An intelligent control system for electric rice cooker

Abstract

The present invention relates to the technical field of intelligent control of electric rice cookers, and in particular to an intelligent control system of electric rice cookers. The invention comprises an electric rice cooker monitoring module, an intelligent temperature control module, a fault diagnosis and protection module, an energy-saving optimization module and a remote control module. The electric rice cooker monitoring module monitors voltage, current, temperature and other state data in real time, and the intelligent temperature control module controls the temperature by a fuzzy self-tuning PID algorithm. The fault diagnosis and protection module analyzes the state of equipment components based on a graph neural network, diagnoses faults in real time and triggers a protection mechanism. The energy-saving optimization module optimizes energy consumption scheduling by using a Monte Carlo tree search algorithm and a Q learning algorithm. The remote control module realizes remote control through GSM and feeds back the state and progress of the electric rice cooker to a user. The present invention improves the intelligence level, operation convenience and use safety of the electric rice cooker by combining multiple functions such as precise monitoring, intelligent control and energy-saving optimization.

Description

Technical Field

[0001] The invention relates to the technical field of intelligent control of electric rice cookers, in particular to an intelligent control system of an electric rice cooker. Background Art

[0002] As a common household appliance, rice cookers have become an indispensable tool in people's daily lives. Traditional rice cookers generally have basic functions such as cooking and keeping warm, and rely on preset simple timing and temperature control mechanisms. However, with the advancement of science and technology and the improvement of people's requirements for food quality, users have put forward higher requirements for the functions and intelligence level of rice cookers. Although some rice cookers on the market currently have multiple cooking modes and timing functions, their control systems still have deficiencies. At present, there are still the following problems: the temperature control of existing systems mostly relies on simple temperature sensing and switch adjustment, and it is impossible to dynamically adjust the temperature according to real-time status data, which leads to temperature fluctuations during the cooking process, affecting the cooking effect and taste of food; the existing technology lacks intelligent fault diagnosis function, which can usually only be detected after the fault occurs, and it is impossible to report the fault type in a timely and accurate manner. The lack of an effective protection mechanism may cause further damage to the equipment and even cause safety hazards; the existing system usually operates according to a preset fixed mode when working, and fails to dynamically adjust energy consumption according to real-time needs, resulting in serious waste of electricity during use and low energy efficiency of the equipment. Summary of the invention

[0003] To solve the above problems, the present invention provides an intelligent control system for an electric rice cooker, which solves the problem of how to dynamically adjust the temperature of the electric rice cooker according to real-time status data, perform intelligent fault diagnosis and optimize energy consumption during the cooking process, thereby improving the intelligence level, operation convenience and use safety of the electric rice cooker.

[0004] To achieve the above object, the technical solution adopted by the present invention is:

[0005] An intelligent control system for an electric rice cooker, comprising an electric rice cooker monitoring module, an intelligent temperature control module, a fault diagnosis and protection module, an energy-saving optimization module and a remote control module which are sequentially connected in communication;

[0006] The electric cooker monitoring module is used for monitoring the status data of the electric cooker in real time, and the status data includes the voltage, current, temperature, resistance and heat load of the electric cooker;

[0007] The intelligent temperature control module is used to control the temperature of the inner pot of the rice cooker through a fuzzy self-tuning PID control algorithm based on the state data;

[0008] The fault diagnosis and protection module is used to monitor the status of components inside the rice cooker in real time, form a multi-node network through a graph neural network algorithm, analyze the working status of each component in the device, perform fault diagnosis and report the fault type in real time, and trigger a protection mechanism at the same time;

[0009] The energy-saving optimization module is used to dynamically optimize the energy consumption scheduling and working mode of the rice cooker through a distributed Monte Carlo tree search algorithm and a Q learning algorithm;

[0010] The remote control module includes a GSM communication unit and a remote operation interface, which is used to remotely control the rice cooker through a terminal device, set the parameters of cooking mode, time and temperature, and report the current working status, cooking progress and fault information to the user in real time through status feedback.

[0011] Furthermore, the operation process of the intelligent temperature control module includes the following steps:

[0012] Based on the state data, a fuzzy self-tuning PID control algorithm is used to execute a fuzzy reasoning process to convert complex temperature changes into precise control output signals and generate real-time temperature adjustment instructions;

[0013] Based on the temperature adjustment instructions, the heating power is adjusted dynamically, the temperature of the inner tank is accurately controlled, and the temperature changes are corrected in real time;

[0014] By real-time monitoring of the temperature fluctuation of the inner tank, the PID control parameters can be adjusted adaptively;

[0015] The temperature control results are sent to the energy-saving optimization module and fed back to the user terminal through the remote control module.

[0016] Furthermore, the intelligent temperature control module specifically sets the target temperature, food type, cooking stage and actual temperature change according to the current cooking state, and optimizes the heating power and regulation strategy.

[0017] Furthermore, the formula of the fuzzy self-tuning PID control algorithm is as follows:

[0018] ;

[0019] in, Represents the control output signal, which is used to adjust the control instruction of the heating power of the rice cooker; Indicates the temperature error, that is, the deviation between the real-time temperature of the rice cooker pot and the set temperature; represents the dynamic fuzzy gain coefficient; Represents the integral time constant, which is used to correct the accumulated error; It represents the integral of the temperature error from the initial time to the current time, and is used to correct the accumulated error in the temperature change; Indicates the heat accumulation inside the rice cooker; Indicates the actual heating power; Indicates the heat load adjustment coefficient; Indicates the real-time working voltage of the rice cooker; Indicates the rate of change of the working voltage of the rice cooker; Indicates the voltage fluctuation regulation coefficient.

[0020] Furthermore, the operation process of the fault diagnosis and protection module includes the following steps:

[0021] Receive the status data of the electric cooker and perform pre-processing operations;

[0022] Based on the processed status data, a multi-node network association diagram of rice cooker components is constructed through the graph neural network algorithm to analyze the health status of each component and predict potential failures.

[0023] When an abnormal signal is detected, the potential fault type is determined by real-time calculation of the fault probability and a fault report is generated; the fault types include heating element failure, power supply failure and communication module failure;

[0024] Based on the fault report, a protection mechanism is automatically triggered according to the severity of the fault, and the working mode is adjusted or the operation of the rice cooker is stopped immediately.

[0025] Furthermore, the process of constructing the multi-node network association graph includes the following steps:

[0026] The key components inside the rice cooker are regarded as network nodes; the network nodes include a heating element, a power supply component, a temperature sensor, a communication component and a control chip inside the rice cooker;

[0027] Constructing edge weights of a multi-node association graph based on the operating voltage change rate, temperature fluctuation amplitude, and heat load transfer between the network nodes;

[0028] Analyze the electrical signals and temperature changes between nodes through the graph neural network algorithm, evaluate the health status of each node in real time, detect anomalies and readjust the network structure;

[0029] Based on the health assessment results of each node and abnormal path changes in the network, the probability of failure is identified in real time, a potential failure path map is constructed, and a detailed failure report is generated.

[0030] Furthermore, the operation process of the energy-saving optimization module includes the following steps:

[0031] Obtaining the status data of the rice cooker and using it as an input parameter;

[0032] Through the distributed Monte Carlo tree search algorithm, we build energy consumption distribution maps for different cooking modes and working stages, predict the power consumption changes and energy efficiency performance under different paths, evaluate the path with the lowest energy consumption for each cooking solution, and determine the optimal scheduling strategy.

[0033] Based on the optimal scheduling strategy, the Q learning algorithm is used to combine the historical working data of the rice cooker to continuously update the energy consumption scheduling strategy and dynamically optimize the heating power and working mode of the rice cooker;

[0034] According to the load fluctuations monitored in real time during the cooking process, the working mode and heating time of the rice cooker are dynamically adjusted, and an energy consumption report is generated and fed back to the remote control module.

[0035] Furthermore, the formula of the distributed Monte Carlo tree search algorithm is as follows:

[0036] ;

[0037] in, represents the total valuation after selecting action a in state s, which is used to determine the optimal energy consumption path; s represents the current working state of the rice cooker; a represents different scheduling decisions during the cooking process; represents the estimated energy efficiency after selecting action a in state s; Represents the characteristic function under state s and action a; Representation characteristics The weight is used to adjust the contribution of each feature to the total energy consumption optimization; Represents the trade-off coefficient, which is used to control the influence of the characteristic function on the total valuation; represents the exploration coefficient; Indicates the number of times state s has been visited, which is used to estimate the degree of exploration of the current state; Indicates the number of visits in which action a is selected in state s.

[0038] Furthermore, the intelligent temperature control module also includes an electromagnetic heating unit, which adopts electromagnetic induction technology for heating, and utilizes the magnetic field generated by the current passing through the coil to directly heat the inner tank, thereby achieving efficient conduction of heat energy.

[0039] The beneficial effects of the present invention are:

[0040] The present invention can monitor the voltage, current, temperature and other state data of the rice cooker in real time through the rice cooker monitoring module, and provide a reliable data basis for the precise control of the system. The intelligent temperature control module uses the fuzzy self-tuning PID control algorithm to automatically adjust the temperature of the inner pot according to the real-time state data, ensure the precise temperature control during the cooking process, and improve the cooking quality and taste of the food. The fault diagnosis and protection module is based on the graph neural network algorithm, which can monitor the status of each component inside the rice cooker in real time, form a multi-node network, and accurately analyze the operation of the equipment. Once a fault occurs, the system can report the fault type in time and trigger the protection mechanism to prevent further damage, thereby improving the safety and reliability of the equipment. The energy-saving optimization module performs dynamic energy consumption scheduling and working mode optimization through the distributed Monte Carlo tree search algorithm and the Q learning algorithm, effectively reducing the energy consumption of the rice cooker, realizing intelligent energy saving, and extending the service life of the equipment. The remote control module uses the GSM communication unit and the operation interface, and the user can use the terminal device to realize the remote operation of the rice cooker, such as setting the cooking mode, time and temperature. At the same time, the real-time status feedback function allows the user to understand the operation status, cooking progress and fault information of the rice cooker at any time, thereby improving the user's convenience and experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 The invention discloses a module schematic diagram of an intelligent control system for an electric rice cooker.

[0042] Figure 2 It is a flowchart of the operation process of the intelligent temperature control module provided by one embodiment of the present invention.

[0043] Figure 3 It is a flowchart of the operation process of the energy-saving optimization module provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0044] See also Figure 1-3 As shown, the present invention relates to an intelligent control system for an electric rice cooker.

[0045] Example

[0046] An intelligent control system for an electric rice cooker, comprising an electric rice cooker monitoring module, an intelligent temperature control module, a fault diagnosis and protection module, an energy-saving optimization module and a remote control module which are sequentially connected in communication;

[0047] The electric cooker monitoring module is used for monitoring the status data of the electric cooker in real time, and the status data includes the voltage, current, temperature, resistance and heat load of the electric cooker;

[0048] It should be noted that the rice cooker monitoring module integrates multiple sensors to collect key status data of the rice cooker in real time. These sensors include:

[0049] Voltage sensor: monitors the input voltage of the rice cooker to ensure that the device is in the appropriate operating voltage range.

[0050] Current sensor: collects current data in real time to help determine whether the power consumption of the rice cooker is normal.

[0051] Temperature sensors: Distributed on the surface of the inner pot and key locations inside the rice cooker, they monitor changes in cooking temperature in real time.

[0052] Resistance sensor: used to detect the resistance change of heaters and other circuit components to facilitate the determination of the working status of the components.

[0053] Heat load sensor: used to monitor the heat load of the inner tank and heating element to determine the current heating efficiency and heat conduction status.

[0054] These data are processed and summarized in real time by the microprocessor of the rice cooker monitoring module and transmitted to the intelligent temperature control module for subsequent temperature control and adjustment.

[0055] The intelligent temperature control module is used to control the temperature of the inner pot of the rice cooker through a fuzzy self-tuning PID control algorithm based on the state data; the intelligent temperature control module also sets the target temperature, food type, cooking stage and actual temperature change according to the current cooking state, and optimizes the heating power and regulation strategy;

[0056] The intelligent temperature control module also includes an electromagnetic heating unit, which adopts electromagnetic induction technology for heating, and utilizes the magnetic field generated by the current passing through the coil to directly heat the inner tank, thereby achieving efficient conduction of heat energy.

[0057] Specifically, the electromagnetic heating unit uses electromagnetic induction technology (IH technology) to directly heat the inner pot of the rice cooker, and converts electrical energy into heat energy through efficient conversion, ensuring that the heating process is fast, uniform, and with low energy loss. The electromagnetic heating unit generates an alternating magnetic field in the heating coil through high-frequency current, and the metal material of the inner pot generates eddy currents under the action of the magnetic field to heat the inner pot itself. This direct heating method avoids the problem of heat conduction delay in traditional heating methods, making the temperature adjustment response faster and more accurate. By monitoring the temperature of the inner pot in real time, the electromagnetic heating unit can quickly change the heating power. Compared with traditional resistance heating, electromagnetic heating has higher heat energy conduction efficiency, and the temperature of the inner pot rises and falls more quickly. It can respond quickly according to different cooking stages (such as heating, boiling, and keeping warm) to avoid temperature lag. The electromagnetic heating unit supports a detailed power adjustment range, and the system can gradually adjust the heating power according to the cooking stage, for example, gradually reducing from the maximum heating power to the temperature holding stage, to ensure that there will be no overheating or insufficient heating during the cooking process.

[0058] In one embodiment, the intelligent temperature control module not only controls the temperature through an algorithm, but also optimizes it in combination with the current cooking mode, so that the temperature control of different ingredients and different cooking stages is more precise.

[0059] Cooking mode recognition: This module can automatically recognize the current cooking mode, such as cooking rice, making soup, stewing meat or steaming vegetables. Different cooking modes require different temperature curves. The system automatically generates a temperature adjustment strategy suitable for the mode by analyzing the cooking mode selected by the user and the sensor feedback data. For example, the rice cooking mode usually requires a rapid temperature rise to about 100°C for boiling, while porridge cooking needs to be maintained in a lower temperature range to prevent sticking or burning the pot.

[0060] Optimization of temperature profile during cooking phase:

[0061] Preheating stage: The system quickly heats up to the preset target temperature, uses high power heating, and closely monitors temperature changes to prevent temperature overshoot.

[0062] Boiling stage: When the temperature reaches the set target temperature (such as 100°C), the system will dynamically adjust the heating power according to the real-time temperature feedback from the sensor to ensure continuous boiling without overheating.

[0063] Stewing / keeping stage: During the stewing stage of cooking rice or stewing soup, the system will adjust the power to a low value to keep the temperature within a low range to prevent overcooking of ingredients. During the keeping warm stage, the temperature control module will automatically monitor and maintain a low temperature to prevent food from cooling or overheating.

[0064] Intelligent adjustment and user intervention: The intelligent temperature control module not only relies on the automatic adjustment mechanism, but also allows users to intervene according to personal preferences. For example, users can manually adjust the temperature or heating time, and the system will generate a new temperature control curve based on the parameters adjusted by the user to ensure a personalized cooking experience.

[0065] The operation process of the intelligent temperature control module includes the following steps:

[0066] Based on the state data, a fuzzy self-tuning PID control algorithm is used to execute a fuzzy reasoning process to convert complex temperature changes into precise control output signals and generate real-time temperature adjustment instructions;

[0067] Specifically, first, real-time status data is obtained from the rice cooker monitoring module. These data include the current temperature, voltage, current of the inner pot, and the overall working status of the rice cooker (for example, whether it has entered the preheating, cooking or insulation stage). Through these status data, the system can understand the current working status of the inner pot in real time. The fuzzy self-tuning PID control algorithm will start the fuzzy logic reasoning system based on the acquired status data. Specifically, the fuzzy reasoning process will generate fuzzy rules based on the type of ingredients (such as rice, porridge, soup, etc.), cooking stage (such as heating, maintenance, insulation) and other conditions. For example, in the preheating stage, the system will set a loose temperature control, while in the boiling stage, the temperature control is more accurate. The fuzzy controller contains a preset fuzzy rule library, which generates corresponding adjustment instructions according to different temperature change amplitudes (such as temperature rise too fast or too slow). The results of fuzzy reasoning will be converted into specific PID parameter adjustments, thereby outputting accurate control signals. After fuzzy reasoning, the system generates a set of specific temperature adjustment instructions, which will determine how the electromagnetic heating unit adjusts its heating power to maintain or adjust the temperature of the inner pot. For example, when the temperature deviates from the set target temperature, the system will output an "increase power" or "reduce power" instruction to ensure that the temperature remains within a reasonable range.

[0068] Based on the temperature adjustment instructions, the heating power is adjusted dynamically, the temperature of the inner tank is accurately controlled, and the temperature changes are corrected in real time;

[0069] Specifically, throughout the heating process, the system continuously monitors the difference between the actual temperature and the target temperature. If a temperature deviation is detected, the system immediately issues a correction instruction to adjust the power to ensure that the temperature of the inner pot is maintained near the set value. This process is highly responsive, ensuring that the temperature will not be too high or too low during the cooking process. In order to ensure control accuracy, the system collects temperature data multiple times per second and analyzes each data change in real time. Based on this data, the system continuously adjusts the output power of the electromagnetic heating unit to form a rapid response and temperature correction mechanism.

[0070] By real-time monitoring of the temperature fluctuation of the inner tank, the PID control parameters can be adjusted adaptively;

[0071] Specifically, during the cooking process, the temperature control module continuously obtains the temperature changes of the inner pot through the sensor network. By analyzing these fluctuation data, the system can determine whether the current temperature control effect meets expectations. For example, when the temperature rises too fast or drops too slowly, the system will determine that the controller's PID parameters need to be adjusted.

[0072] The core feature of the fuzzy self-tuning PID control algorithm is its adaptability. The system will automatically adjust the three parameters of PID: proportional (P), integral (I) and differential (D) according to the actual temperature changes. For example, when the temperature fluctuates greatly, the system will increase the P value to improve the control response speed; when the temperature tends to be stable but there is still a small deviation, the system will appropriately increase the I value to eliminate long-term deviations.

[0073] The system also has a certain self-learning function, which can optimize the control strategy during long-term use. The temperature fluctuation data of each cooking will be recorded and analyzed so that a better PID parameter combination can be used in the future cooking process to gradually improve the control accuracy and efficiency.

[0074] The temperature control results are sent to the energy-saving optimization module and fed back to the user terminal through the remote control module.

[0075] Furthermore, the formula of the fuzzy self-tuning PID control algorithm is as follows:

[0076] ;

[0077] in, Represents the control output signal, which is used to adjust the control instruction of the heating power of the rice cooker; Indicates the temperature error, that is, the deviation between the real-time temperature of the rice cooker pot and the set temperature; represents the dynamic fuzzy gain coefficient; Indicates the integral time constant, which is used to correct the cumulative error and ensure the stability of the inner tank temperature; It represents the integral of the temperature error from the initial time to the current time, and is used to correct the accumulated error in the temperature change; Indicates the heat accumulation inside the rice cooker. According to the heat load of the rice cooker, the heating power is adjusted to prevent overheating or insufficient temperature. Indicates the actual heating power; Indicates the heat load adjustment coefficient; Indicates the real-time working voltage of the rice cooker; Indicates the rate of change of the working voltage of the rice cooker; Indicates the voltage fluctuation regulation coefficient.

[0078] The fault diagnosis and protection module is used to monitor the status of components inside the rice cooker in real time, form a multi-node network through a graph neural network algorithm, analyze the working status of each component in the device, perform fault diagnosis and report the fault type in real time, and trigger a protection mechanism at the same time;

[0079] The operation process of the fault diagnosis and protection module includes the following steps:

[0080] Receive the status data of the electric cooker and perform pre-processing operations;

[0081] Based on the processed status data, a multi-node network association diagram of rice cooker components is constructed through the graph neural network algorithm to analyze the health status of each component and predict potential failures.

[0082] It should be noted that the signal transmission path and electrical signal changes between nodes are analyzed through the graph neural network (GNN) algorithm. For example, when the voltage change of the power component is transmitted to the heating element, the system will detect whether these signal transmissions are normal and identify whether there is abnormal electrical conduction. GNN will also analyze the heat transfer efficiency of the components based on the temperature data between the nodes. The fluctuation information of the temperature sensor is closely related to the working state of the heating element. If the temperature fluctuation amplitude exceeds the normal range, the system will mark the node as "potential abnormality" and conduct a comprehensive evaluation in combination with the status of other components. Through the analysis of the graph network, the system can evaluate the health status of each node in real time and predict the potential failure of the component. For example, if the electrical signal of a node is weak and the temperature fluctuation is abnormal, the system will predict that the node may fail and mark it as a "high-risk" node. The fault prediction model determines the failure probability of these components based on historical data and current signal change trends.

[0083] When an abnormal signal is detected, the potential fault type is determined by real-time calculation of the fault probability and a fault report is generated; the fault types include heating element failure, power supply failure and communication module failure;

[0084] Based on the fault report, a protection mechanism is automatically triggered according to the severity of the fault, and the working mode is adjusted or the operation of the rice cooker is stopped immediately.

[0085] It should be noted that the system evaluates the severity of the fault based on the data in the fault report. The evaluation criteria include: the probability of the fault, the impact of the fault on the safety of the equipment, and whether the fault will cause further damage. For example, if the probability of failure of the heating element exceeds 80% and the equipment is in a high temperature state, the system will determine it as a serious fault.

[0086] Automatically trigger the protection mechanism, as follows:

[0087] Working mode adjustment: In the case of non-fatal minor faults, the system can adjust the working mode to avoid further damage. For example, if the resistance of the heating element is slightly abnormal, but the temperature control is still within the controllable range, the system can reduce the heating power and extend the cooking time to avoid further damage to the element under high load. Similarly, if the communication module signal is weak, the system can try to continue operating through other communication methods (such as switching to a backup network) and send an alert to the user at the same time.

[0088] Safety degradation mode: For situations where a fault may affect the normal operation of the device but will not cause immediate danger, the system can automatically enter safety degradation mode. For example, if the power component detects excessive voltage fluctuations, the system can limit the heating power of the rice cooker and slow down the device to avoid the power system continuing to work under high load. This mode will inform the user through the remote control module and recommend that the user check the device as soon as possible.

[0089] Stop the device immediately: For serious faults, such as short circuit of heating element, power overload, etc., the system will automatically trigger the emergency stop mechanism, immediately cut off the power and stop all operations of the rice cooker. At this time, the system will generate a detailed fault report, record the time of the fault, the specific component status, and notify the user of the fault details. Through the remote control module, the system can push emergency notifications to the user's mobile device, reminding the user that the rice cooker has stopped working and recommending maintenance.

[0090] Power-off protection and alarm: If the system detects a fatal fault that may endanger safety (such as a short circuit or severe overheating), it will not only stop the device from working, but also immediately cut off the power supply and trigger an audible and visual alarm to remind the user that the device has entered a protection state. In this case, the system will also record detailed fault information for subsequent maintenance reference.

[0091] The process of constructing the multi-node network association graph includes the following steps:

[0092] The key components inside the rice cooker are regarded as network nodes; the network nodes include a heating element, a power supply component, a temperature sensor, a communication component and a control chip inside the rice cooker;

[0093] Constructing edge weights of a multi-node association graph based on the operating voltage change rate, temperature fluctuation amplitude, and heat load transfer between the network nodes;

[0094] Analyze the electrical signals and temperature changes between nodes through the graph neural network algorithm, evaluate the health status of each node in real time, detect anomalies and readjust the network structure;

[0095] Specifically, the graph neural network (GNN) evaluates the health status of each component in real time by analyzing the electrical signals between nodes (such as changes in voltage and current). GNN identifies possible abnormal nodes based on the strength of the electrical signals and their changing trends. For example, if the voltage fluctuation of the power component exceeds the normal range, the system will mark the power node as a potential fault and evaluate the health status of other related nodes based on its impact. GNN will determine whether there is an abnormal heat load based on the temperature change of the heating element. For example, if the temperature sensor reading shows that the temperature of the heating element rises too quickly, the system will mark this node as "high risk" and re-evaluate its impact on other nodes. During operation, the system will adjust the network structure in real time based on the signal interaction and health status between nodes. For example, if a node is marked as abnormal, the system will temporarily reduce the strength of its association with other nodes to prevent the fault from spreading further to other components. This dynamic adjustment can respond to changes in the internal state of the device in a timely manner and reduce the spread of potential faults.

[0096] Based on the health assessment results of each node and abnormal path changes in the network, the probability of failure is identified in real time, a potential failure path map is constructed, and a detailed failure report is generated.

[0097] Specifically, through continuous analysis of GNN, the system generates health assessment results for each node. For example, the health index of a heating element can be calculated based on its resistance change, current fluctuation, and temperature change trend. The health assessment result will show whether the node is "normal", "slightly abnormal", or "severely abnormal". If multiple nodes are abnormal at the same time, the system will build a potential fault path diagram to show which node the fault may start from and how it spreads to other nodes. For example, if the voltage fluctuation of the power supply component causes the current abnormality of the heating element, the system will draw a fault path from the power supply node to the heating element node and evaluate other components that may be affected. Finally, the system generates a detailed fault report, including the location of the fault node, fault type, health status, fault probability, and possible fault path. The report will be stored in the device's log system and sent to the user through the remote control module, prompting the user to take further action.

[0098] The energy-saving optimization module is used to dynamically optimize the energy consumption scheduling and working mode of the rice cooker through a distributed Monte Carlo tree search algorithm and a Q learning algorithm;

[0099] The operation process of the energy-saving optimization module includes the following steps:

[0100] Obtaining the status data of the rice cooker and using it as an input parameter;

[0101] Through the distributed Monte Carlo tree search algorithm, we build energy consumption distribution maps for different cooking modes and working stages, predict the power consumption changes and energy efficiency performance under different paths, evaluate the path with the lowest energy consumption for each cooking solution, and determine the optimal scheduling strategy.

[0102] Specifically, the distributed Monte Carlo tree search algorithm is used to explore the energy consumption distribution under different cooking modes and working stages. The specific implementation steps are as follows:

[0103] The energy-saving optimization module decomposes the cooking process into multiple key stages (such as heating, keeping warm, stewing, etc.), each of which constitutes a node in the state space. Each node represents a specific cooking state, such as the current heating power, inner pot temperature, and remaining cooking time.

[0104] Through the distributed Monte Carlo tree search algorithm, the system randomly generates multiple possible paths, each of which represents a cooking strategy from preheating to the end of cooking. The nodes on each path (i.e. different working modes or heating stages) are marked with different energy consumption conditions to form an energy consumption distribution diagram of the cooking mode. The system simulates the temperature changes and power consumption under different paths and calculates the corresponding energy efficiency performance.

[0105] In each path, the system predicts its future energy performance. By evaluating the energy consumption and temperature fluctuations of each node, the Monte Carlo tree search algorithm can identify which paths have the lowest energy consumption under different power output and heating time conditions. The algorithm also compares the energy efficiency performance of each path at different stages and generates energy consumption forecasts for multiple solutions.

[0106] After simulating and evaluating multiple cooking paths, the algorithm will derive the cooking plan with the lowest energy consumption based on the total energy consumption of each path. This plan includes the optimal heating power and heating time for each working stage, which can minimize energy consumption while meeting cooking requirements.

[0107] Based on the optimal scheduling strategy, the Q learning algorithm is used to combine the historical working data of the rice cooker to continuously update the energy consumption scheduling strategy and dynamically optimize the heating power and working mode of the rice cooker;

[0108] Specifically, the energy-saving optimization module first uses the best path obtained by the Monte Carlo tree search algorithm as the initial strategy of the Q learning algorithm. The Q learning algorithm optimizes energy consumption scheduling through a state-action-reward mechanism. The initial state is the current cooking stage, the action is to adjust the heating power or switch the working mode, and the reward is to minimize energy consumption. The Q learning algorithm learns in combination with the historical working data of the rice cooker. By analyzing the actual energy consumption performance of the device under different conditions, Q learning can continuously adjust and optimize the heating power and cooking mode. For example, historical data shows that for certain ingredients, the energy consumption in the insulation stage can be optimized by slightly lowering the temperature. The Q learning algorithm will update the scheduling strategy to reduce power consumption without affecting the cooking effect. As each cooking task is completed, the Q learning algorithm will update the corresponding Q value to dynamically optimize the heating power and working mode. This module can learn the optimal scheduling plan through repeated use and continuously reduce unnecessary energy consumption.

[0109] According to the load fluctuations monitored in real time during the cooking process, the working mode and heating time of the rice cooker are dynamically adjusted, and an energy consumption report is generated and fed back to the remote control module.

[0110] Furthermore, the formula of the distributed Monte Carlo tree search algorithm is as follows:

[0111] ;

[0112] in, represents the total valuation after selecting action a in state s, which is used to determine the optimal energy consumption path; s represents the current working state of the rice cooker; a represents different scheduling decisions during the cooking process; represents the estimated energy efficiency after selecting action a in state s; Represents the characteristic function under state s and action a; Representation characteristics The weight is used to adjust the contribution of each feature to the total energy consumption optimization. The weight of each feature The data collected during the long-term operation of the rice cooker can be dynamically updated to ensure that the features that have a greater impact on energy consumption are given a higher weight. For example, voltage fluctuations may have a significant impact on power consumption in different situations, and the weight of this feature can be adjusted through historical learning; Represents the trade-off coefficient, which is used to control the influence of the characteristic function on the total valuation; represents the exploration coefficient. Especially when the working state of the rice cooker (such as voltage, current, etc.) fluctuates greatly, the algorithm can explore new paths more actively. Indicates the number of times state s has been visited, which is used to estimate the degree of exploration of the current state; Indicates the number of visits in which action a is selected in state s.

[0113] The remote control module includes a GSM communication unit and a remote operation interface, which is used to remotely control the rice cooker through a terminal device, set the parameters of cooking mode, time and temperature, and report the current working status, cooking progress and fault information to the user in real time through status feedback.

[0114] It should be noted that the GSM communication unit is the communication core of the remote control module, which is responsible for connecting the rice cooker with a remote terminal device (such as a mobile phone or tablet). The unit transmits data through the GSM network, enabling users to remotely control the rice cooker.

[0115] The GSM communication unit is connected to the local cellular communication network through a SIM card and has the following communication functions: transmit the status data of the rice cooker (such as current temperature, remaining cooking time, cooking mode, etc.) to the user's terminal device through mobile network protocols such as GPRS / EDGE. Receive operation instructions from the user (such as start, pause, mode switch, temperature adjustment, etc.) and pass these instructions to the central controller of the rice cooker. The GSM communication unit adopts a multi-band design, which can adapt to the network environment of different countries and regions, ensuring the global versatility and stability of communication. Through the optimized data transmission protocol, the module ensures that the data transmission delay in the network is minimal, and the user can see the feedback of the cooking status almost in real time and make timely operations.

[0116] The remote operation interface is an interactive interface between the user and the rice cooker, usually in the form of a mobile app or web interface. The user can complete all operations on the rice cooker through this interface. Specific functions include:

[0117] Cooking mode selection: Users can select different cooking modes (such as cooking rice, making soup, steaming vegetables, etc.) through the remote operation interface, and select the corresponding cooking plan according to the type of ingredients. In each mode, the system will preset the appropriate temperature and time parameters, but users can also manually adjust them according to actual needs.

[0118] Temperature and time adjustment: The system provides real-time temperature and time adjustment functions. Users can remotely adjust the current cooking temperature or extend / shorten the cooking time to ensure that the cooking process matches their personal preferences. For example, if the user wants the rice to be softer, the cooking time can be extended in the middle of the cooking process.

[0119] Start / Pause / Stop Operation: Users can remotely start or pause the rice cooker, or manually stop the device before cooking is finished. In addition, the remote operation interface also supports a timed start function, where users can pre-set a time point and the rice cooker will automatically start working at the specified time.

[0120] Parameter setting and memory function: Through the remote interface, users can save commonly used cooking modes and temperature and time parameters. The system will remember these settings for users to use quickly next time.

[0121] The remote control module can not only receive user instructions, but also provide real-time feedback to the user on the current status of the rice cooker. The rice cooker monitoring module continuously collects internal operating status, including current temperature, remaining cooking time, cooking progress, power output and other information. These data are transmitted to the user's mobile device through the GSM communication unit. Users can view the cooking progress of the rice cooker in real time on the App interface, such as displaying status prompts such as "heating", "keeping warm", and "completed". Users can adjust cooking parameters as needed. When the fault diagnosis module of the rice cooker detects an abnormal situation (such as overheating, short circuit, etc.), the real-time feedback system will immediately send a fault report to the user, including the type of fault, possible causes and recommended treatment solutions. Users can remotely decide whether to continue running the device or take protective measures based on this feedback information. When the cooking task of the rice cooker is completed, the system will automatically push a notification to the user through the App to inform that cooking is completed. The user can choose to turn off the device or keep the keep warm function on.

[0122] Considering that remote control involves user privacy and device security, the remote control module also integrates a series of security measures. During the remote communication process, all transmitted data is encrypted using the Advanced Encryption Standard (AES) or TLS protocol to ensure that the user's operating instructions and device status information will not be maliciously intercepted or tampered with by a third party. Every time a user connects to the rice cooker through a remote device, the system will require the user to pass identity authentication (such as account password or biometrics) to ensure the legitimacy of the operating authority and prevent illegal users from accessing. The user's terminal device is bound to the rice cooker through a unique identification code to ensure that only authorized devices can control the rice cooker. Even if the user's account information is leaked, other devices cannot access the device in an unauthorized manner. The remote control module will record the log of each remote operation, including the operation time, operation content and initiating device. These log information can be queried by users to ensure the transparency and traceability of each operation.

[0123] Even if the remote network connection is interrupted, the rice cooker can still continue to operate and complete the cooking task according to the user's preset program. When the GSM network is temporarily interrupted, the rice cooker will automatically switch to WIFI mode or local control mode to ensure that the cooking process that has been started will not be disturbed. When the network is restored, the system will continue to upload real-time status and synchronize operation information. If the rice cooker loses power due to power problems during the cooking process, the system will continue to operate according to the user's preset program when the power is restored, avoiding the need for users to reset or restart the device.

[0124] In summary, the present invention can monitor the voltage, current, temperature and other status data of the rice cooker in real time through the rice cooker monitoring module, providing a reliable data basis for the precise control of the system. Through the intelligent temperature control module, the fuzzy self-tuning PID control algorithm is combined with the status data of the rice cooker to achieve precise control of the temperature of the inner pot, ensure that the temperature changes smoothly in different cooking stages, avoid temperature overshoot or undershoot, and ensure the best cooking effect of the ingredients. The electromagnetic heating unit uses electromagnetic induction technology to achieve direct heating of the inner pot, with high heat energy conduction efficiency and fast response speed, which significantly reduces energy loss and ensures energy utilization during the cooking process, especially the rapid response capability in different cooking stages, thereby improving cooking efficiency.

[0125] The present invention can monitor the health status of each component of the rice cooker in real time through the fault diagnosis and protection module, and predict potential faults through the graph neural network algorithm, trigger the protection mechanism in time, prevent the equipment from overheating, short circuit and other problems, and ensure the safe and stable operation of the equipment. The energy-saving optimization module dynamically optimizes energy consumption scheduling through the distributed Monte Carlo tree search algorithm and the Q learning algorithm, reduces unnecessary energy consumption, saves energy to the maximum extent without affecting the cooking effect, and improves the overall energy efficiency of the rice cooker. The remote control module supports users to perform real-time operations through terminal devices, remotely set cooking parameters, and can provide real-time feedback on the status information and fault reports of the equipment, which improves the user's convenience and the intelligence of equipment operation.

[0126] The above implementation modes are merely descriptions of the preferred implementation modes of the present invention, and are not intended to limit the scope of the present invention. Without departing from the design spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by ordinary engineering and technical personnel in the field shall fall within the protection scope determined by the claims of the present invention.

Claims

1. An intelligent control system for an electric rice cooker, characterized in that: It includes a rice cooker monitoring module, an intelligent temperature control module, a fault diagnosis and protection module, an energy-saving optimization module and a remote control module which are sequentially connected in communication; The electric cooker monitoring module is used for monitoring the status data of the electric cooker in real time, and the status data includes the voltage, current, temperature, resistance and heat load of the electric cooker; The intelligent temperature control module is used to control the temperature of the inner pot of the rice cooker through a fuzzy self-tuning PID control algorithm based on the state data; The fault diagnosis and protection module is used to monitor the status of components inside the rice cooker in real time, form a multi-node network through a graph neural network algorithm, analyze the working status of each component in the device, perform fault diagnosis and report the fault type in real time, and trigger a protection mechanism at the same time; The energy-saving optimization module is used to dynamically optimize the energy consumption scheduling and working mode of the rice cooker through a distributed Monte Carlo tree search algorithm and a Q learning algorithm; The remote control module includes a GSM communication unit and a remote operation interface, which is used to remotely control the rice cooker through a terminal device, set the parameters of the cooking mode, time and temperature, and report the current working status, cooking progress and fault information to the user in real time through status feedback; The operation process of the intelligent temperature control module includes the following steps: Based on the state data, a fuzzy self-tuning PID control algorithm is used to execute a fuzzy reasoning process to convert complex temperature changes into precise control output signals and generate real-time temperature adjustment instructions; Based on the temperature adjustment instructions, the heating power is adjusted dynamically, the temperature of the inner tank is accurately controlled, and the temperature changes are corrected in real time; By real-time monitoring of the temperature fluctuation of the inner tank, the PID control parameters can be adjusted adaptively; The temperature control results are sent to the energy-saving optimization module and fed back to the user terminal through the remote control module; The formula of the fuzzy self-tuning PID control algorithm is as follows: ； in, Represents the control output signal, which is used to adjust the control instruction of the heating power of the rice cooker; Indicates the temperature error, that is, the deviation between the real-time temperature of the rice cooker pot and the set temperature; represents the dynamic fuzzy gain coefficient; Represents the integral time constant, which is used to correct the accumulated error; It represents the integral of the temperature error from the initial time to the current time, and is used to correct the accumulated error in the temperature change; Indicates the heat accumulation inside the rice cooker; Indicates the actual heating power; Indicates the heat load adjustment coefficient; Indicates the real-time working voltage of the rice cooker; Indicates the rate of change of the working voltage of the rice cooker; Indicates the voltage fluctuation regulation coefficient.

2. The intelligent control system of an electric rice cooker according to claim 1, characterized in that: The intelligent temperature control module also specifically sets the target temperature, food type, cooking stage and actual temperature change according to the current cooking state, and optimizes the heating power and regulation strategy.

3. The intelligent control system of an electric rice cooker according to claim 1, characterized in that: The operation process of the fault diagnosis and protection module includes the following steps: Receive the status data of the electric cooker and perform pre-processing operations; Based on the processed status data, a multi-node network association diagram of rice cooker components is constructed through the graph neural network algorithm to analyze the health status of each component and predict potential failures. When an abnormal signal is detected, the potential fault type is determined by real-time calculation of the fault probability and a fault report is generated; the fault types include heating element failure, power supply failure and communication module failure; Based on the fault report, a protection mechanism is automatically triggered according to the severity of the fault, and the working mode is adjusted or the operation of the rice cooker is stopped immediately.

4. The intelligent control system of an electric rice cooker according to claim 3, characterized in that: The process of constructing the multi-node network association graph includes the following steps: The key components inside the rice cooker are regarded as network nodes; the network nodes include a heating element, a power supply component, a temperature sensor, a communication component and a control chip inside the rice cooker; Constructing edge weights of a multi-node association graph based on the operating voltage change rate, temperature fluctuation amplitude, and heat load transfer between the network nodes; Analyze the electrical signals and temperature changes between nodes through the graph neural network algorithm, evaluate the health status of each node in real time, detect anomalies and readjust the network structure; Based on the health assessment results of each node and abnormal path changes in the network, the probability of failure is identified in real time, a potential failure path map is constructed, and a detailed failure report is generated.

5. The intelligent control system of an electric rice cooker according to claim 1, characterized in that: The operation process of the energy-saving optimization module includes the following steps: Obtaining the status data of the rice cooker and using it as an input parameter; Through the distributed Monte Carlo tree search algorithm, we build energy consumption distribution maps for different cooking modes and working stages, predict the power consumption changes and energy efficiency performance under different paths, evaluate the path with the lowest energy consumption for each cooking solution, and determine the optimal scheduling strategy. Based on the optimal scheduling strategy, the Q learning algorithm is used to combine the historical working data of the rice cooker to continuously update the energy consumption scheduling strategy and dynamically optimize the heating power and working mode of the rice cooker; According to the load fluctuations monitored in real time during the cooking process, the working mode and heating time of the rice cooker are dynamically adjusted, and an energy consumption report is generated and fed back to the remote control module.

6. The intelligent control system of an electric rice cooker according to claim 5, characterized in that: The formula of the distributed Monte Carlo tree search algorithm is as follows: ； in, represents the total valuation after selecting action a in state s, which is used to determine the optimal energy consumption path; s represents the current working state of the rice cooker; a represents different scheduling decisions during the cooking process; represents the estimated energy efficiency after selecting action a in state s; Represents the characteristic function under state s and action a; Representation characteristics The weight is used to adjust the contribution of each feature to the total energy consumption optimization; Represents the trade-off coefficient, which is used to control the influence of the characteristic function on the total valuation; represents the exploration coefficient; Indicates the number of times state s has been visited, which is used to estimate the degree of exploration of the current state; Indicates the number of visits in which action a is selected in state s.

7. The intelligent control system of an electric rice cooker according to claim 1, characterized in that: The intelligent temperature control module also includes an electromagnetic heating unit, which adopts electromagnetic induction technology for heating, and utilizes the magnetic field generated by the current passing through the coil to directly heat the inner tank, thereby achieving efficient conduction of heat energy.

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