An intelligent analysis method for corrugated paper packaging data

Abstract

The present invention belongs to the field of packaging data analysis technology and provides an intelligent analysis method for corrugated paper packaging data. The method mainly includes: obtaining the structural parameters of the corrugated paper, real-time monitoring and recording the changes in internal and external air pressure during storage or transportation, using the ARIMA model to accurately predict the range and duration of air pressure fluctuations in a specified future time period, while considering the direct impact of the quality of the product and its sharpness on the strength of the packaging material, and comprehensively evaluating the actual demand of the product for corrugated paper to accurately calculate the most suitable final demand strength of the sealed corrugated paper. This application carefully compares the strength index of the corrugated paper to be analyzed with the final demand strength to determine whether the corrugated paper to be analyzed meets the use requirements, thereby achieving flexible adjustment of corrugated paper packaging based on the structural characteristics of the product itself and the current or future environment, thereby effectively reducing costs.

Description

Technical Field

[0001] The present invention belongs to the technical field of packaging data analysis, and in particular relates to an intelligent analysis method for corrugated paper packaging data. Background Art

[0002] Corrugated packaging, a widely used packaging material primarily made from raw materials such as paper and cardboard, features a unique corrugated structure. The technology is evolving towards intelligent manufacturing. By applying advanced technologies such as the Internet of Things, cloud computing, and big data, corrugated packaging is enabling refined management of the entire supply chain, from raw material procurement to product delivery.

[0003] Some sealed corrugated paper packages have difficulty in exchanging internal gas freely with the outside world. When the external ambient temperature, altitude or air pressure changes, the internal gas pressure of the package cannot be adjusted in time, resulting in an increase in the internal and external pressure difference. The larger pressure difference will exert additional pressure on the corrugated paper, which may cause its bearing capacity to decrease. Most of the current products are corrugated paper packages with specified specifications, which are difficult to adjust according to the characteristics of the product itself and the changes in air pressure difference. If the strength of the corrugated paper is not enough, it may not be able to bear the weight of the product, resulting in damage to the package and product. On the contrary, if the strength of the corrugated paper is too high, although it can provide better protection, it will also cause waste of materials and lead to increased costs. Summary of the Invention

[0004] This application provides an intelligent analysis method for corrugated paper packaging data, which effectively solves the problem in the prior art that most products have corrugated paper packaging of specified specifications, which is difficult to adjust according to the characteristics of the product itself and changes in air pressure differences. It comprehensively evaluates the actual demand of the product for corrugated paper to accurately calculate the most suitable final demand strength of the sealed corrugated paper, and carefully compares the strength index of the corrugated paper to be analyzed with the final demand strength to determine whether the corrugated paper to be analyzed meets the usage requirements. It realizes flexible adjustment of corrugated paper packaging according to the characteristics of the product itself and the current or future environment, thereby effectively reducing costs.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] In a first aspect, the present application provides an intelligent analysis method for corrugated paper packaging data, comprising: obtaining the quality of a product packaged with corrugated paper, and evaluating the sharpness of the product using image processing technology; obtaining structural parameters of the corrugated paper, monitoring the internal and external air pressure of the corrugated paper during storage or transportation, and recording the monitoring time points; based on the internal and external air pressures and the monitoring time points, using the ARIMA model to predict the range and duration of air pressure fluctuations in a specified future time period; evaluating the degree of influence of the air pressure difference on the strength of sealed corrugated paper according to the air pressure fluctuation range and the duration; evaluating the initial demand strength of the product for non-sealed corrugated paper according to the quality and sharpness of the product; evaluating the final demand strength of the sealed corrugated paper according to the degree of influence of the air pressure difference on the strength of the sealed corrugated paper and the initial demand strength of the product for non-sealed corrugated paper; obtaining the strength index of the corrugated paper to be analyzed, comparing the strength index with the final demand strength, and outputting the analysis results.

[0007] Furthermore, computer vision is combined with image processing technology to evaluate the sharpness of the product, including: obtaining an image of the product and preprocessing the image; extracting edge information of the preprocessed image using an edge detection algorithm, obtaining all contours through a contour detection algorithm, and screening out the product contour according to a preset contour feature standard; for each point on the product contour, calculating the curvature within its neighborhood, setting a curvature threshold, and considering points with curvature greater than the threshold as sharp points; selecting sharp points, connecting them to form the contour of the sharp part, and using the contour of the sharp part with the maximum curvature as an indicator for quantifying the sharpness.

[0008] Furthermore, selecting sharp points for connection includes traversing all sharp points until the traversal is complete. For the currently traversed sharp point, the distance between it and each other sharp point is calculated. All other sharp points with distances less than a set radius are recorded. These recorded sharp points are used as a set of neighboring sharp points. The sharp point with the maximum curvature is retained. The curvature of the current sharp point is compared with that of all sharp points in the set of neighboring sharp points. The sharp point with the maximum curvature is identified and marked as an isolated sharp point. The isolated sharp points are then connected.

[0009] Furthermore, the initial demand intensity of the product for non-sealed corrugated paper is evaluated based on the quality and sharpness of the product, including: obtaining the critical pressure at which the corrugated paper is just not damaged under specified conditions; constructing a design matrix of quality and sharpness and a column vector matrix of critical pressure; constructing normal equations based on the design matrix and column vector matrix, and using NumPy to solve to obtain coefficients; assigning the coefficients to quality and sharpness, and weighting them to obtain the evaluated initial demand intensity of the product for non-sealed corrugated paper.

[0010] Furthermore, based on the internal and external air pressures and the monitoring time points, the ARIMA model is used to predict the range and duration of air pressure fluctuations in a specified future time period, including: screening out air pressure differences greater than a preset threshold value according to structural parameters, marking them as critical air pressure differences, and determining the duration of the critical air pressure differences according to the monitoring time; arranging the critical air pressure differences and their durations in timestamp order, associating the critical air pressure differences and durations corresponding to each timestamp to form a triplet sample set containing timestamps, critical air pressure differences, and durations; performing unit root tests on the critical air pressure difference sequence and the duration sequence respectively. If the test result shows that the sequence is non-stationary, performing a differential operation on the non-stationary sequence, recording the differential order of the critical air pressure difference and the differential order of the duration, until the sequence after differentiation is Through the stationarity test, the stationary key pressure difference sequence and duration sequence are obtained; based on the stationary key pressure difference sequence and duration sequence, the autocorrelation function and partial autocorrelation function are calculated respectively, and the function image characteristics are analyzed; according to the truncation position of the autocorrelation function of the key pressure difference sequence, the moving average order of the key pressure difference is determined; according to the truncation position of the autocorrelation function of the duration sequence, the moving average order of the duration is determined; according to the truncation position of the partial autocorrelation function of the key pressure difference sequence, the autoregressive order of the key pressure difference is determined; according to the truncation position of the partial autocorrelation function of the duration sequence, the autoregressive order of the duration is determined, and the ARIMA model of the key pressure difference and duration is constructed in combination with the difference order; the parameters of the key pressure difference and duration model are optimized by the maximum likelihood estimation method, and it is verified whether the residual sequence conforms to the white noise characteristics. If the residual fails the test, the autoregressive order or moving average order is adjusted and refitted; based on the verified model, the predicted key pressure difference range and predicted duration in the future specified time period are obtained.

[0011] Furthermore, the degree of influence of the air pressure difference on the strength of the sealed corrugated paper is evaluated based on the air pressure fluctuation range and duration, including: establishing a basic mechanical model reflecting the compressive strength and deformation characteristics of the sealed corrugated paper based on the structural parameters of the sealed corrugated paper, and outputting the initial elastic modulus of the model; constructing a basic mechanical model of the sealed corrugated paper based on the structural parameters of the corrugated paper; using the predicted key air pressure difference range as the dynamic load input of the basic mechanical model, calculating the stress distribution cloud map of the sealed corrugated paper under different air pressure differences through finite element analysis, and extracting the maximum stress value; on the basis of the maximum stress value, combined with the predicted duration, fatigue effect correction and creep effect correction are performed to generate a comprehensive strength attenuation model; and calculating the strength loss index of the corrugated paper based on the comprehensive strength attenuation model.

[0012] Furthermore, based on the degree of influence of the air pressure difference on the strength of the sealed corrugated paper and the initial demand strength of the product for the non-sealed corrugated paper, the final demand strength of the sealed corrugated paper is evaluated, including: dividing the standard demand strength by one and the difference between the strength loss index is marked as the final demand strength.

[0013] Furthermore, obtaining the strength index of the corrugated paper to be analyzed includes: obtaining the critical pressure at which the corrugated paper to be analyzed is just not damaged under specified conditions, and marking it as the strength index of the corrugated paper to be analyzed.

[0014] Furthermore, the strength index is compared with the final demand strength, and the analysis results are output, including: establishing an adaptation interval according to the final demand strength; judging whether the strength index of the corrugated paper is within the adaptation interval: if the strength index of the corrugated paper to be analyzed belongs to the adaptation interval, then the output is adapted; if the strength index of the corrugated paper to be analyzed is less than the adaptation interval, then the output is easily damaged; if the strength index of the corrugated paper to be analyzed is greater than the adaptation interval, then the output is too strong.

[0015] Furthermore, the adaptation interval has a lower limit of 1.1 times the final demand intensity and an upper limit of 1.2 times the final demand intensity.

[0016] In a second aspect, the present application provides an intelligent analysis system for corrugated paper packaging data, comprising: a data acquisition module, an air pressure prediction module, a first evaluation module, a second evaluation module, and an output module.

[0017] The data acquisition module is used to obtain the quality of products packaged in corrugated paper, and use image processing technology to evaluate the sharpness of the products; obtain the structural parameters of the corrugated paper, monitor the internal and external air pressure of the corrugated paper during storage or transportation, and record the monitoring time points.

[0018] The air pressure prediction module is used to predict the air pressure fluctuation range and duration within a specified future time period based on the internal and external air pressures and the monitoring time points using the ARI MA model.

[0019] The first evaluation module is used to evaluate the influence of the air pressure difference on the strength of the sealed corrugated paper according to the air pressure fluctuation range and the duration; and to evaluate the initial demand strength of the product for the non-sealed corrugated paper according to the quality and sharpness of the product.

[0020] The second evaluation module is used to evaluate the final demand strength of the sealed corrugated paper according to the influence of the air pressure difference on the strength of the sealed corrugated paper and the initial demand strength of the product for the non-sealed corrugated paper.

[0021] The output module is used to obtain the strength index of the corrugated paper to be analyzed, compare the strength index with the final required strength, and output the analysis results.

[0022] In a third aspect, the present application provides a device comprising a memory and a processor; the memory is used to store a computer program; and the processor is used to implement the steps of the intelligent analysis method for corrugated paper packaging data as described in the first aspect when executing the computer program.

[0023] In a fourth aspect, the present application provides a readable storage medium, which stores computer program instructions. When the computer program instructions are read and executed by a processor, the steps of the intelligent analysis method for corrugated paper packaging data as described in the first aspect are executed.

[0024] Beneficial effects of the present invention:

[0025] The method of the present invention accurately predicts the range and duration of air pressure fluctuations in a specified future time period by utilizing the ARI MA model, while taking into account the direct impact of product quality and sharpness on the strength of packaging materials, and accurately calculates the most suitable final demand strength of sealed corrugated paper by comprehensively evaluating the actual demand of the product for corrugated paper. This effectively solves the problem in the prior art that most products are corrugated paper packages of specified specifications, which are difficult to adjust according to the characteristics of the product itself and changes in air pressure differences. The strength index of the corrugated paper to be analyzed is carefully compared with the final demand strength to determine whether the corrugated paper to be analyzed meets the use requirements, and the corrugated paper packaging can be flexibly adjusted according to the structural characteristics of the product itself and the current or future environment, thereby effectively reducing costs.

[0026] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures pointed out in the description and the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following is a brief introduction to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0028] Figure 1 A schematic diagram showing the flow of the intelligent analysis method for corrugated paper packaging data in the first embodiment of the present invention is shown;

[0029] Figure 2 A schematic diagram of modules of an intelligent analysis system for corrugated paper packaging data in the second embodiment of the present invention is shown. DETAILED DESCRIPTION

[0030] In order to solve the problems raised by the background technology, this application predicts the future range and duration of air pressure fluctuations, evaluates its impact on the strength of sealed corrugated paper, and evaluates the initial demand strength of the product for non-sealed corrugated paper, and finally determines the final demand strength of sealed corrugated paper. The strength index of the corrugated paper to be analyzed is carefully compared with the final demand strength to determine whether the corrugated paper to be analyzed meets the usage requirements.

[0031] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0032] Example 1:

[0033] like Figure 1 As shown, this embodiment provides an intelligent analysis method for corrugated paper packaging data, including:

[0034] S100. Obtain the quality of the product to be packaged with corrugated paper, and use image processing technology to evaluate the sharpness of the product; obtain the structural parameters of the corrugated paper, monitor the internal and external air pressure of the corrugated paper during storage or transportation, and record the monitoring time points.

[0035] S200. Based on the internal and external air pressures and the monitoring time points, use the ARI MA model to predict the range and duration of air pressure fluctuations within a specified future time period.

[0036] S300. Evaluate the degree of influence of the air pressure difference on the strength of the sealed corrugated paper based on the range and duration of the air pressure fluctuation; evaluate the initial demand strength of the product for the non-sealed corrugated paper based on the quality and sharpness of the product.

[0037] S400. Evaluate the final demand strength of the sealed corrugated paper based on the degree of influence of the air pressure difference on the strength of the sealed corrugated paper and the initial demand strength of the product for the non-sealed corrugated paper.

[0038] S500. Obtain the strength index of the corrugated paper to be analyzed, compare the strength index with the final required strength, and output the analysis result.

[0039] The biological activity of some products (such as changes in gas produced by respiration) and seasonal changes can lead to imbalances in the internal and external pressures of sealed corrugated paper packaging, especially in high and low temperature environments. Because sealed corrugated paper has a certain degree of thermal insulation, the temperature difference between its internal and external environment can lead to a large pressure difference between the inside and outside. For example, when moving products to cold storage during hot seasons, the rapid temperature change can cause a significant pressure difference. This large pressure difference puts additional pressure on the corrugated paper, potentially reducing its ability to withstand the pressure.

[0040] By obtaining the structural parameters of corrugated paper, monitoring and recording the changes in internal and external air pressure during storage or transportation in real time, the ARI MA model is used to accurately predict the range and duration of air pressure fluctuations in a specified time period in the future. At the same time, the direct impact of product quality and its sharpness on the strength of packaging materials is considered. By comprehensively evaluating the actual demand of the product for corrugated paper, the most suitable final demand strength of sealed corrugated paper can be accurately calculated, so that the corrugated paper packaging can be flexibly adjusted according to the current or future environment of the product, thereby reducing costs.

[0041] In S100, computer vision combined with image processing technology is used to evaluate the sharpness of the product, including:

[0042] S110. Acquire an image of the product and pre-process the image.

[0043] S120. Use an edge detection algorithm to extract edge information of the preprocessed image, obtain all contours through a contour detection algorithm, and filter out product contours based on preset contour feature standards.

[0044] S130. For each point on the product contour, calculate the curvature within its neighborhood, set a curvature threshold, and regard points with curvature greater than the threshold as sharp points.

[0045] S140. Select sharp points and connect them to form the outline of the sharp part, and use the outline of the sharp part with the maximum curvature as an indicator to quantify the sharpness.

[0046] A digital image of the product is captured using a camera or other image acquisition device. Preprocessing is then performed to reduce noise, uneven lighting, and other effects. Edge detection algorithms, such as the Canny edge detector, are then used to extract edge information from the preprocessed image. After edge detection, contour detection algorithms, such as those based on contour tracking, are applied to capture all contours within the image. Finally, the contours representing the product are selected based on predefined characteristics such as area, perimeter, and shape.

[0047] The sliding window three-point method can be used to calculate the curvature. For the product contour C (coordinate point set {p i =(x i yi )}), if the window size is 5 points, the noise is smoothed by polynomial fitting to improve the curvature stability. For point p i (x i ,y i ), take the neighborhood p i-2 , p i-1 , p i , p i+1 , p i+2 , fitting the quadratic curve y = ax 2 +bx+c, curvature Where x i is the horizontal axis, y i is the vertical axis, a, b, c represent the coefficients of the quadratic polynomial, which are obtained by fitting the 5 neighborhood points by the least squares method. Set the threshold T c , if K i >T c , then mark p i For sharp points.

[0048] After filtering out the sharp points, we get the sharp point set {p s}, can be clustered according to spatial proximity to form a set of sharp contours {S j}, calculate the average curvature K of the contour of each sharp part avgj , K avgj for: And take the largest K avgj As the degree of sharpness.

[0049] In S140, select sharp points for connection, including:

[0050] Traverse all sharp points until the traversal is completed; for the current traversed sharp point: calculate the distance between it and each other sharp point, record all other sharp points whose distance is less than the set radius value, use the recorded sharp points as the set of adjacent sharp points, retain the sharp point with the largest curvature, compare the curvature of the current sharp point with all sharp points in the set of adjacent sharp points, identify the sharp point with the largest curvature, and mark it as an isolated sharp point; connect the isolated sharp points.

[0051] For the sharp point currently being processed, calculate the distance between it and all other sharp points to facilitate finding other sharp points that are spatially close to the current sharp point, that is, neighboring sharp points. By setting a radius value, only points that are less than this radius value from the current sharp point will be considered neighboring sharp points and recorded to form a set of neighboring sharp points. The setting of the radius value is determined according to the needs and scenarios of the actual application, aiming to ensure that the selected neighboring sharp points are neither too sparse nor too dense. Curvature is an important indicator to measure the degree of curvature of a contour or surface. The greater the curvature, the more severe the curvature at that point, that is, the sharper it is. By comparing the curvature of neighboring sharp points, the sharpest point can be identified. Once the sharp point with the largest curvature is identified, we mark it as an isolated sharp point and connect all the points marked as isolated sharp points. Connecting these isolated sharp points can form a complete line or path that can accurately reflect the sharp features of the contour or surface.

[0052] In S300, the initial demand strength of the product for non-sealed corrugated paper is evaluated based on the quality and sharpness of the product, including:

[0053] S310. Obtain the critical pressure at which the corrugated paper does not break under specified conditions.

[0054] S320. Construct the design matrices of mass and sharpness and the column vector matrix of critical pressure.

[0055] S330. Construct normal equations based on the design matrix and column vector matrix, and use NumPy to solve and obtain the coefficients.

[0056] S340. Allocate coefficients to quality and sharpness, and after weighting, obtain the initial demand intensity of the assessed product for non-sealed corrugated paper.

[0057] The critical pressure at which corrugated paper does not break under specified conditions can be obtained through experiments or tests, which can be used to evaluate the performance of corrugated paper.

[0058] If there are n product samples, each product has two independent variables quality x1 and sharpness x2, then the design matrix X is: Among them, x ij represents the jth independent variable of the i-th sample, (i=1,2,…,n; j=1,2); the column vector matrix Y is: Among them, P c,n represents the critical pressure of the i-th sample. The linear regression model can be expressed as: Y = Xβ + ε, where β represents the coefficient vector to be solved and ∈ represents the error term. The normal equation is: X T Xβ=X TY, use NumPy to solve the coefficient vector β, β = [β1, β2], where β1 corresponds to mass and β2 corresponds to sharpness. The initial demand intensity D can be expressed as: D = β1x1 + β2x2. The demand intensity represents the pressure that the corrugated paper needs to withstand when the product of this sharpness is packaged in corrugated paper.

[0059] In S200, based on the internal and external air pressures and the monitoring time points, the ARI MA model is used to predict the range and duration of air pressure fluctuations within a specified time period in the future, including:

[0060] S210. Filter out pressure differences greater than a preset threshold value based on the structural parameters, mark them as critical pressure differences, and determine the duration of the critical pressure differences based on the monitoring time.

[0061] S220. Based on the key air pressure difference values ​​and their durations, arrange them in order of timestamps, associate the key air pressure difference value and duration corresponding to each timestamp, and form a triplet sample set including timestamp, key air pressure difference value, and duration.

[0062] S230. Perform unit root tests on the key pressure difference value series and the duration series respectively. If the test results show that the series are non-stationary, perform a difference operation on the non-stationary series, record the difference order of the key pressure difference value and the difference order of the duration, until the differenced series passes the stationarity test, and obtain the stationary key pressure difference value series and duration series.

[0063] S240. Based on the stabilized key pressure difference sequence and duration sequence, calculate the autocorrelation function and the partial autocorrelation function respectively, and analyze the function image characteristics.

[0064] S250. Determine the moving average order of the key pressure difference value according to the truncation position of the autocorrelation function of the key pressure difference value sequence; determine the moving average order of the duration according to the truncation position of the autocorrelation function of the duration sequence.

[0065] S260. Determine the autoregressive order of the key pressure difference value based on the truncation position of the partial autocorrelation function of the key pressure difference value sequence; determine the autoregressive order of the duration value based on the truncation position of the partial autocorrelation function of the duration value sequence, and construct an ARI MA model of the key pressure difference value and the duration value in combination with the difference order.

[0066] S270. Optimize the parameters of the key pressure difference and duration models through the maximum likelihood estimation method to verify whether the residual sequence conforms to the white noise characteristics. If the residual fails the test, adjust the autoregressive order or moving average order and refit.

[0067] S280. Based on the verified model, obtain the predicted key air pressure difference range and its predicted duration within a specified future time period.

[0068] According to the structural parameters, the air pressure difference greater than the preset threshold is screened out. The critical air pressure difference is the air pressure difference that can have a significant impact on corrugated paper. The threshold is determined based on experimental data, industry standards or historical experience. It is used to distinguish between air pressure differences that have a significant and insignificant impact on corrugated paper. Recording the critical air pressure value data that exceeds the threshold can not only retain the key information that has a significant impact on the performance of corrugated paper, but also significantly reduce the amount of data that needs to be processed, thereby improving the efficiency of data processing.

[0069] The triplet can be expressed as: (t,P d ,D), where t represents the timestamp, P d Represents the key pressure difference, and D represents the duration. Unit root tests (such as ADF test) are performed on the key pressure difference series and the duration series to determine whether the series is stable. If not, perform differential operations until the series is stable. The differential operation formula is: Y t ′ =Y t -Y t-1 , Y t represents the original sequence, Y t ′ represents the differenced sequence.

[0070] The maximum likelihood estimation method is used to optimize the parameters of the ARIMA model. The white noise characteristics of the residual sequence can be verified by statistical tests such as the Ljung-Box test. The Ljung-Box test is: Where Q represents the test statistic, n represents the sample size, and r k represents the sample autocorrelation coefficient of lag k, and h represents the lag order.

[0071] In S300, the influence of the air pressure difference on the strength of the sealed corrugated paper is evaluated based on the air pressure fluctuation range and duration, including:

[0072] S310. Based on the structural parameters of the sealed corrugated paper, a basic mechanical model reflecting its compressive strength and deformation characteristics is established, and the initial elastic modulus of the model is output.

[0073] S320. Construct a basic mechanical model of sealed corrugated paper based on the structural parameters of corrugated paper.

[0074] S330. Use the predicted critical air pressure difference range as the dynamic load input of the basic mechanical model, calculate the stress distribution cloud map of the sealed corrugated paper under different air pressure differences through finite element analysis, and extract the maximum stress value.

[0075] S340. Based on the maximum stress value and combined with the predicted duration, fatigue effect correction and creep effect correction are performed to generate a comprehensive strength attenuation model.

[0076] S350. Calculate the strength loss index of corrugated paper based on the comprehensive strength attenuation model.

[0077] The compressive strength of corrugated paper can be described by classical sheet bending theory or empirical formula. The elastic modulus E0 is calibrated through material testing. Structural parameters may include the number of corrugated layers n, the core paper thickness h, and the corrugated wavelength λ. Combined with the structural parameters, the formula can be expressed as: Among them, k1 and k2 represent the structural influence coefficients fitted by experiments.

[0078] The finite element method is used to solve the static stress distribution, referring to the formula: Ku = F, where K represents the stiffness matrix, u represents the displacement vector, and F represents the load vector. The predicted critical air pressure difference range is used as the dynamic load input, and the stress field of different predicted critical air pressure difference ranges is iteratively calculated to extract the maximum stress value.

[0079] If the predicted duration is expressed as Δt and the fatigue attenuation factor is introduced, the fatigue correction can be expressed as: Among them, k3 and k4 represent the material fatigue coefficient, T r represents the reference time; according to Δt and stress level, the creep attenuation factor is introduced, which can be expressed as: Where γ represents the creep rate coefficient, σ a Represents the maximum stress value; the comprehensive strength attenuation model can be expressed as: α r =α f α c σ0, where σ0 represents the initial compressive strength of the corrugated paper; the strength loss index can be expressed as:

[0080] In S400, the final demand strength of the sealed corrugated paper is evaluated according to the influence of the air pressure difference on the strength of the sealed corrugated paper and the initial demand strength of the product for the non-sealed corrugated paper, including: dividing the standard demand strength by one and the difference between the strength loss index and marking the final demand strength.

[0081] Reference formula: Among them, D ′ Represents the final demand intensity, indicating the pressure that the corrugated paper needs to withstand under the influence of the predicted key air pressure difference range and its predicted duration when choosing corrugated paper packaging for products of this sharpness.

[0082] In S500 , the strength index of the corrugated paper to be analyzed is obtained, including: obtaining the critical pressure at which the corrugated paper to be analyzed is just not damaged under specified conditions, and marking it as the strength index of the corrugated paper to be analyzed.

[0083] For the corrugated paper to be analyzed, the strength index of the corrugated paper to be analyzed is compared with the final required strength to determine whether it can adapt to the specified product. The critical pressure at which the corrugated paper is just not damaged under specified conditions can be obtained through experiments or tests, or calculated using classical thin plate bending theory or empirical formulas.

[0084] In S500, the intensity index is compared with the final demand intensity, and the analysis results are output, including:

[0085] S510. Establish an adaptation range based on the final demand intensity.

[0086] S520. Determine whether the strength index of the corrugated paper is within the adaptation range: if the strength index of the corrugated paper to be analyzed belongs to the adaptation range, output adaptation; if the strength index of the corrugated paper to be analyzed is less than the adaptation range, output easy to break; if the strength index of the corrugated paper to be analyzed is greater than the adaptation range, output excessive strength.

[0087] The strength index of the corrugated paper to be analyzed was carefully compared with the final demand strength to determine whether the corrugated paper to be analyzed meets the usage requirements. First, an adaptation interval was established based on the final demand strength. This interval can be regarded as a safety range, and its minimum value is slightly higher than the final demand to ensure reliability. For example, the adaptation interval has a lower limit of 1.1 times the final demand strength and an upper limit of 1.2 times the final demand strength.

[0088] Determine whether the strength index of the corrugated paper is within the adaptation range. If it is within the range, it means that the strength of the corrugated paper is just right. If it is lower than the range, it means that the strength of the corrugated paper to be analyzed is not enough and it is likely to be damaged during transportation or storage. If it is higher than the range, it means that the strength is too high, which will lead to material waste. You can replace it with corrugated paper with slightly lower strength for packaging.

[0089] Example 2:

[0090] like Figure 2 As shown, this embodiment provides an intelligent analysis system for corrugated paper packaging data, including: a data acquisition module, an air pressure prediction module, a first evaluation module, a second evaluation module, and an output module.

[0091] The data acquisition module is used to obtain the quality of products packaged in corrugated paper, and use image processing technology to evaluate the sharpness of the products; obtain the structural parameters of the corrugated paper, monitor the internal and external air pressure of the corrugated paper during storage or transportation, and record the monitoring time points.

[0092] The air pressure prediction module is used to predict the air pressure fluctuation range and duration within a specified time period in the future based on the internal and external air pressures and monitoring time points using the ARI MA model.

[0093] The first evaluation module is used to evaluate the impact of air pressure difference on the strength of sealed corrugated paper based on the range and duration of air pressure fluctuations; and to evaluate the initial demand strength of the product for non-sealed corrugated paper based on the quality and sharpness of the product.

[0094] The second evaluation module is used to evaluate the final demand strength of the sealed corrugated paper according to the influence of the air pressure difference on the strength of the sealed corrugated paper and the initial demand strength of the product for the non-sealed corrugated paper.

[0095] The output module is used to obtain the strength index of the corrugated paper to be analyzed, compare the strength index with the final required strength, and output the analysis results.

[0096] This embodiment has the advantages of the intelligent analysis method for corrugated paper packaging data in the first embodiment, and can automatically implement the steps of the intelligent analysis method for corrugated paper packaging data.

[0097] Example 3:

[0098] This embodiment provides a device comprising a memory and a processor; the memory is used to store a computer program; the processor is used to implement the steps of the intelligent analysis method for corrugated paper packaging data in the first embodiment when executing the computer program.

[0099] Example 4:

[0100] This embodiment provides a readable storage medium, in which computer program instructions are stored. When the computer program instructions are read and executed by a processor, the steps of the intelligent analysis method for corrugated paper packaging data in the first embodiment are executed.

[0101] Any reference to memory, storage, database, or other media used in the embodiments provided herein may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory.

[0102] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article, or device that includes a series of elements includes not only those elements, but also other elements that are not explicitly listed, or elements that are inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or device that includes the element.

[0103] Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An intelligent analysis method for corrugated paper packaging data, characterized in that: include: Obtain the quality of products used for corrugated packaging and use image processing technology to evaluate the sharpness of the products; Obtain the structural parameters of corrugated paper, monitor the internal and external air pressure of corrugated paper during storage or transportation, and record the monitoring time points; Based on the internal and external air pressures and the monitoring time points, an ARIMA model is used to predict the range and duration of air pressure fluctuations within a specified future time period; Evaluate the influence of the air pressure difference on the strength of the sealed corrugated paper based on the air pressure fluctuation range and the duration; and evaluate the initial demand strength of the product for the non-sealed corrugated paper based on the quality and sharpness of the product; Evaluate the final demand for sealed corrugated paper based on the impact of air pressure difference on the strength of sealed corrugated paper and the initial demand for non-sealed corrugated paper by the product; Obtain the strength index of the corrugated paper to be analyzed, compare the strength index with the final required strength, and output the analysis results; The final demand intensity is the quotient obtained by dividing the standard demand intensity by the difference between one and the intensity loss index; The strength index is the critical pressure at which the device does not break under specified conditions.

2. The method according to claim 1, characterized in that Using computer vision combined with image processing technology, we can evaluate the sharpness of products, including: Acquire an image of the product and pre-process the image; Use edge detection algorithm to extract edge information of pre-processed image, obtain all contours through contour detection algorithm, and filter out product contours according to preset contour feature standards; For each point on the product contour, calculate the curvature in its neighborhood, set a curvature threshold, and regard points with curvature greater than the threshold as sharp points; Sharp points are selected and connected to form the outline of the sharp part, and the outline of the sharp part with the maximum curvature is used as an indicator to quantify the sharpness.

3. The method according to claim 2, characterized in that Select sharp points to connect, including: Traverse all sharp points until the traversal is completed; For the currently traversed sharp point: calculate the distance between it and each other sharp point, record all other sharp points whose distance is less than the set radius value, use the recorded sharp points as the set of neighboring sharp points, retain the sharp point with the largest curvature, compare the curvature of the current sharp point with all sharp points in the set of neighboring sharp points, identify the sharp point with the largest curvature, and mark it as an isolated sharp point; Connect isolated sharp points.

4. The method according to claim 2, characterized in that Evaluate the initial demand strength of the product for non-sealed corrugated paper based on the quality and sharpness of the product, including: Obtain the critical pressure at which corrugated paper does not break under specified conditions; Construct the design matrix of mass and sharpness and the column vector matrix of critical pressure; Constructing normal equations based on the design matrix and the column vector matrix, and solving them using NumPy to obtain coefficients; The coefficients are assigned to quality and sharpness, and after weighting, the initial demand intensity of the assessed product for non-sealed corrugated paper is obtained.

5. The method according to claim 4, characterized in that Based on the internal and external air pressures and the monitoring time points, the ARIMA model is used to predict the range and duration of air pressure fluctuations within a specified future time period, including: According to the structural parameters, the pressure difference values ​​greater than the preset threshold are screened out and marked as critical pressure difference values. The duration of the critical pressure difference values ​​is determined according to the monitoring time. Based on the key pressure difference values ​​and their duration, they are arranged in timestamp order, and the key pressure difference value and duration corresponding to each timestamp are associated to form a triplet sample set containing timestamp, key pressure difference value, and duration; Perform unit root tests on the key pressure difference series and duration series respectively. If the test results show that the series are non-stationary, perform difference operations on the non-stationary series and record the difference orders of the key pressure difference and the duration until the differenced series pass the stationary test, thus obtaining the stationary key pressure difference series and duration series. Based on the stable key pressure difference sequence and duration sequence, the autocorrelation function and partial autocorrelation function are calculated respectively, and the function image characteristics are analyzed; According to the truncation position of the autocorrelation function of the key pressure difference value sequence, the moving average order of the key pressure difference value sequence is determined; according to the truncation position of the autocorrelation function of the duration sequence, the moving average order of the duration is determined; According to the truncation position of the partial autocorrelation function of the key pressure difference series, the autoregressive order of the key pressure difference is determined; according to the truncation position of the partial autocorrelation function of the duration series, the autoregressive order of the duration is determined, and the ARIMA model of the key pressure difference and duration is constructed in combination with the difference order; The parameters of the key pressure difference and duration models are optimized by the maximum likelihood estimation method to verify whether the residual sequence conforms to the white noise characteristics. If the residual fails the test, the autoregressive order or moving average order is adjusted and refitted. Based on the verified model, the predicted critical air pressure difference range and its predicted duration within the specified future time period are obtained.

6. The method according to claim 5, characterized in that Evaluate the impact of air pressure difference on the strength of sealed corrugated paper based on the air pressure fluctuation range and duration, including: Based on the structural parameters of sealed corrugated paper, a basic mechanical model reflecting its compressive strength and deformation characteristics is established, and the initial elastic modulus of the model is output; Constructing a basic mechanical model of sealed corrugated paper according to the structural parameters of the corrugated paper; The predicted critical air pressure difference range is used as the dynamic load input of the basic mechanical model, and the stress distribution cloud diagram of the sealed corrugated paper under different air pressure differences is calculated through finite element analysis to extract the maximum stress value; Based on the maximum stress value, combined with the predicted duration, fatigue effect correction and creep effect correction are performed to generate a comprehensive strength decay model; The strength loss index of corrugated paper is calculated based on the comprehensive strength attenuation model.

7. The method according to claim 6, characterized in that Compare the intensity index with the final demand intensity and output the analysis results, including: Establishing an adaptation interval based on the intensity of the final demand; Determine whether the strength index of corrugated paper is within the adaptation range: If the strength index of the corrugated paper to be analyzed belongs to the adaptation range, the output is adapted; If the strength index of the corrugated paper to be analyzed is less than the adaptation range, the output will be easily damaged; If the strength index of the corrugated paper to be analyzed is greater than the adaptation range, the output intensity is too large.

8. The method according to claim 7, characterized in that The adaptation range has a lower limit of 1.1 times the final demand intensity and an upper limit of 1.2 times the final demand intensity.

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