Meter circuit control system based on household power supply and control method thereof

Abstract

The invention discloses a household power supply-based meter machine circuit control system and a control method thereof, and relates to the technical field of intelligent household appliance control. Comprising a cognitive load quantification module which obtains an interface comprehensive cognitive load score of a user through a user portrait and a terminology, and determines an interface display effect; the progressive guide module is used for carrying out progressive guide according to the Petri network, grading pressing pressure in the use process of the user through a pressure induction touch screen, and determining a pressure threshold value according to a grading result and complexity score; and the state mirror image module is used for carrying out dual-channel operation preview through a set dual-channel memory mirror image, and carrying out node dragging rollback and parameter comparison preview according to a visual operation timeline. The learning cost and the use pressure of the user can be reduced, the problems that a traditional intelligent rice machine is complex in interface and poor in operation fault tolerance are solved, and users with different cognitive levels can efficiently use advanced functions of the equipment.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent household appliance control, and in particular to a rice machine circuit control system based on a household power supply and a control method thereof. Background Art

[0002] With the development of smart home appliances, household rice mills are increasingly integrating automated control functions to ensure the orderly operation of processes such as rice feeding, milling, rice discharging, and impurity removal. These devices typically rely on electrical control systems to regulate motor start / stop, operating sequence, and load status to ensure processing quality and operational safety.

[0003] Chinese invention patent publication number CN105446216A discloses an operation control system and operation control method for a segmented rice mill, including a control single-chip microcomputer, a keyboard input module, a display screen, a buzzer, a rectifier circuit, a light-controlled detection switch located in the segmented rice mill feed hopper, high and low-position light-controlled detection devices, and a time reservation module. The operation control system described in this invention can be applied to a segmented rice mill, enabling it to have the function of reserving the rice milling time. By setting the time and cooperating with light control technology, the rice mill can be automatically started to mill rice at the appointed time. Rice milling can be performed according to different time periods for different groups of people, making it more intelligent.

[0004] While the control system of the aforementioned smart rice machine is feature-rich, its human-computer interaction interface still utilizes a multi-layered menu interaction design. Users unfamiliar with its use will be confused not only by the technical terminology on the main interface but also by the parameterized options that appear in the secondary menu. Furthermore, if the wrong button is accidentally pressed during operation, the system provides no clear error message and no quick return path, forcing the user to exit the current process and start over. This not only increases the user's learning curve and usage pressure, but may also cause users to abandon the product's personalized features, ultimately rendering the device's advanced control functions useless. Summary of the Invention

[0005] The object of the present invention is to provide a rice machine circuit control system based on household power supply and a control method thereof, so as to solve the problems raised in the above background technology.

[0006] To achieve the above object, the present invention provides the following technical solution: a rice machine circuit control system based on household power supply, comprising:

[0007] Cognitive load quantification module: This module uses user profiles and professional terminology to obtain the user's comprehensive cognitive load score for the interface, compares the comprehensive cognitive load score with the preset load threshold range, and determines the interface display effect based on the comparison results, specifically:

[0008] When the comprehensive cognitive load score of the interface is less than the lower limit threshold of the preset load threshold range, the current interface is maintained; when the comprehensive cognitive load score of the interface is within the preset load threshold range, a dynamic help icon is displayed; when the comprehensive cognitive load score of the interface is greater than the upper limit threshold of the preset load threshold range, a progressive guidance module is triggered to forcibly simplify the display interface; including:

[0009] Term cognitive entropy calculation unit: determines the term comprehension difficulty entropy value based on the mapping relationship between user profile data and term library, and monitors user behavior based on the term comprehension difficulty entropy value;

[0010] Operation path weight analysis unit: determines the complexity score based on the menu hierarchy depth and number of options;

[0011] Progressive Guidance Module: This module provides progressive guidance based on Petri nets and uses a pressure-sensitive touch screen to classify the user's pressing pressure during use. It also determines the pressure threshold based on the classification results and complexity score.

[0012] State mirror module: Preview dual-channel operations through the set dual-channel memory mirror, and perform node drag and drop and parameter comparison preview based on the visual operation timeline.

[0013] Furthermore, user behavior is monitored, including:

[0014] SA1: Profile-Term Mapping: Match users of different age groups with the set rating table;

[0015] SA2: Obtaining real-time entropy values: Adjust the initial entropy value based on the term's appearance density and popularity, and make corrections based on the user's age to obtain the final comprehensive entropy value;

[0016] SA3: Animation demonstration: Compare the final comprehensive entropy value with the preset entropy value threshold, and determine the interface display result based on the comparison result, specifically:

[0017] When the final comprehensive entropy value is greater than the preset entropy value threshold, term conversion is performed directly; otherwise, a three-dimensional animation display is performed through a preset 3D model library.

[0018] Furthermore, a basic entropy value is set according to the occurrence density and popularity of the term, and the basic entropy value is combined with the number of user erroneous operations to determine an urgency correction value, specifically:

[0019]

[0020] in: is the urgency modifier, is the frequency of the term appearing in the user manual, is the popularity of the term, The number of incorrect operations by the user.

[0021] Furthermore, the complexity score is compared with a preset score threshold range, and based on the comparison result, an interface display result is determined, specifically:

[0022] When the complexity score is less than the lower limit threshold of the preset score threshold range, the current interface is displayed normally; when the complexity score is within the preset score threshold range, the non-current path options are folded and displayed; when the complexity score is greater than the upper limit threshold of the preset score threshold range, a quick entry is set, and the progressive guidance module is started for progressive guidance.

[0023] Furthermore, the pressure threshold is determined, including:

[0024] SB1: Dynamic Navigation: Decompose user tasks using the Petri net process, and create a colorful halo focus effect using CSS animation.

[0025] SB2: Pressure-Feedback Mapping: The vertical force applied by the user is obtained through the pressure-sensitive touch screen to determine the tactile feedback force, and the tactile feedback force is divided into three intervals according to the magnitude of the vertical force;

[0026] SB3: Determine a pressure threshold: Set an adjustment weight based on the complexity score, and adjust the set standard pressure based on the adjustment weight to obtain the minimum pressing pressure required to trigger tactile feedback.

[0027] Furthermore, the tactile feedback force is divided into three intervals, specifically:

[0028] When the vertical force is less than 0.3N, linear vibration of 50-100Hz is performed; when the vertical force is between 0.3N-1N, exponentially enhanced vibration of 100-200Hz is performed; when the vertical force is greater than 1N, emergency braking is performed.

[0029] Furthermore, you can drag and drop nodes and perform parameter comparison preview, including:

[0030] SC1: Dual-channel setting: Execute the current operation through the primary channel and perform data simulation through the backup channel. The difference between the current operation result and the data simulation result is obtained. The difference value is compared with the preset difference threshold range. The interface display result is determined based on the comparison result. Specifically:

[0031] When the difference value is less than the lower limit threshold of the preset difference threshold range, the difference value is directly displayed; when the difference value is within the preset difference threshold range, the difference value is flashed and a warning signal is triggered; when the difference value is greater than the upper limit threshold of the preset difference threshold range, an alarm signal is directly triggered;

[0032] SC2: Set the operation line: Set the type of operation to be performed, parameters, node code, and operation timestamp accordingly. Obtain the difference fields based on the field differences between nodes, and set the display mode based on the change type.

[0033] Furthermore, it also includes an environmental cognition compensation module for monitoring the user's operating environment and dynamically adjusting the interaction mode based on the environmental monitoring results, including:

[0034] SD1: Tactile monitoring: Detects external environmental noise through a microphone array and adjusts the tactile feedback intensity based on the detection results;

[0035] SD2: Light monitoring: Obtain the frequency of light changes through infrared ambient light sensors, and freeze non-core visual feedback based on the frequency of light changes;

[0036] SD3: Attention Analysis: Millimeter-wave radar is used to detect the frequency of user body movements, and the user's distraction state is graded based on the frequency of the user's body movements. The response strategy is then determined based on the distraction level.

[0037] A rice machine circuit control method based on household power supply uses any one of the above rice machine circuit control systems based on household power supply.

[0038] Compared with the prior art, the present invention has the following beneficial effects:

[0039] Firstly, the present invention can evaluate the user's cognitive level in real time through the cognitive load quantification module and dynamically adjust the interface display mode, so that the interaction complexity can be accurately matched with the user's cognitive ability, thereby reducing learning costs and usage pressure;

[0040] Second, the present invention decomposes complex operations into single-step task chains using Petri nets and uses CSS animations to provide focus prompts, thereby reducing the rate of operational errors. Furthermore, the present invention can also achieve a mapping between force and feedback through a pressure-sensitive touch screen, thereby providing intuitive operational guidance.

[0041] Third, the present invention compares the current operation results of the primary channel with the real-time simulation results of the backup channel, thereby reducing the risk of misoperation. At the same time, through node drag-and-drop recovery and parameter comparison preview, it can also solve the problem of irreversible operation in traditional interfaces.

[0042] Fourthly, the application can ensure the effectiveness of interaction in different environments by real-time monitoring of environmental noise, light changes and user attention through sensors such as microphone arrays and millimeter wave radars. BRIEF DESCRIPTION OF DRAWINGS

[0043] Figure 1 It is a system block diagram of the rice machine circuit control system in the application;

[0044] Figure 2 It is a graph of the relationship between the term cognitive entropy value and the user's age in the application;

[0045] Figure 3 It is a pressure touch grading feedback effect diagram in the application;

[0046] Figure 4 It is a double-channel simulation difference alarm response time diagram in the application. DETAILED DESCRIPTION

[0047] The technical solutions in the embodiments of the application will be described clearly and completely below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, rather than all the embodiments. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the application.

[0048] Although the control system of the existing intelligent rice machine has rich functions, its human-machine interface still adopts multi-layer menu interaction design. Users who are not familiar with the use will not only be confused by the professional terms on the main interface, but also will not know how to choose the parameterized options in the secondary menu. At the same time, if the keys are pressed by mistake during operation, the system does not have clear error prompts, nor does it set a quick return channel. The user can only exit the current process and start over. This not only increases the user's learning cost and use pressure, but also may cause the user to give up using the personalized function of the product, ultimately making the advanced control function of the device a mere formality. The technical solutions of the present application can reduce the user's learning cost and use pressure, solve the problems of complex interface and poor operation fault tolerance of traditional intelligent rice machines, and enable users with different cognitive levels to efficiently use the advanced functions of the device.

[0049] Example 1

[0050] refer to Figures 1-4 This embodiment provides a household power supply-based rice machine circuit control system, which includes a cognitive load quantification module, a progressive guidance module, and a state mirroring module. The cognitive load quantification module is used to calculate and obtain the user's interface complexity index. Based on the size of the interface complexity index, the progressive guidance module is triggered to decompose multi-step operations into single-step task chains. A pressure-sensitive touch screen is used to achieve a nonlinear mapping between operation force and feedback intensity. Simultaneously, based on the dual-channel memory mirroring set in the state mirroring module, the primary channel executes the current operation, while the backup channel simulates the operation results in real time. In the event of an erroneous operation, recovery can be performed by dragging the timeline.

[0051] In this embodiment, the cognitive load quantification module is equipped with a term cognitive entropy calculation unit and an operation path weight analysis unit. The term cognitive entropy calculation unit is used to dynamically evaluate the difficulty of understanding professional terms based on user profiles, and the operation path weight analysis unit is used to generate a complexity score based on the menu hierarchy depth and the number of options. In other words, based on the term cognitive entropy value obtained by the term cognitive entropy calculation unit and the operation path complexity obtained by the operation path weight analysis unit, the corresponding interface comprehensive cognitive load score is determined, specifically:

[0052]

[0053] in: Score the overall cognitive load of the interface. is the basic cognitive entropy value, is the operation path complexity, It is the predefined maximum path complexity benchmark value.

[0054] That is, the obtained comprehensive cognitive load score of the interface is compared with a preset load threshold range (which can be set according to actual data and is not specifically explained in this embodiment, for example, 40-70), and the corresponding interface display effect is determined based on the comparison result, specifically:

[0055] When the obtained interface comprehensive cognitive load score is less than the lower limit of the preset load threshold range, i.e., 40, the current standard professional interface is maintained. When the obtained interface comprehensive cognitive load score is within the preset load threshold range, i.e., 40-70, a dynamic help icon is displayed. Clicking the help icon will expand the corresponding term for explanation. When the obtained interface comprehensive cognitive load score is greater than the upper limit of the preset load threshold range, i.e., 70, the progressive guidance module is triggered, forcibly simplifying the display interface to hide non-core parameters and enter wizard mode.

[0056] Specifically, the term cognitive entropy calculation unit is used to obtain the corresponding term comprehension difficulty entropy value based on the mapping relationship between user portrait data and the term library. At the same time, based on the term comprehension difficulty entropy value, user behavior is monitored and a three-dimensional animation is triggered to interpret the behavior. The details are as follows:

[0057] Step SA1: Profile-Term Mapping. This involves using user actions recorded in device logs to calculate the frequency of queries for specific terms by age group. For example, users over 60 search for "fuzziness" an average of 5.2 times per month, while users aged 18-30 search for the same term only 0.8 times per month. Subsequent user actions after clicking a term are also recorded, such as whether the user immediately takes action, returns to view help, or cancels the setting.

[0058] Furthermore, food engineers and language experts jointly develop terminology classification standards, such as the classification table shown in Table 1 below.

[0059] Table 1: Grading table

[0060] Term Type Example Initial entropy value Daily expressions Start / Cancel 0.1-0.3 Functional Descriptors Quick Cooking / Soup 0.4-0.6 Professional technical parameters Gelatinization degree / heat convection 0.7-0.9

[0061] That is to say, real-time matching is performed based on users of different age groups and the set rating table.

[0062] Step SA2: Obtain real-time entropy values. That is, based on the term's appearance density and popularity, combined with the user's age, determine the corresponding comprehensive entropy value, specifically:

[0063]

[0064] in: is the final comprehensive entropy value, is the initial entropy value, is the urgency modifier, is the frequency of the term appearing in the user manual, The popularity of the term.

[0065] In this embodiment, professional terms are extracted from the product manual based on its content, and the number of times each term is viewed is recorded. At the same time, the latest version of the manual is scanned every month to update the frequency of term appearance and obtain the corresponding term frequency in real time, specifically:

[0066]

[0067] in: is the frequency of the term appearing in the user manual, is the number of times the term appears in the user manual, is the total number of terms that appear in the user manual.

[0068] Further, in the full platform, the proportion of users who query the specific term is counted, and the popularity of the corresponding term is determined according to the proportion, specifically:

[0069]

[0070] Wherein: is the popularity of the term, is the total number of users, is the number of users who have queried the term.

[0071] Further, in the embodiment, the age coefficient is specifically set according to the age of the user. Specifically, when the user is less than 30 years old, the corresponding age coefficient is set to 0.8. When the user is more than 60 years old, the corresponding age coefficient is set to 1.5. When the user is in the range of 30-60 years old, the corresponding age coefficient is set to 1.

[0072] Further, according to the obtained term frequency and term popularity, the basic entropy value is set, and the basic entropy value is combined with the number of user misoperations to determine the corresponding urgency correction value, specifically:

[0073]

[0074] Wherein: is the urgency correction value, is the frequency of the term appearing in the user manual, is the popularity of the term, is the number of user misoperations.

[0075] In the process of specific implementation, the initial entropy value corresponding to the term "pasting degree" is 0.7, the term frequency is 0.072, the term popularity is 1.09, the age coefficient corresponding to the 55-year-old user is 1, and the urgency correction value of continuous 3 misoperations is 0.02, then the corresponding final comprehensive entropy value is: 0.7+0.08+0.02=0.8.

[0076] Step SA3: animation demonstration. That is, the final comprehensive entropy value obtained in step SA2 is compared with the preset entropy value threshold (which can be specifically set according to actual data, so it will not be specifically described in this embodiment, for example, 0.8), and the interface display result is determined according to the comparison result. Specifically:

[0077] When the final comprehensive entropy value obtained is greater than the preset entropy threshold value, i.e., 0.8, term conversion is performed directly. Conversely, when the final comprehensive entropy value obtained is not greater than the preset entropy threshold value, i.e., 0.8, a 3D animation display is performed using the preset 3D model library.

[0078] refer to Figure 2 The regression line shows an increase in comprehensive entropy from approximately 0.4 to 0.8 with increasing age (20 to 80 years old). This indicates that understanding professional terminology becomes more difficult with age. Meanwhile, the entropy for 30-year-old users is approximately 0.45, indicating a relatively low level of comprehension, while the entropy for 65-year-old users is 0.82, clearly indicating an increase in cognitive load. Furthermore, the "entropy after 3D animation interpretation" indicates that visualization tools significantly reduce comprehension difficulty, particularly for younger users.

[0079] In this embodiment, the operation path weight analysis unit sets the corresponding coefficients according to the number of menu levels. Specifically, in this embodiment, the menu levels are divided into three menu levels: main interface, secondary page and tertiary pop-up window, and the corresponding coefficients are 0.5, 1.2 and 2, respectively. The corresponding display instructions are the directly visible first-level menu, the sub-page that needs to be clicked to enter, and the deep setting dialog box.

[0080] Furthermore, based on the user's current operation path (e.g., main interface → taste → gelatinization), the number of visible options at the same level is counted, and the corresponding complexity score is determined based on the statistical results, specifically:

[0081]

[0082] in: Score the complexity, For the The layer coefficient of the layer, is the number of options at the same level, is the total number of menu levels, The menu level index.

[0083] Furthermore, the obtained complexity score is compared with a preset score threshold range (which can be set according to actual data and is not specifically explained in this embodiment, for example, 2.5-5), and the corresponding interface display result is determined based on the comparison result, specifically:

[0084] When the complexity score is less than the lower threshold of the preset score threshold range, i.e., 2.5, the current interface is displayed normally. When the complexity score is within the preset score threshold range, i.e., 2.5-5, the current interface is displayed with options other than the current path collapsed. When the complexity score is greater than the upper threshold of the preset score threshold range, i.e., 5, a quick entry is set up on the current interface and the progressive guidance module is activated for progressive guidance.

[0085] In this embodiment, the progressive guidance module is used to perform progressive guidance based on the Petri net and, through the pressure-sensitive touch screen, classify the pressure applied by the user during use. The corresponding pressure threshold is determined based on the classification results and the complexity score determined by the operation path weight analysis unit. The details are as follows:

[0086] Step SB1: Dynamic Navigation. This involves decomposing the current user task into multiple independent states through the Petri net process, so that each independent state corresponds to a Petri net place, and the switching between independent states corresponds to the Petri net transition.

[0087] Furthermore, in this embodiment, the capacitive touch screen can be used to distinguish between sliding and clicking, so that the difference can be quickly identified during the state transition process. At the same time, after the allowed operation of the current state is completed, the allowed operation of the next state is set accordingly.

[0088] It's worth noting that this embodiment uses CSS animation to achieve a color halo focus effect. Specifically, an amber yellow display color is set at a wavelength of 590nm to improve the color sensitivity of middle-aged and elderly users, and a 3Hz flashing frequency is set as the prompt frequency. At the same time, 120% of the current operation area is set as the coverage area to facilitate the expansion of the operation hotspot.

[0089] Step SB2: Pressure-feedback mapping. This involves obtaining the vertical force applied by the user's finger when contacting the capacitive touch screen through the pressure-sensitive touch screen, and determining the corresponding tactile feedback force based on the obtained vertical force, specifically:

[0090]

[0091] in: is the tactile feedback force, The vertical force applied to the user, is a natural constant.

[0092] That is, according to the vertical force applied by the user's finger when contacting the capacitive touch screen, the corresponding tactile feedback force is divided into three different intervals. Specifically, when the applied vertical force is less than 0.3N, a linear vibration of 50-100Hz is performed to simulate the light touch of a mechanical button. When the applied vertical force is between 0.3N and 1N, an exponentially enhanced vibration of 100-200Hz is performed to simulate the spring pressure resistance. When the applied vertical force is greater than 1N, an emergency brake is applied to prevent the safety lock from being operated accidentally.

[0093] Step SB3: Determine the stress threshold. Specifically, the adjustment weight is determined based on the complexity score determined by the operation path weight analysis unit. Specifically, when the complexity score is less than the lower limit of the preset score threshold range, the corresponding adjustment weight is set to 1.2. When the complexity score is within the preset score threshold range, the corresponding adjustment weight is set to 1. When the complexity score is greater than the upper limit of the preset score threshold range, the corresponding adjustment weight is set to 0.8.

[0094] Furthermore, in this embodiment, the corresponding pressure indicators are measured by multiple representative users with different operating postures (handheld / desktop placement) and operation types (light touch, standard click and heavy pressure), and based on the statistical results obtained, the pressure benchmark corresponding to 75% of the users is set as the corresponding standard pressure threshold.

[0095] In this embodiment, the minimum pressing pressure required to trigger tactile feedback is set according to the determined standard pressure and the adjustment weight, specifically:

[0096]

[0097] in: The minimum pressing pressure required to trigger tactile feedback. To adjust the weights, is standard pressure.

[0098] refer to Figure 3At low pressure (0-0.3N), the feedback frequency increases linearly from 50Hz to 100Hz, reducing the false operation rate from 8% to 5%. This indicates that the system provides gentle feedback under light pressure, effectively reducing false operations and making it suitable for delicate operations (such as light touchscreens). At medium pressure (0.3-1N), the feedback frequency increases exponentially to 200Hz, further reducing the false operation rate to 3%. Moderate pressure triggers significantly enhanced feedback, significantly reducing the false operation rate and making it suitable for confirmation operations (such as button presses). At high pressure (>1N), the feedback frequency stabilizes at 200Hz (maximum), with the false operation rate approaching 0%. Strong pressure provides saturated feedback, completely suppressing false operations and making it suitable for emergency operations or accidental touch prevention scenarios (such as emergency stops). In other words, the false operation rate continuously decreases with increasing feedback frequency (from 8% to 0%), indicating that tactile feedback can effectively improve operational accuracy. Furthermore, false operations almost disappear when the feedback frequency reaches 200Hz, indicating that high-frequency feedback plays a decisive role in eliminating false operations.

[0099] In this embodiment, the state mirror module is used to perform dual-channel operation preview through the set dual-channel memory mirror, and perform node drag and drop and parameter comparison preview according to the visual operation timeline.

[0100] Step SC1: Dual-channel setup. This involves setting up a primary channel using directly drivable actuators such as heating tubes and water pumps. This channel executes the user's current operation and monitors the corresponding temperature and water level data in real time. Simultaneously, a backup channel is set up using a rice-water mixing model. Through data simulation, corresponding real-time simulation results, namely the degree of gelatinization and rice core moisture content, are obtained.

[0101] In this embodiment, the rice-water mixing model is specifically:

[0102]

[0103] in: is the water absorption rate, For soaking time, is a natural constant, For water temperature.

[0104] Furthermore, the current operation result obtained by the main channel and the simulation result obtained by the backup channel are compared to obtain a difference value between the two. At the same time, the obtained difference value is compared with a preset difference threshold range (which can be specifically set according to actual data and is not specifically explained in this embodiment, for example, 5%-10%). Based on the comparison result, the corresponding interface display result is determined, specifically:

[0105] When the obtained difference value is less than the lower limit of the preset difference threshold range, i.e. 5%, the current interface directly displays the difference value between the two. When the obtained difference value is within the preset difference threshold range, i.e. 5%-10%, the current interface flashes the difference value between the two and triggers a warning signal. When the obtained difference value is greater than the upper limit of the preset difference threshold range, i.e. 10%, the current interface directly triggers an alarm signal.

[0106] refer to Figure 4 The larger the difference level (>10%), the shorter the response time (median about 50ms). The smaller the difference level (<5%), the longer the response time (median about 20ms).

[0107] Step SC2: Set the operation line. That is, each operation type currently performed by the user and the corresponding parameters are set accordingly, and are set accordingly with the corresponding node code and operation timestamp. Specifically, in this embodiment, the corresponding difference field is obtained based on the field difference between two adjacent nodes, and its display method is specifically set according to the corresponding change type. For example, the numerical adjustment is displayed through a green pulse halo, the function switch is displayed through a blue border and arrow animation, and the error rollback is displayed through a red flashing mark.

[0108] This embodiment also provides a rice machine circuit control method based on household power supply, and the rice machine circuit control method uses the above-mentioned rice machine circuit control system based on household power supply.

[0109] Example 2

[0110] This embodiment provides a rice machine circuit control system based on a household power supply. Its specific implementation is similar to that of Example 1, differing in that an environmental recognition compensation module monitors changes in the user's operating environment in real time and dynamically adjusts the interaction mode based on the monitoring results. The following describes the present invention with reference to the specific implementation of this embodiment.

[0111] In this embodiment, the corresponding interaction mode is determined based on the monitoring results of the user's operating environment changes, as follows:

[0112] Step SD1: Tactile Monitoring. This involves using a MEMS microphone array to detect the noise level in the user's surrounding environment and, based on the detection results, enhancing the tactile feedback intensity. Specifically, in this embodiment, when the ambient noise level exceeds 70dB, the tactile feedback intensity is automatically enhanced.

[0113] Furthermore, when the tactile feedback intensity is enhanced, the vibration intensity can be adjusted according to the external environment noise level, and the corresponding frequency can be adjusted as follows:

[0114] When the external ambient noise level is between 70-80dB, the vibration intensity increases by 30%, while the base frequency remains unchanged. When the external ambient noise level is between 80-90dB, the vibration intensity increases by 50%, while the base frequency increases by 20Hz. When the external ambient noise level is greater than 90dB, the vibration intensity increases by 80%, while the frequency adopts a pulsed mode.

[0115] Step SD2: Light Monitoring. This involves using an infrared ambient light sensor with a sampling rate of 1kHz to monitor the user's ambient light intensity in real time and obtain the corresponding frequency of light changes. Specifically, in this embodiment, when the frequency of light changes exceeds 5Hz, non-core visual feedback is frozen to reduce cognitive interference.

[0116] Furthermore, when freezing non-core visual feedback, it can determine the corresponding freezing strategy and retained elements based on the corresponding element type. Specifically, when the element type is a background animation, it can pause rendering and retain the current action button. When the element type is an auxiliary prompt, it can reduce the interface display transparency to 20% and display a progress bar / safety warning.

[0117] Step SD3: Attention Analysis. This involves using a 60GHz millimeter-wave radar to monitor a 120° fan-shaped area in front of the device to detect the user's micro-movement frequency. Specifically, if the frequency of micro-movements is less than 0.5Hz for 5 consecutive seconds, the user is distracted. A corresponding response strategy is determined based on the user's level of distraction.

[0118] In this embodiment, if the frequency of limb micro-movements acquired for 5 consecutive seconds is between 0.3Hz and 0.5Hz, the user is in a state of mild distraction, and a voice prompt is provided to the user via the 2x2 speaker array. If the frequency of limb micro-movements acquired for 5 consecutive seconds is less than 0.3Hz, the user is in a state of moderate distraction, and the interface is frozen, with a breathing light providing an alert. If the user leaves the millimeter-wave radar's monitoring range, the user is in a state of severe distraction, and the interface settings are saved and the user is returned to the homepage.

[0119] Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is limited by the accompanying embodiments and their equivalents.

Claims

1. A rice machine circuit control system based on household power supply, characterized in that: Includes: Cognitive load quantification module: This module uses user profiles and professional terminology to obtain the user's comprehensive cognitive load score for the interface, compares the comprehensive cognitive load score with the preset load threshold range, and determines the interface display effect based on the comparison results, specifically: When the comprehensive cognitive load score of the interface is less than the lower limit threshold of the preset load threshold range, the current interface is maintained; when the comprehensive cognitive load score of the interface is within the preset load threshold range, a dynamic help icon is displayed; When the comprehensive cognitive load score of the interface is greater than the upper threshold of the preset load threshold range, the progressive guidance module is triggered to forcibly simplify the display interface; including: Term cognitive entropy calculation unit: determines the term comprehension difficulty entropy value based on the mapping relationship between user profile data and term library, and monitors user behavior based on the term comprehension difficulty entropy value; Operation path weight analysis unit: determines the complexity score based on the menu hierarchy depth and number of options; Progressive Guidance Module: This module provides progressive guidance based on Petri nets and uses a pressure-sensitive touch screen to classify the user's pressing pressure during use. It also determines the pressure threshold based on the classification results and complexity score. State mirror module: Preview dual-channel operations through the set dual-channel memory mirror, and perform node drag and drop and parameter comparison preview based on the visual operation timeline.

2. A rice machine circuit control system based on household power supply according to claim 1, characterized in that: Monitor user behavior, including: SA1: Profile-Term Mapping: Match users of different age groups with the set rating table; SA2: Obtaining real-time entropy values: Adjust the initial entropy value based on the term's appearance density and popularity, and make corrections based on the user's age to obtain the final comprehensive entropy value; SA3: Animation demonstration: Compare the final comprehensive entropy value with the preset entropy value threshold, and determine the interface display result based on the comparison result, specifically: When the final comprehensive entropy value is greater than the preset entropy value threshold, term conversion is performed directly; otherwise, a three-dimensional animation display is performed through a preset 3D model library.

3. A rice machine circuit control system based on household power supply according to claim 2, characterized in that: According to the occurrence density and popularity of the term, a basic entropy value is set, and the basic entropy value is combined with the number of user erroneous operations to determine the urgency correction value, specifically: in: is the urgency modifier, is the frequency of the term appearing in the user manual, is the popularity of the term, The number of incorrect operations by the user.

4. A rice machine circuit control system based on household power supply according to claim 1, characterized in that: The complexity score is compared with a preset score threshold range, and based on the comparison result, the interface display result is determined, specifically: When the complexity score is less than the lower limit threshold of the preset score threshold range, the current interface is displayed normally; when the complexity score is within the preset score threshold range, the non-current path options are folded and displayed; when the complexity score is greater than the upper limit threshold of the preset score threshold range, a quick entry is set, and the progressive guidance module is started for progressive guidance.

5. A rice machine circuit control system based on household power supply according to claim 1, characterized in that: Determine stress thresholds, including: SB1: Dynamic Navigation: Decompose user tasks using the Petri net process, and create a colorful halo focus effect using CSS animation. SB2: Pressure-Feedback Mapping: The vertical force applied by the user is obtained through the pressure-sensitive touch screen to determine the tactile feedback force, and the tactile feedback force is divided into three intervals according to the magnitude of the vertical force; SB3: Determine a pressure threshold: Set an adjustment weight based on the complexity score, and adjust the set standard pressure based on the adjustment weight to obtain the minimum pressing pressure required to trigger tactile feedback.

6. A rice machine circuit control system based on household power supply according to claim 5, characterized in that: The tactile feedback force is divided into three intervals, specifically: When the vertical force is less than 0.3N, linear vibration of 50-100Hz is performed; when the vertical force is between 0.3N-1N, exponentially enhanced vibration of 100-200Hz is performed; when the vertical force is greater than 1N, emergency braking is performed.

7. A rice machine circuit control system based on household power supply according to claim 1, characterized in that: Perform node drag and drop rollback and parameter comparison preview, including: SC1: Dual-channel setting: Execute the current operation through the primary channel and perform data simulation through the backup channel. The difference between the current operation result and the data simulation result is obtained. The difference value is compared with the preset difference threshold range. The interface display result is determined based on the comparison result. Specifically: When the difference value is less than the lower limit threshold of the preset difference threshold range, the difference value is directly displayed; when the difference value is within the preset difference threshold range, the difference value is flashed and a warning signal is triggered; when the difference value is greater than the upper limit threshold of the preset difference threshold range, an alarm signal is directly triggered; SC2: Set the operation line: Set the type of operation to be performed, parameters, node code, and operation timestamp accordingly. Obtain the difference fields based on the field differences between nodes, and set the display mode based on the change type.

8. A rice machine circuit control system based on household power supply according to claim 1, characterized in that: It also includes an environmental cognition compensation module, which is used to monitor the user's operating environment and dynamically adjust the interaction mode based on the environmental monitoring results, including: SD1: Tactile monitoring: Detects external environmental noise through a microphone array and adjusts the tactile feedback intensity based on the detection results; SD2: Light monitoring: Obtain the frequency of light changes through infrared ambient light sensors, and freeze non-core visual feedback based on the frequency of light changes; SD3: Attention Analysis: Millimeter-wave radar is used to detect the frequency of user body movements, and the user's distraction state is graded based on the frequency of the user's body movements. The response strategy is then determined based on the distraction level.

9. A rice machine circuit control method based on household power supply, characterized in that: A rice machine circuit control system based on a household power supply as described in any one of claims 1 to 8 is used.

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