An electric blanket temperature intelligent adjustment control system and control device

Abstract

This invention discloses an intelligent temperature regulation and control system and device for electric blankets. Addressing the technical problems of existing electric blankets' inability to accurately sense the local temperature distribution and aging status of the heating wires, and the difficulty in timely detecting local hot spots, this invention arranges a multi-point temperature sensor array along the heating wire path, collecting resistance, voltage, and current data. After time alignment, normalization, and filtering, the real-time power distribution, temperature rise rate, and resistance deviation rate of each segment are calculated, and combined with spatial location to form a local thermal anomaly feature matrix. Based on this matrix, a health status model of the heating wire is established, and a local thermal runaway risk map marked with high-risk areas is generated through spatial mapping. Based on this, predictive power regulation is performed on high-risk areas, including local power reduction or power cut-off of that segment. This invention can identify local thermal runaway risks in advance, achieving proactive regulation and preventative protection, improving the safety and lifespan of electric blankets.

Description

Technical Field

[0001] This invention relates to the field of electric blanket technology, specifically to an intelligent temperature regulation and control system and control device for electric blankets. Background Technology

[0002] Electric blankets are common household heating devices that provide warmth through internal heating wires.

[0003] Most existing electric blankets use overall power control or single-point temperature control, lacking precise sensing of the local temperature distribution and aging status of the heating wires. During long-term use, the heating wires may develop hot spots due to material aging, poor contact, or localized micro-damage. These hot spots are localized and progressive, easily causing burns or fire risks. Traditional temperature control methods struggle to detect or predict such localized anomalies in a timely manner, only passively cutting off power when the temperature is too high, failing to provide proactive adjustment and preventative protection.

[0004] In addition, the formation of local hotspots is often accompanied by abnormal temperature rise and changes in resistance, and is affected by factors such as user coverings, sleeping posture, and ambient temperature and humidity. This makes it impossible for traditional control methods to effectively intervene before high risks are formed. Summary of the Invention

[0005] In view of the above-mentioned shortcomings mentioned in the background art, the purpose of this invention is to provide an intelligent temperature regulation and control system and control device for electric blankets.

[0006] A first aspect of the present invention provides an intelligent temperature regulation and control system for an electric blanket, the system comprising a control device configured with a computer program, the control device running the computer program to perform the following steps: Acquire sensor temperature data from a multi-point temperature sensor array arranged along the path of the heating wire inside the electric blanket, and collect resistance or voltage and current data of the segment of the heating wire to form the original electrothermal state dataset. Based on the original electrothermal state dataset after time alignment, amplitude normalization and short-term anomaly filtering, the local characteristic parameters of each segment of the heating wire are calculated, including real-time power distribution, temperature rise rate and resistance deviation rate. The local characteristic parameters are combined with the spatial position of the heating wire to form a local thermal anomaly feature matrix. A health status model of the heating wire is established based on the local thermal anomaly feature matrix, and the surface of the entire electric blanket is spatially mapped according to the local feature parameters of each segment of the heating wire to generate a local thermal runaway risk map marked with high-risk areas. Based on the local thermal runaway risk map and the heating wire health status model, predictive power regulation is performed on high-risk areas, including local power reduction or power cut-off of that segment.

[0007] A second aspect of the present invention provides an intelligent temperature regulation and control device for electric blankets, the control device comprising: The temperature and electrical parameter acquisition unit is used to acquire sensor temperature data collected by a multi-point temperature sensor array arranged along the path of the heating wire, and to acquire resistance or voltage and current data of the heating wire segments to form the original electrothermal state dataset. The feature extraction unit is used to calculate the local feature parameters of each segment of the heating wire based on the original electrothermal state dataset after time alignment, amplitude normalization and short-time anomaly filtering, including real-time power distribution, temperature rise rate and resistance deviation rate, and combine the local feature parameters with the spatial position of the heating wire to form a local thermal anomaly feature matrix. The health status modeling and risk map generation unit is used to establish a health status model of the heating wire based on the local thermal anomaly feature matrix, and to perform spatial mapping on the surface of the entire electric blanket according to the local feature parameters of each segment of the heating wire to generate a local thermal runaway risk map marked with high-risk areas. The predictive power regulation unit is used to perform predictive power regulation on high-risk areas based on the local thermal runaway risk map and the heating wire health status model, including locally reducing power or cutting off the power supply to that section.

[0008] Compared with the prior art, the present invention has at least the following beneficial technical effects: This invention employs multiple temperature sensors arranged along the heating wire path to simultaneously collect segmented resistance, voltage, and current data. Combined with time alignment, amplitude normalization, and short-term anomaly filtering, it accurately calculates the real-time power distribution, temperature rise rate, and resistance deviation rate of each heating wire segment. This allows for the construction of a local thermal anomaly feature matrix and the establishment of a heating wire health status model. Spatial rearrangement indexing ensures the matrix accurately reflects the spatial continuity of heat diffusion. Ultimately, a local thermal runaway risk map is generated, marking high-risk areas, and graded predictive power adjustment is implemented. This proactively identifies and eliminates potential local thermal runaway hazards under complex conditions such as user coverings, sleeping posture, and ambient temperature and humidity. It provides early warning and preventative protection against progressive hotspots such as localized aging, micro-damage, and poor contact in the heating wire, significantly improving the safety and lifespan of electric blankets. Attached Figure Description

[0009] Figure 1 This is a schematic diagram of the structure of an intelligent temperature regulation and control system for an electric blanket disclosed in an embodiment of the present invention; Figure 2 This is a first structural schematic diagram of an intelligent temperature regulation and control device for an electric blanket disclosed in an embodiment of the present invention; Figure 3 This is a second structural schematic diagram of an intelligent temperature regulation and control device for an electric blanket disclosed in an embodiment of the present invention.

[0010] Figure 4 This is a third structural schematic diagram of an intelligent temperature regulation and control device for an electric blanket disclosed in an embodiment of the present invention.

[0011] Figure 5 This is a fourth structural schematic diagram of an intelligent temperature regulation and control device for an electric blanket disclosed in an embodiment of the present invention. Detailed Implementation

[0012] 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.

[0013] The solution described in this embodiment can be deployed in the intelligent temperature control module, home energy management gateway, or cloud-based electric heating safety monitoring platform corresponding to the electric blanket. It is used to continuously sense, extract features, and identify the risk of local thermal runaway of the electric heating wire segments, temperature acquisition nodes, power control circuits, and their operating status involved in the actual use of the electric blanket.

[0014] 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 needs. The heating wires in each heating zone independently collect temperature and electrical parameters and adjust power.

[0015] Please see Figure 1 This invention provides an intelligent temperature regulation and control system 100 for electric blankets. The system includes a control device configured with a computer program, which runs the computer program to achieve the following steps: S1, acquire sensor temperature data collected by a multi-point temperature sensor array arranged along the path of the heating wire inside the electric blanket, and acquire resistance or voltage and current data of the segment of the heating wire to form the original electrothermal state dataset. Multiple temperature sensors are arranged at equal or non-equal intervals along the physical path of the heating wire inside the electric blanket, forming a multi-point temperature sensor array. Each sensor independently collects the real-time temperature at its corresponding location. At the same time, the heating wire is divided into multiple continuous segments according to the physical path. Electrical parameter acquisition nodes are set at both ends of each segment to collect the voltage and current flowing through the segment, or to directly measure the segment's resistance value.

[0016] The temperature data and electrical parameter data mentioned above are recorded with a unified timestamp to form a raw electrothermal state dataset containing temperature, voltage, current or resistance.

[0017] S2, based on the original electrothermal state dataset after time alignment, amplitude normalization and short-time anomaly filtering, calculate the local feature parameters of each segment of the heating wire, including real-time power distribution, temperature rise rate and resistance deviation rate, and combine the local feature parameters with the spatial position of the heating wire to form a local thermal anomaly feature matrix. After obtaining the raw electrothermal state dataset, the data is preprocessed to eliminate noise caused by differences in sensor characteristics, inconsistent sampling timing, and transient interference. Preprocessing includes time alignment, amplitude normalization, and short-time anomaly filtering, as detailed below: Time alignment: Since the sampling frequencies of the multi-point temperature sensor array and the electrical parameter acquisition nodes may differ (for example, the temperature sampling period is 1 second, while the voltage and current sampling period is 0.1 seconds), linear interpolation or cubic spline interpolation methods are used to resample all data sequences to a unified time reference to ensure that the temperature, resistance, voltage, and current data at the same moment can be accurately matched.

[0018] Amplitude normalization: Physical quantities such as temperature, voltage, and current have different dimensions and numerical ranges (e.g., temperature is typically 20-60℃, voltage is 220V, and current is 0.1-1A). By using maximum-minimum normalization or Z-score normalization methods, each physical quantity is mapped to a uniform numerical range (e.g., [0,1]).

[0019] Short-term anomaly filtering: In actual use, electric blankets may be affected by electromagnetic interference, poor sensor contact, or instantaneous power fluctuations, resulting in isolated abnormal data points. Median filtering or moving average filtering methods are used to smooth each data channel within a preset time window (e.g., 5 consecutive sampling points), eliminating outliers that significantly deviate from the normal trend and retaining the true thermoelectric change characteristics.

[0020] After the above preprocessing, local characteristic parameters are further calculated for each segment of the heating wire, as follows: Real-time power distribution: For the first The heating wire is segmented, and the voltage across the two ends of the segment is collected. With the current flowing through Calculate real-time power The real-time power of all segments is arranged sequentially according to the spatial location of the heating wire (segment number or spatial coordinates) to form a power distribution vector. It should be understood that this vector can reflect the differences in power distribution along the path of the heating wire, and areas of abnormal power concentration are often potential locations of local hot spots.

[0021] Temperature rise rate: based on the data collected by a multi-point temperature sensor array. Temperature data at the corresponding location of the heating element within a preset time window Calculate the temperature change and rate of temperature rise within (e.g., 10 seconds or 30 seconds). The rate of temperature rise is a key indicator for judging local overheating trends; a higher rate indicates that heat is accumulating faster in the area, and the greater the risk.

[0022] Resistance deviation rate: Pre-obtain the resistance deviation rate of each section of the heating wire at the reference temperature. (e.g., reference resistance at 25°C) The reference resistor can be obtained through factory calibration or self-learning during initial use. During real-time operation, the current resistance value is acquired. (Resistance value directly measured or calculated from voltage and current) and current temperature Temperature compensation correction is performed based on the temperature coefficient of resistance of the heating wire material. Equivalent resistance corrected to reference temperature Then calculate the resistance deviation rate. It should be understood that the resistance deviation rate reflects the change in resistance of the heating wire caused by aging, micro-damage, or local overheating. A significant increase in the deviation rate usually indicates that there is material degradation or poor contact in that section of the heating wire.

[0023] As an example, combining the local feature parameters with the spatial location of the heating wire to form a local thermal anomaly feature matrix includes: S21. According to the physical laying path of the heating wire in the electric blanket, the heating wire is divided into multiple continuous segments, and each segment is assigned spatial coordinates or a number. The heating wires inside an electric blanket are typically laid out evenly within the blanket in a serpentine, zigzag, or spiral pattern, forming a heating network covering the entire surface of the blanket. To achieve precise sensing of localized temperature anomalies, this continuous heating wire needs to be divided into several continuous monitoring units according to its physical orientation.

[0024] The specific division principle is as follows: Starting from the power input end along the heating wire, each segment is marked sequentially according to a preset segment length (e.g., 10 centimeters per segment) or a preset number of segments (e.g., the entire heating wire is evenly divided into 20 segments). Each segment is spatially continuous, and adjacent segments are physically connected end-to-end. For each segment, a unique spatial coordinate or number is assigned. This coordinate can be the segment's sequence number on the path (e.g., segment 1, segment 2... segment N), or it can be mapped to two-dimensional coordinates on the surface of the electric blanket (e.g., grid coordinates calculated based on the segment's row and column position in the serpentine path).

[0025] S22, the real-time power distribution, temperature rise rate, and resistance deviation rate of each segment are used as the multi-dimensional feature vector corresponding to that segment, and the multi-dimensional feature vectors of all segments are arranged according to their spatial positions to form the local thermal anomaly feature matrix.

[0026] After completing the segmentation and spatial numbering of the heating wire, for each segment... ( The real-time power obtained from the aforementioned calculations will be used as a basis for further calculations. Temperature rise rate and resistance deviation rate Organized into three-dimensional feature vectors It should be understood that this vector fully describes the first... The combined thermal and electrical state of the heating element at the current moment.

[0027] Following the spatial numbering order of the segments (i.e., the chronological order of the physical path of the heating wire, from segment 1 to segment N), the feature vectors of each segment are arranged sequentially. Specifically, the arrangement is as follows: the segment number is used as the row index, and the three feature parameters are used as the column index, forming... A two-dimensional matrix, denoted as , of which The first column of the row is The second column is The third column is This matrix is ​​the characteristic matrix of local thermal anomalies.

[0028] It should be understood that the row order of this local thermal anomaly feature matrix directly reflects the spatial position change of the heating wire along the physical path, and the feature vector of each row represents the degree of thermal anomaly of the corresponding segment.

[0029] However, because the heating wires inside electric blankets are typically laid out in a serpentine pattern, the order along the physical path does not correspond to the actual spatial adjacency on the blanket surface. For example, in a serpentine path, two segments in the same column of two adjacent rows may differ by an entire row of segments on the physical path, but they are closely adjacent on the blanket surface. If a matrix arranged according to the path order is used directly for spatial mapping, spatially adjacent segments will have significantly different row indices in the matrix, failing to accurately reflect the diffusion and accumulation characteristics of heat on the blanket surface, thus reducing the accuracy of identifying hotspot areas in the local thermal runaway risk map.

[0030] To address the above problems, the present invention provides the following preferred spatial rearrangement implementation method: As an example, the local thermal anomaly feature matrix is ​​constructed by arranging all segmented multidimensional feature vectors according to their spatial location, including: S221, obtain the multi-dimensional feature vectors of each segment according to the order of the physical path of the heating wire and form an initial feature matrix; establish a spatial rearrangement index based on the actual spatial adjacency relationship of each segment in the two-dimensional grid on the surface of the electric blanket. Specifically, the local thermal anomaly feature matrix arranged in path order is denoted as the initial feature matrix. , its first The row corresponds to the first line on the physical path of the heating wire. Multidimensional feature vectors of segments It should be understood that this initial feature matrix directly reflects the sequence in which the heating wires are laid along the serpentine path starting from the power input terminal.

[0031] To obtain the actual spatial adjacency relationships on the surface of the electric blanket, a two-dimensional spatial coordinate system is further established. The heating area of ​​the electric blanket is divided according to a preset grid granularity (e.g., each 5cm × 5cm is a grid unit). OK, A two-dimensional grid of columns, where each grid cell is identified by its row number. ( ) and column number ( It is uniquely determined.

[0032] Based on the physical layout diagram or measured coordinates of the heating wire segments within the electric blanket, determine the set of grid cells covered by each segment. For each segment... Define its center grid coordinates It is understandable that, since the length of the heating wire segment may be greater than the grid granularity, a segment may cover multiple grid cells. In this case, the coordinates of the grid cell with the largest coverage area or the centroid coordinates can be taken as the representative position of the segment.

[0033] Based on the above grid coordinates, define the spatial adjacency relationship: two segments and On the surface of an electric blanket, they are practically adjacent if and only if their grid coordinates satisfy... (That is, four neighboring regions are adjacent, either vertically or horizontally adjacent). For all segment pairs that satisfy the spatial adjacency condition. Record their position indices in the initial feature matrix. and These adjacent pairs form an undirected graph. , where vertex set For each segment, the edge set It includes all spatially adjacent segment pairs.

[0034] Based on this, a spatial rearrangement index is established, the goal of which is to assign a new row order to each segment, such that segments with spatial adjacency in the rearranged sequence are as close as possible (i.e., the absolute value of the difference between the new indices is as small as possible). This problem is equivalent to the minimum linear permutation problem in graph theory. This invention uses a heuristic algorithm to solve it, specifically: The segment with the highest degree in the graph is selected as the starting vertex, and its new index is set to 1. Then, a breadth-first search is used to traverse the graph, assigning new indices in ascending order according to the traversal sequence; for multiple vertices at the same level, vertices with more adjacencies to already assigned vertices are assigned first. The final mapping function is obtained. ,in Indicates the original number The index of the new row after the rearrangement of each segment is the mapping function, which is the spatial rearrangement index. It can be understood that this index table not only records the target position of each segment, but also implicitly implies the consistency relationship between the row order of the rearranged matrix and the spatial adjacency order of the electric blanket surface.

[0035] S222, according to the spatial rearrangement index, the segmented vectors that are spatially adjacent but not adjacent in path order in the initial feature matrix are adjusted to adjacent positions in the matrix, so that the heating wire segments corresponding to adjacent positions in the matrix are actually adjacent on the surface of the electric blanket, forming a spatial rearrangement feature matrix that reflects the spatial continuity of heat diffusion, and using the spatial rearrangement feature matrix as the local thermal anomaly feature matrix.

[0036] Specifically, creating the initial feature matrix Empty matrices of the same dimension For each segment ( Based on the spatial rearrangement index obtained above , the initial feature vector Copy to The Line, that is:

[0037] After rearranging all segments, the resulting matrix is... This is the spatial rearrangement feature matrix. In this matrix, any two rows... and ( Adjacent in the matrix (i.e.) When the corresponding segments are adjacent on the surface of the electric blanket, the probability of them being actually adjacent increases significantly.

[0038] To illustrate the necessity of spatial rearrangement more clearly, we will use a typical serpentine arrangement of heating wires as an example.

[0039] The serpentine laying method is as follows: the heating wire is laid from left to right in the first row, then turns back at the right end and lays the second row from right to left, then turns back at the left end and lays the third row from left to right, and so on. Under this laying method, there are two types of spatial adjacency relationships: one is that segments within the same row are adjacent left and right, and they are also adjacent in path order, requiring no rearrangement; the other is that segments between different rows are adjacent vertically. Specifically, the segment at the left end of the first row and the segment at the left end of the second row are spatially adjacent vertically, but in terms of path order, the left end of the first row is the starting segment of the entire heating wire (smaller index), while the left end of the second row is the last segment laid in the second row (larger index), and the two are far apart in the initial sequence. Similarly, the rightmost segment of the first row and the rightmost segment of the second row are spatially adjacent, but the rightmost segment of the first row is immediately followed by the rightmost segment of the second row (due to a loop), so they are already adjacent in terms of path order and do not need to be rearranged. Therefore, the spatial rearrangement in this application solves the problem of segments with "leftmost vertical adjacency" having non-adjacent indices in the initial sequence.

[0040] Through the above rearrangement operation, the segments at the left end of the first row and the left end of the second row are adjusted to adjacent rows in the matrix, so that the row order of the matrix is ​​consistent with the two-dimensional spatial adjacency order of the electric blanket surface. When the subsequent step S3 performs spatial mapping based on this matrix to generate a local thermal runaway risk map, the eigenvectors corresponding to adjacent rows in the matrix can be directly mapped to adjacent grid areas on the electric blanket surface, thereby more realistically reflecting the diffusion, conduction, and accumulation process of heat in the two-dimensional plane. It should be understood that this spatial rearrangement only changes the arrangement order of eigenvectors, without changing the eigenvector values ​​of each segment itself, and therefore will not introduce information distortion or change the independent physical state of each segment.

[0041] This invention effectively solves the problem of inconsistent path order and spatial adjacency caused by serpentine laying of heating wires by introducing spatial rearrangement index and matrix rearrangement operation, significantly improving the accuracy of subsequent heating wire health status modeling and local thermal runaway risk map generation, especially in identifying continuous hot spot areas (such as progressive hot spot diffusion caused by local micro-damage).

[0042] S3. Based on the local thermal anomaly feature matrix, establish a health status model of the heating wire, and perform spatial mapping on the surface of the entire electric blanket according to the local feature parameters of each segment of the heating wire to generate a local thermal runaway risk map marked with high-risk areas. After obtaining the local thermal anomaly feature matrix (the matrix obtained after processing in step S22 or step S222), or After that, a health status model of the heating wire is first established. This model is used to evaluate the health of each segment of the heating wire relative to its normal aging or brand-new state, and outputs the health parameters of each segment. ( ),in A higher value indicates a healthier heating wire, while a lower value indicates abnormalities such as aging, micro-damage, or poor contact.

[0043] For example, the health status model can be established using the statistical process control (SPC) method. Specifically, in the initial stage of initial use of the electric blanket or after sufficient aging and stabilization, local thermal anomaly characteristic matrix data for a period of time are collected as a baseline sample. For each segment... Calculate its characteristic parameters (power) Temperature rise rate Resistance deviation rate mean , , and standard deviation , , Then, set the control limit to... ( (Choose 2 or 3). In real-time operation, when any characteristic parameter of a segment exceeds the control limit, the health of that segment is determined to have decreased. Health parameters It can be defined as a weighted sum of the deviations of each feature parameter, for example:

[0044] It should be understood that this formula only produces a positive deviation when the feature parameter exceeds the control upper limit; when it is below the upper limit, the corresponding term is 0, thus ensuring health. It is close to 1 under normal conditions and decreases accordingly under abnormal conditions.

[0045] As another example, health status models can also be built using one-class support vector machines (SVMs) or unsupervised anomaly detection algorithms (such as Isolation Forests). Taking one-class SVMs as an example, the feature vectors of each segment's historical normal state are used as training data to learn a high-dimensional hyperspherical boundary. During real-time detection, for the current feature vector... The model outputs whether it is inside the boundary (normal) or outside the boundary (abnormal), and can map the distance to the boundary to a health score. This invention does not limit the specific modeling method, as long as it can output the health parameters of each heating wire segment.

[0046] After establishing the health status model of the heating wire, the entire surface of the electric blanket is spatially mapped based on the local characteristic parameters of each section of the heating wire, generating a local thermal runaway risk map marked with high-risk areas. This risk map is used to visually display the current thermal runaway risk level of each area on the surface of the electric blanket, serving as a spatial decision-making basis for subsequent predictive power regulation.

[0047] As an example, the entire surface of the electric blanket is spatially mapped based on the local characteristic parameters of each segment of the heating wire to generate a local thermal runaway risk map marked with high-risk areas, including: S31, the real-time power distribution, temperature rise rate and resistance deviation rate of each section of heating wire are weighted and integrated according to preset weights to calculate the local thermal runaway risk index of each section, and the risk level is divided according to the magnitude of the index. Specifically, for each section of heating wire Define its local thermal runaway risk index The weighted sum of the three characteristic parameters mentioned above:

[0048] in, , , These are the normalized values ​​of real-time power, temperature rise rate, and resistance deviation rate (e.g., normalized using the same baseline mean and standard deviation as the health state model, or mapped to the [0,1] interval using maximum-minimum normalization). , , The preset weighting coefficients are used, and they satisfy the following conditions: It should be understood that the weighting can be adjusted according to actual safety requirements. For example, the rate of temperature rise directly reflects the local overheating trend and can be given a higher weight (e.g., The power distribution and resistance deviation rate reflect energy input and material aging, respectively, and can each account for 0.25. It should be understood that users or system administrators can dynamically adjust the above weights according to the usage scenario of the electric blanket (such as the elderly, children, or unattended use) to adapt to different safety sensitivities.

[0049] Obtain the local thermal runaway risk index for each segment. Then, based on preset thresholds, they are divided into multiple risk levels. For example, two thresholds can be set. and (like , The division rule is: if If it is determined to be low risk; It is classified as medium risk; if It was determined to be high-risk.

[0050] It is understood that the risk level classification is not limited to level three, and can also be set to level four or five according to actual needs. This invention does not limit this.

[0051] S32 maps the spatial position of each heating wire segment to a two-dimensional grid on the surface of the electric blanket, assigns the risk level of each segment to the corresponding grid area, and splices them according to spatial position to generate a local thermal runaway risk map covering the entire surface of the electric blanket, and marks the high-risk areas.

[0052] Specifically, a two-dimensional grid coordinate system for the surface of the electric blanket has been established in the aforementioned S221 (total... OK (Section). For each heating wire segment. The set of grid cells it covers is known. The risk level of this segment is determined. Assign a risk level to each grid cell it covers. When a grid cell is covered by multiple segments (e.g., at segment boundaries), the risk level of the segment with the largest coverage area can be taken, or the maximum risk level of all segments can be taken (a conservative approach). For grid cells not covered by any segments (e.g., edge regions), a low risk level can be assigned by default or obtained by interpolation from adjacent cells.

[0053] Then, arrange all grid cells according to their row numbers. and column number Arrange, form The matrix is ​​a two-dimensional matrix, which is the local thermal runaway risk map. The value of each element in the matrix represents the risk level of the corresponding grid cell (for example, using numbers 0, 1, and 2 to represent low, medium, and high risk, respectively, or using continuous risk index values).

[0054] To facilitate quick identification by users or implementing agencies, high-risk areas are prominently marked on the map, for example, by filling them with red, adding flashing borders, or using special patterns. On the control system's user interface, this risk distribution map can be displayed in real-time as a heatmap or a color contour map.

[0055] It should be understood that the generation of the local thermal runaway risk map depends not only on the risk index calculated in S31, but also on the health parameters output from the health status model established in S3. For example, if the health status of a certain segment... Below the threshold (e.g.) Even if the current risk index is not high, its risk level can be raised by one level to reflect the potential risks caused by aging. This correction rule can be preset based on engineering experience.

[0056] S4. Based on the local thermal runaway risk map and the heating wire health status model, perform predictive power regulation on high-risk areas, including locally reducing power or cutting off the power supply to that section.

[0057] After obtaining the local thermal runaway risk map and the segmental health parameters output by the heating wire health status model, predictive power regulation is performed on the heating wire segments marked as high-risk or medium-high-risk. Unlike traditional methods that passively cut off power only when the temperature exceeds an absolute threshold, this invention performs graded and proactive intervention based on risk index and health status, aiming to suppress the further development of local hot spots while maintaining the normal heating function of the electric blanket as much as possible.

[0058] As an example, based on the local thermal runaway risk map and the heating wire health status model, predictive power regulation is performed on high-risk areas, including: S41, Based on the high-risk areas marked in the local thermal runaway risk map, locate the corresponding heating wire segments; Specifically, the local thermal runaway risk map is analyzed in real time, and all grid cells marked as high-risk (or close to the high-risk threshold in the medium-risk category) are extracted. Based on the pre-established mapping relationship between heating wire segments and grid cells, each high-risk grid cell is reverse-located to its corresponding heating wire segment number. For a segment that covers multiple grid cells, if any one of those grid cells is marked as high-risk, the entire segment is identified as a high-risk segment. It should be understood that, due to the spatial continuity of local hotspots, multiple adjacent segments are often marked as high-risk simultaneously; in such cases, the entire contiguous area will be processed as a whole.

[0059] S42, call the health parameter of this segment in the heating wire health status model to determine whether its health status exceeds the preset threshold. Specifically, for each identified high-risk segment Obtain the current health status of the heating wire from the health status model. (Value range [0,1], where 1 represents perfect health). Preset health threshold. (For example This threshold can be determined through aging tests or safety standards for the heating wire. (Comparison) and :like This indicates that although the segment is currently experiencing localized thermal anomalies, the material itself has not yet shown significant aging or damage, and the hotspot can be suppressed by appropriately reducing power; if If the condition is positive, it indicates that the segment has material degradation or damage, posing a high risk and requiring more stringent measures.

[0060] After obtaining the health assessment results, the power is adjusted in stages according to the specific risk index and temperature rise rate: When the health level is below the threshold and the risk index is at a medium-high level, the power of the segment is reduced locally, and the reduction is positively correlated with the risk index. When the health level is below the threshold and the risk index reaches the highest level, or when the temperature rise rate continues to exceed the safety limit, the power supply of the circuit where the segment is located is directly cut off, while maintaining normal power supply to other non-high-risk areas.

[0061] Specifically, if And risk index At a medium to high level (e.g.) If the power level is 0, then a power reduction operation will be performed on that segment. The reduction percentage will be... Risk Index Positive correlation, for example, can be achieved using a linear function:

[0062] in, For the minimum reduction (e.g., 20%). For the maximum reduction (e.g., 70%). This is the lower limit threshold for medium risk. This is a high-risk threshold. In actual operation, the control device reduces the supply voltage or duty cycle of this segment by a corresponding proportion through pulse width modulation (PWM) or silicon controlled rectifier (SCR) phase-shift triggering. Understandably, reducing the power output decreases the heat generation in this segment, slowing the local temperature rise rate and thus preventing further development of hotspots. Simultaneously, other non-high-risk areas maintain their original power output, ensuring that the overall heating effect is not significantly affected.

[0063] An emergency is declared and a power outage is initiated when either of the following two situations occurs: Scenario 1: And risk index (For example At this point, not only has the material itself degraded, but the current thermal anomaly has also entered a high-risk zone, making it highly likely that local overheating and burning or a fire will occur.

[0064] Scenario 2: Rate of temperature rise Continuously exceeding safety limits (For example Furthermore, the continuous monitoring time exceeds the preset duration. (For example, three consecutive sampling cycles, each lasting 10 seconds). A continuously exceeding rate of temperature rise indicates that heat accumulation cannot be alleviated through natural heat dissipation or power regulation. Even if the current risk index has not yet reached the highest level, an emergency shutdown is necessary.

[0065] In any of the above situations, the control device immediately cuts off the power supply to the circuit containing that segment, typically by physically disconnecting it through a relay or SCR switch connected in series in that segment's circuit. Simultaneously, it maintains normal power supply to other non-high-risk areas to prevent the entire electric blanket from failing completely. Furthermore, it can issue audible and visual alarms or push notifications to the user (e.g., notifying the user via a mobile app that "there is a risk of localized overheating in the left side of the electric blanket; power has been automatically cut off"), reminding the user to check or replace it.

[0066] As a preferred supplement, after a power outage, the segmented circuit can be periodically (e.g., every 5 minutes) reclosed and briefly supplied with low power (e.g., 10% of rated power) while monitoring the rate of temperature rise and the risk index. If the anomaly disappears, power is gradually restored; if the anomaly persists, power is cut off again and locked, requiring manual reset. It should be understood that this mechanism can automatically resume function after temporary anomalies (such as poor local heat dissipation due to excessive covering) are resolved.

[0067] Through the aforementioned tiered predictive power regulation, this invention achieves a full-chain safety strategy from proactive power reduction warnings to emergency power outage protection. Compared to traditional passive protection that relies solely on a single temperature threshold, it can identify local thermal runaway risks earlier and adjust power output more precisely, significantly improving the safety and reliability of electric blankets in complex usage scenarios. Furthermore, because intervention only targets high-risk segments while other areas continue to operate normally, the basic heating function of the electric blanket is preserved to the greatest extent possible, contributing to a better user experience.

[0068] Please see Figure 2 This invention provides an intelligent temperature regulation and control device 200 for electric blankets, the control device comprising: The temperature and electrical parameter acquisition unit 11 is used to acquire sensor temperature data collected by a multi-point temperature sensor array arranged along the path of the heating wire, and to acquire resistance or voltage and current data of the segment of the heating wire to form an original electrothermal state dataset. The feature extraction unit 12 is used to calculate the local feature parameters of each segment of the heating wire based on the original electrothermal state dataset after time alignment, amplitude normalization and short-time anomaly filtering, including real-time power distribution, temperature rise rate and resistance deviation rate, and combine the local feature parameters with the spatial position of the heating wire to form a local thermal anomaly feature matrix. The health status modeling and risk map generation unit 13 is used to establish a health status model of the heating wire based on the local thermal anomaly feature matrix, and to perform spatial mapping on the surface of the entire electric blanket according to the local feature parameters of each segment of the heating wire to generate a local thermal runaway risk map marked with high-risk areas. The predictive power regulation unit 14 is used to perform predictive power regulation on high-risk areas based on the local thermal runaway risk map and the heating wire health status model, including locally reducing power or cutting off the power supply to that section.

[0069] As an example, please refer to Figure 3 The feature extraction unit 12 includes: The segmentation module 121 is used to divide the heating wire into multiple continuous segments according to the physical laying path of the heating wire in the electric blanket, and assign spatial coordinates or numbers to each segment. The matrix construction module 122 is used to take the real-time power distribution, temperature rise rate and resistance deviation rate of each segment as the multi-dimensional feature vector corresponding to that segment, and arrange the multi-dimensional feature vectors of all segments according to their spatial positions to form the local thermal anomaly feature matrix.

[0070] As an example, see further. Figure 3 The matrix construction module 122 includes: The initial sequence generation submodule 1221 is used to obtain the multi-dimensional feature vectors of each segment according to the order of the physical path of the heating wire and form the initial feature matrix; The spatial rearrangement index establishment submodule 1222 is used to establish a spatial rearrangement index based on the actual spatial adjacency relationship of each segment in the two-dimensional grid on the surface of the electric blanket. The vector adjustment submodule 1223 is used to adjust the segmented vectors that are spatially adjacent but not adjacent in path order in the initial feature matrix to adjacent positions in the matrix according to the spatial rearrangement index, so that the heating wire segments corresponding to adjacent positions in the matrix are actually adjacent on the surface of the electric blanket, forming a spatial rearrangement feature matrix that reflects the spatial continuity of heat diffusion, and using the spatial rearrangement feature matrix as the local thermal anomaly feature matrix.

[0071] As an example, please refer to Figure 4 The health status modeling and risk map generation unit 13 includes: The risk index calculation module 131 is used to calculate the local thermal runaway risk index of each segment by weighting and integrating the real-time power distribution, temperature rise rate and resistance deviation rate of each segment of heating wire according to preset weights, and classifying the risk level according to the index. The spatial mapping and marking module 132 is used to map the spatial position of each section of heating wire onto a two-dimensional grid on the surface of the electric blanket, assign the risk level of each section to the corresponding grid area, and generate a risk distribution map covering the entire surface of the electric blanket by splicing according to the spatial position, and mark the high-risk areas.

[0072] As an example, please refer to Figure 5 The predictive power regulation unit 14 includes: The segmented positioning module 141 is used to locate the corresponding heating wire segment according to the high-risk area marked in the local thermal runaway risk map; The health status judgment module 142 is used to call the health status parameters of the segment in the electric heating wire health status model to determine whether its health status exceeds the preset threshold. The graded execution module 143 is used to perform a local power reduction on the segment when the health level is below the threshold and the risk index is at a medium-high level. The power reduction is positively correlated with the risk index. When the health level is below the threshold and the risk index reaches the highest level, or when the temperature rise rate continues to exceed the safety limit, the power supply of the circuit where the segment is located is directly cut off, while maintaining normal power supply to other non-high-risk areas.

[0073] 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. An intelligent temperature regulation and control system for electric blankets, characterized in that, The system includes a control device configured with a computer program, which runs the computer program to perform the following steps: Acquire sensor temperature data from a multi-point temperature sensor array arranged along the path of the heating wire inside the electric blanket, and collect resistance or voltage and current data of the segment of the heating wire to form the original electrothermal state dataset. Based on the original electrothermal state dataset after time alignment, amplitude normalization and short-term anomaly filtering, the local characteristic parameters of each segment of the heating wire are calculated, including real-time power distribution, temperature rise rate and resistance deviation rate. The local characteristic parameters are combined with the spatial position of the heating wire to form a local thermal anomaly feature matrix. A health status model of the heating wire is established based on the local thermal anomaly feature matrix, and the surface of the entire electric blanket is spatially mapped according to the local feature parameters of each segment of the heating wire to generate a local thermal runaway risk map marked with high-risk areas. Based on the local thermal runaway risk map and the heating wire health status model, predictive power regulation is performed on high-risk areas, including local power reduction or power cut-off of that segment.

2. The intelligent temperature regulation and control system for an electric blanket according to claim 1, characterized in that: The local feature parameters are combined with the spatial location of the heating wire to form a local thermal anomaly feature matrix, including: According to the physical laying path of the heating wire in the electric blanket, the heating wire is divided into multiple continuous segments, and each segment is assigned spatial coordinates or a number. The real-time power distribution, temperature rise rate, and resistance deviation rate of each segment are used as the multidimensional feature vector corresponding to that segment. The multidimensional feature vectors of all segments are arranged according to their spatial positions to form the local thermal anomaly feature matrix.

3. The intelligent temperature regulation and control system for an electric blanket according to claim 2, characterized in that: The local thermal anomaly feature matrix is ​​constructed by arranging all segmented multidimensional feature vectors according to their spatial location, including: The multidimensional feature vectors of each segment are obtained according to the order of the physical path of the heating wire, and an initial feature matrix is ​​formed; a spatial rearrangement index is established based on the actual spatial adjacency relationship of each segment in the two-dimensional grid on the surface of the electric blanket. Based on the spatial rearrangement index, the segmented vectors that are spatially adjacent but not adjacent in path order in the initial feature matrix are adjusted to adjacent positions in the matrix, so that the heating wire segments corresponding to adjacent positions in the matrix are actually adjacent on the surface of the electric blanket, forming a spatial rearrangement feature matrix that reflects the spatial continuity of heat diffusion, and using this spatial rearrangement feature matrix as the local thermal anomaly feature matrix.

4. The intelligent temperature regulation and control system for an electric blanket according to claim 1, characterized in that: Based on the local characteristic parameters of each heating wire segment, the entire surface of the electric blanket is spatially mapped to generate a local thermal runaway risk map marked with high-risk areas, including: The real-time power distribution, temperature rise rate, and resistance deviation rate of each heating wire are weighted and integrated according to preset weights to calculate the local thermal runaway risk index of each segment, and the risk level is divided according to the magnitude of the index. The spatial positions of each heating wire segment are mapped onto a two-dimensional grid on the surface of the electric blanket. The risk level of each segment is assigned to the corresponding grid area, and the grid is spliced ​​together according to the spatial position to generate a risk distribution map covering the entire surface of the electric blanket, and high-risk areas are marked out.

5. The intelligent temperature regulation and control system for an electric blanket according to claim 1, characterized in that: Based on the aforementioned local thermal runaway risk map and heating wire health status model, predictive power regulation is performed on high-risk areas, including: Based on the high-risk areas marked in the local thermal runaway risk map, locate the corresponding heating wire segments; Call the health status parameter of this segment in the heating wire health status model to determine whether its health status exceeds the preset threshold. When the health level is below the threshold and the risk index is at a medium-high level, the power of the segment is reduced locally, and the reduction is positively correlated with the risk index. When the health level is below the threshold and the risk index reaches the highest level, or when the temperature rise rate continues to exceed the safety limit, the power supply of the circuit where the segment is located is directly cut off, while maintaining normal power supply to other non-high-risk areas.

6. An intelligent temperature regulation and control device for an electric blanket, characterized in that: The control device includes: The temperature and electrical parameter acquisition unit is used to acquire sensor temperature data collected by a multi-point temperature sensor array arranged along the path of the heating wire, and to acquire resistance or voltage and current data of the heating wire segments to form the original electrothermal state dataset. The feature extraction unit is used to calculate the local feature parameters of each segment of the heating wire based on the original electrothermal state dataset after time alignment, amplitude normalization and short-time anomaly filtering, including real-time power distribution, temperature rise rate and resistance deviation rate, and combine the local feature parameters with the spatial position of the heating wire to form a local thermal anomaly feature matrix. The health status modeling and risk map generation unit is used to establish a health status model of the heating wire based on the local thermal anomaly feature matrix, and to perform spatial mapping on the surface of the entire electric blanket according to the local feature parameters of each segment of the heating wire to generate a local thermal runaway risk map marked with high-risk areas. The predictive power regulation unit is used to perform predictive power regulation on high-risk areas based on the local thermal runaway risk map and the heating wire health status model, including locally reducing power or cutting off the power supply to that section.

7. The intelligent temperature regulation and control device for an electric blanket according to claim 6, characterized in that, The feature extraction unit includes: The segmentation module is used to divide the heating wire into multiple continuous segments according to the physical laying path of the heating wire in the electric blanket, and assign spatial coordinates or numbers to each segment. The matrix construction module is used to take the real-time power distribution, temperature rise rate, and resistance deviation rate of each segment as the multi-dimensional feature vector corresponding to that segment, and arrange the multi-dimensional feature vectors of all segments according to their spatial positions to form the local thermal anomaly feature matrix.

8. The intelligent temperature regulation and control device for an electric blanket according to claim 7, characterized in that, The matrix construction module includes: The initial sequence generation submodule is used to obtain the multi-dimensional feature vectors of each segment according to the order of the physical path of the heating wire and form the initial feature matrix; The spatial rearrangement index building submodule is used to build a spatial rearrangement index based on the actual spatial adjacency relationship of each segment in the two-dimensional grid on the surface of the electric blanket. The vector adjustment submodule is used to adjust the segmented vectors that are spatially adjacent but not adjacent in path order in the initial feature matrix to adjacent positions in the matrix according to the spatial rearrangement index, so that the heating wire segments corresponding to adjacent positions in the matrix are actually adjacent on the surface of the electric blanket, forming a spatial rearrangement feature matrix that reflects the spatial continuity of heat diffusion, and using the spatial rearrangement feature matrix as the local thermal anomaly feature matrix.

9. The intelligent temperature regulation and control device for an electric blanket according to claim 6, characterized in that, The health status modeling and risk map generation unit includes: The risk index calculation module is used to weight and integrate the real-time power distribution, temperature rise rate and resistance deviation rate of each heating wire according to preset weights, calculate the local thermal runaway risk index of each segment, and classify the risk level according to the index. The spatial mapping and marking module is used to map the spatial position of each section of heating wire onto a two-dimensional grid on the surface of the electric blanket, assign the risk level of each section to the corresponding grid area, and generate a risk distribution map covering the entire surface of the electric blanket by splicing according to the spatial position, and mark the high-risk areas.

10. The intelligent temperature regulation and control device for an electric blanket according to claim 6, characterized in that, The predictive power regulation unit includes: The segmented positioning module is used to locate the corresponding heating wire segments based on the high-risk areas marked in the local thermal runaway risk map. The health assessment module is used to call the health parameters of this segment in the heating wire health status model and determine whether its health status exceeds the preset threshold. The graded execution module is used to reduce the power of a segment locally when the health level is below the threshold and the risk index is at a medium-high level. The reduction is positively correlated with the risk index. When the health level is below the threshold and the risk index reaches the highest level, or when the temperature rise rate continues to exceed the safety limit, the power supply of the circuit where the segment is located is directly cut off, while maintaining normal power supply to other non-high-risk areas.

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