Intelligent control method and system of food processor, medium and equipment

Abstract

The invention discloses an intelligent control method and system for a food processor, a medium and equipment, and relates to the technical field of smart home. The method comprises the following steps: determining the type of a food material needing to be processed, and determining a corresponding stirring wave band based on the type of the food material; the food processor is controlled to operate according to the stirring wave band, and operation parameters and a version state of the food processor are obtained; determining an upgrading strategy of the food processor based on the operation parameters and the version state; and controlling the food processor to be upgraded according to the upgrading strategy. By implementing the technical scheme provided by the invention, the food processor can keep a long-term stable processing effect.

Description

Technical Field

[0001] The present application relates to the field of smart home technology, and in particular to an intelligent control method, system, medium and device for a food processor. Background Art

[0002] With the improvement of people's living standards and changes in eating habits, food processors have become an indispensable kitchen appliance in modern families. Food processors use high-speed rotating knives to chop and stir ingredients, helping users quickly make juice, soy milk, complementary foods and other foods.

[0003] In the prior art, food processors usually perform processing according to pre-stored fixed parameters. Although this fixed parameter processing method is simple to operate, since similar ingredients from different batches and origins may have different physical properties, using the same processing parameters may lead to unstable processing results of the ingredients. In addition, as the food processor is used for a longer time, the performance of the equipment will gradually change. These changes will affect the actual operating results of the equipment, making it difficult for the food processor to maintain long-term stable processing results. Summary of the invention

[0004] The present application provides an intelligent control method for a food processor, which enables the food processor to maintain a long-term stable processing effect.

[0005] In a first aspect, the present application provides an intelligent control method for a food processor, the method comprising: Determine the type of food to be processed, and determine the corresponding stirring band based on the type of food; Controlling the food processor to operate according to the stirring band, and obtaining the operating parameters and version status of the food processor; Determining an upgrade strategy for the food processor based on the operating parameters and the version status; Control the food processor to upgrade according to the upgrade strategy.

[0006] By adopting the above technical scheme, by determining the type of food and matching the corresponding stirring band, the food processor can automatically adjust the processing parameters according to the physical characteristics of different food ingredients. This intelligent control method based on food characteristics overcomes the technical defect that fixed parameters in the prior art are difficult to adapt to the differences in the characteristics of different food ingredients, and ensures the consistency of processing effects. During the operation of the food processor, the operating parameters are collected in real time, and a comprehensive analysis is conducted in combination with the version status of the equipment to establish a dynamic upgrade strategy determination mechanism. This adaptive upgrade scheme based on actual operating data will automatically adjust the parameter configuration of the stirring band to ensure that a stable processing effect can be maintained when the equipment performance changes. Finally, by implementing the upgrade strategy, dynamic optimization of the food processor control system is achieved, which can not only adapt to changes in equipment performance in a timely manner, but also continuously optimize control parameters according to the processing effects of different batches of food ingredients, so that the food processor can maintain a long-term stable processing effect.

[0007] In a second aspect of the present application, an intelligent control system for a food processor is provided, the system comprising: A stirring band determination module, used to determine the type of food to be processed, and determine the corresponding stirring band based on the type of food; A parameter acquisition module, used to control the food processor to operate according to the stirring band, and to obtain the operating parameters and version status of the food processor; An upgrade strategy determination module, used to determine the upgrade strategy of the food processor based on the operating parameters and the version status; An upgrade control module is used to control the food processor to upgrade according to the upgrade strategy.

[0008] In a third aspect of the present application, a computer storage medium is provided, wherein the computer storage medium stores a plurality of instructions, wherein the instructions are suitable for being loaded by a processor and executing the above method steps.

[0009] In a fourth aspect of the present application, an electronic device is provided, comprising: a processor and a memory; wherein the memory stores a computer program, and the computer program is suitable for being loaded by the processor and executing the above-mentioned method steps.

[0010] In summary, one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: The present application determines the type of food and matches the corresponding stirring band, so that the food processor can automatically adjust the processing parameters according to the physical characteristics of different food ingredients. This intelligent control method based on food characteristics overcomes the technical defect of the prior art that fixed parameters are difficult to adapt to the differences in the characteristics of different food ingredients, and ensures the consistency of processing effects. The operating parameters are collected in real time during the operation of the food processor, and a comprehensive analysis is conducted in combination with the version status of the equipment to establish a dynamic upgrade strategy determination mechanism. This adaptive upgrade solution based on actual operating data will automatically adjust the parameter configuration of the stirring band to ensure that a stable processing effect can be maintained when the equipment performance changes. Finally, through the implementation of the upgrade strategy, dynamic optimization of the food processor control system is achieved, which can not only adapt to changes in equipment performance in a timely manner, but also continuously optimize control parameters according to the processing effects of different batches of food ingredients, so that the food processor can maintain long-term stable processing effects. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 It is a flow chart of an intelligent control method of a food processor provided in an embodiment of the present application; Figure 2 This is a schematic diagram of an intelligent control principle of a food processor provided in an embodiment of the present application; Figure 3 It is a module schematic diagram of an intelligent control system of a food processor provided in an embodiment of the present application; Figure 4 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application.

[0012] Description of reference numerals: 400, electronic device; 401, processor; 402, communication bus; 403, user interface; 404, network interface; 405, memory. DETAILED DESCRIPTION

[0013] In order to enable technicians in this field to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0014] In the description of the embodiments of the present application, words such as "for example" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "for example" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "for example" or "for example" is intended to present related concepts in a specific way.

[0015] In the description of the embodiments of the present application, the meaning of the term "multiple" refers to two or more. For example, multiple systems refer to two or more systems, and multiple screen terminals refer to two or more screen terminals. In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. The terms "include", "comprise", "have" and their variations all mean "including but not limited to", unless otherwise specifically emphasized.

[0016] The following will provide a clear and complete description of the technical solutions in the embodiments of the present application in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments.

[0017] Please refer to Figure 1 , a flowchart of a method for intelligent assessment of food safety is proposed. The method can be implemented by a computer program, a single-chip microcomputer, or run on an intelligent assessment system for food safety. The computer program can be integrated into a computer device or run as an independent tool application. Specifically, the method includes steps 10 to 40, which are as follows: Step 10: Determine the type of food to be processed, and determine the corresponding stirring band based on the type of food.

[0018] In the embodiment of the present application, the food type refers to the physical property classification of the food that needs to be processed, including but not limited to the combination characteristics of the food's hardness level, fiber content, water content, toughness and other physical properties.

[0019] In the embodiment of the present application, the stirring band refers to the running curve of the motor speed of the food processor changing with time during the processing. Specifically, the stirring band is composed of a plurality of different speed intervals combined in a specific time sequence, each speed interval has a corresponding running time, and the complete processing process is formed by the continuous switching of these speed intervals.

[0020] Specifically, the food processor transmits a sound wave signal of a preset frequency to the food in the food cup through a sound wave detection device. Since food with different physical properties has different reflection and attenuation characteristics for sound waves, the reflected wave signal is collected through a receiving device, and characteristic parameters such as waveform characteristics, attenuation coefficient and frequency change are extracted. For example, hard food will produce a stronger reflected wave and decay slowly, while soft and watery food will cause the sound wave to decay rapidly and show specific frequency change characteristics. By comparing and analyzing these characteristic parameters with the pre-established sound wave characteristic mapping table, the type of food can be accurately identified.

[0021] Furthermore, after determining the type of food, the cooking machine will retrieve the physical property parameters corresponding to this type of food from the preset food database. These parameters comprehensively reflect the processing characteristics of the food, such as hardness, toughness, and water content, and are an important basis for determining the mixing parameters. Based on these physical property parameters, the system calculates the initial mixing parameters, including the speed range and processing cycle range suitable for this type of food. Next, the speed range is subdivided into multiple speed intervals, and a reasonable operating time is allocated to each speed interval based on the physical characteristics of the food. By arranging and combining these speed intervals according to physical laws, the system finally generates a complete mixing band curve, which reflects the law of change of speed over time and can adaptively adjust the processing process according to the characteristics of the food.

[0022] Based on the above embodiment, as an optional embodiment, the step of determining the type of food to be processed may further include the following steps: Step 101: transmitting a sound wave signal of a preset frequency, and receiving a reflected wave signal after the sound wave signal interacts with food.

[0023] Specifically, the sound wave transmitter of the food processor first transmits an ultrasonic signal with a frequency of 20kHz to 200kHz into the food cup. This frequency range is selected because sound waves in this range have strong penetrating power, and at the same time, they produce obvious reflection differences for materials of different densities and structures. When the sound waves interact with the ingredients in the food cup, part of the sound waves will be reflected by the surface of the ingredients, and part of the sound waves will propagate inside the ingredients and attenuate, eventually forming a reflected wave signal with characteristic information. These reflected wave signals will be collected by multiple receiving sensors arranged around the food cup and converted into electrical signals.

[0024] Step 102: Obtain the waveform characteristics, attenuation coefficient and frequency change of the reflected wave signal.

[0025] Specifically, the collected reflected wave signals are digitized and feature extracted. First, the waveform characteristics of the reflected wave are analyzed by Fourier transform, including time domain characteristics such as the number of peaks and the degree of waveform fluctuation. These characteristics are closely related to the surface structure and internal tissue characteristics of the food. Secondly, the energy attenuation coefficient of the sound wave during propagation is calculated. Hard and dense food will cause less energy attenuation, while loose and porous food will cause greater energy loss. Finally, the frequency changes of the reflected wave signal relative to the incident wave are analyzed, including the frequency offset and spectrum distribution characteristics. These changes reflect the physical properties of the food, such as elasticity and density.

[0026] Step 103: Determine the type of food according to the waveform characteristics, attenuation coefficient and frequency change by comparing with a preset sound wave characteristic mapping table.

[0027] Specifically, in order to improve the accuracy of recognition, the system uses a multi-dimensional feature fusion method for matching. First, the three types of parameters, waveform characteristics, attenuation coefficients, and frequency changes, are calculated to match the corresponding parameters in the mapping table. Then, they are weighted and fused using the set weight coefficients to obtain a comprehensive matching score. When the comprehensive matching score of a certain food type exceeds the preset threshold, the system confirms the recognition result. If the matching scores of multiple candidate types are close, the system will initiate a secondary confirmation mechanism and recheck by adjusting the frequency of the transmitted signal to further improve the reliability of recognition.

[0028] For example, when testing carrots, due to their dense tissue structure, they will produce strong surface reflection and small energy attenuation, and the frequency change will show typical hard characteristics; while when testing watermelons, their loose and watery characteristics will lead to weaker reflection intensity, large energy attenuation, and obvious low-frequency characteristics. The extracted characteristic parameters are matched and analyzed with the pre-established acoustic wave characteristic mapping table. The mapping table stores the standard acoustic wave characteristic parameters of various common ingredients and their corresponding relationships. By calculating the similarity between the characteristic parameters of the tested ingredients and the standard parameters, the type of ingredients can be accurately determined.

[0029] Based on the above embodiment, as an optional embodiment, the step of determining the corresponding stirring band based on the food type may further include the following steps: Step 201: Obtain physical property parameters corresponding to the food type from a preset food database.

[0030] Specifically, the preset food material database is accessed, which stores a set of physical property parameters of various food materials. These parameters include shear strength, compression modulus, cohesion coefficient, fiber orientation, moisture content and other key indicators that reflect the processing characteristics of food materials. For example, the carrot data set contains parameters such as its high shear strength (about 15-20N / mm²), medium compression modulus (about 8-12MPa) and significant fiber orientation; while the tomato data set records its low shear strength (about 2-4N / mm²), high moisture content (about 94%) and other characteristic data.

[0031] Step 202: Determine initial stirring parameters according to the physical characteristic parameters, where the initial stirring parameters include a rotation speed range and a cycle interval.

[0032] Specifically, after obtaining the physical property parameters, the initial stirring parameters are calculated by the established parameter mapping model. The model comprehensively considers the correlation between the physical properties of the food and the processing effect, and can determine the appropriate speed range and cycle interval. Specifically, first, the required minimum cutting force is determined based on the shear strength and compression modulus, and the minimum speed requirement is obtained by combining the tool specifications; then the maximum speed limit required to achieve the ideal cutting effect is calculated based on the fiber directionality and cohesion coefficient; finally, the appropriate processing cycle is estimated by parameters such as moisture content, thereby forming a complete set of initial stirring parameters. For example, for high-hardness food such as carrots, a larger speed range (such as 3000-12000rpm) will be set based on its higher shear strength to ensure sufficient cutting force; at the same time, considering its significant fiber directionality, the processing cycle will be extended accordingly to fully cut the fiber. For soft and watery food such as tomatoes, a smaller speed range (such as 2000-8000rpm) and a shorter cycle interval will be set to avoid excessive processing and nutrient loss.

[0033] Step 203: Divide the speed range in the initial stirring parameters into a plurality of speed intervals, and determine the operating time of each speed interval.

[0034] Specifically, the speed range in the initial stirring parameters is first divided. Taking the hard food with a speed range of 3000-12000rpm as an example, the system divides it into a low-speed range (3000-5000rpm), a medium-speed range (5000-8000rpm) and a high-speed range (8000-12000rpm). This division is based on the physical property parameters of the food. The low-speed range is mainly used for initial crushing, the medium-speed range is used for deep cutting, and the high-speed range is used for fine homogenization. The basis for the division comes from the shear characteristic curve of the food, ensuring that each range can play a specific processing role. After determining the speed range, the optimal operating time of each range is calculated according to the physical property parameters of the food. The calculation process comprehensively considers factors such as the hardness change law of the food, the fiber breakage characteristics and the dynamic change of the moisture content. For example, for food with a high fiber content, the system will calculate the shortest time required to completely cut the fiber through its fiber breakage time curve, and use this time as the benchmark operating time of the high-speed range, and then adjust it appropriately according to the actual working conditions. At the same time, the system will also take into account the softening characteristics of the ingredients and dynamically adjust the duration ratio of each interval to ensure the continuity and effectiveness of the processing process.

[0035] Step 204: Generate a stirring band consisting of a plurality of continuous speed intervals according to the speed intervals and their corresponding operating time.

[0036] Specifically, after the speed range and running time are determined, a complete mixing band is constructed. This process is not a simple splicing of the speed ranges in sequence, but a scientific speed transition plan is designed based on the physical change laws of the ingredients. For example, for carrot ingredients, the system will generate such a mixing band: first run in the low-speed range (3000-5000rpm) for 15 seconds for preliminary crushing, then smoothly transition to the medium-speed range (5000-8000rpm) at an acceleration of 500rpm / s and maintain for 25 seconds for deep cutting, and finally quickly increase to the high-speed range (8000-12000rpm) and run for 20 seconds to complete the refinement. During the conversion process of each range, the system will dynamically adjust the transition rate according to the immediate state of the ingredients to avoid drastic changes in speed.

[0037] Step 20: Control the food processor to operate according to the blending band, and obtain the operating parameters and version status of the food processor.

[0038] Specifically, the generated stirring band is converted into a motor control instruction sequence, which contains detailed speed change timing information. The controller adjusts the output speed of the motor in real time through the PWM signal to ensure that the actual speed curve is synchronized with the preset stirring band. In this process, the operating parameters of the food processor are synchronously collected through multiple sensors. Among them, the speed sensor collects the real-time speed value of the motor with a sampling period of 20ms; the current sensor records the working current of the motor with the same sampling period; the temperature sensor monitors the motor winding temperature with a sampling period of 1s; the vibration sensor collects the vibration signal of the motor and the transmission system with a sampling frequency of 1kHz. These sensors convert analog signals into digital signals through analog-to-digital conversion circuits and collect them uniformly through the main controller of the device. At the same time, the system also reads the version status information of the food processor. The version status information includes usage status data such as cumulative operating time, cumulative number of starts, tool usage time, and sealing ring usage time. These data are stored in the EEPROM of the device and updated every time it runs. In addition, the system also collects data reflecting the working status of components such as the vibration spectrum characteristics of the bearing and the power curve of the motor. All these operating parameters and version status data are integrated into a unified data stream and transmitted to the control system in real time through the communication interface of the device.

[0039] Step 30: Determine the upgrade strategy of the food processor based on the operating parameters and version status.

[0040] Specifically, the operating parameters are analyzed and evaluated. For example, by comparing the deviation between the actual motor speed and the target speed of the stirring band, the speed control accuracy of each speed range is calculated; by analyzing the dynamic change characteristics of the motor current, the load adaptive ability is evaluated; by the change trend of the temperature curve, the rationality of the current power control strategy is evaluated; by the spectrum analysis of the vibration signal, the smoothness of the speed climbing curve is evaluated. At the same time, the system will also analyze the version status information, including the current program version number, the set values ​​of each control parameter, and historical operation data. Based on these data analysis results, the system uses a preset scoring model to quantitatively evaluate the control performance of the food processor. For example, when the speed tracking error exceeds 5%, the PID parameter score decreases; when the load mutation response is slow, the current loop control score decreases; when the speed switching is not smooth enough, the acceleration and deceleration control score decreases. The system compares the scoring results with the standard performance indicators to identify the control parameters and algorithm modules that need to be optimized first. According to the evaluation results, the system formulates a program upgrade strategy in a targeted manner. For example, when it is detected that the speed control accuracy of a food processor in the high-speed range is insufficient, the system will generate an upgrade package containing optimized PID parameters and speed loop control algorithm; when it is found that the load adaptability is poor, it will generate an upgraded package of improved current limiting strategy and torque control algorithm; when the speed switching is not smooth enough, the system will optimize the acceleration and deceleration control curves and smooth transition algorithm.

[0041] Based on the above embodiment, as an optional embodiment, the step of determining the upgrade strategy of the food processor based on the operating parameters and the version status may further include the following steps: Step 301: Perform data analysis on the operating parameters to obtain equipment operating characteristics.

[0042] Specifically, the operating parameters are analyzed in multiple dimensions to extract the operating characteristics of the equipment. In terms of speed control, the speed tracking accuracy characteristics are obtained by calculating the root mean square error between the actual speed and the target speed; the speed stability characteristics are obtained by analyzing the spectrum characteristics of the speed fluctuation through Fourier transform. In terms of current control, the current dynamic response characteristics are obtained by analyzing the current rise time and overshoot; the load adaptability characteristics are obtained by statistically analyzing the current fluctuation range. In terms of temperature change, the power control characteristics are obtained by fitting the changing trend of the temperature curve. These characteristic parameters together constitute the operating characteristic vector of the equipment.

[0043] Step 302: Compare the device operation characteristics with the standard operation parameters corresponding to the current version status to obtain a comparison result.

[0044] Specifically, the extracted operating characteristics are compared and analyzed with the standard operating parameters corresponding to the current version status. The standard operating parameters are performance benchmarks established based on a large amount of experimental data, and include standard values ​​and allowable deviation ranges for various control indicators. For example, in terms of speed control, the standard operating parameters specify the tracking accuracy requirements for different speed ranges, such as the allowable speed fluctuation range for the low-speed range is ±3%, and the high-speed range is ±5%; in terms of current control, it is specified that the response time for sudden load changes shall not exceed 50ms, and the overshoot shall not exceed 10%. The system obtains detailed comparison results by calculating the deviation value between the operating characteristics and the standard parameters.

[0045] Step 303: Determine the performance deviation item according to the comparison result.

[0046] Specifically, based on the comparison results, the system identifies specific performance deviation items. When the deviation of a characteristic parameter exceeds a preset threshold, the system marks it as a performance deviation item. For example, when the speed fluctuation in the high-speed section exceeds ±5%, a speed control deviation item is generated.

[0047] Based on the above embodiment, as an optional embodiment, the step of determining the performance deviation item according to the comparison result may further include the following steps: Step 3031: Based on the comparison results, extract the deviation data of each operating parameter.

[0048] Specifically, based on the comparison results, the deviation data of each operating parameter is extracted. For speed control parameters, the real-time deviation value between the actual speed and the target speed is calculated; for current control parameters, the deviation value between the actual current response and the standard response curve is calculated; for power control parameters, the deviation value between the actual power and the standard power curve is calculated. These deviation data are recorded at a sampling period of 20ms to form a basic deviation data set.

[0049] Step 3032: Establish a deviation sequence that changes over time based on the real-time deviation data.

[0050] Specifically, these real-time deviation data are arranged in chronological order to establish a deviation trend sequence. For example, for speed control deviation, the system records the change of speed tracking error within 24 consecutive hours; for current response deviation, the change of dynamic response characteristics within the same period is recorded. These sequences not only contain the instantaneous value of the deviation, but also reflect the trend of the deviation over time.

[0051] Step 3033: Perform weighted accumulation of the deviation trend sequence in the time dimension to obtain the cumulative impact value of the parameter deviation.

[0052] Specifically, these deviation trend sequences are weighted and accumulated in the time dimension. In the weighted calculation, the system uses the time decay function to assign different weights to the deviations in different periods. The deviations in the most recent period are given higher weights (such as 0.8), and the deviations in the earlier period are given lower weights (such as 0.2). Through this weighted accumulation, the system obtains the cumulative impact value of each parameter deviation. This processing method not only takes into account the cumulative effect of historical deviations, but also highlights the importance of recent deviations.

[0053] Step 3034: Filter out abnormal parameters whose cumulative impact values ​​exceed the deviation accumulation threshold, and perform correlation clustering on the abnormal parameters.

[0054] Specifically, the cumulative impact value of each parameter is compared with the preset deviation cumulative threshold. For example, when the cumulative impact value of speed control exceeds the threshold of 90%, the parameter is marked as an abnormal parameter; when the cumulative impact value of current response exceeds the threshold of 85%, the parameter is also marked as an abnormal parameter. All marked abnormal parameters then enter the correlation cluster analysis stage.

[0055] Step 3035: Identify the main influencing factors based on the result of the association clustering, and determine the main influencing factors as performance deviation items.

[0056] Specifically, in the correlation cluster analysis, the system uses a hierarchical clustering algorithm to group abnormal parameters. By calculating the correlation coefficient matrix between parameters, the system identifies parameter groups with strong correlation. For example, if it is found that the increase in speed fluctuation has a high correlation with the slow current response, these two parameters will be classified into the same cluster group. This cluster analysis helps the system understand the inherent connection between parameter deviations.

[0057] Step 304: Determine the corresponding functional module and upgrade priority based on the performance deviation item, and generate an upgrade strategy.

[0058] Specifically, firstly, the corresponding relationship between the performance deviation item and the functional module is established. Through the preset functional module mapping table, the system associates each performance deviation item with a specific functional module. For example, when the PID parameter is determined as a performance deviation item, the system maps it to the speed control module; when the current response characteristic is determined as a performance deviation item, the system maps it to the current control module. This mapping relationship ensures that each performance deviation item can find its corresponding optimization target. Subsequently, a multi-dimensional scoring model is used to determine the upgrade priority of each functional module. The scoring model considers three dimensions: the severity, scope of impact, and urgency of the performance deviation. In terms of severity, the system determines the basic score based on the cumulative impact value of the performance deviation item. The larger the cumulative impact value, the higher the basic score; in terms of scope of impact, the system evaluates the degree of correlation and impact of the deviation on other functional modules. The higher the correlation, the greater the additional score; in terms of urgency, the system determines the weighting coefficient based on the impact of the deviation on the safety of product use. The stronger the safety relevance, the greater the weighting coefficient.

[0059] Through the comprehensive scoring of these three dimensions, the system calculates the final priority score for each functional module. For example, when the PID parameter deviation of the speed control module has a large cumulative impact value (such as 95 points), and has a strong correlation with multiple modules (additional 20 points), and has a significant impact on safety (weight 1.2), its final priority score will reach 138 points; and although the parameter deviation of the power control module also exists, because of its small cumulative impact value (such as 70 points), weak correlation (additional 10 points), and limited safety impact (weight 1.0), the final priority score is only 80 points. Based on the priority scores of functional modules, the system generates a hierarchical upgrade strategy. For functional modules with a priority score of more than 120 points, the system classifies them as urgently needed upgrade categories and retrieves the corresponding optimization solutions from the optimization parameter library. For example, for the speed control module, the system may generate an upgrade package containing optimized PID parameter values ​​and improved speed loop control algorithms; for functional modules with priority scores between 80 and 120, the system divides them into categories to be upgraded and generates corresponding optimization suggestions; for functional modules with priority scores below 80, the system puts them into the monitoring category and does not upgrade them for the time being. Finally, the system integrates all functional modules to be upgraded and their optimization solutions into a complete upgrade strategy, which not only includes specific optimization parameters and algorithms, but also detailed upgrade steps and expected results.

[0060] Step 40: Control the food processor to upgrade according to the upgrade strategy.

[0061] Specifically, the current running state of the food processor is first comprehensively tested to ensure that the device is in a state suitable for upgrading. The test contents include power supply voltage stability, communication signal quality, storage space adequacy, etc. The system will start the upgrade process only when all the test indicators meet the requirements. Subsequently, the system gradually implements the upgrade operation according to the priority order of the functional modules. For the speed control module with the highest priority, the system first downloads the optimized PID parameters and control algorithm to the cache area of ​​the device. The download process adopts a packet transmission mechanism, each data packet size is 4KB, and the integrity of the transmission is ensured by CRC verification. When all data packets are successfully transmitted and passed the verification, the system transfers the new control parameters and algorithms to the designated program area. During the parameter update process, the system adopts a double buffer mechanism to ensure the security of the upgrade. The new control parameters are first loaded into the backup buffer area, while the original parameters are retained in the main buffer area. The system gradually transitions to the new control parameters through soft switching. For example, the PID parameters are smoothly transitioned from the old value to the new value according to linear interpolation within 10 seconds. This smooth switching mechanism effectively avoids the control system oscillation caused by parameter mutation.

[0062] Based on the above embodiment, as an optional embodiment, the step of controlling the food processor to upgrade according to the upgrade strategy may further include the following steps: Step 401: When the food processor is in a network connection state, a remote upgrade package is obtained based on an upgrade strategy, and the remote upgrade package is verified for security.

[0063] Specifically, in order to solve the upgrade requirements of the food processor in different network environments, the system has designed a dual-mode upgrade mechanism that supports both online remote upgrades and offline local upgrades. When the food processor is in a network connection state, the system first detects the quality indicators of the network connection. The system requires that the network delay is less than 200ms and the packet loss rate is less than 1% to ensure the reliable transmission of the upgrade package. When the network quality meets the requirements, the system initiates an upgrade package request to the remote server based on the pre-established upgrade strategy. The request information contains information such as the device unique identification code, the current version number, and the function module list. The server matches the corresponding upgrade package based on this information. After obtaining the remote upgrade package, the system immediately conducts multiple security verifications. First, digital signature verification is performed, and the RSA-2048 algorithm is used to verify the source reliability of the upgrade package; then an integrity check is performed, and the hash value of the upgrade package is calculated by the SHA-256 algorithm and compared with the preset check value; finally, version compatibility verification is performed to ensure that the upgrade package is compatible with the current system version. Only when all security verifications are passed will the system proceed to the next step.

[0064] Step 402: When the security verification is passed, the upgrade instruction of the remote upgrade package is written into the local storage unit.

[0065] Specifically, when the security verification is passed, the system writes the upgrade instructions into the local storage unit. In order to ensure the reliability of storage, the system adopts a dual backup mechanism to save the upgrade instructions in both the main storage area and the backup storage area. The upgrade instructions are organized according to the functional modules, and each instruction set contains information such as module identification, upgrade parameters, and execution order. For example, the upgrade instruction set of the speed control module contains new PID parameter values, control algorithm update instructions, execution priority, etc.

[0066] Step 403: When the food processor is not in a network connection state, a preset offline upgrade instruction is retrieved from a local storage unit.

[0067] Specifically, when the food processor is offline, the system will automatically switch to offline upgrade mode. In this mode, the system retrieves the pre-stored upgrade instructions from the local storage unit, such as through USB transmission. In order to ensure the reliability of offline upgrades, the system will first verify the integrity of the locally stored instructions, and ensure that the instructions have not been damaged or tampered with by comparing the data in the primary and backup storage areas.

[0068] Step 404: Execute the upgrade operation of the functional modules in stages according to the execution priority of the upgrade instruction.

[0069] Specifically, whether in online mode or offline mode, the system strictly follows the priority order specified in the upgrade instructions and executes the upgrade operation of the functional modules in stages. For the modules with the highest priority, such as the speed control module, the system first loads its upgrade instructions into the execution queue. Each upgrade stage includes three steps: pre-check, parameter update, and function verification. For example, when updating the PID parameters, the system first checks the controller status, then updates the parameter values ​​in a progressive manner, and finally verifies whether the control effect meets expectations. During the upgrade operation, the system monitors various key indicators in real time. If an abnormality is found at a certain stage, such as the control effect after the parameter update is not up to standard, the system will immediately start the rollback mechanism and restore it to the state before the start of this stage. This method of phased execution and real-time monitoring effectively reduces the upgrade risk and improves the reliability of the upgrade.

[0070] Based on the above embodiment, as an optional embodiment, the intelligent control method of the food processor may further include the following process: Specifically, when obtaining the operating parameters of the food processor, the operating parameters may include but are not limited to key parameters such as motor speed, current, temperature, vibration, etc. The system collects these parameter data with a sampling period of 20ms and sets multiple levels of warning thresholds. For example, for motor temperature, the system sets 75°C as the first-level warning threshold, 85°C as the second-level warning threshold, and 95°C as the third-level warning threshold; for motor speed, the system monitors its fluctuation range and sets ±7% as the first-level warning, ±10% as the second-level warning, and ±15% as the third-level warning.

[0071] When a parameter is detected to be out of the normal range, the system immediately starts the abnormal state assessment process. The assessment adopts a multi-dimensional scoring mechanism, which comprehensively considers the degree of parameter deviation, duration and scope of influence. For example, when the motor temperature reaches 82°C and lasts for 30 seconds, and the speed fluctuates by ±8%, the system will comprehensively calculate the abnormal score. According to the score, the system divides the abnormal state into three levels: a score of 60-80 is a mild abnormality, 80-90 is a moderate abnormality, and 90 points or above is a severe abnormality.

[0072] Based on the abnormality level, the system executes the corresponding emergency handling strategy. For mild abnormalities, the system takes preventive measures, such as appropriately reducing the operating power while maintaining normal operation; for moderate abnormalities, the system performs protective downgrade operation, such as limiting the speed within a safe range and issuing a warning prompt; for severe abnormalities, the system immediately executes emergency protection measures, such as rapidly reducing the speed until it stops, and triggers a fault alarm. For example, when it is detected that the motor temperature continues to rise and reaches 90°C, the system will immediately start the emergency speed reduction program and smoothly reduce the speed to the shutdown state within 3 seconds.

[0073] While executing emergency handling, the key data of the entire process is recorded in detail. The record content includes the parameter status when the abnormality occurs, the basis for determining the abnormality level, the emergency handling measures and their effects. For example, the system records the motor temperature curve, speed change trend, and various parameter responses during the deceleration process when the abnormality occurs. These data are saved in a special abnormality log database as an important basis for subsequent analysis and optimization. The system regularly analyzes the accumulated abnormality records to dynamically optimize the upgrade strategy. The optimization content includes adjusting the step size of parameter updates, modifying the upgrade order of functional modules, and improving the emergency handling mechanism. For example, if the analysis finds that the rapid update of certain parameters is prone to trigger abnormalities, the system will add a progressive update control logic to the upgrade strategy, changing the original one-step update to a multi-step progressive update, and performing stability verification after each step update.

[0074] See also Figure 2, is a schematic diagram of the intelligent control of a food processor provided in an embodiment of the present application, including functional modules such as device initialization, operation monitoring, data processing, fault diagnosis, and remote upgrade. First, the food processor enters the initialization process by starting up, and establishes a data connection with the cloud platform at the same time. This design ensures that the device has real-time data interaction capabilities. Then it enters the trial operation state, and ensures that the device is in normal working condition through a double verification mechanism (machine self-check and manual safety check). Once an abnormality is found, the system will immediately upload the fault information to the cloud platform and start the corresponding troubleshooting process. In the data processing stage, the device operation data is collected and uploaded in real time, and the data is analyzed and processed through the cloud platform. This design realizes real-time monitoring and intelligent analysis of the device operation status. For the remote upgrade mechanism, two upgrade modes are supported. The offline mode can perform system upgrades through the USB interface, and the online mode can perform remote upgrades through the cloud platform. A troubleshooting mechanism is set during the upgrade process to ensure the reliability of the upgrade.

[0075] When working, it can record device status information, save historical working parameters, support interconnection with other smart home appliances, and realize cloud storage of data. Multiple fault diagnosis points are set in the entire control process, including: machine self-check fault diagnosis, manual safety inspection, real-time monitoring of the operation process, and software fault monitoring and maintenance.

[0076] See also Figure 3 , is a module diagram of an intelligent control system of a food processor provided in an embodiment of the present application. The intelligent control system of the food processor may include: a stirring band determination module, a parameter acquisition module, an upgrade strategy determination module and an upgrade control module, wherein: A stirring band determination module, used to determine the type of food to be processed, and determine the corresponding stirring band based on the type of food; A parameter acquisition module, used to control the food processor to operate according to the stirring band, and to obtain the operating parameters and version status of the food processor; An upgrade strategy determination module, used to determine the upgrade strategy of the food processor based on the operating parameters and the version status; An upgrade control module is used to control the food processor to upgrade according to the upgrade strategy.

[0077] Optionally, the stirring band determination module is further used to transmit a sound wave signal of a preset frequency, and receive a reflected wave signal after the sound wave signal interacts with the food; Obtaining waveform characteristics, attenuation coefficient and frequency change of the reflected wave signal; The type of food is determined according to the waveform characteristics, attenuation coefficient and frequency change and by comparing with a preset sound wave characteristic mapping table.

[0078] Optionally, the stirring band determination module is further used to obtain physical characteristic parameters corresponding to the food type from a preset food database; Determine initial stirring parameters according to the physical characteristic parameters, wherein the initial stirring parameters include a rotation speed range and a cycle interval; Dividing the speed range in the initial stirring parameter into a plurality of speed intervals, and determining the running time of each speed interval; A stirring band consisting of a plurality of continuous speed intervals is generated according to the speed intervals and their corresponding operating time.

[0079] Optionally, the upgrade strategy determination module is further used to perform data analysis on the operating parameters to obtain equipment operating characteristics; Comparing the device operation characteristics with the standard operation parameters corresponding to the current version status to obtain a comparison result; determining a performance deviation item according to the comparison result; Based on the performance deviation item, the corresponding functional module and upgrade priority are determined, and an upgrade strategy is generated.

[0080] Optionally, the upgrade strategy determination module is further used to extract deviation data of each operating parameter based on the comparison result; Establishing a deviation sequence that changes over time based on the real-time deviation data; Performing weighted accumulation of the deviation trend sequence in the time dimension to obtain a cumulative impact value of the parameter deviation; Screen out abnormal parameters whose cumulative impact values ​​exceed the deviation cumulative threshold, and perform correlation clustering on the abnormal parameters; Based on the result of the association clustering, main influencing factors are identified and determined as performance deviation items.

[0081] Optionally, the upgrade control module is further used to determine whether the food processor is in an abnormal state based on the operating parameters of the food processor; When the abnormal state is detected, determining the abnormality level of the abnormal state; Execute the corresponding emergency handling strategy according to the abnormal level and record the handling results; The upgrade strategy is optimized based on the abnormal state and the corresponding processing result.

[0082] Optionally, the upgrade control module is further used to obtain a remote upgrade package based on the upgrade strategy and perform security verification on the remote upgrade package when the food processor is in a network connection state; When the security verification is passed, the upgrade instruction of the remote upgrade package is written into the local storage unit; When the food processor is not in a network connection state, retrieving a preset offline upgrade instruction from the local storage unit; The upgrade operation of the functional modules is executed in stages according to the execution priority of the upgrade instruction.

[0083] It should be noted that: when the system provided in the above embodiment realizes its functions, only the division of the above functional modules is used as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the system and method embodiments provided in the above embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0084] An embodiment of the present application also provides a computer storage medium, which can store multiple instructions. The instructions are suitable for being loaded by a processor and executing an intelligent control method for a food processor in the above embodiment. The specific execution process can be found in the specific description of the above embodiment and will not be repeated here.

[0085] Please refer to Figure 4 The application also discloses an electronic device. Figure 4 The electronic device 400 may include: at least one processor 401 , at least one network interface 404 , a user interface 403 , a memory 405 , and at least one communication bus 402 .

[0086] The communication bus 402 is used to realize the connection and communication between these components.

[0087] The user interface 403 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 403 may also include a standard wired interface and a wireless interface.

[0088] The network interface 404 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0089] Among them, the processor 401 may include one or more processing cores. The processor 401 uses various interfaces and lines to connect various parts in the entire server, and executes various functions of the server and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 405, and calling data stored in the memory 405. Optionally, the processor 401 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 401 can integrate one or a combination of a central processing unit (Central Processing Unit, CPU), a graphics processing unit (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 401, and it can be implemented separately through a chip.

[0090] Among them, the memory 405 may include a random access memory (Random Access Memory, RAM) and may also include a read-only memory (Read-Only Memory). Optionally, the memory 405 includes a non-transitory computer-readable storage medium. The memory 405 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 405 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 405 may optionally also be at least one storage device located away from the aforementioned processor 401. Refer to Figure 4 , the memory 305 as a computer storage medium may include an operating system, a network communication module, a user interface module and an application program of an intelligent control method of a food processor.

[0091] exist Figure 4In the electronic device 400 shown, the user interface 403 is mainly used to provide an input interface for the user and obtain the data input by the user; and the processor 401 can be used to call the application program of the intelligent control method of the food processor stored in the memory 405. When executed by one or more processors 401, the electronic device 400 executes one or more of the methods described in the above embodiments. It should be noted that for the aforementioned method embodiments, for the sake of simple description, they are all expressed as a series of action combinations, but those skilled in the art should know that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for the present application.

[0092] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0093] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0094] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0095] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0096] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a memory and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the various embodiments of the present application. The aforementioned memory includes: various media that can store program codes, such as USB flash drives, mobile hard drives, magnetic disks or optical disks.

[0097] The above is only an exemplary embodiment of the present disclosure and cannot be used to limit the scope of the present disclosure. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the disclosure of the specification and the truth of practice, those skilled in the art will easily think of other embodiments of the present disclosure.

[0098] This application is intended to cover any variation, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the art not described in the present disclosure. The description and examples are to be regarded as exemplary only, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. An intelligent control method for a food processor, characterized in that: The method comprises: Determine the type of food to be processed, and determine the corresponding stirring band based on the type of food; Controlling the food processor to operate according to the stirring band, and obtaining the operating parameters and version status of the food processor; Determining an upgrade strategy for the food processor based on the operating parameters and the version status; Control the food processor to upgrade according to the upgrade strategy.

2. The intelligent control method of a food processor according to claim 1, characterized in that: Determining the type of food to be processed includes: Transmitting a sound wave signal of a preset frequency, and receiving a reflected wave signal after the sound wave signal interacts with the food; Obtaining waveform characteristics, attenuation coefficient and frequency change of the reflected wave signal; The type of food is determined according to the waveform characteristics, attenuation coefficient and frequency change and by comparing with a preset sound wave characteristic mapping table.

3. The intelligent control method of a food processor according to claim 1, characterized in that: The determining the corresponding stirring band based on the food type includes: Obtaining physical property parameters corresponding to the food type from a preset food database; Determine initial stirring parameters according to the physical characteristic parameters, wherein the initial stirring parameters include a rotation speed range and a cycle interval; Dividing the speed range in the initial stirring parameter into a plurality of speed intervals, and determining the running time of each speed interval; A stirring band consisting of a plurality of continuous speed intervals is generated according to the speed intervals and the corresponding operating time.

4. The intelligent control method of a food processor according to claim 1, characterized in that: The step of determining the upgrade strategy of the food processor based on the operating parameters and the version status includes: Performing data analysis on the operating parameters to obtain equipment operating characteristics; Comparing the device operation characteristics with the standard operation parameters corresponding to the current version status to obtain a comparison result; determining a performance deviation item according to the comparison result; Based on the performance deviation item, the corresponding functional module and upgrade priority are determined, and an upgrade strategy is generated.

5. The intelligent control method of a food processor according to claim 4, characterized in that: Determining the performance deviation item according to the comparison result includes: Based on the comparison results, extracting deviation data of each operating parameter; Establishing a deviation sequence that changes over time based on the real-time deviation data; Performing weighted accumulation of the deviation trend sequence in the time dimension to obtain a cumulative impact value of the parameter deviation; Screen out abnormal parameters whose cumulative impact values ​​exceed the deviation cumulative threshold, and perform correlation clustering on the abnormal parameters; Based on the result of the association clustering, the main influencing factors are identified and determined as performance deviation items.

6. The intelligent control method of a food processor according to claim 1, characterized in that: The method further comprises: Determining whether the food processor is in an abnormal state based on the operating parameters of the food processor; When the abnormal state is detected, determining the abnormality level of the abnormal state; Execute the corresponding emergency handling strategy according to the abnormal level and record the handling results; The upgrade strategy is optimized based on the abnormal state and the corresponding processing result.

7. The intelligent control method of a food processor according to claim 1, characterized in that: The controlling the food processor to upgrade according to the upgrade strategy includes: When the food processor is in a network connection state, a remote upgrade package is obtained based on the upgrade strategy, and security verification is performed on the remote upgrade package; When the security verification is passed, the upgrade instruction of the remote upgrade package is written into the local storage unit; When the food processor is not in a network connection state, retrieving a preset offline upgrade instruction from the local storage unit; The upgrade operation of the functional modules is executed in stages according to the execution priority of the upgrade instruction.

8. An intelligent control system for a food processor, characterized in that: The system comprises: A stirring band determination module, used to determine the type of food to be processed, and determine the corresponding stirring band based on the type of food; A parameter acquisition module, used to control the food processor to operate according to the stirring band, and to obtain the operating parameters and version status of the food processor; An upgrade strategy determination module, used to determine the upgrade strategy of the food processor based on the operating parameters and the version status; An upgrade control module is used to control the food processor to upgrade according to the upgrade strategy.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a plurality of instructions, and the instructions are suitable for being loaded by a processor and executing the method according to any one of claims 1 to 7.

10. An electronic device, characterized in that: It includes a processor, a memory, a user interface and a network interface, the memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes the method as described in any one of claims 1-7.

Images