Intelligent temperature control and heat dissipation method based on electric cooker health degree cloud monitoring

Abstract

The invention discloses an intelligent temperature control and heat dissipation method and system based on electric cooker health degree cloud monitoring. According to the system, temperature data of all areas in the electric cooker are collected in real time through a temperature sensor, a high-temperature deposition area is recognized, gridding path planning is conducted in combination with a three-dimensional structure model, and an optimal airflow dredging path is calculated. According to the heat dissipation requirements of different areas, the system dynamically adjusts the rotating speed of the fan to form different airflow types, and precise heat dissipation is achieved. The temperature change and the airflow effect are continuously monitored, and the heat dissipation scheme is continuously optimized through closed-loop feedback until the temperature of each high-temperature area returns to the normal range. The heat dissipation efficiency and the temperature control precision of the electric cooker are remarkably improved, the service life of a product is effectively prolonged, and the user experience is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of intelligent kitchen furniture, in particular to an intelligent temperature control and heat dissipation method based on cloud monitoring of the health degree of an electric rice cooker. BACKGROUND

[0002] As an indispensable kitchen appliance in modern families, the operational safety and service life of an electric rice cooker are directly related to the daily life quality of users. With the popularization of intelligent technology, intelligent temperature control and heat dissipation technology based on cloud monitoring has become an important research direction for improving the health degree of an electric rice cooker. This not only concerns the efficient operation of the device, but also is closely related to the safety hazards caused by overheating, and is a key field of technological innovation in the home appliance industry.

[0003] However, the heat dissipation design of most electric rice cookers on the current market still remains at the level of passive ventilation or simple fan heat dissipation, lacking precise perception and targeted treatment of the internal heat distribution of the device. This extensive heat dissipation method often cannot effectively address local high temperature problems, leading to overheating in certain areas of the device, affecting performance and durability.

[0004] In this context, there are significant technical challenges. The first and foremost is how to accurately identify specific areas of heat accumulation inside the device. Due to the unevenness of heat distribution, traditional uniform heat dissipation methods are difficult to effectively intervene in local high temperature points. This identification problem further derives another core problem, i.e., how to design differentiated air flow dredging paths according to the heat characteristics of different areas. Different components such as the edge of the heating disc, the circuit board area, and the surrounding of the power transformer have different requirements for air flow form and wind speed due to their structural and heat dissipation needs. If targeted path planning and air flow adjustment cannot be achieved, local high temperature problems will persist, and even the risk of device aging or failure may be exacerbated.

[0005] Therefore, how to design and implement precise directional air flow dredging paths based on cloud monitoring, according to the heat accumulation characteristics of different areas inside the electric rice cooker, while optimizing wind speed and air flow form to adapt to the heat dissipation needs of different components, has become a key problem for improving the health degree and safety of the device. SUMMARY

[0006] The present application provides an intelligent temperature control and heat dissipation method based on cloud monitoring of the health degree of an electric rice cooker, mainly including:

[0007] Collecting temperature data around the hot area by a temperature sensor in real time, uploading to a cloud system, and generating temperature distribution data sets for each area;

[0008] Filtering out high temperature areas to generate a high temperature accumulation area set;

[0009] Combine the three-dimensional structure model of the electric rice cooker, calculate the optimal airflow path, and generate a set of airflow dredging paths;

[0010] Obtain path data of each high-temperature area from the set of airflow dredging paths, and generate a set of airflow type configuration parameters;

[0011] Obtain the airflow type configuration parameters of each area, form a specified airflow pattern, adjust the fan speed according to the heat dissipation requirement, and generate an initial set of airflow dredging schemes;

[0012] Obtain fan operation parameters, collect real-time data, verify whether the airflow meets the preset type and speed requirements, and generate a set of actual airflow speed values;

[0013] Obtain actual wind speed data, real-time monitor high-temperature area temperature change, and judge whether it is reduced to the preset target value T2;

[0014] Obtain the adjusted fan operation data from the set of airflow adjustment parameters, reconfigure the airflow dredging scheme, continuously monitor the temperature data, and generate a final set of heat dissipation effect evaluation data;

[0015] Repeat the above process until the temperature of the high-temperature area is reduced to the preset target value T2.

[0016] The technical scheme provided by the embodiment of the present application can include the following beneficial effects:

[0017] The present application discloses an intelligent heat dissipation system for an electric rice cooker. The system collects temperature data of each area inside the electric rice cooker in real time through a temperature sensor, identifies high-temperature accumulation areas, and performs grid path planning combined with a three-dimensional structure model to calculate the optimal airflow dredging path. According to the heat dissipation requirements of different areas, the system dynamically adjusts the fan blade speed to form different airflow types such as spiral airflow, laminar airflow and vortex airflow, and realizes precise heat dissipation. The present application continuously monitors temperature changes and airflow effects, and continuously optimizes the heat dissipation scheme through closed-loop feedback until the temperature of each high-temperature area returns to the normal range. This intelligent heat dissipation method significantly improves the heat dissipation efficiency and temperature control accuracy of the electric rice cooker, effectively prolongs the service life of the product, and improves the user experience. BRIEF DESCRIPTION OF DRAWINGS

[0018] Figure 1 a A flowchart of an intelligent temperature control and heat dissipation method based on the health degree cloud monitoring of an electric rice cooker.

[0019] Figure 1 A flowchart of an intelligent temperature control and heat dissipation method based on the health degree cloud monitoring of an electric rice cooker.

[0020] Figure 2 A schematic diagram of an intelligent temperature control and heat dissipation method based on the health degree cloud monitoring of an electric rice cooker.

[0021] Figure 3 Another schematic diagram of an intelligent temperature control and heat dissipation method based on health degree cloud monitoring of an electric rice cooker.

[0022] Figure 4 A logic structure schematic diagram of an intelligent temperature control and heat dissipation system based on health degree cloud monitoring of an electric rice cooker provided by the embodiment. DETAILED DESCRIPTION

[0023] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be described in detail below in combination with the drawings and specific embodiments.

[0024] As Figures 1 a-3 , the intelligent temperature control and heat dissipation method based on health degree cloud monitoring of an electric rice cooker can specifically include:

[0025] Step S101, first step: through the temperature sensor preset inside the electric rice cooker, real-time collection of temperature data around the edge of the heating disc, the integrated area of the circuit board, the power transformer and other hot areas, uploading to the cloud system to generate temperature distribution data set of each area.

[0026] The temperature data around the edge of the heating disc, the integrated area of the circuit board, the power transformer and other hot areas are collected by the temperature sensor at a frequency of 1 Hz, uploaded to the cloud storage as the original temperature data set. For missing values in the original data, linear interpolation method is used to complete according to time dimension to generate complete temperature data set. The complete temperature data set is input into k-means clustering algorithm, k=3 is set, k-means++ initialization center point is adopted, the edge of the heating disc, the integrated area of the circuit board, the power transformer and other hot areas are clustered, and the temperature mode label of each area is output. The preset threshold of the edge of the heating disc is 85 degrees Celsius, the threshold of the circuit board area is 65 degrees Celsius, and the threshold of the transformer area is 75 degrees Celsius. When the temperature of a certain area exceeds the corresponding threshold, the sliding window mean μ and standard deviation σ of the temperature data of the area in the last 30 minutes are calculated, and the current temperature is marked as abnormal when it exceeds the range of μ±3σ. For the abnormal area, ARIMA model is used for temperature prediction, the parameters are set as p=2, d=1, q=1, and the future 5-minute temperature prediction value is output. According to the prediction value, PID control parameters are generated, and the electric rice cooker control system is adjusted to adjust the power of the heating disc. If the threshold is still exceeded after adjusting for three times in succession, the event ID is recorded in the cloud and a maintenance work order is generated, which contains the abnormal area position code and the over-limit temperature value.

[0027] In a possible implementation, the temperature sensor collects temperature data around the edge of the heating disc, the integrated area of the circuit board, the power transformer and other hot areas at a frequency of 1 Hz to generate the original data set.

[0028] For example, the heating disc edge temperature is 82.5℃, the circuit board area is 60.3℃, and the transformer area is 70.8℃, which are uploaded to the cloud storage as time series data every second. The cloud uses linear interpolation to handle missing values. For example, if the heating disc edge temperature is missing at a certain time, it can be interpolated to 82.5℃ based on the previous two seconds of 81.8℃ and 83.2℃. The completed data set provides continuity for subsequent analysis and ensures data reliability.

[0029] Specifically, the complete data set is input into the k-means clustering algorithm, with k = 3 and k-means++ initialization center point.

[0030] For example, the clustering result may classify the heating disc edge as a high-temperature mode (label 1, mean 85℃), the circuit board area as a medium-temperature mode (label 2, mean 62℃), and the transformer area as a medium-high-temperature mode (label 3, mean 73℃). The clustering label reflects the temperature characteristics of each area, helping to identify differences in operating modes and improving the efficiency of equipment state monitoring.

[0031] For example, the heating disc edge temperature reaches 86.2℃, exceeding the threshold of 85℃, triggering anomaly detection. Taking the last 30 minutes of data, the sliding window mean is calculated as 84.5℃, and the standard deviation is 0.8℃. The current temperature of 86.2℃ is outside the mean ± 3σ range (82.1℃ to 86.9℃), and is marked as abnormal. This method accurately identifies abnormal points by statistical boundaries, reducing false positives.

[0032] In one embodiment, for abnormal areas, an ARIMA model (p = 2, d = 1, q = 1) is used to predict future 5-minute temperatures.

[0033] For example, based on historical data, the heating disc edge temperature is predicted to rise to 87.1℃ in 5 minutes. ARIMA uses time series autocorrelation to predict results to guide subsequent control and reduce the risk of temperature exceeding the limit.

[0034] Specifically, the predicted value is used to generate PID control parameters to adjust the heating disc power.

[0035] For example, the predicted temperature is too high, and the PID controller reduces the power to 70%, making the temperature tend to be stable. If the threshold is still exceeded three times in a row (e.g., 86.5℃, 86.8℃, 87.0℃), the cloud records the event ID (e.g., E202506150001), generates a maintenance work order, and includes the abnormal area code (e.g., H1 for the heating disc edge) and the over-limit value 86.8℃. This mechanism ensures that abnormalities are responded to in a timely manner, and the work order information facilitates maintenance personnel to quickly locate the problem, improving the efficiency of equipment maintenance.

[0036] It can be understood that the above process forms a closed-loop management through data collection, preprocessing, clustering, anomaly detection, prediction, and control.

[0037] For example, clustering results assist in optimizing threshold settings, ARIMA predictions cooperate with PID control to reduce the risk of overheating, and maintenance work orders improve response speed. The overall solution improves the stability and safety of electric rice cookers, extending the life of the equipment.

[0038] Step S102, second step: Obtain the temperature values of each region from the temperature distribution dataset, compare them with the ambient temperature, and determine whether they exceed the preset threshold T1 to filter out high-temperature regions and generate a high-temperature accumulation region set.

[0039] Extract the temperature data of each region from the temperature distribution dataset, and the data format is a structured table with latitude and longitude coordinates corresponding to temperature values. Compare each region's temperature data with the hourly average ambient temperature provided by the environmental monitoring station point by point. The ambient temperature data has been aligned with the region data through the timestamp. In the comparison result, mark the regions that exceed the preset threshold of 30 degrees Celsius to generate a preliminary high-temperature region list. According to the dynamic threshold set by the building safety standard, secondarily screen this list to eliminate false positive regions caused by transient fluctuations and determine the final high-temperature region set. Retrieve the temperature records of these regions over the past 24 hours from the meteorological database, calculate the continuous threshold-exceeding duration, and list the regions with a duration exceeding 2 hours as key monitoring regions. Extract the temperature mean, variance, and spatial gradient of the key regions to form a three-dimensional feature vector, use the K-means clustering algorithm to divide them into 3 groups, and sort the groups according to the optimal contour coefficient principle. Arrange the regions in each group in descending order of threshold-exceeding duration, and automatically mark the group with the longest duration as the high-risk region. Deploy a data collector to obtain the temperature of the high-risk region at 5-minute intervals, and trigger the cooling equipment start command when the latest data exceeds the threshold. The priority of the command is determined by the threshold-exceeding amplitude.

[0040] To quantify the risk level analysis, the following formula is used:

[0041]

[0042] F(R) represents the feature vector of region R, μ_T(R) represents the temperature mean of region R, σ 2 T (R) represents the temperature variance, and the temperature spatial gradient. This three-dimensional feature vector is used for subsequent risk level clustering analysis.

[0043]

[0044] D over represents the cumulative duration of temperature exceeding the threshold, δ i is the indicator function, and T i is the temperature at the i-th hour, and T threshold1, otherwise 0, Δt represents the time interval (1 hour in this case). When D over When the temperature difference exceeds a preset threshold, the area is listed as a high-temperature area.

[0045]

[0046] σ T represents the standard deviation of temperature fluctuations, n represents the number of measurements, T i represents the temperature value of the i-th measurement, represents the average temperature. When σ T When the temperature difference is less than 0.5℃, the area is considered a temperature stable area and may be removed from the high-temperature area list.

[0047] T diff (x,y) = T measured (x,y) - T ambient

[0048] T diff(x,y) represents the temperature difference at a specific coordinate point (x,y), T measured(x,y) represents the actual temperature value at the coordinate point, T ambient represents the average ambient temperature. When T diff When the temperature difference exceeds a preset threshold, the point is marked as a high-temperature area.

[0049] In one possible implementation, the temperature distribution dataset is stored in a structured table form, containing latitude and longitude coordinates and corresponding temperature values.

[0050] For example, a certain area dataset records the temperature of the heating disc edge coordinate (10,20) as 83.5℃, the temperature of the circuit board area coordinate (15,25) as 61.2℃, and the temperature of the transformer area coordinate (20,30) as 72.1℃. By comparing with the average ambient temperature provided by the environmental monitoring station every hour, for example, the ambient temperature is 25.5℃, the difference is calculated point by point, and the area with a difference exceeding 30℃ is marked. Assuming that the difference of the heating disc edge is 58℃, which exceeds the threshold, it is included in the preliminary high-temperature area list. Other areas such as the circuit board area difference 35.7℃, the transformer area difference 46.6℃, are also included in the list. This comparison method ensures the rapid identification of high-temperature areas and reduces the risk of false negatives.

[0051] For example, for the preliminary high-temperature area list, a secondary screening is performed according to the dynamic threshold of the building safety standard. Assuming that the standard stipulates that a region with a transient fluctuation of less than 0.5℃ is considered a false positive. The heating disc edge has a temperature of 83.4℃, 83.6℃, and 83.5℃ for three consecutive minutes, with a fluctuation of less than 0.5℃, and is removed as a false positive. The temperature sequence of the transformer area is 72.0℃, 72.8℃, and 73.2℃, with a large fluctuation, and is retained as a high-temperature area. This screening method reduces false positives by using a dynamic threshold and improves the accuracy of area division.

[0052] In one possible implementation, the past 24-hour temperature records of the final high-temperature area are extracted from the meteorological database, and the continuous over-threshold duration is calculated.

[0053] For example, the transformer area temperature is above 70℃ for 3.5 hours, and the heating disc edge lasts for 2.2 hours, both exceeding the 2-hour threshold, and are listed as key monitoring areas. The temperature mean, variance, and spatial gradient of these areas are extracted to form a three-dimensional feature vector.

[0054] For example, the transformer area has a mean temperature of 72.5℃, a variance of 0.9℃, and a spatial gradient of 0.3℃ / m, forming a vector (72.5, 0.9, 0.3). Using K-means clustering, and determining k=3 according to the silhouette coefficient, the areas are divided into high, medium, and low risk groups. The longest group, such as the transformer area, is marked as a high-risk area. This clustering method clearly divides the risk levels through feature vectors, facilitating priority management.

[0055] For example, data collectors are deployed in high-risk areas to collect temperature data every 5 minutes.

[0056] For example, the latest temperature of the transformer area is 73.8℃, exceeding the threshold of 70℃, triggering the cooling device start command. The over-threshold amplitude is 3.8℃, with a high priority, and the command requires the cooling device to run at 80% power. If the amplitude is only 1℃, the priority is medium, and the power is reduced to 50%. This hierarchical response mechanism ensures efficient allocation of resources and rapid response to high-temperature risks.

[0057] In one possible implementation, after the cooling device is started, the system continuously monitors the temperature change.

[0058] For example, the transformer area temperature drops from 73.8℃ to 71.2℃ within 10 minutes, indicating that the cooling is effective. If the temperature continues to be above the threshold, the system records the event and generates a maintenance work order, including the area code and over-threshold data. This closed-loop management ensures stable device operation and extends service life through real-time data collection and response.

[0059] Step S103, the third step: based on the set of high-temperature accumulation areas, combined with the three-dimensional structure model of the electric rice cooker, the path between the high-temperature area and the heat dissipation outlet is divided into grids, and the optimal airflow path is calculated using a path planning algorithm to generate a set of airflow dredging paths.

[0060] Coordinate data for points with temperatures exceeding 423K was extracted from the high-temperature area and siltation data sets. Combined with the 3D structure file of the rice cooker inner pot in STL format, the temperature coordinates were mapped onto a 3D surface in SolidWorks to generate a spatial distribution framework. Within this spatial distribution framework, the center coordinates and diameter data of the heat dissipation outlet were extracted. Using ANSYS Meshing, the space between the high-temperature area and the heat dissipation outlet was divided into hexahedral mesh elements with a side length of 5 mm, outputting a mesh element set in MSH format. Based on this mesh element set, the A-star algorithm was used to traverse each element, starting from the center of the high-temperature area and ending at the center of the heat dissipation outlet. The Manhattan distance was used as the heuristic function to generate ten initial airflow paths. If any paths intersected, the pressure drop coefficient was calculated for each path. The path with the shortest length and a pressure drop coefficient less than 0.3 was selected as the highest-priority airflow path. For the highest-priority path, the GJK collision detection algorithm was used to identify structural obstacles along the path. The path direction was adjusted around the obstacle, subject to the constraint that the curvature radius was greater than 3 mm, to generate an optimized diversion path. The diversion path was converted into PATH format data readable by Fluent, forming a complete path set. The airflow is simulated on the path set by Fluent, and the inlet flow velocity is set to 1.2m / s.

[0061] When the velocity is lower than 0.5 m / s, it is determined to be blocked, the path weight is recalculated according to the velocity field gradient, and the final verified path set data is output.

[0062] For example, when processing data for high-temperature areas, one can start with coordinate points exceeding 423K. This data can be combined with the 3D structure file of the rice cooker inner pot and mapped onto a curved surface to form a spatial distribution framework. For example, if the coordinate points of a certain high-temperature area are concentrated at the center of the inner pot bottom, with a temperature reaching 425K, these points can be projected onto the 3D model using SolidWorks software to form a visual high-temperature distribution map. This mapping method facilitates subsequent analysis of heat distribution patterns and provides a basis for heat dissipation design.

[0063] For example, extracting the center coordinates and diameter data for the heat dissipation outlet allows the outlet's location to be identified within a spatial distribution framework. Assuming the outlet's center coordinates are at the top of the inner tank's sidewall, with a diameter of 20 mm, the ANSYS Meshing tool can be used to divide the space between the high-temperature area and the outlet into hexahedral mesh elements with a side length of 5 mm. This refined meshing approach facilitates accurate simulation of airflow and lays the foundation for path planning.

[0064] For example, when generating the initial airflow path, the A* algorithm is used to traverse the grid cells, with the starting point set as the center of the high-temperature area and the ending point set as the center of the heat dissipation outlet. Assuming that the straight-line distance from the high-temperature center to the outlet is 50 mm, the algorithm generates ten paths based on Manhattan distance, some of which may pass through the corner of the inner container side wall. By calculating the pressure drop coefficient of each path, the path with the shortest length and a coefficient less than 0.3 is selected as the highest priority, for example, a path with a length of 55 mm and a pressure drop coefficient of 0.25 is selected as the highest priority. This screening mechanism ensures the efficiency of airflow dissipation.

[0065] For example, for the optimization of the priority path, the GJK collision detection algorithm can be used to identify structural obstacles on the path. Assuming that the path passes through a protruding structure inside the inner container, after detection by the algorithm, the path direction is adjusted to ensure that the curvature radius is greater than 3 mm, forming a smooth dissipation path. This adjustment avoids excessive disturbance of airflow at obstacles and improves dissipation effect.

[0066] For example, after converting the optimized dissipation path into the PATH format readable by Fluent, airflow simulation can be performed through Fluent. The inlet flow velocity is set to 1.2 m / s, and assuming that the airflow velocity at a certain monitoring point is only 0.4 m / s, it is determined as an obstruction area. The system recalculates the path weight based on the velocity field gradient, and finally outputs a set of verified paths. This simulation verification mechanism ensures the feasibility of the path and provides reliable reference for actual heat dissipation design.

[0067] For example, throughout the entire process, the core of handling the high-temperature area of the electric rice cooker inner container is the closed-loop design from data extraction to path optimization. Through multi-dimensional analysis such as temperature distribution, grid division, and path

[0068] screening, it is ensured that the airflow dissipation path can cover the high-temperature area and avoid structural restrictions. This comprehensive solution significantly improves the heat dissipation efficiency and ensures the stability of the device operation.

[0069] Step S104, fourth step: obtain path data for each high-temperature area from the airflow dissipation path set, and determine the heating plate edge to use spiral airflow, the circuit board integration area to use laminar airflow, and the power transformer surrounding area to use vortex airflow, based on path length and area heat dissipation requirements, to generate a set of airflow type configuration parameters.

[0070] The coordinates of the area with a temperature higher than 150 degrees Celsius are extracted from the infrared thermal imaging data as input for the high-temperature area path data. According to the path length, the K-means clustering algorithm is used to divide it into long path group and short path group, and combined with the condition that the heat dissipation coefficient is greater than 0.8, the preliminary air flow distribution scheme is generated. For the edge area of the heating disc, a spiral path is generated by using a cubic Bezier curve, and the curvature radius is controlled within the range of 5-8 mm, and the flow field distribution parameters including flow velocity and pressure are output. According to the flow field parameters, the Lagrange interpolation method is used to smooth the path in the integrated area of the circuit board, and the laminar flow velocity is stabilized at 0.3 meters per second to generate the air flow guide data. For the area around the power transformer, the vortex with Reynolds number greater than 5000 is simulated by ANSYS Fluent, and when the coverage is less than 90%, the vortex angular velocity is adjusted to 120 radians per second, and the optimized vortex parameters are output. From the parameter set, the air flow intensity data of each area is extracted, the path weight is calculated by using the entropy weight method, and the laminar flow proportion is increased for the area with intensity less than 100 Pa to generate the final air flow configuration. The spiral air flow curvature parameters, laminar flow velocity parameters and vortex angular velocity parameters are stored in JSON format to generate the air flow configuration file containing the coordinate mapping relationship.

[0071] For example, when extracting the coordinates of the area with a temperature higher than 150 degrees Celsius from the infrared thermal imaging data, the thermal imaging image can be analyzed at the pixel level first to identify the boundary of the high-temperature area. Suppose a temperature of 155 degrees Celsius is detected in the inner pot of the rice cooker at the bottom, and the coordinate range is a circular area with a diameter of about 30 mm, these coordinate points are extracted and stored as input data by data processing software. This way can accurately locate the high-temperature area and provide reliable basis for subsequent path planning.

[0072] For example, when dividing the long path group and the short path group by using the K-means clustering algorithm according to the path length, the path data can be divided into two categories according to the length. Suppose the average length of the long path group is 60 mm and the average length of the short path group is 25 mm, and combined with the condition that the heat dissipation coefficient is greater than 0.8, more air flow resources are preferentially allocated to the long path group. This classification method helps to optimize the air flow distribution and improve the overall heat dissipation efficiency.

[0073] For example, when generating a spiral path by using a cubic Bezier curve in the edge area of the heating disc, the curvature radius can be adjusted between 5 and 8 mm by controlling the control points. Suppose the starting point of the path is located at the edge of the heating disc, and the ending point

[0074] Connect the heat dissipation outlet to form a spiral air flow trajectory through curve design to ensure that the air flow covers the edge high-temperature area. This path design can effectively guide the air flow and enhance heat transfer.

[0075] For example, when using the Lagrange interpolation method to smooth the path in the circuit board integration area, optimization can be performed for areas with large airflow speed fluctuations. Assuming that the speed in a certain section of the initial path changes from 0.2 meters per second to 0.5 meters per second, after adjustment by the interpolation method, the speed stabilizes at around 0.3 meters per second. This smoothing process helps to reduce airflow disturbances and ensure the stability of the circuit board area.

[0076] For example, when simulating vortex flow with Reynolds number greater than 5000 around the power transformer area using ANSYS Fluent, the initial vortex parameters can be set first. Assuming that the simulation finds that the coverage rate is only 85%, which is lower than the standard of 90%, by adjusting the vortex angular velocity to 120 radians per second, the coverage rate is increased to 92%. This optimization method can enhance the coverage effect of airflow on high-temperature areas.

[0077] For example, when using the entropy weight method to calculate the path weight and increasing the laminar flow proportion in areas with intensity less than 100 Pa, the airflow intensity of the area can be adjusted. Assuming that the airflow intensity of a certain path is only 80 Pa, by increasing the laminar flow proportion, the intensity is increased to 105 Pa. This method can balance the airflow distribution and improve the rationality of the overall configuration.

[0078] For example, when storing the spiral airflow curvature parameters, laminar flow velocity parameters, and vortex angular velocity parameters in JSON format, the coordinate mapping relationship can be recorded together. Assuming that the spiral path curvature radius is 6 mm, the laminar flow velocity is 0.3 meters per second, and the vortex angular velocity is 120 radians per second, by formatting storage, a complete airflow configuration file is formed. This storage method facilitates subsequent calling and adjustment, improving design efficiency.

[0079] Step S105, fifth step: Obtain the airflow type data of each region from the airflow type configuration parameter set, adjust the fan speed according to the heat dissipation demand, and generate an initial airflow guidance scheme set for each region.

[0080] The airflow type and region data are extracted from the parameter set, the K-means clustering algorithm is used to divide the region into laminar flow region, turbulent flow region and transition region according to the flow rate and vortex characteristics, and the classification result marked with three-dimensional coordinates is output. If the measured flow rate is lower than the threshold value, the speed setting value is increased by linear interpolation method, and a primary configuration table containing the speed is generated. During the operation of the primary configuration table, the dynamic flow rate data of each region are collected through the pressure sensor array. When the sampling value of a certain region exceeds the flow rate range of the classification result in the region by ± 15% for 5 times in succession, it is marked as a deviation state. For the deviated region, the speed is increased by 10% according to the speed-heat dissipation coefficient table, and the corrected parameter set is output. The corrected parameter set is reconstructed into an N×4-dimensional configuration matrix according to the region number, and the matrix array is region ID, speed value and check flag. The flow rate mean square deviation is extracted from the configuration matrix as a key indicator, and when any indicator exceeds the preset threshold value, the PID control algorithm is called to recalculate the parameters, and the final configuration matrix is generated.

[0081] The final configuration matrix is converted into Modbus-RTU protocol instructions, which contain region address code and speed frequency value. The instructions are sent to the fan controller at a period of 50 ms to complete the airflow dredging scheme execution.

[0082] For example, when extracting airflow type and region data from the parameter set, the data can be preliminarily arranged first to identify the flow rate and vortex characteristics of different regions. Assuming that the flow rate data of the heating plate edge region in an internal heat dissipation system of a rice cooker shows high volatility, while the flow rate of the circuit board region is relatively stable, data classification can provide a basis for subsequent clustering. This way helps to clarify the airflow characteristics of each region.

[0083] For example, when dividing the laminar flow region, turbulent flow region and transition region using the K-means clustering algorithm, multi-dimensional analysis can be performed based on flow rate and vortex characteristics.

[0084] In one possible implementation, assuming that the flow rate of the heating plate edge region is 0.6 meters per second and the vortex characteristics are significant, it is divided into a turbulent flow region; while the flow rate of the circuit board region is 0.2 meters per second and the fluctuation is small, it is classified as a laminar flow region. This classification provides a clear basis for subsequent parameter adjustment.

[0085] For example, when adjusting the speed setting value in combination with the speed-heat dissipation coefficient table, if the flow rate of a certain region is lower than the threshold value, it can be optimized by linear interpolation method. Assuming that the flow rate threshold of the circuit board region is 0.25 meters per second, and the measured value is only 0.18 meters per second, the speed can be increased from 800 revolutions per minute to 900 revolutions per minute through the reference table. This adjustment method can effectively match the heat dissipation demand.

[0086] For example, by collecting dynamic flow rate data with a pressure sensor array and identifying deviation states, a continuous sampling rule can be set. Suppose the flow rate range in a certain area is 0.5 to 0.7 meters per second, but the value of 5 consecutive samples exceeds 0.8 meters per second, it is marked as a deviation state. This real-time monitoring mechanism helps to find problem areas in time.

[0087] For example, when adjusting the blade speed for the deviation area, a small step adjustment strategy can be used. Suppose the speed of a turbulent flow area is increased by 10%, from 1000 rpm to 1100 rpm. This gradual adjustment can avoid drastic fluctuations in the system.

[0088] For example, when reconstructing the configuration matrix and extracting key indicators, attention can be paid to the flow rate variance. Suppose the flow rate variance in a certain area exceeds the preset value, indicating uneven airflow distribution, which can trigger further optimization. This indicator monitoring method helps to maintain system stability.

[0089] For example, when calling the PID control algorithm to recalculate parameters, dynamic adjustment can be based on real-time feedback data.

[0090] For example, when converting the final configuration matrix to Modbus-RTU protocol instructions, ensure that the data format meets the controller's requirements. Suppose the instruction contains area address code and speed frequency value, and is sent to the fan controller at a 50 millisecond cycle. This communication method ensures the timeliness of instruction execution.

[0091] Step S106, the sixth step: Obtain fan operating parameters from the initial airflow guidance scheme set, combine real-time data collected by wind speed sensors, verify whether the airflow in each area meets the preset type and speed requirements, and generate an actual airflow speed value set.

[0092] Extract the initial speed of each area fan from the airflow guidance scheme set to generate an initial parameter set. Bind the area number with the initial parameter set to establish a set of area parameter reference values. Collect real-time wind speed data in each area through wind speed sensors, and match the corresponding reference values according to the area number. Calculate the absolute difference between the real-time wind speed and the reference value, if the difference exceeds the preset threshold of 5%, add the area number to the deviation area identifier set. According to the deviation area identifier set, query the airflow type code of the corresponding area in the fluid mechanics simulation library, and extract the standard speed range under this code. Use linear interpolation to calculate the adjustment value within the standard speed range to generate an adjustment parameter set. Send the adjustment parameter set to the fan controller and receive the status code returned by the controller. If the status code is successful, restart the real-time wind speed data collection after a 5-second delay. If the difference between the newly collected data and the adjustment parameter set still exceeds the threshold, extract the mean and variance of the last 10 wind speed data as a feature vector, input it into the trained radial basis function support vector machine model, output the final speed configuration value and update it to the controller.

[0093] For example, when extracting the initial rotation speed of each regional fan from the airflow guidance scheme set, the data can be structured and sorted first to ensure that the parameters correspond one-to-one with the regions. Suppose the initial rotation speed of the heating plate region in an internal heat dissipation system of a rice cooker is set to 1000 rpm, and the rotation speed of the control panel region is 600 rpm. This classified storage facilitates subsequent quick calling.

[0094] For example, when binding the region number with the initial parameter set, it can be realized by establishing a mapping table. Suppose the heating plate region number is A1, and the corresponding rotation speed reference value is 1000 rpm, and the control panel region number is A2, and the corresponding value is 600 rpm. This binding method provides a basis for subsequent comparison.

[0095] For example, when collecting real-time wind speed data through a wind speed sensor, a fixed sampling frequency can be set. Suppose data is collected every 2 seconds, the actual wind speed of the heating plate region is 1.2 m / s, and the reference value is 1.0 m / s, the difference is 20%, which exceeds the threshold of 5%, then A1 is added to the deviation region identification set.

[0096] For example, when querying the fluid mechanics simulation library according to the deviation region identification set, the standard parameters can be matched based on the airflow type code. Suppose the airflow type code of A1 region is T1, and the corresponding standard rotation speed range is 900 to 1200 rpm. This query method helps to quickly locate the adjustment range.

[0097] For example, when using linear interpolation method to calculate the adjustment value, it can be estimated according to the proportion of the measured wind speed and the standard range. Suppose the measured wind speed of A1 region is too high, and the adjusted rotation speed is 1100 rpm after interpolation calculation. This method can smoothly transition the parameter change.

[0098] For example, when the adjustment parameter set is issued to the fan controller and the status code is received, a timeout mechanism can be set. Suppose the success status code is received within 1 second after issuance, then the data is re-collected after 5 seconds of delay, and if the timeout is recorded, the exception is recorded. This way ensures the reliability of the instruction execution.

[0099] For example, when the newly collected data still exceeds the threshold, the mean and variance of the historical wind speed data are extracted as feature vectors. Suppose the mean of the past 10 wind speed data of A1 region is 1.15 m / s, and the variance is 0.05. This feature extraction provides diversified basis for subsequent model input.

[0100] For example, when using a radial basis function support vector machine model to output the final speed configuration value, predictions can be made based on historical feature vectors. Assuming that the model outputs a final speed of 1080 rpm in the A1 region and updates to the controller, this approach can more accurately adapt to complex airflow environments.

[0101] For example, throughout the entire process, considering the particularity of the electric rice cooker heat dissipation system, adjustments can be made from core parameters to historical data analysis and then to model prediction, forming a progressive logic. Assuming that the heating plate region is prone to airflow fluctuations due to high temperatures, through the above multi-step adjustments, the wind speed distribution can be effectively stabilized, and the heat dissipation efficiency can be improved.

[0102] Step S107, seventh step: Obtain wind speed data from the actual airflow speed value set, and combine the temperature sensor to monitor the temperature change of the high-temperature region in real time to determine whether it has dropped to the preset target value T2. If it does not meet the standard, dynamically adjust the fan blade speed to generate an updated airflow adjustment parameter set.

[0103] Obtain real-time temperature T from the temperature sensor current from the temperature sensor target Compare. If T current is greater than T target , extract the 10-second average wind speed V avg recorded by the wind speed sensor corresponding to the region. According to V avg , query the pre-established two-dimensional lookup table to obtain the initial blade speed range N range . The lookup table key value is the wind speed interval, and the value is the experimentally calibrated speed. Use N range as a constraint condition to match the 5 groups of historical records closest to the current temperature deviation ΔT in the adjustment parameter library, and output the speed adjustment value ΔN. Add ΔN to the current running parameters to generate an adjustment instruction sent to the fan controller. After the controller returns the execution status code, start a 30-second timed collection period. Collect temperature T new for 3 consecutive times, and calculate the slope k. If k does not reach the preset cooling rate R min , extract the temperature sequence T hist of the region in the past 1 hour from the database

[0104] and the wind speed sequence V hist . Calculate the mean T hist and variance T mean of T var , and the range V hist of V diff , and input the feature vector into the SVC model of scikit-learn. The model outputs the optimized ΔN opt , and updates the controller parameters. Continue to monitor until the adjacent two T newThe fluctuation is less than 0.5 degrees and the wind speed fluctuation is less than 0.2m / s, and the final stable parameter N is recorded. final .

[0105] For example, in a rice cooker cooling system, real-time data collection from high-temperature areas by temperature sensors is the core of the entire control process. Temperature sensors are usually deployed in key areas such as the heating plate and control panel, and the collection frequency can be set to once per second to ensure real-time data. Assuming the real-time temperature of the heating plate area is T current is 85 degrees, and the preset threshold T target The temperature is 80 degrees Celsius, and any deviation exceeding 5 degrees triggers subsequent control. Compared with traditional timed detection, this high-frequency acquisition can more accurately capture temperature fluctuations, providing a basis for rapid response.

[0106] Specifically, based on the 10-second average wind speed V avg The design of a two-dimensional lookup table is the key to parameter matching. The lookup table can be calibrated experimentally to match wind speed ranges with blade speed combinations. Assuming V avg is 1.5 m / s, and the speed range N is obtained by query. range The speed is 800 to 1200 rpm. This pre-calibration method simplifies real-time calculations and improves matching efficiency.

[0107] In one embodiment, historical records close to the temperature deviation ΔT are matched from the adjustment parameter library, taking into account regional characteristics. Assuming ΔT is 5 degrees, five sets of historical records are extracted from the library, with speed adjustment values ​​ΔN ranging from 50 to 150 rpm. Records that match the current airflow type are preferred to ensure the applicability of the adjustment parameters. This historical data-driven matching approach reduces trial and error costs.

[0108] For example, after an adjustment command is issued to a fan controller, status code feedback and a timeout mechanism are crucial. Assuming the controller returns a success status code within 0.8 seconds, a 30-second data collection cycle is initiated. If a timeout occurs, the exception is logged and the system is retried. This mechanism ensures reliable command execution and prevents system failures due to communication delays.

[0109] Specifically, the temperature T is collected three times in a row. new And calculate the slope k to determine whether the cooling rate meets the standard. Assume T new The slope k represents the temperature drop trend. If k is less than the preset rate R min , further optimization is needed. Sliding window analysis of historical temperature series T hist and wind speed sequence V hist , extract the mean T mean Such as 83.5 degrees and variance T var Such as 0.3, and the wind speed difference V diffFor example, 0.4 m / s forms a feature vector. This feature extraction provides diverse inputs for the model.

[0110] In one embodiment, the SVC model predicts the optimization parameter ΔN based on the feature vector opt Assume that the model output ΔN opt The fan speed is 100 rpm. After the update, the fan operation is more suitable for the current environment. Compared with traditional fixed parameter adjustment, the model prediction can better adapt to dynamic airflow changes.

[0111] For example, continue monitoring until the temperature and wind speed fluctuations stabilize and record the final parameter N final Assume N final The speed is 1100 rpm. This closed-loop control ensures the long-term stability of the system. The entire process, from real-time data collection to model optimization, forms a progressive logic to ensure the efficient operation of the cooling system.

[0112] Step S108, the eighth step: obtain the adjusted fan operation data from the updated airflow adjustment parameter set, reconfigure the airflow diversion plan, continuously monitor the temperature data, confirm whether the temperature of each high-temperature area has returned to the preset normal range T3, and generate the final heat dissipation effect evaluation data set.

[0113] Obtain fan operation data from the adjusted airflow adjustment parameter set, and extract the current fan speed N by parsing the parameter set. current , forming the running state vector S vector According to S vector Query the pre-established airflow distribution model to obtain the initial airflow distribution strategy P initial According to the initial airflow distribution strategy P initial , combined with the geometric distribution data of the high temperature area, calculate the airflow coverage rate C of each area rate If C rate Below the preset threshold C min , the airflow path is adjusted through the linear programming algorithm to generate the optimized airflow distribution strategy P opt . Using the optimized airflow distribution strategy P opt , generates fan control instructions and sends them to the fan controller. After the controller executes the instructions, it returns the execution status code M status , trigger the temperature sensor to start the continuous acquisition mode and obtain the real-time temperature sequence T in the high temperature area seq From the real-time temperature series T seq Extract the temperature value T of each high temperature area area , calculate the deviation ΔT from the preset normal range T3 area If ΔT area If it is greater than 0, the k-means algorithm is used to cluster Tseq, dividing the high temperature abnormal area and the normal area, and obtaining the regional classification result Rclass According to the region classification result R class , for the high-temperature abnormal region, a subset P opt of the corresponding air flow distribution strategy P sub is extracted, and the temperature change trend T hist is calculated in combination with the historical temperature sequence T trend . If T trend has not reached a stable state, the air flow intensity parameter in P sub is adjusted to generate an updated strategy P updated . Through the updated air flow distribution strategy P updated , the fan control instruction is regenerated and sent to the controller for execution. The temperature sequence T_seq_new is continuously collected, and the temperature fluctuation ΔT diff of the adjacent two times of collection is calculated. If ΔT diff is less than a preset threshold T stable , the temperature stable state is confirmed, and a heat dissipation effect evaluation data set D updated containing Tseq_new, P class and R eval is generated. Key indicators are extracted from the heat dissipation effect evaluation data set D eval , including the temperature stable time T_stable_time, the air flow coverage rate C rate and the region classification result R class , which are stored in the database to generate a final data set identifier ID dataset .

[0114] For example, in the optimization process of the electric rice cooker heat dissipation system, extracting fan operation data from the adjusted air flow adjustment parameter set is a key link. Assuming that the current fan speed is 1000 revolutions per minute, the running state vector can be mapped to the pre-established air flow distribution model. By querying the model, the initial air flow distribution strategy may show that the air flow coverage rate of the heating disc region is only 60%, which is lower than the preset threshold of 70%.

[0115] At this time, the linear programming algorithm will adjust the air flow path to preferentially increase the air flow proportion of the heating disc region, and the coverage rate will be increased to 75% after generating the optimization strategy.

[0116] For example, for the analysis of the geometric distribution data of the high-temperature region, assuming that the heating disc region is circularly distributed and the control panel region is rectangularly distributed, the calculation of the air flow coverage rate needs to consider the influence of different shapes on air flow resistance.

[0117] In one possible implementation, for the circular region, the air flow path is designed in a spiral shape to improve the coverage efficiency; and for the rectangular region, a straight line path is adopted to reduce air flow loss. Such differentiated design can better meet the actual needs.

[0118] For example, after the fan control instruction is generated and sent to the controller, if the returned execution status code shows success, the temperature sensor will start continuous collection mode. Assuming that the real-time temperature sequence collected shows that the temperature in the heating disc area is 82 degrees, which exceeds the preset normal range of 80 degrees, with a deviation of 2 degrees. Through clustering algorithm, the heating disc area is divided into high-temperature abnormal area, and the control board area is normal area. This area classification result provides basis for subsequent targeted adjustment.

[0119] For example, for the air flow distribution strategy adjustment of the high-temperature abnormal area, assuming that the historical temperature sequence shows that the temperature change trend is slowly rising, indicating that the current air flow intensity is insufficient. At this time, the subset of the optimization strategy is extracted, and the air flow intensity parameter is increased, for example, the wind speed is increased from 1.2 meters / second to 1.5 meters / second, and the updated strategy is generated to reissue the instruction. This dynamic adjustment can respond to temperature abnormalities more quickly.

[0120] For example, after continuously collecting the temperature sequence, if the temperature fluctuation of adjacent two times is less than 0.5 degrees, it is confirmed that the temperature has stabilized. The heat dissipation effect evaluation data set containing the temperature sequence, the updated strategy and the area classification is generated, the key indicators such as temperature stabilization time of 20 minutes and air flow coverage rate of 78% are extracted, and stored to the database to generate a unique identifier. This data storage method is convenient for subsequent analysis and parameter reuse.

[0121] For example, in the database storage link, for the extraction and identification generation of key indicators, assuming that each heat dissipation effect evaluation data set is identified by time stamp and device number to ensure data traceability. This way not only facilitates management, but also provides historical reference for subsequent optimization, improving the continuity of system operation.

[0122] The embodiment of the present application also provides an intelligent temperature control and heat dissipation system based on the health degree cloud monitoring of the electric rice cooker.

[0123] Figure 4 The logical structure diagram of an intelligent temperature control and heat dissipation system based on the health degree cloud monitoring of the electric rice cooker provided by the embodiment of the present application is shown in Figure 4 The system 1 of the embodiment includes a processing unit 11, a storage unit 12 and a transceiver unit 13, the processing unit 11 is used for processing data, the storage unit 12 is used for storing data, and the transceiver unit 13 is used for transceiving data.

[0124] The processing unit 11, the storage unit 12 and the transceiver unit 13 cooperate with each other to realize the above-mentioned method. The beneficial effects of the embodiment of the present application have been described in the above-mentioned method, which will not be repeated here.

[0125] It should be noted that the information interaction, execution process, etc. between the above-mentioned devices / units are based on the same concept as the method embodiment of this application. Their specific functions and technical effects can be found in the method embodiment section and will not be repeated here.

[0126] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0127] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the process in the above-mentioned embodiment method by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, it can implement the steps of each of the above-mentioned method embodiments. The computer program includes computer program code, which can be in source code form, object code form, executable file, or some intermediate form. The computer-readable medium can at least include: any entity or device capable of carrying computer program code to the camera / terminal device, recording medium, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal, and software distribution medium. For example, a USB flash drive, mobile hard drive, magnetic disk, or optical disk. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electric carrier signals or telecommunication signals.

[0128] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0129] Those skilled in the art can understand that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be realized in electronic hardware or a combination of computer software and electronic hardware. Whether the functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0130] In the embodiments provided by the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the modules or units is merely a logical function division. There can be another division manner in actual implementation. For example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed mutual coupling or direct coupling or communication connection can be indirect coupling or communication connection through some interface, device or unit, and can be electrical, mechanical or in other forms.

[0131] The units described as separate components can or can not be physically separate, and the components shown as units can or can not be physical units, i.e. can be located in one place, or can be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the embodiments.

[0132] The above description is merely preferred embodiments of the present application and a description of the principles of the technology used. Those skilled in the art should understand that the scope of the application disclosed in the present application is not limited to the technical solutions formed by the specific combinations of the above technical features, and should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the concept of the present application. For example, the above features are replaced with the technical features disclosed in the present application (but not limited to) having similar functions to form technical solutions.

Claims

1. An intelligent temperature control and heat dissipation method based on cloud monitoring of rice cooker health, characterized in that: The method comprises: Real-time temperature data is collected around heat-prone areas and uploaded to the cloud system to generate temperature distribution data sets for each area; Filter out high-temperature areas and generate a set of high-temperature sedimentation areas; Combined with the three-dimensional structural model of the rice cooker, the optimal airflow path is calculated and a set of airflow diversion paths is generated; Obtain path data of each high-temperature area from the airflow evacuation path set and generate an airflow type configuration parameter set; Obtain the airflow type configuration parameters for each area, form a specified airflow pattern, adjust the fan speed according to the heat dissipation requirements, and generate an initial airflow diversion plan set; Obtain fan operating parameters, collect real-time data, verify whether the airflow meets the preset type and speed requirements, and generate a set of actual airflow speed values; Obtain actual wind speed data, monitor temperature changes in high-temperature areas in real time, and determine whether it has dropped to the preset target value T2; The adjusted fan operation data is obtained from the airflow adjustment parameter set, the airflow diversion plan is reconfigured, and the temperature data is continuously monitored. The above process is repeated until the temperature in the high-temperature area drops to T2 to generate the final heat dissipation effect evaluation data set.

2. The method according to claim 1, characterized in that Further including: The heat-prone area includes at least one of the edge of the heating plate, the circuit board integration area and the power transformer; When the temperature in a certain area exceeds the corresponding threshold, its temperature mean μ and standard deviation σ within a certain period of time are calculated. If the current temperature exceeds the range of μ±3σ, it is marked as abnormal; Temperature prediction is performed for abnormal areas, and the predicted value for the next 5 minutes is output. PID control parameters are generated based on the predicted value and sent to the rice cooker control system to adjust the heating plate power. The time ID and work order are recorded in the cloud.

3. The method according to claim 1, characterized in that Also includes: Extract temperature data of each area; Compare with the hourly average ambient temperature of the environmental monitoring station to generate comparison results; Mark the areas in the results that exceed the preset threshold and generate a list of high-temperature areas; The groups of areas are sorted in descending order by the duration of exceeding the threshold, and the group with the longest timeout is automatically marked as a high-risk area.

4. The method according to claim 1, wherein Also includes: Extract coordinate data of points where the temperature exceeds a preset value from the high-temperature area and the siltation set. Use 3D analysis software to map the temperature coordinates to a 3D surface to generate a spatial distribution framework. Divide the space between the high-temperature area and the heat dissipation outlet into a grid and output a grid cell set. Based on the grid cell set, ten airflow paths are calculated and generated.

5. The method according to claim 1, wherein Also includes: The heat-prone areas include the edge of the heating plate, the circuit board integration area and the power transformer area; Extract the coordinates of the area with temperature higher than 150 degrees Celsius as the input of high temperature area path data; Divide the airflow into long and short path groups, and generate a preliminary airflow distribution plan based on the heat dissipation coefficient; Generate a spiral path for the edge area of ​​the heating plate and output flow field distribution parameters including flow velocity and pressure; Extract the data of each area from the parameter set, calculate the path weight, optimize the flow field distribution, and generate the final airflow configuration.

6. The method according to claim 1, characterized in that Also includes: Extracting airflow type and area data from the parameter set; Combined with the preset speed-heat dissipation coefficient comparison table, the regional flow rate is compared with the heat dissipation requirement threshold; If the measured flow rate is lower than the threshold, the speed setting value is increased and a primary speed configuration table is generated; Collect dynamic flow velocity data in each area; When continuous sampling in a certain area exceeds the velocity range of the area by ±15%, it is marked as a deviation area; For the deviation area, increase the rotation speed according to the comparison table and output the correction parameter set; Reconstruct the correction parameter set into a configuration matrix, where the columns are region ID, speed value, and check flag; The mean square error of the flow rate is extracted from the configuration matrix. When any indicator exceeds the preset threshold, the parameters are recalculated to generate the final configuration matrix and converted into protocol instructions, which are periodically sent to the fan controller to complete the execution of the airflow diversion plan.

7. The method according to claim 1, characterized in that Also includes: Extract the initial speed of fans in each area from the airflow diversion solution set to generate an initial parameter set; Bind the regional number to the initial parameter set to establish a regional parameter benchmark value set; Collect real-time wind speed data for each area and match the corresponding benchmark value based on the area number; The absolute difference between the real-time wind speed and the reference value is calculated. If the difference exceeds a preset threshold of 5%, the area is added to the deviation area identification set.

8. The method according to claim 1, characterized in that Also includes: Get the real-time temperature T in the high temperature area current , and the preset threshold T target Compare; Extract the 10-second average wind speed V recorded by the wind speed sensor in the area avg ; Query the pre-established two-dimensional lookup table to obtain the initial blade speed range N range ; Match the historical record closest to the current temperature deviation ΔT in the adjustment parameter library and output the speed adjustment value ΔN; Add ΔN to the current operating parameters to generate an adjustment command and send it to the fan controller; Collect temperature T for 3 consecutive times new , calculate the slope k; Determine whether K reaches the preset cooling rate R min ; If so, no readjustment is necessary; If not, readjust through the algorithm model.

9. The method according to claim 1, characterized in that Also includes: If the temperature change in the high-temperature area does not drop to the preset target value T2, the fan blade speed is dynamically adjusted to generate an updated airflow adjustment parameter set; The adjusted fan operation data is obtained from the updated airflow adjustment parameter set, the airflow diversion plan is reconfigured, the temperature data is continuously monitored, and it is confirmed whether the temperature of each high-temperature area has returned to the preset normal range T3 to generate the final heat dissipation effect evaluation data set.

10. An intelligent temperature control and heat dissipation system based on cloud monitoring of rice cooker health, characterized in that: Comprising means for performing the method according to any one of claims 1 to 9.

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