Cooking pressure early warning method and device based on user safety behavior analysis

Abstract

The application relates to the field of cooking pressure control, and discloses a cooking pressure early warning method and device based on user safety behavior analysis. The method comprises the following steps: obtaining material characteristic data of food materials cooked by an electric pressure cooker, obtaining historical pressure state information of the electric pressure cooker, and obtaining current operation data of the electric pressure cooker by a user; obtaining current food material state information corresponding to the material characteristic data and the historical pressure state information through a food material state monitoring database; determining a pressure early warning estimate value of the electric pressure cooker according to the historical pressure state information, the current food material state information and the current operation data through a food material state monitoring model; and when the pressure early warning estimate value is greater than or equal to a preset pressure early warning estimate value, issuing a pressure relief prompt information to the electric pressure cooker. The application can dynamically evaluate the risk of user operation instructions combined with the physical characteristics of food materials and actively protect the pressure state.

Description

Technical Field

[0001] This application relates to the field of cooking pressure control technology, and more specifically, to a cooking pressure early warning method and device based on user safety behavior analysis. Background Technology

[0002] Most electric pressure cookers on the market currently employ a pressure control method based on preset programs. This means that electric pressure cookers are typically pre-programmed with fixed parameters such as pressure level, heating rate, pressure holding time, and pressure release method for different recipes (e.g., rice, meat, soup). After the user selects the corresponding program, the electric pressure cooker automatically executes the entire cooking process strictly following the pre-stored time-pressure curve. Pressure control relies on the mechanical execution of the program logic. However, this static, predefined model lacks the ability to perceive and respond to dynamic changes in the actual cooking environment, especially in response to the physical properties of different ingredients (e.g., water content, viscosity, starch / protein content) or real-time user operations. Essentially, it is a "program-driven" rather than a "data- or state-driven" control logic.

[0003] Furthermore, existing electric pressure cookers are capable of receiving and executing user commands (such as mode selection, pressure holding time setting, manual start / stop / venting). The control logic of electric pressure cookers is primarily based on explicit user input and preset program rules. However, the existing control methods of electric pressure cookers cannot proactively understand the specific characteristics of the ingredients in the cooking pot and the impact of these characteristics on the pressure state. For example, during user settings or program execution, the system cannot identify the risk that ingredients that easily produce a lot of foam (such as beans, rice porridge, and soups that easily burn) may block the venting passage under high pressure, nor can it detect the potential dry-burning hazard caused by insufficient liquid addition by the user. Therefore, existing electric pressure cookers lack an intelligent mechanism for dynamically assessing risks and proactively protecting the pressure state by combining user commands with the physical characteristics of the ingredients, making it difficult to effectively avoid safety risks caused by the characteristics of the ingredients. Summary of the Invention

[0004] The purpose of this application is to provide a cooking pressure early warning method and device based on user safety behavior analysis, which solves the technical problem that existing electric pressure cookers cannot perform dynamic risk assessment and active pressure state protection based on user operation commands combined with the physical characteristics of food. It achieves the technical effect of dynamic risk assessment and active pressure state protection based on user operation commands combined with the physical characteristics of food.

[0005] This application provides a cooking pressure warning method based on user safety behavior analysis. The method includes: acquiring ingredient characteristic data of the food being cooked in the electric pressure cooker, acquiring historical pressure status information of the electric pressure cooker, and acquiring current operation data of the user on the electric pressure cooker; acquiring current ingredient status information corresponding to the ingredient characteristic data and historical pressure status information through an ingredient status monitoring database; determining a pressure warning estimate of the electric pressure cooker based on historical pressure status information, current ingredient status information, and current operation data through an ingredient status monitoring model; and issuing a pressure relief prompt to the electric pressure cooker when the pressure warning estimate is greater than or equal to a preset pressure warning estimate.

[0006] In one possible implementation, a pressure warning estimate for the electric pressure cooker is determined using a food condition monitoring model based on historical pressure condition information, current food condition information, and current operational data. This includes: obtaining target food condition information corresponding to historical pressure condition information and food characteristic data from a food condition monitoring database; determining the difference between the current food condition information and the target food condition information as the current food condition offset value; determining the subsequent pressure condition information of the electric pressure cooker based on the current pressure condition information and current operational data using the electric pressure cooker condition monitoring model; wherein, historical pressure condition information includes cooking stage information corresponding to historical pressure conditions; and determining the pressure warning estimate of the electric pressure cooker based on the subsequent pressure condition information and the current food condition offset value using the food condition monitoring model.

[0007] In another possible implementation, the method further includes: obtaining the pressure relief valve orifice information of the electric pressure cooker and obtaining the food particle size information from the food characteristic data; determining the pressure relief state influencing factor based on the pressure relief valve orifice information, food particle size information, and current food state information through a food state monitoring model; wherein, the food state information includes the food viscosity index and the food expansion index; and adjusting the pressure warning estimate by determining the product of the pressure warning estimate and the pressure relief state influencing factor.

[0008] In another possible implementation, the method further includes: acquiring multiple historical cooking records of the user cooking with an electric pressure cooker, acquiring the user's operation delay time after issuing a pressure relief prompt in each historical cooking record, and determining the average operation delay time corresponding to multiple historical cooking records; acquiring the current pressure rise rate inside the electric pressure cooker; determining the pre-pressure relief probability value based on the current pressure rise rate, food viscosity index, and average operation delay time using a pre-pressure relief control model; and controlling the electric pressure cooker to pre-relieve pressure when the pre-pressure relief probability value is greater than or equal to the preset pre-pressure relief probability value.

[0009] In another possible implementation, the method further includes: when cooking with an electric pressure cooker, obtaining the real-time operation delay time of the user after the electric pressure cooker issues a pressure relief prompt; when the real-time operation delay time is greater than or equal to the average operation delay time of a preset multiple, controlling the electric pressure cooker to pre-depressurize and then enter the pressure holding mode.

[0010] In another possible implementation, the method further includes: obtaining the user's operation delay time after issuing a pressure relief prompt in each historical cooking record, and determining the number of times the operation delay time corresponding to multiple historical cooking records is greater than a preset operation delay time, as the user's pressure relief alarm ignore count; when the pressure relief alarm ignore count is greater than or equal to the preset pressure relief alarm ignore count, reducing the preset pressure warning estimate by a preset ratio; when the pressure relief alarm ignore count is less than the preset pressure relief alarm ignore count, increasing the preset pressure warning estimate by a preset ratio.

[0011] In another possible implementation, the method further includes: acquiring the ingredient characteristic data of the food being cooked in the electric pressure cooker, and determining a preset ratio for adjusting the preset pressure warning estimate and the preset operation delay time based on the ingredient characteristic data.

[0012] In another possible implementation, the method further includes: when the user has multiple operation data on the electric pressure cooker in a time sequence, obtaining the pressure warning estimates corresponding to the multiple operation data on the electric pressure cooker in a time sequence; when the first pressure warning estimate is greater than or equal to the preset pressure warning estimate, and the second pressure warning estimate that follows the first pressure warning estimate in a time sequence is greater than or equal to the preset pressure warning estimate, and after issuing the corresponding first pressure relief prompt message for the first pressure warning estimate, if the electric pressure cooker does not perform pressure relief, issuing a pressure relief prompt message to the electric pressure cooker and starting the forced pressure relief program of the electric pressure cooker.

[0013] In another possible implementation, the method further includes: when the electric pressure cooker releases pressure according to the first pressure warning estimate, the heating power is reduced by a first proportion; when the electric pressure cooker releases pressure according to the second pressure warning estimate, the heating power is reduced by a second proportion; wherein the first proportion is proportional to the first pressure warning estimate, and the second proportion is proportional to the sum of the first pressure warning estimate and the second pressure warning estimate.

[0014] This application also provides a cooking pressure warning device based on user safety behavior analysis, including a unit for performing the method described in any of the preceding claims.

[0015] The beneficial effects of the embodiments in this application compared with the prior art are:

[0016] This application provides a cooking pressure warning method based on user safety behavior analysis. The method includes: acquiring food characteristic data of the food being cooked in the electric pressure cooker, acquiring historical pressure status information of the electric pressure cooker, and acquiring current user operation data of the electric pressure cooker; acquiring current food status information corresponding to the food characteristic data and historical pressure status information through a food status monitoring database; determining a pressure warning estimate of the electric pressure cooker based on the historical pressure status information, current food status information, and current operation data through a food status monitoring model; and issuing a pressure relief prompt to the electric pressure cooker when the pressure warning estimate is greater than or equal to a preset pressure warning estimate. This application improves the safety of electric pressure cooker use by determining whether a pressure warning is needed based on the food status, reduces the probability of false alarms, and ensures that users can proactively respond under high pressure risks. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 A flowchart illustrating the first cooking pressure early warning method based on user safety behavior analysis provided in this application embodiment;

[0019] Figure 2 A schematic diagram of the workflow of the first cooking pressure early warning method based on user safety behavior analysis provided in the embodiments of this application;

[0020] Figure 3 A flowchart illustrating the second cooking pressure early warning method based on user safety behavior analysis provided in this application embodiment;

[0021] Figure 4 A flowchart illustrating the third cooking pressure early warning method based on user safety behavior analysis provided in this application embodiment;

[0022] Figure 5 A flowchart illustrating the fourth cooking pressure early warning method based on user safety behavior analysis provided in this application embodiment;

[0023] Figure 6 A schematic diagram of the logical structure of a first cooking pressure early warning device based on user safety behavior analysis provided in this application embodiment. Detailed Implementation

[0024] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0025] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0026] As used in this application specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if detected [the described condition or event]" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once detected [the described condition or event]," or "in response to detection [the described condition or event]."

[0027] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0028] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0029] Existing electric pressure cookers lack intelligent mechanisms for dynamically assessing risks and actively protecting pressure states based on user operation commands and the physical characteristics of ingredients, making it difficult to effectively avoid safety risks caused by the characteristics of ingredients.

[0030] Based on the above reasons, this application provides a cooking pressure warning method based on user safety behavior analysis. The method includes: acquiring food characteristic data of the food being cooked in the electric pressure cooker, acquiring historical pressure status information of the electric pressure cooker, and acquiring current operation data of the user on the electric pressure cooker; acquiring current food status information corresponding to the food characteristic data and historical pressure status information through a food status monitoring database; determining a pressure warning estimate of the electric pressure cooker based on historical pressure status information, current food status information, and current operation data through a food status monitoring model; and issuing a pressure relief prompt to the electric pressure cooker when the pressure warning estimate is greater than or equal to a preset pressure warning estimate. This application improves the safety of using the electric pressure cooker by determining whether a pressure warning is needed based on the food status, reduces the probability of false judgments, and ensures that users can proactively respond under high pressure risks.

[0031] In some scenarios, a cooking pressure warning method based on user safety behavior analysis according to an embodiment of this application can be applied to the pressure control of electric pressure cookers. It can determine whether a pressure warning is needed based on the state of the ingredients, thereby improving the safety of using electric pressure cookers.

[0032] The following describes in detail, with specific examples, a cooking pressure early warning method based on user safety behavior analysis provided in the embodiments of this application.

[0033] Figure 1 A flowchart illustrating the first cooking pressure early warning method based on user safety behavior analysis provided in this application embodiment is shown below. Figure 1 As shown, this cooking pressure early warning method based on user safety behavior analysis includes S110 to S120, and S110 to S120 are described in detail below.

[0034] S110. Obtain the ingredient characteristic data of the food being cooked in the electric pressure cooker, obtain the historical pressure status information of the electric pressure cooker, and obtain the user's current operation data on the electric pressure cooker.

[0035] Figure 2 A schematic diagram of the workflow of the first cooking pressure early warning method based on user safety behavior analysis provided in this application embodiment is shown below. Figure 2 As shown in this implementation, when controlling the cooking pressure of the electric pressure cooker, the ingredient characteristic data of the ingredients being cooked can be obtained. The ingredient characteristic data includes information such as the type of ingredients, the weight of the ingredients during cooking, the expansion range of the ingredients during cooking, and the viscosity changes during cooking. Then, the degree of influence of the ingredient characteristic data on the pressure state of the electric pressure cooker can be determined by the ingredient characteristic data.

[0036] In this implementation, the historical pressure status information of the electric pressure cooker can also be obtained. The historical pressure status information includes the historical information on the pressure change trend of the electric pressure cooker during the current cooking process. Then, the impact on the food can be judged by the historical pressure status information, thereby improving the pressure control information for controlling the state of the electric pressure cooker.

[0037] In this implementation, the user's current operation data on the electric pressure cooker can also be obtained. The current operation data includes the temperature parameters or time control parameters set by the user on the electric pressure cooker during the current cooking process. The pressure status of the electric pressure cooker can then be determined by the current operation data, and the pressure control strategy of the electric pressure cooker can be adjusted accordingly.

[0038] like Figure 2 As shown, food characteristic data, historical pressure status information, and current operation data can work together to form the basic elements of early warning monitoring, ensuring comprehensive coverage of dynamic factors in the cooking process.

[0039] For example, when cooking beans, users can obtain the characteristics of the ingredients in the electric pressure cooker through the control panel or the app linked to the electric pressure cooker. They can also obtain historical pressure status information of the electric pressure cooker during the current cooking process, and obtain manual operation data such as the user's adjustment of the heating power and other working parameters of the electric pressure cooker in real time.

[0040] S120. Obtain the current food status information corresponding to food characteristic data and historical pressure status information through the food status monitoring database. Using the food status monitoring model, determine the pressure warning estimate for the electric pressure cooker based on historical pressure status information, current food status information, and current operation data. When the pressure warning estimate is greater than or equal to the preset pressure warning estimate, issue a pressure relief prompt to the electric pressure cooker.

[0041] In this implementation, the current status information of the ingredients can be obtained through the ingredient status monitoring database based on the ingredient characteristic data and historical pressure status information. The ingredient status monitoring database stores pre-collected data mapping relationships, which can convert the ingredient characteristic data and historical pressure status information into real-time status feedback, such as determining whether the ingredients are in a state of easy expansion.

[0042] For example, when cooking vegetables with high water content, the ingredient condition monitoring database can combine the ingredient characteristic data in the current cooking record with historical pressure state information to output the current tenderness and viscosity of the ingredients, improving the accuracy and flexibility of judging the condition of the ingredients.

[0043] like Figure 2As shown, after obtaining the current food status information, the food status monitoring model can calculate the pressure warning estimate of the electric pressure cooker based on historical pressure status information, current food status information, and current operation data. The food status monitoring model can integrate multi-dimensional data features and use a pre-trained model to predict the pressure warning estimate. When the pressure warning estimate indicates a high risk, it can generate a reliable warning signal. The pressure warning estimate represents the stability level of the current cooking environment.

[0044] For example, if a user frequently increases the temperature setting and historical data shows that similar operations lead to pressure runaway, the model will output a higher pressure warning estimate based on the current boiling state of the vegetables, the soft and mushy state of the vegetables, the viscosity state, and the trend of changes in the user's operations. The pressure warning estimate reflects the potential pressure relief demand. This calculation process ensures the timeliness and personalization of the warning response.

[0045] For example, the food condition monitoring model can be a deep learning model based on neural networks. The food condition monitoring model can be trained using historical stress status information of samples, current food condition information of samples, current operation data of samples, and stress warning estimates of samples.

[0046] After obtaining the pressure warning estimate, when the pressure warning estimate is greater than or equal to the preset pressure warning estimate, a pressure relief prompt can be issued to the electric pressure cooker. The preset pressure warning estimate is set based on safety standards and serves as the trigger limit. The pressure relief prompt reminds the user to intervene through visual or auditory means to prevent safety hazards caused by high pressure.

[0047] For example, if the warning value exceeds the limit, the electric pressure cooker's LCD screen will display a message "It is recommended to release the pressure immediately" and at the same time, it will sound a prompt for the user to check the pressure of the pot or release the pressure.

[0048] The beneficial effects of the above implementation method are that by judging whether the pressure warning of the electric pressure cooker needs to be triggered based on the state of the ingredients, the safety of using the electric pressure cooker is improved; this warning method reduces the probability of false alarms and ensures that users can respond proactively under high pressure risks.

[0049] In some implementations, in S120 above, the pressure warning estimate of the electric pressure cooker is determined by the food status monitoring model based on historical pressure status information, current food status information and current operation data, including S121 to S122. S121 to S122 will be explained in detail below.

[0050] S121. Obtain the target ingredient status information corresponding to historical pressure status information and ingredient characteristic data through the ingredient status monitoring database. Determine the difference between the current ingredient status information and the target ingredient status information as the current ingredient status offset value. Using the electric pressure cooker status monitoring model, determine the subsequent pressure status information of the electric pressure cooker based on the current pressure status information and current operation data. The historical pressure status information includes the cooking stage information corresponding to the historical pressure status.

[0051] When controlling the pressure warning of an electric pressure cooker, the target ingredient status information corresponding to historical pressure status information and ingredient characteristic data can be obtained through the ingredient status monitoring database. The target ingredient status information represents the state that the ingredient should be in under ideal cooking conditions, such as expansion standard, moisture saturation level, tenderness, and viscosity.

[0052] It should be noted that historical pressure status information includes cooking stage information corresponding to historical pressure states. Specifically, historical pressure status information can include data from previous high-pressure stable stages, enhancing the contextual relevance of the prediction. Furthermore, the target ingredient's state information can be determined by analyzing the historical pressure status of the current cooking process, and then the ingredient's state can be assessed based on this target ingredient state information.

[0053] For example, when a user cooks rice, a type of food with high water absorption, the food characteristic data includes the water absorption expansion coefficient and weight. The system retrieves the pressure holding stage information of similar historical cooking methods from the food condition monitoring database and outputs the target food condition information corresponding to the rice, which has high water absorption.

[0054] After obtaining the target ingredient status information, the difference between the current ingredient status information and the target ingredient status information can be determined as the current ingredient status offset value. The current ingredient status offset value quantifies the degree of deviation between the actual state and the ideal standard, providing a basic indicator for subsequent risk analysis.

[0055] During subsequent pressure monitoring, the electric pressure cooker's status monitoring model can be used to determine the subsequent pressure status information of the electric pressure cooker based on the current pressure status information and current operation data. The electric pressure cooker status monitoring model can combine the current pressure and the heating control data input by the user to predict the short-term pressure trend in the future.

[0056] For example, if the current pressure information shows that it is stable at a high level, and the user suddenly increases the heating power, the electric pressure cooker status monitoring model can predict the subsequent pressure status information within a few minutes based on the current pressure status information and current operation data, taking into account the dynamic changes in user operation and ensuring timely response to warnings.

[0057] For example, the electric pressure cooker status monitoring model can be a deep learning model based on neural networks. The electric pressure cooker status monitoring model can be trained using the current pressure status information of the sample, the current operation data of the sample, and the subsequent pressure status information of the sample.

[0058] S122. Using the food condition monitoring model, determine the pressure warning estimate of the electric pressure cooker based on subsequent pressure condition information and the current food condition offset value.

[0059] In this implementation, a food condition monitoring model can be used to determine the pressure warning estimate of the electric pressure cooker based on subsequent pressure condition information and the current food condition offset value. The food condition monitoring model can integrate the previous output, subsequent pressure condition information and the current food condition offset value as input parameters, and output the pressure warning estimate through weighted calculation. When the pressure warning estimate reaches or exceeds the limit, it can be used as the decision basis for pressure relief prompts.

[0060] For example, in scenarios where rice has a small expansion offset but a high subsequent pressure prediction, the food condition monitoring model calculates a medium-to-high risk warning estimate, and the system then guides the user to check the pressure relief valve to prevent overpressure hazards.

[0061] The beneficial effect of the above implementation method is that by estimating the offset value of the food state, it is possible to comprehensively judge whether the electric pressure cooker will be affected by the change of the food state, avoiding the misjudgment problem caused by a single static indicator and improving the accuracy of early warning analysis.

[0062] The beneficial effect of the above implementation method is that, based on the prediction of the subsequent pressure status information of the electric pressure cooker, it is possible to determine whether pressure relief is needed based on dynamic trends, ensuring timely intervention and preventive control under high pressure risks, and enhancing the safety and stability of the cooking process.

[0063] Figure 3 A flowchart illustrating the second cooking pressure early warning method based on user safety behavior analysis provided in this application embodiment is shown below. Figure 3 As shown, the above method also includes S210 to S220, which will be described in detail below.

[0064] S210. Obtain the orifice diameter information of the pressure relief valve of the electric pressure cooker, and obtain the food particle size information from the food characteristic data. Using a food state monitoring model, determine the pressure relief state influencing factors based on the pressure relief valve orifice diameter information, food particle size information, and current food state information. The food state information includes the food viscosity index and the food expansion index.

[0065] When controlling the pressure of an electric pressure cooker, the pressure relief valve orifice information can be obtained, as well as the particle size information of the ingredients from the ingredient characteristic data. The pressure relief valve orifice information reflects the physical size characteristics of the pressure relief channel, and the ingredient particle size information characterizes the particle size of the ingredients. Together, they constitute the basic parameters that affect the pressure relief effect.

[0066] After obtaining the pressure relief valve orifice information and the food particle size information, the food status monitoring model can be used to comprehensively calculate the pressure relief status influence factor based on the pressure relief valve orifice information, food particle size information and current food status information. The pressure relief status influence factor characterizes the magnitude of the impact on the pressure relief process of the electric pressure cooker.

[0067] It should be noted that the current food status information includes the food viscosity index and the food expansion index. These indicators dynamically reflect the changes in the physical properties of the food during the cooking process.

[0068] For example, when a user is cooking root vegetables with high starch content, the system can obtain the particle size information of the ingredients and the orifice information of the pressure relief valve by reading the design parameter database of the pressure relief valve. In the current state information of the ingredients, the viscosity index shows the degree of starch gelatinization and the swelling index reflects the swelling state of the ingredients after absorbing water. Thus, the system can determine the degree of influence on the pressure relief state of the electric pressure cooker by using the orifice information of the pressure relief valve, the particle size information of the ingredients, and the current state information of the ingredients.

[0069] In this implementation, the calculation process of the pressure relief state influencing factor takes into account a variety of risk factors. When the viscosity index of the food is high, it may increase the risk of blockage of the pressure relief channel; when the expansion index of the food is large, it may lead to obstruction of the pressure relief space; by combining the matching degree between the pressure relief valve orifice diameter and the particle size of the food, the degree of influence on the pressure relief efficiency can be quantitatively assessed.

[0070] It should be noted that the value range of the pressure relief status influence factor is greater than 1. The larger the value of the pressure relief status influence factor, the higher the risk of pressure relief obstruction.

[0071] For example, if the particle size of the food is detected to be close to the orifice of the pressure relief valve and the viscosity index continues to rise, the model will output a large pressure relief state influence factor, indicating that the pressure relief channel may be blocked. This situation is particularly obvious when cooking glutinous rice, which is prone to gelatinization, and can detect risk characteristics in advance.

[0072] S220. Determine the product of the pressure warning estimate and the influencing factors of the pressure relief status, and adjust the pressure warning estimate accordingly.

[0073] After determining the influencing factors of the pressure relief state, the product of the pressure warning estimate and the influencing factors of the pressure relief state can be calculated to achieve dynamic adjustment of the pressure warning estimate. This adjustment mechanism enables the warning value to be sensitively compensated according to the actual pressure relief conditions, triggering protective measures in advance.

[0074] For example, when the original pressure warning estimate is at the critical point, and the pressure relief state influencing factor increases to 1.1 due to high-viscosity ingredients, the adjusted pressure warning estimate will be greater than the preset pressure warning estimate, thus activating the pressure relief prompt function in advance.

[0075] The beneficial effect of the above implementation method is that by monitoring the viscosity index and expansion index of the food in its state, the influence of these factors on the pressure relief state can be accurately assessed, thus avoiding pressure relief failure in the electric pressure cooker due to the characteristics of the food and greatly improving the safety of pressure control.

[0076] The beneficial effects of the above implementation method are that, by combining the matching analysis of the pressure relief valve orifice diameter and the particle size of the food, the pressure warning estimate is dynamically compensated and adjusted to ensure that the warning mechanism can respond to changes in actual working conditions in a timely manner, forming an intelligent protection mechanism for different food characteristics, and enhancing the reliability of equipment operation.

[0077] Figure 4 A flowchart illustrating the third cooking pressure early warning method based on user safety behavior analysis provided in this application embodiment is shown below. Figure 4 As shown, the above method also includes S310 to S320, which will be described in detail below.

[0078] S310. Obtain multiple historical cooking records when the user cooks using an electric pressure cooker, and obtain the user's operation delay time after issuing a pressure relief prompt in each historical cooking record, and determine the average operation delay time corresponding to multiple historical cooking records. Obtain the current pressure rise rate inside the electric pressure cooker.

[0079] In this implementation, when controlling the pressure warning of the electric pressure cooker, multiple historical cooking records of the user cooking with the electric pressure cooker can be obtained. For each historical record, the operation delay time after the user receives the pressure relief prompt can be analyzed. By statistically analyzing the operation delay time values ​​in multiple historical records, the average operation delay time can be calculated. The average operation delay time reflects the user's actual response efficiency.

[0080] For example, the system automatically retrieves the ten most recent cooking records, recording the time difference between when the user receives the screen prompt and when they manually confirm the pressure release each time. The system then calculates the average of these time differences through the statistics module to form the average operation delay time.

[0081] At the same time, it can also monitor the pressure change trend inside the electric pressure cooker in real time and obtain the current pressure rise rate, which represents the urgency of the pressure increase.

[0082] For example, a pressure sensor can detect the rate at which the pressure rises in an electric pressure cooker by 0.5 atmospheres per minute.

[0083] S320: Using a pre-pressure relief control model, a pre-pressure relief probability value is determined based on the current pressure rise rate, food viscosity index, and average operation delay time. When the pre-pressure relief probability value is greater than or equal to the preset pre-pressure relief probability value, the electric pressure cooker is controlled to perform pre-pressure relief.

[0084] After obtaining the current pressure rise rate, food viscosity index, and average operation delay time, the pre-depression control model can comprehensively assess the risk by combining these three parameters. The pre-depression control model uses a weighted algorithm to integrate and analyze the three factors: when the pressure rises rapidly, the food viscosity is high, and the average user response is slow, the pre-depression control model will output a higher pre-depression probability value. The preset pre-depression probability value is used as a safety baseline to trigger subsequent control actions.

[0085] For example, when the current pressure is detected to be rising by 0.6 atmospheres per minute, the viscosity index of the food reaches the high viscosity range, and the user's historical average response time exceeds 15 seconds, the pre-depression control model may calculate a probability value of 0.85.

[0086] For example, the pre-depression control model can be a support vector machine model, which can determine the pre-depression probability value by combining the current pressure rise rate, food viscosity index and average operation delay time.

[0087] In this implementation, when the pre-depression probability value reaches or exceeds the preset pre-depression probability value, the pre-depression mechanism can be automatically triggered. The pre-depression mechanism adopts a graded depression control logic. First, a small amount of steam is released to alleviate the pressure trend. If the pressure continues to be abnormal, it is upgraded to a complete depressurization procedure.

[0088] For example, when the pre-depressurization probability value reaches 0.9 (the preset threshold is 0.8), the controller will open the pressure relief valve by 5% for 10 seconds to stabilize the pressure inside the pot within a safe range.

[0089] The beneficial effect of the above implementation method is that, through statistical analysis of the user's historical operation delay time, the early warning control strategy can be optimized in combination with individual operation habit characteristics. This personalized adaptation mechanism is particularly suitable for user groups with slower response speeds.

[0090] The beneficial effect of the above implementation method is that by combining the dynamic rate of pressure change and the viscosity characteristics of food to make pre-pressure relief decisions, the upward trend of pressure can be controlled by feedforward. This proactive intervention before the formation of dangerous pressure values ​​significantly reduces the incidence of overpressure accidents.

[0091] In some implementations, the above method also includes S330 to S340, which will be described in detail below.

[0092] S330: When cooking with an electric pressure cooker, obtain the real-time operation delay time of the user after the electric pressure cooker issues a pressure relief prompt.

[0093] When controlling the pressure warning of an electric pressure cooker, the user's operation response can be monitored in real time after a pressure relief warning is issued. The electric pressure cooker can automatically record the cumulative interval between the triggering of the warning and the user's actual operation as the real-time operation delay time. The real-time operation delay time can dynamically reflect the current user's response to the warning and provide real-time basis for safety intervention.

[0094] For example, when the display shows a "Please release pressure immediately" warning, the built-in timer starts recording the time. If the user performs other operations next to the pressure cooker or does not release pressure, the system continues to track until it detects the user touching the pressure release button or knob or the total accumulated time of not releasing pressure. The time difference recorded at this time is the real-time operation delay time, which is automatically completed by the pressure cooker control module.

[0095] S340. When the real-time operation delay time is greater than or equal to the average operation delay time of the preset multiplier, control the electric pressure cooker to pre-depressurize and then enter the pressure holding mode.

[0096] In this implementation, the control system of the electric pressure cooker can retain the average operation delay time calculated in history as a benchmark reference value.

[0097] When the detected real-time operation delay time is greater than or equal to the average operation delay time of the preset multiplier, the preset multiplier is based on a safety redundancy design, for example, set to a threshold of 1.5 times the historical average; when this threshold is reached, it is judged as a high-risk response delay, and at this time, a dual protection mechanism of pre-pressure relief control and mode switching is executed.

[0098] For example, the user's historical average response time is 10 seconds, and the preset multiplier is 1.8 times the threshold. When the actual delay time reaches 18 seconds without any operation, the automatic protection program is triggered.

[0099] In the pressure holding mode, the control program first initiates a pre-pressure relief operation, releasing an appropriate amount of steam through a solenoid valve to bring the pressure back to the safe zone. After the pre-pressure relief is completed, the system automatically switches the operating mode to the pressure holding state. The pressure holding mode maintains a constant sub-safe pressure, which avoids both cooking interruption and the risk of continuous pressure rise.

[0100] For example, after a 2-second intelligent pressure relief, the pressure inside the pot drops from the dangerous value to below the safe threshold. At this time, the control unit automatically switches the heating power to keep the pressure inside the pot stable at 100 kPa.

[0101] The beneficial effect of the above implementation method is that by monitoring the user response delay in real time, the protection mechanism is automatically triggered when the safety threshold is exceeded. This design effectively avoids the risk of operation delay caused by the user temporarily leaving or being distracted.

[0102] The beneficial effect of the above implementation method is that the dual-stage control strategy of switching between pre-depressurization and pressure holding modes can quickly eliminate the danger of overpressure and maintain the basic cooking process. This mechanism maintains the cooking continuity to the greatest extent while ensuring safety, and is particularly suitable for use scenarios where it is necessary to temporarily leave the kitchen.

[0103] In some implementations, the above method also includes S350 to S360, which will be described in detail below.

[0104] S350: Obtain the user's operation delay time after issuing a pressure relief prompt in each historical cooking record, and determine the number of times the operation delay time corresponding to multiple historical cooking records is greater than the preset operation delay time, as the number of times the user's pressure relief alarm is ignored.

[0105] When implementing intelligent pressure management for electric pressure cookers, the system can collect users' historical cooking records and record the user's operation delay time for each pressure relief alert event. By comparing the operation delay time with the preset operation delay time, the system can count the number of times the operation delay time exceeds the preset operation delay time. This number of times the operation delay time exceeds the preset operation delay time is defined as the number of times the user ignores the pressure relief alarm. The number of times the pressure relief alarm is ignored can objectively reflect the user's level of attention to safety warnings and provide a quantitative basis for adjusting system parameters.

[0106] For example, the system can automatically analyze user operation records after the pressure relief alarm is triggered in the last 20 cooking sessions. When the preset operation delay time is set to 12 seconds, if a user's response time exceeds this threshold 6 times, the number of times the pressure relief alarm is ignored will be recorded as 6.

[0107] S360. When the number of times a pressure relief alarm is ignored is greater than or equal to the preset number of times a pressure relief alarm is ignored, the preset pressure warning estimate is reduced by a preset ratio. When the number of times a pressure relief alarm is ignored is less than the preset number of times a pressure relief alarm is ignored, the preset pressure warning estimate is increased by a preset ratio.

[0108] When the number of times the pressure relief alarm is ignored reaches or exceeds the preset threshold, the preset pressure warning estimate can be dynamically reduced according to a preset ratio. This adjustment mechanism makes the warning trigger more sensitive and activates the safety mechanism in advance when the user's response habits are poor.

[0109] Conversely, when the number of times the pressure relief alarm is ignored is lower than the preset pressure warning estimate, the preset pressure warning estimate is increased by a preset ratio, thereby providing a more lenient operating window for users who respond promptly.

[0110] For example, if the preset threshold for the number of times to ignore is 5 and the preset ratio is 0.9, when the user actually ignores 7 times, the preset pressure warning estimate of 120 kPa can be adjusted to 108 kPa according to the preset ratio.

[0111] For example, if the number of times the pressure relief alarm is ignored is only 3, the preset pressure warning estimate of 120 kPa can be adjusted to 128 kPa according to the preset ratio.

[0112] The beneficial effects of the above implementation method are that it automatically optimizes the warning sensitivity based on the user's historical response characteristics. For users who often ignore alarms, the warning threshold can be lowered to achieve earlier safety intervention and greatly improve the timeliness of stress anomaly monitoring.

[0113] The beneficial effect of the above implementation method is that, for users who respond promptly, the operation time limit can be appropriately relaxed by increasing the preset pressure warning value. This personalized adaptation mechanism avoids excessive intervention while ensuring the reliability of safety control, enabling the warning system to intelligently distinguish the operating habits of different users.

[0114] In some implementations, the method in S360 above further includes: acquiring the food characteristic data of the food being cooked in the electric pressure cooker, and determining a preset ratio for adjusting the preset pressure warning estimate based on the food characteristic data.

[0115] When optimizing the pressure warning of an electric pressure cooker, the characteristics of the ingredients being cooked in the pressure cooker can be obtained. This data can then be used to determine a preset ratio for adjusting the pressure warning estimate. This preset ratio represents a reference coefficient for adjusting the pressure warning estimate, ensuring that the warning system can flexibly adjust its sensitivity according to the characteristics of different ingredients.

[0116] For example, the food characteristic data includes, but is not limited to, food type, particle size, and viscosity information. This data is obtained through user input to ensure the adaptive capability of the early warning mechanism.

[0117] For example, when the food characteristic data indicates that the food is highly expandable, the preset ratio is set to be lower to reduce the adjustment range; conversely, for low-risk food, the preset ratio is set to be higher to increase the adjustment range.

[0118] The beneficial effect of the above implementation method is that by determining the preset ratio for adjusting the preset pressure warning estimate based on the food characteristics data, the pressure warning sensitivity can be intelligently adjusted according to the food characteristics, thereby improving the warning response capability of the electric pressure cooker during use.

[0119] The beneficial effect of the above implementation method is that by adjusting the proportion driven by the characteristics of the ingredients, the flexibility and accuracy of the pressure warning system are optimized, thereby enhancing the overall safety protection performance.

[0120] Figure 5 A flowchart illustrating the fourth cooking pressure early warning method based on user safety behavior analysis provided in this application embodiment is shown below. Figure 5 As shown, the above method also includes S410 to S420, which are described in detail below.

[0121] S410. When the user has multiple operation data for the electric pressure cooker in a time sequence, obtain the pressure warning estimate corresponding to each of the multiple operation data for the electric pressure cooker in a time sequence.

[0122] When controlling the pressure warning of an electric pressure cooker, if the user has multiple operation data points in the time sequence, that is, during the user's continuous operation, multiple pressure warning estimates are obtained in the time series. The electric pressure cooker can automatically record the pressure warning estimate corresponding to each operation time point to form a time axis data of pressure change trend.

[0123] For example, multiple operation data in time sequence may include user operation data such as adjusting heating time and adjusting heating temperature.

[0124] S420. When the first pressure warning estimate is greater than or equal to the preset pressure warning estimate, and the second pressure warning estimate that follows the first pressure warning estimate in time sequence is greater than or equal to the preset pressure warning estimate, and after issuing the corresponding first pressure relief prompt message for the first pressure warning estimate, if the electric pressure cooker does not perform pressure relief, issue a pressure relief prompt message to the electric pressure cooker and start the forced pressure relief program of the electric pressure cooker.

[0125] During the operation of the electric pressure cooker, when the first pressure warning estimate is greater than or equal to the preset pressure warning estimate, and the second pressure warning estimate that follows the first pressure warning estimate in time sequence is greater than or equal to the preset pressure warning estimate, that is, when two adjacent pressure warning estimates in time sequence are detected to exceed the preset pressure warning estimate, and the first pressure relief prompt does not receive a response, a pressure relief prompt message can be issued to the electric pressure cooker, and the forced pressure relief program of the electric pressure cooker can be started to improve the safety control level of the electric pressure cooker.

[0126] For example, if the user adjusts the heating power during cooking, the system records a pressure warning estimate of 125 kPa at 10:05 (preset threshold 120 kPa), triggering the first pressure relief prompt. If no pressure relief operation is detected by 10:08, the system records the estimate rising to 128 kPa. That is, when two consecutive pressure warning estimates in time sequence are detected that exceed the preset pressure warning estimate, and the user has not yet performed pressure relief on the electric pressure cooker, the system issues a pressure relief prompt message to the electric pressure cooker and starts the electric pressure cooker's forced pressure relief program, which can specifically execute a 30-second forced venting program.

[0127] For example, after forced pressure relief is initiated, the pressure can be fully released for the first 10 seconds to quickly drop the pressure to 110 kPa, and then the system can be switched to intermittent pressure relief mode to maintain a safe pressure level.

[0128] The beneficial effect of the above implementation method is that, through time series analysis of continuous pressure early warning estimates, it is possible to accurately capture the risk state of continuous abnormal pressure. This dynamic monitoring mechanism avoids the problems of misjudgment or omission that may occur in a single early warning.

[0129] The beneficial effect of the above implementation method is that the forced pressure relief procedure is activated in a timely manner after the first warning fails, forming a complete safety protection closed loop. This mechanism effectively eliminates the danger of continuous high pressure caused by user negligence and significantly improves the equipment's self-protection capability under extreme working conditions.

[0130] In some implementations, the above method further includes: when the electric pressure cooker releases pressure according to a first pressure warning estimate, reducing the heating power by a first proportion; when the electric pressure cooker releases pressure according to a second pressure warning estimate, reducing the heating power by a second proportion. The first proportion is directly proportional to the first pressure warning estimate, and the second proportion is directly proportional to the sum of the first and second pressure warning estimates.

[0131] When performing pressure-coordinated control of an electric pressure cooker, the heating power can be adjusted synchronously when a pressure relief operation is triggered. When the system performs pressure relief based on the first pressure warning estimate, the heating power can be reduced according to a first proportional coefficient that is proportional to the first pressure warning estimate. Subsequently, if pressure relief is performed again based on a second pressure warning estimate in the time series, a greater power reduction can be implemented according to a second proportional coefficient that is proportional to the sum of the first and second pressure warning estimates. This graded adjustment mechanism can ensure that the power reduction magnitude is dynamically matched with the risk level.

[0132] For example, when the first pressure warning estimate is detected to reach 130 kPa (preset threshold 120 kPa), the control module calculates the first proportional coefficient to be 0.85, and adjusts the original 1000W power to 850W to perform pressure relief. This can be precisely controlled through the power adjustment circuit.

[0133] In continuous pressure anomaly scenarios, the system can enhance intervention based on the superposition effect of two warning values. For example, if the second pressure warning value rises to 135 kPa after the initial pressure relief, the second proportional coefficient is calculated to be 0.7 based on the sum of 130 + 135 = 265 kPa, and the power is further reduced to 595 W.

[0134] The beneficial effects of the above implementation method are that the heating power is reduced simultaneously during pressure relief to form a dual protection mechanism, effectively blocking the energy source for continuous pressure increase; the use of dynamic proportional coefficient to achieve risk-level response enhances the power reduction for continuous warning scenarios, significantly improving the safety control capability of electric pressure cookers under repeated abnormal operating conditions.

[0135] This application also provides a cooking pressure warning device based on user safety behavior analysis, including a unit for performing the method described in any of the preceding claims.

[0136] Figure 6 A schematic diagram of the logical structure of a first type of cooking pressure early warning device based on user safety behavior analysis provided in this application embodiment is shown below. Figure 6 As shown, the device 1 in this embodiment includes a processing unit 11, a storage unit 12, and a transceiver unit 13. The processing unit 11 is used to process data, the storage unit 12 is used to store data, and the transceiver unit 13 is used to send and receive data. The processing unit 11, the storage unit 12, and the transceiver unit 13 cooperate with each other to implement the above-described method. The beneficial effects of the embodiments of this application have been described in the above-described method and will not be repeated here.

[0137] It should be noted that the information interaction and execution process between the above-mentioned devices / units are based on the same concept as the method embodiments of this application. For details on their specific functions and technical effects, please refer to the method embodiments section, and they will not be repeated here.

[0138] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0139] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of this application can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include at least: any entity or device capable of carrying computer program code to a photographing device / terminal device, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium. Examples include USB flash drives, portable hard drives, magnetic disks, or optical disks. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electrical carrier signals or telecommunication signals.

[0140] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0141] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0142] In the embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0143] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0144] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A cooking stress early warning method based on user safety behavior analysis, characterized in that, The method includes: The system acquires data on the characteristics of the food being cooked in the electric pressure cooker, historical pressure status information of the electric pressure cooker, and current user operation data of the electric pressure cooker. The system obtains current food status information corresponding to food characteristic data and historical pressure status information through a food status monitoring database; it determines the pressure warning estimate of the electric pressure cooker based on historical pressure status information, current food status information, and current operation data through a food status monitoring model; and it issues a pressure relief prompt to the electric pressure cooker when the pressure warning estimate is greater than or equal to the preset pressure warning estimate. Using a food condition monitoring model, based on historical pressure status information, current food condition information, and current operational data, a pressure warning estimate for the electric pressure cooker is determined, including: By using a food condition monitoring database, the target food condition information corresponding to historical pressure condition information and food characteristic data is obtained; the difference between the current food condition information and the target food condition information is determined as the current food condition offset value; and the electric pressure cooker condition monitoring model is used to determine the subsequent pressure condition information of the electric pressure cooker based on the current pressure condition information and the current operation data. Among them, the historical pressure condition information includes the cooking stage information corresponding to the historical pressure condition. By using a food condition monitoring model, the pressure warning estimate of the electric pressure cooker is determined based on subsequent pressure condition information and the current food condition offset value. The method further includes: Obtain the pressure relief valve orifice information of the electric pressure cooker, and obtain the food particle size information from the food characteristic data; through the food state monitoring model, determine the pressure relief state influencing factors based on the pressure relief valve orifice information, food particle size information, and current food state information; among which, the food state information includes the food viscosity index and the food expansion index; The pressure warning estimate is adjusted by determining the product of the pressure warning estimate and the factors affecting the pressure relief status.

2. The method as described in claim 1, characterized in that, The method further includes: The system acquires multiple historical cooking records of the user cooking in an electric pressure cooker, and obtains the user's operation delay time after the pressure relief prompt message is issued in each historical cooking record, and determines the average operation delay time corresponding to multiple historical cooking records; it also obtains the current pressure rise rate inside the electric pressure cooker. The pre-pressure relief control model determines the pre-pressure relief probability value based on the current pressure rise rate, food viscosity index, and average operation delay time. When the pre-pressure relief probability value is greater than or equal to the preset pre-pressure relief probability value, the electric pressure cooker is controlled to perform pre-pressure relief.

3. The method as described in claim 2, characterized in that, The method further includes: When cooking with an electric pressure cooker, obtain the real-time operation delay time of the user after the electric pressure cooker issues a pressure release prompt message; When the real-time operation delay time is greater than or equal to the average operation delay time of the preset multiplier, the electric pressure cooker is controlled to pre-depressurize and then enter the pressure holding mode.

4. The method as described in claim 3, characterized in that, The method further includes: The system obtains the user's operation delay time after issuing a pressure relief prompt in each historical cooking record, and determines the number of times the operation delay time for multiple historical cooking records is greater than the preset operation delay time, which is used as the number of times the user's pressure relief alarm is ignored. When the number of times a pressure relief alarm is ignored is greater than or equal to the preset number of times a pressure relief alarm is ignored, the preset pressure warning estimate is reduced by a preset ratio; when the number of times a pressure relief alarm is ignored is less than the preset number of times a pressure relief alarm is ignored, the preset pressure warning estimate is increased by a preset ratio.

5. The method as described in claim 4, characterized in that, The method further includes: Obtain the ingredient characteristic data of the food being cooked in the electric pressure cooker, and determine the preset ratio for adjusting the preset pressure warning estimate and preset operation delay time based on the ingredient characteristic data.

6. The method as described in claim 5, characterized in that, The method further includes: When a user has multiple operation data points for the electric pressure cooker in a time sequence, obtain the pressure warning estimate corresponding to each of the multiple operation data points for the electric pressure cooker in a time sequence. When the first pressure warning estimate is greater than or equal to the preset pressure warning estimate, and the second pressure warning estimate that follows the first pressure warning estimate in time sequence is greater than or equal to the preset pressure warning estimate, and after issuing the corresponding first pressure relief prompt message for the first pressure warning estimate, if the electric pressure cooker does not perform pressure relief, a pressure relief prompt message is issued to the electric pressure cooker, and the forced pressure relief program of the electric pressure cooker is started.

7. The method as described in claim 6, characterized in that, The method further includes: When the electric pressure cooker releases pressure according to the first pressure warning estimate, the heating power is reduced by a first proportion; when the electric pressure cooker releases pressure according to the second pressure warning estimate, the heating power is reduced by a second proportion; wherein, the first proportion is proportional to the first pressure warning estimate, and the second proportion is proportional to the sum of the first pressure warning estimate and the second pressure warning estimate.

8. A cooking pressure early warning device based on user safety behavior analysis, characterized in that, Includes a unit for performing the method according to any one of claims 1 to 7.

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