Cooking data processing method and device and cooking equipment
By employing an adaptive keypoint extraction algorithm and dynamic threshold adjustment, the problems of large amounts of cooking data and improper keypoint selection were solved, achieving efficient and accurate dish reproduction and improving user experience and device performance.
Patent Information
- Application Number
- CN202511132813.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-08-13
- Publication Date
- 2025-11-14
AI Technical Summary
In existing technologies, the large amount of cooking data increases the burden on equipment, affecting the efficiency and accuracy of reproduction. Furthermore, it fails to effectively select key data points, ignores changes in stove settings and data point density, and affects the reproduction effect of dishes.
An adaptive keypoint extraction algorithm is used to extract keypoint data from the cooking dataset, including start point, end point, power change point, and inflection point. Reconstructed data is generated through data volume and span rules, and the threshold is dynamically adjusted to adapt to different dishes and cooking methods.
It improves the accuracy and efficiency of dish reproduction, reduces data transmission, lowers equipment load, enhances user experience and equipment processing efficiency, and adapts to diverse cooking needs.
Smart Images
Figure CN120950485A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the technical field of cooking control, and in particular to a method, apparatus, and cooking equipment for processing cooking data. Background Technology
[0002] With the development of technology and the increasing demand for delicious food, culinary techniques are no longer merely a reflection of a chef's individual skills, but are gradually evolving towards a more scientific and data-driven approach. Through data collection and algorithmic analysis, precise control of the cooking process can be achieved, thus reproducing the flavors of professional chefs. In this process, data processing systems and technologies from the field of computer science play a crucial role.
[0003] In related technologies, data such as temperature, time, and stove heat during the cooking process can be collected and stored in the cloud. When a recipe needs to be recreated, this data is transmitted to the cooking equipment via the cloud, thereby controlling the operation of the cooking equipment and reproducing the dish.
[0004] However, since the amount of raw data is very large, transmitting it all to the device would not only increase the device's burden but also affect the efficiency and accuracy of the restoration, thereby reducing the user experience. Summary of the Invention
[0005] In view of this, the purpose of the present invention is to provide a method, apparatus and cooking equipment for processing cooking data to alleviate the above-mentioned technical problems.
[0006] In a first aspect, embodiments of the present invention provide a method for processing cooking data, the method comprising: acquiring a cooking dataset, wherein the cooking dataset includes multiple cooking data entries, each of which includes a time parameter, a temperature parameter, and a cooking power parameter; and wherein the cooking data is uploaded at preset time intervals when a target device cooks a target recipe, the target device being a pre-configured intelligent cooking device; extracting key point data from the cooking dataset according to preset data extraction rules; wherein the key point data includes cooking start point data, cooking end point data, power change point data, and cooking inflection points; wherein the cooking inflection points are extreme points of the cooking data and points where the slope of the cooking data change exceeds a preset threshold; determining whether the key point data satisfies the preset data rules; and if so, generating restoration data of the target recipe based on the key point data, the restoration data being used to restore the target recipe.
[0007] In conjunction with the first aspect, this embodiment of the invention provides a first possible implementation of the first aspect, wherein the data rules include data volume rules and data span rules; the data volume rules are used to control the quantity of the key point data, and the data span rules are used to control the span between adjacent key point data; the step of determining whether the key point data satisfies the pre-configured data rules includes: determining whether the key point data simultaneously satisfies the data volume rules and the data span rules; if yes, determining that the key point data satisfies the pre-configured data rules; if no, determining that the key point data does not satisfy the pre-configured data rules.
[0008] In conjunction with the first possible implementation of the first aspect, this embodiment of the invention provides a second possible implementation of the first aspect, wherein the step of determining whether the key point data satisfies the data volume rule includes: determining whether the number of key point data exceeds a pre-configured quantity threshold range; if not, determining that the key point data satisfies the data volume rule; if yes, determining that the key point data does not satisfy the data volume rule.
[0009] In conjunction with the second possible implementation of the first aspect, this embodiment of the invention provides a third possible implementation of the first aspect, wherein the above method further includes: if it is determined that the number of key point data exceeds a pre-configured quantity threshold range, adjusting the preset threshold in the cooking inflection point so that the number of key point data meets the quantity threshold range.
[0010] In conjunction with the first possible implementation of the first aspect, this embodiment of the invention provides a fourth possible implementation of the first aspect, wherein the step of determining whether the key point data satisfies the data span rule includes: determining whether the span between adjacent key point data is greater than a preset first span threshold and less than a preset second span threshold; wherein the second span threshold is greater than the first span threshold; if so, determining that the key point data satisfies the data span rule; if the span between adjacent key point data is less than the preset first span threshold or greater than the preset second span threshold, then determining that the key point data does not satisfy the data span rule.
[0011] In conjunction with the fourth possible implementation of the first aspect, this embodiment of the invention provides a fifth possible implementation of the first aspect, wherein the above method further includes: if the span between adjacent key point data is less than a preset first span threshold, then one of the key point data is deleted according to a pre-configured deletion principle; if the span between adjacent key point data is greater than the preset first span threshold, then a preset number of interpolation data is inserted between the two key point data according to a pre-configured interpolation principle.
[0012] In conjunction with the first aspect, this embodiment of the invention provides a sixth possible implementation of the first aspect, wherein the step of generating the reconstruction data of the target recipe based on the key point data includes: generating a cooking curve based on the key point data; and determining the cooking curve as the reconstruction data of the target recipe.
[0013] In conjunction with the first aspect, the present invention provides a seventh possible implementation of the first aspect, wherein the above method further includes: sending the restored data to a smart cooking device so that the smart cooking device cooks the target recipe based on the restored data.
[0014] Secondly, embodiments of the present invention also provide a cooking data processing apparatus, the apparatus comprising: an acquisition module, configured to acquire a cooking dataset, wherein the cooking dataset includes multiple cooking data entries, each of which includes a time parameter, a temperature parameter, and a cooking power parameter; and the cooking data is uploaded at preset time intervals when a target device cooks a target recipe, the target device being a pre-configured intelligent cooking device; an extraction module, configured to extract key point data from the cooking dataset according to preset data extraction rules; wherein the key point data includes cooking start point data, cooking end point data, power change point data, and cooking inflection points; wherein the cooking inflection points are extreme points of the cooking data and points where the slope of the cooking data change exceeds a preset threshold; a judgment module, configured to judge whether the key point data satisfies the preset data rules; and a restoration module, configured to generate restored data of the target recipe based on the key point data when the judgment result of the judgment module is yes, the restored data being used to restore the target recipe.
[0015] Thirdly, embodiments of the present invention also provide a cooking device for acquiring restoration data and restoring a target recipe based on the restoration data; wherein the restoration data is obtained by the method described in any one of claims 1-8.
[0016] Fourthly, embodiments of the present invention also provide a computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, performs the steps of the method described in the first aspect above.
[0017] The embodiments of the present invention bring the following beneficial effects: This invention provides a method, apparatus, and cooking equipment for processing cooking data. The method can acquire a cooking dataset, which includes multiple cooking data points, each containing time, temperature, and cooking power parameters. Key point data is extracted from the cooking dataset according to pre-configured data extraction rules. The method determines whether the key point data meets the pre-configured data rules. If so, it generates reconstruction data for a target recipe based on the key point data, facilitating the reconstruction of the target recipe. Furthermore, by configuring the data rules satisfied by the key point data, the method ensures that the number of key point data points is appropriate and representative, thereby improving the adaptability and robustness of the entire processing. This enables the reconstruction of different types of dishes and cooking methods, ultimately enhancing the user experience.
[0018] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention are realized and obtained in accordance with the structures particularly pointed out in the description, claims and drawings.
[0019] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description
[0020] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.
[0021] Figure 1 A flowchart illustrating a method for processing cooking data provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of the structure of a cooking data processing device provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation
[0022] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions 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, 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.
[0023] Currently, in related technologies, a large amount of raw data is collected during the dish reproduction process. Due to the sheer volume of raw data, it's impossible to transmit all of it to the equipment; therefore, key data points need to be selected for transmission. However, there is currently no unified standard or method for selecting these key data points. This leads to the potential loss of important information during data transmission, thus affecting the dish reproduction effect. Secondly, existing technologies typically only consider factors such as temperature and time when selecting key data points, neglecting changes in stove settings, which is also a significant factor affecting the reproduction effect. Furthermore, existing technologies often overlook the density and distribution of data points when selecting key data points, which may result in data points being too densely or sparsely distributed in certain time periods, thus affecting the accuracy of the reproduction.
[0024] Based on this, the cooking data processing method, apparatus and cooking equipment provided in this embodiment of the invention can extract key points from a large amount of raw data through an efficient adaptive key point extraction algorithm, greatly reducing the amount of data transmission and thus improving the accuracy of dish reproduction.
[0025] To facilitate understanding of this embodiment, a method for processing cooking data disclosed in this embodiment of the invention will first be described in detail.
[0026] In one possible implementation, embodiments of the present invention provide a method for processing cooking data. This method can be applied to a cloud server, which can communicate with smart cooking devices and user terminals. Simultaneously, the user terminal and the smart cooking device can also establish communication.
[0027] Specifically, such as Figure 1 The flowchart shown illustrates a method for processing cooking data, which includes the following steps: Step S102: Obtain the cooking dataset; In this embodiment of the invention, the cooking data includes multiple cooking data, each of which includes time parameters, temperature parameters, and cooking power parameters; and the cooking data is uploaded at a preset time interval when the target device cooks the target recipe, and the target device is a pre-configured smart cooking device. In practical use, the aforementioned cooking power parameters are characterized by the setting or heat level of the cooking equipment. For example, a high setting or high heat indicates greater cooking power, while a low setting or low heat indicates less cooking power. Furthermore, the target equipment refers to the equipment used by professional chefs during cooking. After establishing communication with the cloud server, this equipment can upload data to the cloud server at a certain frequency, such as once every 2 seconds. Therefore, a relatively large amount of raw data is generated. For example, a dish that requires 120 minutes of cooking time will generate 3600 raw data entries. In this embodiment of the invention, the cooking dataset used in step S102 is obtained by preprocessing these raw data, such as data cleaning and formatting, to facilitate the subsequent extraction of key data.
[0028] Specifically, the data cleaning process can remove invalid data: for example, it can check the validity of each data point and remove abnormal temperature values caused by sensor malfunctions (such as temperatures outside the normal range); it can also remove duplicate data: for example, if there are multiple identical data points at the same time, one of them can be kept.
[0029] Furthermore, the data formatting process includes: time alignment, which aligns the time of all data points to a unified time base, for example, starting recording when the cooking start time is 0 seconds; and data standardization, which standardizes temperature parameters and cooking power parameters to ensure data consistency and comparability. The purpose is to ensure the data quality of the cooking dataset and improve the accuracy and reliability of key point data extraction.
[0030] In practical use, to obtain the above cooking dataset, the target device and data acquisition process can be pre-configured, which may include the following: (1) Equipment preparation: That is, use professional intelligent cooking equipment. The intelligent cooking equipment can be set to record data such as temperature, time and stove setting during the cooking process. Therefore, the target equipment in the present invention should have a high-precision temperature sensor and setting detector to ensure the accuracy and reliability of the data.
[0031] (2) Data Upload: Typically, the target device is operated by a professional chef to cook the dish corresponding to the target recipe, and the target device is configured to upload data to the cloud server at a fixed frequency (e.g., every 2 seconds). For example, assuming the entire cooking process takes 120 minutes, and the target device uploads data every 2 seconds, the target device will generate 3600 pieces of raw data.
[0032] (3) Data format: Each data entry is set to include three main parameters: time parameter (unit: seconds), temperature parameter (unit: degrees Celsius), and cooking power parameter (unit: gear number).
[0033] (4) Data storage: The uploaded raw data is stored on the cloud server to ensure the security and accessibility of the data, so as to collect detailed cooking process data, form a cooking dataset, and provide a basis for the subsequent extraction of key point data.
[0034] Step S104: Extract key point data from the cooking dataset according to the pre-configured data extraction rules; The key data points include cooking start point data, cooking end point data, power change point data, and cooking inflection point data. Specifically, the aforementioned cooking start point data and cooking end point data are also called cooking start and end points. Including these cooking start and end points ensures that the start point (time = 0 seconds) and end point (time = 7200 seconds) of the subsequently obtained cooking curve are preserved, thereby guaranteeing the integrity and accuracy of the cooking process. Furthermore, the power change data extracted above can be recorded when the stove's setting or firepower changes. For example, whenever the stove's setting changes, the recorded data point at that moment can ensure that key operation points are completely preserved and reflect changes in firepower.
[0035] Furthermore, the aforementioned cooking inflection points are extreme points in the cooking data and points where the slope of the cooking data change exceeds a preset threshold. These can be implemented using adaptive keypoint extraction algorithms, such as the Ramsey algorithm or the Douglas-Peucker algorithm. Typically, the extraction of cooking inflection points can be configured according to actual usage requirements. For example, extreme points of temperature change (e.g., maximum and minimum values) or points where the slope of temperature change exceeds a certain threshold can be extracted as cooking inflection points. In addition, cooking inflection point data can also be extracted from the cooking dataset according to predicted time intervals based on the cooking time. The specific extraction process can be set according to actual usage conditions, and this embodiment of the invention does not impose any limitations on this.
[0036] Step S106: Determine whether the key point data meets the pre-configured data rules; Step S108: If yes, generate restoration data of the target recipe based on the key point data. This restoration data is used to restore the target recipe.
[0037] In specific implementation, the data rules in this embodiment of the invention include data volume rules and data span rules; the data volume rules are used to control the number of key point data, and the data span rules are used to control the span between adjacent key point data; based on the above data rules, it is possible to avoid an excessive number of key point data and to avoid a large span between key point data, thereby obtaining a more appropriate number of key point data as reconstruction data.
[0038] Therefore, the cooking data processing method provided in this embodiment of the invention can acquire a cooking dataset, wherein the cooking dataset includes multiple cooking data points, each of which includes time parameters, temperature parameters, and cooking power parameters; extract key point data from the cooking dataset according to pre-configured data extraction rules; determine whether the key point data meets the pre-configured data rules; if so, generate reconstruction data of the target recipe based on the key point data, so as to facilitate the reconstruction of the target recipe; and, by configuring the data rules satisfied by the key point data, it can ensure that the number of key point data is moderate and representative, thereby improving the adaptability and robustness of the entire processing process, and being able to cope with the reconstruction process of different types of dishes and cooking methods, thus helping to improve the user experience.
[0039] In practical use, the aforementioned restored data can take the form of a cooking curve, such as generating a cooking curve based on key point data; then, the cooking curve is determined as the restored data for the target recipe. Specifically, since each piece of cooking data includes time parameters, temperature parameters, and cooking power parameters, the cooking curve can be a curve showing the changes in temperature and power over time, and is stored on a cloud server.
[0040] Furthermore, the cloud server can also send the restored data to the smart cooking device, enabling the device to cook the target recipe based on the restored data. Specifically, the process of sending the restored data typically includes data packaging, data transmission, and device execution, as follows: (1) Data Packaging: The extracted key data points are packaged into a standard data format. Each data point contains time parameters, temperature parameters, and gear information parameters.
[0041] (2) Data transmission: The packaged key data is sent to the smart cooking device via the cloud server as the restored data. The data transmission should use a reliable data transmission protocol to ensure the integrity and real-time performance of the data.
[0042] (3) Equipment execution: After receiving the restored data, the intelligent cooking equipment adjusts the cooking temperature, time, cooking power, and the corresponding stove setting or heat level based on this data, so as to cook according to the cooking method of a professional chef. The purpose is to ensure that the intelligent cooking equipment can accurately reproduce the cooking process of a professional chef and improve the fidelity of the dish.
[0043] In practical use, considering that the above data rules include data volume rules and data span rules; data volume rules are used to control the quantity of key point data, and data span rules are used to control the span between adjacent key point data; therefore, in the above step S106, when determining whether the key point data meets the pre-configured data rules, it is necessary to determine whether the key point data meets both the data volume rules and the data span rules; if yes, it is determined that the key point data meets the pre-configured data rules; if no, that is, the key point data does not meet the data volume requirements, or does not meet the data span requirements, it is determined that the key point data does not meet the pre-configured data rules.
[0044] Specifically, when determining whether key point data meets the data volume rules, it can be determined whether the number of key point data exceeds the pre-configured quantity threshold range; if not, it is determined that the key point data meets the data volume rules; if so, it is determined that the key point data does not meet the data volume rules.
[0045] In specific implementation, the data volume rule can control the number of key point data points selected in the end by configuring different quantity thresholds, ensuring that the number of key point data points is within a certain range (e.g., no more than 20 and no less than 15). That is, in this embodiment of the invention, the above-mentioned quantity threshold range actually includes an upper quantity threshold and a lower quantity threshold. If the number of key point data points finally extracted is between the lower quantity threshold and the upper quantity threshold, it is determined that the number of key point data points does not exceed the pre-configured quantity threshold range. If the number of key point data points is less than the lower quantity threshold or more than the upper quantity threshold, it indicates that the number of key point data points exceeds the pre-configured quantity threshold range, that is, the key point data does not meet the data volume rule.
[0046] If the number of key point data points exceeds a pre-configured threshold range, the preset threshold in the cooking inflection point needs to be adjusted to ensure the number of key point data points meets the threshold range. Specifically, the preset threshold can be dynamically adjusted based on the characteristics of the cooking curve. For example, taking a temperature-time curve as an example, curves with drastic temperature changes often result in more extreme points and points where the slope of the cooking data changes exceeds the preset threshold, i.e., more cooking inflection points. This can easily lead to a number of key point data points exceeding the upper limit threshold. In this case, the preset threshold in the cooking inflection point can be appropriately increased. In this embodiment of the invention, the slope threshold of temperature change can be increased. This way, inflection points with low temperature change rates will not be extracted as key point data, keeping the key point data within a certain range. At the same time, since the slope threshold for extracted temperature changes has been increased, inflection points with significant slope changes can also be extracted, ensuring that the extracted cooking inflection points are not inaccurate due to the increased slope threshold.
[0047] Furthermore, for curves where the rate of temperature change is not very significant, a higher slope threshold may result in fewer extracted cooking inflection points, causing the number of key point data to fall below the lower limit threshold. In this case, the preset threshold for cooking inflection points can be appropriately lowered. In this embodiment of the invention, the slope threshold for temperature change can be appropriately lowered to extract more cooking inflection points with insignificant temperature changes, thereby increasing the number of key point data. The specific amount of increase or decrease in the preset threshold each time can be set according to actual usage, and this embodiment of the invention does not impose any restrictions on this.
[0048] The process of adjusting the preset threshold in the cooking inflection point described above can be adaptively adjusted according to the actual amount of key point data extracted, so as to dynamically adjust the preset threshold of key point data according to the characteristics of different cooking data.
[0049] Furthermore, based on different cooking methods, such as steaming, baking, frying, and stir-frying, different quantity threshold ranges can be set. The specific settings can be made according to the actual usage situation, and the embodiments of the present invention do not impose any restrictions on this.
[0050] Furthermore, when determining whether key point data meets the data span rule, it can be determined whether the span between adjacent key point data is greater than a preset first span threshold and less than a preset second span threshold; wherein the second span threshold is greater than the first span threshold; if so, it is determined that the key point data meets the data span rule; if the span between adjacent key point data is less than the preset first span threshold or greater than the preset second span threshold, it is determined that the key point data does not meet the data span rule.
[0051] Specifically, based on the above data span rules, the continuity and smoothness of the restored data or the final cooking curve can be guaranteed, avoiding abrupt changes and discontinuities.
[0052] Furthermore, if the span between adjacent keypoint data is less than a preset first span threshold, one of the keypoint data is deleted according to the pre-configured deletion principle; if the span between adjacent keypoint data is greater than the preset first span threshold, a preset number of interpolation data is inserted between the two keypoint data according to the pre-configured interpolation principle, so that the span of the keypoint data meets the above data span rules.
[0053] In practical use, considering that the above cooking data includes time parameters, temperature parameters and cooking power parameters, the above span can be a time span, a temperature span, or a span that considers both time and temperature. The specific span depends on the actual usage, and the embodiments of the present invention do not impose any restrictions on this.
[0054] Furthermore, based on the aforementioned data span rules, corresponding data addition and subtraction mechanisms can be set. For example, taking the aforementioned span as a time span, mechanism 1 can be set: if the time span or time interval between two key data points is too short (e.g., less than 10 seconds), then one of the key data points is deleted. This aims to avoid data redundancy and improve the simplicity of the reconstructed data. Further, if the time span between two key data points is too long (e.g., more than 30 seconds) or the temperature difference is too large (e.g., more than 10°C), then additional points are appropriately inserted, i.e., interpolated data is inserted into the reconstructed data to avoid discontinuities or data abrupt changes.
[0055] Furthermore, the restored data or cooking curves obtained above can be validated. For example, the restored data can be sent to a smart cooking device, which can then cook the target recipe based on the restored data. The cooking process and results can then be compared with those of the original target device when cooking the target recipe. Alternatively, images of the cooking process can be captured and compared using visualization tools to verify the rationality of the extracted key data. The specific validation process can be set according to actual usage, and this embodiment of the invention does not impose any limitations on it.
[0056] In practical use, based on the cooking data processing method provided in the embodiments of the present invention, a recipe restoration system can be formed. That is, the recipe restoration system includes a target device, a cloud server, and an intelligent cooking device. Its functions may include a data acquisition module, a cloud processing module, and an intelligent cooking module, as detailed below: (1) Data acquisition module: Hardware equipment: The target equipment is a pre-configured smart cooking device operated by a professional chef and equipped with a high-precision temperature sensor, gear position detector, etc.
[0057] Data Upload: The target device uploads the collected raw data to the cloud server in real time via its built-in communication module (such as Wi-Fi, Bluetooth, etc.).
[0058] Data content: Record time parameters, temperature parameters, and cooking power parameters at preset time intervals, such as every 2 seconds, to form a day's cooking data for uploading.
[0059] In practical use, the target device can be calibrated before data acquisition to ensure the accuracy of the sensors and detectors. Simultaneously, the acquisition frequency should be set, for example, the target device can be configured to upload data to the cloud server every 2 seconds. The data content should also be configured to ensure that each uploaded data includes time, temperature, and cooking power parameters.
[0060] Once the data collection is complete, a complete original cooking dataset can be generated.
[0061] (2) Cloud processing module Data storage: Cloud servers are used to store raw data and perform backups.
[0062] Data preprocessing: The uploaded data is cleaned, formatted, and validated to obtain a cooking dataset.
[0063] Key point extraction algorithm: Corresponding to the process in step S104 above, an adaptive key point extraction algorithm is used to extract key point data from the cooking dataset according to the data extraction rules. For example, it extracts cooking start point data, cooking end point data, power change point data, and cooking inflection points. It also determines whether the key point data meets the above data rules. For example, it adds interpolated data between inflection points with excessively long time spans or large temperature changes, or adds interpolated data where necessary to maintain the accuracy of the cooking process. In addition, it controls the number of key data points to ensure that the final extracted key point data does not exceed the number threshold. For details, please refer to the above process, which will not be repeated here.
[0064] Data Packaging and Transmission: The extracted key data is packaged to generate restored data and sent to the smart cooking device.
[0065] In this cloud processing module, the aforementioned data cleaning is to remove invalid and duplicate data. That is, the cleaning process may include checking the validity of each cooking data, removing outliers, and retaining one of the multiple identical cooking data at the same time point.
[0066] Data formatting: This can include time alignment, aligning the time of all cooking data to a unified time base; data standardization: standardizing temperature and cooking power parameters; and data verification, including ensuring data integrity and continuity, as well as ensuring data accuracy.
[0067] (3) Intelligent cooking module The intelligent cooking equipment used in this intelligent cooking module differs from the target equipment mentioned above; it can be equipment operated by non-professional chefs, including: Data reception: Receive restoration data or cooking curves sent from the cloud server.
[0068] Parameter adjustment: Adjust the temperature, time, and cooking power parameters based on the received restoration data.
[0069] Real-time monitoring: Monitors temperature and setting in real time during cooking to ensure accuracy.
[0070] Log recording: Records key events and parameter changes during the cooking process to facilitate subsequent analysis and optimization.
[0071] Specifically, the cloud server and the smart cooking device use reliable data transmission protocols for data transmission, such as HTTPS or MQTT, and error handling mechanisms can also be added to ensure the reliability of data transmission.
[0072] After receiving the restored data, the intelligent cooking device analyzes and stores the data. Then, based on the analyzed data, it adjusts its own temperature, time, and cooking power parameters. During the cooking process, it monitors the temperature and power level in real time to ensure the accuracy of the cooking process. At the same time, it records key events and parameter changes during the cooking process for subsequent analysis and optimization.
[0073] In summary, the cooking data processing method provided in this embodiment of the invention extracts key point data from the cooking dataset through an efficient adaptive key point data extraction algorithm, which significantly reduces the amount of data transmission while maintaining the integrity and accuracy of the original data. This not only improves the efficiency of data transmission but also reduces the computational burden on intelligent cooking devices, thereby enhancing the overall processing efficiency.
[0074] Furthermore, in related technologies, key point extraction algorithms use fixed thresholds, which cannot be dynamically adjusted according to different data characteristics. This leads to unstable quantity and quality of extracted key point data, affecting the accuracy and consistency of cooking results. Additionally, different dishes and cooking methods have different requirements for the quantity of key point data, making fixed-threshold algorithms difficult to adapt to diverse cooking needs. The cooking data processing method in this embodiment of the invention can dynamically adjust the quantity threshold of the key point data extraction algorithm based on an adaptive quantity threshold adjustment mechanism, according to the characteristics of different cooking data. This not only ensures that the quantity of key point data is moderate and representative, but also improves the adaptability and robustness of the algorithm, enabling it to handle different types of dishes and cooking methods.
[0075] Furthermore, in related technologies, the extraction of key point data often fails to fully consider important nodes in the cooking process, such as the beginning and end points and stove power level changes. These nodes are crucial for accurately reconstructing the cooking process. For example, the beginning and end points determine the start and end states of cooking, and power level changes reflect the changes in heat during cooking. The absence of this key point data can lead to significant deviations between the cooking results and the actual operation of a professional chef, affecting the quality and taste of the dish. In this embodiment of the invention, the beginning and end points of the cooking data and the data points for each stove power level change are forcibly retained during the key point data extraction process. This ensures that the start and end states of the cooking data and important operational nodes are completely preserved, improving the accuracy and completeness of the cooking results.
[0076] Furthermore, considering that in some cases, the span between two key data points can be large, potentially leading to discontinuities in the reconstructed data and affecting cooking results, and that existing technologies lack effective interpolation mechanisms to guarantee the smoothness and continuity of the reconstructed data. For example, when the time span between two key data points is long or the temperature change is significant, directly connecting these two points may cause the cooking device to drastically adjust the temperature within a short period, affecting the stability and consistency of cooking. Based on these issues, this embodiment of the invention employs an intelligent interpolation mechanism. When the time span between two key data points is too long or the temperature difference is too large, additional interpolated data points can be automatically inserted to ensure the continuity and smoothness of the reconstructed data. This ensures that the intelligent cooking device can smoothly adjust parameters during the cooking process, avoiding abrupt changes and discontinuities, and improving the stability and consistency of the cooking results.
[0077] Furthermore, considering that many data points change relatively smoothly during the cooking process and have little impact on the cooking result, directly using all cooking data to reconstruct the cooking curve would lead to data redundancy, increase computational complexity, and reduce cooking efficiency. At the same time, redundant data may also introduce noise, affecting the final cooking effect. Therefore, in the extraction process of the key point data in the embodiments of the present invention, a large number of smoothly changing data points are removed, and only key point data that have a significant impact on the cooking result are retained, such as the cooking inflection point in the embodiments of the present invention. This not only reduces computational complexity but also avoids noise introduced by redundant data, improving the accuracy and stability of the cooking result.
[0078] In summary, the cooking data processing method provided in this embodiment of the invention has the following beneficial effects: (1) Improve cooking precision: Through an adaptive key point data extraction algorithm and an intelligent interpolation mechanism, the system ensures high-precision restoration of the data and corresponding cooking curves. Furthermore, the key point data extraction algorithm can dynamically adjust the threshold according to the characteristics of different cooking curves to extract the most representative key point data, avoiding the deviation caused by fixed thresholds. This also helps improve the cooking accuracy of intelligent cooking equipment, enabling users to achieve cooking results close to those of professional chefs.
[0079] (2) Reduce data transmission volume: By extracting key data, the amount of data transmitted is significantly reduced, improving data transmission efficiency. For example, the original 3,600 data points generated during a 120-minute cooking time can be reduced to no more than 20 key points after key data extraction. This effectively reduces the burden of data transmission, avoids transmission delays and packet loss, and improves the system's real-time performance and stability.
[0080] (3) Enhance user experience: Based on the cooking data processing method provided in this invention, users can easily achieve professional chef-level cooking results, enhancing the user experience of smart kitchens. The key point data extraction algorithm and intelligent interpolation mechanism ensure the continuity and smoothness of the restored data and cooking curves, avoiding abrupt changes and discontinuities during the cooking process. This not only improves user satisfaction and loyalty but also promotes the market promotion and application of smart cooking equipment.
[0081] (4) Improve processing efficiency: By reducing data transmission volume and optimizing the key point data extraction algorithm, the processing efficiency of intelligent cooking equipment has been improved. The intelligent cooking equipment no longer needs to process a large amount of raw data, but only needs to operate based on the extracted key point data, which greatly reduces the computational complexity. This not only improves the overall performance of the entire intelligent cooking system, but also extends the service life of the equipment and reduces maintenance costs.
[0082] (5) Adapt to diverse cooking needs Through an adaptive quantity threshold adjustment mechanism, the quantity threshold of the key point data extraction algorithm can be dynamically adjusted according to different characteristics, ensuring that different types of dishes and cooking methods can be reproduced with high accuracy. This not only improves the universality and flexibility of the intelligent cooking system, but also makes it suitable for various cooking scenarios and meets the diverse needs of users.
[0083] Furthermore, based on the above embodiments, this invention also provides a cooking data processing device, such as... Figure 2 The diagram shows a structural schematic of a cooking data processing device, which includes: The acquisition module 20 is used to acquire a cooking dataset, wherein the cooking dataset includes multiple cooking data, each of which includes time parameters, temperature parameters, and cooking power parameters; and the cooking data is uploaded at preset time intervals when the target device cooks the target recipe, wherein the target device is a pre-configured smart cooking device. Extraction module 22 is used to extract key point data from the cooking dataset according to pre-configured data extraction rules; wherein, the key point data includes cooking start point data, cooking end point data, power change point data, and cooking inflection points; wherein, the cooking inflection points are the extreme points of the cooking data and the points where the slope of the cooking data change exceeds a preset threshold; The judgment module 24 is used to determine whether the key point data meets the pre-configured data rules; The restoration module 26 is used to generate restoration data of the target recipe based on the key point data when the judgment result of the judgment module is yes. The restoration data is used to restore the target recipe.
[0084] Furthermore, the aforementioned data rules include data volume rules and data span rules; the data volume rules are used to control the quantity of the key point data, and the data span rules are used to control the span between adjacent key point data. The above-mentioned step of determining whether the key point data meets the pre-configured data rules includes: determining whether the key point data simultaneously meets the data volume rule and the data span rule; if yes, determining that the key point data meets the pre-configured data rules; if no, determining that the key point data does not meet the pre-configured data rules.
[0085] The step of determining whether the key point data meets the data volume rule includes: determining whether the number of key point data exceeds a pre-configured quantity threshold range; if not, determining that the key point data meets the data volume rule; if yes, determining that the key point data does not meet the data volume rule.
[0086] Furthermore, the device is also used to: if it is determined that the number of key point data exceeds a pre-configured quantity threshold range, adjust the preset threshold in the cooking inflection point so that the number of key point data meets the quantity threshold range.
[0087] Furthermore, the step of determining whether the key point data satisfies the data span rule includes: determining whether the span between adjacent key point data is greater than a preset first span threshold and less than a preset second span threshold; wherein the second span threshold is greater than the first span threshold; if so, determining that the key point data satisfies the data span rule; if the span between adjacent key point data is less than the preset first span threshold or greater than the preset second span threshold, then determining that the key point data does not satisfy the data span rule.
[0088] Furthermore, the above-mentioned device is also used to: if the span between adjacent key point data is less than a preset first span threshold, delete one of the key point data according to a pre-configured deletion principle; if the span between adjacent key point data is greater than the preset first span threshold, insert a preset number of interpolation data between the two key point data according to a pre-configured interpolation principle.
[0089] Furthermore, the step of generating the reconstruction data of the target recipe based on the key point data includes: generating a cooking curve based on the key point data; and determining the cooking curve as the reconstruction data of the target recipe.
[0090] Furthermore, the aforementioned device is also used to: send the restored data to a smart cooking device, so that the smart cooking device can cook the target recipe based on the restored data.
[0091] The cooking data processing device provided in this embodiment of the invention has the same technical features as the cooking data processing method provided in the above embodiments, so it can also solve the same technical problems and achieve the same technical effects.
[0092] Furthermore, this embodiment of the invention also provides a cooking device for acquiring restoration data and restoring a target recipe based on the restoration data; wherein the restoration data is obtained through the above-described cooking data processing method.
[0093] Furthermore, embodiments of the present invention also provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above method.
[0094] This invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, performs the steps of the above-described method.
[0095] Furthermore, embodiments of the present invention also provide a schematic diagram of the structure of an electronic device, such as... Figure 3 The diagram shows the structure of the electronic device, which includes a processor 31 and a memory 30. The memory 30 stores computer-executable instructions that can be executed by the processor 31, and the processor 31 executes the computer-executable instructions to implement the above-described method.
[0096] exist Figure 3 In the illustrated embodiment, the electronic device further includes a bus 32 and a communication interface 33, wherein the processor 31, the communication interface 33, and the memory 30 are connected via the bus 32.
[0097] The memory 30 may include high-speed random access memory (RAM) and may also include non-volatile memory, such as at least one disk storage device. Communication between this system network element and at least one other network element is achieved through at least one communication interface 33 (which can be wired or wireless), such as the Internet, wide area network, local area network, metropolitan area network, etc. The bus 32 may be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus, etc. The bus 32 can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 3 The symbol is represented by a single double-headed arrow, but this does not mean that there is only one bus or one type of bus.
[0098] Processor 31 may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method can be completed by the integrated logic circuitry in the hardware of processor 31 or by software instructions. Processor 31 can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in the embodiments of this invention can be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software modules can reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. The storage medium is located in the memory, and the processor 31 reads the information in the memory and uses its hardware to complete the aforementioned method.
[0099] The computer program products of the cooking data processing method, apparatus and electronic device provided in the embodiments of the present invention include a computer-readable storage medium storing program code. The instructions included in the program code can be used to execute the methods described in the preceding method embodiments. For specific implementation, please refer to the method embodiments, which will not be repeated here.
[0100] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the device described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.
[0101] Furthermore, in the description of the embodiments of the present invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in the present invention based on the specific circumstances.
[0102] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.
[0103] In the description of this invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.
[0104] Finally, it should be noted that the above embodiments are merely specific implementations of the present invention, used to illustrate the technical solutions of the present invention, and not to limit it. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments within the technical scope disclosed in the present invention, or make equivalent substitutions for some of the technical features; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.
Claims
1. A method for processing cooking data, characterized in that, The method includes: A cooking dataset is obtained, wherein the cooking dataset includes multiple cooking data points, each of which includes time parameters, temperature parameters, and cooking power parameters; and the cooking data is uploaded at preset time intervals when the target device cooks the target recipe, wherein the target device is a pre-configured smart cooking device. Key point data is extracted from the cooking dataset according to pre-configured data extraction rules; wherein, the key point data includes cooking start point data, cooking end point data, power change point data, and cooking inflection points; wherein, the cooking inflection points are the extreme points of the cooking data and the points where the slope of the cooking data change exceeds a preset threshold; Determine whether the key point data meets the pre-configured data rules; If so, the restoration data of the target recipe is generated based on the key point data, and the restoration data is used to restore the target recipe.
2. The method according to claim 1, characterized in that, The data rules include data volume rules and data span rules; the data volume rules are used to control the number of key point data, and the data span rules are used to control the span between adjacent key point data. The step of determining whether the key point data meets the pre-configured data rules includes: Determine whether the key point data simultaneously satisfies the data volume rule and the data span rule; If so, determine that the key point data meets the pre-configured data rules; If not, then it is determined that the key point data does not meet the pre-configured data rules.
3. The method according to claim 2, characterized in that, The step of determining whether the key point data meets the data volume rule includes: Determine whether the number of key point data exceeds a pre-configured threshold range; If not, then the key point data is determined to satisfy the data volume rule; If so, then it is determined that the key point data does not meet the data volume rule.
4. The method according to claim 3, characterized in that, The method further includes: If it is determined that the number of key point data exceeds a pre-configured threshold range, the preset threshold in the cooking inflection point is adjusted so that the number of key point data meets the threshold range.
5. The method according to claim 2, characterized in that, The step of determining whether the key point data satisfies the data span rule includes: Determine whether the span between adjacent key point data is greater than a preset first span threshold and less than a preset second span threshold; wherein the second span threshold is greater than the first span threshold. If so, determine that the key point data satisfies the data span rule; If the span between adjacent key point data is less than a preset first span threshold, or greater than a preset second span threshold, then the key point data is determined not to meet the data span rule.
6. The method according to claim 5, characterized in that, The method further includes: If the span between adjacent key point data is less than a preset first span threshold, then one of the key point data is deleted according to the pre-configured deletion principle; If the span between adjacent key point data is greater than a preset first span threshold, then a preset number of interpolated data are inserted between the two key point data according to a pre-configured interpolation principle.
7. The method according to claim 1, characterized in that, The steps for generating the reconstruction data of the target recipe based on the key point data include: A cooking curve is generated based on the key point data; The cooking curve is determined as the reconstruction data of the target recipe.
8. The method according to claim 1, characterized in that, The method further includes: The restored data is sent to the smart cooking device so that the smart cooking device can cook the target recipe based on the restored data.
9. A cooking data processing device, characterized in that, The device includes: The acquisition module is used to acquire a cooking dataset, wherein the cooking dataset includes multiple cooking data points, each of which includes time parameters, temperature parameters, and cooking power parameters; and the cooking data is uploaded at preset time intervals when the target device cooks the target recipe, wherein the target device is a pre-configured smart cooking device. An extraction module is used to extract key point data from the cooking dataset according to pre-configured data extraction rules; wherein, the key point data includes cooking start point data, cooking end point data, power change point data, and cooking inflection points; wherein, the cooking inflection points are the extreme points of the cooking data and the points where the slope of the cooking data change exceeds a preset threshold; The judgment module is used to determine whether the key point data meets the pre-configured data rules; The restoration module is used to generate restoration data of the target recipe based on the key point data when the judgment result of the judgment module is yes. The restoration data is used to restore the target recipe.
10. A cooking device, characterized in that, The cooking device is used to acquire restoration data and restore the target recipe based on the restoration data; The restored data is obtained by the method described in any one of claims 1-8.