Portable blowing comb multi-point temperature monitoring method and system

Abstract

The invention discloses a portable blowing comb multi-point temperature monitoring method and system, and relates to the technical field of temperature measurement and temperature distribution monitoring, and the method comprises the steps: arranging temperature sensing units at different spatial positions of a comb tooth structure, and collecting temperature data, thereby obtaining a multi-point temperature data set; the temperature gradient of adjacent spatial positions is calculated based on the multi-point temperature data set, the change process of the temperature gradient is tracked to obtain gradient evolution information, the temperature propagation incidence relation is identified according to the gradient evolution information, the temperature propagation source position is determined, the temperature anomaly diffusion direction is predicted, and the potential affected position is identified. And establishing a temperature distribution matrix to generate a temperature field evolution curve, and finally outputting a time sequence change trend of a heat accumulation degree and a heat diffusion degree as a temperature monitoring result. According to the invention, dynamic monitoring and abnormal early warning of the comb tooth structure temperature field can be realized, and the precision and reliability of temperature monitoring are improved.

Description

Technical Field

[0001] This invention relates to the field of temperature measurement and temperature distribution monitoring technology, specifically to a convenient multi-point temperature monitoring method and system for a blower comb. Background Technology

[0002] In existing hair dryers and similar heated hair care devices, temperature monitoring primarily relies on single-point or limited temperature measurements. Temperature sensors are typically placed only near the heating element or at the air outlet for temperature control or overheat protection. This monitoring method only reflects localized or average temperature conditions and cannot accurately characterize the overall temperature distribution of the comb's structure. When abnormal temperature rises in a localized area, single-point monitoring often fails to detect changes in the temperature gradient and its spatial propagation, potentially leading to localized overheating, hair damage, or even safety risks.

[0003] On the other hand, with the development of intelligent sensing and data processing technologies, multi-point temperature measurement and temperature distribution analysis are gradually being applied to fields such as industrial inspection and heat dissipation analysis of electronic devices. However, existing technologies typically focus on real-time temperature control or alarm functions, paying less attention to the propagation correlation of temperature between different spatial locations, and failing to predict and warn of potential temperature anomalies based on the evolution trend of temperature gradients. This limits the equipment's ability to sense the state of heat accumulation and diffusion under complex operating conditions, making it difficult to provide effective data support for refined temperature control and safety monitoring.

[0004] Based on the above situation, it is necessary to provide a method that can perform multi-point temperature measurement on the comb tooth structure of a blower, and comprehensively monitor the heat accumulation and diffusion state through temperature gradient analysis, propagation correlation identification, and temperature field evolution modeling, so as to improve the accuracy and reliability of blower temperature monitoring. Summary of the Invention

[0005] The purpose of this invention is to provide a convenient method and system for multi-point temperature monitoring of a blower comb, aiming to solve at least one of the technical problems existing in the prior art.

[0006] The technical solution of this invention is: a convenient method for multi-point temperature monitoring of a hair dryer comb, comprising the following steps: By using temperature sensing units located at different spatial positions of the comb structure, temperature data from multiple spatial positions of the comb structure during operation are collected to obtain a multi-point temperature dataset. The temperature gradient between adjacent spatial locations is calculated based on a multi-point temperature dataset, and the change process of the temperature gradient over time is tracked to obtain gradient evolution information. Based on gradient evolution information, spatial locations with temperature gradient propagation associations are identified, temperature propagation associations between spatial locations are established, the locations of temperature propagation sources are identified, and the associated propagation paths are obtained. Based on the correlation propagation path, the evolution direction of the temperature anomaly spread is predicted, the potentially affected spatial locations are identified, and the early warning location information is obtained; Based on the early warning location information, a temperature distribution matrix is ​​established between the temperature propagation source location and the potentially affected spatial location. The temperature change rate of adjacent spatial locations in the temperature distribution matrix is ​​calculated, and a temperature field evolution curve is generated. The degree of heat accumulation at the source of temperature propagation and the degree of heat diffusion at potentially affected spatial locations are calculated based on the temperature field evolution curve. The temporal variation trend of the degree of heat accumulation and the degree of heat diffusion is output as the temperature monitoring result.

[0007] Temperature data from multiple spatial locations of the comb structure during operation are collected by temperature sensing units positioned at different spatial locations within the comb structure, resulting in a multi-point temperature dataset including: Temperature sensing units are set at different spatial positions of the comb structure according to the heat transfer law. The temperature sensing unit includes a main temperature sensing unit and a backup temperature sensing unit. The main temperature sensing unit collects temperature data of the comb structure in its working state to generate an initial temperature data sequence. The initial temperature data sequence is compared with the historical temperature data sequence collected within the preset time window. When the temperature data collected by the main temperature sensing unit changes abruptly, the system automatically switches to the backup temperature sensing unit to continue collecting the temperature data of the comb structure in working condition and records the corresponding spatial location as the abnormal monitoring location. Based on the temperature change rate at the abnormal monitoring location, the sampling time interval between adjacent spatial locations on the comb structure is calculated to generate sampling control information; According to the sampling control information, the temperature data of the comb structure under working conditions are collected from the temperature sensing unit, the temperature difference between adjacent spatial positions is calculated, and the temperature gradient distribution information is obtained. The direction of heat transfer in the comb structure is determined based on the temperature gradient distribution information, and a multi-point temperature dataset is generated by combining the sampling control information.

[0008] Based on a multi-point temperature dataset, the temperature gradient between adjacent spatial locations is calculated, and the change of the temperature gradient over time is tracked to obtain gradient evolution information, including: The temperature change rate is calculated for adjacent sampling times in a multi-point temperature dataset. The segmentation points are determined based on the fluctuation amplitude of the temperature change rate, and a time-series segmented temperature dataset is generated. Based on the time-series segmented temperature dataset, the geometric distance between adjacent spatial locations is used as a weighting factor to calculate the heat loss and heat absorption at spatial locations, generating a set of heat transfer parameters. The temperature gradient values ​​between spatial locations are calculated based on the heat transfer parameter set. The temperature gradient values ​​are arranged in time sequence, the heat migration rate is calculated, and a temperature gradient prediction function is constructed by combining the heat accumulation effect. The temperature gradient prediction function is used to predict the temperature gradient at each spatial location in the temperature gradient distribution set, generating a predicted temperature gradient value. The temperature gradient deviation value is obtained by comparing it with the measured temperature gradient value. The heat transfer parameter set is corrected and updated based on the temperature gradient deviation value. The corrected heat transfer parameter set is then substituted into the temperature gradient prediction function to obtain gradient evolution information.

[0009] Based on gradient evolution information, spatial locations with temperature gradient propagation associations are identified, temperature propagation relationships between spatial locations are established, the locations of temperature propagation sources are identified, and the associated propagation paths are obtained, including: The gradient evolution information is segmented according to a preset time window. The temperature change rate of the spatial location within each time period is calculated. The fluctuation period and fluctuation amplitude of the temperature change rate are extracted. The spatial locations are grouped according to the fluctuation period and fluctuation amplitude to generate temperature association groups. The temperature propagation delay time between the spatial locations is calculated based on the temperature association grouping. The spatial locations within the temperature association grouping are divided into temperature propagation sequences according to the temperature propagation delay time. The temperature propagation source location and the temperature propagation end location in the temperature propagation sequence are determined. Calculate the amount of temperature propagation from the source location to the end location, and construct a temperature propagation intensity matrix; Based on the distribution characteristics of temperature transfer efficiency in the temperature transfer intensity matrix, spatial location pairs with temperature transfer intensity greater than a preset intensity threshold are screened to identify temperature propagation channels. Calculate the attenuation value of temperature propagation in the temperature propagation channel, filter the target propagation channel according to the attenuation value, sort the target propagation channel according to the temperature propagation delay time, and generate associated propagation paths.

[0010] Calculating the amount of temperature propagation from the source location to the destination location, and constructing the temperature propagation intensity matrix includes: The coordinates of multiple monitoring points between the temperature propagation source and the temperature propagation end point are obtained, the real-time temperature values ​​of the multiple monitoring points are recorded, and the temperature propagation time difference is calculated based on the monitoring coordinates and the real-time temperature values. The temperature change rate between multiple monitoring points is calculated based on the temperature propagation time difference and the monitoring location coordinates, and a temperature attenuation coefficient is generated based on the temperature change rate. The temperature attenuation coefficient is accumulated according to the monitoring location coordinates to generate the temperature propagation amount from the temperature propagation source location to the temperature propagation end location. The temperature attenuation coefficient is then classified according to the attenuation efficiency based on the temperature propagation amount. The attenuation efficiency classification results of the temperature attenuation coefficient are reorganized into a matrix form to generate the temperature propagation intensity matrix.

[0011] Based on the correlation propagation path, the evolution direction of temperature anomaly spread is predicted, and potentially affected spatial locations are identified, yielding early warning location information including: Calculate the temperature gradient at the location of the temperature propagation source in the associated propagation path, obtain the amount of change and direction of the temperature gradient, and determine the evolution direction of the temperature anomaly diffusion. Based on the evolution direction and combined with the spatial distribution of the associated propagation paths, a temperature propagation rate map is generated, and the temperature anomaly diffusion rate along the evolution direction is extracted from the temperature propagation rate map. Based on the temperature anomaly diffusion rate, the surrounding spatial location is extended along the associated propagation path. The propagation time of the temperature anomaly from the temperature propagation source location to the surrounding spatial location is calculated. The propagation time is combined with the temperature anomaly diffusion rate to generate the temperature anomaly diffusion time sequence. The temperature impact value of the surrounding spatial location is calculated based on the diffusion time series of temperature anomalies and the change in temperature gradient. The surrounding spatial locations are classified according to the temperature impact value, and the surrounding spatial locations that exceed the preset impact value are identified as potentially affected spatial locations, thus obtaining early warning location information.

[0012] Based on the early warning location information, a temperature distribution matrix is ​​established between the temperature propagation source location and the potentially affected spatial locations. The temperature change rate of adjacent spatial locations in the temperature distribution matrix is ​​calculated, and temperature field evolution curves are generated, including: Collect real-time temperature data of the temperature propagation source location and the potentially affected spatial location in the early warning location information, establish the temperature correspondence between the temperature propagation source location and the potentially affected spatial location based on the real-time temperature data, and generate an initial temperature distribution matrix; Real-time temperature data at multiple time points are collected, and the initial temperature distribution matrix is ​​updated based on the temperature correspondence to obtain a temperature distribution matrix that reflects the temperature distribution state at different time points. Calculate the rate of temperature change between the location of the temperature propagation source and the potentially affected spatial location based on the temperature distribution matrix; The temperature change rate is arranged in chronological order to form a temperature change trend, and the temperature change trend is mapped to a temperature field evolution curve.

[0013] Based on the temperature field evolution curve, the degree of heat accumulation at the location of the temperature propagation source and the degree of heat diffusion at potentially affected spatial locations are calculated. The time-series variation trends of the degree of heat accumulation and the degree of heat diffusion are output as temperature monitoring results, including: Temperature temporal variation data of temperature propagation source locations and temperature distribution data of potentially affected spatial locations are extracted from the temperature field evolution curve to calculate temperature field transmission characteristics; The characteristics of temperature field transfer are decomposed into heat storage component and heat diffusion component; The degree of heat accumulation at the location of the temperature propagation source is calculated based on the time-series integration of the heat accumulation component, and the degree of heat diffusion at the potentially affected spatial location is calculated based on the time-series integration of the heat diffusion component. Calculate the changes in the degree of heat accumulation and the degree of heat diffusion over time, generate the time-series change trends of the degree of heat accumulation and the degree of heat diffusion, and output the time-series change trends as the temperature monitoring results.

[0014] This invention provides a portable multi-point temperature monitoring system for a hair dryer comb, the system comprising: The temperature acquisition module, which consists of temperature sensing units located at different spatial positions of the comb structure, is used to acquire temperature data from multiple spatial positions of the comb structure during operation, thereby obtaining a multi-point temperature dataset. The gradient calculation module is used to calculate the temperature gradient between adjacent spatial locations based on a multi-point temperature dataset, and to track the change of the temperature gradient over time to obtain gradient evolution information. The correlation analysis module is used to identify spatial locations with temperature gradient propagation correlations based on gradient evolution information, establish temperature propagation correlations between spatial locations, identify the location of temperature propagation sources, and obtain the associated propagation paths. The early warning analysis module is used to predict the evolution direction of the spread of temperature anomalies based on the associated propagation path, identify potentially affected spatial locations, and obtain early warning location information. The matrix calculation module is used to establish a temperature distribution matrix between the temperature propagation source location and the potentially affected spatial location based on the early warning location information, calculate the temperature change rate of adjacent spatial locations in the temperature distribution matrix, and generate temperature field evolution curves. The results output module is used to calculate the degree of heat accumulation at the location of the temperature propagation source and the degree of heat diffusion at the potentially affected spatial location based on the temperature field evolution curve, and output the time-series change trend of the degree of heat accumulation and the degree of heat diffusion as temperature monitoring results.

[0015] One technical solution provided in this embodiment of the invention is 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 in any of the aforementioned methods.

[0016] This invention, by placing temperature sensing units at different spatial locations within the comb structure, enables comprehensive multi-point temperature data collection and complete monitoring of the temperature field distribution. Time-series analysis based on temperature gradients accurately identifies the propagation patterns of temperature anomalies, allowing for early detection of potential temperature anomalies. Establishing temperature propagation correlations allows for rapid location of temperature anomaly sources and prediction of their diffusion trends. The establishment of a temperature distribution matrix enables quantitative analysis of the dynamic evolution of the temperature field, improving the accuracy of temperature monitoring. Calculations of heat accumulation and diffusion levels enhance the readability of temperature monitoring results, facilitating timely detection and prevention of localized overheating. The overall solution improves the accuracy, real-time performance, and reliability of air blower comb temperature monitoring, enhancing operational safety. Attached Figure Description

[0017] Figure 1 A flowchart illustrating a convenient multi-point temperature monitoring method for a hair dryer comb, as provided in an embodiment of the present invention; Figure 2 This is a flowchart of the adaptive temperature gradient prediction and parameter correction process according to an embodiment of the present invention. Figure 3 This is a schematic diagram illustrating the preventative temperature control effect based on associated paths in an embodiment of the present invention. Detailed Implementation

[0018] like Figure 1 As shown, Figure 1 A flowchart of a convenient multi-point temperature monitoring method for a hair dryer comb provided by an embodiment of the present invention, the method comprising the following steps: By using temperature sensing units located at different spatial positions of the comb structure, temperature data from multiple spatial positions of the comb structure during operation are collected to obtain a multi-point temperature dataset. The temperature gradient between adjacent spatial locations is calculated based on a multi-point temperature dataset, and the change process of the temperature gradient over time is tracked to obtain gradient evolution information. Based on gradient evolution information, spatial locations with temperature gradient propagation associations are identified, temperature propagation associations between spatial locations are established, the locations of temperature propagation sources are identified, and the associated propagation paths are obtained. Based on the correlation propagation path, the evolution direction of the temperature anomaly spread is predicted, the potentially affected spatial locations are identified, and the early warning location information is obtained; Based on the early warning location information, a temperature distribution matrix is ​​established between the temperature propagation source location and the potentially affected spatial location. The temperature change rate of adjacent spatial locations in the temperature distribution matrix is ​​calculated, and a temperature field evolution curve is generated. The degree of heat accumulation at the source of temperature propagation and the degree of heat diffusion at potentially affected spatial locations are calculated based on the temperature field evolution curve. The temporal variation trend of the degree of heat accumulation and the degree of heat diffusion is output as the temperature monitoring result.

[0019] Temperature data from multiple spatial locations of the comb structure during operation are collected by temperature sensing units positioned at different spatial locations within the comb structure, resulting in a multi-point temperature dataset including: Temperature sensing units are set at different spatial positions of the comb structure according to the heat transfer law. The temperature sensing unit includes a main temperature sensing unit and a backup temperature sensing unit. The main temperature sensing unit collects temperature data of the comb structure in its working state to generate an initial temperature data sequence. The initial temperature data sequence is compared with the historical temperature data sequence collected within the preset time window. When the temperature data collected by the main temperature sensing unit changes abruptly, the system automatically switches to the backup temperature sensing unit to continue collecting the temperature data of the comb structure in working condition and records the corresponding spatial location as the abnormal monitoring location. Based on the temperature change rate at the abnormal monitoring location, the sampling time interval between adjacent spatial locations on the comb structure is calculated to generate sampling control information; According to the sampling control information, the temperature data of the comb structure under working conditions are collected from the temperature sensing unit, the temperature difference between adjacent spatial positions is calculated, and the temperature gradient distribution information is obtained. The direction of heat transfer in the comb structure is determined based on the temperature gradient distribution information, and a multi-point temperature dataset is generated by combining the sampling control information.

[0020] First, temperature sensing units are installed on the comb teeth structure of the air blower according to the heat transfer law. The temperature sensing units include a main temperature sensing unit and a backup temperature sensing unit, both installed side-by-side at the same monitoring location to ensure reliable data acquisition. The temperature sensing units use miniature thermocouple sensors with a measurement range of -50℃ to 200℃, meeting the monitoring requirements for rapid temperature changes in the air blower.

[0021] The arrangement of temperature sensing units on the comb structure follows the laws of heat transfer, with monitoring points typically placed near the heat source, at the root of the comb teeth, in the middle of the comb teeth, and at the tips of the comb teeth. Taking a portable hair dryer with 20 comb teeth as an example, two temperature sensing units are placed near the heat source, four at the root of the comb teeth, six in the middle of the comb teeth, and four at the tips of the comb teeth, for a total of 16 monitoring points, covering the key temperature change areas of the comb structure.

[0022] When the air blower is in operation, the main temperature sensing unit continuously collects temperature data from each monitoring point, generating an initial temperature data sequence. The sampling frequency is set to 10 times / second to ensure accurate capture of rapid temperature changes. The collected temperature data is converted into digital signals by an analog-to-digital converter and then transmitted to the data processing unit. The data processing unit is equipped with a high-speed cache, capable of storing nearly 30 minutes of historical temperature data, forming a continuously updated historical temperature data sequence.

[0023] The data processing unit compares the initial temperature data sequence with historical temperature data sequences within a preset time window in real time. The preset time window is typically set to 5 seconds; within this time range, normal temperature fluctuations should not exceed 5°C. When a sudden change is detected in the data from a primary temperature sensor unit—that is, a temperature change exceeding 10°C within 100ms, or a temperature change trend in three consecutive samples significantly deviating from historical data—it is considered an anomaly for that sensor unit. At this point, the system automatically switches to the backup temperature sensor unit at the corresponding location to continue data acquisition, and marks that location as the anomaly monitoring location.

[0024] Based on the temperature change rate at the abnormal monitoring location, the sampling time interval between adjacent spatial locations on the comb structure is calculated. The temperature change rate is the ratio of the temperature difference between two consecutive samples to the sampling time interval. When the temperature change rate exceeds 2℃ / s, the sampling time interval between adjacent spatial locations is halved; when the temperature change rate is below 0.5℃ / s, the sampling time interval is doubled, but not exceeding 200ms, to balance the real-time performance of data acquisition and resource consumption. Sampling control information, including sampling frequency adjustment instructions for each monitoring point, is generated based on the calculation results.

[0025] Temperature data of the comb structure under operating conditions is collected from the temperature sensing unit according to the sampling control information. For abnormal monitoring locations, the sampling frequency is increased to 20 times / s; for areas with stable temperature changes, the sampling frequency can be reduced to 5 times / s. The collected temperature data is used to calculate the temperature difference between adjacent spatial locations, thereby obtaining temperature gradient distribution information. The temperature gradient calculation takes into account the actual distance between spatial locations and is usually expressed as the temperature change per unit distance.

[0026] The direction of heat transfer in the comb structure is determined based on the temperature gradient distribution information. The heat transfer direction is consistent with the temperature gradient direction, i.e., from the high-temperature region to the low-temperature region. By analyzing the temperature gradient change trends at multiple adjacent monitoring points, a heat conduction path diagram of the comb structure can be constructed, visually displaying the main channels and directions of heat transfer.

[0027] By combining sampling control information and temperature gradient distribution information, a multi-point temperature dataset is generated. This dataset includes the spatial coordinates of each monitoring point, temperature values, sampling timestamps, temperature change rates, and temperature gradients between adjacent points. The dataset uses a structured storage format for easy subsequent analysis and processing. In practical applications, when the temperature in a certain area exceeds the safety threshold (usually 70℃), an overheat protection mechanism will be triggered, suspending the heating function or reducing power output to ensure safe operation.

[0028] This invention achieves redundant backup of temperature data acquisition by setting up primary and backup temperature sensing units, thereby improving the reliability and stability of the monitoring system. By analyzing the temperature gradient distribution, it enables precise depiction of the heat transfer path, providing data support for optimizing the thermal management of hair dryers. Furthermore, by monitoring multi-point temperature changes in real time, it promptly detects potential overheating risks, improving product safety. This method can be applied not only to portable hair dryers but also extended to other electrically heated beauty and hair styling tools requiring precise temperature management, demonstrating broad application prospects.

[0029] like Figure 2 As shown, the temperature gradient between adjacent spatial locations is calculated based on a multi-point temperature dataset, and the change of the temperature gradient over time is tracked to obtain gradient evolution information, including: The temperature change rate is calculated for adjacent sampling times in a multi-point temperature dataset. The segmentation points are determined based on the fluctuation amplitude of the temperature change rate, and a time-series segmented temperature dataset is generated. Based on the time-series segmented temperature dataset, the geometric distance between adjacent spatial locations is used as a weighting factor to calculate the heat loss and heat absorption at spatial locations, generating a set of heat transfer parameters. The temperature gradient values ​​between spatial locations are calculated based on the heat transfer parameter set. The temperature gradient values ​​are arranged in time sequence, the heat migration rate is calculated, and a temperature gradient prediction function is constructed by combining the heat accumulation effect. The temperature gradient prediction function is used to predict the temperature gradient at each spatial location in the temperature gradient distribution set, generating a predicted temperature gradient value. The temperature gradient deviation value is obtained by comparing it with the measured temperature gradient value. The heat transfer parameter set is corrected and updated based on the temperature gradient deviation value. The corrected heat transfer parameter set is then substituted into the temperature gradient prediction function to obtain gradient evolution information.

[0030] When processing multi-point temperature datasets, the first step is to calculate the rate of temperature change between adjacent sampling times. Taking a portable hair dryer as an example, the temperature data from its 16 monitoring points under continuous operation can be processed to obtain the rate of temperature change curve for each monitoring point. The rate of temperature change curve typically exhibits phased changes, requiring identification of the boundary points between these phases. Segmentation points are determined by setting a fluctuation amplitude threshold. When the fluctuation amplitude of the rate of temperature change exceeds a preset threshold, that moment is marked as a segmentation point. This threshold is typically set to 30% of the average rate of temperature change. For example, at a certain monitoring point, when a sudden change in the rate of temperature change is detected from 0.8℃ / s to 2.4℃ / s, the fluctuation amplitude is 200%, far exceeding the preset threshold. At this point, the corresponding sampling time is marked as a segmentation point. The original temperature dataset is then divided into multiple time periods according to these segmentation points, forming a time-series segmented temperature dataset.

[0031] Based on a time-series segmented temperature dataset, the heat loss and heat absorption at spatial locations are calculated, taking into account the geometric distance between adjacent locations as a weighting factor. The actual physical distance between each monitoring point is measured, and the normalized distance values ​​are used as weighting factors. In a portable hair dryer comb, the distance between monitoring points at the root and middle of the comb teeth is approximately 15 mm, and the distance between the middle and tip of the comb teeth is approximately 20 mm. Closer distances result in higher heat transfer efficiency and larger weighting factors. Combining the temperature change rate, spatial distance weighting factors, and heat capacity parameters at each monitoring point, the heat loss and heat absorption at each monitoring point are calculated. The heat capacity parameters are determined based on the comb tooth material properties. The heat loss and heat absorption calculated above constitute a heat transfer parameter set, reflecting the heat flow characteristics between spatial locations during the operation of the hair dryer comb.

[0032] Temperature gradient values ​​between spatial locations are calculated based on a set of heat transfer parameters, by dividing the temperature difference between adjacent monitoring points by the actual physical distance. Arranging the temperature gradient values ​​chronologically allows observation of the temperature gradient's trend over time. The heat migration rate is calculated by analyzing the rate of temperature gradient change. The heat migration rate reflects the speed of heat transfer within the comb structure. Simultaneously, the heat accumulation effect—the impact of heat accumulation at a specific spatial location on subsequent temperature changes—is considered. The heat accumulation effect typically manifests as a non-linear change in the rate of temperature rise, with a slower initial stage followed by a faster rate after accumulation. Based on the above analysis, a temperature gradient prediction function is constructed. This function employs a piecewise fitting method, incorporating the temperature gradient change characteristics at different working stages to achieve accurate prediction of the temperature gradient at various parts of the comb structure.

[0033] A temperature gradient prediction function is used to predict the temperature gradient at each spatial location within the temperature gradient distribution set, generating predicted temperature gradient values. For example, temperature data within 60 seconds of the blower's activation can be selected to establish an initial prediction function, predicting the temperature gradient changes at each monitoring point over the subsequent 30 seconds. The predicted values ​​are compared with the measured temperature gradient values ​​to calculate the temperature gradient deviation. The deviation is calculated as the percentage difference between the predicted and measured values. For instance, if the predicted temperature gradient at a monitoring point is 0.8℃ / mm and the measured value is 0.75℃ / mm, the deviation is 6.67%.

[0034] The heat transfer parameter set is corrected and updated based on the temperature gradient deviation. When the deviation exceeds a preset threshold, the relevant parameters in the heat transfer parameter set are adjusted according to the direction of the deviation. The preset threshold is usually set to 10%. Parameter adjustment follows a progressive principle, i.e., small adjustments made multiple times to avoid oscillations caused by over-correction. Adjusted heat transfer parameters include heat dissipation coefficient, heat absorption rate, and conduction efficiency. The corrected heat transfer parameter set is substituted into the temperature gradient prediction function to recalculate the predicted temperature gradient value. This value is compared with the measured value. If the deviation decreases and is below the threshold, the correction process is complete; if the deviation still exceeds the threshold, the parameters are adjusted further until the accuracy requirements are met. The final temperature gradient prediction function and the corrected heat transfer parameter set together constitute gradient evolution information, comprehensively describing the changing patterns of the temperature gradient at various spatial locations during the operation of the air blower.

[0035] In practical applications of portable hair dryers, this gradient evolution information can guide the implementation of intelligent temperature control. When it is predicted that the temperature gradient in a certain area will exceed the safety threshold, the heating power can be reduced in advance to prevent local overheating; when it is predicted that the temperature in sensitive areas of the user, such as the hair roots, will exceed the comfort threshold, the air outlet angle or temperature can be adjusted to improve user comfort.

[0036] This invention achieves dynamic tracking and prediction of the heat transfer process by accurately calculating and predicting temperature gradient evolution information, significantly improving the temperature control accuracy and safety of portable hair dryers. Through time-series segmented processing and adaptive parameter correction, it solves the problem of response lag in traditional temperature control methods, transforming temperature control from a passive response to an active prediction. It considers spatial geometric relationships and heat accumulation effects, making temperature prediction more closely aligned with actual physical processes. By constructing a temperature gradient prediction function, it achieves accurate prediction of temperature change trends in various parts of the hair dryer, providing a reliable basis for intelligent temperature control, effectively preventing the risk of localized overheating, improving product safety and user experience, optimizing energy efficiency, and extending equipment lifespan.

[0037] Based on gradient evolution information, spatial locations with temperature gradient propagation associations are identified, temperature propagation relationships between spatial locations are established, the locations of temperature propagation sources are identified, and the associated propagation paths are obtained, including: The gradient evolution information is segmented according to a preset time window. The temperature change rate of the spatial location within each time period is calculated. The fluctuation period and fluctuation amplitude of the temperature change rate are extracted. The spatial locations are grouped according to the fluctuation period and fluctuation amplitude to generate temperature association groups. The temperature propagation delay time between the spatial locations is calculated based on the temperature association grouping. The spatial locations within the temperature association grouping are divided into temperature propagation sequences according to the temperature propagation delay time. The temperature propagation source location and the temperature propagation end location in the temperature propagation sequence are determined. Calculate the amount of temperature propagation from the source location to the end location, and construct a temperature propagation intensity matrix; Based on the distribution characteristics of temperature transfer efficiency in the temperature transfer intensity matrix, spatial location pairs with temperature transfer intensity greater than a preset intensity threshold are screened to identify temperature propagation channels. Calculate the attenuation value of temperature propagation in the temperature propagation channel, filter the target propagation channel according to the attenuation value, sort the target propagation channel according to the temperature propagation delay time, and generate associated propagation paths.

[0038] First, the system is segmented according to a preset time window. The preset time window is determined based on the changing characteristics of the air blower's working state and is typically set to 10 seconds. Taking the working process of a portable air blower after it is turned on as an example, the entire monitoring process is divided into three main stages: preheating, stabilization, and cooling. Within each preset time window, the rate of temperature change at each spatial location is calculated. The rate of temperature change represents the amount of temperature change per unit time, reflecting the speed of heat transfer. For monitoring points near the heat source in the air blower, the rate of temperature change typically reaches its peak at the initial stage of operation, such as 2.5℃ / s, and then gradually decreases to a stable value, such as 0.3℃ / s. Time-domain analysis is performed on the calculated rate of temperature change to extract the fluctuation period and fluctuation amplitude. The fluctuation period refers to the time required for the rate of temperature change to complete one periodic change, and the fluctuation amplitude refers to the maximum change in the rate of temperature change within one period. In a portable air blower, monitoring points directly exposed to the heat source typically exhibit shorter fluctuation periods (e.g., 3 seconds) and larger fluctuation amplitudes (e.g., 1.2℃ / s); while monitoring points farther from the heat source exhibit longer fluctuation periods (e.g., 8 seconds) and smaller fluctuation amplitudes (e.g., 0.4℃ / s). Based on the similarity of fluctuation periods and amplitudes, spatial locations are grouped to generate temperature association groups. The similarity criterion is that monitoring points with fluctuation period differences not exceeding 20% ​​and fluctuation amplitude differences not exceeding 30% are grouped into the same association group. A typical portable air blower can usually be divided into 3 to 5 temperature association groups, such as a heat source area association group, a conduction intermediate area association group, and an end area association group.

[0039] The temperature propagation delay time between spatial locations is calculated based on temperature correlation grouping. This delay time refers to the time required for heat to transfer from one spatial location to another, determined by comparing the phase difference between the temperature change rate curves of the two locations. Characteristic points on the temperature change rate curves, such as peak points or inflection points, are identified, and the time difference between the occurrence of these characteristic points at the two spatial locations is calculated; this is the temperature propagation delay time. Taking the monitoring points in the heat source area and the intermediate conduction area of ​​a blower comb as an example, when the temperature change rate at the heat source area monitoring point reaches its peak, the temperature change rate at the intermediate conduction area monitoring point typically reaches its peak 1.5 seconds later; therefore, the temperature propagation delay time between them is 1.5 seconds. Based on the temperature propagation delay time, the spatial locations within the temperature correlation group are divided into temperature propagation sequences. The location with the shortest delay time is the start of the sequence, and the location with the longest delay time is the end of the sequence. By analyzing the start of each temperature propagation sequence, the location of the temperature propagation source is determined; by analyzing the end of each temperature propagation sequence, the location of the temperature propagation endpoint is determined. In portable blower combs, the temperature propagation source is usually a monitoring point near the heating element, and the temperature propagation endpoint is usually a monitoring point at the far end of the comb teeth.

[0040] The amount of temperature propagation from the source location to the destination location is calculated. This amount refers to the amount of heat transferred from the source location to the destination location, determined by the temperature change relationship between them. The response change at the destination location after a temperature change at the source location is observed, and the response coefficient is calculated. A larger response coefficient indicates higher heat transfer efficiency. The response coefficients between all spatial location pairs are organized into a matrix to construct a temperature propagation intensity matrix. In portable air blowers, the response coefficient between the heat source area and adjacent monitoring points is typically higher, such as 0.85; while the response coefficient between the heat source area and distant monitoring points is typically lower, such as 0.25.

[0041] Based on the distribution characteristics of temperature transfer efficiency in the temperature transfer intensity matrix, spatial location pairs with temperature transfer intensities greater than a preset intensity threshold are selected. The preset intensity threshold needs to balance coverage and screening effectiveness, and is usually set to 1.2 times the average response coefficient. The selected spatial location pairs form temperature propagation channels, which represent the dominant paths for heat transfer in the blower comb. The temperature propagation channels typically exhibit a tree-like structure, extending outward from the heat source location and covering the main structural components of the blower comb.

[0042] The attenuation value of temperature propagation in a temperature propagation channel represents the degree of heat loss during propagation, determined by comparing the temperature changes at the source and destination locations. The ratio of the temperature change at the source to the temperature change at the destination is considered the attenuation value; the closer this ratio is to 1, the smaller the attenuation and the higher the heat transfer efficiency. Target propagation channels are selected based on the attenuation value, typically choosing those with an attenuation value less than 0.4. These target propagation channels are then sorted according to their temperature propagation delay time, forming associated propagation paths. In portable hair dryers, these associated propagation paths usually start from the heat source area, extend along the comb structure towards the comb teeth, and finally reach the tooth tips. Identifying these associated propagation paths helps understand the flow patterns of heat in the hair dryer, providing a basis for optimizing heat distribution.

[0043] In practical applications, the identification results of the associated propagation path can be directly used for intelligent temperature control of the hair dryer comb. When a temperature change in the heat source area is detected, the future temperature change trend of each monitoring point can be accurately predicted based on the established propagation delay time and propagation intensity, thus achieving preventive temperature control. For example, if it is predicted that a certain area of ​​the comb teeth will exceed the safe temperature threshold in 5 seconds, the control unit can reduce the heating power in advance to prevent excessive temperature from causing discomfort or injury to the user.

[0044] like Figure 3The diagram illustrates the preventative temperature control effect based on the associated path in this embodiment, where the horizontal axis represents time (s) and the vertical axis represents the temperature at the monitoring point (°C). The solid curve represents the "predictive control" effect of this solution, the dashed line represents the existing technology (PID feedback control), and the straight line represents the 60°C safety threshold. When it is predicted that the comb area will exceed the threshold after 5 seconds, this solution intervenes in advance to adjust and smoothly stabilize the temperature within the safe range. In contrast, the existing technology, lacking the ability to predict propagation delays, only begins adjustment when the temperature approaches the threshold, causing the temperature to surge to over 65°C (overshoot), creating a safety hazard. This demonstrates the significant technical effectiveness of this solution in preventing localized overheating and improving operational safety.

[0045] This invention achieves precise identification and quantitative expression of temperature correlations between spatial locations through in-depth analysis of temperature gradient propagation characteristics. The method treats the temperature propagation process as a dynamically evolving physical process, comprehensively revealing the flow law of heat in the air blower structure through fluctuation characteristic analysis, propagation delay calculation, and propagation intensity assessment. By identifying the location of temperature propagation sources and key propagation channels, it achieves accurate depiction of heat transfer paths, effectively preventing local overheating and improving safety and comfort.

[0046] Calculating the amount of temperature propagation from the source location to the destination location, and constructing the temperature propagation intensity matrix includes: The coordinates of multiple monitoring points between the temperature propagation source and the temperature propagation end point are obtained, the real-time temperature values ​​of the multiple monitoring points are recorded, and the temperature propagation time difference is calculated based on the monitoring coordinates and the real-time temperature values. The temperature change rate between multiple monitoring points is calculated based on the temperature propagation time difference and the monitoring location coordinates, and a temperature attenuation coefficient is generated based on the temperature change rate. The temperature attenuation coefficient is accumulated according to the monitoring location coordinates to generate the temperature propagation amount from the temperature propagation source location to the temperature propagation end location. The temperature attenuation coefficient is then classified according to the attenuation efficiency based on the temperature propagation amount. The attenuation efficiency classification results of the temperature attenuation coefficient are reorganized into a matrix form to generate the temperature propagation intensity matrix.

[0047] The system acquires the coordinates of multiple monitoring points between the source and destination of temperature propagation. These coordinates are represented in three-dimensional space to precisely describe the position of each temperature sensor within the hair dryer. Taking a portable hair dryer as an example, it houses 16 temperature sensors located at key positions such as the handle, the connection point between the handle and body, the root of the teeth, the middle of the teeth, and the tips of the teeth. Precision positioning technology is used to determine the spatial coordinates of each monitoring point, with the origin set at the center of the bottom of the hair dryer, the X-axis representing the vertical direction, the Y-axis the horizontal direction, and the Z-axis the height. Real-time temperature values ​​from multiple monitoring points are recorded at a sampling frequency of 5 times per second to ensure the capture of subtle temperature changes. When the hair dryer is activated with hot air, the temperature of sensors closer to the heat source rises rapidly, while the temperature of sensors farther from the heat source rises relatively slowly. The temperature propagation time difference is calculated based on the monitoring coordinates and real-time temperature values. This temperature propagation time difference represents the time required for heat to transfer from one monitoring point to another. Identify characteristic points on the temperature curves of each monitoring point, such as inflection points of temperature rise or the moment when a specific temperature threshold is reached, and calculate the time difference between the occurrence of characteristic points between different monitoring points. When the hot air function of the blower comb is activated, the sensor temperature near the heating element rises rapidly to 60°C within 3 seconds, while the sensor temperature in the middle of the comb teeth begins to rise significantly after 6 seconds. It takes 9 seconds to reach the same temperature rise, thus the temperature propagation time difference between the two points is 6 seconds.

[0048] The rate of temperature change between multiple monitoring points was calculated based on the temperature propagation time difference and the coordinates of the monitoring locations. This rate of temperature change reflects the speed of heat transfer in space, and is calculated by dividing the spatial distance between two monitoring points by the temperature propagation time difference. Taking a hair dryer comb as an example, the spatial distance between the sensor near the heating element and the sensor in the middle of the comb teeth is 50mm, and the temperature propagation time difference is 6s. Therefore, the calculated temperature change rate is 8.33mm / s. Similar calculations were performed on all adjacent monitoring points within the hair dryer comb to obtain a complete dataset of temperature change rates. A temperature attenuation coefficient was generated based on the temperature change rate. The temperature attenuation coefficient represents the degree of heat loss during transfer; a smaller value indicates more severe attenuation. It is calculated by comparing the temperature change amplitudes of two monitoring points; the ratio of the temperature change amplitude at the endpoint to that at the starting point is the temperature attenuation coefficient. In the hair dryer comb, when the temperature at the heat source rises by 20℃, the temperature in the middle of the comb teeth rises by 15℃. Therefore, the calculated temperature attenuation coefficient is 0.75, indicating that 25% of the heat is lost during transfer. The temperature decay coefficient between the middle and tip of the comb teeth is 0.6, indicating that 40% of the heat is lost during the transfer process. The temperature decay coefficient is affected by the thermal conductivity of the material, the shape of the structure, and environmental conditions, and is a key parameter for evaluating heat transfer efficiency.

[0049] The temperature attenuation coefficient is accumulated according to the monitoring location coordinates to generate the temperature propagation amount from the temperature propagation source location to the temperature propagation destination location. The accumulation calculation involves multiplying the temperature attenuation coefficients of each segment along the propagation path to obtain the total attenuation coefficient from the source location to the destination location. The product of the total attenuation coefficient and the initial temperature change is the temperature propagation amount. In the air blower comb, the temperature attenuation coefficients for each segment along the path from the heat source to the comb tooth tip are 0.85, 0.75, and 0.6, respectively. After accumulation, the total attenuation coefficient is 0.38. If the initial temperature rise of the heat source is 50℃, the temperature rise transmitted to the comb tooth tip is 19℃, i.e., the temperature propagation amount is 19℃. The temperature attenuation coefficient is graded according to the temperature propagation amount for attenuation efficiency. The grading criteria are: attenuation coefficient > 0.8 is high-efficiency propagation, with a temperature propagation amount loss < 20%; attenuation coefficient between 0.6 and 0.8 is medium-efficiency propagation, with a temperature propagation amount loss between 20% and 40%; attenuation coefficient < 0.6 is low-efficiency propagation, with a temperature propagation amount loss > 40%. In a hair dryer comb, the heat transfer between the heat source and the comb body is highly efficient, the heat transfer between the comb body connection and the root of the comb teeth is moderately efficient, and the heat transfer between the root of the comb teeth and the tip of the comb teeth is inefficient.

[0050] The attenuation efficiency classification results of the temperature attenuation coefficient are reorganized into a matrix form. The generated temperature propagation intensity matrix is ​​a two-dimensional table, with the horizontal and vertical axes representing different monitoring points, and the matrix element values ​​indicating the heat transfer efficiency level of the monitoring points from row to column. The attenuation efficiency between each pair of monitoring points is rated: high efficiency is assigned a value of 3, medium efficiency 2, low efficiency 1, and no direct transfer relationship 0. Taking a blower comb with 16 monitoring points as an example, a 16×16 temperature propagation intensity matrix is ​​generated. Elements with a value of 3 in the matrix indicate a high-efficiency heat transfer channel between the two monitoring points, elements with a value of 2 indicate a medium-efficiency heat transfer channel, elements with a value of 1 indicate a low-efficiency heat transfer channel, and elements with a value of 0 indicate no direct heat transfer relationship between the two points. By analyzing the characteristics of the temperature propagation intensity matrix, the main paths and bottleneck locations of heat transfer in the blower comb can be identified. For example, elements with a value of 3 are concentrated around the heat source, indicating high heat transfer efficiency in that area; elements with a value of 1 are mainly distributed at the distal end of the comb teeth, indicating low heat transfer efficiency and representing a heat transfer bottleneck in that area.

[0051] The construction of the temperature propagation intensity matrix provides a scientific basis for the intelligent temperature control of the portable hair dryer. Based on the matrix information, the heat transfer path and rate in the hair dryer structure can be predicted, enabling accurate prediction of temperature changes. When a change in the heat source temperature is detected, the control unit can predict the future temperature change trend of each monitoring point based on the temperature propagation intensity matrix, and adjust the heating power in advance to prevent local overheating or uneven temperature distribution.

[0052] This invention achieves precise characterization and quantitative analysis of the heat transfer process inside a blower comb by constructing a temperature propagation intensity matrix. It simplifies the complex heat conduction phenomenon into quantifiable attenuation coefficients and propagation amounts, visualizing the heat transfer law and providing a theoretical basis for intelligent temperature control. The predictive control function based on the temperature propagation intensity matrix significantly improves temperature control accuracy and response speed, effectively preventing local overheating and temperature fluctuations, enhancing user safety and comfort. It also enables dynamic monitoring and evaluation of the heat transfer path, transforming temperature control from a passive response to an active prediction, greatly improving energy efficiency and extending equipment lifespan.

[0053] Based on the correlation propagation path, the evolution direction of temperature anomaly spread is predicted, and potentially affected spatial locations are identified, yielding early warning location information including: Calculate the temperature gradient at the location of the temperature propagation source in the associated propagation path, obtain the amount of change and direction of the temperature gradient, and determine the evolution direction of the temperature anomaly diffusion. Based on the evolution direction and combined with the spatial distribution of the associated propagation paths, a temperature propagation rate map is generated, and the temperature anomaly diffusion rate along the evolution direction is extracted from the temperature propagation rate map. Based on the temperature anomaly diffusion rate, the surrounding spatial location is extended along the associated propagation path. The propagation time of the temperature anomaly from the temperature propagation source location to the surrounding spatial location is calculated. The propagation time is combined with the temperature anomaly diffusion rate to generate the temperature anomaly diffusion time sequence. The temperature impact value of the surrounding spatial location is calculated based on the diffusion time series of temperature anomalies and the change in temperature gradient. The surrounding spatial locations are classified according to the temperature impact value, and the surrounding spatial locations that exceed the preset impact value are identified as potentially affected spatial locations, thus obtaining early warning location information.

[0054] A temperature gradient represents the degree of temperature change per unit distance, reflecting the strength and direction of heat transfer. The temperature gradient calculation method involves setting up multiple sampling points around the temperature source, processing the sampled temperature data, and calculating the rate of temperature change in each direction. In a portable hair dryer, the heat source is typically located at the connection between the handle and the body. When a temperature sensor at this location detects an abnormal temperature rise, the temperature gradient calculation program is immediately initiated. Taking a portable hair dryer as an example, its heat source has five temperature sensors, located at the center point and in four surrounding directions. When the temperature at the center point is 85℃, and the surrounding temperatures are 82℃, 79℃, 83℃, and 80℃ respectively, the temperature gradients in the four directions can be calculated. Obtaining the magnitude and direction of the temperature gradient change requires continuous monitoring of the temperature gradient over time. The magnitude of change represents the increase or decrease in the temperature gradient per unit time, and the direction of change indicates the spatial orientation of the temperature gradient's strengthening or weakening. When the portable hair dryer detected that the temperature gradient from the heat source towards the comb teeth increased from 0.6 to 0.9 within 10 seconds, the change in temperature gradient was 0.3, and the direction of change was positively increasing, indicating that heat was being transferred towards the comb teeth at an accelerated rate. Based on the characteristics of the temperature gradient change, the evolution direction of the temperature anomaly diffusion was determined. The evolution direction is usually consistent with the direction of the largest temperature gradient, but it is also affected by the thermal conductivity of the material and the shape of the structure. In the portable hair dryer, the temperature gradient in the direction of the comb teeth is the largest and shows an increasing trend; therefore, the evolution direction of the temperature anomaly diffusion is mainly determined to be towards the comb teeth.

[0055] Based on a defined evolutionary direction and the spatial distribution of associated propagation paths, the generated temperature propagation rate map is a spatial distribution map showing the rate of heat transfer in different regions. The generation method involves calculating the time delay between the temperature change of each node on the associated propagation path and the temperature changes of adjacent nodes, deriving the heat transfer rate, and mapping the rate data to a spatial coordinate system. In the portable hair dryer, the heat transfer rate is highest at the heat source, reaching 8 mm / s; followed by the root of the comb teeth, approximately 6 mm / s; then the middle of the comb teeth, approximately 4 mm / s; and lowest at the tips of the comb teeth, approximately 2 mm / s. The rate distribution shows a gradual decrease from the heat source to the tips of the comb teeth, forming a clear gradient of heat transfer rate. The temperature anomaly diffusion rate along the evolutionary direction is extracted from the temperature propagation rate map. The extraction method involves setting sampling points on the rate map along the defined evolutionary direction, reading the rate values ​​at each sampling point, and calculating a continuous diffusion rate curve through interpolation. In the portable hair dryer, the temperature anomaly diffusion rate from the heat source to the comb teeth can be expressed as a function of distance, approximately exhibiting an exponential decay.

[0056] Based on the temperature anomaly diffusion rate, the method for expanding the temperature anomaly along the associated propagation path to surrounding spatial locations involves starting from the temperature propagation source location and gradually expanding towards the surrounding space according to the topology of the associated propagation path, considering the propagation characteristics of each branch. In a portable hair dryer, the temperature anomaly at the heat source location first diffuses along the main comb body and then disperses to each comb tooth. The propagation time of the temperature anomaly from the temperature propagation source location to the surrounding spatial locations is calculated. The calculation method is to accumulate the propagation delay time of each segment on the associated propagation path. In the portable hair dryer, it takes 4 seconds for the temperature anomaly at the heat source to propagate to the root of the comb tooth, 9 seconds to the middle of the comb tooth, and 15 seconds to the tip of the comb tooth. Combining the propagation time with the temperature anomaly diffusion rate generates the diffusion time sequence of the temperature anomaly. The diffusion time sequence is a spatiotemporal relationship table that records the spatial range affected by the temperature anomaly at different time points. In a portable hair dryer comb, within 4 seconds of an abnormal heat source temperature, the affected area is limited to a 10mm radius around the heat source; from 4 to 9 seconds, the affected area expands to the comb body and the root of the comb teeth; from 9 to 15 seconds, the affected area further expands to the middle of the comb teeth; after 15 seconds, the affected area covers the entire comb teeth.

[0057] Based on the diffusion timeline of temperature anomalies combined with changes in temperature gradients, the temperature impact value of surrounding spatial locations is calculated. This temperature impact value represents the degree of influence of the temperature anomaly on the surrounding area, considering propagation time, initial temperature anomaly intensity, and attenuation factors. The calculation method involves multiplying the initial temperature anomaly value by the propagation attenuation coefficient, and then adjusting for a time delay factor. In a portable hair dryer comb, it is assumed that the temperature anomaly at the heat source increases by 20°C, the propagation attenuation coefficient decreases exponentially with distance, and the time delay factor decreases linearly with time. Calculations show that the temperature impact value at the root of the comb teeth is 15°C, the temperature impact value in the middle of the comb teeth is 10°C, and the temperature impact value at the tip of the comb teeth is 6°C. The surrounding spatial locations are then classified according to the temperature impact value. The classification criteria are: a temperature impact value > 15°C is a high-impact zone, a temperature impact value between 8°C and 15°C is a medium-impact zone, a temperature impact value between 3°C and 8°C is a low-impact zone, and a temperature impact value < 3°C is a micro-impact zone. In portable hair dryer combs, the area around the heat source and the base of the comb teeth is a high-impact zone, the middle of the comb teeth is a medium-impact zone, the tips of the comb teeth are a low-impact zone, and the far end of the comb handle is a micro-impact zone.

[0058] The preset impact value is determined according to the safety standards of hair dryers, typically set at 8°C, indicating that a temperature rise exceeding 8°C may affect user comfort and safety. In portable hair dryers, the preset impact value is 8°C, therefore the area around the heat source, the root of the comb teeth, and the middle of the comb teeth are identified as potentially affected spatial locations. Warning location information is generated, including the spatial coordinates of the potentially affected location, the expected temperature impact value, and the expected impact time. The warning location information is output in tabular form for the control unit to perform preventative adjustments. When an abnormal rise in heat source temperature is detected, the control unit, based on the warning location information, reduces heating power or activates cooling measures in advance to prevent excessively high temperatures from causing discomfort or injury to the user.

[0059] This invention achieves proactive temperature safety management through forward-looking prediction of the evolution of temperature anomalies. By employing temperature gradient analysis, rate mapping, and time-series modeling, it comprehensively reveals the propagation patterns of temperature anomalies. Based on diffusion timelines and impact value assessments, it accurately identifies potentially affected areas and generates precise early warning information. The introduction of this early warning mechanism transforms temperature control from a passive response to proactive prediction, significantly improving the safety and comfort of using the air blower. Precise control guided by early warning location information also effectively avoids global energy waste and improves energy efficiency.

[0060] Based on the early warning location information, a temperature distribution matrix is ​​established between the temperature propagation source location and the potentially affected spatial locations. The temperature change rate of adjacent spatial locations in the temperature distribution matrix is ​​calculated, and temperature field evolution curves are generated, including: Collect real-time temperature data of the temperature propagation source location and the potentially affected spatial location in the early warning location information, establish the temperature correspondence between the temperature propagation source location and the potentially affected spatial location based on the real-time temperature data, and generate an initial temperature distribution matrix; Real-time temperature data at multiple time points are collected, and the initial temperature distribution matrix is ​​updated based on the temperature correspondence to obtain a temperature distribution matrix that reflects the temperature distribution state at different time points. Calculate the rate of temperature change between the location of the temperature propagation source and the potentially affected spatial location based on the temperature distribution matrix; The temperature change rate is arranged in chronological order to form a temperature change trend, and the temperature change trend is mapped to a temperature field evolution curve.

[0061] Temperature data acquisition utilizes a high-precision temperature sensor array with a sampling accuracy of 0.1℃ and a sampling frequency of 10 times / s. Temperature sensors are distributed around the heating element, the comb body, the roots of each comb tooth, the middle section of the comb tooth, and the tips of the comb tooth. When the hot air function of the blow-dry comb is activated, the temperature of the heating element rises from 25℃ to 85℃, the comb body reaches 65℃, the comb tooth roots reach 55℃, the middle section of the comb tooth reaches 45℃, and the tips of the comb tooth reach 35℃. Temperature correlations are established based on real-time temperature data. Through correlation analysis, when the heating element temperature increases by 10℃, the comb body temperature increases by 7℃, the comb tooth root temperature increases by 5℃, the middle section of the comb tooth increases by 3℃, and the tips of the comb tooth increase by 1℃. The initial temperature distribution matrix is ​​a two-dimensional data structure, with rows representing different spatial locations and columns representing temperature values. The matrix is ​​constructed by arranging the real-time temperatures of each monitoring point according to spatial coordinates, forming a discrete representation of the temperature field.

[0062] Real-time temperature data at multiple time points were collected to track dynamic changes in the temperature field. Starting from the activation of the hot air function, a complete temperature dataset was collected every 5 seconds for 60 seconds, resulting in 13 sets of temperature data. The initial temperature distribution matrix was updated based on temperature correspondences, replacing the corresponding values ​​in the matrix with the newly collected temperature data, forming a matrix sequence that changes over time. After 60 seconds of continuous monitoring, 13 temperature distribution matrices reflecting the temperature distribution at different time points were obtained. These matrices record the complete process of heat transfer in the air blower comb, showing that the temperature gradient from the heating element towards the comb teeth gradually establishes and stabilizes. Initially, the temperature at each monitoring point is close to the ambient temperature with small differences; as time progresses, the temperature gradient becomes more pronounced, with the temperature rising rapidly at the heat source and slowly increasing at locations farther from the heat source.

[0063] The rate of temperature change between the temperature source location and the potentially affected spatial location was calculated based on the temperature distribution matrix. This rate of temperature change represents the magnitude of temperature change per unit time, reflecting the speed of heat transfer. By analyzing the continuously collected temperature distribution matrix, the temperature change at each monitoring point was calculated every 5 seconds. Within the first 5 seconds after activation, the temperature of the heating element rose from 25℃ to 65℃, with a rate of change of 8℃ / s; the temperature of the comb body rose from 35℃ to 50℃ between 5 and 10 seconds, with a rate of change of 3℃ / s; and the temperature at the root of the comb teeth rose from 30℃ to 42℃ between 10 and 15 seconds, with a rate of change of 2.4℃ / s. The rate of temperature change exhibited a pattern over time: the temperature change at the heating element reached its peak first and then gradually decreased; the rate of temperature change at other locations showed a trend of first increasing and then decreasing, and the peak time was delayed with distance from the heat source. This trend reflects the diffusion process of heat in space, exhibiting fluctuating characteristics of diffusion from the heat source to the surrounding area.

[0064] The temperature change rate is arranged chronologically to form a temperature change trend. A time series of temperature change rates is generated for each monitoring point, with each series containing 12 data points representing the temperature change process within 60 seconds. The temperature change trend reflects the temporal characteristics of heat transfer and can be used to predict the evolution direction of the temperature field. The temperature change trend is mapped to a temperature field evolution curve, with time on the horizontal axis and the temperature change rate on the vertical axis; different curves represent different monitoring points. The temperature field evolution curve visually displays the dynamic process of heat transfer from the heating element to the comb teeth. The curve at the heating element shows a high peak followed by a rapid decline; the curve at the main body of the comb has a lower peak but a longer duration; the peak values ​​of the curves at different parts of the comb teeth decrease sequentially, and the occurrence time is successively delayed, forming a stepped distribution. The temperature field evolution curve reveals the fluctuation characteristics of heat transfer; heat is transferred from the heat source to the surrounding area in the form of waves, with the wave amplitude decreasing with increasing distance, and the wave speed varying due to the influence of the material's thermal conductivity. By analyzing the curve characteristics, abnormal heat transfer conditions can be identified; for example, abrupt changes in the curve indicate interruption of heat transfer, and abnormal curve slopes indicate changes in heat transfer efficiency.

[0065] The temperature field evolution curve provides a theoretical basis and data support for the intelligent temperature control of the portable hair dryer. Based on the established temperature field evolution model, the hair dryer can predict the temperature distribution at future points in time, achieving proactive temperature control. When a temperature change in the heating element is detected, the control unit estimates the future temperature change trend at each monitoring point based on the temperature field evolution curve, adjusting the heating power in advance to prevent excessively high temperatures or uneven temperature distribution. The temperature field evolution curve is also used for thermal runaway early warning. When the actual temperature change curve deviates from the expected value, an early warning mechanism is triggered, and emergency cooling measures are taken to ensure safe use.

[0066] This invention achieves accurate description and prediction of dynamic changes in the temperature field by establishing a temperature distribution matrix and generating temperature field evolution curves. By analyzing the temperature field evolution characteristics, this method can accurately predict temperature change trends at various spatial locations, enabling proactive temperature control and preventing local overheating and temperature fluctuations. The establishment of temperature field evolution curves facilitates the identification of abnormal conditions in the heat transfer process, enhancing fault detection capabilities and safety assurance levels.

[0067] Based on the temperature field evolution curve, the degree of heat accumulation at the location of the temperature propagation source and the degree of heat diffusion at the potentially affected spatial locations are calculated. The time-series variation trends of the degree of heat accumulation and the degree of heat diffusion are output as temperature monitoring results, including: Temperature temporal variation data of temperature propagation source locations and temperature distribution data of potentially affected spatial locations are extracted from the temperature field evolution curve to calculate temperature field transmission characteristics; The characteristics of temperature field transfer are decomposed into heat storage component and heat diffusion component; The degree of heat accumulation at the location of the temperature propagation source is calculated based on the time-series integration of the heat accumulation component, and the degree of heat diffusion at the potentially affected spatial location is calculated based on the time-series integration of the heat diffusion component. Calculate the changes in the degree of heat accumulation and the degree of heat diffusion over time, generate the time-series change trends of the degree of heat accumulation and the degree of heat diffusion, and output the time-series change trends as the temperature monitoring results.

[0068] When extracting the time-series temperature variation data of the temperature propagation source location and the temperature distribution data of the potentially affected spatial locations from the temperature field evolution curve, a sliding window technique is used to process the raw temperature data. The window size is set to 60 data points, and the data within each window is smoothed to eliminate the influence of instantaneous noise. For the heating element area of ​​the blower comb, the average temperature of each measuring point in this area is extracted as the time-series temperature data of the temperature propagation source location; for the potentially affected areas such as the blower comb teeth and handle, the temperature distribution data of each measuring point in each area is extracted, and the highest temperature, lowest temperature, and temperature gradient are recorded.

[0069] When calculating the temperature field transfer characteristics, the temperature correlation between the temperature propagation source and the potentially affected area is analyzed. Specifically, this is achieved by calculating the ratio of temperature change rates in different regions to determine the heat transfer path and efficiency. In practical applications of air blowers, once the heating element temperature rises to the set operating temperature, the temperature of the heating element is taken as T. 源 The temperature of the comb teeth is T. 齿 Calculate T 齿 rate of change and T 源 The ratio of the rate of change is used as a characteristic value of temperature field transmission.

[0070] In the decomposition of temperature field transfer characteristics into heat storage and heat diffusion components, based on the physical principle of heat conduction, the transfer characteristics are decomposed along the time dimension. The heat storage component characterizes the heat storage capacity of the temperature source location; for the heating element of a hair dryer, it is calculated as the product of the temperature change value and time within the temperature rise range. The heat diffusion component characterizes the ability of heat to diffuse into the surrounding space, and is calculated as the product of the temperature gradient and time. During the use of the hair dryer, when the heating function is activated, as the heating element temperature rises from room temperature to the operating temperature, temperature values ​​T1 and T2 are recorded, and the temperature difference ΔT is calculated, yielding the heat storage component S = ΔT × Δt, where Δt is the time interval. The heat diffusion component D is calculated as D = G × Δt, where G is the temperature gradient, representing the temperature change value per unit distance.

[0071] When calculating the degree of heat accumulation at the location of the temperature propagation source based on the time-series integration of the heat accumulation, the trapezoidal integration method is used to integrate the heat accumulation over time. For the heating element of the blower comb, at each sampling moment after the start of use, the current heat accumulation S1 and the heat accumulation S0 at the previous moment are recorded. The heat accumulation increment ΔA = (S0 + S1) / 2 × Δt for that time period is calculated. The heat accumulation increments of all time periods are summed to obtain the total degree of heat accumulation.

[0072] When calculating the degree of heat diffusion at potentially affected spatial locations based on the temporal integral of the heat diffusion components, the trapezoidal integration method is also used. For the comb teeth of the blower, at each sampling time, the current heat diffusion component D1 and the heat diffusion component D0 at the previous time are measured, and the heat diffusion increment ΔB=(D0+D1) / 2×Δt is calculated. The heat diffusion increments of all time periods are accumulated to obtain the total degree of heat diffusion B.

[0073] When calculating the changes in heat accumulation and heat diffusion over time, a first-order difference operation is performed on both. For the heat accumulation level A1 at each sampling moment, the difference ΔA = A1 - A0 between it and the value A0 at the previous moment is calculated to obtain the change in heat accumulation; similarly, the change in heat diffusion ΔB = B1 - B0 is calculated. In practical applications of the air blower comb, the change is calculated every 5 seconds, and continuous monitoring is performed for 3 minutes to form a time series of the change.

[0074] When generating the time-series trends of heat accumulation and heat diffusion, trend analysis is performed on the calculated time series of changes. A local weighted regression scatter smoothing method is used to smooth the series and extract the overall trend. Specifically, for each time point, the values ​​of its neighboring points are considered, with closer points receiving higher weights, and a weighted average is calculated as the smoothed value for that point. In the temperature monitoring of the air blower comb, the trend curve after this smoothing clearly shows the changes in the heat accumulation rate of the heating element and the heat diffusion rate of the comb teeth.

[0075] When outputting time-series trends as temperature monitoring results, the changes in heat accumulation and heat diffusion are transformed into intuitive charts and numerical indicators. For a hair dryer, a safe threshold for heat accumulation (Amax) and a warning threshold for heat diffusion (Bmax) are set. When A>Amax or B>Bmax is detected, a temperature anomaly alarm is triggered, prompting the user to adjust usage or stop using the product. The temperature monitoring results include peak heat accumulation, peak heat diffusion, and the time points of their occurrence, as well as the ratio between the two, providing users with comprehensive temperature safety reference information.

[0076] Taking a real-world test as an example, within 20 seconds of startup, the temperature of the heating element in the hair dryer rose from 25°C to 75°C, with a calculated heat accumulation component of 250°C·s. The temperature in the comb teeth area rose from 25°C to 35°C, with a calculated heat diffusion component of 50°C·s. After continuous monitoring for 3 minutes, the heat accumulation reached 1500°C·s, and the heat diffusion reached 600°C·s. By calculating the magnitude of these changes and performing trend analysis, it was found that the heat accumulation rate peaked at 50 seconds after startup, while the heat diffusion rate peaked at 80 seconds. The significant time lag between these two peaks indicates that the hair dryer has good thermal conductivity and will not cause rapid overheating of the comb teeth.

[0077] This invention comprehensively assesses the temperature safety of a hair dryer during use by employing both heat accumulation and heat diffusion as indicators, avoiding the limitations of traditional single-point temperature monitoring. By analyzing the heat transfer relationship between the temperature propagation source and the potentially affected area, it achieves precise control over the dynamic changes in the temperature field. Monitoring the temporal trend provides an early warning mechanism for temperature anomalies, significantly improving the safety of hair dryer use. It requires no complex computing equipment, can be integrated into portable devices for real-time monitoring, and is highly adaptable, providing strong assurance for the safe use of portable hair dryers.

[0078] This invention provides a portable multi-point temperature monitoring system for a hair dryer comb, the system comprising: The temperature acquisition module, which consists of temperature sensing units located at different spatial positions of the comb structure, is used to acquire temperature data from multiple spatial positions of the comb structure during operation, thereby obtaining a multi-point temperature dataset. The gradient calculation module is used to calculate the temperature gradient between adjacent spatial locations based on a multi-point temperature dataset, and to track the change of the temperature gradient over time to obtain gradient evolution information. The correlation analysis module is used to identify spatial locations with temperature gradient propagation correlations based on gradient evolution information, establish temperature propagation correlations between spatial locations, identify the location of temperature propagation sources, and obtain the associated propagation paths; The early warning analysis module is used to predict the evolution direction of the spread of temperature anomalies based on the associated propagation path, identify potentially affected spatial locations, and obtain early warning location information. The matrix calculation module is used to establish a temperature distribution matrix between the temperature propagation source location and the potentially affected spatial location based on the early warning location information, calculate the temperature change rate of adjacent spatial locations in the temperature distribution matrix, and generate temperature field evolution curves. The results output module is used to calculate the degree of heat accumulation at the location of the temperature propagation source and the degree of heat diffusion at the potentially affected spatial location based on the temperature field evolution curve, and output the time-series change trend of the degree of heat accumulation and the degree of heat diffusion as temperature monitoring results.

[0079] One technical solution provided in this embodiment of the invention is 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 in any of the aforementioned methods.

[0080] The specific embodiments described above are preferred embodiments of the present invention and are not intended to limit the specific scope of the present invention. The scope of the present invention includes, but is not limited to, these specific embodiments. All equivalent changes made in accordance with the shape and structure of the present invention are within the protection scope of the present invention.

Claims

1. A convenient method for multi-point temperature monitoring of a hair dryer comb, characterized in that, Includes the following steps: By using temperature sensing units located at different spatial positions of the comb structure, temperature data from multiple spatial positions of the comb structure during its working state are collected to obtain a multi-point temperature dataset. The temperature gradient between adjacent spatial locations is calculated based on a multi-point temperature dataset, and the change process of the temperature gradient over time is tracked to obtain gradient evolution information. Based on gradient evolution information, spatial locations with temperature gradient propagation associations are identified, temperature propagation associations between spatial locations are established, the locations of temperature propagation sources are identified, and the associated propagation paths are obtained. Based on the correlation propagation path, the evolution direction of the temperature anomaly spread is predicted, the potentially affected spatial locations are identified, and the early warning location information is obtained; Based on the early warning location information, a temperature distribution matrix is ​​established between the temperature propagation source location and the potentially affected spatial location. The temperature change rate of adjacent spatial locations in the temperature distribution matrix is ​​calculated, and a temperature field evolution curve is generated. The degree of heat accumulation at the source of temperature propagation and the degree of heat diffusion at potentially affected spatial locations are calculated based on the temperature field evolution curve. The temporal trends of the degree of heat accumulation and the degree of heat diffusion are then output as temperature monitoring results.

2. The method according to claim 1, characterized in that, Temperature data from multiple spatial locations of the comb structure during operation are collected by temperature sensing units positioned at different spatial locations within the comb structure, resulting in a multi-point temperature dataset including: Temperature sensing units are set at different spatial positions of the comb structure according to the heat transfer law. The temperature sensing unit includes a main temperature sensing unit and a backup temperature sensing unit. The main temperature sensing unit collects temperature data of the comb structure in its working state to generate an initial temperature data sequence. The initial temperature data sequence is compared with the historical temperature data sequence collected within the preset time window. When the temperature data collected by the main temperature sensing unit changes abruptly, the system automatically switches to the backup temperature sensing unit to continue collecting the temperature data of the comb structure in working condition and records the corresponding spatial location as the abnormal monitoring location. Based on the temperature change rate at the abnormal monitoring location, the sampling time interval between adjacent spatial locations on the comb structure is calculated to generate sampling control information; According to the sampling control information, the temperature data of the comb structure under working conditions are collected from the temperature sensing unit, the temperature difference between adjacent spatial positions is calculated, and the temperature gradient distribution information is obtained. The direction of heat transfer in the comb structure is determined based on the temperature gradient distribution information, and a multi-point temperature dataset is generated by combining the sampling control information.

3. The method according to claim 1, characterized in that, Based on a multi-point temperature dataset, the temperature gradient between adjacent spatial locations is calculated, and the change of the temperature gradient over time is tracked to obtain gradient evolution information, including: The temperature change rate is calculated for adjacent sampling times in a multi-point temperature dataset. The segmentation points are determined based on the fluctuation amplitude of the temperature change rate, and a time-series segmented temperature dataset is generated. Based on the time-series segmented temperature dataset, the geometric distance between adjacent spatial locations is used as a weighting factor to calculate the heat loss and heat absorption at spatial locations, generating a set of heat transfer parameters. The temperature gradient values ​​between spatial locations are calculated based on the heat transfer parameter set. The temperature gradient values ​​are arranged in time sequence, the heat migration rate is calculated, and a temperature gradient prediction function is constructed by combining the heat accumulation effect. The temperature gradient prediction function is used to predict the temperature gradient at each spatial location in the temperature gradient distribution set, generating a predicted temperature gradient value. The temperature gradient deviation value is obtained by comparing it with the measured temperature gradient value. The heat transfer parameter set is corrected and updated based on the temperature gradient deviation value. The corrected heat transfer parameter set is then substituted into the temperature gradient prediction function to obtain gradient evolution information.

4. The method according to claim 1, characterized in that, Based on gradient evolution information, spatial locations with temperature gradient propagation associations are identified, temperature propagation relationships between spatial locations are established, the locations of temperature propagation sources are identified, and the associated propagation paths are obtained, including: The gradient evolution information is segmented according to a preset time window. The temperature change rate of the spatial location within each time period is calculated. The fluctuation period and fluctuation amplitude of the temperature change rate are extracted. The spatial locations are grouped according to the fluctuation period and fluctuation amplitude to generate temperature association groups. The temperature propagation delay time between the spatial locations is calculated based on the temperature association grouping. The spatial locations within the temperature association grouping are divided into temperature propagation sequences according to the temperature propagation delay time. The temperature propagation source location and the temperature propagation end location in the temperature propagation sequence are determined. Calculate the amount of temperature propagation from the source location to the end location, and construct a temperature propagation intensity matrix; Based on the distribution characteristics of temperature transfer efficiency in the temperature transfer intensity matrix, spatial location pairs with temperature transfer intensity greater than a preset intensity threshold are screened to identify temperature propagation channels. Calculate the attenuation value of temperature propagation in the temperature propagation channel, filter the target propagation channel according to the attenuation value, sort the target propagation channel according to the temperature propagation delay time, and generate associated propagation paths.

5. The method according to claim 4, characterized in that, Calculating the amount of temperature propagation from the source location to the destination location, and constructing the temperature propagation intensity matrix includes: The coordinates of multiple monitoring points between the temperature propagation source and the temperature propagation end point are obtained, the real-time temperature values ​​of the multiple monitoring points are recorded, and the temperature propagation time difference is calculated based on the monitoring coordinates and the real-time temperature values. The temperature change rate between multiple monitoring points is calculated based on the temperature propagation time difference and the monitoring location coordinates, and a temperature attenuation coefficient is generated based on the temperature change rate. The temperature attenuation coefficient is accumulated according to the monitoring location coordinates to generate the temperature propagation amount from the temperature propagation source location to the temperature propagation end location. The temperature attenuation coefficient is then classified according to the attenuation efficiency based on the temperature propagation amount. The attenuation efficiency classification results of the temperature attenuation coefficient are reorganized into a matrix form to generate the temperature propagation intensity matrix.

6. The method according to claim 1, characterized in that, Based on the correlation propagation path, the evolution direction of temperature anomaly spread is predicted, and potentially affected spatial locations are identified, yielding early warning location information including: Calculate the temperature gradient at the location of the temperature propagation source in the associated propagation path, obtain the amount of change and direction of the temperature gradient, and determine the evolution direction of the temperature anomaly diffusion. Based on the evolution direction and combined with the spatial distribution of the associated propagation paths, a temperature propagation rate map is generated, and the temperature anomaly diffusion rate along the evolution direction is extracted from the temperature propagation rate map. Based on the temperature anomaly diffusion rate, the surrounding spatial location is extended along the associated propagation path. The propagation time of the temperature anomaly from the temperature propagation source location to the surrounding spatial location is calculated. The propagation time is combined with the temperature anomaly diffusion rate to generate the temperature anomaly diffusion time sequence. The temperature impact value of the surrounding spatial location is calculated based on the diffusion time series of temperature anomalies and the change in temperature gradient. The surrounding spatial locations are classified according to the temperature impact value, and the surrounding spatial locations that exceed the preset impact value are identified as potentially affected spatial locations, thus obtaining early warning location information.

7. The method according to claim 1, characterized in that, Based on the early warning location information, a temperature distribution matrix is ​​established between the temperature propagation source location and the potentially affected spatial locations. The temperature change rate of adjacent spatial locations in the temperature distribution matrix is ​​calculated, and temperature field evolution curves are generated, including: Collect real-time temperature data of the temperature propagation source location and the potentially affected spatial location in the early warning location information, establish the temperature correspondence between the temperature propagation source location and the potentially affected spatial location based on the real-time temperature data, and generate an initial temperature distribution matrix; Real-time temperature data at multiple time points are collected, and the initial temperature distribution matrix is ​​updated based on the temperature correspondence to obtain a temperature distribution matrix that reflects the temperature distribution state at different time points. Calculate the rate of temperature change between the location of the temperature propagation source and the potentially affected spatial location based on the temperature distribution matrix; The temperature change rate is arranged in chronological order to form a temperature change trend, and the temperature change trend is mapped to a temperature field evolution curve.

8. The method according to claim 1, characterized in that, Based on the temperature field evolution curve, the degree of heat accumulation at the location of the temperature propagation source and the degree of heat diffusion at the potentially affected spatial locations are calculated. The time-series variation trends of the degree of heat accumulation and the degree of heat diffusion are output as temperature monitoring results, including: Temperature temporal variation data of temperature propagation source locations and temperature distribution data of potentially affected spatial locations are extracted from the temperature field evolution curve to calculate temperature field transmission characteristics; The characteristics of temperature field transfer are decomposed into heat storage component and heat diffusion component; The degree of heat accumulation at the location of the temperature propagation source is calculated based on the time-series integration of the heat accumulation component, and the degree of heat diffusion at the potentially affected spatial location is calculated based on the time-series integration of the heat diffusion component. Calculate the changes in the degree of heat accumulation and the degree of heat diffusion over time, generate the time-series change trends of the degree of heat accumulation and the degree of heat diffusion, and output the time-series change trends as the temperature monitoring results.

9. A portable multi-point temperature monitoring system for a hair dryer comb, used to implement the method described in any one of claims 1-8, characterized in that, The system includes: The temperature acquisition module, which consists of temperature sensing units located at different spatial positions of the comb structure, is used to acquire temperature data from multiple spatial positions of the comb structure during operation, thereby obtaining a multi-point temperature dataset. The gradient calculation module is used to calculate the temperature gradient between adjacent spatial locations based on a multi-point temperature dataset, and to track the change of the temperature gradient over time to obtain gradient evolution information. The correlation analysis module is used to identify spatial locations with temperature gradient propagation correlations based on gradient evolution information, establish temperature propagation correlations between spatial locations, identify the location of temperature propagation sources, and obtain the associated propagation paths; The early warning analysis module is used to predict the evolution direction of the spread of temperature anomalies based on the associated propagation path, identify potentially affected spatial locations, and obtain early warning location information. The matrix calculation module is used to establish a temperature distribution matrix between the temperature propagation source location and the potentially affected spatial location based on the early warning location information, calculate the temperature change rate of adjacent spatial locations in the temperature distribution matrix, and generate temperature field evolution curves. The results output module is used to calculate the degree of heat accumulation at the location of the temperature propagation source and the degree of heat diffusion at the potentially affected spatial location based on the temperature field evolution curve, and output the time-series change trend of the degree of heat accumulation and the degree of heat diffusion as temperature monitoring results.

10. An electronic device, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the steps of the method as described in any one of claims 1 to 8.

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