Intelligent speed regulation method and system for stirrer

Abstract

The invention discloses an intelligent speed regulation method for a stirrer, which comprises the following steps: according to initial speed regulation parameter configuration, acquiring motor rotating speed fluctuation data by adopting a real-time sensor, and when the rotating speed fluctuation exceeds a preset threshold value, judging whether the rotating speed fluctuation deviates from a reference value range to obtain rotating speed deviation data; for the rotating speed deviation data, calculating a difference value between the current rotating speed fluctuation and a reference value through a preset rotating speed correction algorithm, and determining a corrected rotating speed adjustment value; for a uniformity evaluation result which does not reach a preset standard, recalculating a rotation speed fluctuation frequency and amplitude combination through a short-period dynamic adjustment mechanism, and determining a new speed regulation parameter; according to the new speed regulation parameter, a motor control instruction is updated, the food material mixing state in the stirring process is continuously monitored, and latest uniformity feedback data is obtained; and according to the optimized control parameters, continuously executing motor rotating speed adjustment and uniformity detection circulation until a preset mixing uniformity standard is reached, and determining a final stirring control scheme.

Description

Technical Field

[0001] The present invention relates to the technical field of food blender control, and in particular to an intelligent speed regulation method and system for a blender. Background Art

[0002] The intelligent speed regulation technology of food blenders, as an important research direction in the field of kitchen appliances, is of crucial significance for improving the efficiency of food processing and the user experience. With the increasing demands of consumers for refined and personalized food processing, the intelligent speed regulation technology has become one of the core driving forces for promoting industry innovation, and its importance is self-evident.

[0003] However, the speed regulation methods of most blenders on the current market still remain at the simple speed adjustment level and lack targeted processing for the characteristics of different ingredients. Such a single speed regulation method often has difficulty adapting to the mixing requirements of complex ingredients, resulting in uneven mixing effects 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 precisely control the motor speed fluctuation to adapt to the physical characteristics of different ingredients. For example, batter-like ingredients require a larger stirring amplitude, while nuts require a more delicate processing method. Due to the insufficient control precision of speed fluctuation, it is difficult to form effective regular changes during the stirring process, which further leads to another problem, that is, it is impossible to break the static structure between ingredients through short-period dynamic adjustment to achieve a more uniform mixing effect. These two problems are interrelated. The former is the basis for technical implementation, and the latter is the key link directly affecting the user experience, jointly constituting the bottleneck for the breakthrough of intelligent speed regulation technology. Summary of the Invention

[0005] The present invention provides an intelligent speed regulation method and system for a blender to solve the above-mentioned existing technical problems.

[0006] The technical solution of the present invention is realized as follows: An intelligent speed regulation method for a blender, comprising: Pre-establish an ingredient characteristic database, and for ingredients with different physical characteristics, determine the speed fluctuation range and frequency reference value to obtain the initial speed regulation parameter configuration; According to the initial speed regulation parameter configuration, collect the motor speed fluctuation data, and when the speed fluctuation exceeds the preset threshold, determine whether it deviates from the reference value range to obtain the speed deviation data; For the speed deviation data, through a preset speed correction algorithm, calculate the difference between the current speed fluctuation and the reference value, and determine the corrected speed adjustment value; According to the adjusted rotational speed adjustment value, monitor the rotational speed fluctuation in real time, dynamically optimize the rotational speed curve, adjust the rotational speed fluctuation frequency and amplitude within a short period, and obtain the adjusted real-time rotational speed data.

[0007] Further, the obtaining of the initial speed regulation parameter configuration includes: Through a pre-constructed food ingredient characteristic database, obtain the information on the type of food ingredient being processed currently, classify and process different physical characteristics of the food ingredient, and determine the preliminary type classification result; According to the type classification result, extract the physical characteristic data related to different food ingredients, match the corresponding rotational speed fluctuation range, and obtain the preliminary rotational speed configuration plan; If there is a deviation between the rotational speed configuration plan and the frequency reference value, then by comparing the historical frequency data in the database, adjust the rotational speed fluctuation range to generate the corrected rotational speed parameter; For the corrected rotational speed parameter, obtain the associated frequency reference value, judge whether the frequency reference value meets the preset threshold range, and output the verified frequency parameter; According to the verified frequency parameter, combined with the physical characteristic data in the food ingredient characteristics, through a preset speed regulation model, determine the final initial speed regulation parameter configuration and generate a set of speed regulation parameters.

[0008] Further, the obtaining of the rotational speed deviation data includes: Through the motor data collected in real time, use a sensor to continuously monitor the rotational speed fluctuation, judge whether the fluctuation exceeds the preset threshold, and obtain the preliminary fluctuation state data; According to the preliminary fluctuation state data, conduct a comparative analysis of the rotational speed fluctuation and the reference range. If the rotational speed fluctuation exceeds the reference range, extract the deviation information and determine the specific deviation degree data.

[0009] Further, the obtaining of the rotational speed deviation data further includes: Through the deviation degree data, combined with the speed regulation parameters in the initial configuration, use a preset mapping rule to process the deviation information and obtain the adjusted parameter correction value; According to the adjusted parameter correction value, update the motor data in real time, and use a logical verification method to judge whether the correction value meets the preset threshold to obtain the verified parameter data; Through the verified parameter data, combined with the result of the fluctuation judgment, conduct a secondary analysis of the rotational speed fluctuation. If the fluctuation still exceeds the reference range, conduct a parameter fine-tuning through the historical database record to determine the final speed regulation plan; According to the final speed regulation plan, continuously compare the sensor values collected in real time, and use the support vector machine algorithm to optimize the speed regulation plan to obtain the control parameters adapted to the current environment; Dynamically update the results of data detection by adapting the control parameters of the current environment, determine whether the requirements of range comparison are met, and obtain stable operation status data.

[0010] Further, the determination of the corrected rotational speed adjustment value includes: Analyze the difference between the current rotational speed and the reference value by using a preset correction algorithm through rotational speed deviation data, calculate the specific degree of fluctuation, and obtain preliminary correction parameters; According to the preliminary correction parameters, perform comparison processing on the rotational speed fluctuation data. If the degree of fluctuation exceeds the preset threshold, correct the parameters through the reference comparison logic to determine the adjusted correction value; Monitor the current rotational speed in real time through the adjusted correction value in combination with the rotational speed data, and use the fluctuation analysis method to determine whether the correction value meets the expected range to obtain verified correction data.

[0011] Further, the determination of the corrected rotational speed adjustment value also includes: Perform a secondary verification on the degree of fluctuation according to the verified correction data. If the degree of fluctuation still exceeds the reference comparison range, conduct a comparison analysis through historical rotational speed data to determine a further adjustment plan; Optimize the adjustment plan by using the support vector machine algorithm in combination with the real-time state of the current rotational speed through the further adjustment plan to obtain control parameters adapted to the environment; Dynamically update the rotational speed fluctuation data according to the control parameters adapted to the environment. If the updated degree of fluctuation still does not meet the reference comparison conditions, perform fine-tuning of the parameters through a preset logic verification tool to determine the final correction result; Continuously track the rotational speed deviation data through the final correction result, and verify the correction result by using a data calculation method to obtain stable operation parameters.

[0012] Further, the acquisition of the adjusted real-time rotational speed data includes: For the corrected rotational speed adjustment value, use an adaptive intelligent algorithm to monitor the rotational speed fluctuation value in real time, and obtain the fluctuation state data by comparing the analyzed fluctuation monitoring value with the preset threshold range; According to the obtained fluctuation state data, if the fluctuation state data exceeds the preset threshold range, preliminarily correct the frequency adjustment value in the rotational speed curve graph by using the dynamic adjustment method to obtain preliminary frequency parameters; For the preliminary frequency parameters, if the preliminary frequency parameters do not match the target frequency range, synchronize the amplitude adjustment value, and analyze the matching degree between the amplitude adjustment value and the frequency adjustment value through an information comparison tool to determine the corrected amplitude parameters; With the corrected amplitude parameter, combined with the real-time data stream, the optimized rotational speed value is updated within a short time period. A data verification tool is used to determine whether the updated rotational speed value meets the preset range, and the verified rotational speed data is obtained.

[0013] Further, the obtaining of the adjusted real-time rotational speed data further includes: Based on the verified rotational speed data, if there are still deviations in the verified rotational speed data, data comparison and analysis are performed in combination with the historical fluctuation monitoring value, and further adjustment parameters are obtained through a pre-established mapping relation table; For the further adjustment parameters, the support vector machine algorithm is used to comprehensively analyze the rotational speed fluctuation value and the optimized rotational speed value. With the support of the real-time monitoring method, the final rotational speed control parameter is determined; With the final rotational speed control parameter, the rotational speed curve graph is dynamically updated, and the fluctuation monitoring value is continuously tracked in combination with the real-time data stream to determine whether the updated fluctuation state is stable, and the stable control data is obtained.

[0014] An intelligent speed regulation system for a mixer, comprising: A parameter configuration and deviation detection module, which is used to obtain the information of the type of food material being processed through a pre-established food material characteristic database, determine the corresponding rotational 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, a real-time sensor is used to collect the rotational speed fluctuation data of the motor, and when the rotational speed fluctuation exceeds the preset threshold, it is judged whether it deviates from the reference value range to obtain the rotational speed deviation data; A rotational speed correction and optimization module, which is used to calculate the difference between the current rotational speed fluctuation and the reference value through a preset rotational speed correction algorithm for the rotational speed deviation data, and determine the corrected rotational speed adjustment value; according to the corrected rotational speed adjustment value, an adaptive intelligent algorithm is introduced to monitor the rotational speed fluctuation in real time, dynamically optimize the rotational speed curve, and adjust the rotational speed fluctuation frequency and amplitude within a short period to obtain the adjusted real-time rotational speed data A uniformity detection and adjustment module, which is used to analyze the structural change state of the food material during the stirring process by using a mixing uniformity detection module for the adjusted real-time rotational speed data, judge whether the preset uniformity standard is reached to obtain the uniformity evaluation result; for the uniformity evaluation result that does not reach the preset standard, the rotational speed fluctuation frequency and amplitude combination are recalculated through a short-period dynamic adjustment mechanism to determine the new speed regulation parameter.

[0015] The control instruction and strategy adjustment module is used to update the motor control instruction according to the new speed regulation parameters, continuously monitor the ingredient mixing state during the stirring process, and obtain the latest uniformity feedback data; for the latest uniformity feedback data, if it is detected that the static structure has not been broken, the dynamic structure breaking strategy is adjusted by increasing the number of speed fluctuations within a short period to obtain optimized control parameters.

[0016] The loop control module is used to continuously execute the motor speed adjustment and uniformity detection loop according to the optimized control parameters until the preset mixing uniformity standard is reached, and determine the final stirring control scheme.

[0017] Compared with the prior art, the present invention has the following beneficial effects: 1. By pre-establishing a database containing the physical characteristics of various ingredients and obtaining the ingredient type information of the currently processed ingredients through sensors, the corresponding speed fluctuation range and frequency reference value can be retrieved from the database, enabling the blender to perform targeted speed regulation according to the physical characteristics of different ingredients, and solving the problem that a single speed regulation method in the prior art is difficult to adapt to different ingredients. 2. During the stirring process, the present invention collects the motor speed fluctuation data through a real-time sensor, and when the speed fluctuation exceeds the preset threshold, it judges whether it deviates from the reference value range to obtain the speed deviation data; for the speed deviation data, through a preset speed correction algorithm, the difference between the current speed fluctuation and the reference value is calculated to determine the corrected speed adjustment value, ensuring that the blender always maintains the best speed fluctuation when processing different ingredients, and further improving the adaptability and accuracy of the stirring effect. 3. By introducing an adaptive intelligent algorithm, according to the corrected speed adjustment value, the speed fluctuation is monitored in real time and the speed curve is dynamically optimized; the frequency and amplitude of the speed fluctuation are adjusted within a short period. Through this dynamic adjustment strategy, the static structure between ingredients is broken; the adaptive intelligent algorithm can automatically adjust the regular change of the speed fluctuation according to the real-time monitoring data, thereby achieving a more uniform mixing effect. 4. For the adjusted real-time speed data, the present invention adopts a mixing uniformity detection module to analyze the structural change state of the ingredients during the stirring process and judge whether the preset uniformity standard is reached; if the preset standard is not reached, through a short-period dynamic adjustment mechanism, the speed fluctuation frequency and amplitude combination are recalculated to determine the new speed regulation parameters. This feedback optimization mechanism based on mixing uniformity ensures that the blender can gradually optimize the stirring effect after each adjustment and finally reach the ideal mixing uniformity standard. Description of the Drawings

[0018] Figure 1 It is a flowchart of an intelligent speed regulation method for a blender in Embodiment 1. Figure 2 It is a flowchart of an intelligent speed regulation method for a blender in Embodiment 2; Figure 3 It is a framework diagram of an intelligent speed regulation system for a blender in Embodiment 3. Specific implementation manners

[0019] To make the objectives, features, and advantages of the present invention more obvious and understandable, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the embodiments described below are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention. Embodiment

[0020] As Figure 1 shown, this embodiment provides an intelligent speed regulation method for a blender, including: Pre-establish a food ingredient characteristic database, determine the rotational speed fluctuation range and frequency reference value for food ingredients with different physical characteristics, and obtain the initial speed regulation parameter configuration; According to the initial speed regulation parameter configuration, collect the rotational speed fluctuation data of the motor, and when the rotational speed fluctuation exceeds the preset threshold, determine whether it deviates from the reference value range to obtain the rotational speed deviation data; For the rotational speed deviation data, calculate the difference between the current rotational speed fluctuation and the reference value through a preset rotational speed correction algorithm, and determine the corrected rotational speed adjustment value; According to the corrected rotational speed adjustment value, monitor the rotational speed fluctuation in real time, dynamically optimize the rotational speed curve, adjust the rotational speed fluctuation frequency and amplitude within a short period, and obtain the adjusted real-time rotational speed data.

[0021] Furthermore, the obtaining of the initial speed regulation parameter configuration includes: Obtain the food ingredient type information currently being processed through the pre-constructed food ingredient characteristic database, classify and process the different physical characteristics of the food ingredients, and determine the preliminary type classification result; According to the type classification result, extract the physical characteristic data related to different food ingredients, match the corresponding rotational speed fluctuation range, and obtain the preliminary rotational speed configuration scheme; If there is a deviation between the rotational speed configuration scheme and the frequency reference value, then adjust the rotational speed fluctuation range by comparing the historical frequency data in the database to generate the corrected rotational speed parameter; For the corrected rotational speed parameter, obtain the associated frequency reference value, determine whether the frequency reference value meets the preset threshold range, and output the verified frequency parameter; According to the verified frequency parameters, combined with the physical property data in the ingredient characteristics, through a preset speed regulation model, determine the final initial speed regulation parameter configuration, and generate a set of speed regulation parameters; Through the set of speed regulation parameters, combined with the ingredient type information currently being processed, use the support vector machine algorithm to optimize the parameter set to obtain an optimized speed regulation configuration result; If there is a mismatch between the optimized speed regulation configuration result and the actual processing environment, then perform secondary calibration through the historical processing records in the database, and output the finally adapted speed regulation parameter scheme.

[0022] Specifically, in some embodiments, it can be as described below: Through a pre-constructed ingredient characteristics database, the ingredient type information currently being processed can be automatically obtained. For example, assume that the ingredient currently being processed is yogurt. The database stores the physical property data of yogurt, including key parameters such as viscosity of 1500 centipoise and acidity of 120°T. Automatically match the characteristic record of yogurt through the query interface, and extract the corresponding processing requirements; Next, for the viscosity characteristic of yogurt, determine the speed fluctuation range according to the built-in algorithm. The calculation formula is: speed fluctuation range = base speed × (viscosity coefficient / 1000), where the base speed is set to 1800 revolutions per minute, and the viscosity coefficient is 1500 / 1000 = 1.5. It is obtained that the upper and lower limits of the speed fluctuation range are 1800 × 1.5 = 2700 revolutions per minute, that is, the speed can fluctuate between 900 and 3600 revolutions per minute. At the same time, considering the influence of acidity of 120°T, by analyzing the influence of acidity on the speed frequency, set the frequency reference value to 45 Hz. The calculation method is: frequency reference value = base frequency + (acidity - 100) × 0.25, where the base frequency is 40 Hz, and the acidity adjustment value is (120 - 100) × 0.25 = 5, resulting in 45 Hz as the reference; Subsequently, if processing ingredients such as oatmeal, the database shows that its hardness is 4 (on a scale of 10) and the fiber content is 10%. According to the positive correlation between hardness and speed, use the formula speed fluctuation range = hardness × 400 to calculate the fluctuation range as 4 × 400 = 1600 revolutions per minute, that is, the speed is between 1200 and 2800 revolutions per minute. At the same time, the frequency reference value is set to 60 Hz through the hardness linear mapping algorithm to ensure the mixing efficiency of high-fiber ingredients; Finally, generate the initial speed regulation parameter configuration according to the above calculation results. For example, the configuration for yogurt is a speed range of 900 - 3600 revolutions per minute and a frequency of 45 Hz, and for oatmeal, it is a speed range of 1200 - 2800 revolutions per minute and a frequency of 60 Hz.

[0023] Furthermore, the obtaining of the rotational speed deviation data includes: By collecting motor data in real time, a sensor is used to continuously monitor the rotational speed fluctuation, determine whether the fluctuation exceeds a preset threshold, and obtain preliminary fluctuation status data; According to the preliminary fluctuation status data, a comparative analysis is carried out on the rotational speed fluctuation and the reference range. If the rotational speed fluctuation exceeds the reference range, deviation information is extracted to determine specific deviation degree data.

[0024] Furthermore, obtaining the rotational speed deviation data further includes: By using the deviation degree data, combining the speed regulation parameters in the initial configuration, and processing the deviation information using a preset mapping rule, an adjusted parameter correction value is obtained; According to the adjusted parameter correction value, the motor data is updated in real time, and a logic verification method is used to determine whether the correction value meets the preset threshold to obtain verified parameter data; By using the verified parameter data and combining the result of the fluctuation judgment, a secondary analysis of the rotational speed fluctuation is carried out. If the fluctuation still exceeds the reference range, parameter fine-tuning is performed through the historical database record to determine the final speed regulation scheme; According to the final speed regulation scheme, continuous comparison is made on the sensor values collected in real time, and the support vector machine algorithm is used to optimize the speed regulation scheme to obtain control parameters adapted to the current environment; By using the control parameters adapted to the current environment, the result of the data detection is dynamically updated to determine whether the requirements of the range comparison are met to obtain stable operation status data.

[0025] Specifically, in some embodiments, it can be as follows: Based on the initial speed regulation parameter configuration, the rotational speed fluctuation data of the motor is collected and analyzed through a real-time sensor to ensure the stability of the equipment operation.

[0026] For example, assume that the set rotational speed range in the initial configuration is 1500 to 3500 revolutions per minute, the reference value is 2500 revolutions per minute, the rotational speed data is collected 10 times per second through a built-in high-precision sensor, and the current rotational speed fluctuation value is 1480 revolutions per minute, which is lower than the preset lower limit of 1500 revolutions per minute; Immediately start the deviation detection algorithm to calculate the deviation value: deviation value = reference value - current rotational speed, that is, 2500 - 1480 = 1020 revolutions per minute, and compare this deviation with the preset threshold of 800 revolutions per minute, and it is found that the threshold range is exceeded; Next, it automatically enters the deviation analysis module. Combining historical operation data, it determines whether the rotational speed deviation is caused by a sudden change in load. After analysis, the load increase coefficient is obtained as 1.2. Through the formula adjustment value = deviation value / load increase coefficient, that is, 1020 / 1.2 = 850 revolutions per minute, it is determined that the rotational speed needs to be increased by 850 revolutions per minute to approach the reference value. Subsequently, the adjustment instruction is transmitted to the motor control unit, the rotational speed is updated in real time to 2330 revolutions per minute, and the fluctuation data is continuously monitored to form a closed-loop feedback mechanism; To ensure logical integrity, the current fluctuation detection service is also associated. If the current value exceeds the safe range of 1.5 amperes after the rotational speed is adjusted, the rotational speed is further finely adjusted to the safe range, for example, reduced to 2200 revolutions per minute, to avoid the risk of overload. Through the above series of automated processes, a complete logical chain from data acquisition to deviation judgment and then to parameter adjustment is realized, ensuring the stable operation of the equipment under complex working conditions.

[0027] Further, the determination of the corrected rotational speed adjustment value includes: Through the rotational speed deviation data, a preset correction algorithm is used to analyze the difference between the current rotational speed and the reference value, calculate the specific fluctuation degree, and obtain the preliminary correction parameters; According to the preliminary correction parameters, the rotational speed fluctuation data is processed for comparison. If the fluctuation degree exceeds the preset threshold, the parameters are corrected through the reference comparison logic to determine the adjusted correction value; 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 determine whether the correction value meets the expected range to obtain the verified correction data.

[0028] Further, the determination of the corrected rotational speed adjustment value also includes: 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 used for comparison and analysis to determine a further adjustment plan; Through the further adjustment plan, combined with the real-time state of the current rotational speed, the support vector machine algorithm is used to optimize the adjustment plan to obtain the control parameters adapted to the environment; According to the control parameters adapted to the environment, the rotational speed fluctuation data is dynamically updated. If the updated fluctuation degree still does not meet the reference comparison conditions, the parameter is finely adjusted through a preset logic verification tool to determine the final correction result; Through the final correction result, the rotational speed deviation data is continuously tracked, and the data calculation method is used to verify the correction result to obtain stable operation parameters.

[0029] Specifically, in some embodiments, it can be as follows: For the processing of rotational speed deviation data, a complete logical chain from deviation calculation to rotational speed correction is completed through a series of automated algorithms and analysis processes; First, based on a preset reference rotational speed value of 1800 revolutions per minute, combined with the real-time collected rotational speed data, for example, the current rotational speed is 1650 revolutions per minute, calculate the difference between the two, that is, 1800 - 1650 = 150 revolutions per minute, as the initial deviation data; Next, call the rotational speed correction algorithm, compare this deviation value with the historical fluctuation trend, analyze that the rotational speed decrease may be related to the increase in ambient temperature, and the temperature influence coefficient is 1.1. Then, through the correction formula: adjustment value = deviation value * temperature influence coefficient, that is, 150 * 1.1 = 165 revolutions per minute, determine that the preliminary adjustment amount is to increase by 165 revolutions per minute; At the same time, introduce the vibration frequency detection service as an associated logic. If the vibration frequency exceeds the standard value of 0.5 Hz, further correct the adjustment value. Assume that the current vibration frequency is 0.6 Hz, automatically reduce the adjustment amplitude by 10%, that is, 165 * 0.9 = 148.5 revolutions per minute, and finally determine that the corrected rotational speed adjustment value is to increase by 148.5 revolutions per minute; Subsequently, convert this adjustment value into a control signal, transmit it to the drive module, update the target rotational speed to 1798.5 revolutions per minute, and record the adjusted rotational speed data and the change in vibration frequency in real time to form a data feedback chain; if it is detected later that the vibration frequency still does not return to the normal range, the secondary correction process will be automatically started, and the adjustment value will be recalculated in combination with the dual coefficients of temperature and vibration to ensure a logical closed-loop; Through this series of automated processes, the full-process intelligent management from deviation identification, influence factor analysis to parameter correction is realized.

[0030] Furthermore, the obtaining of the adjusted real-time rotational speed data includes: For the corrected rotational speed adjustment value, use an adaptive intelligent algorithm to monitor the rotational speed fluctuation value in real time, and obtain the fluctuation state data by analyzing the comparison between the fluctuation monitoring value and the preset threshold range; According to the obtained fluctuation state data, if the fluctuation state data exceeds the preset threshold range, preliminarily correct the frequency adjustment value in the rotational speed curve graph by the dynamic adjustment method to obtain the preliminary frequency parameter; For the preliminary frequency parameter, if the preliminary frequency parameter does not match the target frequency range, synchronously process the amplitude adjustment value, analyze the matching degree between the amplitude adjustment value and the frequency adjustment value through an information comparison tool, and determine the corrected amplitude parameter; Through the corrected amplitude parameter, combined with the real-time data stream, update the optimized rotational speed value within a short time period, and use a data verification tool to determine whether the updated rotational speed value meets the preset range to obtain the verified rotational speed data.

[0031] The preliminary correction of the frequency adjustment value in the rotational speed curve graph includes: Real-time monitoring of rotational speed fluctuations, and calculating the smoothing coefficient according to the optimization curve generated based on the fluctuation characteristics; Optimizing the rotational speed increment : ; Introducing a load compensation factor: load volatility , load compensation factor , adjusted increment : ; Updating the real-time rotational speed data ; ; Preliminarily correcting the frequency adjustment value : ; Synchronously processing the amplitude adjustment value, the corrected amplitude parameter , if the preliminary frequency adjustment value is within the target frequency range, then keep the current amplitude adjustment value unchanged; if it exceeds the target frequency range, then adjust according to the matching degree between the frequency adjustment value and the target frequency range; The updated optimized rotational speed value , if the updated optimized rotational speed value is within the preset range, then the verification passes; if it exceeds the preset range, then further adjustment is required.

[0032] Furthermore, the obtaining of the adjusted real-time rotational speed data further includes: According to the verified rotational speed data, if there are still deviations in the verified rotational speed data, then perform data comparison and analysis in combination with historical fluctuation monitoring values, and obtain further adjustment parameters through a pre-established mapping relation table; For the further adjustment parameters, use the support vector machine algorithm to comprehensively analyze the rotational speed fluctuation value and the optimized rotational speed value, and determine the final rotational speed control parameters with the support of the real-time monitoring method; Through the final rotational speed control parameters, dynamically update the rotational speed curve graph, continuously track the fluctuation monitoring value in combination with the real-time data stream, and judge whether the updated fluctuation state is stable to obtain stable control data.

[0033] Specifically, in some embodiments, it may be as follows: For the corrected rotational speed adjustment value, realize the complete process of real-time monitoring and dynamic optimization of the rotational speed curve through an adaptive intelligent algorithm; Assume that the adjusted rotational speed adjustment value has been determined to be increased by 120 revolutions per minute. First, the rotational speed data is collected by the built-in sensor every second. The current real-time rotational speed is 1720 revolutions per minute, and there is still a gap of 30 revolutions per minute compared with the target value of 1750 revolutions per minute; Next, start the adaptive algorithm to analyze the rotational speed fluctuation frequency within a short period. Assume that the fluctuation frequency is 3 times per minute and the amplitude is ±10 revolutions per minute. The algorithm generates an optimization curve according to the fluctuation characteristics, calculates the smoothing coefficient as 0.8. Through the formula: optimized rotational speed increment = fluctuation amplitude * smoothing coefficient, that is, 10 * 0.8 = 8 revolutions per minute, and initially determines that the adjustment increment per cycle is 8 revolutions per minute; Subsequently, combined with historical data analysis, it is found that the rotational speed fluctuation is related to the load change, and the load volatility is 5%. Therefore, introduce the load compensation factor 1.05, and further adjust the increment to 8 * 1.05 = 8.4 revolutions per minute to ensure that the adjusted rotational speed is closer to the target value; Finally, update the adjusted real-time rotational speed data to 1728.4 revolutions per minute, and record the changes in the fluctuation frequency and amplitude in the database to form a closed-loop feedback; At the same time, automatically associate with the power consumption monitoring service. If the power fluctuation exceeds the standard value of 2%, trigger the auxiliary algorithm to recalculate the optimization curve to ensure the balance between rotational speed adjustment and energy consumption; Through this series of automated processes, the dynamic management from fluctuation monitoring to curve optimization is realized. Embodiment

[0034] As Figure 2 shown, this embodiment provides an intelligent speed regulation method for a blender, including: Pre-establish a food ingredient characteristic database. For food ingredients with different physical characteristics, determine the rotational speed fluctuation range and frequency reference value to obtain the initial speed regulation parameter configuration; According to the initial speed regulation parameter configuration, collect the motor rotational speed fluctuation data. When the rotational speed fluctuation exceeds the preset threshold, determine whether it deviates from the reference value range to obtain the rotational speed deviation data; For the rotational speed deviation data, through the preset rotational speed correction algorithm, calculate the difference between the current rotational speed fluctuation and the reference value to determine the adjusted rotational speed adjustment value; According to the adjusted rotational speed adjustment value, monitor the rotational speed fluctuation in real time, dynamically optimize the rotational speed curve, adjust the rotational speed fluctuation frequency and amplitude within a short period, and obtain the adjusted real-time rotational speed data.

[0035] Furthermore, the obtaining of the initial speed regulation parameter configuration includes: Through the pre-constructed food ingredient characteristic database, obtain the information of the type of food ingredient being processed currently, classify and process the different physical characteristics of the food ingredient to determine the preliminary type classification result; According to the classification results by type, extract the physical property data related to different food ingredients, match the corresponding rotational speed fluctuation range, and obtain a preliminary rotational speed configuration plan; If there is a deviation between the rotational speed configuration plan and the frequency reference value, then by comparing the historical frequency data in the database, adjust the rotational speed fluctuation range to generate corrected rotational speed parameters; 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; According to the verified frequency parameters, combined with the physical property data in the food ingredient characteristics, through a preset speed regulation model, determine the final initial speed regulation parameter configuration and generate a set of speed regulation parameters.

[0036] Furthermore, the obtaining of the rotational speed deviation data includes: Through the motor data collected in real time, use a sensor to continuously monitor the rotational speed fluctuation, judge whether the fluctuation exceeds the preset threshold, and obtain preliminary fluctuation state data; According to the preliminary fluctuation state data, conduct a comparative analysis of the rotational speed fluctuation and the reference range. If the rotational speed fluctuation exceeds the reference range, extract the deviation information and determine the specific deviation degree data.

[0037] Furthermore, the obtaining of the rotational speed deviation data also includes: Through the deviation degree data, combined with the speed regulation parameters in the initial configuration, use a preset mapping rule to process the deviation information and obtain an adjusted parameter correction value; According to the adjusted parameter correction value, update the motor data in real time, and use a logical verification method to judge whether the correction value meets the preset threshold to obtain verified parameter data; Through the verified parameter data, combined with the result of the fluctuation judgment, conduct a secondary analysis of the rotational speed fluctuation. If the fluctuation still exceeds the reference range, perform parameter fine-tuning through the historical database record to determine the final speed regulation plan; According to the final speed regulation plan, continuously compare the sensor values collected in real time, and use the support vector machine algorithm to optimize the speed regulation plan to obtain control parameters adapted to the current environment; Through the control parameters adapted to the current environment, dynamically update the result of the data detection, judge whether it meets the requirements of the range comparison, and obtain stable operation state data.

[0038] Furthermore, the determination of the corrected rotational speed adjustment value includes: Through the rotational speed deviation data, use a preset correction algorithm to analyze the difference between the current rotational speed and the reference value, calculate the specific fluctuation degree, and obtain preliminary correction parameters; According to the preliminary calibration parameters, the rotational speed fluctuation data is processed for comparison. If the degree of fluctuation exceeds the preset threshold, the parameters are corrected through the reference comparison logic to determine the adjusted calibration value. Using the adjusted calibration value and combining with the rotational speed data, the current rotational speed is monitored in real time. The fluctuation analysis method is adopted to determine whether the calibration value meets the expected range, and the verified calibration data is obtained.

[0039] Furthermore, the determination of the adjusted rotational speed adjustment value further includes: According to the verified calibration data, a secondary verification is performed on the degree of fluctuation. If the degree of fluctuation still exceeds the reference comparison range, a comparative analysis is carried out through historical rotational speed data to determine a further adjustment plan. Through the further adjustment plan and combining with the real-time state of the current rotational speed, the support vector machine algorithm is used to optimize the adjustment plan to obtain the control parameters adapted to the environment. According to the control parameters adapted to the environment, the rotational speed fluctuation data is dynamically updated. If the degree of fluctuation after the update still does not meet the reference comparison conditions, parameter fine-tuning is performed through a preset logic verification tool to determine the final calibration result. Through the final calibration result, continuous tracking is performed on the rotational speed deviation data, and the data calculation method is used to verify the calibration result to obtain stable operating parameters.

[0040] Furthermore, the acquisition of the adjusted real-time rotational speed data includes: For the adjusted rotational speed adjustment value, an adaptive intelligent algorithm is used to monitor the rotational speed fluctuation value in real time. By analyzing the comparison between the fluctuation monitoring value and the preset threshold range, the fluctuation state data is obtained. According to the obtained fluctuation state data, if the fluctuation state data exceeds the preset threshold range, the frequency adjustment value in the rotational speed curve diagram is preliminarily corrected through the dynamic adjustment method to obtain the preliminary frequency parameter. For the preliminary frequency parameter, if the preliminary frequency parameter does not match the target frequency range, the amplitude adjustment value is synchronously processed, and the matching degree between the amplitude adjustment value and the frequency adjustment value is analyzed through an information comparison tool to determine the corrected amplitude parameter. Using the corrected amplitude parameter and combining with the real-time data stream, the optimized rotational speed value is updated within a short time period, and a data verification tool is used to determine whether the updated rotational speed value meets the preset range to obtain the verified rotational speed data.

[0041] Furthermore, the acquisition of the adjusted real-time rotational speed data further includes: According to the verified rotational speed data, if there are still deviations in the verified rotational speed data, then combined with the historical fluctuation monitoring values, data comparison and analysis are carried out, and through a pre-established mapping relation table, further adjustment parameters are obtained; For the further adjustment parameters, the support vector machine algorithm is used to comprehensively analyze the rotational speed fluctuation value and the optimized rotational speed value, and with the support of the real-time monitoring method, the final rotational speed control parameters are determined; Through the final rotational speed control parameters, the rotational speed curve graph is dynamically updated, and combined with the real-time data stream, the fluctuation monitoring value is continuously tracked to determine whether the updated fluctuation state is stable, and stable control data is obtained.

[0042] Furthermore, this method also includes: For the adjusted real-time rotational speed data, analyze the structural change state of the ingredients during the stirring process, judge whether the preset uniformity standard is reached, and obtain the uniformity evaluation result; For the uniformity evaluation result that does not reach the preset standard, then through the short-period dynamic adjustment mechanism, recalculate the combination of rotational speed fluctuation frequency and amplitude, and determine the new speed regulation parameters; According to the new speed regulation parameters, update the motor control instruction, continuously monitor the ingredient mixing state during the stirring process, and obtain the latest uniformity feedback data; For the latest uniformity feedback data, if it is detected that the static structure has not been broken yet, then by increasing the number of rotational speed fluctuations within a short period, adjust the dynamic structure breaking strategy to obtain the optimized control parameters; According to the optimized control parameters, continuously execute the motor rotational speed adjustment and uniformity detection loop until the preset mixing uniformity standard is reached, and determine the final stirring control scheme.

[0043] Furthermore, the obtaining of the uniformity evaluation result includes: The real-time rotational speed data is preliminarily processed by the mixing module, the change situation of the ingredient state during the stirring process is analyzed, and the preliminary structural change data is obtained; According to the preliminary structural change data, a detection and analysis tool is used to continuously monitor the uniformity state of the ingredients, judge whether it is close to the preset standard, and obtain the monitoring data of the uniformity state; For the monitoring data of the uniformity state, if the monitoring data does not reach the preset standard, then through the information processing link, the rotational speed data during the stirring process is compared and analyzed to determine the adjustment direction data; According to the adjustment direction data, obtain the real-time rotational speed fluctuation information during the stirring process, and compare it with the preset threshold range to obtain the reference data for rotational speed adjustment; For the reference data of rotational speed adjustment, the support vector machine algorithm is used to comprehensively analyze the structural change and the uniformity state to obtain the optimized rotational speed control parameters; With the optimized rotational speed control parameters, the rotational speed data during the stirring process is dynamically updated. Combining with real-time monitoring information, it is judged whether the target data of the uniform state is reached; According to the target data of the uniform state, if the target data still does not meet the preset standard, information comparison is carried out by combining the structural change data in the historical stirring process to determine the final rotational speed adjustment parameters.

[0044] Specifically, in some embodiments, it can be as follows: For the adjusted real-time rotational speed data, the mixing uniformity detection module analyzes the structural change state of the ingredients during the stirring process to judge whether the preset uniformity standard is reached and generates an evaluation result; First, the high-precision image sensor collects the ingredient distribution image data in the stirring container every 5 seconds. Assuming that the current image analysis shows that the ingredient particle size distribution range is 2.5 to 8.3 mm, while the preset uniformity standard requires the particle size distribution range to be within 3.0 to 5.0 mm, the current uniformity deviation value is calculated to be 35%; Subsequently, the built-in uniformity analysis algorithm is started, the image data is converted into a grayscale value matrix, and the variance of the grayscale distribution is calculated. Assuming that the variance value is 12.7 and the standard variance threshold is 5.0, it indicates that the current mixing state has a high degree of dispersion; Then, according to the variance value and the particle distribution range, the weighted calculation formula is used: uniformity score = 100 - (variance value * 2 + deviation value * 1.5). It is obtained that the current uniformity score is 100 - (12.7 * 2 + 35 * 1.5) = 22.1 points, which is much lower than the preset passing score of 80 points; Furthermore, combining 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. Therefore, 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 Newtons, the resistance compensation algorithm is triggered to adjust the stirring mode to intermittent operation to reduce the equipment load; Finally, the uniformity score, particle distribution range and adjusted stirring parameters are stored in the database to form a data feedback chain, providing a reference basis for subsequent batches of stirring; Through this automated process, the whole process informatization processing from image acquisition to uniformity evaluation is realized.

[0045] Furthermore, the determination of the new speed regulation parameters includes: Through the short-cycle adjustment mechanism, data collection is carried out for the uniformity evaluation result, and the rotational speed fluctuation related information is extracted from it to obtain the preliminary distribution data of the fluctuation frequency and amplitude; According to the preliminary distribution data, classify the rotational speed fluctuations using a preset threshold range to determine whether the fluctuation frequency and amplitude are within the acceptable range, and obtain the classified fluctuation characteristic data; For the classified fluctuation characteristic data, if the fluctuation frequency exceeds the preset threshold range, analyze and compare the historical rotational speed data through the information processing link to obtain a reference value for frequency adjustment; According to the reference value for frequency adjustment, combined with the amplitude data, use the support vector machine algorithm to comprehensively analyze the two to determine the preliminary combination plan of the speed regulation parameters; For the preliminary combination plan of the speed regulation parameters, continuously monitor the rotational speed fluctuations during the stirring process through a real-time data acquisition tool to obtain the latest updated fluctuation characteristic data; According to the latest updated fluctuation characteristic data, if the amplitude still does not meet the preset standard, fine-tune the parameter combination through the information processing link to determine the final speed regulation parameter plan; Through the final speed regulation parameter plan, dynamically adjust the rotational speed fluctuations during the stirring process, and combine the real-time monitoring information to determine whether the relevant requirements for uniformity evaluation are met.

[0046] Specifically, in some embodiments, it can be as follows: For the uniformity evaluation results that do not meet the preset standard, through a short-cycle dynamic adjustment mechanism, automatically start a series of information processing processes to recalculate the combination of rotational speed fluctuation frequency and amplitude, and finally determine the new speed regulation parameters; First, based on the historical stirring data and the current evaluation results, extract the initial value of the rotational speed fluctuation frequency. Assume that the current fluctuation frequency is 15 times per minute and the amplitude is 10% of the rotational speed, while the database analysis shows that the optimal fluctuation frequency range is 18 to 22 times per minute and the amplitude is 8% to 12% of the rotational speed; Calculate the frequency adjustment coefficient through the built-in dynamic optimization algorithm. The formula is: new frequency = current frequency + (median of target frequency - current frequency) * 0.6. It is obtained that the new frequency is 15 + (20 - 15) * 0.6 = 18 times / minute. At the same time, the amplitude adjustment uses the linear interpolation method to obtain the new amplitude of 9.5%; Next, input the calculated new frequency and amplitude combination into the simulation analysis module to predict the stirring stability after adjustment. Assume that the simulation results show that the stability index is 0.85, which is lower than the preset threshold of 0.9. Automatically associate with the load distribution monitoring service, and it is detected that the current load peak is 8.2 kW, exceeding the standard value of 7.5 kW. Therefore, further fine-tune the amplitude to 9.2% to balance the load and stability; Finally, the newly determined speed regulation parameters, namely a frequency of 18 times per minute and an amplitude of 9.2%, are transmitted to the stirring control unit, and the load change data during the adjustment process is recorded in real time. Assuming that the load peak drops to 7.6 kW after adjustment and the stability index increases to 0.91, it meets the operation requirements; These parameters and predicted data are stored in the log to form a closed-loop feedback mechanism, providing data support for subsequent adjustments; Through this process, full-automatic dynamic adjustment from parameter calculation to stability verification is achieved.

[0047] Further, the obtaining of the latest uniformity feedback data includes: By updating the speed regulation parameters, the motor control instructions are reconfigured to obtain the adjusted instruction data; According to the adjusted instruction data, the mixing state of the ingredients during the stirring process is monitored in real time to obtain the current mixing state information; 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; According to the determined parameter direction, a pre-established regression model is used to predict the parameter adjustment amplitude to obtain an optimized combination of speed regulation parameters; Through the optimized combination of speed regulation parameters, the motor control instructions are updated, and the status monitoring is continuously carried out to obtain the latest uniformity feedback information; If the latest uniformity feedback information still shows that the mixing state does not meet the standard, the historical state data is compared through the information processing link to determine whether there are periodic fluctuation characteristics; According to the determined periodic fluctuation characteristics, the control instruction frequency during the stirring process is adjusted to obtain the final stable mixing state data.

[0048] Specifically, in some embodiments, it can be as follows: Based on the newly calculated speed regulation parameters, motor control instructions are automatically generated and updated. At the same time, the mixing state of the ingredients during the stirring process is continuously monitored through information means, and the latest uniformity feedback data is obtained to optimize the operation effect; First, the new speed regulation parameters, such as the rotation speed set to 1200 revolutions per minute and the fluctuation range controlled within plus or minus 5%, are converted into specific motor control instructions. The corresponding voltage adjustment value is calculated 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). The target voltage is obtained as 24 * (1 + 0.05 * 0.8) = 24.96 volts. Subsequently, this instruction is transmitted to the motor drive module through the data interface to ensure accurate execution of the rotation speed; Next, start the real-time monitoring program. Using the multi-point sensor array installed in the mixing tank, collect the ingredient particle distribution data every 2 seconds. Suppose the current collected distribution variance value is 0.12, while the standard uniformity variance threshold is 0.08. Automatically analyze that there is a local aggregation phenomenon in the mixing state; To solve this problem, associate with the flow field simulation service in the mixing tank. Predict the moving trajectories of ingredient particles through the flow field analysis algorithm. Calculate that the current mixing paddle angle deviates from the optimal value by 3.5 degrees, resulting in uneven flow field. Then generate a temporary adjustment instruction to fine-tune the paddle angle to a deviation value of 2.0 degrees, and predict that the distribution variance can be reduced to 0.09 after adjustment; Finally, upload all monitoring data and adjustment records to the cloud analysis platform. Use historical data to compare the current uniformity feedback. Suppose 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 once every 3 seconds to reduce resource occupancy. At the same time, store the analysis results in the database to form a data closed-loop, providing a reference basis for subsequent optimization; Through this series of automated processes, the full-process informatization management from instruction update to status monitoring is realized.

[0049] Further, the obtained optimized control parameters include: By analyzing the uniformity feedback data, judge whether the static structure has not been broken yet, and obtain the detailed characteristic information of the current mixing state; According to the obtained mixing state characteristic information, for the case where the static structure has not been broken, adjust the frequency of rotational speed fluctuation, and determine the range of frequency change within a short period; Adopt the adjusted fluctuation frequency range to dynamically configure the rotational speed fluctuation within a short period to obtain the preliminary adjustment result of the dynamic structure; 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, analyze the difference between the feedback data and the historical data through the information processing link, and judge whether there are continuous static structure characteristics; If continuous static structure characteristics are judged, optimize and predict the control parameters through a pre-established regression model to obtain an optimized combination of control parameters; According to the optimized combination of control parameters, adjust the specific execution method of the breaking strategy to obtain the updated dynamic structure state information; For the updated dynamic structure state information, determine whether the final mixing state meets the preset standard by continuously monitoring the uniformity feedback data.

[0050] Specifically, in some embodiments, it can be as described below: Analyze according to the latest uniformity feedback data. Assume that the currently detected distribution variance is 0.15, which is much higher than the target threshold of 0.07, indicating that the static structure has not been effectively broken, and the dynamic structure breaking strategy needs to be adjusted; First, through the built-in uniformity analysis algorithm, combined with historical data records, calculate that the aggregation index of the static structure in the current mixing state is 0.22, which is higher than the normal range of 0.10. It is determined that the number of rotational speed fluctuations within a short period needs to be increased to enhance the perturbation effect; Next, call the rotational speed fluctuation optimization module, set the short period to 30 seconds, and the initial number of rotational speed fluctuations per period to 3. Through the algorithm formula: adjustment value of fluctuation times = aggregation index * 10, the adjustment value is obtained as 0.22 * 10 = 2.2. After rounding up, the new number of fluctuations per period is determined to be 5, and at the same time, the rotational speed fluctuation amplitude is set to plus or minus 8% to ensure the perturbation intensity; Subsequently, convert the optimized parameters into control instructions. Assume that the base rotational speed is 1000 revolutions per minute, and the rotational speed range after fluctuation is 920 to 1080 revolutions per minute, and transmit them to the drive unit for execution through the data bus; Immediately afterwards, associate with the dynamic structure analysis service, and collect the data of the adjusted mixing state in real time. Assume that after 5 minutes, the distribution variance drops to 0.09, approaching the target threshold, indicating that the strategy is effective, but still needs fine-tuning; Further analyze the relationship between the number of fluctuations and the uniformity degradation rate, and obtain a predicted value that each increase of 1 fluctuation can reduce the variance by 0.01. Therefore, generate a secondary optimization instruction to increase the number of fluctuations to 6 per period, and it is predicted that the variance can be further reduced to 0.08; Finally, synchronize all parameter adjustment records and feedback data to the background analysis to form a dynamic strategy optimization file, providing data support for subsequent similar scenarios to ensure that the entire adjustment process is automatically completed through information means.

[0051] Furthermore, the determination of the final stirring control scheme includes: Through obtaining the current control parameter data, initially configure the motor rotational speed, complete the setting of the initial adjustment frequency, and obtain the initial rotational speed configuration result; According to the initial rotational speed configuration result, synchronously execute the uniformity detection process, obtain the real-time information of the mixing state from the detection link, and determine whether the current state is close to the preset standard; If the current state does not meet the preset standard, then analyze the deviation between the real-time information and the historical data through the information processing link, and judge whether there are continuous uneven mixing characteristics to obtain the deviation analysis result; For the deviation analysis result, dynamically correct the control parameters using the pre-established regression model, obtain the corrected parameter combination, and determine the new adjustment frequency range; According to the corrected parameter combination and the adjusted frequency range, the motor speed is cyclically adjusted and the status monitoring is continuously performed to obtain the updated hybrid status data; Based on the updated hybrid status data, it is verified by comparing with the preset standard. If it still does not meet the standard, the deviation analysis in the information processing link is repeated to obtain further optimized parameters; According to the further optimized parameters, the execution mode of the stirring scheme is adjusted, and the status monitoring process is synchronously updated to determine the final stirring control scheme.

[0052] Specifically, in some embodiments, it can be as follows: According to the optimized control parameters, the cyclic process of automatically adjusting the motor speed and detecting the uniformity is started, with the goal of reaching the preset standard value of the mixing uniformity of 0.05, and finally determining the stirring control scheme; First, extract the current optimized parameters from the database. Assume that the base speed is set to 1200 revolutions per minute, the speed fluctuation range is plus or minus 5%, the fluctuation period is 20 seconds, and the number of fluctuations per period is 4 times. These parameters are converted into control signals through the data bus and sent to the motor drive module for execution and adjustment; At the same time, activate the uniformity detection sensor, and collect the distribution data of the mixed material every 2 minutes. Assume that the uniformity index detected for the first time is 0.12, which is much higher than the target value. Calculate the uniformity deviation value of 0.07 through the built-in analysis algorithm, and combine the historical trend data to derive the correlation formula between the number of speed fluctuations and the uniformity index: index decrease value = increase in the number of fluctuations * 0.02. Based on this, calculate that the number of fluctuations needs to be increased by 3.5 times, and after rounding, it is adjusted to 7 times per period; Subsequently, automatically update the control instruction, send the new parameters to the drive unit, and obtain the uniformity index of 0.08 in the next round of detection. The deviation is reduced to 0.03, indicating that the adjustment direction is correct, but still needs to be optimized; Further call the material viscosity analysis module, and combine the current viscosity value of 1.5 Pa·s to calculate that the speed fluctuation range needs to be increased to plus or minus 7% to enhance the mixing disturbance effect, and record the adjusted data in the cloud log in real time; Finally, when the detected uniformity index drops to 0.05, automatically lock the current parameter combination, including the speed of 1200 revolutions per minute, the fluctuation range of plus or minus 7%, the fluctuation period of 20 seconds, and the number of fluctuations of 7 times, to form the final stirring control scheme, and synchronize it to the production management through the information interface to provide a reference basis for subsequent batches. The whole process does not require manual intervention and is completed by relying on algorithms and data driving. Embodiment

[0053] As Figure 3 shown, this embodiment provides an intelligent speed regulation system for a mixer to implement an intelligent speed regulation method for a mixer. The system includes: The parameter configuration and deviation detection module is used to obtain the information of the type of food ingredient being processed through a pre-established food ingredient characteristic database, determine the corresponding rotational 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, use a real-time sensor to collect the rotational speed fluctuation data of the motor, and when the rotational speed fluctuation exceeds the preset threshold, determine whether it deviates from the reference value range to obtain the rotational speed deviation data; The rotational speed correction and optimization module is used to calculate the difference between the current rotational speed fluctuation and the reference value through a preset rotational speed correction algorithm for the rotational speed deviation data, and determine the corrected rotational speed adjustment value; according to the corrected rotational speed adjustment value, introduce an adaptive intelligent algorithm to continuously monitor the rotational speed fluctuation, dynamically optimize the rotational speed curve, and adjust the rotational speed fluctuation frequency and amplitude within a short period to obtain the adjusted real-time rotational speed data The uniformity detection and adjustment module is used to analyze the structural change state of the food ingredient during the stirring process by using a mixing uniformity detection module for the adjusted real-time rotational speed data, determine whether the preset uniformity standard is reached to obtain the uniformity evaluation result; for the uniformity evaluation result that does not reach the preset standard, recalculate the combination of rotational speed fluctuation frequency and amplitude through a short-period dynamic adjustment mechanism to determine the new speed regulation parameters.

[0054] The control instruction and strategy adjustment module is used to update the motor control instruction according to the new speed regulation parameters, continuously monitor the food ingredient mixing state during the stirring process, and obtain the latest uniformity feedback data; for the latest uniformity feedback data, if it is detected that the static structure has not been broken, adjust the dynamic structure breaking strategy by increasing the number of rotational speed fluctuations within a short period to obtain the optimized control parameters.

[0055] The loop control module is used to continuously execute the loop of motor rotational speed adjustment and uniformity detection according to the optimized control parameters until the preset mixing uniformity standard is reached to determine the final stirring control scheme.

[0056] The specific embodiments of the invention have been described in detail above, but they are only examples, and the invention is not limited to the specific embodiments described above. Those skilled in the art of this industry should understand that the above embodiments and the descriptions in the specification only illustrate the principle of the invention. Without departing from the spirit and scope of the invention, the invention will have various changes and improvements, and these changes and improvements all fall within the scope of the invention claimed. The scope of protection of the invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent speed regulation method for a blender, characterized in that Including: Pre - establish a database of ingredient characteristics. For ingredients with different physical characteristics, determine the speed fluctuation range and frequency reference value to obtain the initial speed regulation parameter configuration; According to the initial speed regulation parameter configuration, collect the motor speed fluctuation data. When the speed fluctuation exceeds the preset threshold, determine whether it deviates from the reference value range to obtain the speed deviation data; For the speed deviation data, through a preset speed correction algorithm, calculate the difference between the current speed fluctuation and the reference value to determine the corrected speed adjustment value; According to the corrected speed adjustment value, 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.

2. The intelligent speed regulation method of a blender according to claim 1, characterized in that: The obtaining of the initial speed regulation parameter configuration includes: Through the pre - constructed database of ingredient characteristics, obtain the information of the ingredient type currently being processed, classify and process the different physical characteristics of the ingredients to determine the preliminary type classification result; According to the type classification result, extract the physical characteristic data related to different ingredients, match the corresponding speed fluctuation range to obtain the preliminary speed configuration scheme; If there is a deviation between the speed configuration scheme and the frequency reference value, then by comparing the historical frequency data in the database, adjust the speed fluctuation range to generate the corrected speed parameter; For the corrected speed parameter, obtain the associated frequency reference value, determine whether the frequency reference value meets the preset threshold range, and output the verified frequency parameter; According to the verified frequency parameter, combined with the physical characteristic data in the ingredient characteristics, through a preset speed regulation model, determine the final initial speed regulation parameter configuration and generate a set of speed regulation parameters.

3. A method for intelligent speed regulation of a blender according to claim 1, characterized in that: The obtaining of the speed deviation data includes: Through the real - time collected motor data, use sensors to continuously monitor the speed fluctuation, determine whether the fluctuation exceeds the preset threshold to obtain the preliminary fluctuation state data; According to the preliminary fluctuation state data, conduct a comparative analysis of the speed fluctuation and the reference range. If the speed fluctuation exceeds the reference range, extract the deviation information to determine the specific deviation degree data.

4. The intelligent speed regulation method of a blender according to claim 3, characterized in that: The obtaining of the speed deviation data also includes: Through the deviation degree data, combined with the speed regulation parameters in the initial configuration, use a preset mapping rule to process the deviation information to obtain the adjusted parameter correction value; according to the adjusted parameter correction value, update the motor data in real - time, and use a logical verification method to determine whether the correction value meets the preset threshold to obtain the verified parameter data; Through the verified parameter data, combined with the result of the fluctuation judgment, conduct a secondary analysis of the speed fluctuation. If the fluctuation still exceeds the reference range, then conduct parameter fine - tuning through the historical database record to determine the final speed regulation scheme; According to the final speed regulation scheme, continuously compare the real - time collected sensor values, and use the support vector machine algorithm to optimize the speed regulation scheme to obtain the control parameters adapted to the current environment; Through the control parameters adapted to the current environment, dynamically update the result of the data detection, determine whether it meets the requirements of the range comparison to obtain the stable operation state data.

5. A method for intelligent speed regulation of a blender according to claim 1, characterized in that: The determining of the corrected speed adjustment value includes: Analyze the difference between the current rotational speed and the reference value using a preset correction algorithm based on the rotational speed deviation data, calculate the specific degree of fluctuation, and obtain preliminary correction parameters; Based on the preliminary correction parameters, perform comparison processing on the rotational speed fluctuation data. If the degree of fluctuation exceeds the preset threshold, correct the parameters through the reference comparison logic to determine the adjusted correction value; Using the adjusted correction value and combining with the rotational speed data, monitor the current rotational speed in real time, and use the fluctuation analysis method to determine whether the correction value meets the expected range to obtain the verified correction data.

6. The intelligent speed regulation method of a blender according to claim 5, characterized in that: The determination of the adjusted rotational speed correction value further includes: According to the verified correction data, conduct a secondary verification of the degree of fluctuation. If the degree of fluctuation still exceeds the reference comparison range, conduct a comparison analysis through historical rotational speed data to determine a further adjustment plan; Using the further adjustment plan and combining with the real-time state of the current rotational speed, optimize the adjustment plan using the support vector machine algorithm to obtain control parameters adapted to the environment; According to the control parameters adapted to the environment, dynamically update the rotational speed fluctuation data. If the updated degree of fluctuation still does not meet the reference comparison conditions, perform fine-tuning of the parameters through a preset logic verification tool to determine the final correction result; Using the final correction result, continuously track the rotational speed deviation data, and verify the correction result using a data calculation method to obtain stable operating parameters.

7. A method for intelligent speed regulation of a blender according to claim 1, characterized in that: The acquisition of the adjusted real-time rotational speed data includes: Regarding the adjusted rotational speed correction value, use an adaptive intelligent algorithm to monitor the rotational speed fluctuation value in real time, and obtain the fluctuation state data by comparing the analyzed fluctuation monitoring value with the preset threshold range; Based on the obtained fluctuation state data, if the fluctuation state data exceeds the preset threshold range, preliminarily correct the frequency adjustment value in the rotational speed curve graph through the dynamic adjustment method to obtain preliminary frequency parameters; Regarding the preliminary frequency parameters, if the preliminary frequency parameters do not match the target frequency range, synchronize the amplitude adjustment value, and analyze the matching degree between the amplitude adjustment value and the frequency adjustment value through an information comparison tool to determine the corrected amplitude parameters; Using the corrected amplitude parameters and combining with the real-time data stream, update the optimized rotational speed value within a short time period, and use a data verification tool to determine whether the updated rotational speed value meets the preset range to obtain the verified rotational speed data.

8. A method for intelligent speed regulation of a blender according to claim 7, characterized in that: The acquisition of the adjusted real-time rotational speed data further includes: According to the verified rotational speed data, if there are still deviations in the verified rotational speed data, conduct a data comparison analysis by combining with historical fluctuation monitoring values, and obtain further adjustment parameters through a pre-established mapping relation table; Regarding the further adjustment parameters, comprehensively analyze the rotational speed fluctuation value and the optimized rotational speed value using the support vector machine algorithm, and determine the final rotational speed control parameters with the support of the real-time monitoring method; Using the final rotational speed control parameters, dynamically update the rotational speed curve graph, continuously track the fluctuation monitoring value by combining with the real-time data stream, and determine whether the updated fluctuation state is stable to obtain stable control data.

9. The intelligent speed regulation method of a blender according to claim 1, characterized in that: It also includes: Analyze the structural change state of the ingredients during the stirring process for the adjusted real-time rotation speed data, determine whether the preset uniformity standard is reached, and obtain the uniformity evaluation result; For the uniformity evaluation result that does not reach the preset standard, recalculate the combination of rotation speed fluctuation frequency and amplitude through a short-cycle dynamic adjustment mechanism to determine the new speed regulation parameters; According to the new speed regulation parameters, update the motor control instruction, continuously monitor the ingredient mixing state during the stirring process, and obtain the latest uniformity feedback data; For the latest uniformity feedback data, if it is detected that the static structure has not been broken, adjust the dynamic structure breaking strategy by increasing the number of rotation speed fluctuations within a short cycle to obtain the optimized control parameters; According to the optimized control parameters, continuously execute the motor speed adjustment and uniformity detection loop until the preset mixing uniformity standard is reached to determine the final stirring control scheme.

10. An intelligent speed regulation system for a blender, which is used to implement the intelligent speed regulation method for the blender, is characterized in that, Including: A parameter configuration and deviation detection module, which is used to obtain the ingredient type information of the current process through a pre-established ingredient characteristic database, determine the corresponding rotation speed fluctuation range and frequency reference value for different physical characteristics to obtain the initial speed regulation parameter configuration; according to the initial speed regulation parameter configuration, use a real-time sensor to collect the motor speed fluctuation data, and when the speed fluctuation exceeds the preset threshold, determine whether it deviates from the reference value range to obtain the speed deviation data; A rotation speed correction and optimization module, which is used to calculate the difference between the current rotation speed fluctuation and the reference value through a preset rotation speed correction algorithm for the speed deviation data to determine the corrected rotation speed adjustment value; according to the corrected rotation speed adjustment value, introduce an adaptive intelligent algorithm to continuously monitor the rotation speed fluctuation, dynamically optimize the rotation speed curve, and adjust the rotation speed fluctuation frequency and amplitude within a short cycle to obtain the adjusted real-time rotation speed data; A uniformity detection and adjustment module, which is used to analyze the structural change state of the ingredients during the stirring process by using a mixing uniformity detection module for the adjusted real-time rotation speed data, determine whether the preset uniformity standard is reached, and obtain the uniformity evaluation result; for the uniformity evaluation result that does not reach the preset standard, recalculate the combination of rotation speed fluctuation frequency and amplitude through a short-cycle dynamic adjustment mechanism to determine the new speed regulation parameters; A control instruction and strategy adjustment module, which is used to update the motor control instruction according to the new speed regulation parameters, continuously monitor the ingredient mixing state during the stirring process, and obtain the latest uniformity feedback data; for the latest uniformity feedback data, if it is detected that the static structure has not been broken, adjust the dynamic structure breaking strategy by increasing the number of rotation speed fluctuations within a short cycle to obtain the optimized control parameters; A loop control module, which is used to continuously execute the motor speed adjustment and uniformity detection loop according to the optimized control parameters until the preset mixing uniformity standard is reached to determine the final stirring control scheme.

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