Material adhesion degree evaluation method, system, equipment and medium for heat sealing performance test

Abstract

The invention discloses a material adhesion degree evaluation method, system, equipment and medium for a heat sealing performance test, and relates to the technical field of data processing. The method comprises the step of locally heating a lowest temperature point area by acquiring temperature distribution data of a sealed area. In the heating process, time-space coordinates of a temperature peak value are identified, a material heat conduction path is determined, and heating power of different positions is adjusted accordingly. And when the temperature reaches the preset melting temperature, obtaining the adhesion degree value of a detection point on the sealing line and generating a distribution curve. Judging the cause of adhesion deterioration by analyzing the overlapping rate of the adhesion trough position and the temperature peak value space coordinate: when the overlapping rate is greater than or equal to a preset threshold value, judging that the adhesion deterioration is caused by overheating and generating a cooling suggestion; when the overlapping rate is smaller than a preset threshold value, it is judged that heating is insufficient, and a heat compensation suggestion is generated. By implementing the technical scheme provided by the invention, the effect of quickly and accurately evaluating the nutriology literature is achieved.

Description

Technical Field

[0001] This application relates to the field of data processing technology, specifically to a method, system, equipment, and medium for evaluating the adhesion of materials in a heat-sealing test. Background Technology

[0002] With the continuous development of industrial manufacturing technology, heat sealing technology is being used more and more widely in packaging, medical devices, automotive parts and other fields. During the heat sealing process, the adhesion of materials is an important indicator of sealing quality; its value reflects the tightness of the material interface bonding and directly affects the sealing performance and service life of the product.

[0003] Currently, industrial production generally uses fixed heating parameters to heat-seal materials, and then conducts sampling inspections of the sealed products through post-production testing methods such as peel strength testing and airtightness testing to evaluate the adhesion level of the materials.

[0004] However, in practical applications, due to the inherent inhomogeneity of the material, heating with fixed parameters often fails to guarantee uniform temperature distribution across the sealing area. In such cases, existing testing methods struggle to accurately assess changes in material adhesion at different locations within the sealing area, leading to significant discrepancies between the adhesion assessment results and actual conditions, thus reducing the accuracy of material adhesion assessment. Summary of the Invention

[0005] This application provides a method, system, equipment, and medium for evaluating the adhesion of materials in a heat-sealing test, which can improve the accuracy of material adhesion evaluation.

[0006] In a first aspect, this application provides a method for evaluating the adhesion of materials in a heat-sealing test, comprising: Acquire first temperature distribution data of the sealed area, identify the lowest temperature point region in the first temperature distribution data, and perform local heating on the lowest temperature point region; During the local heating process, second temperature distribution data of the sealed area is collected, the spatial coordinates and occurrence time of the local temperature peak in the second temperature distribution data are identified, and the material heat conduction path is determined based on the spatial coordinates and occurrence time. According to the heat conduction path of the material, adjust the heating power of the sealing area at different positions and perform the heating operation; When the temperature of the sealing area reaches the preset material melting temperature, the adhesion values ​​of multiple detection points on the sealing line are obtained, and an adhesion distribution curve is generated based on each adhesion value. Determine the location of the adhesion trough in the adhesion distribution curve; When the overlap rate between the spatial coordinates corresponding to the adhesion trough and the local temperature peak is greater than or equal to a preset threshold, it is determined that there is adhesion degradation caused by overheating at the adhesion trough and cooling suggestion information is generated. When the overlap rate between the spatial coordinates corresponding to the adhesion valley position and the local temperature peak is less than the preset threshold, it is determined that there is insufficient heating at the adhesion valley position and heat replenishment suggestion information is generated.

[0007] By adopting the above technical solution, firstly, by identifying the lowest temperature point region in the first temperature distribution data and performing local heating, the problem of uneven temperature distribution can be improved in a targeted manner. Secondly, by collecting second temperature distribution data during the local heating process and identifying the spatiotemporal characteristics of local temperature peaks, the heat conduction path of the material can be accurately obtained, providing a basis for precise adjustment of subsequent heating power. Thirdly, by adjusting the heating power at different locations based on the obtained heat conduction path, dynamic optimization of the temperature distribution in the sealing area can be achieved. Finally, by analyzing the overlap between the trough positions and the spatial coordinates of local temperature peaks in the adhesion distribution curve, the cause of adhesion degradation can be accurately determined as overheating or underheating, and corresponding improvement suggestions can be generated. This evaluation method based on dynamic monitoring of temperature distribution and analysis of adhesion distribution characteristics can promptly detect and diagnose temperature anomalies during the sealing process, provide targeted improvement measures, and thus improve the accuracy of material adhesion evaluation.

[0008] Optionally, the second temperature distribution data is divided into multiple temperature distribution snapshots according to the time series. A Laplace operator operation is performed on each temperature distribution snapshot to identify candidate temperature peak points where the second spatial derivatives of the temperature in both the horizontal and vertical directions are negative. The temperature values ​​of each candidate temperature peak point are extracted, and candidate temperature peak points whose temperature values ​​are greater than those of adjacent candidate temperature peak points and whose temperature rise relative to the lowest temperature point region exceeds a preset temperature rise threshold are selected as local temperature peak points. The time slice time corresponding to the temperature distribution snapshot where each local temperature peak point is located is recorded as the occurrence time, and the spatial coordinates of each local temperature peak point in the sealed area are extracted.

[0009] Optionally, the spatial coordinates corresponding to the local temperature peaks are arranged chronologically according to the occurrence time to obtain a temperature peak spatiotemporal sequence; the heat conduction rate of multiple path segments is calculated based on the distance and time interval between adjacent spatial coordinates in the temperature peak spatiotemporal sequence; the heat conduction rate is compared with a preset standard heat conduction rate, and when the heat conduction rate of any path segment deviates from the preset standard heat conduction rate by more than a preset deviation range, the path segment is marked as an abnormal heat conduction path segment; the lowest temperature point region is connected to each of the abnormal heat conduction path segments to generate a material heat conduction path.

[0010] Optionally, the thermal conductivity rate deviation value of each abnormal thermal conductivity path segment in the material's thermal conductivity path is extracted, and the sealing area is divided into multiple thermal conductivity characteristic regions based on the thermal conductivity rate deviation value. For each thermal conductivity characteristic region, the expected heating time from the current temperature to the preset material melting temperature is calculated based on the corresponding thermal conductivity rate deviation value. Using the median value of the expected heating time as the reference heating time, the time deviation value between the expected heating time of each thermal conductivity characteristic region and the reference heating time is calculated. Based on the time deviation value, the heating power and heating start time of each thermal conductivity characteristic region are determined. According to the heating start time and heating power of each thermal conductivity characteristic region, the heating device of each thermal conductivity characteristic region is started sequentially to perform the heating operation.

[0011] Optionally, heat conduction characteristic regions with positive time deviation values ​​are marked as advanced heating regions, and heat conduction characteristic regions with negative time deviation values ​​are marked as delayed heating regions. For the advanced heating region, a power attenuation coefficient and a start-up delay duration are calculated based on the absolute value of the time deviation value, wherein the power attenuation coefficient is negatively correlated with the absolute value of the time deviation value, and the start-up delay duration is positively correlated with the absolute value of the time deviation value. For the delayed heating region, a power enhancement coefficient and a start-up advance duration are calculated based on the absolute value of the time deviation value, wherein the power enhancement coefficient and the start-up advance duration are positively correlated with the absolute value of the time deviation value. The power attenuation coefficient and the power enhancement coefficient are multiplied by a preset reference heating power to obtain the adjusted heating power for each heat conduction characteristic region. A standard time corresponding to the reference heating duration is obtained, and the start-up delay duration of the advanced heating region is added to the standard time, and the start-up advance duration of the delayed heating region is subtracted from the standard time to obtain the heating start-up time corresponding to each heat conduction characteristic region.

[0012] Optionally, the starting end of the sealing line is set as the zero point, and a distance reference line is determined along the extension direction of the sealing line; the positions of each detection point are projected onto the distance reference line to obtain the projected distance value of each detection point on the distance reference line; the adhesion value of the detection point is normalized to obtain the normalized target adhesion value; the projected distance value and the target adhesion value are used as input data for interpolation calculation; based on the input data, interpolation calculation is performed between adjacent detection points on the sealing line to generate the adhesion distribution curve.

[0013] Optionally, the spatial coordinate range of the adhesion valley location on the sealing line is extracted, and a preset valley influence radius is extended outward from the adhesion valley location as the center to determine the adhesion valley influence area; a preset peak influence radius is extended outward from each of the spatial coordinates to determine multiple temperature peak influence areas; the spatial overlap area between the adhesion valley influence area and each of the temperature peak influence areas is calculated; the sum of the spatial overlap areas is divided by the total area of ​​the adhesion valley influence area to obtain the overlap rate between the adhesion valley location and the spatial coordinates corresponding to the local temperature peak.

[0014] A second aspect of this application provides a material adhesion evaluation system for heat-sealing testing, the system comprising: The data acquisition module is used to acquire first temperature distribution data of the sealed area, identify the lowest temperature point area in the first temperature distribution data, and perform local heating on the lowest temperature point area. The path determination module is used to collect second temperature distribution data of the sealed area during the local heating process, identify the spatial coordinates and occurrence time of the local temperature peak in the second temperature distribution data, and determine the material heat conduction path based on the spatial coordinates and occurrence time. The adhesion acquisition module is used to adjust the heating power corresponding to different positions of the sealing area and perform heating operations according to the heat conduction path of the material; when the temperature of the sealing area reaches the preset material melting temperature, the adhesion values ​​of multiple detection points on the sealing line are acquired, and an adhesion distribution curve is generated according to each adhesion value. An adhesion evaluation module is used to determine the location of adhesion troughs in the adhesion distribution curve; when the overlap rate between the adhesion trough location and the spatial coordinates corresponding to the local temperature peak is greater than or equal to a preset threshold, it is determined that the adhesion trough location has adhesion degradation caused by overheating and cooling suggestion information is generated; when the overlap rate between the adhesion trough location and the spatial coordinates corresponding to the local temperature peak is less than the preset threshold, it is determined that the adhesion trough location has insufficient heating and heating suggestion information is generated.

[0015] A third aspect of this application provides an electronic device including a memory, a processor, and a program stored in the memory and executable on the processor, the program being loaded and executed by the processor to implement a material adhesion evaluation method for a heat-sealing test.

[0016] A fourth aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, causes the processor to implement a method for evaluating the adhesion of materials in a heat-sealing test.

[0017] In summary, one or more technical solutions provided in this application have at least the following technical effects or advantages: By adopting the above technical solution, firstly, by identifying the lowest temperature point region in the first temperature distribution data and performing local heating, the problem of uneven temperature distribution can be improved in a targeted manner. Secondly, by collecting second temperature distribution data during the local heating process and identifying the spatiotemporal characteristics of local temperature peaks, the heat conduction path of the material can be accurately obtained, providing a basis for precise adjustment of subsequent heating power. Thirdly, by adjusting the heating power at different locations based on the obtained heat conduction path, dynamic optimization of the temperature distribution in the sealing area can be achieved. Finally, by analyzing the overlap between the trough positions and the spatial coordinates of local temperature peaks in the adhesion distribution curve, the cause of adhesion degradation can be accurately determined as overheating or underheating, and corresponding improvement suggestions can be generated. This evaluation method based on dynamic monitoring of temperature distribution and analysis of adhesion distribution characteristics can promptly detect and diagnose temperature anomalies during the sealing process, provide targeted improvement measures, and thus improve the accuracy of material adhesion evaluation. Attached Figure Description

[0018] Figure 1 This is a schematic flowchart of a material adhesion evaluation method for a heat-sealing test provided in an embodiment of this application; Figure 2 This is another schematic flowchart of a material adhesion evaluation method for a heat-sealing test provided in an embodiment of this application; Figure 3 This is a schematic diagram of the structure of a material adhesion evaluation system for heat sealing testing provided in an embodiment of this application; Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0019] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0020] In the description of the embodiments of this application, the words "for example" or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design that is described as "for example" or "for instance" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design options. Rather, the use of the words "for example" or "for instance" is intended to present the relevant concepts in a specific manner.

[0021] In the description of the embodiments of this application, the term "multiple" means two or more. For example, multiple systems means two or more systems, and multiple screen terminals means two or more screen terminals. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the indicated technical features. Thus, a feature defined with "first" or "second" may explicitly or implicitly include one or more of that feature. The terms "comprising," "including," "having," and variations thereof all mean "including but not limited to," unless otherwise specifically emphasized.

[0022] This application provides a method for evaluating the adhesion of materials in a heat-sealing test. In one embodiment, please refer to... Figure 1 , Figure 1 This is a flowchart illustrating a material adhesion evaluation method for heat-sealing performance testing provided in this application. This method can be implemented using a computer program, which can be integrated into an application or run as a standalone tool application. The method can also be implemented using a microcontroller or run on a material adhesion evaluation system for heat-sealing performance testing based on the von Neumann architecture. Specifically, the method may include the following steps: Step 101: Obtain the first temperature distribution data of the sealed area, identify the lowest temperature point area in the first temperature distribution data, and perform local heating on the lowest temperature point area.

[0023] The sealing area refers to the overlapping portion between two materials to be sealed, typically appearing as a strip or line, and is the area where the heat sealing process takes effect. Temperature distribution data refers to the collection of temperature values ​​at various points within the sealing area, acquired through temperature sensor arrays or infrared thermal imagers, reflecting the spatial distribution of temperature within the sealing area. The lowest temperature point region refers to the localized area with the lowest temperature value in the temperature distribution data; this area may be formed due to uneven heat conduction, insufficient heating, or other reasons. Localized heating refers to the operation of using point-like or small-area heating devices, such as laser heaters or infrared heaters, to directionally heat a specific area.

[0024] Specifically, firstly, an infrared thermal imager is used to scan the temperature of the sealed area at a sampling period of 0.1 seconds, acquiring a temperature distribution data matrix with a resolution of 0.1mm × 0.1mm. Next, a data processing algorithm analyzes the temperature distribution data, comparing each temperature value in the temperature data matrix with its eight neighbors. When the temperature value of a point is lower than the temperature values ​​of all points in its eight neighbors, that point is marked as the local minimum temperature point. Then, a 2mm × 2mm area is extended outward from this local minimum temperature point, defining the area within this range as the minimum temperature region. Finally, a 50W point-shaped infrared heater is activated, with the heating focus aligned with the geometric center of the minimum temperature region, locally heating the area at a heating rate of 20℃ / s. During the heating process, the infrared thermal imager continuously collects temperature data until the temperature of the minimum temperature region reaches the average temperature value of the surrounding area. This method effectively identifies and improves temperature unevenness within the sealed area.

[0025] Step 102: During the local heating process, collect the second temperature distribution data of the sealed area, identify the spatial coordinates and occurrence time of the local temperature peak in the second temperature distribution data, and determine the material heat conduction path based on the spatial coordinates and occurrence time.

[0026] The second temperature distribution data refers to the temperature data matrix of the sealed area collected in real time during the local heating process, recording the dynamic changes in temperature during heating. The local temperature peak refers to the point in the temperature distribution data where the temperature is higher than the surrounding area, represented as the local highest point on the temperature surface. Spatial coordinates refer to the position of the temperature peak point on the two-dimensional plane of the sealed area, represented by an XY rectangular coordinate system. The occurrence time refers to the time point corresponding to the formation of the temperature peak, recorded with the start of heating as time zero. The heat conduction path refers to the actual route of heat propagation within the material, reflecting the characteristics of heat conduction within the material.

[0027] Specifically, firstly, an infrared thermal imager is used to continuously collect temperature data of the sealed area during the local heating process at a sampling period of 0.05 seconds, obtaining a temperature distribution data sequence with a resolution of 0.1mm × 0.1mm. For the temperature distribution data at each sampling time, the second derivative is calculated using the Laplace operator, specifically the formula: ∂²T / ∂x² + ∂²T / ∂y², where T represents the temperature value, and x and y represent the spatial coordinates in the horizontal and vertical directions, respectively. When the Laplace operator calculation result at a point is negative, and the temperature value at that point is greater than the temperature values ​​of all points within its 8-neighborhood, that point is marked as a local temperature peak. For each temperature peak, its position coordinates (x, y) in the XY coordinate system and the corresponding timestamp t are recorded. All temperature peaks are sorted by timestamp to obtain the spatiotemporal evolution sequence of the temperature peaks. Based on this sequence, the spatial distance d and time interval Δt between adjacent temperature peaks are calculated, yielding the heat propagation rate v = d / Δt. By connecting adjacent temperature peaks in the time series, a heat conduction path diagram is drawn. This path diagram visually shows the trajectory of heat propagation in the material and can be used to analyze the heat conduction characteristics of the material.

[0028] In one possible implementation, identifying the spatial coordinates and occurrence time of local temperature peaks in the second temperature distribution data specifically includes steps 1021-1023, as follows: Step 1021: Divide the second temperature distribution data into multiple temperature distribution snapshots according to the time series, perform Laplace operator operation on each temperature distribution snapshot, and identify candidate temperature peak points where the second spatial derivatives of the temperature in both the horizontal and vertical directions are negative.

[0029] A temperature distribution snapshot refers to a temperature distribution data matrix acquired at a specific moment, recording the temperature values ​​of all points within a sealed area at that moment. The Laplace operator is a mathematical operation used to detect local extrema in a two-dimensional data field; in a temperature field, it is represented as the sum of the second-order partial derivatives of the temperature value with respect to spatial coordinates. A candidate temperature peak point is a point that satisfies the characteristic of a local maximum temperature.

[0030] Specifically, firstly, the collected second temperature distribution data is divided into segments at 0.05-second intervals, with each time slice corresponding to a temperature distribution snapshot, forming a time series {T1, T2, ..., Tn}. For each temperature distribution snapshot, a temperature value matrix T(x, y) is constructed, where x and y represent the spatial coordinates in the horizontal and vertical directions, respectively. The Laplace operator is then applied to the temperature value matrix to calculate the second derivative of each point (i, j): the horizontal second derivative Dxx = [T(i+1, j) - 2T(i, j) + T(i-1, j)] / (Δx)², and the vertical second derivative Dyy = [T(i, j+1) - 2T(i, j) + T(i, j-1)] / (Δy)², where Δx and Δy are the spatial sampling intervals, both 0.1 mm. When both Dxx and Dyy at a point are less than 0, it indicates that the point exhibits a downward convex characteristic in both directions, and this point is marked as a candidate temperature peak point. In this way, all candidate points that meet the characteristics of local maximum temperature can be identified in each time slice, providing basic data for subsequent temperature peak analysis.

[0031] Step 1022: Extract the temperature values ​​of each candidate temperature peak point, filter out the candidate temperature peak points whose temperature values ​​are greater than those of adjacent candidate temperature peak points and whose temperature rise value relative to the lowest temperature point area exceeds the preset temperature rise threshold, and determine them as local temperature peak points.

[0032] The temperature value of a candidate temperature peak point refers to the actual temperature value corresponding to the location marked as a candidate point in the temperature distribution snapshot, in degrees Celsius. Adjacent candidate temperature peak points refer to other candidate points that are spatially less than 2 mm from the target candidate point. The temperature rise value is the difference between the temperature value of a candidate point and the average temperature of the lowest temperature area. The preset temperature rise threshold is the temperature difference standard used to judge the significance of temperature peaks, set at 5°C. Local temperature peak points refer to the actual temperature peak locations determined after screening.

[0033] Specifically, first, for each candidate temperature peak point, its temperature value T(i, j) is read. Then, other candidate points are searched within a 2mm × 2mm area around this point, and the coordinates and temperature values ​​of these points are recorded as a set of adjacent candidate points {(x1, y1, T1), (x2, y2, T2), ..., (xn, yn, Tn)}. The arithmetic mean Tbase of all temperature values ​​in the region of the lowest temperature point is calculated. For the current candidate point, the temperature rise ΔT = T(i, j) - Tbase between it and Tbase is calculated. At the same time, T(i, j) is compared with the temperature values ​​of all adjacent candidate points, and the comparison results are recorded in the flag array flag[]. When T(i, j) is greater than the temperature value of a certain adjacent point, the corresponding flag is set to 1; otherwise, it is set to 0. A candidate point is identified as a local temperature peak point only if all flag values ​​are 1 (indicating that the temperature at this point is higher than all adjacent candidate points) and ΔT is greater than a preset temperature rise threshold of 5℃. Its coordinates (i, j) and temperature value T(i, j) are then recorded in the temperature peak point dataset. This multi-stage filtering mechanism ensures that the identified temperature peak points have significant local maximum temperature characteristics, avoiding false peak interference caused by temperature fluctuations.

[0034] Step 1023: Record the time slice corresponding to the temperature distribution snapshot of each local temperature peak point as the occurrence time, and extract the spatial coordinates of each local temperature peak point in the sealed area.

[0035] A time slice moment refers to a marker point on the time axis representing a snapshot of the temperature distribution, with the start of local heating as the zero point, and the unit of time is seconds. The occurrence moment refers to the time slice moment corresponding to the first detection of the local temperature peak.

[0036] Specifically, first, a spatiotemporal data recording table is established, containing four fields: timestamp, X coordinate, Y coordinate, and temperature value. For each determined local temperature peak point, the timestamp t of the snapshot of its temperature distribution is read. This timestamp is calculated by multiplying the snapshot number by the sampling period (0.05 seconds). The row and column indices (i, j) of the peak point in the temperature distribution matrix are extracted and converted into actual physical coordinates: X coordinate = i × 0.1 mm, Y coordinate = j × 0.1 mm, where 0.1 mm is the spatial sampling interval. The timestamp t and spatial coordinates (X, Y) are recorded in the corresponding rows of the spatiotemporal data recording table. After recording the data for all peak points, the recording table is sorted in ascending order of timestamp to obtain a complete spatiotemporal distribution sequence of temperature peak points: {(t1, X1, Y1, T1), (t2, X2, Y2, T2), ..., (tn, Xn, Yn, Tn)}. This sequence records the spatiotemporal information of all significant temperature peaks formed during heat propagation in the material, providing a data foundation for subsequent heat conduction path analysis. Each data point precisely records the time and location information of the temperature peak, with a temporal accuracy of 0.05 seconds and a spatial accuracy of 0.1 mm.

[0037] In one possible implementation, the material heat conduction path is determined based on spatial coordinates and the time of occurrence, specifically including steps 1024-1026, as follows: Step 1024: Arrange the spatial coordinates corresponding to the local temperature peaks in chronological order according to the time of occurrence to obtain the spatiotemporal sequence of temperature peaks.

[0038] Temporal arrangement refers to the operation of sorting data according to the order of time. A temperature peak spatiotemporal sequence is a data sequence that organizes the spatial location and occurrence time of temperature peak points in chronological order. Each sequence element contains a timestamp, spatial coordinates, and temperature value information. The spatial coordinates include X and Y components, representing the horizontal and vertical positions of the peak point, respectively, in millimeters. The occurrence time is recorded as the time from the start of local heating to zero, in seconds.

[0039] Specifically, firstly, a data structure PeakPoint{time, x, y, temperature} is constructed to store information for each temperature peak point. An array of type PeakPoint, peakArray[], is created to store all peak point data. For each identified local temperature peak point, a PeakPoint object is created, and its timestamp, X coordinate, Y coordinate, and temperature value are assigned to the corresponding fields. The peakArray[] is sorted in ascending order using the time field as the key. After sorting, the peakArray[] is traversed to generate formatted spatiotemporal sequence data. Each sequence element is denoted as: S(i) = {ti, (xi, yi), Ti}, where i is the sequence index, ti is the timestamp, (xi, yi) are the spatial coordinates, and Ti is the temperature value. All sequence elements are written into the output array in ascending order of index i, forming a complete temperature peak spatiotemporal sequence S = {S(1), S(2), ..., S(n)}. This sequence clearly records the evolution of the temperature peak in both time and space, with a time interval of no less than 0.05 seconds (sampling period) between each sequence element and spatial coordinates accurate to 0.1 mm. This temporal arrangement provides a structured data foundation for subsequent analysis of thermal conductivity characteristics.

[0040] Step 1025: Calculate the heat conduction rate of multiple path segments based on the distance and time interval between adjacent spatial coordinates in the temperature peak spatiotemporal sequence.

[0041] Adjacent spatial coordinates refer to the positional coordinates of two consecutive temperature peak points in a spatiotemporal sequence. Distance refers to the Euclidean distance between two adjacent spatial coordinate points, measured in millimeters. Time interval refers to the difference in the times when two adjacent peak points occur, measured in seconds. Path segment refers to the straight line segment connecting two adjacent temperature peak points. Thermal conductivity rate refers to the speed at which heat propagates through a material, measured in millimeters per second, reflecting the material's thermal conductivity characteristics.

[0042] Specifically, first, all adjacent element pairs {S(i), S(i+1)} in the spatiotemporal sequence S of the temperature peak are traversed. For each pair of adjacent elements, their spatial coordinates (xi, yi) and (xi+1, yi+1), as well as the corresponding timestamps ti and ti+1, are extracted. The spatial distance di,i+1 is calculated by calculating the distance between adjacent coordinate points in the X-axis and Y-axis directions respectively, and then calculating the actual distance between the two points using the Euclidean distance formula, with the result accurate to 0.1 mm. The time interval Δti,i+1 = ti+1 - ti is calculated, accurate to 0.05 seconds. For each path segment, its heat conduction rate vi,i+1 = di,i+1 / Δti,i+1 is calculated, with the unit being mm / s. The calculated heat conduction rate data is stored in the array v[], where each array element contains five fields: the starting coordinates, ending coordinates, distance, time interval, and heat conduction rate of the path segment. At the same time, the direction angle θi,i+1 of each path segment is calculated, obtained by the arctangent function of the displacement in the X-axis and Y-axis directions, which is used to characterize the directional features of heat conduction. This calculation yields a complete dataset of thermal conductivity rates {(di, i+1, Δti, i+1, vi, i+1, θi, i+1)}, which contains information on the speed and direction of heat propagation in the material, providing a quantitative basis for subsequent analysis of the material's thermal conductivity characteristics.

[0043] Step 1026: Compare the thermal conduction rate with the preset standard thermal conduction rate. When the thermal conduction rate of any path segment deviates from the preset standard thermal conduction rate by more than the preset deviation range, mark the path segment as an abnormal thermal conduction path segment; connect the lowest temperature point area with each abnormal thermal conduction path segment to generate the material thermal conduction path.

[0044] The standard thermal conductivity rate refers to the benchmark value of a material's thermal conductivity rate under ideal conditions, obtained through standard testing, and is measured in millimeters per second. The preset deviation range refers to the allowable fluctuation range of the thermal conductivity rate, set at ±15% of the standard value. Abnormal thermal conductivity path segments refer to path segments where the thermal conductivity rate exceeds the preset deviation range.

[0045] Specifically, first, read the pre-set standard heat conduction rate Vs (for a specific material, such as PVC, the standard value is 2.5 mm / s). Calculate the upper and lower threshold values: Vupper = Vs × 1.15, Vlower = Vs × 0.85. Traverse the heat conduction rate data {vi, i+1} of all path segments. When the heat conduction rate vi, i+1 of a certain path segment is greater than Vupper or vi, i+1 is less than Vlower, mark this path segment as an abnormal path segment, and record its starting coordinates (xi, yi) and ending coordinates (xi+1, yi+1) into the abnormal path array abnormalPath[]. Calculate the geometric center coordinates (x0, y0) of the lowest temperature point region. Create a two-dimensional plane coordinate system, where the X-axis and Y-axis represent the spatial positions in the horizontal and vertical directions respectively, with the unit of millimeter. Mark the center (x0, y0) of the lowest temperature point region in the coordinate system with red. Plot the starting and ending coordinates of all abnormal path segments on the plane with blue. Connect the center of the lowest temperature point region with the starting point of each abnormal path segment with a solid yellow line. Connect the starting point and the ending point of the abnormal path segment with a solid red line. Mark the heat conduction rate value vi, i+1 beside each abnormal path segment. The finally generated heat conduction path diagram visually shows the abnormal heat conduction phenomenon in the material, including the conduction direction, the position of rate abnormality, and the degree of abnormality.

[0046] Step 103: Adjust the heating power corresponding to different positions in the sealing area according to the material heat conduction path and perform the heating operation.

[0047] The heating power refers to the heat output by the heating device per unit time, with the unit of watt. The heating operation refers to the process of applying heat to the material by controlling the heating device. Different positions refer to each spatial point within the sealing area, and each position has its unique spatial coordinates. Adjustment refers to the operation of quantitatively modifying the heating power according to the characteristics of the heat conduction path.

[0048] Specifically, firstly, the sealed area is divided into 10mm × 10mm grid cells, each corresponding to an independently controllable heating unit. Abnormal path information in the heat conduction path diagram is read, including the location coordinates and heat conduction rate of the abnormal path segment. For each grid cell, the spatial distance *d* between it and the nearest abnormal path segment is calculated. Based on the distance *d* and the heat conduction rate *v* of the abnormal path segment, the heating power adjustment coefficient *k* for that grid cell is calculated: when *v* is greater than the standard heat conduction rate, *k* = 1 - 0.2 × (*v* - *vs*) / *vs; when *v* is less than the standard heat conduction rate, *k* = 1 + 0.2 × (*vs* - *v*) / *vs, where *vs* is the standard heat conduction rate. The initial heating power *P0* (standard value 50W) is multiplied by the adjustment coefficient *k* to obtain the target heating power *P* = *P0* *k* for that grid cell. The output power of each heating unit of the heating device is adjusted to the calculated target value. A PID control algorithm is used to adjust the heating power in real time. The control parameters are set as follows: proportional coefficient Kp = 0.8, integral time Ti = 2s, and derivative time Td = 0.5s. Temperature data is collected every 0.1 seconds, and the heating power is dynamically adjusted based on the deviation between the actual temperature and the target temperature to ensure uniform and stable heating at all locations. This dynamic power adjustment mechanism based on the heat conduction path achieves precise temperature control in the sealed area.

[0049] Step 104: When the temperature of the sealing area reaches the preset material melting temperature, obtain the adhesion values ​​of multiple detection points on the sealing line, and generate an adhesion distribution curve based on each adhesion value.

[0050] The preset material melting temperature refers to the threshold temperature at which a specific material begins to melt, such as 165℃ for PVC. The sealing line is a continuous line segment formed by the contact edges of two materials to be sealed. Test points are test locations selected at fixed intervals along the sealing line. Adhesion value is a quantitative indicator of the interfacial bonding strength of materials. The adhesion distribution curve is a graph plotted with the sealing line location as the independent variable and the adhesion value as the dependent variable.

[0051] Specifically, firstly, a detection point is set every 5mm along the sealing line, for a total of 20 detection points for a 100mm long sealing line. An infrared thermometer is used to monitor the temperature of the sealing area in real time. When the temperature reaches the preset material melting temperature, an adhesion test is performed. For each detection point, the adhesion is measured using a peel strength test: the test sample is fixed on a tensile testing machine, the tensile rate is set to 50mm / min, and the maximum tensile force F during the peeling process is recorded. The adhesion value for each detection point is calculated: σi = F / w, where w is the width of the test sample (standard value 10mm). The spatial position xi (range 0-100mm) of the detection point and the corresponding adhesion value σi are recorded in a data array. Cubic spline interpolation is used to fit the discrete adhesion data points, with an interpolation interval of [0, 100mm] and an interpolation step of 1mm, generating a continuous adhesion distribution curve. The adhesion distribution curve is plotted on a coordinate system, with the horizontal axis representing the sealing line position and the vertical axis representing the adhesion value. This curve visually illustrates the distribution of adhesion quality at various locations within the sealing area, providing a quantitative basis for assessing sealing quality.

[0052] In one possible implementation, an adhesion distribution curve is generated based on each adhesion value, specifically including steps 1041-1043, as follows: Step 1041: Set the starting end of the sealing line as the zero point of distance, and determine the distance reference line along the extension direction of the sealing line.

[0053] The starting point of the sealing line refers to its initial spatial position, serving as the origin for positional reference. The extension direction refers to the spatial trajectory of the sealing line from its starting point to its ending point. The distance baseline is a one-dimensional coordinate axis established with the starting point of the sealing line as its origin, along the extension direction of the sealing line, used to determine the position of each point on the sealing line. The distance zero point refers to the starting point of the distance baseline, corresponding to the starting position of the sealing line.

[0054] Specifically, firstly, the starting point coordinates (x0, y0) are identified in the geometric data of the sealing line, and this point is set as the zero point of distance. A local coordinate system is established, with the starting point as the origin, the tangential direction of the sealing line at this point as the positive X-axis, and the direction perpendicular to the tangential direction as the positive Y-axis. The direction vector of the sealing line is calculated as v=(dx, dy), where dx and dy are the components of the sealing line in the X-axis and Y-axis directions, respectively. The sealing line is discretized, dividing the continuous sealing line into tiny line segments of 1mm in length. For the endpoint of each line segment, its projected distance relative to the starting point is calculated as: si=(xi-x0)×dx+(yi-y0)×dy, where (xi, yi) are the coordinates of the endpoint of the line segment. Based on the calculated projected distance, a distance baseline is established, the domain of which is [0, L], where L is the total length of the sealing line. Scales are marked on the distance baseline, with the main scale interval being 10mm and the sub-scale interval being 1mm. In this way, a one-dimensional reference system for describing the position on the sealing line is established, providing a unified spatial positioning benchmark for subsequent adhesion measurement.

[0055] Step 1042: Project the position of each detection point onto the baseline and obtain the projection distance value of each detection point on the baseline; normalize the adhesion value of the detection points to obtain the normalized target adhesion value.

[0056] The projection position refers to the orthogonal projection point of the detection point onto the baseline. The projection distance value refers to the linear distance from the projection position of the detection point to the zero point. Normalization is the mathematical process of converting the adhesion value to the [0, 1] interval according to a specific rule. The target adhesion value refers to the dimensionless adhesion value after normalization. Orthogonal projection means drawing a perpendicular line from the detection point to the baseline; the intersection of the perpendicular line and the baseline is the projection point.

[0057] Specifically, firstly, for each detection point P(x, y), calculate its orthogonal projection to the baseline. The baseline direction vector is v=(dx, dy), and the vector from the detection point to the starting point is w=(x-x0, y-y0). Calculate the projected distance value di=w·v=(x-x0)×dx+(y-y0)×dy, which represents the position of the detection point on the baseline. Normalize the original adhesion values ​​{σ1, σ2, ..., σn} of all detection points: First, find the maximum value σmax and the minimum value σmin in the dataset. For each adhesion value σi, calculate its normalized value according to the formula σi_norm=(σi-σmin) / (σmax-σmin). Create a data table containing three columns: detection point number i, projected distance value di, and normalized adhesion value σi_norm. Sort the data table in ascending order of projected distance value to generate an ordered position-adhesion correspondence. The projected distance values ​​were grouped at 5mm intervals, and the average normalized adhesion value within each group was calculated to eliminate local fluctuations. This resulted in a set of standardized data points {(di, σi_norm)}, which reflect the relative variation in adhesion strength along the seal line direction.

[0058] Step 1043: Use the projected distance value and the target adhesion value as input data for interpolation calculation; perform interpolation calculation between adjacent detection points on the sealing line based on the input data to generate an adhesion distribution curve.

[0059] Input data refers to the set of known data points used for interpolation calculations, including projected distance values ​​and corresponding target adhesion values. Adjacent detection points refer to two detection points that are adjacent to each other on the baseline. Interpolation calculation refers to the mathematical calculation of values ​​at any position between two known data points. The adhesion distribution curve is a function curve representing the continuous change in adhesion along the sealing line.

[0060] Specifically, firstly, the projected distance values ​​of all detection points and the target adhesion values ​​are combined to form data point pairs {(di, σi_norm)}, which are then sorted in ascending order of projected distance values. Cubic spline interpolation is performed on the intervals between adjacent data points: the entire sealing line length interval [0, L] is divided into n-1 sub-intervals [di, di+1], where n is the total number of detection points. Within each sub-interval, a cubic polynomial function Si(x) = ai(x-di)³ + bi(x-di)² + ci(x-di) + di is constructed, where ai, bi, ci, and di are undetermined coefficients. Based on the condition of equal function values ​​at the data points: Si(di) = σi_norm, Si(di+1) = σi+1_norm, and the condition of continuity of the first and second derivatives of adjacent polynomials at the connection points, a system of linear equations is established to solve for the interpolation coefficients. For each sub-interval, the interpolation function value is calculated with a step size of 0.1 mm, resulting in a dense sequence of data points. The interpolation results of all sub-intervals are connected to form a complete adhesion distribution curve. This curve is plotted on a Cartesian coordinate system, with the horizontal axis representing the projected distance and the vertical axis representing the normalized adhesion value. This curve smoothly reflects the continuous variation trend of adhesion along the seal line, avoiding abrupt changes caused by discrete data points, and providing an intuitive visual representation for evaluating seal quality.

[0061] Step 105: Determine the location of the adhesion trough in the adhesion distribution curve.

[0062] An adhesion distribution curve is a continuous function curve describing the change in adhesion along a sealing line. The horizontal axis represents location, and the vertical axis represents adhesion value. An adhesion trough is a local minimum point on the curve, indicating a location with relatively low adhesion strength. The trough location refers to the spatial coordinates corresponding to the adhesion trough point, including the projected distance from the baseline. A local minimum is a function value that is less than that of all surrounding points within a certain range.

[0063] Specifically, firstly, the data sequence {(xi, yi)} of the adhesion distribution curve is sampled at 0.1 mm intervals to generate a discrete data point set. Numerical differentiation is performed on the data sequence to calculate the first derivative of each point: dy / dx = (yi+1-yi) / (xi+1-xi). Valley points are identified by the sign change of the first derivative: the position where the first derivative changes from negative to positive is the valley point. To eliminate noise, a derivative threshold of ±0.01 is set; only changes where the absolute value of the derivative is greater than the threshold are considered valid. For each candidate valley point, samples are taken within a 2 mm range to its left and right for verification: if the function value of that point is less than the values ​​of all other points within that range, it is confirmed as a valid valley point. The location coordinates and corresponding adhesion values ​​of all valley points are recorded to form a valley dataset {(xv, yv)}. Valley points are sorted by valley depth (the difference relative to the nearest local maximum), with a depth threshold set to 20% of the normalized adhesion value; only significant valley points exceeding the threshold are retained. Significant troughs are marked on the adhesion distribution curve with red markers, along with their location coordinates and adhesion values. These trough locations indicate areas where quality issues may exist during the sealing process, requiring focused attention and improvement.

[0064] Step 106: When the overlap rate between the spatial coordinates corresponding to the adhesion trough and the local temperature peak is greater than or equal to a preset threshold, it is determined that there is adhesion degradation caused by overheating at the adhesion trough and cooling suggestion information is generated.

[0065] Overlap rate refers to the degree of coincidence between the spatial coordinates of the adhesion trough and the local temperature peak, expressed as a percentage. The preset threshold is the standard value for judging the degree of overlap, set at 80%. Adhesion degradation refers to the phenomenon of decreased interfacial bonding strength. Cooling recommendations provide specific temperature adjustment schemes for overheated areas. Overheating refers to a state where the local temperature exceeds the material's optimal adhesion temperature. Spatial coordinate overlap means that the distance between two points is less than the preset judgment distance.

[0066] Specifically, firstly, a spatial location determination standard is established, considering two points less than 2 mm apart as overlapping. The coordinate set of all adhesion trough locations {(xv1, yv1), (xv2, yv2), ...} and the coordinate set of local temperature peak points {(xt1, yt1), (xt2, yt2), ...} are read. For each adhesion trough location (xvi, yvi), its distance to all temperature peak points is calculated: dij = |xvi - xtj| + |yvi - ytj|. The number of temperature peak points overlapping with this trough location, n (the number of points satisfying dij ≤ 2 mm), is counted, and the overlap rate r = n / N is calculated, where N is the total number of temperature peak points within a 10 mm radius of this location. When the overlap rate r ≥ 80%, this trough location is marked as an adhesion degradation point caused by overheating. For each point of adhesion degradation, read the temperature value T at its corresponding peak temperature point and calculate the temperature adjustment ΔT = T - T0, where T0 is the optimal bonding temperature of the material (e.g., 165℃ for PVC). Generate cooling suggestion information, including: coordinates of the degradation point, current temperature value, suggested cooling range (50% of ΔT), and suggested heating power adjustment (70% of the current power). Compile all the information for all degradation points into a table, including fields such as location, temperature, overlap rate, and suggested adjustment parameters, to provide a basis for subsequent process parameter optimization.

[0067] Step 107: When the overlap rate between the spatial coordinates corresponding to the adhesion valley position and the local temperature peak is less than a preset threshold, it is determined that there is insufficient heating at the adhesion valley position and heat replenishment suggestion information is generated.

[0068] Overlap rate refers to the degree of coincidence between the spatial coordinates of the adhesion trough and the temperature peak, expressed as a percentage. Insufficient heating refers to a localized temperature that has not reached the minimum temperature required for effective material adhesion. Heating compensation suggestions provide specific heating parameter adjustment schemes for areas with insufficient heating. Spatial coordinates refer to location information used for positioning on a two-dimensional plane, including both X and Y components. The preset threshold is the standard value for determining the degree of overlap, set at 80%.

[0069] Specifically, first, collect the coordinate set of the adhesion trough locations {(xv1, yv1), (xv2, yv2), ...} and the coordinate set of the local temperature peak points {(xt1, yt1), (xt2, yt2), ...}. For each adhesion trough location (xvi, yvi), calculate its Manhattan distance to all temperature peak points within a 10mm radius: dij = |xvi - xtj| + |yvi - ytj|. Count the number n of temperature peak points that meet the criterion of a distance less than 2mm, and calculate the overlap rate r = n / N, where N is the total number of temperature peak points within this range. When the overlap rate r < 80%, mark the trough location as an underheated point. Read the current temperature value Tc at this location and calculate the difference ΔT = Tmin - Tc between it and the material's lowest adhesion temperature Tmin (e.g., 155℃ for PVC). Generate supplementary heating recommendations, including the following: coordinates of the underheated point, current temperature value, underheating amount ΔT, recommended increase in heating power (130% of the current power), and recommended supplementary heating time (calculated based on a heating rate of 2℃ / s). For all underheated points, sort them in descending order of underheating amount ΔT, generating a priority table containing information such as location, temperature difference, and recommended heating parameters. Organize the supplementary heating recommendations into structured data to guide the adjustment of heating process parameters, ensuring that each location reaches the temperature conditions required for effective bonding.

[0070] In the above embodiments, a basic temperature control framework for heat sealing processing was achieved through local temperature monitoring, heat conduction path analysis, and heating power adjustment. To further improve the adaptability of multi-material heat sealing and reduce the impact of material property changes on sealing quality, this application also provides another material adhesion evaluation method for heat sealing performance testing. This method intelligently adjusts the heating strategy by identifying changes in material thermal conductivity and analyzing the temperature-time coupling relationship, enabling the system to more accurately handle the heat sealing requirements of complex multilayer materials and heterogeneous material combinations. The following section combines... Figure 2 Another method for evaluating material adhesion in a heat-sealing test, as described in the embodiments of this application, is as follows: Please see Figure 2 This is another flowchart illustrating a material adhesion evaluation method for a heat-sealing test in an embodiment of this application.

[0071] Step 201: Extract the thermal conduction rate deviation value of each abnormal thermal conduction path segment in the material's thermal conduction path, and divide the sealed area into multiple thermal conduction characteristic areas based on the thermal conduction rate deviation value.

[0072] The thermal conductivity deviation value refers to the difference between the actual thermal conductivity rate and the standard thermal conductivity rate, measured in millimeters per second. An abnormal thermal conductivity path segment refers to the portion of the path where the thermal conductivity rate exceeds a preset range. A thermal conductivity characteristic region refers to a continuous spatial region with similar thermal conductivity characteristics. A sealed area refers to the material contact surface area that requires heat sealing. A thermal conductivity path diagram is a two-dimensional schematic diagram depicting the heat transfer path within a material.

[0073] Specifically, firstly, data for all abnormal heat conduction path segments are extracted from the material's heat conduction path, including the starting coordinates (xs, ys), ending coordinates (xe, ye), and heat conduction rate v. The heat conduction rate deviation for each path segment is calculated: Δv = v - vs, where vs is the standard heat conduction rate (e.g., 2.5 mm / s). The deviation values ​​are then categorized as follows: severely low (Δv < -0.5 mm / s), slightly low (-0.5 mm / s ≤ Δv < -0.2 mm / s), normal (-0.2 mm / s ≤ Δv ≤ 0.2 mm / s), slightly high (0.2 mm / s < Δv ≤ 0.5 mm / s), and severely high (Δv > 0.5 mm / s). A circular influence area with a radius of 5 mm is constructed, centered on the midpoint of each abnormal path segment. When multiple influence areas overlap, the larger deviation value is taken as the characteristic value of that overlapping area. A region growing algorithm is used for region division: the point with the largest deviation value is selected as the seed point, and the algorithm expands outwards. When the deviation value difference between adjacent points is less than 0.1 mm / s, they are included in the same region. This process is repeated until all points are classified. Finally, the sealed area is divided into multiple thermal conductivity characteristic regions, each marked with a different color and labeled with its average thermal conductivity rate and deviation value. A distribution map of the thermal conductivity characteristic regions is generated, visually displaying the extent and thermal conductivity characteristics of each region.

[0074] Step 202: For each heat conduction characteristic region, calculate the estimated heating time from the current temperature to the preset material melting temperature based on the heat conduction rate deviation value corresponding to the heat conduction characteristic region.

[0075] Current temperature refers to the actual temperature value of the heat conduction characteristic region at the start of heating. Preset material melting temperature refers to the specific temperature value required for the material to begin melting, such as 165℃ for PVC. Estimated heating time refers to the time required to rise from the current temperature to the melting temperature, in seconds. Heat conduction rate deviation refers to the difference between the actual heat conduction rate and the standard value. Heat conduction characteristic region refers to a continuous area with similar heat conduction characteristics.

[0076] Specifically, firstly, for each heat conduction characteristic region, obtain its average heat conduction rate deviation value Δv and current temperature T0. According to the fundamental law of heat conduction, calculate the temperature transfer coefficient α = λ / (ρ × c), where λ is the thermal conductivity (W / m·K), ρ is the density (kg / m³), and c is the specific heat capacity (J / kg·K). For standard PVC material, λ = 0.15, ρ = 1380, and c = 1000. Calculate the effective thermal diffusivity of the region: αeff = α × (1 + Δv / vs), where vs is the standard heat conduction rate. Establish the one-dimensional heat conduction equation: ∂T / ∂t = αeff × ∂²T / ∂x². Solve this equation using the explicit finite difference method: set the spatial step size to Δx = 1 mm and the time step size to Δt = 0.1 s. Initial condition: at t = 0, T(x, 0) = T0. Boundary condition: the temperature at the heating end is maintained at 180℃. Through iterative calculation, the temperature evolution over time is obtained: T(x, t + Δt) = T(x, t) + αeff × Δt × [T(x + Δx, t) - 2T(x, t) + T(x - Δx, t)] / Δx². The time t when the temperature at the center point of the region reaches the melting temperature of 165℃ is recorded as the expected heating time. The above calculation process is repeated for all regions with characteristic heat conduction, generating a heating time data table containing information such as region number, location range, current temperature, heat conduction deviation value, and expected heating time.

[0077] Step 203: Using the median value of the expected heating time as the reference heating time, calculate the time deviation between the expected heating time and the reference heating time for each heat conduction characteristic region.

[0078] The estimated heating time refers to the time required for a specific area to rise from its current temperature to its melting temperature, measured in seconds. The reference heating time is the heating time value used as a standard for comparison. The time deviation value is the difference between the estimated heating time for a specific area and the reference heating time, measured in seconds. A region with similar thermal conductivity characteristics refers to a continuous spatial region with similar thermal conductivity properties.

[0079] Specifically, firstly, collect the estimated heating duration data {t1, t2, ..., tn} for all heat conduction characteristic regions. Sort these duration data in ascending order to obtain an ordered sequence {t(1), t(2), ..., t(n)}. When the total number of regions n is odd, the baseline heating duration tb = t((n+1) / 2); when n is even, tb = (t(n / 2) + t(n / 2+1)) / 2. For each heat conduction characteristic region i, calculate its time deviation value Δti = ti - tb. Establish a time deviation analysis table containing the following fields: region number, region center coordinates (xi, yi), estimated heating duration ti, and time deviation value Δti. The time deviation values ​​are sorted in descending order of absolute value and categorized by severity: severe lag (Δti>5s), slight lag (2s<Δti≤5s), normal range (-2s≤Δti≤2s), slight advance (-5s≤Δti<-2s), and severe advance (Δti<-5s). A time deviation distribution map is generated: using a plan view of the sealed area as the base map, different colors are used to indicate the degree of time deviation in each area, and the specific deviation value is labeled for each area. The overall time distribution characteristics are calculated: maximum deviation Δtmax=max(|Δti|), average deviation Δtavg=Σ|Δti| / n, and standard deviation σt=sqrt(Σ(Δti-Δtavg)² / (n-1)). These data are used to evaluate heating uniformity and formulate heating power adjustment strategies.

[0080] Step 204: Determine the heating power and heating start time of each heat conduction characteristic area based on the time deviation value; start the heating device of each heat conduction characteristic area in sequence according to the heating start time and heating power of each heat conduction characteristic area to perform the heating operation.

[0081] Heating power refers to the amount of heat output by a heating device per unit time, measured in watts. Heating start-up time refers to the point in time when heating begins in a specific area, expressed as an offset from a reference time. A heating device is an execution unit capable of generating and transferring heat. Heating operation refers to the process of applying heat to materials by controlling the heating device. Time deviation value refers to the time difference relative to a reference heating duration. A thermal conductivity characteristic region refers to a continuous spatial region with similar thermal conductivity characteristics.

[0082] Specifically, first, establish a heating power baseline value P0 = 50W. For each heat conduction characteristic region i, calculate the power adjustment coefficient ki based on its time deviation value Δti: when Δti > 0, ki = 1 + 0.1 × Δti; when Δti < 0, ki = 1 / (1 - 0.1 × Δti). Calculate the target heating power Pi = P0 × ki for this region. Determine the heating sequence: sort all regions in descending order of time deviation value Δti. Calculate the heating start time ti_start for each region: for the region with the largest time deviation value, ti_start = 0; for other regions, ti_start = max(0, Δti). Generate a heating control sequence list, including: region number, region coordinate range, heating power Pi, start time ti_start, and heating duration ti. Divide the heating device into independent control units of 10mm × 10mm, each unit corresponding to a PID controller with parameters set as follows: proportional coefficient Kp = 0.8, integral time Ti = 2s, and derivative time Td = 0.5s. Heating operation: Timing begins at t=0. For each control unit, when the current time t≥ti_start, its heating power is set to Pi, and PID control is initiated. Temperature data is collected every 0.1 seconds, and the output power is dynamically adjusted based on the deviation between the actual temperature and the target temperature (165℃). When the area temperature reaches the target value or the heating time reaches the preset value, the heating power of that area is reduced to the heat preservation power (30% of P0). Through this zoned and time-sequential heating control strategy, uniform heating of the sealed area is achieved.

[0083] In one possible implementation, the heating power and heating start-up time of each heat conduction characteristic region are determined based on the time deviation value, specifically including steps 2041-2043, as follows: Step 2041: Mark the heat conduction characteristic regions with positive time deviation values ​​as advanced heating regions, and mark the heat conduction characteristic regions with negative time deviation values ​​as delayed heating regions.

[0084] The time deviation value refers to the difference between the expected heating time and the reference heating time for a specific area, expressed in seconds. An advanced heating area refers to a region where the heating time is shorter than the reference time, requiring delayed startup or reduced power. A delayed heating area refers to a region where the heating time is longer than the reference time, requiring earlier startup or increased power. A thermal conduction characteristic region refers to a continuous spatial region with similar thermal conduction characteristics.

[0085] Specifically, first, read the time deviation data {Δt1, Δt2, ..., Δtn} of all heat conduction characteristic regions. Create two region sets: A for advanced heating regions and B for delayed heating regions. Iterate through each heat conduction characteristic region i: when Δti > 0, add the region to set A, recording its number, coordinate range (xi1, yi1, xi2, yi2), and time deviation value Δti; when Δti < 0, add the region to set B, recording the same information. Sort the regions in both sets in descending order of |Δti|. Generate a region classification table, including: region number, region type (advanced / delayed), coordinate range, and time deviation value. On the sealed region plan, mark advanced heating regions in red and delayed heating regions in blue, labeling the specific deviation values ​​of each region. Calculate the statistical characteristics of the two types of regions: number of regions, average deviation value, and maximum deviation value.

[0086] Step 2042: For the advanced heating area, calculate the power attenuation coefficient and the start-up delay time based on the absolute value of the time deviation. The power attenuation coefficient is negatively correlated with the absolute value of the time deviation, and the start-up delay time is positively correlated with the absolute value of the time deviation.

[0087] The power attenuation coefficient refers to the reduction ratio relative to the standard heating power, ranging from (0, 1). Start-up delay time refers to the delay time relative to the baseline start-up time, measured in seconds. Negative correlation means that as one variable increases, another decreases. Positive correlation means that the two variables change in the same direction. Advanced heating zone refers to the area where heating power needs to be reduced or start-up delayed.

[0088] Specifically, first, set the baseline heating power P0 = 50W. For each advanced heating zone i, obtain the absolute value of its time deviation |Δti|. Calculate the power attenuation coefficient ki = 1 / (1 + 0.2 × |Δti|), which ensures that: when |Δti| increases, ki decreases; when |Δti| = 0, ki = 1; when |Δti| = 5s, ki ≈ 0.5. Calculate the start-up delay time di = 0.8 × |Δti|, which ensures that: when |Δti| increases, di increases; when |Δti| = 0, di = 0. Generate an advanced zone control parameter table, including: zone number, coordinate range, deviation value |Δti|, power attenuation coefficient ki, target heating power Pi = P0 × ki, and start-up delay time di. For all advanced heating zones, verify the calculation results: 0.3 ≤ ki ≤ 1.0, ensuring the power will not decrease excessively; 0 ≤ di ≤ |Δti|, ensuring the delay time will not exceed the deviation value. These parameters will be used for subsequent heating control to achieve precise adjustment of the advanced area.

[0089] Step 2043: For the delayed heating region, calculate the power enhancement coefficient and start-up advance time based on the absolute value of the time deviation value. The power enhancement coefficient and start-up advance time are positively correlated with the absolute value of the time deviation value.

[0090] The power enhancement factor refers to the increase in heating power relative to the reference heating power, with a value range of [1, 2]. Start-up advance time refers to the time difference between the reference start-up time and the actual start-up time, measured in seconds. Positive correlation refers to the relationship where two variables change in the same direction. The lag heating region refers to the region where heating power needs to be increased or start-up needs to be advanced. The time deviation value refers to the time difference relative to the reference heating time.

[0091] Specifically, firstly, for each delayed heating region i, obtain the absolute value of its time deviation, |Δti|. Calculate the power enhancement coefficient ki = 1 + 0.15 × |Δti|. This formula ensures that: ki increases linearly as |Δti| increases; ki = 1 when |Δti| = 0; and ki = 2 when |Δti| = 6.67s. Set the upper limit of power enhancement to 2 times to avoid overheating. Calculate the start-up advance time di = 0.9 × |Δti|. This formula ensures that: di increases linearly as |Δti| increases; and di = 0 when |Δti| = 0. Generate a delayed region control parameter table, including: region number, coordinate range (xi1, yi1, xi2, yi2), deviation value |Δti|, power enhancement coefficient ki, and start-up advance time di. For all delayed heating regions, verify the parameter validity: 1.0 ≤ ki ≤ 2.0, ensuring power enhancement is within a reasonable range; 0 ≤ di ≤ |Δti|, ensuring the advance time does not exceed the deviation value. These parameters will be used to achieve heating compensation in the hysteresis region.

[0092] Step 2044: Multiply the power attenuation coefficient and power enhancement coefficient by the preset reference heating power to obtain the adjusted heating power for each heat conduction characteristic region.

[0093] The preset reference heating power refers to the reference value of heating power under standard operating conditions, measured in watts. The adjusted heating power refers to the actual operating power after considering the power adjustment factor. The power attenuation factor and power enhancement factor are multipliers used to adjust the heating power. The heat conduction characteristic region refers to a continuous spatial region with similar heat conduction characteristics.

[0094] Specifically, first, a preset baseline heating power P0 = 50W is set. A power adjustment table is created, including all heat conduction characteristic regions. For each advanced heating region i, its power attenuation coefficient ki (0.3 ≤ ki ≤ 1.0) is read, and the adjusted power Pi = P0 × ki is calculated. For each lagging heating region j, its power enhancement coefficient kj (1.0 ≤ kj ≤ 2.0) is read, and the adjusted power Pj = P0 × kj is calculated. A heating power distribution table is generated, including: region number, region type (advanced / lagging), original power P0, adjustment coefficient k, and adjusted power P. The rationality of the adjusted power for all regions is verified: advanced regions 15W ≤ Pi ≤ 50W, lagging regions 50W ≤ Pj ≤ 100W. The total power demand Ptotal = ΣPi is calculated, ensuring it does not exceed the maximum output capacity of the heating system (e.g., 200W). The power data is converted into control parameters for the heating device, including PWM duty cycle, voltage modulation value, etc. These adjusted power parameters will be directly used for the execution of the heating control system.

[0095] Step 2045: Obtain the standard time corresponding to the reference heating time, add the start-up delay time of the advanced heating region to the standard time, and subtract the start-up advance time of the lagging heating region from the standard time to obtain the heating start-up time corresponding to each heat conduction characteristic region.

[0096] The standard time refers to the point in time corresponding to the baseline heating duration, serving as a reference time for heating start-up. The heating start-up time refers to the actual time at which heating begins in each heat conduction characteristic region. The start-up delay duration refers to the time that the advanced heating region needs to be delayed. The start-up advance duration refers to the time that the lagging heating region needs to be advanced. The baseline heating duration refers to the time required from the start of heating to reaching the target temperature under standard process conditions. A heat conduction characteristic region refers to a continuous spatial region with similar heat conduction characteristics.

[0097] Specifically, first, determine the baseline heating duration tb as the standard time ts = 0. Create a start-up timing table containing all heat conduction characteristic regions. For each advanced heating region i, read its start-up delay duration di and calculate the actual start-up time ti_start = ts + di. For example, when the start-up delay duration of an advanced region is 3 seconds, its start-up time is t = 3s. For each lagging heating region j, read its start-up advance duration dj and calculate the actual start-up time tj_start = ts - dj. For example, when the start-up advance duration of a lagging region is 2 seconds, its start-up time is t = -2s. Generate a heating timing arrangement table, including: region number, region type (advanced / lagging), coordinate range (x1, y1, x2, y2), adjustment duration d, and start-up time t_start. Sort all regions in ascending order of start-up time to generate a heating control timing sequence. Verify the rationality of the timing arrangement: all start-up times should be distributed within the range of [-10s, 10s]; the start-up time interval between adjacent areas should not be less than 0.1s to ensure the response time of the control system. Create a timed trigger event for each start-up time, including: trigger time, target area, target power, and heating duration. These timing parameters will be used for precise control of the heating system to ensure that each area starts heating at the optimal time.

[0098] In the above embodiments, a dynamic heating control framework for the sealing area is achieved through predicted heating time, zone division, and power adjustment. To further improve the identification accuracy of abnormal heat conduction areas and achieve precise adjustment of heating parameters, this application also provides a heating anomaly localization method based on spatial overlap analysis. This method establishes a quantitative mapping relationship between temperature distribution and adhesion quality by accurately calculating the spatial correlation between adhesion trough areas and temperature anomaly areas. This enables the system to accurately identify poor adhesion caused by abnormal heat conduction, thereby providing reliable data support for optimizing heating parameters.

[0099] Step 301: Extract the spatial coordinate range of the adhesion valley position on the sealing line, and extend the preset valley influence radius around the adhesion valley position to determine the adhesion valley influence area.

[0100] The adhesion trough location refers to the point where the adhesion value reaches a local minimum. The seal line refers to the path along which the material needs to be heat-sealed. The spatial coordinate range refers to the range of positions represented by coordinate pairs on a two-dimensional plane. The trough influence radius refers to the radius of the influence area centered at the trough location, measured in millimeters. The adhesion trough influence region refers to the spatial range within which the trough location affects the surrounding area.

[0101] Specifically, firstly, the coordinate set {(xv1, yv1), (xv2, yv2), ...} of all valley locations is extracted from the seal adhesion data. For each valley location, its adhesion value vi and its position parameter si on the seal line (arc length from the start of the seal line) are recorded. The valley influence radius is set to r = 5mm. For each valley location (xvi, yvi), an influence region is constructed: a circular area centered at (xvi, yvi) with a radius of r. When multiple valley influence regions overlap, the following merging rule is adopted: the lowest adhesion value in the overlapping region is taken as the feature value of that region; the coordinate set of the boundary points of the overlapping region {(xb1, yb1), (xb2, yb2), ...} is recorded. A valley influence region description table is generated, containing: region number, center coordinates, influence radius, coverage area coordinate set, and minimum adhesion value. The influence area was discretized using a rasterization method: the region was divided into a 0.5mm × 0.5mm grid, and the degree of influence of the trough on each grid was calculated as I = exp(-d² / 2r²), where d is the distance from the grid center to the trough. An intensity distribution map of the influence area was created, using different color depths to represent the degree of influence. This data will be used for subsequent heating parameter adjustments and control strategy formulation.

[0102] Step 302: Extend the preset peak influence radius outward from each spatial coordinate to determine multiple temperature peak influence areas.

[0103] Spatial coordinates refer to the position of the temperature peak point on a two-dimensional plane, denoted by (x, y). The peak influence radius refers to the range of heat influence exerted by the temperature peak point on the surrounding area, measured in millimeters. The temperature peak influence area refers to the circular region affected by a single temperature peak point. The temperature peak refers to the highest point of a localized temperature. A preset value refers to a fixed parameter value determined based on material properties and process requirements.

[0104] Specifically, first, obtain the coordinate set of all temperature peak points {(xp1, yp1), (xp2, yp2), ...} and the corresponding temperature value set {T1, T2, ...}. Set the peak influence radius r = 4mm. For each temperature peak point (xpi, ypi), perform the following operations: Construct a circular influence region centered at (xpi, ypi) with radius r; divide this region into 0.2mm × 0.2mm grid cells; for each grid cell, calculate its influence degree from the temperature peak point: F(x, y) = Ti × exp(-d² / 2r²), where d is the distance from the center of the grid cell to the peak point, and Ti is the peak temperature. When the influence regions of multiple temperature peaks overlap, the temperature superposition principle is adopted: for each grid cell within the overlapping region, calculate the cumulative value of all influencing temperatures. Generate a peak influence region description table, including: region number, center coordinates (xpi, ypi), peak temperature Ti, influence radius r, and the coordinate set of the maximum influence range boundary {(xb1, yb1), (xb2, yb2), ...}. Establish a temperature influence intensity distribution map: represent influences of different intensities with different colors to generate isotherm plots; mark the location of the temperature peak point and the boundary of the influence range; calculate the area and average influence intensity of each influence region. These data will be used to evaluate the uniformity of heat distribution and analyze heat conduction characteristics.

[0105] Step 303: Calculate the spatial overlap area between the adhesion trough influence area and the temperature peak influence area; divide the sum of the spatial overlap areas by the total area of ​​the adhesion trough influence area to obtain the overlap rate of the spatial coordinates corresponding to the adhesion trough location and the local temperature peak.

[0106] The spatial overlap area refers to the intersection area of ​​the adhesion trough influence region and the temperature peak influence region on a plane, measured in square millimeters. The total area refers to the complete area of ​​the adhesion trough influence region. The overlap rate is the percentage of the overlap area to the total area of ​​the trough influence region. The adhesion trough influence region refers to the range of influence of the local minimum adhesion point. The temperature peak influence region refers to the range of influence of the local maximum temperature point.

[0107] Specifically, firstly, for each adhesion trough influence region Vi and each temperature peak influence region Tj, the following calculations are performed: The two regions are divided into 0.1mm × 0.1mm grids; for the trough influence region Vi, its total area Ai = πr², where r is the trough influence radius (5mm); the intersection of the two circular regions is identified: when the distance d between the two centers is greater than the sum of the two radii, the overlap area is 0; when one circle completely contains the other circle, the overlap area is relatively large. The area of ​​the small circle; when the two circles partially overlap, the overlapping area is calculated using analytical geometry: S = r1²arccos((d²+r1²-r2²) / (2dr1)) + r2²arccos((d²+r2²-r1²) / (2dr2)) - 0.5sqrt((-d+r1+r2)(d+r1-r2)(d-r1+r2)(d+r1+r2)), where r1 and r2 are the radii of the two circles, and d is the distance between their centers. For the trough influence region Vi, the overlapping area with all temperature peak influence regions is accumulated: Total = ΣSij, where Sij is the overlapping area of ​​Vi and Tj. The overlap rate Ri = Total / Ai is calculated. An overlap analysis report is generated, including: trough region number, total area, overlapping area of ​​each temperature peak region, total overlapping area, and overlap rate. Visualize the overlap: Mark overlapping areas with different colors on a plan view and label important values, including overlapping area and overlap rate. This data is used to assess the correlation between heating effect and adhesion quality.

[0108] Reference Figure 3 This application provides a material adhesion evaluation system for heat sealing performance testing. The system includes: a data acquisition module, a path determination module, an adhesion acquisition module, and an adhesion evaluation module, wherein: The data acquisition module is used to acquire the first temperature distribution data of the sealed area, identify the lowest temperature point area in the first temperature distribution data, and perform local heating on the lowest temperature point area. The path determination module is used to collect second temperature distribution data of the sealed area during the local heating process, identify the spatial coordinates and occurrence time of the local temperature peak in the second temperature distribution data, and determine the material heat conduction path based on the spatial coordinates and occurrence time. The adhesion acquisition module is used to adjust the heating power of the sealing area at different positions and perform heating operations according to the material heat conduction path; when the temperature of the sealing area reaches the preset material melting temperature, the adhesion values ​​of multiple detection points on the sealing line are acquired, and an adhesion distribution curve is generated based on each adhesion value. The adhesion evaluation module is used to determine the location of adhesion troughs in the adhesion distribution curve. When the overlap rate between the adhesion trough location and the spatial coordinates corresponding to the local temperature peak is greater than or equal to a preset threshold, it is determined that there is adhesion degradation caused by overheating at the adhesion trough location and cooling suggestion information is generated. When the overlap rate between the adhesion trough location and the spatial coordinates corresponding to the local temperature peak is less than a preset threshold, it is determined that there is insufficient heating at the adhesion trough location and heating supplementation suggestion information is generated.

[0109] Based on the above embodiments, the path determination module is further configured to divide the second temperature distribution data into multiple temperature distribution snapshots according to the time series, perform Laplace operator operation on each temperature distribution snapshot, identify candidate temperature peak points whose second spatial derivatives in both the horizontal and vertical directions are negative; extract the temperature values ​​of each candidate temperature peak point, filter out candidate temperature peak points whose temperature values ​​are greater than those of adjacent candidate temperature peak points and whose temperature rise relative to the lowest temperature point region exceeds a preset temperature rise threshold, and determine them as local temperature peak points; record the time slice time corresponding to the temperature distribution snapshot where each local temperature peak point is located as the occurrence time, and extract the spatial coordinates of each local temperature peak point in the sealed area.

[0110] Based on the above embodiments, the path determination module is further used to arrange the spatial coordinates corresponding to the local temperature peaks in chronological order according to the time of occurrence to obtain a temperature peak spatiotemporal sequence; calculate the heat conduction rate of multiple path segments based on the distance and time interval between adjacent spatial coordinates in the temperature peak spatiotemporal sequence; compare the heat conduction rate with a preset standard heat conduction rate, and mark the path segment as an abnormal heat conduction path segment when the heat conduction rate of any path segment deviates from the preset standard heat conduction rate by more than a preset deviation range; and connect the lowest temperature point region with each abnormal heat conduction path segment to generate a material heat conduction path.

[0111] Based on the above embodiments, the adhesion acquisition module is further used to extract the thermal conduction rate deviation value of each abnormal thermal conduction path segment in the material's thermal conduction path, and divide the sealing area into multiple thermal conduction characteristic regions according to the thermal conduction rate deviation value; for each thermal conduction characteristic region, the estimated heating time from the current temperature to the preset material melting temperature is calculated based on the thermal conduction rate deviation value corresponding to the thermal conduction characteristic region; using the median value of the estimated heating time as the reference heating time, the time deviation value between the estimated heating time of each thermal conduction characteristic region and the reference heating time is calculated; based on the time deviation value, the heating power and heating start time of each thermal conduction characteristic region are determined; according to the heating start time and heating power of each thermal conduction characteristic region, the heating device of each thermal conduction characteristic region is started sequentially to perform the heating operation.

[0112] Based on the above embodiments, the adhesion acquisition module is further configured to mark heat conduction characteristic regions with positive time deviation values ​​as advanced heating regions and heat conduction characteristic regions with negative time deviation values ​​as delayed heating regions; for advanced heating regions, a power attenuation coefficient and a start-up delay duration are calculated based on the absolute value of the time deviation value, wherein the power attenuation coefficient is negatively correlated with the absolute value of the time deviation value, and the start-up delay duration is positively correlated with the absolute value of the time deviation value; for delayed heating regions, a power enhancement coefficient and a start-up advance duration are calculated based on the absolute value of the time deviation value, wherein the power enhancement coefficient and the start-up advance duration are positively correlated with the absolute value of the time deviation value; the power attenuation coefficient and the power enhancement coefficient are multiplied by a preset reference heating power to obtain the adjusted heating power for each heat conduction characteristic region; a standard time corresponding to the reference heating duration is obtained, the start-up delay duration of the advanced heating region is added to the standard time, and the start-up advance duration of the delayed heating region is subtracted from the standard time to obtain the heating start-up time corresponding to each heat conduction characteristic region.

[0113] Based on the above embodiments, the adhesion acquisition module is further configured to set the starting end of the sealing line as the distance zero point, determine the distance baseline along the extension direction of the sealing line; project the position of each detection point onto the distance baseline to obtain the projected distance value of each detection point on the distance baseline; normalize the adhesion value of the detection points to obtain the normalized target adhesion value; use the projected distance value and the target adhesion value as input data for interpolation calculation; and perform interpolation calculation between adjacent detection points on the sealing line based on the input data to generate an adhesion distribution curve.

[0114] Based on the above embodiments, the adhesion evaluation module is also used to extract the spatial coordinate range of the adhesion valley position on the sealing line, and extend a preset valley influence radius around the adhesion valley position to determine the adhesion valley influence area; extend a preset peak influence radius around each spatial coordinate to determine multiple temperature peak influence areas; calculate the spatial overlap area between the adhesion valley influence area and each temperature peak influence area; divide the sum of the spatial overlap areas by the total area of ​​the adhesion valley influence area to obtain the overlap rate between the adhesion valley position and the spatial coordinates corresponding to the local temperature peak.

[0115] It should be noted that the above embodiments of the apparatus are only illustrated by the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the apparatus and method embodiments provided in the above embodiments belong to the same concept, and the specific implementation process can be found in the method embodiments, which will not be repeated here.

[0116] This application also discloses an electronic device. (See reference...) Figure 4 , Figure 4 This is a schematic diagram of the structure of an electronic device disclosed in an embodiment of this application. The electronic device 400 may include: at least one processor 401, at least one network interface 404, a user interface 403, a memory 405, and at least one communication bus 402.

[0117] The communication bus 402 is used to enable communication between these components.

[0118] The user interface 403 may include a display interface and a camera interface. Optionally, the user interface 403 may also include a standard wired interface and a wireless interface.

[0119] The network interface 404 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface).

[0120] The processor 401 may include one or more processing cores. The processor 401 connects to various parts of the server using various interfaces and lines, and performs various server functions and processes data by running or executing instructions, programs, code sets, or instruction sets stored in memory 405, and by calling data stored in memory 405. Optionally, the processor 401 may be implemented using at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor 401 may integrate one or a combination of several of the following: Central Processing Unit (CPU), Graphics Processing Unit (GPU), and modem. The CPU primarily handles the operating system, user interface graphics, and applications; the GPU is responsible for rendering and drawing the content required for display; and the modem handles wireless communication. It is understood that the modem may also not be integrated into the processor 401 and may be implemented as a separate chip.

[0121] The memory 405 may include random access memory (RAM) or read-only memory. Optionally, the memory 405 may include a non-transitory computer-readable storage medium. The memory 405 may be used to store instructions, programs, code, code sets, or instruction sets. The memory 405 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-described method embodiments, etc.; the data storage area may store data involved in the above-described method embodiments, etc. Optionally, the memory 405 may also be at least one storage device located remotely from the aforementioned processor 401. (Refer to...) Figure 4 The memory 405, which serves as a computer storage medium, may include an operating system, a network communication module, a user interface module, and an application program for evaluating the material adhesion of a heat-sealing test.

[0122] exist Figure 4 In the illustrated electronic device 400, the user interface 403 is mainly used to provide an input interface for the user and to acquire user input data; while the processor 401 can be used to call an application program stored in the memory 405 for evaluating the adhesion of a material in a heat-sealing test. When executed by one or more processors 401, the electronic device 400 performs one or more methods as described in the above embodiments. It should be noted that, for the foregoing method embodiments, for the sake of simplicity, they are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, because according to this application, some steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also understand that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0123] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0124] In the various embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some service interface; the indirect coupling or communication connection between apparatuses or units may be electrical or other forms.

[0125] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0126] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0127] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as USB flash drives, portable hard drives, magnetic disks, or optical disks.

[0128] The above are merely exemplary embodiments of this disclosure and should not be construed as limiting the scope of this disclosure. Any equivalent changes and modifications made in accordance with the teachings of this disclosure shall still fall within the scope of this disclosure. Other embodiments of this disclosure will be readily apparent to those skilled in the art upon consideration of the specification and practical disclosure.

[0129] This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not described in this disclosure. The specification and embodiments are to be considered exemplary only.

Claims

1. A method for evaluating the adhesion of materials in a heat-sealing test, characterized in that, include: Acquire first temperature distribution data of the sealed area, identify the lowest temperature point region in the first temperature distribution data, and perform local heating on the lowest temperature point region; During the local heating process, second temperature distribution data of the sealed area is collected, the spatial coordinates and occurrence time of the local temperature peak in the second temperature distribution data are identified, and the material heat conduction path is determined based on the spatial coordinates and occurrence time. According to the heat conduction path of the material, adjust the heating power of the sealing area at different positions and perform the heating operation; When the temperature of the sealing area reaches the preset material melting temperature, the adhesion values ​​of multiple detection points on the sealing line are obtained, and an adhesion distribution curve is generated based on each adhesion value. Determine the location of the adhesion trough in the adhesion distribution curve; When the overlap rate between the spatial coordinates corresponding to the adhesion trough and the local temperature peak is greater than or equal to a preset threshold, it is determined that there is adhesion degradation caused by overheating at the adhesion trough and cooling suggestion information is generated. When the overlap rate between the spatial coordinates corresponding to the adhesion valley position and the local temperature peak is less than the preset threshold, it is determined that there is insufficient heating at the adhesion valley position and heat replenishment suggestion information is generated.

2. The method of claim 1, wherein, The step of identifying the spatial coordinates and occurrence time of local temperature peaks in the second temperature distribution data includes: The second temperature distribution data is divided into multiple temperature distribution snapshots according to the time series. The Laplace operator operation is performed on each temperature distribution snapshot to identify candidate temperature peak points where the second spatial derivatives of the temperature in both the horizontal and vertical directions are negative. Extract the temperature values ​​of each candidate temperature peak point, and filter out the candidate temperature peak points whose temperature values ​​are greater than those of adjacent candidate temperature peak points and whose temperature rise value relative to the lowest temperature point region exceeds a preset temperature rise threshold, and determine them as local temperature peak points. Record the time slice corresponding to the temperature distribution snapshot of each local temperature peak point as the occurrence time, and extract the spatial coordinates of each local temperature peak point in the sealed area.

3. The method of claim 1, wherein, Determining the material heat conduction path based on the spatial coordinates and the time of occurrence includes: Arrange the spatial coordinates corresponding to the local temperature peaks in chronological order according to the times of occurrence to obtain the spatiotemporal sequence of temperature peaks. The heat conduction rate of multiple path segments is calculated based on the distance and time interval between adjacent spatial coordinates in the spatiotemporal sequence of the temperature peak. The heat conduction rate is compared with a preset standard heat conduction rate. When the heat conduction rate of any path segment deviates from the preset standard heat conduction rate by more than a preset deviation range, the path segment is marked as an abnormal heat conduction path segment. Connect the region of the lowest temperature point with each of the abnormal heat conduction path segments to generate a material heat conduction path.

4. The method of claim 1, wherein, The step of adjusting the heating power of the sealing area at different positions and performing the heating operation according to the material's heat conduction path includes: Extract the thermal conduction rate deviation value of each abnormal thermal conduction path segment in the material's thermal conduction path, and divide the sealing area into multiple thermal conduction characteristic areas based on the thermal conduction rate deviation value; For each of the heat conduction characteristic regions, the estimated heating time from the current temperature to the preset material melting temperature is calculated based on the heat conduction rate deviation value corresponding to the heat conduction characteristic region. Using the median value of the expected heating time as the reference heating time, the time deviation value between the expected heating time of each of the heat conduction characteristic regions and the reference heating time is calculated; Based on the time deviation value, determine the heating power and heating start time of each of the heat conduction characteristic regions; According to the heating start time and heating power of each of the heat conduction characteristic regions, the heating devices of each of the heat conduction characteristic regions are started in sequence to perform the heating operation.

5. The method of claim 4, wherein, The step of determining the heating power and heating start-up time of each heat conduction characteristic region based on the time deviation value includes: The heat conduction characteristic regions with positive time deviation values ​​are marked as advanced heating regions, and the heat conduction characteristic regions with negative time deviation values ​​are marked as delayed heating regions. For the advanced heating area, a power attenuation coefficient and a start-up delay duration are calculated based on the absolute value of the time deviation value, wherein the power attenuation coefficient is negatively correlated with the absolute value of the time deviation value, and the start-up delay duration is positively correlated with the absolute value of the time deviation value. For the delayed heating region, the power enhancement coefficient and the start-up advance time are calculated based on the absolute value of the time deviation value, wherein the power enhancement coefficient and the start-up advance time are positively correlated with the absolute value of the time deviation value; The power attenuation coefficient and the power enhancement coefficient are multiplied by the preset reference heating power to obtain the adjusted heating power for each of the heat conduction characteristic regions; Obtain the standard time corresponding to the reference heating duration, add the start-up delay duration of the advanced heating region to the standard time, and subtract the start-up advance duration of the lagging heating region from the standard time to obtain the heating start-up time corresponding to each of the heat conduction characteristic regions.

6. The method of claim 1, wherein, The step of generating an adhesion distribution curve based on each of the adhesion values ​​includes: Set the starting end of the sealing line as the zero point and determine the distance reference line along the extension direction of the sealing line; The positions of each detection point are projected onto the distance baseline, and the projected distance value of each detection point on the distance baseline is obtained; The adhesion values ​​at the detection points are normalized to obtain the normalized target adhesion values. The projected distance value and the target adhesion value are used as input data for interpolation calculation; Based on the input data, interpolation is performed between adjacent detection points on the sealing line to generate the adhesion distribution curve.

7. The method of claim 1, wherein, After determining the location of the adhesion trough in the adhesion distribution curve, the method further includes: Extract the spatial coordinate range of the adhesion valley position on the sealing line, and extend a preset valley influence radius around the adhesion valley position to determine the adhesion valley influence area. A preset peak influence radius is extended outward from each of the aforementioned spatial coordinates to determine multiple temperature peak influence areas; Calculate the spatial overlap area between the adhesion trough influence region and each of the temperature peak influence regions; Divide the sum of the spatially overlapping areas by the total area of ​​the adhesion valley influence region to obtain the overlap rate between the spatial coordinates corresponding to the adhesion valley location and the local temperature peak.

8. A material adhesion evaluation system for heat sealability testing, characterized by, The system includes: The data acquisition module is used to acquire first temperature distribution data of the sealed area, identify the lowest temperature point area in the first temperature distribution data, and perform local heating on the lowest temperature point area. The path determination module is used to collect second temperature distribution data of the sealed area during the local heating process, identify the spatial coordinates and occurrence time of the local temperature peak in the second temperature distribution data, and determine the material heat conduction path based on the spatial coordinates and occurrence time. The adhesion acquisition module is used to adjust the heating power corresponding to different positions of the sealing area and perform heating operations according to the heat conduction path of the material; when the temperature of the sealing area reaches the preset material melting temperature, the adhesion values ​​of multiple detection points on the sealing line are acquired, and an adhesion distribution curve is generated according to each adhesion value. An adhesion evaluation module is used to determine the location of adhesion troughs in the adhesion distribution curve; when the overlap rate between the adhesion trough location and the spatial coordinates corresponding to the local temperature peak is greater than or equal to a preset threshold, it is determined that the adhesion trough location has adhesion degradation caused by overheating and cooling suggestion information is generated; when the overlap rate between the adhesion trough location and the spatial coordinates corresponding to the local temperature peak is less than the preset threshold, it is determined that the adhesion trough location has insufficient heating and heating suggestion information is generated.

9. An electronic device, comprising: The device includes a processor, a memory, a user interface, and a network interface. The memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory to cause the electronic device to perform the material adhesion evaluation method for the heat-sealing test as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions that, when executed, perform the material adhesion evaluation method for the heat-sealing test as described in any one of claims 1-7.

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