Jade mattress temperature monitoring control system based on temperature sensor

Abstract

The invention relates to the technical field of mattress temperature control, and discloses a jade mattress temperature monitoring control system based on a temperature sensor. A temperature data monitoring module of the system monitors the temperature of the mattress in real time and obtains a temperature value; the temperature state analysis module compares the temperature value with a preset interval, and generates a temperature abnormal signal if the temperature value is not in the interval; the temperature influence analysis module monitors the environment humidity based on the abnormal signal, obtains a temperature influence value and compares the temperature influence value with a threshold value to generate an influence size signal; the temperature correlation evaluation module obtains a temperature change trend value and a similarity value based on the influence magnitude signal, and obtains a temperature correlation value through correlation calculation; the temperature adjusting module obtains an adjusting coefficient based on the correlation value and further adjusts the heating temperature of the mattress. According to the system, real-time monitoring, anomaly recognition, multi-factor analysis and accurate adjustment of the temperature of the jade mattress are achieved through cooperation of multiple modules, the use comfort and reliability are improved, and the system adapts to the temperature adjustment requirements in different environments.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of mattress temperature control, in particular to a jade mattress temperature monitoring and control system based on temperature sensors. BACKGROUND

[0002] With the improvement of people's living quality, jade mattresses are widely used in the field of health and wellness due to the trace elements and warm effects contained in jade. Jade mattresses usually achieve temperature regulation through built-in heating devices to meet the user's use requirements in different seasons and environments. However, the current temperature control technology of jade mattresses still has many problems to be improved.

[0003] The temperature control of existing jade mattresses mostly adopts a simple constant temperature control method, which can only perform single heating or stopping heating operation according to the preset temperature, and lacks real-time dynamic monitoring and accurate regulation of the temperature. In actual use, the mattress temperature is easily affected by environmental factors, for example, changes in environmental humidity will change the human perception of temperature, and may also affect the working efficiency of the mattress heating element. When the environmental humidity is too high, the heat transfer efficiency decreases, which may cause the actual temperature of the mattress to be lower than the set value; while the humidity is too low, the heat is gathered too quickly, which may cause the temperature to exceed the safety range.

[0004] Traditional temperature control systems often rely on only a single temperature sensor for data collection, lacking an effective identification mechanism for temperature abnormalities. When the temperature fluctuates abnormally, the system cannot determine the cause of the abnormality in time and can only perform simple power-off or alarm processing, making it difficult to make targeted adjustments according to the actual situation. In addition, the existing system does not consider the trend and similarity of temperature changes, and cannot deeply analyze the temperature change law, resulting in temperature regulation lag, making it difficult to adapt to the dynamic changes of the environment and user needs.

[0005] In the temperature regulation process, the existing technology is mostly based on fixed adjustment parameters, without comprehensive evaluation combined with the associated factors of temperature change, resulting in insufficient adjustment accuracy. This not only affects the user's experience, causing discomfort due to excessively high or low temperature, but also may affect the service life of the jade mattress due to long-term temperature abnormalities, and even pose a safety risk due to temperature loss of control. Therefore, how to realize real-time and accurate monitoring, multi-factor comprehensive analysis and intelligent adjustment of the temperature of the jade mattress has become a problem to be solved in the development of current jade mattress technology. SUMMARY

[0006] The purpose of the present application is to provide a jade mattress temperature monitoring and control system based on temperature sensors to solve the problems raised in the background.

[0007] To achieve the above purpose, the present application provides a jade mattress temperature monitoring and control system based on temperature sensors, which comprises: a temperature data monitoring module, configured to monitor temperature data of the jade mattress in real time and obtain a temperature value; a temperature state analysis module, configured to compare the temperature value with a preset temperature interval based on the temperature value, and generate a temperature anomaly signal if the temperature value does not exist in the preset temperature interval; a temperature influence analysis module, configured to monitor environmental humidity data of the jade mattress based on the temperature anomaly signal, obtain a temperature influence value, and compare the temperature influence value with a temperature influence threshold value to generate a temperature influence size signal; a temperature correlation evaluation module, configured to obtain a temperature change trend value and a temperature change similarity value based on the temperature influence size signal, perform correlation calculation on the temperature change trend value and the temperature change similarity value, and obtain a temperature correlation value; a temperature adjustment module, configured to obtain a temperature adjustment coefficient based on the temperature correlation value, and adjust a heating temperature of the jade mattress based on the temperature adjustment coefficient.

[0008] Preferably, the temperature data monitoring module comprises: a region division unit, configured to divide the jade mattress into a plurality of monitoring sub-regions; a temperature obtaining unit, configured to obtain a real-time temperature value in each monitoring sub-region; a temperature representation value calculation unit, configured to add and average the real-time temperature value in each monitoring sub-region to obtain a sub-region temperature average value, and perform ratio calculation on the sub-region temperature average value and a preset temperature standard value to obtain a temperature representation value.

[0009] Preferably, the temperature influence analysis module comprises: a humidity data acquisition unit, configured to acquire environmental humidity data of the jade mattress; an influence value calculation unit, configured to add a temperature fluctuation influence value and a temperature distribution influence value to obtain the temperature influence value.

[0010] Preferably, the temperature fluctuation influence value is obtained in the following manner: a time node division unit, configured to divide a monitoring period into a plurality of time nodes; a fluctuation synchronization value calculation unit, configured to perform ratio calculation on a temperature peak-valley slope value and a humidity peak-valley slope value in a same time period to obtain a fluctuation synchronization value, and extract a time period with similar fluctuation synchronization values and mark the time period as an influence time period; a fluctuation influence value calculation unit, configured to obtain a time length value of the influence time period, perform ratio calculation on the time length value of the influence time period and a total time length value of the monitoring period to obtain the temperature fluctuation influence value.

[0011] Preferably, the temperature peak-valley slope value is obtained in the following way: A coordinate system establishing unit is configured to establish a coordinate system with an X-axis representing time nodes and a Y-axis representing temperature values. A temperature curve drawing unit is configured to draw a temperature change curve by substituting the temperature value of each time node into the coordinate system. A peak-valley slope calculating unit is configured to obtain temperature peak points and temperature valley points based on the temperature change curve, and calculate the slope of the coordinates of adjacent temperature peak points and temperature valley points to obtain the temperature peak-valley slope value. The humidity peak-valley slope value is obtained in the following way: A coordinate system establishing unit is configured to establish a coordinate system with an X-axis representing time nodes and a Y-axis representing humidity values. A humidity curve drawing unit is configured to draw a humidity change curve by substituting the humidity value of each time node into the coordinate system. A peak-valley slope calculating unit is configured to obtain humidity peak points and humidity valley points based on the humidity change curve, and calculate the slope of the coordinates of adjacent humidity peak points and humidity valley points to obtain the humidity peak-valley slope value.

[0012] Preferably, the temperature distribution influence value is obtained in the following way: A temperature deviation comparing unit is configured to compare the sub-region temperature deviation value with a sub-region temperature deviation threshold value, and mark a sub-region as a temperature abnormal sub-region if the sub-region temperature deviation value is greater than or equal to the sub-region temperature deviation threshold value. A humidity deviation comparing unit is configured to compare the sub-region humidity deviation value with a sub-region humidity deviation threshold value, and mark a sub-region as a humidity abnormal sub-region if the sub-region humidity deviation value is greater than or equal to the sub-region humidity deviation threshold value. An abnormal region pairing unit is configured to overlap and pair the temperature abnormal sub-region and the humidity abnormal sub-region to obtain an abnormal overlap sub-region, and obtain the area of the abnormal overlap sub-region. A distribution influence value calculating unit is configured to calculate the ratio of the area of the abnormal overlap sub-region to the total area of the monitoring region to obtain the temperature distribution influence value.

[0013] Preferably, the sub-region temperature deviation value is obtained in the following way: A temperature monitoring unit is configured to obtain the measured temperature value of each monitoring sub-region. A temperature deviation calculating unit is configured to calculate the difference between the measured temperature value and a sub-region temperature standard value to obtain the sub-region temperature deviation value. The sub-region humidity deviation value is obtained in the following way: A humidity monitoring unit is configured to obtain the measured humidity value of each monitoring sub-region. A humidity deviation calculation unit is configured to calculate the difference between the measured humidity value and the sub-region humidity standard value to obtain the sub-region humidity deviation value.

[0014] Preferably, the temperature change similarity value is obtained by: A slope ratio calculation unit is configured to obtain the temperature peak-valley slope value and the humidity peak-valley slope value in the influence period, and calculate the ratio of the temperature peak-valley slope value to the humidity peak-valley slope value to obtain the slope ratio value. A similarity value calculation unit is configured to add up the slope ratio values of each influence period and take the average to obtain the temperature change similarity value.

[0015] Preferably, the temperature change trend value is obtained by: A synchronization deviation calculation unit is configured to calculate the difference between all fluctuation synchronization values in the monitoring period and the fluctuation synchronization standard value to obtain the synchronization deviation value. A trend value calculation unit is configured to calculate the standard deviation of the synchronization deviation value to obtain the temperature change trend value.

[0016] Preferably, the temperature adjustment coefficient is obtained by: An adjustment coefficient calculation unit is configured to add up all temperature correlation values and take the average to obtain the temperature adjustment coefficient.

[0017] Compared with the prior art, the present application has the following advantages: The jade mattress temperature monitoring control system based on the temperature sensor brings multiple practical values through the cooperative work of various modules. The temperature data monitoring module can capture the temperature value of the jade mattress in real time, so that the system always grasps the real-time temperature state of the mattress, avoiding the regulation lag problem caused by the untimely acquisition of temperature information. This real-time nature ensures the rapid response of the system to temperature changes, providing timely basic data for subsequent analysis and adjustment.

[0018] The temperature state analysis module can quickly identify temperature abnormal conditions and generate abnormal signals by comparing the real-time temperature value with the preset temperature interval. This process does not require manual intervention and automatically judges whether the temperature deviates from the reasonable range, reducing the omissions of human monitoring, so that the temperature abnormality is discovered at the first time, avoiding the adverse effects of the abnormal temperature on user experience or the mattress itself.

[0019] After receiving the temperature abnormality signal, the temperature influence analysis module introduces environmental humidity data for monitoring and generates a temperature influence size signal. This design takes into account the actual influence of environmental factors on the temperature of the mattress, breaking through the limitation of traditional control relying only on a single temperature parameter, making the temperature analysis more comprehensive, and enabling the system to more accurately grasp the cause of temperature abnormalities.

[0020] The temperature correlation evaluation module obtains temperature change trend values and temperature change similarity values based on the temperature influence size signal and performs correlation calculation to obtain a temperature correlation value. Through analysis of the temperature change trend and similarity law, the system can deeply understand the internal law of temperature change, avoid isolated judgment of temperature anomalies, and make the temperature state evaluation more accurate, providing a more scientific basis for subsequent adjustment.

[0021] The temperature adjustment module obtains an adjustment coefficient based on the temperature correlation value and adjusts the heating temperature, realizing the precision and intelligence of temperature adjustment. This adjustment method no longer relies on fixed parameters, but dynamically adjusts according to the correlation value obtained through comprehensive analysis, which can adapt to the adjustment needs under different environmental humidity and different temperature change trends, keep the temperature of the jade mattress within the range of user comfort and safety, improve the overall use experience, and also reduce various problems caused by improper temperature. The coordinated operation of each module forms an organic whole from temperature monitoring, anomaly identification, influence analysis to final adjustment, significantly improving the reliability and adaptability of jade mattress temperature control. BRIEF DESCRIPTION OF DRAWINGS

[0022] Figure 1 The timing diagram of the jade mattress temperature monitoring control system based on temperature sensors according to the present application; Figure 2 The flowchart of the temperature data monitoring module; Figure 3 The flowchart of obtaining temperature peak-valley slope values and humidity peak-valley slope values; Figure 4 The flowchart of obtaining temperature change trend values. DETAILED DESCRIPTION

[0023] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative labor are within the scope of protection of the present application.

[0024] Please refer to Figure 1 The present application provides a jade mattress temperature monitoring control system based on temperature sensors, which comprises: The surface temperature data of the jade mattress is collected in real time by the temperature data monitoring module, and the obtained temperature value is compared and analyzed with the preset temperature interval. When it is detected that the temperature exceeds the preset range, the temperature state analysis module generates a temperature abnormality signal to trigger the subsequent processing process. The temperature influence analysis module then starts the environmental humidity monitoring function, determines the influence degree of environmental factors on the temperature anomaly by calculating the comprehensive index of temperature fluctuation influence value and distribution influence value. The temperature correlation evaluation module further analyzes the similarity characteristics of temperature change trend and humidity change according to the influence degree, and generates a temperature adjustment parameter through a correlation algorithm. Finally, the final temperature adjustment module dynamically adjusts the heating power of each region of the jade mattress according to the calculated adjustment coefficient, so as to realize precise temperature control.

[0025] Embodiment 1: refer to Figure 2 , covering three technical levels of monitoring network construction, data processing algorithm and environmental parameter coupling analysis. In the physical deployment stage, the surface of the jade mattress is planned as a rectangular monitoring area with uniform size, and a DS18B20 digital temperature sensor is arranged at the center point of each region, and the sensor spacing is set to 10 centimeters according to the heat conduction characteristics. Each sensor is connected through a single bus protocol network, and the bus terminal is connected to the GPIO pin of the STM32F407 master chip, and the device address mapping table is loaded during system initialization to realize partition identification. The temperature sampling period is fixed at 1 second, and the DMA controller built-in the master chip directly transmits the raw data to the cache area, eliminating the CPU interrupt delay.

[0026] The temperature data processing adopts a three-stage pipeline architecture: the first stage performs sliding average filtering, sets a window width of 10 samples, and performs noise suppression on the original temperature sequence of each monitoring point; the second stage performs regional aggregation operation, calculates the arithmetic mean value of all sensors in each sub-region within a 10-second window period; and the third stage performs standardization conversion, and calculates the ratio of the average value to the preset physiological suitable temperature of 37℃. When the temperature value of a region is 36.5℃, the output value is 0.986, which is stored in the ring buffer with three decimal places.

[0027] The environmental humidity monitoring network is independently deployed in the same partition grid, and a HIH6130 humidity sensor is installed in each corner of each sub-region to form a spatial complement. The humidity data is collected at a period of 5 minutes, and is transmitted to the dedicated processing unit through the I 2 C bus. When the temperature fluctuation analysis module is started, the time stamps of the temperature and humidity data are automatically aligned, and a double-parameter time series is established based on a 30-minute monitoring period. The data processing unit generates a continuous curve by performing cubic spline interpolation on the discrete data points, and uses curvature analysis method to locate the coordinates of the wave peak and wave trough when extracting feature points from the temperature curve: calculate the change rate of the tangent vector of the curve, and when the derivative sign changes from positive to negative, it is determined as a wave peak, and vice versa.

[0028] The fluctuation synchronization value calculation adopts a dynamic time warping algorithm: first, the peak and valley slope sequence of the temperature and humidity curves is normalized to eliminate dimensional differences; second, a distance matrix is constructed to calculate the Euclidean distance between each slope of the two sequences; and finally, similar feature points are matched through dynamic programming path optimization. When the cumulative distance of the path is below the 0.85 similarity threshold, the time period is marked as an effective impact time period. The temperature fluctuation impact value is obtained by integral operation: the cumulative duration of all impact time periods is divided by the total duration of the monitoring period, 1800 seconds, and the result is retained as a percentage such as 32.67% indicating the impact intensity.

[0029] The temperature distribution impact analysis is activated synchronously, and the system scans the temperature representation value deviation record of each sub-region. When the deviation of a certain region exceeds ±0.05 standardized units (equivalent to an actual temperature of ±2°C) for three consecutive sampling periods, the region is marked as a red abnormal area. The humidity abnormality determination adopts a relative threshold mechanism: if the difference between the current humidity value and the standard value of 60% RH exceeds ±15% for three consecutive times, a humidity abnormal area is generated. The spatial correlation unit superimposes and analyzes the two types of abnormal areas, calculates the overlapping area based on spatial coordinates: uses the scan line algorithm to calculate the vertex coordinates of the irregular polygon to determine the overlapping area in millimeters, which accounts for a percentage of the total surface area of the mattress (1.44 million square centimeters), and this value is used as the distribution impact factor in the final calculation.

[0030] Embodiment 2: refer to Figure 3 , which involves the accurate extraction and analysis process of temperature and humidity fluctuation characteristics, the core of which is to establish a multi-dimensional parameter coordinate system and realize dynamic feature matching. When the system is initialized, two independent two-dimensional rectangular coordinate systems are constructed, both with a unified time reference on the horizontal axis, marked in seconds, and the vertical axis corresponds to the temperature value (unit: Celsius) and relative humidity value (unit: percentage) respectively. The scale range of the coordinate system is dynamically adjusted according to the actual working conditions of the jade mattress, with a default temperature axis display range of 30-40°C and a default humidity axis display range of 40-80% RH. The system automatically expands the coordinate range according to real-time monitoring data.

[0031] The data mapping process adopts a discrete point set conversion method to convert the preprocessed sensor raw data into discrete points in the coordinate system. Temperature data points are generated into continuous and smooth curves through a cubic spline interpolation algorithm, which maintains data continuity while avoiding overfitting. Humidity data is fitted using a Bezier curve, focusing on preserving trend characteristics. During curve generation, an adaptive smoothing parameter is set to automatically increase the control point density when the fluctuation amplitude of adjacent data points exceeds the threshold. The generated curves are stored in a ring buffer, retaining the complete waveform data of the last 60 minutes for feature analysis.

[0032] The extreme point detection uses an improved derivative analysis method, and the system calculates the first and second derivatives of the curve in real time. For the temperature curve, when the first derivative changes from positive to negative and the second derivative is negative, it is determined that the point is a temperature peak; when the first derivative changes from negative to positive and the second derivative is positive, it is determined that the point is a temperature valley. The extreme point detection of the humidity curve adds a lag compensation mechanism to avoid false positives caused by humidity change inertia. All detected extreme point coordinates are recorded in the feature point list, including timestamp and numerical information.

[0033] The slope calculation module dynamically pairs adjacent extreme points and establishes a peak-valley or valley-peak combination according to the time sequence. The slope value of each combination is obtained by coordinate difference calculation, and the result is kept to four significant digits. The slope sequence of the temperature curve is standardized to eliminate the dimensional influence caused by absolute temperature differences. The system maintains two independent but time-synchronized slope queues: temperature slope queue and humidity slope queue, each with a fixed length of 30 samples, and uses a first-in, first-out strategy to update the data.

[0034] The feature matching process introduces an improved version of the dynamic time warping algorithm, which can handle similarity calculation of non-equal length time series. The system first normalizes the length of the two slope sequences, then constructs a cost matrix to calculate the optimal matching path. A slope difference tolerance threshold is set during the matching process, and when the difference of a single slope pair exceeds the threshold, the weight of that matching pair is automatically reduced. The final output similarity index is nonlinearly transformed and mapped to the standard range of 0-1. The system continuously monitors the trend of the similarity index, and when the similarity of three consecutive periods exceeds the preset threshold, a temperature-humidity correlation event is triggered.

[0035] The fluctuation feature analysis uses a multi-scale decomposition method, and the system simultaneously monitors short-term (5-minute window) and long-term (30-minute window) fluctuation patterns. The short-term analysis focuses on capturing transient features that change rapidly, and the long-term analysis reveals periodic variation patterns. The analysis results of the two time scales are fused by weighted integration to generate a comprehensive fluctuation index, where the short-term feature weight is set to 0.6 and the long-term feature weight is set to 0.4. This index is used to assess the actual impact of environmental humidity on temperature fluctuations, serving as an important input parameter for subsequent temperature adjustment decisions.

[0036] The abnormal fluctuation detection mechanism monitors the statistical properties of the slope sequence in real time. The system maintains a dynamically updated reference baseline, and when the current slope value deviates from the baseline by more than three standard deviations, it is marked as an abnormal fluctuation event. The abnormal event triggers detailed data recording function, saving the complete waveform data of 30 seconds before and after the event for offline analysis. At the same time, the system automatically adjusts the filtering parameters to enhance the suppression of sudden disturbances. All detected abnormal events generate log records, including timestamp, fluctuation amplitude and duration, etc. metadata.

[0037] The real-time visualization module converts the processing results into graphical elements, and synchronously displays the temperature curve, humidity curve and their characteristic point markers on the monitoring interface. The user interface adopts a layered rendering technology, the basic waveform layer displays the original data trend, the characteristic marker layer highlights the extreme points and slope direction, and the analysis result layer superimposes the similarity index and fluctuation intensity. The graphical rendering frame rate is locked at 30fps, ensuring smooth display of dynamic changes. The interface elements support multi-point touch zooming and panning operations, facilitating users to view the waveform characteristics of a specific time period in detail.

[0038] Example 3: Spatial correlation analysis focusing on temperature and humidity abnormal areas, precise distribution impact calculation is achieved by establishing a multi-dimensional deviation evaluation matrix. In the system initialization stage, a three-dimensional coordinate mapping system is constructed, in which the x-axis and y-axis represent the physical position coordinates of the mattress surface, and the z-axis stores the double-layer data of temperature deviation and humidity deviation. The size of each monitoring sub-area is set to a 10cm×10cm square grid, forming 144 independent analysis units on a standard mattress surface of 180cm×80cm. The temperature deviation data collection uses an array network of DS18B20 sensors, updating the measured temperature value at the center of each grid every 5 seconds, and calculating the absolute difference with the preset standard value of 37℃:

[0039] wherein: represents the temperature deviation value of the i-th row and j-th column sub-area, is the measured temperature value of the sub-area, is the standard temperature value of 37℃. Humidity deviation monitoring is carried out synchronously, and the HIH6130 sensor network collects the relative humidity data of each grid at a period of 1 minute, and calculates the absolute deviation with the standard value of 60%RH. The system sets double judgment conditions: when the temperature deviation of a sub-area is ≥0.5℃ for 3 consecutive temperature sampling periods, the area is marked as a red temperature abnormal area; when the humidity deviation is ≥15%RH for 3 consecutive humidity sampling periods, it is marked as a blue humidity abnormal area.

[0040] The spatial matching of abnormal areas uses an improved gridding analysis method. The system discretizes the mattress surface into a fine pixel grid of 1cm×1cm, and performs boundary extraction and filling processing for each abnormal sub-area. The temperature abnormal area generates a binary matrix , where the abnormal grid is set to 1 and the normal grid is set to 0; the humidity abnormal area generates a matrix . The spatial correlation unit performs a bitwise AND operation on the matrices, and the pixel points with a value of 1 in the result matrix are the abnormal overlap areas. The pixel counting module counts the number of 1s in , which is converted into the actual physical area .

[0041] The calculation process of distribution impact value introduces an area weight adjustment mechanism. The system first calculates the total monitoring area of the mattress , and then processes it according to the distribution characteristics of the abnormal overlapping area: when , the area proportion is directly calculated; when , a segmented linear function is used to amplify the impact coefficient; when , a nonlinear transformation algorithm is started. The final output of the distribution impact value is rounded to three decimal places for subsequent comprehensive evaluation.

[0042] The abnormal area dynamic tracking module establishes a spatiotemporal correlation model. The system maintains a circular queue with a length of 10, recording the evolution data of abnormal areas in the last 10 analysis periods. The spatial change analysis unit calculates the overlap rate index of adjacent periods of abnormal areas, and determines that the abnormal area migration event occurs when the overlap rate is lower than 50%. For migration events, the system automatically increases the monitoring frequency of the surrounding area, and generates a red warning trajectory line on the visualization interface. The temperature-humidity coupling analyzer compares the morphological characteristics of the abnormal areas, calculates the contour similarity and centroid distance, and determines that it is a strong correlation anomaly when the centroid distance is less than 5 cm.

[0043] The visualization system uses layered rendering technology to present the analysis results. The basic layer displays the two-dimensional plan view of the mattress surface, the middle layer superimposes the red translucent mask of the temperature abnormal area and the blue mask of the humidity abnormal area, and the top layer uses purple to identify the abnormal overlapping area. The interactive exploration function allows users to click on any sub-area to view detailed data, including real-time temperature and humidity values, historical deviation curves, and correlation indicators. The system automatically generates a heat map snapshot every 30 seconds, saving the spatial distribution evolution record of the last 10 minutes.

[0044] The abnormal warning mechanism adopts a multi-level triggering strategy. The primary warning is activated when an abnormality occurs in a single sub-area, which is manifested as flashing icons on the interface; the secondary warning is triggered when 3 adjacent sub-areas are abnormal at the same time, accompanied by a sound prompt; the senior warning is started when the area of the abnormal overlapping area exceeds , automatically reduces the heating power and sends a mobile phone notification. The warning response module integrates an adaptive adjustment algorithm, which adjusts the response strategy according to the distribution position of the abnormal area: the abnormality of the edge area of the mattress triggers a gentle adjustment, and the abnormality of the center area triggers a rapid response.

[0045] The data persistence module realizes the long-term storage of the analysis results. A structured record is generated every monitoring period, containing fields such as timestamp, number of abnormal areas, overlapping area, distribution impact value, etc. The storage adopts a ring buffer and cloud synchronization dual mechanism, retaining the detailed data of the last 7 days locally and storing monthly statistical reports in the cloud. The query service supports multi-dimensional retrieval according to time range, abnormal level and area location, etc., and the returned results include original data and visualization charts. The system regularly generates distribution feature reports to analyze the frequency, duration and spatial distribution of abnormal areas.

[0046] Example 4: Dynamic similarity analysis is carried out around temperature and humidity changes, and multi-scale feature matching is realized by establishing a time window mechanism. The sensor array deployed by the system in the upper left corner area (coordinates X1-Y3) of the mattress records a set of typical data, demonstrating the complete processing flow. The monitoring data is collected in 5-minute cycles, Table 1: The temperature and humidity change observations of this area for the last 6 cycles are as follows.

[0047]

[0048] The standard value calibration process is automatically executed when the system starts, and the main control chip drives the heating element to gradually heat up each area of the mattress. When the temperature in the X1-Y3 area reaches 37°C and remains stable, the measured value of 36.8°C at that time is recorded as the temperature reference for this sub-area. The humidity reference value is the median of the last 10 measurements after the environment stabilizes, which is determined to be 55% RH. The deviation calculation module performs detection every 30 seconds, and when the absolute difference between the measured temperature and the reference value exceeds 0.5°C, it is marked as a temporary deviation event; if it lasts for 3 cycles, it is upgraded to a continuous deviation event.

[0049] The change trend analysis uses a sliding time window technique, with a window width dynamically adjusted in the range of 2-10 minutes. During the 08:10-08:20 period, the system detects that the temperature drops from 37.1°C to 36.3°C while the humidity rises from 49% RH to 57% RH, forming an inverse change trend. The feature extraction unit calculates the correlation coefficient of the temperature and humidity change rates within this window, and when the absolute value exceeds 0.7, it is determined to be a significant correlation. The similarity evaluation module introduces a time alignment algorithm to compensate for the inherent difference in response time between the temperature and humidity sensors, ensuring the consistency of the comparison reference.

[0050] The dynamic weight adjustment mechanism adjusts the analysis parameters according to the environmental state. The night mode automatically extends the analysis window to 10 minutes, reducing the sensitivity to transient fluctuations; the daytime mode shortens the window to 3 minutes, enhancing the ability to capture rapid changes. At 08:15, the system detects that the synchronization of temperature drop rate and humidity rise rate reaches a peak, at which time the similarity weight of the current window is raised to 0.7, while the previous window weight is reduced to 0.3. When the weighted calculation generates a comprehensive similarity index converted into an adjustment coefficient, a piecewise linear mapping strategy is adopted: 0-0.3 interval corresponds to fine tuning, 0.3-0.7 interval corresponds to medium tuning, and 0.7-1.0 interval corresponds to strong tuning.

[0051] The abnormal event processing flow presents typical application scenarios. When the temperature rises and the humidity drops during 08:05-08:10, the system first checks the sensor state flag to exclude the possibility of device failure; secondly, it analyzes the surrounding area data to confirm whether it is a local phenomenon; finally, it queries the historical database to compare with the typical patterns under similar environmental conditions. If it is confirmed as a real anomaly, an event record containing fields such as timestamp, region coordinates, and deviation degree is generated, triggering a secondary response strategy: adjusting the target region heating power to 90% of the standard value, while activating the monitoring enhancement mode of adjacent regions.

[0052] The visualization interface realizes multi-dimensional data fusion display. In the region detail panel, the temperature curve is drawn with a red solid line, the humidity curve is represented with a blue dashed line, and the matching period of change trend is displayed as a purple background. Interactive controls allow users to drag the time axis to view the double waveform comparison of any time period, and click on specific data points to view the original sampling values, processing intermediate results, and final determination basis. The system automatically generates a similarity trend chart every 5 minutes, displaying the analysis result changes of the last 1 hour in line form.

[0053] The data persistence adopts a layered storage architecture. Raw sampling data is cached locally in binary format, retaining the last 48 hours of records; feature extraction results and event records are stored in a structured format and synchronized to the cloud database. Query interfaces support combined retrieval by time range, region location, event type, etc., with results returned in both raw data table and visualization chart formats. The system maintenance module regularly performs data quality checks, identifies and repairs abnormal records, ensuring the reliability of the analysis foundation.

[0054] Example 5: see Figure 4, focusing on quantitative analysis of temperature fluctuation patterns and decision-making generation, precise environmental adaptation control is achieved through time series data processing. The system initializes the ring-shaped data buffer, continuously stores the fluctuation synchronization value records of the last 20 monitoring periods. The synchronization value of each period is derived from the dynamic matching analysis of temperature and humidity changes, and the value is stored in the time series queue with three decimal places. The synchronization deviation calculation module performs difference operation when new data arrives each time, and the absolute deviation of the current fluctuation synchronization value from the preset reference value 0.75 is stored in the temporary register.

[0055] The time series processing unit uses a window moving mechanism to process the synchronization deviation sequence. The observation window is fixed at 20 samples, and the oldest record is automatically squeezed out when new data enters to maintain a constant queue length. During the standardization process, the system calculates the arithmetic mean and root mean square dispersion of the data in the window in real time. Each deviation value is subtracted from the mean and divided by the dispersion to obtain the normalized variable, which dynamically reflects the deviation of the current state from the overall fluctuation pattern. The trend strength index is obtained by squaring operation: after squaring all normalized variables, the sum is divided by the sample size to generate the change trend value. This value is updated every 5 seconds, continuously evaluating the development trend of the fluctuation pattern.

[0056] The integrated processing of temperature correlation values introduces a time decay mechanism. The system maintains records of correlation values for five consecutive calculation periods, with different weight coefficients assigned in reverse chronological order. The latest period data occupies a central position, with a decision weight of 0.5; the previous four periods obtain coefficients of 0.25, 0.125, 0.0625, and 0.0625, respectively. The weighted processing process first multiplies each correlation value by the corresponding weight, then adds the five product results to synthesize the comprehensive index. This synthesized value is mapped to the 0.8-1.2 regulation interval through an S-shaped curve conversion function, generating the final temperature adjustment coefficient. The coefficient conversion process sets a smooth transition mechanism to prevent sudden changes in adjacent period output values.

[0057] The power actuator receives the adjustment coefficient and executes a multi-level response strategy. The main controller analyzes the coefficient value, and when it is in the 0.95-1.05 interval, it maintains the current power; below 0.95, it starts the heating program, with each 0.01 deviation corresponding to an increase of 2% pulse width modulation duty cycle; above 1.05, it starts the cooling program, with each 0.01 deviation corresponding to a decrease of 3% working period. The power regulation adopts a ramp control algorithm, setting a gradual change rate of not more than 5% per minute to avoid sudden temperature changes affecting user experience. The execution process monitors the heating element current in real time, and automatically switches to the safety mode when there is abnormal fluctuation.

[0058] The running state visualization interface sets up a three-layer display structure. The background layer shows the trend value curve of the last 30 minutes, with a green gradient fill indicating stability; the middle layer superimposes the associated value change line, with key turning points labeled with timestamps; the foreground layer displays the current coefficient in real time with a red adjustment pointer. Users can view detailed parameters at any time point through touch interaction, and the system provides a voice broadcast mode to report important state changes. The data recording system creates dual backup storage, with local flash memory saving original time series data and cloud synchronous storage storing decision parameter logs for remote review.

[0059] The abnormality handling mechanism establishes a hierarchical response system. When the trend value exceeds the threshold for three consecutive periods, the system issues a primary audio alert and displays a yellow warning sign on the interface; if the associated value continues to be abnormal, it triggers a secondary response, automatically locking the adjustment coefficient at a safe range of 1.0; in extreme cases, if electrical parameter abnormalities are detected, the third-level protection is immediately activated, disconnecting the heating circuit and sending an emergency notification. All abnormal events generate encrypted log records, including complete environmental parameter snapshots and decision path tracking data. The self-maintenance subsystem regularly calibrates sensor reference values, and performs full-system diagnostic tests every month to ensure monitoring accuracy.

[0060] The control parameter optimization module dynamically fine-tunes based on long-term running data. The system analyzes the adjustment effect data in the historical records, automatically adjusting the weight distribution ratio and the conversion function curvature. A running characteristic report is generated every quarter, comparing the environmental adaptability performance in different seasons. The user preference learning unit records manual intervention records, gradually adapting to individual temperature control needs to form customized strategies. The remote service interface supports expert diagnosis mode, allowing authorized technical personnel to correct core parameters online to cope with special working conditions.

[0061] It should be noted that, in the present text, relational terms such as first and second are used merely to distinguish one entity or action from another, without necessarily requiring or implying any actual such relationship or order between or among the entities or actions. Also, the terms "comprises", "comprising", or any other variations thereof, are intended to cover non-exclusive inclusions, so that a process, method, article, or apparatus that comprises a list of elements does not only include those elements, but can also include other elements not expressly listed or inherent to such process, method, article, or apparatus.

[0062] Although embodiments of the present application have been shown and described, it will be understood by those having ordinary skill in the art that various changes, modifications, alternatives, and variations can be made thereto without departing from the principles and spirit of the application, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A temperature sensor jade mattress temperature monitoring control system based on, characterized by, The method comprises the following steps: A temperature data monitoring module is used to monitor the temperature data of the jade mattress in real time and obtain a temperature value; A temperature state analysis module is used to compare the temperature value with a preset temperature interval based on the temperature value, generate a temperature anomaly signal if the temperature value does not exist in the preset temperature interval; A temperature influence analysis module is used to monitor the environmental humidity data of the jade mattress based on the temperature anomaly signal, obtain a temperature influence value, compare the temperature influence value with a temperature influence threshold, and generate a temperature influence size signal; A temperature correlation evaluation module is used to obtain a temperature change trend value and a temperature change similarity value based on the temperature influence size signal, perform correlation calculation on the temperature change trend value and the temperature change similarity value, and obtain a temperature correlation value; A temperature adjustment module is used to obtain a temperature adjustment coefficient based on the temperature correlation value, and adjust the heating temperature of the jade mattress based on the temperature adjustment coefficient.

2. The temperature sensor based jade mattress temperature monitoring control system according to claim 1, wherein, The temperature data monitoring module comprises: A region division unit is used to divide the jade mattress into a plurality of monitoring sub-regions; A temperature acquisition unit is used to obtain real-time temperature values in each monitoring sub-region; A temperature representation value calculation unit is used to add and average the real-time temperature values in each monitoring sub-region to obtain a sub-region temperature average value, and perform ratio calculation on the sub-region temperature average value and a preset temperature standard value to obtain a temperature representation value.

3. The temperature sensor based jade mattress temperature monitoring control system according to claim 2, wherein, The temperature influence analysis module comprises: A humidity data acquisition unit is used to acquire the environmental humidity data of the jade mattress; An influence value calculation unit is used to add a temperature fluctuation influence value and a temperature distribution influence value to obtain the temperature influence value.

4. The temperature sensor based jade mattress temperature monitoring control system according to claim 3, wherein, The temperature fluctuation influence value is obtained in the following manner: A time node division unit is used to divide a monitoring period into a plurality of time nodes; A fluctuation synchronization value calculation unit is used to perform ratio calculation on a temperature peak-valley slope value and a humidity peak-valley slope value in the same time period to obtain a fluctuation synchronization value, and extract a time period with similar fluctuation synchronization values and mark it as an influence time period; A fluctuation influence value calculation unit is used to obtain a time length value of the influence time period, perform ratio calculation on the time length value of the influence time period and a total time length value of the monitoring period to obtain the temperature fluctuation influence value.

5. The temperature sensor based jade mattress temperature monitoring control system according to claim 4, wherein, The temperature peak-valley slope value is obtained in the following manner: A coordinate system establishment unit is used to establish a coordinate system with an X-axis representing time nodes and a Y-axis representing temperature values; A temperature curve drawing unit is used to substitute the temperature values of each time node into the coordinate system to draw a temperature change curve; A peak-valley slope calculation unit is used to obtain a temperature peak point and a temperature valley point based on the temperature change curve, perform slope calculation on the coordinates of adjacent temperature peak points and temperature valley points to obtain the temperature peak-valley slope value; The humidity peak-valley slope value is obtained in the following manner: A coordinate system establishment unit is used to establish a coordinate system with an X-axis representing time nodes and a Y-axis representing humidity values; A humidity curve drawing unit is used to substitute the humidity values of each time node into the coordinate system to draw a humidity change curve; A peak-valley slope calculation unit is configured to obtain a humidity peak point and a humidity valley point based on the humidity change curve, and calculate a slope of coordinates of adjacent humidity peak points and humidity valley points to obtain the humidity peak-valley slope value.

6. The temperature sensor based jade mattress temperature monitoring control system according to claim 5, wherein, The temperature distribution influence value is obtained in the following manner: A temperature deviation comparison unit is configured to compare the sub-region temperature deviation value with a sub-region temperature deviation threshold value, and mark a temperature abnormal sub-region if the sub-region temperature deviation value is greater than or equal to the sub-region temperature deviation threshold value. A humidity deviation comparison unit is configured to compare the sub-region humidity deviation value with a sub-region humidity deviation threshold value, and mark a humidity abnormal sub-region if the sub-region humidity deviation value is greater than or equal to the sub-region humidity deviation threshold value. An abnormal region pairing unit is configured to pair the temperature abnormal sub-region and the humidity abnormal sub-region to obtain an abnormal overlap sub-region, and obtain an area of the abnormal overlap sub-region. A distribution influence value calculation unit is configured to calculate a ratio of the area of the abnormal overlap sub-region to a total area of the monitoring region to obtain the temperature distribution influence value.

7. The temperature sensor based jade mattress temperature monitoring control system according to claim 6, wherein, The sub-region temperature deviation value is obtained in the following manner: A temperature monitoring unit is configured to obtain a measured temperature value of each monitoring sub-region. A temperature deviation calculation unit is configured to calculate a difference between the measured temperature value and a sub-region temperature standard value to obtain the sub-region temperature deviation value. The sub-region humidity deviation value is obtained in the following manner: A humidity monitoring unit is configured to obtain a measured humidity value of each monitoring sub-region. A humidity deviation calculation unit is configured to calculate a difference between the measured humidity value and a sub-region humidity standard value to obtain the sub-region humidity deviation value.

8. The temperature sensor based jade mattress temperature monitoring control system according to claim 7, wherein, The temperature change similarity value is obtained in the following manner: A slope ratio calculation unit is configured to obtain a temperature peak-valley slope value and a humidity peak-valley slope value in an influence time period, calculate a ratio of the temperature peak-valley slope value to the humidity peak-valley slope value to obtain a slope ratio, and calculate a sum of the slope ratios in each influence time period to obtain the temperature change similarity value. The temperature change trend value is obtained in the following manner:

9. The temperature sensor based jade mattress temperature monitoring control system according to claim 8, wherein, A synchronous deviation calculation unit is configured to calculate a difference between all fluctuation synchronization values in a monitoring period and a fluctuation synchronization standard value to obtain a synchronous deviation value. A trend value calculation unit is configured to calculate a standard deviation of the synchronous deviation value to obtain the temperature change trend value. The temperature adjustment coefficient is obtained in the following manner:

10. The temperature sensor based jade mattress temperature monitoring control system according to claim 9, wherein, An adjustment coefficient calculation unit is configured to calculate a sum of all temperature correlation values to obtain the temperature adjustment coefficient. ​

Images