Copper foil thickness analysis method and system

Abstract

The invention provides a copper foil thickness analysis method and device. The copper foil thickness analysis method comprises the steps that tension values of all layer number positions and thickness measurement results of the corresponding layer number positions in the copper foil winding process are obtained; analyzing a deformation mechanism by combining the obtained interlayer relative displacement and torsion angle according to the compression deformation analysis result to obtain stress distribution mode data; compensating thickness measurement data based on the stress distribution mode data to obtain compensated thickness measurement data; setting a normal thickness range and an anomaly judgment threshold based on the compensated thickness measurement data, and determining a sensitivity identification threshold parameter of thickness anomaly detection; judging a thickness anomaly state according to the sensitivity identification threshold parameter of the thickness anomaly detection to obtain a thickness anomaly detection result; and in combination with the thickness anomaly detection result and historical winding process thickness measurement data, thickness anomaly detection combination parameters are determined.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of information technology, and in particular to a copper foil thickness analysis method and system. BACKGROUND

[0002] As a core basic material of the electronic industry, the thickness precision of copper foil directly determines the performance of the circuit board and the quality of electronic products, and plays an irreplaceable key role in the field of high-precision electronic manufacturing. With the development of electronic products towards lightness, thinness and high integration, the requirement for copper foil thickness control precision is increasingly stringent, making accurate thickness measurement an important link to ensure product quality. Traditional copper foil thickness measurement methods mainly rely on static detection or simple online measurement, which often ignores the influence of dynamic mechanical factors in the winding process, resulting in significant deviation between the measurement results and the actual thickness. The existing measurement system lacks in-depth analysis of the compression mechanism of multiple layers of copper foil under winding state, the shear deformation transmission law between layers, and the coupling relationship between tension, deformation and thickness, and cannot effectively compensate for the measurement error caused by winding tension, seriously affecting the accuracy and reliability of quality control. Specifically, due to the influence of factors such as winding speed variation, tension control system response delay and copper foil material property difference, the unevenness of tension distribution will be highlighted at different layer positions during the copper foil winding process. The inner layer copper foil is subjected to the cumulative pressure from the outer layer, and obvious compression deformation occurs, resulting in a decrease in apparent thickness, and the compression degree gradually increases with the increase of the number of winding layers. The existence of compression deformation further triggers the shear deformation accumulation effect of the intermediate layer copper foil. This shear deformation not only changes the geometric shape of the copper foil, but also accumulates continuously in the continuous winding process. Therefore, when the dynamic change of winding tension distribution occurs, the deformation response of copper foil at different layers presents a highly nonlinear characteristic of coupling between shear deformation and radial position, and the traditional fixed compensation strategy for different layer thickness measurement values cannot adapt to this dynamic change, resulting in a sharp decline in measurement accuracy. How to combine the relationship between winding tension and copper foil deformation and develop a dynamic tension compensation measurement strategy based on the deformation mechanism under different winding layers has become a key problem in the development of copper foil thickness precision measurement technology. SUMMARY

[0003] The present application provides a copper foil thickness analysis method, mainly comprising:

[0004] Obtaining the tension value at each layer position during the copper foil winding process and the thickness measurement result at the corresponding layer position;

[0005] Identifying the compression deformation law of the copper foil at different layers by analyzing the correlation between the thickness change of the copper foil at different winding layers and the tension distribution, and generating a compression deformation analysis result according to the compression deformation law of the copper foil at different layers;

[0006] According to the compression deformation analysis result, the deformation mechanism is analyzed in combination with the obtained interlayer relative displacement and torsion angle, and stress distribution mode data is obtained;

[0007] Based on the stress distribution mode data, the thickness measurement data is compensated to obtain compensated thickness measurement data;

[0008] Based on the compensated thickness measurement data, a normal thickness range and an abnormality determination threshold are set, and a sensitivity identification threshold parameter of thickness abnormality detection is determined;

[0009] According to the sensitivity identification threshold parameter of thickness abnormality detection, the judgment of thickness abnormality state is carried out, and the thickness abnormality detection result is obtained;

[0010] In combination with the thickness abnormality detection result and the historical winding process thickness measurement data, the thickness abnormality detection combination parameter is determined.

[0011] Further, the tension value of each layer position in the copper foil winding process and the thickness measurement result corresponding to the layer position are obtained, including:

[0012] The tension sensor is arranged at each layer position of the copper foil winding device, the tension signal in the winding process is collected, the tension signal is amplified and filtered, the real-time tension value data sequence is generated, and the layer number mark and the collection time information in the real-time tension value data sequence are marked. The thickness measuring device is arranged to distribute a plurality of measuring points along the width direction of the copper foil, the thickness data of each measuring point is collected, the average value of all measuring point thickness data is calculated, the average thickness value of the layer copper foil is generated, and the layer number mark corresponding to the average thickness value and the measurement time are marked. Match the real-time tension value data sequence and the average thickness value, determine the corresponding relationship through the layer number mark and the time difference value, generate the corresponding data group containing the layer number, the tension value and the thickness value, judge the comparison result of the tension value of the corresponding data group and the preset tension threshold, mark the corresponding data group and its adjacent layer data group which exceeds the threshold, and store all corresponding data groups.

[0013] Further, the compression deformation rule of the copper foil under different layer numbers is identified by analyzing the correlation between the thickness change and the tension distribution of the copper foil under different winding layer numbers, and the compression deformation analysis result is generated according to the compression deformation rule of the copper foil under different layer numbers, including:

[0014] According to the real-time tension value at different radial positions and the original thickness measurement result at the corresponding layer position, a three-dimensional data matrix of layer number, tension and thickness is constructed, a thickness change value of each layer is calculated, and a correlation quantization index of tension value and thickness change value is generated; based on the correlation quantization index, the radial pressure generated by each layer is calculated in combination with the winding radius and the copper foil width, and the cumulative pressure value borne by the inner layer copper foil is accumulated; based on the cumulative pressure value and the elastic modulus of the copper foil material obtained in advance, a compression deformation analysis result is generated.

[0015] Further, according to the compression deformation analysis result, the deformation mechanism is analyzed in combination with the obtained interlayer relative displacement and torsion angle, and stress distribution mode data is obtained, including:

[0016] The relative displacement data and the torsion angle data of adjacent layers are extracted, the vertical distance between the surfaces of adjacent copper foil layers is measured, the tangential shear strain is calculated, the torsional shear strain is calculated in combination with the torsion angle and the current layer radius, the tangential shear strain and the torsional shear strain are added, the total shear strain value is generated, and the total shear strain value is accumulated layer by layer to generate the shear deformation cumulative amount of each layer; the compression deformation amount in the compression deformation analysis result is extracted, the relationship between the shear deformation cumulative amount and the compression deformation amount is fitted, the regression coefficient is determined, the coupling function form is substituted into the compression deformation amount and the shear deformation cumulative amount, the equivalent stress value in the composite deformation state is calculated, and the stress distribution mode data is generated.

[0017] Further, the thickness measurement data is compensated based on the stress distribution mode data to obtain compensated thickness measurement data, including:

[0018] The stress value and the real-time tension value are extracted, the deformation amount data in the compression deformation analysis result is combined, the thickness deviation is calculated, and a dynamic compensation parameter library is generated; based on the real-time tension value and the layer position, a matching category is found in the dynamic compensation parameter library, the tension mean value, the deformation amount mean value and the thickness deviation mean value of the category are extracted, and a dynamic compensation coefficient is calculated; the original thickness measurement value is corrected by using the dynamic compensation coefficient to obtain the compensated thickness measurement data.

[0019] Further, the dynamic compensation coefficient is calculated, including: finding a category matching the real-time tension value, extracting the parameter mean value of the category, calculating the distance proportion of the real-time tension value and the category mean value, and generating a dynamic compensation coefficient.

[0020] Further, the thickness abnormality detection combination parameter is determined in combination with the thickness abnormality detection result and the historical winding process thickness measurement data, including:

[0021] In combination with the abnormality detection result and historical winding process thickness measurement data, the abnormality occurrence times of each layer position are counted and divided by the total measurement times of the layer to obtain layer position abnormality frequency, a layer position with abnormality frequency greater than twice the historical average abnormality frequency is identified as a key adjustment layer position, and a deviation characteristic value of the key adjustment layer position is extracted; the current compensation coefficient of the key adjustment layer position is searched in the dynamic compensation parameter library, the current compensation coefficient is multiplied by the deviation characteristic value to obtain a new compensation coefficient, and the parameters of the corresponding layer position in the dynamic compensation parameter library are updated; the original thickness data of the most recent production batch is compensated and calculated using the updated dynamic compensation parameter library, the number of abnormality detections of the compensated thickness data is counted, and the thickness abnormality detection combination parameter is determined.

[0022] The application provides a copper foil thickness analysis system, mainly comprising:

[0023] A data acquisition module is configured to acquire tension values of each layer position and thickness measurement results of corresponding layer positions during a copper foil winding process.

[0024] An association analysis module is configured to identify compression deformation rules of the copper foil under different layer numbers by analyzing the association between copper foil thickness changes and tension distribution under different layer numbers, and generate a compression deformation analysis result according to the compression deformation rules of the copper foil under different layer numbers.

[0025] A compression deformation analysis module is configured to analyze deformation mechanisms according to the compression deformation analysis result in combination with the acquired interlayer relative displacement and torsion angle, and obtain stress distribution mode data.

[0026] A deformation mechanism analysis module is configured to compensate thickness measurement data based on the stress distribution mode data, and obtain compensated thickness measurement data.

[0027] A data compensation module is configured to set a normal thickness range and an abnormality determination threshold based on the compensated thickness measurement data, and determine a sensitivity identification threshold parameter of thickness abnormality detection.

[0028] A threshold setting module is configured to judge a thickness abnormality state according to the sensitivity identification threshold parameter of thickness abnormality detection, and obtain a thickness abnormality detection result.

[0029] An abnormality detection module is configured to determine a thickness abnormality detection combination parameter in combination with the thickness abnormality detection result and historical winding process thickness measurement data.

[0030] A computer readable storage medium stores a computer program, which is executed by a processor to implement the method described above.

[0031] A computer program product includes a computer program, which is executed by a processor to implement the steps of the above method.

[0032] The technical scheme provided by the embodiment of the present application can include the following beneficial effects:

[0033] The present application discloses a copper foil thickness analysis method and system, aiming at the problem of complex correlation between thickness change and tension distribution, and the coupling influence of interlayer compression and shear deformation on measurement accuracy during the winding process of copper foil, by real-time acquisition of tension sensor and thickness measurement data, analyzing the correlation between thickness change and tension distribution under different layers, constructing a nonlinear relationship model of compression deformation and layer position, calculating the cumulative pressure of the inner copper foil, and then establishing a coupling model of compression and shear deformation through the relative displacement and torsion angle between layers, the stress distribution under the composite deformation state is predicted. When the cumulative shear deformation exceeds the threshold value, the stress distribution pattern is obtained by finite element analysis, a dynamic compensation parameter library based on tension sensing is constructed, and the thickness measurement compensation coefficient is dynamically adjusted. With the compensated thickness data as input, a reference thickness range model is established, the deviation is monitored in real time and the model parameters are updated, the compensation parameter library and threshold value are optimized through abnormal frequency and position deviation distribution, realizing high-precision dynamic measurement and abnormal detection. The present application significantly improves the copper foil thickness measurement accuracy and the robustness of quality monitoring, and provides reliable technical support for high-performance copper foil production. BRIEF DESCRIPTION OF DRAWINGS

[0034] Fig. 1 A flowchart of a copper foil thickness analysis method of the present application.

[0035] Fig. 2 A structural schematic diagram of a copper foil thickness analysis system of the present application. DETAILED DESCRIPTION

[0036] In order to make the purpose, technical scheme and advantages of the present application clearer, the present application will be described in detail below in combination with the drawings and specific embodiments.

[0037] As Figs. 1-2 , the copper foil thickness analysis method and device of the present embodiment can specifically include:

[0038] S101, acquiring the tension value of each layer position and the thickness measurement result of the corresponding layer position during the winding process of the copper foil.

[0039] The tension sensor is arranged at each layer position of the copper foil winding device, and the sensor collects the tension signal in the winding process according to a preset sampling frequency. The collected tension signal is amplified and filtered by a signal conditioning circuit to obtain a real-time tension value data sequence of each layer position. Each tension value data contains layer number identification and collection time information. A laser thickness gauge or an ultrasonic thickness measuring device is used to measure the thickness of the copper foil in the winding process. The thickness measuring device is uniformly distributed with multiple measuring points along the width direction of the copper foil to obtain the thickness data of each measuring point. The sum of the thickness data of all measuring points is calculated by an arithmetic average method and divided by the number of measuring points to obtain the average thickness value of the copper foil. The layer number identification and measurement time corresponding to the thickness value are recorded. According to the layer number identification and collection time information in the real-time tension value data sequence, the layer number identification and measurement time of the average thickness value are matched. When the layer number identification is the same and the time difference is within a preset range, it is determined that the tension value corresponds to the thickness value to form a corresponding data group containing the layer number, the tension value and the thickness value. For each corresponding data group, it is judged whether the tension value exceeds a preset tension threshold. If the threshold is exceeded, the corresponding data group and the corresponding data group of the adjacent layer number are marked as key data for storage. If the threshold is not exceeded, the corresponding data group is stored in a standard format to obtain complete records of the tension sensor data and the thickness measurement data of each layer position in the copper foil winding process.

[0040] Specifically, in the copper foil winding process, tension control and thickness monitoring are key factors to ensure product quality. The arrangement of tension sensors needs to consider the structural characteristics of the winding device. Usually, multiple strain or piezoelectric sensors are installed at different radius positions of the winding shaft. These sensors can real-time sense the tensile stress changes of the copper foil during the winding process.

[0041] Specifically, the signal conditioning circuit plays an important role in tension signal processing. The original tension signal often contains high-frequency noise and electromagnetic interference. Through the operation amplifier, the weak sensor output signal is amplified to reach the voltage level required for subsequent processing. The filter processing uses a low-pass filter to remove high-frequency interference and retain low-frequency signal components reflecting the actual tension changes. The conditioned signal forms a continuous time series data, and each data point contains accurate time markers and corresponding layer number information.

[0042] In one possible implementation, the laser thickness gauge calculates the thickness according to the time difference of the reflected light by emitting a laser beam to the surface of the copper foil. Multiple measuring points arranged along the width direction of the copper foil can capture the changes in the transverse thickness of the copper foil. The application of the arithmetic average method is to reduce the random error of single-point measurement. By adding the thickness values of all measuring points and dividing by the total number of measuring points, a more stable and reliable average thickness value is obtained. This multi-point measurement and average processing method can effectively reduce the measurement deviation caused by local unevenness of the copper foil surface.

[0043] It should be noted that the time matching of tension data and thickness data is a key link to achieve accurate correspondence. Due to the possible difference in physical location between the tension sensor and the thickness measuring device, the time of the same layer of copper foil passing through the two sensors will be different. By setting a reasonable time window, when the layer number identifiers of the two data are the same and the time difference is within the allowed range, it can be considered that they belong to the measurement results of the same layer of copper foil. This matching mechanism ensures the accurate correspondence of the data and provides a reliable foundation for subsequent quality analysis.

[0044] Preferably, the setting of the tension threshold is based on the mechanical properties of the copper foil material and the production process requirements. When the tension value of a certain layer is detected to exceed the threshold, it indicates that there may be abnormal conditions at this position, such as material defects or equipment failure. At this time, the data of this layer and adjacent layers are marked and stored, which helps subsequent quality traceability and problem analysis. The data within the normal range is stored in the standard format, which not only ensures the integrity of the data, but also avoids wasting storage resources.

[0045] S102, the compression deformation rule of the copper foil under different layer numbers is identified by analyzing the correlation between the thickness change and the tension distribution of the copper foil under different winding layer numbers, and a compression deformation analysis result is generated according to the compression deformation rule of the copper foil under different layer numbers.

[0046] According to the real-time tension value of different radial positions and the original thickness measurement result of the corresponding layer position, a three-dimensional data matrix of layer number, tension and thickness is constructed, the difference between the original thickness and the standard thickness of each layer is calculated to obtain the thickness change value, the correlation coefficient between the tension value and the thickness change value of each layer is calculated by Pearson correlation coefficient, when the absolute value of the correlation coefficient is greater than a preset threshold, it is determined that the layer has significant tension and thickness correlation characteristics, and the correlation quantization index of each layer is obtained. Based on the correlation quantization index, for the layers with significant correlation, the winding radius and copper foil width parameters of the layer are obtained, and a radial pressure calculation method is adopted, wherein the radial pressure is equal to the tension value of the layer divided by the winding radius and multiplied by the copper foil width. The cumulative pressure value borne by each inner layer position is obtained by accumulating the radial pressure generated by each layer from the outermost layer to the inner layer. The cumulative pressure value and the elastic modulus of the copper foil material are used to calculate the theoretical compression amount of each layer. The original thickness measurement result is subtracted by the theoretical compression amount to obtain the thickness after compression. The difference between the original thickness and the thickness after compression is the compression deformation amount. The power function relationship between the compression deformation amount and the layer number position is fitted by the least square method, two parameters of power index and proportion coefficient are determined, and a nonlinear relationship expression describing the compression deformation rule of the copper foil is obtained; the power function relationship expression between the compression deformation amount and the layer number position is

[0047] y=a·x b

[0048] where y represents the compression deformation amount, x represents the layer position, a represents the proportional coefficient, and b represents the power index. The formula describes the nonlinear power function law of the copper foil compression deformation amount with the layer position. According to the nonlinear relationship expression and any layer position value, the theoretical compression deformation amount of the layer is calculated by substituting the expression, the original thickness measurement result of the layer is subtracted by the theoretical compression deformation amount, and the actual thickness prediction value considering the compression effect is obtained. The compression deformation analysis result is formed by summarizing the compression deformation amount, the cumulative pressure value, and the actual thickness prediction value of each layer.

[0049] Specifically, the compression deformation analysis during the copper foil winding process involves complex relationships between multiple physical quantities. The Pearson correlation coefficient, as a statistical indicator to measure the linear correlation degree between two variables, has a value range of -1 to 1. When the calculated correlation coefficient is close to 1 or -1, it indicates that there is a strong correlation between the tension and the thickness change, which means that the change in tension will directly cause the change in copper foil thickness.

[0050] Specifically, the construction of the three-dimensional data matrix is the basis for precise analysis. Each element of the matrix contains the tension value and the thickness measurement value of a specific layer position, and a large amount of data can be processed in batches through matrix operations. The calculation of the thickness change value requires a standard thickness as a reference benchmark, which is usually derived from copper foil production specifications or quality standards. When the measured thickness is less than the standard thickness, the thickness change value is negative, indicating that the copper foil is compressed; otherwise, it indicates that the copper foil may be stretched or have other abnormalities.

[0051] In one possible implementation, the calculation of the radial pressure is based on the hoop stress theory in material mechanics. During the winding process, the tension of the outer layer of copper foil is converted into radial pressure on the inner layer. This pressure transmission mechanism is similar to the stress distribution of a wrapped pressure vessel, where each layer of copper foil not only bears pressure from the outer layer but also exerts pressure on the inner layer. The winding radius gradually increases with the increase of the number of layers, and this radius change directly affects the size and distribution of the pressure.

[0052] It should be noted that the calculation of the cumulative pressure value adopts a layer-by-layer superposition method. Starting from the outermost layer, the radial pressure generated by each layer is transmitted to the inner layer, and the total pressure borne by the inner layer is the cumulative effect of all the outer layer pressures. This cumulative effect causes the innermost layer to bear the maximum pressure and is most likely to experience significant compression deformation. The elastic modulus of the copper foil material is a key parameter connecting pressure and deformation, which reflects the material's ability to resist elastic deformation.

[0053] Preferably, a power function form is selected to describe the relationship between the compression deformation and the number of layers in the least squares fitting process. The power function can well reflect the nonlinear change characteristics, especially the law that the deformation grows rapidly at the initial stage of winding and tends to be stable at the later stage. The power exponent parameter determines the degree of curvature of the curve, and the proportional coefficient controls the size of the overall deformation. By fitting the measured data points, the best parameter combination can be obtained to minimize the deviation of the fitted curve from the actual data. The advantage of this analysis method is that it can predict the compression deformation at any layer position, even if there is no direct measurement data at that position. By substituting the layer number value into the fitted expression, the theoretical deformation can be calculated, and the actual thickness after compression can be calculated. This prediction can help identify the risk area that may appear excessive compression.

[0054] S103、According to the compression deformation analysis result, the relative displacement between layers and the torsion angle are obtained to analyze the deformation mechanism and obtain stress distribution mode data.

[0055] Through the compression deformation analysis result, the relative displacement data and the torsion angle data between adjacent layers are obtained, the vertical distance between the surfaces of adjacent copper foil layers is measured as the interlayer distance, the tangential shear strain is calculated according to the relationship that the shear strain is equal to the interlayer relative displacement divided by the interlayer distance, the torsional shear strain is calculated according to the torsion angle multiplied by the current layer radius and then divided by the interlayer distance, the total shear strain value is obtained by adding the tangential shear strain and the torsional shear strain, and the total shear strain value of each layer is accumulated from the inner layer to the outer layer to obtain the shear deformation accumulation of each layer. Based on the shear deformation accumulation and the compression deformation amount in the compression deformation analysis result, a multiple regression method is used to fit the relationship between the shear deformation and the compression deformation, and the regression coefficient is determined as the influence coefficient of the compression deformation on the shear deformation. The compression deformation, the shear deformation and the influence coefficient are substituted into the preset coupling function form to obtain the coupling relationship expression and the parameter value describing the interaction of the two deformations. According to the coupling relationship expression and the parameter value, the compression deformation and the shear deformation of each layer are substituted into the expression to calculate the equivalent stress value in the complex deformation state. If the shear deformation accumulation of a layer exceeds the preset shear deformation threshold, a geometric model of finite element analysis is established for the layer, triangular or quadrilateral elements are divided, displacement constraints and stress loads are applied to the boundaries of the model, and the stress distribution mode data of each element in the region is obtained by solving the node displacement and element stress.

[0056] Specifically, shear deformation is a complex mechanical phenomenon in the copper foil winding process, mainly caused by the relative movement between adjacent layers. When the outer copper foil generates circumferential strain under the action of tension, it will drive the inner layer to generate tangential displacement, and this displacement difference forms a shear effect. The measurement of interlayer relative displacement can be realized by setting marker points on the surface of the copper foil and tracking the position change of the marker points in the winding process by using image recognition technology.

[0057] Specifically, the calculation of tangential shear strain is based on the shear deformation theory in material mechanics. When two adjacent layers slip relative to each other, the shear strain is equal to the ratio of relative displacement to interlayer distance. This ratio reflects the degree of shear deformation of the material. The torsional shear strain takes into account the torsional effect during winding. When the copper foil is wound, there is a certain helix angle, which will produce additional shear effect. The product of the torsion angle and the radius represents the circumferential displacement caused by torsion, and then divided by the interlayer distance to get the shear strain component caused by torsion.

[0058] In one possible implementation, the application of multiple regression method requires collecting a large amount of experimental data. By changing the winding tension, speed and other process parameters, the compression deformation and shear deformation under different conditions are measured to establish a data sample set. In the regression analysis process, the compression deformation is taken as the independent variable and the shear deformation is taken as the dependent variable. By minimizing the sum of squares of errors between the predicted value and the measured value, the optimal regression coefficient is determined.

[0059]

[0060] , S represents the sum of squares of errors, n represents the number of samples, Y i represents the measured value of the i-th shear deformation, Y i hat represents the predicted value of the i-th shear deformation, X i represents the i-th compression deformation, β0 represents the regression intercept coefficient, and β1 represents the regression slope coefficient. This formula determines the optimal regression coefficient by minimizing the sum of squares of errors between the predicted value and the measured value. These coefficients quantify the degree of influence of compression deformation on shear deformation.

[0061] It should be noted that the construction of the coupling relationship expression considers the interaction mechanism between the two deformations. Compression deformation will change the contact state and friction condition between layers, and then affect the development of shear deformation. The coupling function usually adopts a polynomial form, including linear and cross terms, which can describe the nonlinear relationship between deformations. By substituting the actual deformation data into the expression, the shear deformation response under a certain compression state can be predicted.

[0062] Preferably, the meshing in finite element analysis is the process of discretizing the continuous copper foil area. Triangular elements are suitable for irregular boundary areas, while quadrilateral elements have higher calculation accuracy in regular areas. The selection of element size needs to balance the calculation accuracy and efficiency, using denser mesh in stress concentration areas and sparser mesh in areas with gentle stress changes. The application of boundary conditions simulates the actual constraint state, such as fixed end constraint limiting node displacement, while stress load simulates external force. The calculation of equivalent stress considers the joint action of normal stress and shear stress, and through the conversion of multi-directional stress state to a single numerical value, it is convenient to evaluate the stress state of the material. When the shear deformation accumulation exceeds the threshold value, it indicates that the material performance degradation or damage may occur in this area, and detailed stress analysis is needed. The finite element solution process solves the linear equation system by establishing the overall stiffness matrix and load vector to obtain the node displacement, and then calculates the element stress to form the stress distribution map of the entire analysis area.

[0063] S104, compensating the thickness measurement data based on the stress distribution mode data to obtain compensated thickness measurement data.

[0064] Using the stress distribution mode data, the stress values and corresponding real-time tension values of each layer position are extracted, the deformation data of each layer is obtained from the compression deformation analysis results, the difference between the actual measured thickness and the designed thickness is calculated as the thickness deviation, the K-means clustering method is used to classify data points with similar stress, tension and thickness deviation characteristics into the same category, the tension mean value, deformation mean value and thickness deviation mean value of all data points in each category are calculated, and a corresponding relationship table of category identifier and three mean value parameters is established to form a dynamic compensation parameter library. According to the real-time tension value and the layer position of the current measurement position, the category closest to the current tension value is found in the dynamic compensation parameter library, and the tension mean value, deformation mean value and thickness deviation mean value of the category are extracted. When the real-time tension value is between the tension mean values of two adjacent categories, the thickness deviation mean values of the two categories are weighted and averaged according to the distance proportion of the real-time tension value and the tension mean values of the two categories to obtain the dynamic compensation coefficient of the current position. The original thickness measurement value is corrected by using the dynamic compensation coefficient, and the compensated thickness value is obtained by subtracting the dynamic compensation coefficient from the original measurement value. The tension value difference between the current time and the previous sampling time is calculated, and if the absolute value of the difference exceeds the preset tension change threshold, the corresponding category is found again in the parameter library, the updated dynamic compensation coefficient is calculated according to the new tension value, and the thickness measurement value is corrected again by using the updated compensation coefficient to obtain the dynamic compensation thickness measurement data.

[0065] Specifically, the construction of the dynamic compensation parameter library is based on statistical analysis of a large amount of historical measurement data. During the copper foil winding process, different tension conditions will cause different degrees of elastic deformation of the material, which directly affects the accuracy of thickness measurement. The designed thickness refers to the standard specification thickness of the copper foil during manufacturing, while the actual measured thickness will deviate due to the effect of stress.

[0066] Specifically, the K-means clustering method groups data points through iterative optimization. Initially, K center points are randomly selected, then the Euclidean distance of each data point to each center point is calculated, and the data point is attributed to the class represented by the nearest center point. Then the center point position of each class is recalculated, i.e. the average value of all data points in each dimension of the class. This process is repeated until the center point position no longer changes significantly. In the application of copper foil thickness compensation, each data point contains three dimensions: stress value, tension value and thickness deviation value.

[0067] In one possible implementation, the selection of the number of classes K needs to balance accuracy and efficiency. Too few classes will result in too large differences in data within the same class, reducing compensation accuracy; too many classes will increase the complexity of storage and lookup. The optimal K value can usually be determined by the elbow rule, i.e. plotting the within-group sum of squares curve corresponding to different K values, and selecting the position where the curve has a clear inflection point.

[0068] It should be noted that the calculation method of weighted average embodies the idea of linear interpolation. When the real-time tension value is between the tension means of two adjacent classes, the compensation coefficient should not jump, but should be smoothly transitioned. Assuming that the tension means of two adjacent classes are T1 and T2, the corresponding thickness deviation means are D1 and D2, and the real-time tension value is T, then the interpolation weight can be determined according to the relative distance of T, T1 and T2. The closer to T1, the greater the weight of D1; otherwise, the greater the weight of D2.

[0069] Preferably, the setting of the tension change threshold needs to consider the response characteristics of the measurement system and the mechanical properties of the material. If the threshold is set too small, it will result in frequent parameter updates, increasing the computational burden and possibly introducing noise; if the threshold is too large, it will not respond to significant changes in tension in a timely manner, affecting the compensation effect. The threshold is usually set to 2 to 3 times the normal tension fluctuation range. The application of the compensation coefficient uses subtraction rather than addition, because under the action of tension, the copper foil is usually compressed and thinned, and the actual measurement value is less than the true thickness. By subtracting a positive compensation coefficient, the true thickness without stress can be restored. This compensation mechanism can eliminate the interference of tension changes on thickness measurement, improving the reliability of the measurement data. The dynamic updating mechanism ensures that the compensation parameters can adapt to changes in working conditions during winding. When a significant change in tension is detected, a new compensation coefficient is obtained by repositioning in the parameter library. This real-time adjustment capability enables the thickness measurement to maintain high precision throughout the winding process, even in cases of large tension fluctuations, ensuring stable and reliable measurement results.

[0070] S105, based on the compensated thickness measurement data, set the normal thickness range and the abnormality determination threshold, and determine the sensitivity identification threshold parameter of the thickness abnormality detection.

[0071] With the compensated thickness measurement data as input, arrange the thickness data sequence in time sequence, calculate the arithmetic mean of all data points in the sequence as the center reference value, calculate the deviation square sum of each data point from the center reference value divided by the total number of data points to get the variance, take the square root of the variance to get the standard deviation, and add or subtract the standard deviation of a preset multiple to the center reference value to determine the upper and lower limits of the normal thickness range, to construct the reference thickness range parameter group containing the center reference value, the standard deviation and the range upper and lower limits. For the thickness data sequence and the reference thickness range parameter group, set a fixed length data window and a window moving step, calculate the average value of the data points in each window as the local mean, calculate the difference between the local means of adjacent windows divided by the window moving step to get the local change gradient, and count the local mean and local change gradient of all window positions to form a statistical feature sequence describing the time sequence change characteristics of the thickness data. According to the local change gradient data in the statistical feature sequence, sort all gradient absolute values from small to large, select the gradient value corresponding to the preset percentile as the gradient abnormality detection threshold, when the local change gradient of more than a preset number of windows continuously exceeds the detection threshold, it is determined that the thickness change trend is abnormal, and combined with the range upper and lower limits in the reference thickness range parameter group and the gradient abnormality detection threshold, the sensitivity identification threshold parameter for thickness abnormality detection is determined.

[0072] Specifically, the establishment of the benchmark thickness range model relies on the normal distribution theory in statistics. In the production process of copper foil, the thickness measurements usually fluctuate around a central value, and this fluctuation presents a normal distribution characteristic under normal production conditions. The central benchmark value represents the expected value of the thickness, and the standard deviation reflects the degree of dispersion of the thickness fluctuation.

[0073] Specifically, the calculation process of variance involves the deviation of each measurement point from the average value. Assuming there are 100 thickness measurements, first calculate the arithmetic mean of these 100 values, then calculate the difference between each value and the average value, square the difference values and sum them up, and then divide by 100 to get the variance. The standard deviation is the square root of the variance, which has the same dimension as the original data and more intuitively reflects the degree of dispersion of the data. The preset multiple is usually 2.5 or 3, which is based on the confidence interval theory in statistics and can cover most normal data points.

[0074] In one possible implementation, the application of the sliding window method requires reasonable setting of window parameters. The window length determines the amount of data for local statistics, and too short will result in unstable statistical results, and too long may mask the characteristics of local changes. The window moving step determines the degree of analysis, and a step of 1 indicates point-by-point movement, which can capture subtle changes; a step greater than 1 improves calculation efficiency but may miss some change points. The calculation of the local change gradient reflects the rate of change of the thickness in the time dimension, and a sharp gradient change often indicates an abnormal situation in the production process.

[0075] It should be noted that the choice of percentile directly affects the sensitivity of anomaly detection. After sorting all the gradient absolute values, the 90th percentile means that 90% of the gradient values are less than the threshold, and only 10% of the gradients are considered abnormal. Selecting a higher percentile will reduce the false positive rate but may miss real anomalies; selecting a lower percentile will have the opposite effect. This trade-off needs to be determined according to the quality requirements of actual production.

[0076] Preferably, the abnormality determination mechanism of the continuous window can distinguish between incidental fluctuations and trend abnormalities. The gradient of a single window may be caused by measurement noise or transient disturbances, while the abnormality of multiple consecutive windows indicates a systematic problem. The default number is usually set to 3 to 5 consecutive windows, which can filter incidental interference and detect real quality problems in time. The determination of the sensitivity recognition threshold parameter is a comprehensive consideration process. It not only contains the upper and lower limits of the range based on statistical distribution, but also combines the gradient threshold based on time series change. This dual detection mechanism can identify abnormalities from different dimensions: range detection focuses on whether the absolute value deviates from the normal interval, and gradient detection focuses on whether the change speed is abnormal. The two detection methods complement each other, improving the accuracy and reliability of abnormality identification. By adjusting each threshold parameter, it can adapt to different product specifications and quality standards, and realize flexible quality control.

[0077] S106, judging the thickness abnormality state according to the sensitivity recognition threshold parameter of the thickness abnormality detection, and obtaining a thickness abnormality detection result.

[0078] According to the sensitivity recognition threshold parameter and the reference thickness range parameter group of the abnormality detection, the compensated thickness data at the current time is obtained, the difference between the data and the center reference value in the reference thickness range parameter group is calculated as the current deviation value, the dynamic threshold is obtained by adjusting the sensitivity recognition threshold according to the current layer position, if the absolute value of the current deviation value exceeds the dynamic threshold, it is determined that the current measurement point is in an abnormal state, and the layer position, timestamp and deviation value of the measurement point are recorded to form an abnormal record. Based on the abnormal record, the deviation value is stored in the abnormal data cache area in chronological order, and the abnormal occurrence frequency of the same layer position is counted, when the number of abnormal data points in the cache area reaches the preset update trigger number, the arithmetic mean of all deviation values in the cache area is calculated as the deviation mean, and the standard deviation of the deviation value is calculated as the deviation dispersion, to obtain an abnormal statistical parameter group. Using the abnormal statistical parameter group and the preset exponential smoothing coefficient, the center reference value in the reference thickness range parameter group is updated, and the updated center reference value is equal to the product of the original center reference value and the exponential smoothing coefficient plus the product of the deviation mean and the exponential smoothing coefficient complement. The same method is used to update the standard deviation parameter, and the updated reference thickness range parameter group is stored and used for subsequent detection. All abnormal records, abnormal statistical parameter groups and updated reference thickness range parameter groups are summarized to obtain a thickness abnormality detection result.

[0079] Specifically, the dynamic threshold adjustment mechanism in real-time monitoring is the key to precise anomaly detection. The normal thickness fluctuation range of copper foils at different layer positions differs due to the cumulative pressure they bear. The inner layer copper foils bear more pressure, so their allowed thickness deviation range is relatively small; the outer layer copper foils bear less pressure, so their allowed deviation range can be appropriately relaxed. This differentiated threshold setting is more in line with the actual production rules.

[0080] Specifically, the calculation of the dynamic threshold is based on the mapping relationship between the layer position and the sensitivity identification threshold. Assuming that the basic sensitivity identification threshold is T0, for the nth layer copper foil, its dynamic threshold can be corrected by an adjustment coefficient. The adjustment coefficient is determined according to the historical abnormal rate and physical characteristics of the layer. The adjustment coefficient of the inner layer is usually less than 1, making the threshold more stringent; the adjustment coefficient of the outer layer may be greater than 1, appropriately relaxing the detection standard. This hierarchical threshold management improves the detection specificity.

[0081] In one possible implementation, the design of the abnormal data buffer area adopts a circular queue structure, which not only ensures the time sequence of the data, but also avoids unlimited growth of the storage space. When new abnormal data enters, the earliest data will be removed if the buffer area is full. The selection of the preset update trigger quantity needs to balance the update frequency and the statistical reliability. Too small will lead to frequent updates and unstable statistical results, and too large will reduce the response speed of the system. Usually set to 20 to 50 abnormal points, this range can both guarantee statistical significance and timely reflect changes in production status.

[0082] It should be noted that the application of the exponential smoothing method in parameter updating reflects the trade-off between new and old information. The exponential smoothing coefficient determines the relative importance of historical data and new data. When the coefficient is close to 1, the updated parameter mainly retains the original value, and changes slowly; when the coefficient is close to 0, the influence of new data is greater, and the parameter update is more aggressive. The exponential smoothing coefficient complement is 1 minus the smoothing coefficient value, which represents the weight of new data. This updating mechanism makes the system both stable and adaptable to gradual changes in production conditions.

[0083] Preferably, the utilization of the layer position information is not only reflected in the dynamic threshold adjustment, but also used for abnormal pattern recognition. By counting the abnormal occurrence frequency of different layers, it can be found whether there is a systematic problem in a certain layer position. If the abnormal frequency of a layer is significantly higher than that of other layers, it may imply that the equipment at that position needs maintenance or the process parameters need to be adjusted. The timestamp information is used to analyze the time distribution characteristics of the abnormality, to identify periodic or trend quality fluctuations. The comprehensive output of the abnormal detection results provides a comprehensive decision basis for subsequent quality control. The abnormal record provides specific problem positioning information, which is convenient for tracing and analysis; the abnormal statistical parameter group reflects the current quality fluctuation situation; the updated reference thickness range parameter group ensures that the detection standard can adapt to the changes of production state. This multi-level information output mechanism not only meets the needs of real-time monitoring, but also provides data support for long-term quality improvement. Through continuous learning and parameter updating, the detection performance is continuously optimized, and false positives and false negatives are reduced.

[0084] S107, determine the thickness abnormality detection combined parameters in combination with the thickness abnormality detection results and historical winding process thickness measurement data.

[0085] In combination with the abnormality detection results and historical winding process thickness measurement data, the abnormality occurrence frequency of each layer position is obtained by dividing the number of abnormalities by the total number of measurements of the layer, the absolute value of each abnormal point deviation value is calculated, the average value of all deviation absolute values of the same layer is taken as the average deviation of the layer, the deviation ratio sequence is obtained by dividing each abnormal point deviation absolute value by the average deviation of the layer, and a quality feature data set containing the layer number, abnormal frequency and deviation ratio sequence is constructed. According to the layer position abnormal frequency in the quality feature data set, the layer position with an abnormal frequency greater than twice the historical average abnormal frequency is identified as a key adjustment layer position, the ninety-fifth percentile is extracted from the deviation ratio sequence as the deviation characteristic value of the layer, the current compensation coefficient of the key adjustment layer position is found in the dynamic compensation parameter library, the current compensation coefficient is multiplied by the deviation characteristic value to obtain a new compensation coefficient, and the parameters of the corresponding layer position in the dynamic compensation parameter library are updated. The original thickness data of the last production batch is compensated and calculated using the updated dynamic compensation parameter library, the number of abnormal detections of the compensated data is counted, and if the number of abnormal detections is reduced by more than a preset proportion compared with before the update, the updated parameters are retained; if the preset proportion is not reached, the abnormality determination threshold is increased or decreased by a preset step, and the compensation and counting process is repeated until the number of abnormal detections meets the requirements, and the thickness abnormality detection combined parameters are determined.

[0086] Specifically, the feedback adjustment mechanism of quality monitoring is based on in-depth analysis of historical data. The calculation of abnormal frequency of layer reveals the difference in quality stability of different positions. Some layers may show a higher abnormal rate due to equipment wear, process parameter deviation or material property change. By quantifying this difference, the system can identify the weak link that needs to be focused on.

[0087] Specifically, the construction of deviation ratio sequence provides a standardized abnormality evaluation method. Assuming that the deviation of a certain measurement point of the 10th layer is 0.5 microns, and the average deviation of the layer is 0.2 microns, the deviation ratio is 2.5, indicating that the deviation of the point is 2.5 times the average level. This relative treatment eliminates the difference in absolute deviation between different layers, making the comparison of abnormality more fair and reasonable.

[0088] In one possible implementation, twice the historical average abnormal frequency is statistically significant as a recognition standard. Normal production fluctuations usually do not cause abnormal frequency to double, and when the abnormal frequency of a layer reaches this level, it often means that there is a systematic problem. The choice of the 95th percentile ensures the robustness of the adjustment, which represents 95% of the deviations that are less than this value, and only 5% of the extreme cases are excluded, which takes into account most of the abnormal cases and avoids the excessive influence of individual extreme values.

[0089] It should be noted that the update of the compensation coefficient uses multiplication adjustment rather than addition adjustment, because the compensation requirement is usually proportional to the original deviation. If the deviation eigenvalue of a layer is 1.8 and the original compensation coefficient is 0.5, the updated compensation coefficient is 0.9, which means that the layer needs stronger compensation effort to correct systematic deviation. This adjustment method maintains the relative relationship of compensation and avoids overcompensation or insufficient compensation.

[0090] Preferably, the verification process of feedback adjustment uses the data of the latest production batch instead of all historical data, which has several advantages. The latest data can better reflect the current production state, avoiding the interference of factors such as equipment aging or process improvement in early data. At the same time, using a smaller data set for verification can quickly obtain feedback results, improving the timeliness of adjustment. The setting of the preset proportion needs to balance the improvement effect and system stability. If the number of abnormal detections must be reduced by more than 50% to accept updates, it may lead to the rejection of many beneficial small improvements; if the threshold is too low, such as requiring a reduction of only 10%, it may accept some false improvements caused by random fluctuations. Usually, 20% to 30% improvement is set as the acceptance standard. Incremental and decremental adjustment of the threshold forms an iterative optimization process. When there are still many abnormalities after the compensation parameter is updated, it may be that the threshold is too strict and needs to be appropriately relaxed; on the contrary, if there are too few abnormalities, there may be a risk of missed detection, and the threshold needs to be tightened. This two-way adjustment mechanism ensures that the best balance point between detection sensitivity and stability is found, achieving the coordinated optimization of measurement accuracy and detection performance.

[0091] The present application provides a copper foil thickness analysis system, mainly comprising:

[0092] A data acquisition module is configured to acquire tension values at different layer positions during the winding process of the copper foil and thickness measurement results corresponding to the layer positions.

[0093] An association analysis module is configured to identify compression deformation rules of the copper foil at different layers by analyzing the association between thickness changes and tension distribution of the copper foil at different winding layers, and to generate a compression deformation analysis result according to the compression deformation rules of the copper foil at different layers.

[0094] A compression deformation analysis module is configured to analyze deformation mechanisms based on the compression deformation analysis result and the acquired interlayer relative displacement and torsion angle, and to obtain stress distribution mode data.

[0095] A deformation mechanism analysis module is configured to compensate thickness measurement data based on the stress distribution mode data, and to obtain compensated thickness measurement data.

[0096] A data compensation module is configured to set a normal thickness range and an abnormality determination threshold based on the compensated thickness measurement data, and to determine a sensitivity identification threshold parameter of thickness abnormality detection.

[0097] A threshold setting module is configured to determine a thickness abnormality state based on the sensitivity identification threshold parameter of thickness abnormality detection, and to obtain a thickness abnormality detection result.

[0098] An abnormality detection module is configured to determine a thickness abnormality detection combination parameter by combining the thickness abnormality detection result and historical winding process thickness measurement data.

[0099] A computer readable storage medium storing a computer program which, when executed by a processor, implements the method described above.

[0100] A computer program product comprising a computer program which, when executed by a processor, implements the steps of the method described above.

[0101] The above description is merely the preferred embodiments of the present application, but the protection scope of the present application is not limited to this. Any person skilled in the art can make equivalent replacements or changes according to the technical solution and the inventive concept of the present application within the technical range disclosed by the present application, and all of them should be covered within the protection scope of the present application.

Claims

1. A copper foil thickness analysis method characterized by, The method comprises: Obtaining the tension value of each layer position in the copper foil winding process and the thickness measurement result of the corresponding layer position; By analyzing the correlation between the thickness change of the copper foil under different winding layers and the tension distribution, the compression deformation rule of the copper foil under different layers is identified, and a compression deformation analysis result is generated according to the compression deformation rule of the copper foil under different layers; According to the compression deformation analysis result, the interlayer relative displacement and the torsion angle obtained are combined to analyze the deformation mechanism, and stress distribution mode data is obtained; Based on the stress distribution mode data, the thickness measurement data is compensated to obtain compensated thickness measurement data; Based on the compensated thickness measurement data, a normal thickness range and an abnormal judgment threshold are set, and a sensitivity identification threshold parameter of thickness abnormality detection is determined; According to the sensitivity identification threshold parameter of thickness abnormality detection, the judgment of thickness abnormality state is carried out, and a thickness abnormality detection result is obtained; Combining the thickness abnormality detection result and the historical winding process thickness measurement data, a thickness abnormality detection combination parameter is determined.

2. The copper foil thickness analysis method according to claim 1, wherein The method comprises: Arranging a tension sensor at each layer position of the copper foil winding device, collecting the tension signal in the winding process, amplifying and filtering the tension signal, generating real-time tension value data sequence, marking the layer number identifier and collection time information in the real-time tension value data sequence; deploy a thickness measuring device along the width direction of the copper foil to distribute multiple measurement points, collect the thickness data of each measurement point, calculate the average value of all measurement point thickness data, generate the average thickness value of the layer copper foil, mark the layer number identifier and the measurement time corresponding to the average thickness value; match the real-time tension value data sequence and the average thickness value, determine the corresponding relationship through the layer number identifier and the time difference value, generate the corresponding data group containing the layer number, tension value and thickness value, judge the comparison result of the tension value of the corresponding data group and the preset tension threshold, mark the corresponding data group and its adjacent layer data group which exceeds the threshold, and store all corresponding data groups.

3. The copper foil thickness analysis method according to claim 1, wherein The method comprises: According to the real-time tension value of different radial positions and the original thickness measurement result of the corresponding layer position, a three-dimensional data matrix of layer number, tension and thickness is constructed, the thickness change value of each layer is calculated, and the correlation quantization index of tension value and thickness change value is generated; based on the correlation quantization index, the winding radius and the copper foil width are combined to calculate the radial pressure generated by each layer, and the cumulative pressure value borne by the inner layer copper foil is accumulated; based on the cumulative pressure value and the elastic modulus of the copper foil material obtained in advance, a compression deformation analysis result is generated.

4. The copper foil thickness analysis method according to claim 1, wherein The method comprises: extracting relative displacement data and torsion angle data between adjacent layers, measuring the vertical distance between the surfaces of adjacent copper foil layers, calculating the tangential shear strain, combining the torsion angle and the current layer radius to calculate the torsional shear strain, adding the tangential shear strain and the torsional shear strain to generate a total shear strain value, accumulating the total shear strain value layer by layer to generate the shear deformation accumulation of each layer; extracting the compression deformation amount in the compression deformation analysis result, fitting the relationship between the shear deformation accumulation and the compression deformation amount, determining the regression coefficient, substituting into the preset coupling function form to generate a coupling relationship expression, substituting the compression deformation amount and the shear deformation accumulation to calculate the equivalent stress value in the composite deformation state, and generating stress distribution mode data.

5. The copper foil thickness analysis method according to claim 1, wherein The compensation of the thickness measurement data based on the stress distribution mode data obtains compensated thickness measurement data, which comprises: extracting stress values and real-time tension values, combining deformation amount data in the compression deformation analysis result to calculate thickness deviation, and generating a dynamic compensation parameter library; based on the real-time tension value and the layer position, finding a matching category in the dynamic compensation parameter library, extracting the tension average value, deformation amount average value and thickness deviation average value of the category, and calculating a dynamic compensation coefficient; using the dynamic compensation coefficient to correct the original thickness measurement value to obtain the compensated thickness measurement data.

6. The copper foil thickness analysis method according to claim 5, wherein The calculation of the dynamic compensation coefficient comprises: finding a category matching the real-time tension value, extracting the parameter average value of the category, calculating the distance ratio of the real-time tension value and the category average value, and generating a dynamic compensation coefficient.

7. The copper foil thickness analysis method according to claim 1, wherein The combination of the thickness abnormality detection result and the historical winding process thickness measurement data to determine the thickness abnormality detection combination parameter comprises: combining the abnormality detection result and the historical winding process thickness measurement data, calculating the abnormality frequency of each layer position by dividing the total measurement times of the layer by the abnormality occurrence times of the layer, identifying the layer position with an abnormality frequency greater than twice the historical average abnormality frequency as a key adjustment layer position, and extracting the deviation characteristic value of the key adjustment layer position; finding the current compensation coefficient of the key adjustment layer position in the dynamic compensation parameter library, multiplying the current compensation coefficient by the deviation characteristic value to obtain a new compensation coefficient, and updating the parameters of the corresponding layer position in the dynamic compensation parameter library; using the updated dynamic compensation parameter library to compensate and calculate the original thickness data of the most recent production batch, counting the number of abnormal detections of the compensated thickness data, and determining the thickness abnormality detection combination parameter.

8. A copper foil thickness analysis system characterized by comprising: The system comprises: a data acquisition module configured to acquire tension values of each layer position and thickness measurement results of corresponding layer positions during a copper foil winding process; an association analysis module configured to identify compression deformation rules of the copper foil at different layer numbers by analyzing the association between copper foil thickness changes and tension distributions at different layer numbers, and to generate a compression deformation analysis result according to the compression deformation rules of the copper foil at different layer numbers; a compression deformation analysis module configured to analyze deformation mechanisms according to the compression deformation analysis result and combining the acquired interlayer relative displacement and torsion angle to obtain stress distribution mode data; a deformation mechanism analysis module, configured to compensate the thickness measurement data based on the stress distribution mode data to obtain compensated thickness measurement data; a data compensation module, configured to set a normal thickness range and an abnormality determination threshold based on the compensated thickness measurement data, and determine a sensitivity identification threshold parameter of the thickness abnormality detection; a threshold setting module, configured to determine a thickness abnormality state according to the sensitivity identification threshold parameter of the thickness abnormality detection to obtain a thickness abnormality detection result; an abnormality detection module, configured to determine a thickness abnormality detection combination parameter in combination with the thickness abnormality detection result and historical winding process thickness measurement data. 9.A computer readable storage medium storing a computer program, which, when executed by a processor, implements the method of any one of claims 1-7.

10. A computer program product comprising a computer program, characterized in that, The computer program, when executed by a processor, implements the steps of the method of claim 1.

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