Visualized cooking process recording method and device, electronic equipment and storage medium

Abstract

This application relates to the field of visual cooking process recording technology, and discloses a method, device, electronic device, and storage medium for visual cooking process recording. The method includes: synchronously timestamping and precisely binding temperature data with operation events, solving the problem of isolated data in existing technologies. This allows users to accurately trace the instantaneous temperature at the time of any operation. Furthermore, by designing a unified timeline event recording unit and encoding format, efficient structured storage and transmission of multi-dimensional process data are achieved, ultimately generating a visual timeline chart that integrates temperature curves and operation event markers. The beneficial effects of this invention are: achieving automatic spatiotemporal fusion and integrated presentation of temperature data and operation events during the cooking process, intuitively revealing the influence of operations on temperature changes, and greatly improving the efficiency and scientific rigor of cooking process review, problem analysis, and experience transfer.

Description

Technical Field

[0001] This invention relates to the field of visual cooking process recording technology, and more particularly to a visual cooking process recording method, apparatus, electronic device, and storage medium. Background Technology

[0002] In existing technologies, the monitoring and recording of the cooking process typically relies on independent tools or simple data collection. For example, smart kitchen appliances may be equipped with temperature probes to record temperature curves or use timers to remind users of key steps. However, these solutions have significant shortcomings: temperature data and user operation events are isolated in the time dimension and cannot be automatically and accurately correlated. Users find it difficult to trace the precise temperature inside the pot when specific operations such as "flipping" or "adding water" occur, making it impossible to scientifically analyze the impact of operations on the cooking results. The recorded results are often presented as numbers or simple charts, failing to generate a unified and visual timeline that intuitively integrates temperature curves and operation event markers, which is not conducive to reviewing, analyzing, and sharing experiences of the cooking process. Summary of the Invention

[0003] Therefore, it is necessary to address the existing problem of visualizing the cooking process by proposing a method, device, electronic equipment, and storage medium for recording the cooking process.

[0004] A method for visually recording the cooking process, the method comprising: During the cooking process, temperature data is continuously collected by a temperature sensor at a preset sampling frequency, and a first timestamp is generated for each collected temperature data point. User operation events are identified through a preset interaction module. Whenever an operation event is identified, a corresponding event tag containing an event label and a second timestamp of the event occurrence time is generated. For each event marker, a target temperature data value corresponding to the time of the event is determined from the temperature data based on its second timestamp; The event tag, the second timestamp, and the target temperature data value are bound together to generate a timeline event recording unit, and each timeline event recording unit is encoded to form a cooking process dataset. The cooking process dataset is parsed to extract a structured data sequence consisting of timestamps, temperature values, and event labels; Based on the parsed structured data sequence, a curve of temperature change over time is plotted on the time axis coordinate system; At the time corresponding to each event label on the curve, a visual marker is added to indicate the operation event, thereby generating a visual timeline that integrates the temperature curve and the operation event.

[0005] Furthermore, after the step of adding visual markers to the curve at the time corresponding to each event label to indicate the operation event, thereby generating a visual timeline graph that integrates the temperature curve and the operation event, the method further includes: Obtain the target food type for cooking and the target event type for each operation event; The theoretical dimension vector corresponding to the operation event is obtained based on the target food type and the target event type; wherein, the theoretical dimension vector is the set of theoretical dimension values ​​for each preset dimension corresponding to the operation event; Obtain the set of actual dimension values ​​for each preset dimension corresponding to the second timestamp to obtain the actual dimension vector; The theoretical dimension vector and the actual dimension vector are compared to obtain the comparison result; The operation events are marked and described on the curve based on the comparison results.

[0006] Furthermore, before the step of obtaining the theoretical dimension vector corresponding to the operation event based on the target food type and the target event type, the method further includes: Obtain the historical dimension vector of multiple historical operation events corresponding to the target food type and the target event type; wherein, the historical operation events are operation events marked as normal cooking processes; The values ​​in each of the historical dimension vectors are extracted according to a preset dimension to obtain a set of historical dimension values ​​corresponding to each preset dimension. Calculate the mean and standard deviation of the historical dimension set; The averages are aggregated to obtain a theoretical dimension vector, and the normal range of the corresponding preset dimension is set according to the standard deviation.

[0007] Further, the step of comparing the theoretical dimension vector and the actual dimension vector to obtain the comparison result includes: Based on the theoretical dimension vector and the normal range of the corresponding preset dimension, an evaluation standard for each preset dimension is established. For each preset dimension, determine whether the corresponding actual dimension value in the actual dimension vector falls within its corresponding normal range; Record all actual dimension values ​​that fall within the normal range and mark them as normal dimensions; Record all actual dimension values ​​that do not fall within the normal range, mark them as abnormal dimensions, and calculate the degree of deviation between the actual dimension value of the abnormal dimension and its corresponding normal range boundary value; The comparison results are generated by summarizing the judgment results and the degree of deviation of all normal and abnormal dimensions.

[0008] Further, the interaction module is a motion sensor. In the steps of continuously collecting temperature data at a preset sampling frequency using a temperature sensor during the cooking process, generating a first timestamp for each collected temperature data point, and identifying user operation events through the preset interaction module, and generating an event marker containing an event label and a second timestamp of the event occurrence time whenever an operation event is identified, the step of identifying user operation events through the preset interaction module and generating an event marker containing an event label and a second timestamp of the event occurrence time whenever an operation event is identified includes: The motion sensor collects the user's cooking actions. The cooking operation actions are identified to determine whether they match preset operation events; When an operation event is detected, an event tag is generated that includes an event label and a second timestamp of the time the event occurred.

[0009] Further, after the step of binding the event tag, the second timestamp, and the target temperature data value to generate timeline event recording units and encoding each timeline event recording unit to form a cooking process dataset, the method further includes: Based on the cooking process dataset, calculate one or more preset key quantitative indicators; The key quantitative indicators are then integrated with the visualization timeline to generate a cooking process report; the key quantitative indicators include at least one of the following: average heating rate, duration of each preset temperature range, and frequency of operation events. Share the cooking process report to a designated terminal.

[0010] Furthermore, after the step of adding visual markers to the curve at the time corresponding to each event label to indicate the operation event, thereby generating a visual timeline graph that integrates the temperature curve and the operation event, the method further includes: In response to user interaction on the visualized timeline, the user-selected time interval is obtained; Displays detailed temperature data changes within the specified time interval, as well as all operational events occurring within that time interval; Receive user instructions to modify the event tag or the second timestamp of the operation event, and update the cooking process dataset and the visualization timeline based on the modification instructions.

[0011] A visual recording device for the cooking process, the device comprising: The data acquisition module is used to continuously acquire temperature data at a preset sampling frequency through a temperature sensor during the cooking process, generate a first timestamp for each acquired temperature data point, and identify user operation events through a preset interaction module. Whenever an operation event is identified, an event tag containing an event label and a second timestamp of the event occurrence time is generated. The determination module is used to determine, for each event marker, a target temperature data value corresponding to the time of occurrence of the event from the temperature data based on its second timestamp; The binding module is used to bind the event tag, the second timestamp, and the target temperature data value, generate timeline event recording units, and encode each timeline event recording unit to form a cooking process dataset; The parsing module is used to parse the cooking process dataset to extract a structured data sequence consisting of timestamps, temperature values, and event labels; The plotting module is used to plot the temperature change over time on the time axis coordinate system based on the parsed structured data sequence. The generation module is used to add visual markers to the curve at the time corresponding to each event label to indicate the operation event, thereby generating a visual timeline that integrates the temperature curve and the operation event.

[0012] An electronic device includes a memory and a processor, the memory storing a computer program that, when executed by the processor, causes the processor to perform the following steps: During the cooking process, temperature data is continuously collected by a temperature sensor at a preset sampling frequency, and a first timestamp is generated for each collected temperature data point. User operation events are identified through a preset interaction module. Whenever an operation event is identified, a corresponding event tag containing an event label and a second timestamp of the event occurrence time is generated. For each event marker, a target temperature data value corresponding to the time of the event is determined from the temperature data based on its second timestamp; The event tag, the second timestamp, and the target temperature data value are bound together to generate a timeline event recording unit, and each timeline event recording unit is encoded to form a cooking process dataset. The cooking process dataset is parsed to extract a structured data sequence consisting of timestamps, temperature values, and event labels; Based on the parsed structured data sequence, a curve of temperature change over time is plotted on the time axis coordinate system; At the time corresponding to each event label on the curve, a visual marker is added to indicate the operation event, thereby generating a visual timeline that integrates the temperature curve and the operation event.

[0013] A computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the following steps: During the cooking process, temperature data is continuously collected by a temperature sensor at a preset sampling frequency, and a first timestamp is generated for each collected temperature data point. User operation events are identified through a preset interaction module. Whenever an operation event is identified, a corresponding event tag containing an event label and a second timestamp of the event occurrence time is generated. For each event marker, a target temperature data value corresponding to the time of the event is determined from the temperature data based on its second timestamp; The event tag, the second timestamp, and the target temperature data value are bound together to generate a timeline event recording unit, and each timeline event recording unit is encoded to form a cooking process dataset. The cooking process dataset is parsed to extract a structured data sequence consisting of timestamps, temperature values, and event labels; Based on the parsed structured data sequence, a curve of temperature change over time is plotted on the time axis coordinate system; At the time corresponding to each event label on the curve, a visual marker is added to indicate the operation event, thereby generating a visual timeline that integrates the temperature curve and the operation event.

[0014] The beneficial effects of this invention are as follows: By synchronously timestamping and precisely binding temperature data with operation events, the problem of the two types of data being isolated in the prior art is solved, enabling users to accurately trace the instantaneous temperature at the time of any operation. Furthermore, by designing a unified timeline event recording unit and encoding format, efficient structured storage and transmission of multi-dimensional process data are achieved. The resulting visualized timeline chart, which integrates temperature curves and operation event markers, realizes the automatic spatiotemporal fusion and unified presentation of temperature data and operation events during the cooking process. It intuitively reveals the influence of operations on temperature changes, greatly improving the efficiency and scientific nature of cooking process review, problem analysis, and experience transfer. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, 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 the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] in: Figure 1 This is a diagram illustrating the application environment of a method for visualizing the cooking process recording in one embodiment. Figure 2 A flowchart illustrating a method for recording a visualized cooking process in one embodiment; Figure 3 A structural block diagram of a visual cooking process recording device in one embodiment; Figure 4 This is a structural block diagram of an electronic device in one embodiment. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] Figure 1 A diagram illustrating the application environment for visualizing the cooking process in one embodiment. (Refer to...) Figure 1 This visualized cooking process recording method is applied to a visualized cooking process recording system. The visualized cooking process recording system includes a terminal 110 and a server 120. The terminal 110 and server 120 are connected via a network. The terminal 110 can be a desktop terminal or a mobile terminal, specifically a mobile phone, tablet computer, laptop computer, or at least one of these. The server 120 can be a standalone server or a server cluster consisting of multiple servers. The terminal 110 is used to collect temperature data, and the server 120 is used to generate a visualized timeline chart.

[0019] like Figure 2 As shown, in one embodiment, a method for visually recording the cooking process is provided. This method can be applied to either a terminal or a server; this embodiment illustrates its application to a terminal. The method specifically includes the following steps: S1: During the cooking process, temperature data is continuously collected by a temperature sensor at a preset sampling frequency, and a first timestamp is generated for each collected temperature data point. User operation events are identified through a preset interaction module. Whenever an operation event is identified, an event tag containing an event label and a second timestamp of the event occurrence time is generated. S2: For each event marker, determine the target temperature data value corresponding to the time of the event occurrence from the temperature data based on its second timestamp; S3: Bind the event tag, the second timestamp, and the target temperature data value to generate a timeline event recording unit and encode each timeline event recording unit to form a cooking process dataset; S4: Parse the cooking process dataset to extract a structured data sequence consisting of timestamps, temperature values, and event labels; S5: Based on the parsed structured data sequence, plot the temperature change curve over time on the time axis coordinate system; S6: At the time corresponding to each event label on the curve, add a visual marker to indicate the operation event, thereby generating a visual timeline that integrates the temperature curve and the operation event.

[0020] As described in step S1 above, data acquisition and event tag generation are performed. During the cooking process, temperature data needs to be continuously monitored and collected in real time by a temperature sensor. To ensure the reliability and accuracy of the data, the temperature sensor continuously collects temperature data according to a preset sampling frequency, records the temperature data, and attaches a timestamp to each data point for subsequent data analysis and backtracking. In addition, every operation performed by the user during the cooking process (such as turning on the heat, adding ingredients, stirring, etc.) is recognized by the interaction module, generating corresponding event tags. Whenever an operation event is recognized, the system generates a second timestamp containing an event label (such as "add salt", "stir-fry", "turn off the heat source", etc.) and the time of the event. In this way, each operation is recorded in detail, forming an event timeline. This combination of data acquisition and event tagging enables comprehensive tracking of the cooking process, providing basic data for subsequent analysis.

[0021] As described in step S2 above, the target temperature data is determined. The event markers generated by user operation events are processed. Specifically, based on the accompanying second timestamp information, the target temperature data value corresponding to the time of the event is found from the pre-collected temperature data. The key to this step is the time alignment operation, which allows for comparison between actual temperature changes and user operation events, thereby demonstrating how the temperature changes due to user operations during cooking and understanding the relationship between each operation and the temperature state at that time. Then, the target temperature data values ​​corresponding to all event markers provide the necessary data foundation for the subsequent generation of recording units.

[0022] As described in step S3 above, timeline event recording units are generated. Each recording unit consists of three main elements: an event tag, a second timestamp, and a defined target temperature data value. By bundling this information together, a series of ordered timeline event recording units are generated, resulting in a dataset of the cooking process. Users can subsequently review and analyze their cooking behavior and corresponding temperature changes. This not only facilitates archiving but also provides a basis for cooks to improve their skills and learn from their experience, contributing to more scientific cooking.

[0023] As described in step S4 above, the structured data sequence is parsed. The generated cooking process dataset is parsed to extract a structured data sequence composed of timestamps, temperature values, and event labels. The purpose is to decode the previously generated timeline event record units, extract key data information, and transform it into operable structured data. By analyzing these data sequences, users can obtain real-time feedback, understand the key factors in their cooking process, and the impact of temperature changes on the cooking results. This parsing not only helps users re-examine the cooking process but also lays the foundation for subsequent data display and visualization.

[0024] As described in step S5 above, a temperature change curve is plotted. In the fifth step, based on the parsed structured data sequence, the system will plot a curve showing the temperature change over time. Using a time axis coordinate system, with time as the X-axis and temperature as the Y-axis, the corresponding temperature curve is plotted. This visualization provides users with an intuitive understanding of temperature change trends, helping them better understand the correlation between temperature fluctuations during cooking and user actions. Furthermore, by observing the temperature curve, users can visually see how the temperature rises or falls over time at each cooking stage, and how drastic temperature changes occur when operational events occur. This visual data display greatly enhances everyone's understanding of culinary science and, to some extent, encourages users to make more informed decisions in future cooking.

[0025] As described in step S6 above, a visual timeline is generated. Based on the time corresponding to each event label, visual markers are added to the plotted temperature change curve, forming a visual timeline that organically combines the temperature curve with user operation events. Each marker intuitively represents the specific time when the user performed an operation, such as adding ingredients or stirring. Users can not only review the temperature changes during the cooking process but also see the direct relationship between their actions and temperature changes. This fusion effect not only enhances the user experience but also provides a scientific basis for users to summarize and optimize cooking techniques.

[0026] In one embodiment, after step S6, which involves adding visual markers to the curve at the times corresponding to each event label to generate a visual timeline graph that integrates the temperature curve and the operation events, the method further includes: S701: Obtain the target food type for cooking and the target event type for each operation event; S702: Obtain the theoretical dimension vector corresponding to the operation event based on the target food type and the target event type; wherein, the theoretical dimension vector is the set of theoretical dimension values ​​for each preset dimension corresponding to the operation event; S703: Obtain the set of actual dimension values ​​for each preset dimension corresponding to the second timestamp, so as to obtain the actual dimension vector; S704: Compare the theoretical dimension vector with the actual dimension vector to obtain the comparison result; S705: Mark and describe the operation event on the curve according to the comparison results.

[0027] As described in step S701 above, the target food type and target event type are obtained. The target food type refers to the specific ingredient or dish selected by the user during the cooking process, such as "beef" or "steamed vegetables," while the target event type refers to the specific operation performed by the user during cooking, such as "stir-frying," "boiling," or "adding seasonings." This information can be obtained through the user's selection in the application or through the system's intelligent recognition and recommendation functions. The purpose of this step is to provide basic information for subsequent data analysis and comparison. For example, different foods may have different requirements for temperature and time during cooking. Accurately identifying the target food type and target event type will help the system generate corresponding theoretical dimension vectors, laying the foundation for quantitative evaluation of the correctness of the operation event and providing users with more targeted cooking guidance and suggestions.

[0028] As described in step S702 above, a theoretical dimension vector is obtained. This vector is created by acquiring the target food type and target event type. This theoretical dimension vector is a set of theoretical values ​​containing all relevant dimensions, such as the ideal temperature range, time limits, and the required order and frequency of operations. Based on previously defined standards and data models, and combined with preset expected values ​​for the target food and the operation event, these theoretical dimension values ​​are generated. For example, for a certain food, the theoretical dimension vector might specify a cooking temperature of 180 degrees Celsius, a cooking time of 30 minutes, and ideal frequencies for stirring and adding water. These theoretical dimension values ​​will help the system improve its intelligence level, enabling direct comparison and analysis between the user's actual operations and theoretical standards in subsequent steps. This not only enhances the user's cooking experience but also provides real-time feedback, helping them determine whether their current cooking behavior conforms to best practices.

[0029] As described in step S703 above, the actual dimension value set is obtained. The actual dimension value set for each preset dimension corresponding to the second timestamp is obtained to obtain the actual dimension vector. The core of this process is to perform real-time evaluation using previously recorded temperature data and user operation events. Through the timestamp of each user operation, the system can capture the corresponding temperature data, duration, and executed operation, thus forming an actual dimension vector. These actual dimension values ​​are used to compare with the theoretical dimension vector to verify whether the user's current operation meets the ideal standard. By establishing the actual dimension vector, more intuitive feedback on the cooking effect can be provided to the user, enabling them to better grasp the dynamic changes in cooking and adjust their operations in a timely manner to achieve the best cooking effect.

[0030] As described in step S704 above, the theoretical dimension vector and the actual dimension vector are compared. The core objective of this step is to verify the degree of matching between the user's actual operation and their theoretical expectation. A specific algorithm compares the two sets of vectors one by one, judging each dimension value. For example, if the theoretical dimension vector sets the optimal temperature to 180 degrees Celsius, but the user's actual recorded temperature is 190 degrees Celsius, this deviation is marked. This comparison result can be effectively represented as statistical data, such as the deviation ratio (e.g., the percentage higher or lower than the theoretical value), consistency indicators, etc. This process not only helps the system determine whether the user's cooking operation is performed according to the established standards, but also provides key data support for subsequent labeling explanations and dynamic visualizations.

[0031] As described in step S705 above, the comparison results are marked and explained. Based on the comparison results obtained in step S704, the operation events are marked and explained on the temperature change curve. This aims to visually present the analysis results of the comparison to the user. When a significant deviation is found between an operation event and its corresponding theoretical standard, a mark will be added to the corresponding time point on the temperature curve, indicating that the operation event performed "beyond expectations" or "below expectations." This visual marking can be a color change, a symbol shape, or additional text to help users quickly understand the state of the relevant event. For example, if the actual temperature is significantly higher than the theoretical value, the system may use a red mark with a prompt to remind the user of the potential risks of excessively high temperatures. In addition, through these intuitive markings, users can have a more comprehensive understanding of the impact and effect of each operation, thus being more confident and making timely adjustments in future cooking activities. This also effectively improves the user's data visualization experience of the cooking process.

[0032] In one embodiment, before step S702 of obtaining the theoretical dimension vector corresponding to the operation event based on the target food type and the target event type, the method further includes: S7011: Obtain the historical dimension vector of multiple historical operation events corresponding to the target food type and the target event type; wherein, the historical operation events are operation events marked as normal cooking processes; S7012: Extract the values ​​from each of the historical dimension vectors according to a preset dimension to obtain a set of historical dimension values ​​corresponding to each preset dimension. S7013: Calculate the mean and standard deviation of the historical dimension numerical set; S7014: Set the various averages together to obtain a theoretical dimension vector, and set the normal range of the corresponding preset dimension according to the standard deviation.

[0033] As described in step S7011 above, historical dimension vectors of historical operation events are obtained. This involves acquiring historical dimension vectors of multiple historical operation events corresponding to the target food type and target event type. These historical operation events refer to those marked as "normal cooking" in previous cooking processes. The system extracts information from these events from the database and organizes them into a structured dataset. Historical dimension vectors typically include factors such as temperature, operation duration, required space or equipment, etc. These factors are recorded in previous normal cooking procedures. In this way, a reference based on real experience can be formed, providing data support for subsequent calculations of theoretical dimension vectors. This process helps strengthen the system's intelligent analysis capabilities when handling new cooking stages, making subsequent theoretical presuppositions more practical and feasible.

[0034] As described in step S7012 above, the historical dimension value set is extracted. The historical dimension values ​​are extracted from the obtained historical dimension vector and categorized according to preset dimensions. This data extraction process aims to ensure that the obtained values ​​directly correspond to the preset dimensions required by the object, thus forming a corresponding historical dimension value set. For example, assuming the historical dimension vector contains information such as temperature, time, and operation type in past cooking processes, values ​​belonging to temperature should be categorized into the temperature set, time-related values ​​into the time set, and so on. After extraction, clearer dimensional data can be obtained. This is important because in subsequent steps, only by comparing these value sets with theoretical dimensions can the accuracy and effectiveness of the comparison be ensured. Through hierarchical value sets, users can better review and understand the contribution of each dimension to the ideal cooking result under different historical operations, which is helpful for subsequent analysis and feedback.

[0035] As described in step S7013 above, the mean and standard deviation of the historical dimension value set are calculated. Statistical analysis is performed on the extracted historical dimension value set to calculate the mean and standard deviation of each preset dimension. The mean provides a benchmark for the normal values ​​of various dimensions; for example, if the historical average temperature is 200 degrees Celsius, the system will use this value as the theoretically preset temperature standard. Simultaneously, the calculation of the standard deviation allows the system to understand the dispersion of these historical values, i.e., the fluctuations in historical operations across dimensions such as temperature and time. A smaller standard deviation indicates more consistent past operations, allowing the system to set a stricter standard range when generating the theoretical dimension vector. Conversely, a larger standard deviation indicates greater variability in the same operation, requiring consideration of a wider normal range. This statistical analysis not only improves data accuracy but also provides a basis for setting subsequent theoretical dimension vectors, helping to enhance the system's intelligence level.

[0036] As described in step S7014 above, a theoretical dimension vector and normal range settings are generated. The calculated averages of each dimension are aggregated to generate the corresponding theoretical dimension vector. Simultaneously, based on the obtained standard deviation, a normal range is set for each preset dimension. The construction of the theoretical dimension vector is a high-level summary of previous historical analysis. It provides users with intuitive, historically data-based ideal operating parameters. For example, assuming the average of the temperature dimension is determined to be 200 degrees Celsius and the standard deviation is 5 degrees Celsius, the normal temperature range can be set to 195-205 degrees Celsius. This structure not only makes the feedback during cooking more targeted but also helps users adjust their cooking strategies faster and more accurately, ensuring the final ideal cooking effect is achieved. The generation of the theoretical dimension vector and its normal range is the core key to providing guidance and judgment for subsequent processes, driving the system towards intelligent development.

[0037] In one embodiment, step S704, which compares the theoretical dimension vector and the actual dimension vector to obtain the comparison result, includes: S7041: Based on the theoretical dimension vector and the normal range of the corresponding preset dimension, establish the evaluation criteria for each preset dimension; S7042: For each preset dimension, determine whether the corresponding actual dimension value in the actual dimension vector falls within its corresponding normal range; S7043: Record all actual dimension values ​​that fall within the normal range and mark them as normal dimensions; S7044: Record all actual dimension values ​​that do not fall within the normal range, mark them as abnormal dimensions, and calculate the degree of deviation between the actual dimension value of the abnormal dimension and its corresponding normal range boundary value; S7045: Summarize the judgment results and deviation degree of all normal and abnormal dimensions to generate the comparison result.

[0038] As described in step S7041 above, an evaluation standard is established for each preset dimension. Based on the generated theoretical dimension vector and the corresponding normal range for each preset dimension, an evaluation standard is established for each preset dimension. The establishment of the evaluation standard is the basis for analyzing whether the operation is effective. These standards can be quantitative or qualitative. For example, for the temperature dimension, if the theoretical value is 200 degrees Celsius and the normal range is 195-205 degrees Celsius, the system will set that the actual measured temperature should be within this range to be considered effective; if it is outside this range, further review is required. The establishment of this standard not only provides a clear basis for subsequent data comparison but also provides users with a clear judgment reference so that they can adjust their operations in a timely manner according to the set evaluation standard when cooking. Through this step, systematic judgment can be made in subsequent operation analysis, improving the level of intelligence.

[0039] As described in step S7042 above, it is determined whether the actual dimension value falls within the normal range. For each preset dimension, it is determined whether the corresponding actual dimension value in the actual dimension vector falls within the corresponding normal range. This is done by sequentially traversing each preset dimension in the theoretical dimension vector and comparing it with the actual dimensions vector. The intersection of the actual value and its corresponding normal range is found to confirm whether the actual value is within a reasonable range. If the actual value, such as a temperature of 197 degrees Celsius, falls within the normal range of 195-205 degrees Celsius, then this value will be considered valid; conversely, if the actual value is 210 degrees Celsius, it will be marked as exceeding the normal range. Through comparison, potential operational problems can be identified in a timely manner, providing a clear direction for subsequent corrections.

[0040] As described in step S7043 above, record all actual dimension values ​​that fall within the normal range. Record all actual dimension values ​​that have been determined to fall within the normal range and mark them as normal dimensions. All normal dimension values ​​will be saved to a dedicated record. This record not only includes the numerical value itself but may also include relevant timestamps or operation event tags for future tracking and review. Establish a reliable data source library so that status backtracking and root cause analysis can be performed quickly when problems occur in the future.

[0041] As described in step S7044 above, record all actual dimension values ​​that do not fall within the normal range. Record all actual dimension values ​​that do not fall within the normal range and mark them as abnormal dimensions. Calculate the deviation between the recorded actual value of the abnormal dimension and its corresponding normal range boundary value. For example, if the actual temperature is 210 degrees Celsius, while the theoretical range is 195-205 degrees Celsius, this temperature will be marked as "abnormal," and the deviation will be calculated as 5 degrees Celsius (i.e., 210-205=5). Focusing on the statistical analysis of abnormal values ​​helps the system provide targeted information feedback during the user's cooking process, thereby achieving efficient and accurate cooking results.

[0042] As described in step S7045 above, a comparison result is generated. The judgment results and deviation levels of all normal and abnormal dimensions are summarized to generate the comparison result. This comparison result will include two parts of data: first, the values, labels, and related parameters of all normal dimensions; second, the values ​​of all abnormal dimensions, the corresponding deviation levels, and whether user action is required. This summarized result will be presented to the user in a concise and clear manner, helping the user quickly understand the effectiveness of their actions. Furthermore, the deviation levels and action suggestions attached to the abnormal dimensions will further help the user make necessary adjustments or corrections. The final comparison result will provide the user with complete feedback on the cooking status, enabling them to adjust their cooking strategies more flexibly to ensure that the final cooking effect matches expectations.

[0043] In one embodiment, the interaction module is a motion sensor. In step S1, during the cooking process, a temperature sensor continuously collects temperature data at a preset sampling frequency, generates a first timestamp for each collected temperature data point, and identifies user operation events through the preset interaction module. Whenever an operation event is identified, an event marker containing an event label and a second timestamp of the event's occurrence is generated. The step S1, which involves identifying user operation events through the preset interaction module and generating an event marker containing an event label and a second timestamp of the event's occurrence whenever an operation event is identified, includes: S101: The user's cooking actions are collected through the motion sensor; S102: Identify the cooking operation action to determine whether it matches a preset operation event; S103: When an operation event is detected, generate an event tag that includes the event label and the time when the event occurred.

[0044] As described in step S101 above, the user's cooking actions are collected using motion sensors. Motion sensors are used to collect various actions performed by the user during cooking. These sensors can be various sensors installed in the kitchen environment, such as accelerometers, gyroscopes, or other types of motion detection devices. These sensors work by capturing the user's real-time physical actions, such as stirring, cutting, flipping, and adding ingredients, thereby acquiring relevant motion data. This data typically manifests as motion trajectory, speed, frequency, and other information. By analyzing this data, the system determines the user's specific cooking behavior. The purpose of collecting these actions is to provide foundational data for subsequent event recognition, accurately identifying each action event and establishing a data basis for each user operation. This ensures that every actual cooking action performed by the user can be captured in a timely manner during subsequent processing, thereby improving the system's responsiveness to user behavior and the interactive experience.

[0045] As described in step S102 above, the cooking operation actions are identified. The collected user cooking operation actions are analyzed and identified to determine whether these actions conform to preset operation events. To achieve this, a feature recognition algorithm needs to be established internally, typically including machine learning or deep learning algorithms, enabling it to learn and recognize specific motion patterns. These motion patterns can be known operation behaviors, such as "cutting," "stirring," and "adding salt." When the motion sensor captures the user's actions, this data is analyzed and compared with preset operation events stored in the database to determine their similarity and matching degree. If the identified action conforms to a preset operation event, it is marked as valid. The accuracy and efficiency of this identification process directly affect the user interaction experience, effectively reducing misjudgments and enhancing the intelligence of the system's interaction.

[0046] As described in step S103 above, an event tag is generated. When a user's operation event is successfully identified, a corresponding event tag is generated. This tag contains two main parts of information: an event label and the specific time the event occurred (recorded in the form of a second timestamp). First, the event label is a concise and clear description that specifically identifies the operation performed by the user, such as "add salt," "stir-fry," or "turn off the heat source." Second, the timestamp of the event is recorded so that each user operation can be accurately located in subsequent cooking process reviews. The importance of generating event tags lies in the fact that it not only enables the system to track user operations but also helps users analyze the impact of each operation on the final cooking result when reviewing their cooking process.

[0047] In one embodiment, after step S3, which involves binding the event tag, the second timestamp, and the target temperature data value to generate timeline event recording units and encoding each timeline event recording unit to form a cooking process dataset, the method further includes: S401: Based on the cooking process dataset, calculate one or more preset key quantitative indicators; S402: Integrate the key quantitative indicators with the visualization timeline to generate a cooking process report; the key quantitative indicators include at least one of the following: average heating rate, duration percentage of each preset temperature range, and frequency of operation events. S403: Share the cooking process report to the designated terminal.

[0048] As described in step S401 above, one or more preset key quantitative indicators are calculated. Based on the previously generated cooking process dataset, one or more preset key quantitative indicators are calculated. These indicators are important parameters used to measure the efficiency, effectiveness, and accuracy of the cooking process. They provide users with clear feedback on cooking results. Example indicators include average heating rate, the time percentage within each preset temperature range, and the statistical frequency of various operational events. For instance, when calculating the average heating rate, the ratio of temperature change to time within a given time period is analyzed to obtain a value reflecting heating efficiency. The percentage of temperature ranges helps users understand how long the ingredients are maintained within a specific temperature range throughout the cooking process, assisting users in better adjusting their operations. Furthermore, the frequency statistics of operational events can reveal high-frequency actions performed by the user during cooking, such as frequent stirring or heating. The results of this analysis not only help users understand their own cooking habits but also enable the system to provide suggestions for future cooking based on historical data, thereby improving users' cooking skills and satisfaction.

[0049] As described in step S402 above, a cooking process report is generated. Based on the calculated key quantitative indicators, these indicators are integrated with a visualized timeline to generate a cooking process report. This report summarizes key data from the entire cooking process, allowing users to review and analyze the cooking results. The report may include various visualizations, such as temperature change curves, event frequency distribution charts, average heating rate charts, and intuitive displays of the relationship between operation events and temperature. This report not only serves as a record of historic data but also provides users with an intelligent feedback mechanism. Users can quickly identify factors affecting cooking quality within this report and adjust their cooking methods accordingly to achieve the desired results.

[0050] As described in step S403 above, the cooking process report is shared to the designated terminal. The main purpose of sharing the generated cooking process report to the user's designated terminal is to enable users to easily access their cooking data, whether viewed on the same device or on other devices (such as smartphones, tablets, or computers). Multiple sharing methods are provided, potentially including in-app sharing functions, email, or social media. Through these sharing functions, users can not only share their cooking results with family and friends but also showcase their cooking skills, experience, and data on social platforms. Furthermore, this allows users to offer suggestions for improving the cooking process or seek opinions and suggestions from others, further enhancing their cooking skills.

[0051] In one embodiment, after step S6, which involves adding visual markers to the curve at the times corresponding to each event label to generate a visual timeline graph that integrates the temperature curve and the operation events, the method further includes: S711: In response to user interaction on the visualized timeline, obtain the time interval selected by the user; S712: Displays details of temperature data changes within the time interval, as well as all operational events within that time interval; S713: Receive a user's instruction to modify the event tag or the second timestamp of the operation event, and update the cooking process dataset and the visualization timeline based on the modification instruction.

[0052] As described in step S711 above, the user-selected time interval is obtained. The system responds to user interactions on the visualized timeline chart, thereby obtaining the user-selected time interval. This interaction typically involves the user selecting a time period on the visualization chart through touch, click, or dragging, capturing the user's intent. Users can select different time periods to view temperature changes and events within those periods. The obtained time interval information is recorded by the system for subsequent detailed analysis and display of temperature data and events within that time period. Furthermore, the user-selected time interval can influence subsequent data display, thereby more accurately meeting the user's needs.

[0053] As described in step S712 above, the details of temperature data changes within the time interval are displayed. This includes the detailed temperature data changes within the user-selected time interval, as well as all operational events occurring within that time interval. This display process is a crucial step in data interpretation and provides important feedback to the user. Various visualization techniques, such as numerical charts and graphs, are used to visually present the temperature trend. For operational events occurring within the time interval, all event tags are listed, and their specific times are marked. This allows users to clearly see the relationship between temperature and each operational event within a specific time period, analyzing how these operations affected temperature fluctuations. This information helps users review and reflect on their cooking decisions, identify areas for improvement, and further enhance their cooking skills and experience.

[0054] As described in step S713 above, the system receives user modification instructions for operation events. It receives user instructions to modify the event tags or second timestamps of operation events and updates the cooking process dataset and visualization timeline based on these instructions. The core of this process lies in user interaction, allowing users to adjust content while reviewing the cooking process, making the recorded and displayed data more accurate and consistent with the user's true operational intentions. For example, a user might find that the tag of an operation event is inaccurate or that the timestamp is off. In this case, the user can correct it by inputting or selecting. Upon receiving these modification instructions, the system automatically updates the corresponding dataset, ensuring the accuracy and reliability of the cooking records. After the update, the visualization timeline is also adjusted accordingly to ensure consistency in information presentation. This function not only enhances the user's sense of control over the system but also improves data accuracy, helping users create more accurate cooking records, thus providing a more realistic basis for subsequent analysis and feedback.

[0055] Reference Figure 3 The present invention also provides a visual cooking process recording device, the device comprising: The acquisition module 902 is used to continuously acquire temperature data at a preset sampling frequency through a temperature sensor during the cooking process, generate a first timestamp for each acquired temperature data point, and identify user operation events through a preset interaction module. Whenever an operation event is identified, an event tag containing an event label and a second timestamp of the event occurrence time is generated. The determination module 904 is used to determine, for each event marker, a target temperature data value corresponding to the time of occurrence of the event from the temperature data based on its second timestamp; The binding module 906 is used to bind the event tag, the second timestamp, and the target temperature data value, generate timeline event recording units, and encode each timeline event recording unit to form a cooking process dataset; The parsing module 908 is used to parse the cooking process dataset to extract a structured data sequence consisting of timestamps, temperature values, and event tags; The plotting module 910 is used to plot the temperature change over time curve on the time axis coordinate system based on the parsed structured data sequence. The generation module 912 is used to add a visual marker to indicate the operation event at the time corresponding to each event label on the curve, thereby generating a visual timeline that integrates the temperature curve and the operation event.

[0056] In one embodiment, the visual cooking process recording device further includes: The target food type acquisition module is used to acquire the target food type for cooking and the target event type for each operation event; The theoretical dimension vector acquisition module is used to acquire the theoretical dimension vector corresponding to the operation event based on the target food type and the target event type; wherein, the theoretical dimension vector is a set of theoretical dimension values ​​for each preset dimension corresponding to the operation event; The actual dimension vector acquisition module is used to acquire the set of actual dimension values ​​for each preset dimension corresponding to the second timestamp, so as to obtain the actual dimension vector; The comparison result acquisition module is used to compare the theoretical dimension vector and the actual dimension vector to obtain the comparison result. The marking and description module is used to mark and describe the operation event on the curve according to the comparison results.

[0057] In one embodiment, the visual cooking process recording device further includes: The historical dimension vector acquisition module is used to acquire historical dimension vectors of multiple historical operation events corresponding to the target food type and the target event type; wherein, the historical operation events are operation events marked as normal cooking processes; The historical dimension value set acquisition module is used to extract the values ​​in each of the historical dimension vectors according to preset dimensions to obtain the historical dimension value set corresponding to each preset dimension. The mean calculation module is used to calculate the mean and standard deviation of the historical dimension value set; The normal range setting module is used to aggregate the various means to obtain a theoretical dimension vector, and set the normal range of the corresponding preset dimension according to the standard deviation.

[0058] In one embodiment, the comparison result acquisition module includes: The evaluation criteria establishment submodule is used to establish the evaluation criteria for each preset dimension based on the theoretical dimension vector and the normal range of the corresponding preset dimension. The judgment submodule is used to determine, for each preset dimension, whether the corresponding actual dimension value in the actual dimension vector falls within its corresponding normal range; The Actual Dimension Value Recording Submodule is used to record all actual dimension values ​​that fall within the normal range and mark them as normal dimensions; The deviation degree calculation submodule is used to record all actual dimension values ​​that do not fall within the normal range, mark them as abnormal dimensions, and calculate the degree of deviation between the actual dimension value of the abnormal dimension and its corresponding normal range boundary value. The summary submodule is used to summarize the judgment results and deviation degree of all normal and abnormal dimensions, and generate the comparison result.

[0059] In one embodiment, the acquisition module 902 includes: The cooking operation action acquisition submodule is used to acquire the user's cooking operation actions through the motion sensor; The cooking operation action recognition submodule is used to recognize the cooking operation action in order to determine whether it matches a preset operation event; The event tag generation submodule is used to generate an event tag that includes an event label and a second timestamp of the event occurrence time when an operation event is detected.

[0060] In one embodiment, the visual cooking process recording device further includes: The key quantitative indicator calculation module is used to calculate one or more preset key quantitative indicators based on the cooking process dataset. A cooking process report generation module is used to integrate the key quantitative indicators with the visualization timeline to generate a cooking process report; the key quantitative indicators include at least one of the following: average heating rate, duration percentage of each preset temperature range, and frequency of operation events. The sharing module is used to share the cooking process report to a designated terminal.

[0061] In one embodiment, the visual cooking process recording device further includes: The time interval acquisition module is used to acquire the time interval selected by the user in response to the user's interactive operation on the visualized timeline chart; The temperature data change details display module is used to display the temperature data change details within the time interval, as well as all operation events within that time interval; The update module is used to receive a user's instruction to modify the event tag or the second timestamp of the operation event, and update the cooking process dataset and the visualization timeline based on the modification instruction.

[0062] Figure 4 An internal structural diagram of an electronic device in one embodiment is shown. This electronic device can specifically be a terminal or a server, and more specifically, a computer device. Figure 4 As shown, the electronic device includes a processor, a memory, and a network interface connected via a system bus. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and may also store a computer program. When executed by the processor, this computer program enables the processor to implement a method for recording a visual cooking process. The internal memory may also store a computer program, which, when executed by the processor, enables the processor to implement the method for recording a visual cooking process. Those skilled in the art will understand that... Figure 4 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the electronic device to which the present application is applied. The specific electronic device may include more or fewer components than shown in the figure, or combine certain components, or have different component arrangements.

[0063] In one embodiment, an electronic device is provided, including a memory and a processor, the memory storing a computer program that, when executed by the processor, causes the processor to perform the following steps: During the cooking process, temperature data is continuously collected by a temperature sensor at a preset sampling frequency, and a first timestamp is generated for each collected temperature data point. User operation events are identified through a preset interaction module. Whenever an operation event is identified, a corresponding event tag containing an event label and a second timestamp of the event occurrence time is generated. For each event marker, a target temperature data value corresponding to the time of the event is determined from the temperature data based on its second timestamp; The event tag, the second timestamp, and the target temperature data value are bound together to generate a timeline event recording unit, and each timeline event recording unit is encoded to form a cooking process dataset. The cooking process dataset is parsed to extract a structured data sequence consisting of timestamps, temperature values, and event labels; Based on the parsed structured data sequence, a curve of temperature change over time is plotted on the time axis coordinate system; At the time corresponding to each event label on the curve, a visual marker is added to indicate the operation event, thereby generating a visual timeline that integrates the temperature curve and the operation event.

[0064] By synchronously timestamping and precisely binding temperature data with operation events, the problem of the two types of data being isolated in existing technologies is solved. This allows users to accurately trace the instantaneous temperature at the time of any operation. Furthermore, by designing a unified timeline event recording unit and encoding format, efficient structured storage and transmission of multi-dimensional process data are achieved. The resulting visualized timeline chart, which integrates temperature curves and operation event markers, realizes the automatic spatiotemporal fusion and unified presentation of temperature data and operation events during the cooking process. It intuitively reveals the impact of operations on temperature changes, greatly improving the efficiency and scientific nature of cooking process review, problem analysis, and experience transfer.

[0065] In one embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, causes the processor to perform the following steps: During the cooking process, temperature data is continuously collected by a temperature sensor at a preset sampling frequency, and a first timestamp is generated for each collected temperature data point. User operation events are identified through a preset interaction module. Whenever an operation event is identified, a corresponding event tag containing an event label and a second timestamp of the event occurrence time is generated. For each event marker, a target temperature data value corresponding to the time of the event is determined from the temperature data based on its second timestamp; The event tag, the second timestamp, and the target temperature data value are bound together to generate a timeline event recording unit, and each timeline event recording unit is encoded to form a cooking process dataset. The cooking process dataset is parsed to extract a structured data sequence consisting of timestamps, temperature values, and event labels; Based on the parsed structured data sequence, a curve of temperature change over time is plotted on the time axis coordinate system; At the time corresponding to each event label on the curve, a visual marker is added to indicate the operation event, thereby generating a visual timeline that integrates the temperature curve and the operation event.

[0066] By synchronously timestamping and precisely binding temperature data with operation events, the problem of the two types of data being isolated in existing technologies is solved. This allows users to accurately trace the instantaneous temperature at the time of any operation. Furthermore, by designing a unified timeline event recording unit and encoding format, efficient structured storage and transmission of multi-dimensional process data are achieved. The resulting visualized timeline chart, which integrates temperature curves and operation event markers, realizes the automatic spatiotemporal fusion and unified presentation of temperature data and operation events during the cooking process. It intuitively reveals the impact of operations on temperature changes, greatly improving the efficiency and scientific nature of cooking process review, problem analysis, and experience transfer.

[0067] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments described above. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM), etc.

[0068] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0069] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A method for visually recording the cooking process, characterized in that, The method includes: During the cooking process, temperature data is continuously collected by a temperature sensor at a preset sampling frequency, and a first timestamp is generated for each collected temperature data point. User operation events are identified through a preset interaction module. Whenever an operation event is identified, a corresponding event tag containing an event label and a second timestamp of the event occurrence time is generated. For each event marker, a target temperature data value corresponding to the time of the event is determined from the temperature data based on its second timestamp; The event tag, the second timestamp, and the target temperature data value are bound together to generate a timeline event recording unit, and each timeline event recording unit is encoded to form a cooking process dataset. The cooking process dataset is parsed to extract a structured data sequence consisting of timestamps, temperature values, and event labels; Based on the parsed structured data sequence, a curve of temperature change over time is plotted on the time axis coordinate system; At the time corresponding to each event label on the curve, a visual marker is added to indicate the operation event, thereby generating a visual timeline that integrates the temperature curve and the operation event.

2. The method for recording a visualized cooking process according to claim 1, characterized in that, After the step of adding visual markers to the curve at the times corresponding to each event label to indicate the operation event, thereby generating a visual timeline graph that integrates the temperature curve and the operation event, the method further includes: Obtain the target food type for cooking and the target event type for each operation event; Based on the target food type and the target event type, obtain the theoretical dimension vector corresponding to the operation event; wherein, the theoretical dimension vector is the set of theoretical dimension values ​​for each preset dimension corresponding to the operation event; Obtain the set of actual dimension values ​​for each preset dimension corresponding to the second timestamp to obtain the actual dimension vector; The theoretical dimension vector and the actual dimension vector are compared to obtain the comparison result; The operation events are marked and described on the curve based on the comparison results.

3. The method for recording a visualized cooking process according to claim 2, characterized in that, Before the step of obtaining the theoretical dimension vector corresponding to the operation event based on the target food type and the target event type, the method further includes: Obtain the historical dimension vector of multiple historical operation events corresponding to the target food type and the target event type; wherein, the historical operation events are operation events marked as normal cooking processes; The values ​​in each of the historical dimension vectors are extracted according to a preset dimension to obtain a set of historical dimension values ​​corresponding to each preset dimension. Calculate the mean and standard deviation of the historical dimension set; The averages are aggregated to obtain a theoretical dimension vector, and the normal range of the corresponding preset dimension is set according to the standard deviation.

4. The method for recording a visualized cooking process according to claim 3, characterized in that, The step of comparing the theoretical dimension vector and the actual dimension vector to obtain the comparison result includes: Based on the theoretical dimension vector and the normal range of the corresponding preset dimension, an evaluation standard for each preset dimension is established. For each preset dimension, determine whether the corresponding actual dimension value in the actual dimension vector falls within its corresponding normal range; Record all actual dimension values ​​that fall within the normal range and mark them as normal dimensions; Record all actual dimension values ​​that do not fall within the normal range, mark them as abnormal dimensions, and calculate the degree of deviation between the actual dimension value of the abnormal dimension and its corresponding normal range boundary value; The comparison results are generated by summarizing the judgment results and the degree of deviation of all normal and abnormal dimensions.

5. The method for recording a visualized cooking process according to claim 1, characterized in that, The interaction module is a motion sensor. In the step of continuously collecting temperature data at a preset sampling frequency using a temperature sensor during the cooking process, generating a first timestamp for each collected temperature data point, and identifying user operation events through the preset interaction module, and generating an event marker containing an event label and a second timestamp of the event occurrence time whenever an operation event is identified, the step of identifying user operation events through the preset interaction module and generating an event marker containing an event label and a second timestamp of the event occurrence time whenever an operation event is identified includes: The motion sensor collects the user's cooking actions. The cooking operation actions are identified to determine whether they match preset operation events; When an operation event is detected, an event tag is generated that includes an event label and a second timestamp of the time the event occurred.

6. The method for recording a visualized cooking process according to claim 1, characterized in that, After the steps of binding the event tag, the second timestamp, and the target temperature data value to generate timeline event recording units and encoding each timeline event recording unit to form a cooking process dataset, the method further includes: Based on the cooking process dataset, calculate one or more preset key quantitative indicators; The key quantitative indicators are then integrated with the visualization timeline to generate a cooking process report; the key quantitative indicators include at least one of the following: average heating rate, duration of each preset temperature range, and frequency of operation events. Share the cooking process report to a designated terminal.

7. The method for recording a visualized cooking process according to claim 1, characterized in that, After the step of adding visual markers to the curve at the times corresponding to each event label to indicate the operation event, thereby generating a visual timeline graph that integrates the temperature curve and the operation event, the method further includes: In response to user interaction on the visualized timeline, the user-selected time interval is obtained; Displays detailed temperature data changes within the specified time interval, as well as all operational events occurring within that time interval; Receive user instructions to modify the event tag or the second timestamp of the operation event, and update the cooking process dataset and the visualization timeline based on the modification instructions.

8. A visual recording device for the cooking process, characterized in that, The device includes: The data acquisition module is used to continuously acquire temperature data at a preset sampling frequency through a temperature sensor during the cooking process, generate a first timestamp for each acquired temperature data point, and identify user operation events through a preset interaction module. Whenever an operation event is identified, an event tag containing an event label and a second timestamp of the event occurrence time is generated. The determination module is used to determine, for each event marker, a target temperature data value corresponding to the time of occurrence of the event from the temperature data based on its second timestamp; The binding module is used to bind the event tag, the second timestamp, and the target temperature data value, generate timeline event recording units, and encode each timeline event recording unit to form a cooking process dataset; The parsing module is used to parse the cooking process dataset to extract a structured data sequence consisting of timestamps, temperature values, and event labels; The plotting module is used to plot the temperature change over time on the time axis coordinate system based on the parsed structured data sequence. The generation module is used to add visual markers to the curve at the time corresponding to each event label to indicate the operation event, thereby generating a visual timeline that integrates the temperature curve and the operation event.

9. A computer-readable storage medium, characterized in that, The system contains a computer program that, when executed by a processor, causes the processor to perform the steps of the visual cooking process recording method as described in any one of claims 1 to 7.

10. An electronic device, characterized in that, The device includes a memory and a processor, the memory storing a computer program that, when executed by the processor, causes the processor to perform the steps of the visual cooking process recording method as described in any one of claims 1 to 7.

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