Cooking equipment control method, device, equipment and medium

Abstract

The present invention discloses a cooking equipment control method, apparatus, device, and medium. The cooking equipment control method utilizes a gas sensor array to monitor gas signals released by food in real time. Through a preset algorithm model, the method dynamically generates a control strategy suitable for the current cooking state based on user-input time adjustment instructions during the cooking process. Furthermore, the method predicts and verifies the convergence of the gas signal to the target response value in subsequent stages. If the expected cooking quality is not achieved, the cooking parameters are continuously optimized to ensure that the ideal food cooking effect is achieved within the adjusted time. By combining gas sensing technology and intelligent models, dynamic adjustment and real-time fine control are achieved during the cooking process, addressing the shortcomings of traditional cooking equipment in terms of control flexibility and adaptability.

Description

Technical Field

[0001] The present invention relates to the technical field of cooking equipment control methods, and in particular to a cooking equipment control method, device, equipment and medium. Background Art

[0002] With the accelerated pace of people's lives and the constant pursuit of gourmet food experience, smart kitchen equipment has gradually become an important part of modern kitchens. Among them, steam ovens, as multifunctional cooking equipment, have the advantages of large heating area, high thermal efficiency and convenient operation, and are favored by the majority of users. However, existing steam ovens mostly rely on temperature sensors or time controllers to realize the automation of the cooking process. Their control logic is relatively simple and it is difficult to flexibly respond to different ingredients, environmental changes and user personalized needs. Traditional control methods are usually difficult to dynamically adjust cooking time or parameters during the cooking process, which can easily lead to uneven cooking or poor quality of food, especially when facing ingredients of different specifications, shapes or freshness. In addition, relying solely on temperature or time adjustment cannot fully reflect the cooking status inside the food, and cannot dynamically detect the actual degree of cooking maturity of the food, thereby affecting the final edible quality. Therefore, there is an urgent need for a control method that can freely adjust the time during cooking and ensure quality consistency by real-time monitoring of the chemical state of food. Summary of the Invention

[0003] Embodiments of the present invention provide a cooking device control method, apparatus, device, and medium, aiming to solve the problem of unstable quality of finished cooking products caused by insufficient flexibility in cooking control of cooking devices in the prior art.

[0004] In a first aspect, an embodiment of the present invention provides a cooking device control method, which is applied to a cooking device equipped with a gas sensor array. The method includes:

[0005] The gas signal response data released by the food is continuously monitored through the gas sensor array; when the cooking time reaches a first preset cooking time node, a cooking time adjustment instruction input by the user is received, and based on the cooking time adjustment instruction, a cooking parameter control strategy and target gas signal response data adapted to the optimal cooking quality are generated through a preset variable speed algorithm model; when the cooking time reaches a second preset cooking time node, whether the gas signal response data within the remaining cooking time approaches the target gas signal response data is verified through a prediction model; if not, the cooking parameter control strategy is dynamically optimized, and the prediction of whether the gas signal response data approaches the target gas signal response data is continuously executed until it approaches the target gas signal response data.

[0006] In a second aspect, an embodiment of the present invention further provides a cooking device control apparatus for executing the cooking device control method described above.

[0007] In a third aspect, an embodiment of the present invention further provides a computer device, comprising a memory and a processor connected to the memory; the memory is used to store a computer program; and the processor is used to run the computer program stored in the memory to execute the steps of the above-mentioned cooking device control method.

[0008] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, wherein the storage medium stores a computer program, wherein the computer program includes program instructions, and when the program instructions are executed by a processor, the steps of the above-mentioned cooking device control method can be implemented.

[0009] Compared with the prior art, the present invention has the following beneficial effects:

[0010] In the technical solution of the present invention, the cooking equipment control method utilizes a gas sensor array to monitor the gas signals released by food in real time. Using a preset algorithm model, the system dynamically generates a control strategy tailored to the current cooking state based on user-input time adjustment instructions during the cooking process. Subsequently, the system predicts and verifies the convergence of the gas signal to the target response value. If the desired cooking quality is not achieved, the cooking parameters are continuously optimized to ensure the desired cooking effect within the adjusted timeframe. By combining gas sensing technology and intelligent models, dynamic adjustment and real-time fine control are achieved during the cooking process, addressing the shortcomings of traditional cooking equipment in terms of control flexibility and adaptability. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0012] Figure 1 A flowchart of the cooking equipment control method provided by the present invention;

[0013] Figure 2 A first sub-flowchart of the cooking device control method provided by the present invention;

[0014] Figure 3 A sub-flowchart of the second sub-flowchart of the cooking device control method provided by the present invention;

[0015] Figure 4 A third sub-flowchart of the cooking device control method provided by the present invention;

[0016] Figure 5 A fourth sub-flowchart of the cooking device control method provided by the present invention;

[0017] Figure 6 A fifth sub-flowchart of the cooking device control method provided by the present invention;

[0018] Figure 7 A sixth sub-flowchart of the cooking device control method provided by the present invention;

[0019] Figure 8 A schematic block diagram of a unit of a cooking device control device provided by the present invention;

[0020] Figure 9 A schematic block diagram of a computer device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0021] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0022] It will be understood that when used in this specification and the appended claims, the terms “comprises” and “comprising” indicate the presence of described features, integers, steps, operations, elements and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof.

[0023] It should also be understood that the terms used in this specification are for the purpose of describing embodiments only and are not intended to limit the present invention. As used in the specification and appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms unless the context clearly indicates otherwise.

[0024] It should be further understood that the term "and / or" used in the present description and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0025] To address the problem of unstable cooking product quality caused by inflexible cooking control in conventional cooking equipment, the present invention provides a cooking equipment control method, which is applied to a cooking equipment equipped with a gas sensor array. The method comprises:

[0026] S110, continuously monitoring gas signal response data released by food through the gas sensor array;

[0027] S120: When the cooking time reaches a first preset cooking time node, receiving a cooking time adjustment instruction input by a user, and generating a cooking parameter control strategy and target gas signal response data adapted to optimal cooking quality using a preset variable speed algorithm model based on the cooking time adjustment instruction;

[0028] S130: When the cooking time reaches a second preset cooking time node, verifying through a prediction model whether the gas signal response data within the remaining cooking time approaches the target gas signal response data;

[0029] S140 : If not, dynamically optimize the cooking parameter control strategy, and continue to predict whether the gas signal response data approaches the target gas signal response data until it approaches the target gas signal response data.

[0030] The cooking device used in this method is an intelligent steam oven equipped with a gas sensor array. This array, integrated into the air duct, contains multiple metal oxide semiconductor gas sensor channels, each specifically targeting volatile compounds such as aldehydes, ketones, and pyrazines produced by the Maillard reaction. When cooking is started, the device automatically activates the gas sensor array and begins synchronously collecting gas signal response data. These signals reflect the internal state of the food at different cooking stages, such as changes in volatile gases caused by water evaporation, fat volatilization, and protein denaturation. The collected gas signal data is filtered and feature extracted by a preprocessing module to eliminate noise and highlight key changing parameters, ensuring the accuracy of subsequent model judgments.

[0031] When the cooking time reaches the preset first node, such as 50% of the total cooking time, the system will receive cooking time adjustment instructions input by the user through the control panel or smart terminal. These instructions may include "speeding up" or "extending" the cooking time. The system uses a preset variable speed algorithm model, such as an algorithm based on historical data and regular learning, to convert user instructions into cooking parameter control strategies. These strategies involve adjusting parameters such as heating power, fan speed or cooking time to ensure that the predetermined cooking effect can be achieved even if the time is adjusted. The preset variable speed algorithm model is a mapping model based on neural network training. At the same time, the target gas signal response data set by the system is obtained by analyzing the characteristic parameters of the gas signal when the food is in the ideal cooking state, and plays the role of a benchmark.

[0032] When the cooking time reaches a second preset threshold, such as 75% of the total cooking time, the system uses a predictive model to analyze the gas signal for the remaining time. This predictive model can be a deep learning model or a short-term memory (LSTM) network. This model uses previously collected gas signal changes and historical data to determine whether the gas response data will approach the set target gas signal response data within the remaining cooking time, indicating that the food has reached the ideal state of doneness. If the test results indicate that these targets are not met, the system dynamically adjusts the cooking parameters based on the current gas signal changes, such as further increasing the heating power or adjusting the fan mode, to optimize heat conduction and heat distribution, ensuring that the cooking state within the food gradually approaches the ideal trend. This process repeats until the gas signal response data meets the preset target gas signal response data, indicating that the food has reached the ideal state of doneness.

[0033] At the end of the cooking process, the system automatically adjusts the heat source output parameters based on the final gas signal response and pre-set quality standards to ensure sufficient and even heat transfer, preventing overcooking or undercooking. Simultaneously, the system issues a warning signal, prompting the user to safely remove the food and enjoy it. Throughout this entire process, continuous monitoring by the gas sensor array and real-time adjustments by the dynamic control model enable scientific control of the cooking state, significantly improving the inflexibility and difficulty in ensuring consistent quality of the finished product caused by traditional control methods.

[0034] In one embodiment, the step S120 includes:

[0035] S121, calling the variable speed algorithm model with constant gas response data as a constraint condition to determine the cooking time adjustment instruction;

[0036] S122: When the cooking time adjustment instruction is to shorten the original total cooking time, generating the cooking parameter control strategy for improving cooking efficiency through the variable speed algorithm model;

[0037] S123: When the cooking time adjustment instruction is to extend the original total cooking time, generating the cooking parameter control strategy for reducing cooking efficiency through the speed-changing algorithm model;

[0038] S124. Outputting target gas signal response data that matches the cooking parameter control strategy through the variable speed algorithm model.

[0039] Upon program startup, the control system first invokes a preset variable-speed algorithm model. This model analyzes the gas response data collected by the gas sensor array and, in conjunction with the preset constant-gas response data constraints, determines the user's cooking time adjustment instructions. The variable-speed algorithm model is a trained or designed mathematical model that generates corresponding cooking parameter control strategies based on different adjustment instructions, such as "shortening" or "extending" the total cooking time. Specifically, when a user requests a "shorten" cooking time instruction, the control model adjusts the heating power of the heating element and the fan operating mode, ensuring that the gas response data is consistent with the preset constant-gas response conditions. This accelerates heat transfer in a fast-cooking mode, and adds auxiliary measures, such as fan speed or temperature control strategies, to improve overall cooking efficiency. Conversely, if the user requests an "extend" cooking time instruction, the control model reduces heating power, switches to a slow-cooking mode, or activates a waste heat utilization strategy to slow down heat transfer, thereby prolonging the cooking process and ensuring that the food is heated evenly over a longer period of time.

[0040] During this process, the control system also outputs target gas signal response data that matches the cooking parameter control strategy. These target values ​​represent the gas signal response parameters required to achieve the ideal cooking state for the food under the new cooking parameter conditions. This target gas signal response data is determined through gas signal analysis, sensory verification, and model training, ensuring that the food maintains the expected quality and taste when different cooking strategies are used. Simultaneously, the system adjusts the heat source, fan, or other actuators in the cooking equipment in real time to ensure that their motion conforms to the designed control strategy as closely as possible. This ensures that the gas response data remains consistent with the preset constant gas response to the greatest extent possible, even when improving cooking efficiency or extending cooking time, achieving effective and flexible cooking control.

[0041] In one embodiment, the step S110 includes:

[0042] S111, continuously collecting gas signal response data of multiple channels of the gas sensor array;

[0043] S112, extracting the change rate and change direction characteristics of the gas signal response data in real time;

[0044] S113: Cache the change direction feature as a time series data set.

[0045] The gas sensor array consists of multiple gas sensor units installed at various locations within the cooking cavity, designed to comprehensively and multi-channelly collect volatile gas signals released by food during the cooking process. These multiple gas sensor units can monitor the initial products of the Maillard reaction, intermediate products of starch pyrolysis, and final products of the caramelization reaction, such as acetaldehyde, 2,3-butanedione, and furfural. Once cooking is started, the gas sensor array continuously measures the gas composition within the cavity, generating a multi-channel gas signal response data stream. These signals include analog or digital output values ​​generated by the sensors, representing changes in the concentrations of different gas components.

[0046] During data processing, the system analyzes the gas signal response data collected by the sensor array in real time, extracting key change characteristics, including the rate of change and direction of change. The rate of change refers to the speed of change in the signal value between two consecutive acquisition time points, while the direction of change reflects whether the gas concentration is increasing or decreasing. Using differential or finite difference methods, the signal change rate at each moment is quickly calculated and the trend of the gas concentration change is determined—whether it is continuously increasing, decreasing, or remaining stable.

[0047] To enable dynamic monitoring and subsequent analysis, these change direction features are cached as a time series dataset. This means that the continuously collected change direction values ​​are stored in a queue or buffer in chronological order, forming a time series library. This dataset not only reflects the dynamics of gas release during the cooking process but also serves as the basis for subsequent model training or real-time judgment. During storage, the system synchronizes and tags the data to ensure that the corresponding relationship between the response data of each channel remains consistent, providing an accurate input basis for subsequent gas change trend prediction and cooking status judgment. Through this continuous, multi-channel, multi-feature gas signal response data collection and feature extraction method, the system can achieve dynamic and precise monitoring of the actual state of food during the cooking process, providing the necessary data support for subsequent intelligent adjustment and cooking quality assurance.

[0048] Furthermore, the step S130 includes:

[0049] S131, loading the time series data set from the start of cooking to the judgment node;

[0050] S132, extracting cumulative change trend characteristics of the gas signal response data;

[0051] S133. Based on the cumulative change trend characteristics, deduce the evolution trajectory of the gas signal response data within the remaining time through the prediction model;

[0052] S134 : Compare the deviation between the evolution trajectory and the target gas signal response data.

[0053] In a specific implementation of the present invention, the system first continuously collects and stores all gas signal response data from the start of cooking to the current judgment node to form a complete time series data set. This data set may include information on changes in gas component concentrations across multiple channels collected by the gas sensor array, and undergoes necessary synchronization processing and storage to ensure that the corresponding relationships between the data in each channel are complete and accurate. Next, the system analyzes the stored time series data set to extract the cumulative change trend characteristics of the gas signal. This characteristic is typically manifested as the overall direction of the signal response change, the cumulative trend of the change rate, and the overall tendency of the change amplitude. These trend characteristics reveal the overall dynamics of food gradually reaching a certain state of maturity during the cooking process.

[0054] Subsequently, a predictive model, such as a deep learning-based time series model or momentum analysis algorithm, leverages the extracted cumulative trend features to infer the evolutionary trajectory of the gas signal response data for the remaining time. Specifically, based on the gas change trajectory from the start to the current node, the model infers the path and trend of the gas response for the remaining time. This inference process considers that gas signals may be influenced by multiple factors. The model incorporates known cooking patterns to dynamically simulate the future direction of the signal.

[0055] Finally, after obtaining the evolution trajectory, the system compares the predicted trajectory with the preset target gas signal response data and calculates the deviation between the two. This deviation reflects whether the gas signal response is expected to reach the target state within the remaining time, that is, whether the food can reach the ideal level of doneness within the predetermined time. A large deviation indicates that further adjustment of cooking parameters is needed to shorten or extend the cooking time. A small deviation indicates that the remaining time and adjustment strategy are close to the ideal state, and cooking can be continued or terminated.

[0056] Furthermore, the step of S140 includes:

[0057] S141. When the deviation exceeds a preset tolerance threshold, generating a cooking efficiency adjustment instruction;

[0058] S142: After executing the cooking efficiency adjustment instruction, updating the time series data set;

[0059] S143. Repeatedly deduce the evolution trajectory and compare the deviation based on the updated time series data set;

[0060] S144. When the deviation falls within the preset tolerance threshold, stop deducing the evolution trajectory and comparing the deviation.

[0061] When the prediction model verifies that the evolution trajectory of the gas signal within the remaining time fails to reach the preset target gas signal response data, and the deviation exceeds the preset tolerance threshold, the control system automatically generates a cooking efficiency adjustment instruction. This adjustment instruction mainly includes adjusting the heating power of the heating tube, the fan speed, or other related thermal control parameters to enhance the efficiency of heat transfer or adjust the thermal field distribution, thereby promoting the gas release state to approach the target value. The generated adjustment instruction will be executed in real time, and the specific operation may involve adjusting the temperature setting of the equipment, the fan operating parameters, or the cooking time.

[0062] The system then updates the existing time series dataset in real time, adding the newly collected gas response data to the original data to form an updated, complete time series dataset. This ensures that subsequent trend analysis and trajectory deduction are based on the latest cooking status information, ensuring the adaptability and real-time performance of the adjustment strategy. The system then extracts trend features from the updated time series dataset and, based on the pre-trained prediction model, re-derives the evolution trajectory of the gas signal for the remaining time and compares the deviations.

[0063] This process forms a feedback loop: after each adjustment, the gas signal trajectory is re-evaluated, the deviation is calculated, and the cooking parameter control strategy is continuously optimized until the deviation falls within the preset tolerance threshold. At this point, the gas signal response for the remaining time has basically reached the target, and the adjustment process can be stopped. The cooking equipment will maintain control based on the current status to ensure the food is cooked to the desired effect.

[0064] Through this dynamic adjustment and continuous feedback mechanism, the system can effectively respond to possible variations and interferences during the cooking process, ensure that the gas signal is stable and meets the predetermined cooking specifications, fundamentally improve the stability of cooking quality, and avoid problems caused by complex changes that cannot be solved by a single adjustment strategy, thereby achieving smarter and more precise cooking control.

[0065] Furthermore, the step S144 includes:

[0066] S145. When the remaining cooking time is lower than the preset adjustment node critical value, if the deviation still exceeds the tolerance threshold, switch to the maximum cooking efficiency and skip the loop judgment to terminate directly.

[0067] After multiple adjustments, deductions, and deviation comparisons, the system detects that the deviation has successfully fallen within the preset tolerance threshold, indicating that the gas signal response within the remaining cooking time has basically met the target value and the cooking state has reached the expected state. Based on this, the system further checks whether the remaining cooking time is below the preset adjustment node critical value, namely the cooking time critical point. This is to ensure that the adjustment effect is fully achieved when the remaining time is short. If the deviation is still outside the tolerance range, it means that it is difficult to achieve the ideal gas signal response within the remaining time through conventional adjustment of cooking parameter control strategies.

[0068] To ensure high cooking efficiency and energy savings, the system switches to maximum cooking efficiency mode, maximizing heat output, heating speed, and fan speed. This bypasses the subsequent, smaller-scale, repetitive adjustments and immediately terminates the deviation adjustment process. This avoids wasted adjustments in the final stages, ensuring a quick cooking completion, ensuring the food is cooked to the desired quality, and significantly saving energy and time.

[0069] In addition, when implementing the maximum cooking efficiency strategy, the device automatically adjusts all heat source control parameters to the limit state, while maintaining gas monitoring to ensure that the gas signal response is stable and reaches or approaches the preset target gas signal response data. This strategy is particularly suitable for scenarios near the end of cooking and when time is tight, and it also ensures that high-quality cooking results can be quickly achieved under special circumstances. Through the above measures, the present invention achieves rapid and efficient processing when the deviation has reached the standard and the remaining time is insufficient, avoiding delays caused by multiple cycle judgments, and ensuring the efficiency, intelligence, and energy saving of the entire cooking process.

[0070] In one embodiment, the step S120 includes:

[0071] S12a, when the cooking time reaches the first preset cooking time node, unlocking the cooking time change option;

[0072] S12b. Generate the cooking time adjustment instruction according to the cooking result expectation given by the user through the cooking time change option.

[0073] When the cooking time reaches the first preset threshold, 50% of the total cooking time, the control system automatically unlocks the cooking time change option, allowing the user to adjust the remaining cooking time. This operation is typically completed through the device's control panel or a companion smart terminal, where the user can select adjustment modes such as "Speed ​​Up," "Extend," or "Keep Original Time." This unlocking process ensures that users can adjust the cooking time mid-cook based on actual conditions or preferences, thereby achieving personalized customization.

[0074] Based on the adjustment options selected by the user, the system intelligently generates corresponding cooking time adjustment instructions to match the user's expectations. For example, if the user selects "Speed ​​Up" mode, the system converts this instruction into an operational strategy to shorten the remaining cooking time, adjusting heating parameters or fan control to accelerate cooking. If the user selects "Extend" mode, the system adjusts the device's heat source power or waste heat utilization to extend cooking time. Throughout this process, the system translates user preferences into specific control instructions based on pre-set control strategies or rules, ensuring that subsequent cooking adjustments meet the user's expectations.

[0075] Figure 8 FIG. 6 is a schematic block diagram of a cooking equipment control device 600 provided by an embodiment of the present invention. Figure 8 As shown, corresponding to the above cooking device control method, the present invention also provides a cooking device control device 600. The cooking device control device 600 includes a unit for executing the above cooking device control method, and the device can be configured in a desktop computer, tablet computer, smart phone, etc.

[0076] Specifically, see Figure 8 , the cooking equipment control device 600 includes:

[0077] a data acquisition unit 610, configured to continuously monitor the gas signal response data released by food through the gas sensor array;

[0078] The first adjustment unit 620 receives a cooking time adjustment instruction input by a user when the cooking time reaches a first preset cooking time node, and generates a cooking parameter control strategy and target gas signal response data adapted to the optimal cooking quality based on the cooking time adjustment instruction using a preset variable speed algorithm model;

[0079] The second adjustment unit 630 verifies, through the prediction model, whether the gas signal response data approaches the target gas signal response data within the remaining cooking time when the cooking time reaches a second preset cooking time node;

[0080] The strategy dynamic generation unit 640 is configured to dynamically optimize the cooking parameter control strategy if no, and continuously perform the prediction of whether the gas signal response data approaches the target gas signal response data until it approaches the target gas signal response data.

[0081] In one embodiment, the first adjustment unit 620 includes:

[0082] an adjustment instruction judging unit, configured to call the variable speed algorithm model with the constant gas response data as a constraint condition to judge the cooking time adjustment instruction;

[0083] a shortening control strategy unit, configured to generate the cooking parameter control strategy for improving cooking efficiency through the speed-changing algorithm model when the cooking time adjustment instruction is to shorten the original total cooking time;

[0084] an extension control strategy unit, configured to generate the cooking parameter control strategy for reducing cooking efficiency through the speed-changing algorithm model when the cooking time adjustment instruction is to extend the original total cooking time;

[0085] The target gas signal response data generating unit is configured to output target gas signal response data matching the cooking parameter control strategy through the variable speed algorithm model.

[0086] In one embodiment, the data acquisition unit 610 includes:

[0087] A gas sensor array signal acquisition unit, configured to continuously acquire gas signal response data from multiple channels of the gas sensor array;

[0088] A data feature extraction unit, configured to extract the change rate and change direction features of the gas signal response data in real time;

[0089] The time series data set generating unit is configured to cache the change direction feature as a time series data set.

[0090] Furthermore, the second adjustment unit 630 includes:

[0091] A time series data set loading unit, configured to load the time series data set from the start of cooking to the judgment node;

[0092] a cumulative change trend feature extraction unit, configured to extract cumulative change trend features of the gas signal response data;

[0093] an evolution trajectory deduction unit, configured to deduce the evolution trajectory of the gas signal response data within the remaining time through the prediction model based on the cumulative change trend characteristics;

[0094] The deviation comparison unit is used to compare the deviation between the evolution trajectory and the target gas signal response data.

[0095] Furthermore, the policy dynamic generation unit 640 includes:

[0096] an exceeding preset tolerance threshold value adjustment unit, configured to generate a cooking efficiency adjustment instruction when the deviation exceeds the preset tolerance threshold value;

[0097] a time series data set updating unit, configured to update the time series data set after executing the cooking efficiency adjustment instruction;

[0098] An advanced deduction unit, configured to repeatedly deduce the evolution trajectory and the comparison deviation based on the updated time series data set;

[0099] The cyclic stop node unit is used to stop deducing the evolution trajectory and comparing the deviation when the deviation falls within the preset tolerance threshold.

[0100] Furthermore, after the loop stops the node unit, the process further includes:

[0101] When the remaining cooking time is lower than the preset adjustment node critical value, if the deviation still exceeds the tolerance threshold, it switches to the maximum cooking efficiency and skips the loop judgment and terminates directly.

[0102] In one embodiment, the first adjustment unit 620 includes:

[0103] a cooking time change option unlocking unit, configured to unlock the cooking time change option when the cooking time reaches the first preset cooking time node;

[0104] The user demand acquisition unit is configured to generate the cooking time adjustment instruction according to the cooking result expectation given by the user through the cooking time change option.

[0105] The cooking device control device 600 can be implemented in the form of a computer program. Figure 9 Runs on the computer device shown.

[0106] See also Figure 9 , Figure 9 This is a schematic block diagram of a computer device provided in an embodiment of the present application. The computer device 500 can be a terminal or a server. The terminal can be a desktop computer, tablet computer, smartphone, or other electronic device with communication capabilities. The server can be a standalone server or a server cluster consisting of multiple servers.

[0107] See Figure 9 The computer device 500 includes a processor 502 , a memory, and a network interface 505 connected via a system bus 501 , wherein the memory may include a non-volatile storage medium 503 and an internal memory 504 .

[0108] The non-volatile storage medium 503 can store an operating system 5031 and a computer program 5032. The computer program 5032 includes program instructions, which, when executed, can cause the processor 502 to execute a cooking device control method.

[0109] The processor 502 is used to provide computing and control capabilities to support the operation of the entire computer device 500.

[0110] The internal memory 504 provides an environment for the operation of the computer program 5032 in the non-volatile storage medium 503. When the computer program 5032 is executed by the processor 502, the processor 502 can execute a cooking device control method.

[0111] The network interface 505 is used to communicate with other devices over the network. Figure 9 The structure shown in the figure is merely a block diagram of a portion of the structure related to the solution of the present application, and does not constitute a limitation on the computer device 500 to which the solution of the present application is applied. The specific computer device 500 may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0112] The processor 502 is configured to run a computer program 5032 stored in the memory to implement the steps of the above method.

[0113] It should be understood that in the embodiment of the present application, the processor 502 may be a central processing unit (CPU), and the processor 502 may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc.

[0114] Those skilled in the art will appreciate that all or part of the steps in the method of the above-described embodiment can be implemented by instructing the relevant hardware through a computer program. The computer program includes program instructions, which can be stored in a storage medium that is computer-readable. The program instructions are executed by at least one processor in the computer system to implement the steps in the method of the above-described embodiment.

[0115] Therefore, the present invention also provides a storage medium. The storage medium may be a computer-readable storage medium. The storage medium stores a computer program, wherein the computer program includes program instructions. When the program instructions are executed by a processor, the processor performs the steps of the above method.

[0116] The storage medium may be any computer-readable storage medium that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a magnetic disk, or an optical disk.

[0117] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the above description has generally described the composition and steps of each example according to function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0118] In the several embodiments provided herein, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the various units is merely a logical functional division, and actual implementation may employ other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be omitted or not implemented.

[0119] The steps in the methods of the embodiments of the present invention may be adjusted in order, combined, or deleted as needed. The units in the devices of the embodiments of the present invention may be combined, divided, or deleted as needed. Furthermore, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit.

[0120] If this integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product, stored in a storage medium, includes instructions for enabling a computer device (such as a personal computer, terminal, or network device) to execute all or part of the steps of the method described in various embodiments of the present invention.

[0121] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and such modifications or substitutions are intended to be within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be subject to the scope of protection of the claims.

Claims

1. A cooking equipment control method, characterized in that: Applied to a cooking device equipped with a gas sensor array, the method comprises: Continuously monitoring the gas signal response data released by the food through the gas sensor array; When the cooking time reaches a first preset cooking time node, receiving a cooking time adjustment instruction input by a user, and generating a cooking parameter control strategy and target gas signal response data adapted to the optimal cooking quality through a preset variable speed algorithm model based on the cooking time adjustment instruction; When the cooking time reaches a second preset cooking time node, verifying through a prediction model whether the gas signal response data within the remaining cooking time approaches the target gas signal response data; If not, the cooking parameter control strategy is dynamically optimized, and the prediction of whether the gas signal response data approaches the target gas signal response data is continuously performed until the gas signal response data approaches the target gas signal response data.

2. The cooking equipment control method according to claim 1, characterized in that: The step of generating a cooking parameter control strategy and target gas signal response data adapted to the optimal cooking quality through a preset variable speed algorithm model based on the cooking time adjustment instruction includes: Invoking the variable speed algorithm model with constant gas response data as a constraint condition to determine the cooking time adjustment instruction; When the cooking time adjustment instruction is to shorten the original total cooking time, the cooking parameter control strategy for improving cooking efficiency is generated by the variable speed algorithm model; When the cooking time adjustment instruction is to extend the original total cooking time, the cooking parameter control strategy for reducing cooking efficiency is generated by the speed-changing algorithm model; The variable speed algorithm model outputs target gas signal response data that matches the cooking parameter control strategy.

3. The cooking equipment control method according to claim 1, wherein: The step of continuously monitoring the gas signal response data released by food through the gas sensor array includes: Continuously collecting gas signal response data of multiple channels of the gas sensor array; Extracting the change rate and change direction characteristics of the gas signal response data in real time; The change direction feature is cached as a time series data set.

4. The cooking equipment control method according to claim 3, characterized in that: The step of verifying whether the gas signal response data approaches the target gas signal response data within the remaining cooking time by using the prediction model includes: Loading the time series data set from the start of cooking to the judgment node; extracting cumulative change trend characteristics of the gas signal response data; Based on the cumulative change trend characteristics, the prediction model is used to deduce the evolution trajectory of the gas signal response data within the remaining time; The deviation between the evolution trajectory and the target gas signal response data is compared.

5. The cooking equipment control method according to claim 4, characterized in that: If not, the step of dynamically optimizing the cooking parameter control strategy includes: When the deviation exceeds a preset tolerance threshold, generating a cooking efficiency adjustment instruction; After executing the cooking efficiency adjustment instruction, updating the time series data set; Repeatedly deducing the evolution trajectory and comparing the deviation based on the updated time series data set; When the deviation falls within the preset tolerance threshold, the deduction of the evolution trajectory and the comparison of the deviation are stopped.

6. The cooking equipment control method according to claim 5, characterized in that: When the deviation falls within the preset tolerance threshold, the method further includes: When the remaining cooking time is lower than the preset adjustment node critical value, if the deviation still exceeds the tolerance threshold, it switches to the maximum cooking efficiency and skips the loop judgment and terminates directly.

7. The cooking equipment control method according to claim 1, characterized in that: When the cooking time reaches the first preset cooking time node, the step of receiving a cooking time adjustment instruction input by the user includes: When the cooking time reaches the first preset cooking time node, unlocking the cooking time change option; The cooking time adjustment instruction is generated according to the cooking result expectation given by the user through the cooking time change option.

8. A cooking equipment control device, characterized in that: Used to execute the cooking device control method according to any one of claims 1 to 7.

9. A computer device, characterized in that: The computer device includes a memory and a processor connected to the memory; the memory is used to store a computer program; the processor is used to run the computer program stored in the memory to perform the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The storage medium stores a computer program, which includes program instructions. When the program instructions are executed by a processor, the steps of the method according to any one of claims 1 to 7 can be implemented.

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