Electric blanket fault monitoring method and monitoring device thereof

Abstract

The invention belongs to the technical field of fault monitoring, and discloses an electric blanket fault monitoring method and a monitoring device thereof. A heating area of the electric blanket is divided into a plurality of monitoring sub-areas, temperature data of the monitoring sub-areas and corresponding human body compression state data are collected respectively, human body compression distribution characteristics and compression change characteristics changing along with time are extracted in a preset time window, and local temperature anomaly characteristics are extracted. A compression-temperature correlation mode is constructed by performing correlation analysis on local temperature anomaly characteristics, human body compression distribution characteristics and compression change characteristics, and is compared with a correlation reference mode in a normal use state; whether the local temperature abnormity is a heat accumulation phenomenon caused by human body compression or a fault heat abnormity caused by an internal abnormity of the electric blanket is judged, and then a fault processing strategy is executed. According to the invention, a normal use state and an internal abnormal state can be effectively distinguished, the misjudgment rate is reduced, and the safety and reliability of the electric blanket in a complex use scene are improved.

Description

Technical Field

[0001] This invention relates to the field of fault monitoring technology, specifically to a fault monitoring method and device for electric blankets. Background Technology

[0002] In existing technologies, fault monitoring and safety protection methods for electric blankets mainly fall into the following categories: first, over-temperature detection based on temperature sensors, which activates protection when the local or overall temperature exceeds a preset threshold; second, anomaly detection based on current, voltage, or power, used to identify open circuits, short circuits, or load abnormalities; and third, cutting off the circuit in extreme cases using passive safety components such as thermal fuses. These technologies improve the safety of electric blankets to a certain extent.

[0003] However, in actual use, electric blankets are usually not in an ideal, uniform heat dissipation state. While lying down, users apply continuous or intermittent pressure to localized areas of the electric blanket, and it is often covered by bedding or mattresses, leading to significant differences in heat dissipation conditions in different areas. Under these conditions, even if the electric blanket itself does not suffer from structural failure, localized areas may still experience temperature increases due to limited heat dissipation.

[0004] Existing technologies generally employ fixed temperature thresholds or overheating detection methods based on single parameter variations. These methods struggle to distinguish between heat buildup caused by normal user behavior such as pressure from the body or stacking of bedding, and dangerous thermal anomalies caused by internal malfunctions such as aging heating wires, poor contact, or controller malfunctions. In this situation, on the one hand, normal usage is easily misjudged as a malfunction, frequently triggering power outages or power limiting controls, impacting user experience; on the other hand, increasing the temperature threshold to reduce the probability of misjudgment may prevent genuine malfunctions from being identified in a timely manner, posing safety hazards.

[0005] Furthermore, during sleep, the human body undergoes movements such as turning over and shifting, causing the pressure on different areas of the electric blanket to change over time. Existing fault monitoring solutions typically fail to effectively model and utilize the distribution and changing characteristics of human body pressure, and do not fully consider the correlation between pressure status and localized thermal anomalies. This makes it difficult for current technologies to accurately determine the causes of localized thermal anomalies in complex usage scenarios. Summary of the Invention

[0006] In view of the above-mentioned shortcomings mentioned in the background art, the purpose of this invention is to provide a fault monitoring method and monitoring device for electric blankets. This invention can comprehensively consider the distribution and changing characteristics of human body pressure in actual use environment, and make more accurate judgment on local thermal anomalies, so as to improve the safety and reliability of electric blankets in complex use scenarios.

[0007] A first aspect of the present invention provides a fault monitoring method for an electric blanket, the method comprising the following steps: S1. Divide the heating area of ​​the electric blanket into multiple independent monitoring sub-areas, collect temperature data of each monitoring sub-area during operation, and obtain human body pressure state data corresponding to the monitoring sub-area. S2. Within a preset time window, analyze the human body compression state data and extract the human body compression distribution characteristics of each monitoring sub-region and its compression change characteristics over time. S3. Analyze the temperature changes in each monitoring sub-region based on the temperature data to extract local temperature anomaly features, and perform correlation analysis between the local temperature anomaly features and the corresponding human body pressure distribution features and pressure change features. S4. Based on the correlation analysis results, the local temperature anomaly is determined: When the local temperature anomaly is consistent with the human body pressure distribution characteristics and pressure change characteristics of the corresponding monitoring sub-area, it is determined to be a heat accumulation phenomenon caused by normal use; otherwise, it is determined to be a faulty thermal anomaly caused by an internal abnormality of the electric blanket, and the corresponding fault handling strategy is executed.

[0008] A second aspect of the present invention provides a fault monitoring device for electric blankets, the device comprising: The area division and data acquisition module is used to divide the heating area of ​​the electric blanket into multiple independent monitoring sub-areas, collect temperature data of each monitoring sub-area during operation, and obtain human body pressure state data corresponding to the monitoring sub-area. The human body compression feature extraction module is used to analyze the human body compression state data within a preset time window and extract the human body compression distribution characteristics and the compression change characteristics over time in each monitoring sub-region. The temperature anomaly and correlation analysis module is used to analyze the temperature changes of each monitoring sub-region based on the temperature data, extract local temperature anomaly features, and perform correlation analysis between the local temperature anomaly features and the corresponding human body pressure distribution features and pressure change features. The anomaly detection and processing module is used to detect the local temperature anomaly based on the correlation analysis results. When the local temperature anomaly is consistent with the human body pressure distribution characteristics and pressure change characteristics of the corresponding monitoring sub-area, it is determined to be a heat accumulation phenomenon caused by normal use; otherwise, it is determined to be a faulty thermal anomaly caused by an internal abnormality of the electric blanket, and the corresponding fault handling strategy is executed.

[0009] A third aspect of the present invention provides a non-transitory computer-readable storage medium storing computer instructions for causing the computer to perform the method described in any of the preceding claims.

[0010] A fourth aspect of the present invention provides a computer program product including a computing program stored on a non-transitory computer-readable storage medium, the computer program including program instructions that, when executed by a computer, cause the computer to perform the method described in any of the preceding claims.

[0011] Compared with the prior art, the present invention has at least the following beneficial technical effects: This invention uses multi-layered correlation and discrimination between local temperature anomalies and the distribution and variation characteristics of human body pressure. It can accurately distinguish between normal heat accumulation and dangerous thermal anomalies caused by internal malfunctions in complex usage scenarios such as human body covering and bedding stacking, reducing the risk of misjudgment and missed judgment, and improving the operational safety and reliability of electric blankets. Attached Figure Description

[0012] Figure 1 This is a schematic diagram of the overall process of a fault monitoring method for an electric blanket disclosed in an embodiment of the present invention; Figure 2 This is a schematic diagram of the electric blanket control method disclosed in an embodiment of the present invention; Figure 3 This is a schematic diagram of the structure of a fault monitoring device for an electric blanket disclosed in an embodiment of the present invention. Detailed Implementation

[0013] The specific embodiments of this disclosure will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for illustration and explanation only and are not intended to limit this disclosure.

[0014] Please see Figure 2 The solution described in this embodiment can be deployed in the smart control module of the corresponding household appliance, smart home management terminal, or cloud device operation monitoring platform for electric blankets. It is used to continuously sense, comprehensively analyze, and determine the safety status of the heating unit, temperature control unit, and their operating status during actual use of the electric blanket. The operating objects include, but are not limited to, the heating wire partitions, temperature acquisition units, human pressure sensing units, power control modules, and related data acquisition and processing nodes inside the electric blanket. Their corresponding temperature status, pressure status, and operating parameters are acquired through multi-source data acquisition.

[0015] The human body pressure sensing unit may include pressure sensing elements disposed inside or on the surface of the electric blanket, which can be distributed to each monitoring sub-area according to a preset spatial distribution. When a human body lies on the electric blanket, the pressure sensing elements in different monitoring sub-areas generate corresponding pressure response signals. After being collected and processed, the pressure response signals form the human body pressure state data, which is used to characterize whether each monitoring sub-area is under pressure and the degree of pressure.

[0016] For ease of understanding, the following embodiments use multiple monitoring sub-areas and their operation processes in actual usage scenarios such as electric blankets covering the human body and layering bedding as examples. It should be noted that the monitoring sub-areas are not necessarily required to have a direct physical isolation relationship or independent control relationship. The correlation between local temperature anomalies and human body pressure distribution described in this invention is constructed based on the actual operating state of the electric blanket within a specific time window, changes in human body pressure, and temperature change characteristics. Its essence is a structured analysis and judgment of the operating status of the electric blanket under complex usage scenarios, rather than an isolated judgment of the temperature state of a single area or a single operating parameter.

[0017] Furthermore, the specific form of the electric blanket in this invention is not limited; it can be any of the different structural types such as a pad-type electric blanket, a cover-type electric blanket, an electric mattress, or a small electric blanket. The heating unit inside the electric blanket can adopt heating structures such as heating wires, carbon fiber heating elements, or heating films, and can be configured as a single-zone or multi-zone heating structure according to product design requirements.

[0018] Please see Figure 1 This invention provides a fault monitoring method for electric blankets, the method comprising the following steps: S1. Divide the heating area of ​​the electric blanket into multiple independent monitoring sub-areas, collect temperature data of each monitoring sub-area during operation, and obtain human body pressure state data corresponding to the monitoring sub-area. In this embodiment, the overall heating area of ​​the electric blanket is divided into multiple independent monitoring sub-areas according to a preset spatial distribution rule. These monitoring sub-areas can be defined based on the layout of the heating units inside the electric blanket, the direction of the heating wires, or the placement of the temperature acquisition units, so that each monitoring sub-area corresponds to a section of the heating area or a group of heating units within the electric blanket. This area division method avoids masking local anomalies by only monitoring the overall temperature, thereby improving the accuracy of detecting local thermal anomalies.

[0019] After the area division is completed, temperature data for each monitoring sub-area is collected during operation. This temperature data can be acquired in real time by temperature acquisition units located within or near the corresponding monitoring sub-area, reflecting the temperature changes in that sub-area during the heating process. By collecting temperature data from different monitoring sub-areas separately, the spatial temperature distribution information of the electric blanket can be obtained.

[0020] Simultaneously, human pressure state data corresponding to each monitoring sub-region is acquired. This human pressure state data characterizes the pressure exerted by the human body on each monitoring sub-region during the use of an electric blanket. It can be collected by the human pressure sensing unit or generated by inferring equivalent human pressure state data from temperature change characteristics. By mapping the human pressure state data one-to-one with the monitoring sub-regions, the pressure distribution in different areas caused by actions such as lying down and turning over can be accurately reflected.

[0021] S2. Within a preset time window, analyze the human body compression state data and extract the human body compression distribution characteristics of each monitoring sub-region and its compression change characteristics over time. In this embodiment, within a preset time window, data on the human body compression status corresponding to each monitoring sub-region are continuously collected and statistically analyzed. The preset time window can be set according to the usage characteristics of the electric blanket and safety monitoring requirements, for example, covering a complete lying down process or multiple turning cycles, thereby reflecting the stability and changes in the human body compression status more completely.

[0022] As an example, the human body pressure distribution characteristics include: Compression stability characteristics used to characterize whether the corresponding monitoring sub-region is continuously under pressure within a preset time window; The pressure stability feature is used to reflect whether the human body continuously compresses a certain monitoring sub-area for a long period of time. For example, if the human body maintains the same sleeping position for a long time, a specific area will be continuously compressed, thus providing a basis for subsequent judgment on whether the temperature rise in the area is related to human body compression.

[0023] Compression change frequency characteristics are used to characterize the frequency of changes in the human body's compression state within the preset time window; The pressure change frequency characteristic is used to reflect the dynamic impact of human body movements such as turning over and moving during use on the pressure state of each monitoring sub-area of ​​the electric blanket. When the pressure state of a certain monitoring sub-area changes frequently within a time window, it indicates that the area is subjected to intermittent or unstable pressure.

[0024] The compression spatial continuity feature is used to characterize whether adjacent monitoring sub-regions are simultaneously under pressure.

[0025] Specifically, by combining the spatial adjacency relationships of each monitoring sub-region, a compression spatial continuity feature is extracted to characterize whether adjacent monitoring sub-regions are simultaneously under pressure. This compression spatial continuity feature reflects the spatial distribution pattern of human body compression. For example, when a person is lying down, multiple adjacent regions are usually subjected to compression simultaneously, rather than a single region being isolated under pressure.

[0026] Understandably, the extracted human body compression distribution features not only reflect the stability and variability of human body compression in the time dimension, but also its continuous distribution characteristics in the spatial dimension. This can be used to subsequently correlate human body compression features with local temperature anomaly features, thereby improving the accuracy of determining the cause of local thermal anomalies.

[0027] S3. Analyze the temperature changes in each monitoring sub-region based on the temperature data to extract local temperature anomaly features, and perform correlation analysis between the local temperature anomaly features and the corresponding human body pressure distribution features and pressure change features. In this embodiment, this step is used to analyze and process the temperature data of each monitoring sub-region obtained in step S1, so as to extract local temperature anomaly features that can reflect the local abnormal heating state, and to perform correlation analysis between the local temperature anomaly features and the human body pressure distribution features and pressure change features extracted in step S2.

[0028] As an example, the characteristics of the local temperature anomaly include: Temperature deviation characteristics where the temperature of the corresponding monitoring sub-region exceeds the preset reference temperature range; abnormal temperature rise rate characteristics where the temperature rise rate of the corresponding monitoring sub-region changes abnormally relative to the historical operating state; and local gradient anomaly characteristics where the temperature gradient changes are inconsistent between the corresponding monitoring sub-region and adjacent monitoring sub-regions.

[0029] Specifically, by comparing the temperature data currently collected in each monitoring sub-area with a pre-set reference temperature range, when the temperature exceeds the reference temperature range, a temperature deviation feature is formed to reflect whether there is an obvious abnormal temperature rise in the monitoring sub-area.

[0030] Meanwhile, by analyzing the temperature changes of the monitored sub-region over a continuous period of time and comparing them with the temperature change rate under historical operating conditions, when the temperature rise rate shows a significant anomaly, the abnormal temperature rise rate characteristic is formed, which is used to reflect whether there is an abnormal acceleration in the local heating process.

[0031] Furthermore, by comparing temperature changes between adjacent monitoring sub-regions, when the temperature gradient change of a certain monitoring sub-region is significantly inconsistent with that of its adjacent monitoring sub-regions, a local gradient anomaly feature is formed. This feature is used to reflect whether there is abnormal heating behavior in a local area that is inconsistent with the overall heating trend. It is understandable that by introducing comparative analysis between adjacent regions, misjudgments arising from relying solely on temperature changes in a single region can be effectively avoided.

[0032] After extracting the local temperature anomaly features, these features are correlated with the distribution and variation characteristics of human body pressure in the corresponding monitoring sub-region. By combining the manifestations of the local temperature anomaly in the temporal and spatial dimensions with the stability, frequency of change, and spatial continuity of human body pressure within the corresponding time window, a comprehensive analysis is performed to determine whether the local temperature anomaly is a heat accumulation phenomenon caused by human body pressure or a malfunctioning thermal anomaly caused by an internal abnormality of the electric blanket.

[0033] As an example, the correlation analysis between the local temperature anomaly characteristics and the corresponding human body pressure distribution characteristics and pressure change characteristics includes: S31. Within the preset time window, the local temperature anomaly characteristics and the human body pressure distribution characteristics of the corresponding monitoring sub-region are processed by time segmentation, and the time window is divided into multiple continuous sub-time periods. In practice, the preset time window is used to cover a relatively complete segment of usage behavior, such as a person maintaining a sleeping position and possibly turning over once. To clearly characterize the relationship between pressure changes and temperature response, this step performs time segmentation processing on the preset time window, dividing the time window into multiple continuous sub-time periods.

[0034] Specifically, let the preset time window length be W, and divide it into m consecutive sub-time periods. The length of each sub-time period is ,satisfy .in, The settings need to take the following two points into account: 1) It can capture changes in the human body's compression state. For example, the migration of the compressed area caused by turning over usually manifests as a change on the scale of several seconds to tens of seconds; 2) It can reflect the gradual change in temperature under local heat dissipation limitation conditions (temperature response usually has thermal inertia).

[0035] Therefore, in this embodiment, it can be The time intervals are set to the range of several seconds to tens of seconds, so that the pressure state is approximately stable within each sub-time period, while the pressure migration and temperature accumulation trends can be reflected across sub-time periods.

[0036] By using the above time segmentation process, the changes in pressure state and temperature change trends can be mapped to the same time granularity, which can be used to construct a pressure-temperature correlation model later.

[0037] S32. In each of the sub-time periods, determine whether the monitored sub-area is under pressure and calculate the corresponding temperature change trend parameters. In practice, within each sub-time period, for the same monitoring sub-area, parameters reflecting whether the area is under human pressure (or pressure level) and temperature change trends reflecting the temperature change pattern of the area within that sub-time period are obtained, as follows: For any monitoring sub-region With any sub-time period Based on the human body compression status data corresponding to the monitored sub-area, the compression status is determined. In one implementation, pressure data can be statistically analyzed (e.g., by calculating the average or duty cycle) within a sub-time period and compared with a preset pressure threshold: if the average pressure within the sub-time period is higher than the threshold, or the proportion of pressure sampling points is higher than the threshold, the sub-time period is considered to be under pressure; otherwise, the sub-time period is considered to be under non-pressure.

[0038] To enhance robustness, this embodiment can employ hysteresis determination to avoid critical jitter. For example, different thresholds can be used for entering and exiting pressure, or a condition can be required to be met for a certain number of consecutive sampling points before the state can be switched, thereby reducing short-term misjudgments caused by minor movements.

[0039] For temperature data, this embodiment calculates temperature change trend parameters within each sub-time period. It may include at least: the temperature difference between the start and end of a sub-time period. Estimated rate of temperature rise (e.g., based on the slope of the linear fit within a sub-time period); temperature fluctuation (e.g., standard deviation) is used to identify abnormal fluctuations or noise interference.

[0040] Additionally, the trend difference with neighboring regions can be calculated as a local gradient trend quantity, for example:

[0041] in, for The set of adjacent monitoring sub-regions is used to reflect the difference in temperature rise trends between the local area and the surrounding area.

[0042] Through the above processing, a set of structured data pairs is formed within each sub-time period. , serving as the smallest composable unit for constructing subsequent patterns.

[0043] S33. Based on the combined relationship between the pressure state and temperature change trend parameters in the multiple sub-time periods, construct the pressure-temperature correlation model of the monitoring sub-region; In practice, for the monitoring sub-area Based on multiple sub-time periods Combination relationships construct a pressure-temperature correlation model In one implementation, the pressure-temperature correlation mode can be represented as one or a combination of the following structures: 1) State sequence pattern: Pressure state sequence With temperature rise rate sequence The correspondence; 2) Grouped Statistical Model: Sub-time periods are grouped by pressure / non-pressure, and the average temperature rise rate, average temperature difference, and fluctuation are calculated separately to form... Parameters, etc. Among them, This indicates the average rate of temperature rise or the average temperature change of the corresponding monitoring sub-region within the preset time window under pressure, which is used to characterize the typical temperature rise level of a local area under continuous human body pressure. This represents the average rate of temperature rise or the average temperature change of the corresponding monitoring sub-region within the preset time window under non-pressurized conditions, and is used to characterize the normal heat dissipation and temperature rise characteristics of the region when it is not under human pressure. This indicates the fluctuation or dispersion of temperature change characteristics in the corresponding monitoring sub-region under pressure, and is used to reflect the stability of local temperature changes under human body compression conditions. This indicates the fluctuation or dispersion of temperature change characteristics in the corresponding monitoring sub-region under non-pressure conditions, reflecting the natural fluctuation of temperature changes under normal heat dissipation conditions.

[0044] 3) Hysteresis-related model: Considering the thermal inertial response of temperature to pressure, in and Establish a connection between them ( (where lag step size) is used to characterize the pattern of initial pressure followed by cumulative temperature rise; 4) Spatial collaborative mode: Trend difference with adjacent regions The inclusion mode is used to distinguish between the difference between simultaneous temperature rise in multiple areas caused by large-area pressure on the human body and abnormal temperature rise in a single area.

[0045] For example: If a person maintains a sleeping position for an extended period of time, it will manifest as follows: It remains under pressure for multiple consecutive sub-time periods; correspondingly or During periods of sustained pressure, temperatures accumulate steadily or rise slowly; the temperature rise trends in adjacent areas (within the human body's coverage area) show a certain degree of synchronicity.

[0046] S34. Compare the pressure-temperature correlation pattern obtained during the current operation with the pressure-temperature correlation benchmark pattern under normal use conditions established in advance, and determine whether the local temperature anomaly characteristics are consistent with the heat accumulation characteristics caused by human body pressure.

[0047] In practice, the pressure-temperature correlated reference model can be obtained at either of the following stages: Factory or first-use initialization stage: Data is collected over several time windows under known normal conditions to form... Historical self-update phase: Within windows where normal heat accumulation is identified, the baseline model is selectively updated to accommodate differences in mattress and bedding thickness. To avoid baseline drift, this embodiment can set update conditions, such as allowing updates only when multiple consecutive windows are consistently classified as normal.

[0048] The above comparisons are not limited to a single indicator, but rather involve a multi-dimensional consistency judgment from the perspective of pressure-temperature causal consistency, including at least: Consistency of compressive stability: Whether the length and distribution of the compressive stability segment in the current model are consistent with the typical range of the reference model; Consistency of temperature rise trend: Whether the difference between the temperature rise rate / temperature difference statistics in the pressure section and the corresponding statistics of the baseline model is within the allowable range; Hysteresis Consistency: Does a hysteresis response consistent with the baseline exist between changes in pressure and changes in temperature? Spatial coordination consistency: Whether adjacent areas within the human body coverage area show a synchronous warming trend consistent with the baseline.

[0049] For example: (1) A region is continuously under pressure (pressure stability) for multiple sub-time periods. The temperature rise rate of this region accumulates slowly during the pressure period, and the temperature rise trends of several adjacent regions are synchronous. and The differences in the above dimensions are small, and it can be determined that the local temperature anomaly is more likely to be caused by heat dissipation restriction due to human body pressure. (2) The pressure state of a certain area changes frequently or is mostly unpressurized, but the rate of temperature rise continues to accumulate, or the temperature rise trend of this area is significantly higher than that of the adjacent areas and lacks spatial synchronicity. At this time, and The significant difference indicates that the local temperature anomaly does not conform to the characteristics of heat accumulation and is more likely caused by internal anomalies.

[0050] As an example, the pressure-temperature correlation pattern obtained during the current operation is compared with the pre-established pressure-temperature correlation benchmark pattern under normal use conditions to determine whether the local temperature anomaly characteristics conform to the characteristics of heat accumulation caused by human body pressure, including: S341. Within the preset time window, based on the pressure-temperature correlation mode and the pressure-temperature correlation benchmark mode, respectively, calculate a first difference index for characterizing the temperature change trend, a second difference index for characterizing the pressure state change, and a third difference index for characterizing the synergy of adjacent monitoring sub-regions. In specific implementation, within the preset time window, based on the pressure-temperature correlation pattern obtained during the current operation and the pre-established pressure-temperature correlation benchmark pattern under normal use conditions, at least the following three types of difference indicators are calculated: The first difference indicator is used to characterize the difference in temperature change trends. This indicator can be obtained by comparing the current model with the baseline model in terms of temperature rise rate, temperature accumulation, or temperature change curve shape during the compressed sub-period. For example, the difference or ratio between the average temperature rise rate during the current compressed phase and the average temperature rise rate during the baseline compressed phase can be calculated to reflect whether the current temperature change deviates from the typical trend under normal heat accumulation conditions.

[0051] The second difference indicator is used to characterize the difference in compression state changes. This indicator reflects the difference between human compression behavior under current operating conditions and normal use conditions. For example, it can compare the duration of the stable compression period and the frequency of compression changes within the current time window with the corresponding parameters of the baseline mode, thereby reflecting whether human compression behavior has abnormally frequent changes or unstable characteristics.

[0052] The third difference index is used to characterize the coordination differences between adjacent monitoring sub-regions. This index reflects whether the temperature change in the current monitoring sub-region maintains a spatial coordination relationship with adjacent monitoring sub-regions consistent with the baseline model. For example, it calculates the correlation between the temperature rise trend of this region and the temperature rise trend of adjacent regions under the current model, and compares it with the corresponding correlation in the baseline model to identify whether there are isolated anomalous temperature rises.

[0053] The differences between the current model and the baseline model are characterized by the three types of difference indicators mentioned above, respectively, from the three dimensions of time trend, behavioral stability and spatial structure.

[0054] S342. Combine the first difference index, the second difference index and the third difference index according to a preset rule to obtain the comprehensive difference degree of the monitoring sub-region, and generate the corresponding initial discrimination result according to the level to which the comprehensive difference degree belongs within the preset grading threshold range. In practice, the preset rules may include, but are not limited to, weighted summation, segmented mapping, or logical combination methods, wherein the weights or influence factors of each difference indicator can be set according to the degree of safety sensitivity. For example, the first difference indicator directly related to temperature anomalies can be given a higher weight, while the second and third difference indicators related to behavioral changes are used to provide auxiliary criteria.

[0055] After obtaining the overall difference degree, it is compared with a preset grading threshold range, and an initial discrimination result is generated according to its level. For example, the overall difference degree can be divided into low difference, medium difference, and high difference levels, where low difference usually corresponds to a situation that is close to the baseline pattern, while high difference indicates that the current pattern deviates significantly from the normal thermal accumulation characteristics.

[0056] S343. After generating the initial discrimination result, further conflict resolution discrimination is performed, specifically: When the second difference index indicates frequent changes in the compression state and the first difference index indicates a continuous accumulation trend in the temperature anomaly, it is determined that the local temperature anomaly does not conform to the heat accumulation characteristics caused by changes in human body compression; when the second difference index indicates a stable compression state and the third difference index indicates that the temperature changes in adjacent monitoring sub-regions are synchronous, it is determined that the local temperature anomaly conforms to the heat accumulation characteristics caused by human body compression. In practice, after generating the initial discrimination result, this embodiment further introduces a conflict resolution discrimination mechanism to conduct targeted analysis on situations where potential conflicts exist.

[0057] Specifically, when the second difference index indicates frequent changes in the compression state, while the first difference index indicates a continuous accumulation trend in temperature abnormalities, it indicates that the current temperature rise does not correspond to stable human compression behavior. In this case, even if the overall difference is at a moderate level, the heat accumulation characteristics caused by changes in human compression can be ruled out, thus determining that it does not meet the heat accumulation characteristics.

[0058] Conversely, when the second difference index indicates a relatively stable compression state, and the third difference index indicates a high degree of synchronicity in temperature changes between adjacent monitoring sub-regions, it indicates that the temperature changes conform to the typical characteristics of heat dissipation limitation caused by continuous human body pressure in both time and spatial dimensions. In this case, even if the first difference index deviates to a certain extent, it can be determined that the local temperature anomaly conforms to the characteristics of heat accumulation caused by human body pressure.

[0059] Understandably, by using the above-mentioned conflict resolution judgment, we can avoid the misleading results that may be produced by simple weighting or threshold comparison in complex usage scenarios, and make the judgment logic more in line with the actual physical and usage behavior characteristics.

[0060] S344. Perform consistency verification on the initial discrimination result and the conflict resolution discrimination result. When the two are consistent within multiple consecutive preset time windows, output the final judgment result that the local temperature anomaly feature conforms to the heat accumulation feature caused by human body pressure. Otherwise, output the final judgment result that the local temperature anomaly feature does not conform to the heat accumulation feature caused by human body pressure.

[0061] In practice, the stability and reliability of the initial discrimination results and conflict resolution discrimination results obtained above are verified in the time dimension to avoid misjudgment caused by short-term fluctuations, occasional actions or transient interference, and output a final judgment result with engineering credibility.

[0062] Specifically, within multiple consecutive preset time windows, a consistency check is performed between the initial discrimination result and the conflict resolution discrimination result. It is understood that the consistency check does not merely determine whether the discrimination results within a single time window are identical, but rather comprehensively considers the persistence, stability, and trend of the discrimination results over time.

[0063] In one implementation, for N consecutive preset time windows... The initial discrimination result and the conflict resolution discrimination result within each time window are recorded respectively, forming a corresponding discrimination sequence. If the initial discrimination result and the conflict resolution discrimination result remain consistent in each of the N consecutive time windows, and the discrimination results do not show reversal or alternation, then the local temperature anomaly is considered to have a stable discrimination conclusion in the time dimension.

[0064] The consistency mentioned includes at least one or a combination of the following situations: 1) within multiple consecutive time windows, both the initial discrimination result and the conflict resolution discrimination result point to the characteristics of heat accumulation caused by human body pressure; 2) within multiple consecutive time windows, both the initial discrimination result and the conflict resolution discrimination result point to the characteristics of heat accumulation caused by human body pressure.

[0065] When the above consistency conditions are met, the final judgment result is output: whether the local temperature anomaly characteristics conform to the heat accumulation characteristics caused by human body pressure, or do not conform to the heat accumulation characteristics caused by human body pressure.

[0066] In another implementation, to further enhance the robustness of the judgment, a fault-tolerance mechanism can be introduced into the consistency verification process. For example, a preset number of short-term inconsistencies can be allowed within N consecutive time windows, but the overall judgment trend must remain stable over time. When the number of inconsistencies exceeds the preset number, or when the judgment results frequently reverse between adjacent time windows, it is determined that the current local temperature anomaly does not possess a stable human body pressure-temperature response correspondence.

[0067] In the above situation, even if the initial judgment result or conflict resolution judgment result within a single time window points to the heat accumulation characteristics caused by human body pressure, this embodiment will still output the final judgment result that the local temperature anomaly characteristics do not conform to the heat accumulation characteristics caused by human body pressure, so as to avoid misjudging the internal abnormal or potentially dangerous state as normal use behavior.

[0068] Through the continuous time window consistency verification mechanism in this step, this embodiment abandons the local thermal anomaly discrimination method based on transient decision-making of single analysis, and instead adopts steady-state decision-making based on time evolution characteristics, so that the final judgment result is more in line with the human behavior characteristics and thermal response law of electric blanket in real use scenarios, thereby reducing unnecessary power outages or false alarms while ensuring safety.

[0069] S4. Based on the correlation analysis results, the local temperature anomaly is determined: When the local temperature anomaly is consistent with the human body pressure distribution characteristics and pressure change characteristics of the corresponding monitoring sub-area, it is determined to be a heat accumulation phenomenon caused by normal use; otherwise, it is determined to be a faulty thermal anomaly caused by an internal abnormality of the electric blanket, and the corresponding fault handling strategy is executed.

[0070] In this embodiment, this step is used to clearly distinguish the causes of local temperature anomalies and execute a fault handling strategy that matches the judgment result, thereby ensuring safety while avoiding excessive intervention in normal use behavior.

[0071] Specifically, when the final determination result output in step S344 indicates that the local temperature anomaly characteristics match the heat accumulation characteristics caused by human body pressure, the local temperature anomaly is determined to be a heat accumulation phenomenon caused by normal use. In this case, the local temperature rise and the continuous human body pressure and the limited local heat dissipation of the electric blanket have stable consistency in both time and space dimensions, and there are no abnormal characteristics that significantly deviate from the baseline pattern. In this embodiment, this type of thermal anomaly is regarded as a non-fault state, and an emergency power-off operation is not directly executed, thereby avoiding the impact on user experience caused by frequent triggering of protection due to normal lying down, covering with bedding, and other usage behaviors.

[0072] In cases where the above-mentioned phenomenon is determined to be normal heat accumulation, this embodiment can implement flexible adjustment methods according to the safety strategy. For example, it can smoothly adjust the heating power of the corresponding monitoring sub-area, reduce the upper limit of the power, or extend the heating interval to slow down the trend of temperature continuing to rise; or it can output prompt information to the user through indicator lights, display interface, or sound prompts to guide the user to adjust the sleeping position or improve the coverage status, thereby reducing potential risks without interrupting use.

[0073] When the final determination result output in step S344 indicates that the local temperature anomaly does not conform to the heat accumulation characteristics caused by human body pressure, it is determined that the local temperature anomaly is more likely a faulty thermal anomaly caused by an internal abnormality of the electric blanket. In this case, there is no stable and consistent correspondence between the local temperature anomaly and the distribution and changing characteristics of human body pressure, or it may exhibit significant deviation behavior during the time evolution process, posing a high safety risk.

[0074] In response to the aforementioned cases of thermal anomalies identified as malfunctions, this embodiment implements a mandatory safety protection-type fault handling strategy. For example, it immediately cuts off the power supply circuit to the corresponding monitored sub-area or the entire electric blanket, outputs a clear fault alarm message, or enters a locked state to prevent power from being restored. This method can promptly prevent further heat accumulation when a potential internal anomaly is detected, reducing the possibility of overheating, fire, or other hazards.

[0075] This embodiment transforms the judgment results obtained from the aforementioned correlation analysis between changes in human body pressure distribution and local temperature anomalies into differentiated processing strategies adapted to the causes of anomalies, thereby achieving classified handling and closed-loop control of local thermal anomalies in electric blankets. Compared to the method of triggering unified protection solely based on temperature thresholds, this embodiment can simultaneously ensure safety and continuity of use in complex real-world usage scenarios, improving the reliability and intelligence level of electric blanket operation.

[0076] Please see Figure 3 This invention also provides a fault monitoring device 100 for electric blankets, comprising: The area division and data acquisition module 101 is used to divide the heating area of ​​the electric blanket into multiple independent monitoring sub-areas, collect temperature data of each monitoring sub-area during operation, and obtain human body pressure state data corresponding to the monitoring sub-area. The human body compression feature extraction module 102 is used to analyze the human body compression state data within a preset time window and extract the human body compression distribution features and the compression change features over time in each monitoring sub-region. The temperature anomaly and correlation analysis module 103 is used to analyze the temperature changes of each monitoring sub-region based on the temperature data, extract local temperature anomaly features, and perform correlation analysis between the local temperature anomaly features and the corresponding human body pressure distribution features and pressure change features. The anomaly detection and processing module 104 is used to detect the local temperature anomaly based on the correlation analysis results. When the local temperature anomaly is consistent with the human body pressure distribution characteristics and pressure change characteristics of the corresponding monitoring sub-area, it is determined to be a heat accumulation phenomenon caused by normal use; otherwise, it is determined to be a faulty thermal anomaly caused by an internal abnormality of the electric blanket, and the corresponding fault handling strategy is executed.

[0077] As an example, the human body compression distribution characteristics include: compression stability characteristics, which characterize whether the corresponding monitoring sub-region is continuously under compression within a preset time window; compression change frequency characteristics, which characterize the frequency of changes in the human body compression state within the preset time window; and compression spatial continuity characteristics, which characterize whether adjacent monitoring sub-regions are simultaneously under compression.

[0078] As an example, the temperature deviation characteristic of the corresponding monitoring sub-region exceeding the preset reference temperature range, the abnormal temperature rise rate characteristic of the corresponding monitoring sub-region where the temperature rise rate changes abnormally relative to the historical operating state, and the local gradient abnormality characteristic where the temperature gradient change between the corresponding monitoring sub-region and adjacent monitoring sub-regions is inconsistent.

[0079] As an example, the temperature anomaly and correlation analysis module 103 is configured as follows: Within the preset time window, the local temperature anomaly characteristics and the human body pressure distribution characteristics of the corresponding monitoring sub-region are processed by time segmentation, and the time window is divided into multiple continuous sub-time periods. Within each of the sub-time periods, it is determined whether the monitored sub-region is under pressure, and the corresponding temperature change trend parameters are calculated. Based on the combined relationship between the pressure state and temperature change trend parameters in the multiple sub-time periods, a pressure-temperature correlation model for the monitored sub-region is constructed. The pressure-temperature correlation pattern obtained during the current operation is compared with the pressure-temperature correlation benchmark pattern under normal use conditions to determine whether the local temperature anomaly characteristics are consistent with the heat accumulation characteristics caused by human body pressure.

[0080] As an example, the temperature anomaly and correlation analysis module 103 is configured as follows: Within the preset time window, based on the pressure-temperature correlation model and the pressure-temperature correlation benchmark model, a first difference index for characterizing the temperature change trend, a second difference index for characterizing the pressure state change, and a third difference index for characterizing the synergy of adjacent monitoring sub-regions are calculated respectively. The first difference index, the second difference index, and the third difference index are combined according to a preset rule to obtain the comprehensive difference degree of the monitored sub-region, and the corresponding initial discrimination result is generated according to the level to which the comprehensive difference degree belongs within the preset grading threshold range. After generating the initial discrimination result, further conflict resolution discrimination is performed, specifically: When the second difference index indicates frequent changes in the compression state and the first difference index indicates a continuous accumulation trend in the temperature anomaly, it is determined that the local temperature anomaly does not conform to the heat accumulation characteristics caused by changes in human body compression; when the second difference index indicates a stable compression state and the third difference index indicates that the temperature changes in adjacent monitoring sub-regions are synchronous, it is determined that the local temperature anomaly conforms to the heat accumulation characteristics caused by human body compression. The initial discrimination result and the conflict resolution discrimination result are checked for consistency. When the two are consistent within multiple consecutive preset time windows, the final judgment result is output that the local temperature anomaly characteristics are consistent with the heat accumulation characteristics caused by human body pressure. Otherwise, the final judgment result is output that the local temperature anomaly characteristics are not consistent with the heat accumulation characteristics caused by human body pressure.

[0081] This invention also provides a non-transitory computer-readable storage medium that stores computer instructions for causing the computer to perform any of the methods described above.

[0082] This invention also provides a computer program product, including a computing program stored on a non-transitory computer-readable storage medium, the computer program including program instructions that, when executed by a computer, cause the computer to perform the method described in any of the preceding claims.

[0083] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A fault monitoring method for an electric blanket, characterized in that, The method includes the following steps: S1. Divide the heating area of ​​the electric blanket into multiple independent monitoring sub-areas, collect temperature data of each monitoring sub-area during operation, and obtain human body pressure state data corresponding to the monitoring sub-area. S2. Within a preset time window, analyze the human body compression state data and extract the human body compression distribution characteristics of each monitoring sub-region and its compression change characteristics over time. S3. Analyze the temperature changes in each monitoring sub-region based on the temperature data to extract local temperature anomaly features, and perform correlation analysis between the local temperature anomaly features and the corresponding human body pressure distribution features and pressure change features. S4. Based on the correlation analysis results, the local temperature anomaly is determined: When the local temperature anomaly is consistent with the human body pressure distribution characteristics and pressure change characteristics of the corresponding monitoring sub-area, it is determined to be a heat accumulation phenomenon caused by normal use; otherwise, it is determined to be a faulty thermal anomaly caused by an internal abnormality of the electric blanket, and the corresponding fault handling strategy is executed.

2. The fault monitoring method for an electric blanket according to claim 1, characterized in that: The human body compression distribution characteristics include: compression stability characteristics, which characterize whether the corresponding monitoring sub-region is continuously under compression within a preset time window; compression change frequency characteristics, which characterize the frequency of changes in the human body compression state within the preset time window; and compression spatial continuity characteristics, which characterize whether adjacent monitoring sub-regions are simultaneously under compression.

3. The fault monitoring method for an electric blanket according to claim 1, characterized in that: The local temperature anomaly features include: temperature deviation features where the temperature of the corresponding monitoring sub-region exceeds the preset reference temperature range; temperature rise rate anomaly features where the temperature rise rate of the corresponding monitoring sub-region changes abnormally relative to the historical operating state; and local gradient anomaly features where the temperature gradient changes are inconsistent between the corresponding monitoring sub-region and adjacent monitoring sub-regions.

4. The fault monitoring method for an electric blanket according to claim 1, characterized in that: The correlation analysis between the local temperature anomaly characteristics and the corresponding human body pressure distribution and pressure change characteristics includes: S31. Within the preset time window, the local temperature anomaly characteristics and the human body pressure distribution characteristics of the corresponding monitoring sub-region are processed by time segmentation, and the time window is divided into multiple continuous sub-time periods. S32. In each of the sub-time periods, determine whether the monitored sub-area is under pressure and calculate the corresponding temperature change trend parameters. S33. Based on the combined relationship between the pressure state and temperature change trend parameters in the multiple sub-time periods, construct the pressure-temperature correlation model of the monitoring sub-region; S34. Compare the pressure-temperature correlation pattern obtained during the current operation with the pressure-temperature correlation benchmark pattern under normal use conditions established in advance, and determine whether the local temperature anomaly characteristics are consistent with the heat accumulation characteristics caused by human body pressure.

5. The fault monitoring method for an electric blanket according to claim 4, characterized in that: The pressure-temperature correlation pattern obtained during the current operation is compared with the pre-established pressure-temperature correlation benchmark pattern under normal use conditions to determine whether the local temperature anomaly characteristics conform to the heat accumulation characteristics caused by human body pressure, including: S341. Within the preset time window, based on the pressure-temperature correlation mode and the pressure-temperature correlation benchmark mode, respectively, calculate a first difference index for characterizing the temperature change trend, a second difference index for characterizing the pressure state change, and a third difference index for characterizing the synergy of adjacent monitoring sub-regions. S342. Combine the first difference index, the second difference index and the third difference index according to a preset rule to obtain the comprehensive difference degree of the monitoring sub-region, and generate the corresponding initial discrimination result according to the level to which the comprehensive difference degree belongs within the preset grading threshold range. S343. After generating the initial discrimination result, further conflict resolution discrimination is performed, specifically: When the second difference index indicates frequent changes in the compression state and the first difference index indicates a continuous accumulation trend in the temperature anomaly, it is determined that the local temperature anomaly does not conform to the heat accumulation characteristics caused by changes in human body compression; when the second difference index indicates a stable compression state and the third difference index indicates that the temperature changes in adjacent monitoring sub-regions are synchronous, it is determined that the local temperature anomaly conforms to the heat accumulation characteristics caused by human body compression. S344. Perform consistency verification on the initial discrimination result and the conflict resolution discrimination result. When the two are consistent within multiple consecutive preset time windows, output the final judgment result that the local temperature anomaly feature conforms to the heat accumulation feature caused by human body pressure. Otherwise, output the final judgment result that the local temperature anomaly feature does not conform to the heat accumulation feature caused by human body pressure.

6. A fault monitoring device for an electric blanket, characterized in that: The device includes: The area division and data acquisition module is used to divide the heating area of ​​the electric blanket into multiple independent monitoring sub-areas, collect temperature data of each monitoring sub-area during operation, and obtain human body pressure state data corresponding to the monitoring sub-area. The human body compression feature extraction module is used to analyze the human body compression state data within a preset time window and extract the human body compression distribution characteristics and the compression change characteristics over time in each monitoring sub-region. The temperature anomaly and correlation analysis module is used to analyze the temperature changes of each monitoring sub-region based on the temperature data, extract local temperature anomaly features, and perform correlation analysis between the local temperature anomaly features and the corresponding human body pressure distribution features and pressure change features. The anomaly detection and processing module is used to detect the local temperature anomaly based on the correlation analysis results. When the local temperature anomaly is consistent with the human body pressure distribution characteristics and pressure change characteristics of the corresponding monitoring sub-area, it is determined to be a heat accumulation phenomenon caused by normal use; otherwise, it is determined to be a faulty thermal anomaly caused by an internal abnormality of the electric blanket, and the corresponding fault handling strategy is executed.

7. The fault monitoring device for an electric blanket according to claim 6, characterized in that: The temperature anomaly and correlation analysis module is configured as follows: Within the preset time window, the local temperature anomaly characteristics and the human body pressure distribution characteristics of the corresponding monitoring sub-region are processed by time segmentation, and the time window is divided into multiple continuous sub-time periods. Within each of the sub-time periods, it is determined whether the monitored sub-region is under pressure, and the corresponding temperature change trend parameters are calculated. Based on the combined relationship between the pressure state and temperature change trend parameters in the multiple sub-time periods, a pressure-temperature correlation model for the monitored sub-region is constructed. The pressure-temperature correlation pattern obtained during the current operation is compared with the pressure-temperature correlation benchmark pattern under normal use conditions to determine whether the local temperature anomaly characteristics are consistent with the heat accumulation characteristics caused by human body pressure.

8. The fault monitoring device for an electric blanket according to claim 7, characterized in that: The temperature anomaly and correlation analysis module is configured as follows: Within the preset time window, based on the pressure-temperature correlation model and the pressure-temperature correlation benchmark model, a first difference index for characterizing the temperature change trend, a second difference index for characterizing the pressure state change, and a third difference index for characterizing the synergy of adjacent monitoring sub-regions are calculated respectively. The first difference index, the second difference index, and the third difference index are combined according to a preset rule to obtain the comprehensive difference degree of the monitored sub-region, and the corresponding initial discrimination result is generated according to the level to which the comprehensive difference degree belongs within the preset grading threshold range. After generating the initial discrimination result, further conflict resolution discrimination is performed, specifically: When the second difference index indicates frequent changes in the compression state and the first difference index indicates a continuous accumulation trend of temperature abnormality, it is determined that the local temperature abnormality does not conform to the heat accumulation characteristics caused by changes in human body compression. When the second difference index indicates that the compression state is stable and the third difference index indicates that the temperature changes of adjacent monitoring sub-regions are synchronous, it is determined that the local temperature anomaly is consistent with the heat accumulation characteristics caused by human body compression. The initial discrimination result and the conflict resolution discrimination result are checked for consistency. When the two are consistent within multiple consecutive preset time windows, the final judgment result is output that the local temperature anomaly characteristics are consistent with the heat accumulation characteristics caused by human body pressure. Otherwise, the final judgment result is output that the local temperature anomaly characteristics are not consistent with the heat accumulation characteristics caused by human body pressure.

9. A non-transitory computer-readable storage medium, characterized in that, The non-transitory computer-readable storage medium stores computer instructions for causing the computer to perform the method described in any one of claims 1-5.

10. A computer program product, characterized in that, The computer program includes a computer program stored on a non-transitory computer-readable storage medium, the computer program including program instructions that, when executed by a computer, cause the computer to perform the method described in any one of claims 1-5.

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