Paper thickness and surface roughness detection method

Abstract

The invention relates to the technical field of paper detection, and discloses a paper thickness and surface roughness detection method, which comprises the following steps: acquiring thickness characteristic parameters, surface roughness characteristic parameters and environmental parameters of target paper, processing to obtain index parameter data, constructing a dynamic compensation model to obtain predicted deviation parameters, and calculating the thickness and surface roughness of the target paper. And a correction parameter index is obtained through an adaptive optimization algorithm so as to adjust a detection result. Through the combination of the dynamic compensation model and the adaptive optimization algorithm, the influence of various factors such as environment temperature and humidity, paper material characteristics and the like on paper thickness and surface roughness detection results can be comprehensively considered, so that the accuracy and reliability of detection data are improved. The comprehensive detection method abandons the limitation of a traditional single detection means, avoids detection deviation caused by neglecting external and internal factors, has higher adaptability and practicability, and is suitable for large-scale popularization and application.

Description

Technical Field

[0001] This invention relates to the field of paper testing technology, specifically a method for testing paper thickness and surface roughness. Background Technology

[0002] Paper, a widely used material in daily production and life, has thickness and surface roughness as important indicators affecting its performance. Paper thickness determines its strength, flexibility, and applicability, while surface roughness directly affects print quality, writing smoothness, and adhesion to other materials. Therefore, accurate measurement of paper thickness and surface roughness is of great significance in the papermaking industry and related fields.

[0003] Paper thickness measurement is typically achieved using mechanical thickness gauges, which measure the paper thickness by the distance between the probe and a reference surface. Surface roughness measurement, on the other hand, often employs contact or non-contact methods, such as stylus methods or optical interferometry. While these methods can meet certain testing requirements, they still have limitations in practical applications. For example, mechanical thickness gauges may produce inaccurate measurement results due to uneven probe pressure, while contact roughness measurement methods may cause minor damage to the paper surface, affecting subsequent use.

[0004] While some existing detection devices can measure either thickness or surface roughness separately, there is still room for improvement in their overall performance and adaptability. For example, some devices are only suitable for specific types of paper, making it difficult to meet diverse detection needs; others are limited in their widespread application due to their complex structure and cumbersome operation. Therefore, developing a more efficient, accurate, and adaptable method for detecting paper thickness and surface roughness is particularly important. Summary of the Invention

[0005] The purpose of this invention is to provide a method for detecting paper thickness and surface roughness, solving the problem of insufficient data accuracy caused by the failure to comprehensively consider multiple factors during the detection of paper thickness and surface roughness in the prior art. Existing technologies typically employ single mechanical or optical detection methods, neglecting the influence of external and internal factors such as ambient temperature and humidity, and paper material characteristics, thus leading to deviations in the detection results.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] A method for detecting paper thickness and surface roughness includes the following steps:

[0008] The following steps are taken: collecting thickness characteristic parameters of the target paper; collecting surface roughness characteristic parameters of the target paper; collecting environmental parameters affecting paper properties; extracting and processing the collected parameters to obtain the data of each index parameter; constructing a dynamic compensation model; obtaining the prediction deviation parameter Ω within a future time threshold based on the constructed dynamic compensation model; constructing an adaptive optimization algorithm using the prediction deviation parameter Ω within the future time threshold and the data of each index parameter; obtaining the correction parameter index through the adaptive optimization algorithm; and adjusting the thickness and surface roughness detection results of the target paper based on the correction parameter index.

[0009] Preferably, the acquisition of the thickness characteristic parameters specifically includes the thickness distribution uniformity parameter of the target paper, the maximum thickness value of the target paper, and the minimum thickness value of the target paper.

[0010] Preferably, the process of acquiring the surface roughness characteristic parameters includes at least a stylus-type detection unit, an optical interference detection unit, and a pressure feedback unit; the parameter data of the stylus-type detection unit includes the maximum and minimum probe displacement of all stylus-type detection units; the parameter data of the optical interference detection unit includes the maximum and minimum optical path difference of all optical interference detection units.

[0011] Preferably, the collection of environmental parameters includes at least the ambient temperature, ambient humidity, and material density distribution data of the detection area where the target paper is located.

[0012] Preferably, the specific steps for obtaining the various index parameter data in the processing are as follows: extracting the thickness characteristic parameters of the target paper; aggregating the maximum and minimum thickness values ​​of the target paper and adding a relative floating range to obtain a thickness distribution index; extracting the surface roughness characteristic parameters of the target paper; aggregating the maximum and minimum probe displacements of all stylus-type detection units and adding a relative floating range to obtain a surface roughness distribution index; aggregating the maximum and minimum optical path difference values ​​of all optical interference detection units and adding a relative floating range to obtain an optical interference distribution index; extracting the environmental parameters of the target paper; setting several time period thresholds, and extracting environmental temperature, environmental humidity, and material density distribution data using the set time period thresholds; aggregating the environmental parameter data within at least three time period thresholds to obtain environmental impact indicators within each time period threshold.

[0013] Preferably, the dynamic compensation model includes a future environmental parameter acquisition module, a future environmental parameter impact analysis module, and a future environmental parameter impact verification module; the future environmental parameter acquisition module and the future environmental parameter impact analysis module are electrically connected; the future environmental parameter impact analysis module and the future environmental parameter impact verification module are electrically connected. The function of the future environmental parameter acquisition module is to collect parameter data on environmental temperature variation range, environmental humidity variation range, and material density distribution variation within the future time threshold. The function of the future environmental parameter impact analysis module is to analyze the impact of the parameter data collected by the future environmental parameter acquisition module on the thickness and surface roughness of the target paper and obtain the prediction deviation parameter Ω. The function of the future environmental parameter impact verification module is to verify the prediction deviation parameter Ω obtained by the future environmental parameter impact analysis module. The verification condition is to use the parameter data collected by the future environmental parameter acquisition module as a benchmark and then verify it using the previously collected parameter data impact index Ωmax. The verification formula is as follows: Ωmax·C ≤ Ω ≤ Ωmax·D. In the verification formula, C is the minimum lower threshold and D is the maximum upper threshold. If the verification formula is true, the prediction deviation parameter Ω is confirmed; otherwise, the prediction deviation parameter Ω is invalid.

[0014] Preferably, the specific steps for constructing the adaptive optimization algorithm include: firstly, receiving the prediction deviation parameter Ω, the thickness characteristic parameter, surface roughness characteristic parameter, and environmental parameter of the target paper, and substituting them into the adaptive optimization algorithm; extracting the thickness and surface roughness detection results within at least five time thresholds as a benchmark; substituting the extracted thickness and surface roughness detection results into the adaptive optimization algorithm; confirming the required parameter data, substituting it into the adaptive optimization algorithm, and performing iterative optimization; performing data analysis and parameter adjustment through the adaptive optimization algorithm to obtain the correction parameter index; and confirming the reliability of the obtained correction parameter index.

[0015] Preferably, in the verification of the reliability of the correction parameter index, the upper reliability Pw and the lower reliability Pg are confirmed by combining two types of data. The upper reliability Pw is calculated as follows: Pw = NUMp×M + (RSSI1+RSSI2 +......)×N. In the formula, NUMp represents the thickness distribution index of the target paper, RSSI represents the surface roughness distribution index of several stylus-type detection units, M and N are both specified weight values, and the sum of the weight values ​​of M and N is 1. Then, the lower reliability Pg is calculated. The specific lower reliability Pg calculation formula is as follows: Pg = CN1×Q + Ω×S. In the formula, CN1 represents the environmental impact index, Ω represents the prediction deviation parameter, Q and S are both specified weight values, and the sum of the weight values ​​of Q and S is 1. If the verification formulas for the upper reliability Pw and the lower reliability Pg are both valid, then the first reliability Pw is used as the basis for further verification. The second confidence level, Pg, is used to determine the final confidence level, Ep, using the following formula: Ep = Pg / (Pg + Pw).

[0016] Preferably, after obtaining the correction parameter index through the adaptive optimization algorithm, the thickness detection result of the target paper is adjusted according to the obtained correction parameter index, and the surface roughness detection result of the target paper is also adjusted according to the correction parameter index.

[0017] Compared with the prior art, the beneficial effects achieved by the present invention are:

[0018] 1. By combining a dynamic compensation model and an adaptive optimization algorithm, the method comprehensively considers the impact of various factors such as environmental temperature and humidity, and paper material characteristics on the test results of paper thickness and surface roughness, thereby improving the accuracy and reliability of the test data. This comprehensive testing method overcomes the limitations of traditional single testing methods, avoids testing deviations caused by ignoring external and internal factors, and has stronger adaptability and practicality, making it suitable for large-scale application.

[0019] 2. During use, it can effectively predict and correct based on the collected target paper thickness characteristic parameters, surface roughness characteristic parameters, and environmental parameters, combined with the constructed dynamic compensation model, and obtain the prediction deviation parameters. This management method is more predictive, and the prediction deviation parameters have a high degree of objectivity. At the same time, it is verified by combining a large amount of past data, avoiding product quality problems caused by inaccurate test results. Attached Figure Description

[0020] Figure 1 This is a schematic flowchart of the paper thickness and surface roughness detection method provided in an embodiment of the present invention.

[0021] Figure 2 This is a flowchart illustrating the dynamic compensation model in this invention.

[0022] Figure 3 This is a schematic diagram of the operation flow of the adaptive optimization algorithm in this invention;

[0023] Figure 4 This is a schematic diagram of the calculation process for confirming the credibility of the calibration parameter index in this invention. Detailed Implementation

[0024] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0025] This invention provides a method for detecting paper thickness and surface roughness, combined with Figures 1 to 4 The accompanying reference numerals illustrate the specific implementation method in detail. The target paper, as the object of inspection, has its thickness characteristic parameters, surface roughness characteristic parameters, and environmental parameters collected through a stylus-type detection unit, an optical interferometry detection unit, and a pressure feedback unit. These detection units work in conjunction with a dynamic compensation model and an adaptive optimization algorithm to ultimately generate correction parameter indices to adjust the inspection results.

[0026] In practice, the first step is to collect the thickness characteristic parameters of the target paper. This process includes measuring the thickness distribution uniformity parameter, the maximum thickness value, and the minimum thickness value of the target paper. The thickness distribution uniformity parameter is used to assess the overall thickness variation of the target paper, while the maximum and minimum thickness values ​​represent the extreme thickness values ​​of the target paper within specific regions. These data are acquired using a stylus-type detection unit. The probe of the stylus-type detection unit records the displacement as it moves across the surface of the target paper. The maximum and minimum probe displacements are extracted and aggregated into a thickness distribution index. The calculation of the thickness distribution index requires adding a relative fluctuation range to reflect the magnitude of the thickness variation of the target paper.

[0027] Simultaneously, the surface roughness characteristic parameters of the target paper are collected. This process is completed collaboratively by a stylus-type detection unit, an optical interferometry detection unit, and a pressure feedback unit. The stylus-type detection unit records the microscopic undulations of the target paper surface through probe displacement, and its maximum and minimum probe displacements are extracted and aggregated into a surface roughness distribution index. The optical interferometry detection unit performs non-contact detection of the target paper surface using the optical path difference principle, and its maximum and minimum optical path differences are extracted and aggregated into an optical interferometry distribution index. The pressure feedback unit is used to monitor the pressure applied by the stylus-type detection unit during the detection process to ensure the consistency of detection conditions. These data collectively constitute the surface roughness characteristic parameters of the target paper.

[0028] Environmental parameter collection is a crucial component of this method, including ambient temperature, humidity, and density distribution data of the target paper in the detection area. Ambient temperature and humidity are collected in real-time using sensors, while density distribution data is obtained by analyzing the density of different regions of the target paper. To improve data accuracy, several time-period thresholds are set. Ambient temperature, humidity, and density distribution data are extracted using these thresholds, and environmental impact indicators within each time-period threshold are obtained by aggregating environmental parameter data from at least three time-period thresholds.

[0029] The collected data, after processing, enters the dynamic compensation model. The dynamic compensation model includes a future environmental parameter acquisition module, a future environmental parameter impact analysis module, and a future environmental parameter impact verification module. The future environmental parameter acquisition module is responsible for collecting parameter data on environmental temperature variation ranges, environmental humidity variation ranges, and material density distribution variation data within future time thresholds. The future environmental parameter impact analysis module analyzes the impact of each parameter data collected by the future environmental parameter acquisition module on the target paper thickness and surface roughness, and obtains the prediction deviation parameter Ω. The future environmental parameter impact verification module verifies the prediction deviation parameter Ω obtained by the future environmental parameter impact analysis module using the verification formula: Ωmax·C≤Ω≤Ωmax·D, where C is the minimum lower threshold and D is the maximum upper threshold. If the verification formula holds true, the prediction deviation parameter Ω is confirmed; otherwise, the prediction deviation parameter Ω is invalid.

[0030] In constructing the adaptive optimization algorithm, the prediction deviation parameter Ω, along with the thickness, surface roughness, and environmental parameters of the target paper, are first received and substituted into the algorithm. Thickness and surface roughness detection results within at least five time thresholds are extracted as benchmarks, and these results are then substituted into the algorithm. After confirming the required parameter data, these parameters are iteratively optimized using the adaptive optimization algorithm. Data analysis and parameter adjustment are performed through the adaptive optimization algorithm to obtain the correction parameter index. The reliability of the correction parameter index is confirmed by calculating the upper confidence level Pw and the lower confidence level Pg. The formula for calculating the upper confidence level Pw is Pw = NUMp × M + (RSSI1 + RSSI2 + ...) × N, where NUMp is the thickness distribution index of the target paper, RSSI is the surface roughness distribution index of several stylus-type detection units, and M and N are specified weight values ​​that sum to 1. The formula for calculating the lower confidence level Pg is Pg = CN1 × Q + Ω × S, where CN1 is the environmental impact indicator, Ω is the prediction bias parameter, and Q and S are specified weight values ​​that sum to 1. If the verification formulas for both the upper confidence level Pw and the lower confidence level Pg are valid, then the confidence level Ep is finally determined based on the first confidence level Pw and the second confidence level Pg, and its formula is Ep = Pg / (Pg + Pw).

[0031] After obtaining the calibration parameters using an adaptive optimization algorithm, the thickness and surface roughness of the target paper are adjusted based on these parameters. The adjustment process fully considers the thickness uniformity, surface roughness distribution, optical interference distribution, and environmental impact parameters of the target paper, thereby improving the accuracy and reliability of the detection results.

[0032] In practical applications, this method can be widely used in quality control within the papermaking industry. For example, when inspecting the thickness and surface roughness of mass-produced paper at the end of the production line, various parameters of the target paper are collected using a stylus-type detection unit, an optical interferometry detection unit, and a pressure feedback unit. These parameters are then combined with a dynamic compensation model and adaptive optimization algorithm to generate correction parameters, ultimately achieving precise adjustment of the inspection results. This method not only effectively avoids inspection deviations caused by changes in environmental temperature and humidity or differences in paper material properties but also significantly improves inspection efficiency and product quality.

[0033] To enable those skilled in the art to fully understand and implement this invention, the specific implementation principle of this invention will be further explained below in conjunction with a specific application scenario.

[0034] At the end of the paper manufacturing production line, the target paper is placed on a testing platform. A stylus-type detection unit, an optical interferometry detection unit, and a pressure feedback unit work together to detect thickness and surface roughness. First, the stylus-type detection unit moves its probe across the target paper surface, recording its displacement. The maximum and minimum probe displacements are extracted and combined to form a thickness distribution index. Simultaneously, the optical interferometry detection unit uses the principle of optical path difference to perform non-contact detection on the target paper surface, extracting the maximum and minimum optical path differences and combining them into an optical interference distribution index. The pressure feedback unit monitors the pressure applied by the stylus-type detection unit in real time to ensure consistent testing conditions.

[0035] Environmental parameters are collected by sensors deployed in the detection area, including ambient temperature, ambient humidity, and material density distribution data in different areas of the target paper. These data are extracted using set time-period thresholds and combined with environmental parameter data from at least three time periods to generate environmental impact indicators. The collected data is then input into a dynamic compensation model. The future environmental parameter acquisition module is responsible for collecting data on ambient temperature variations, ambient humidity variations, and material density distribution variations within future time thresholds. The future environmental parameter impact analysis module analyzes the impact of the collected parameter data on the thickness and surface roughness of the target paper and generates a prediction deviation parameter Ω. The future environmental parameter impact verification module confirms the effectiveness of the prediction deviation parameter Ω using the formula Ωmax·C≤Ω≤Ωmax·D, where C is the minimum lower threshold and D is the maximum upper threshold.

[0036] When constructing the adaptive optimization algorithm, the prediction deviation parameter Ω, along with the thickness characteristic parameters, surface roughness characteristic parameters, and environmental parameters of the target paper, are substituted into the algorithm. Thickness and surface roughness detection results within at least five time thresholds are extracted as baseline data, and correction parameter indices are obtained through iterative optimization of these data. The reliability of the correction parameter indices is confirmed by calculating the upper reliability Pw and the lower reliability Pg. The formula for calculating the upper reliability Pw is Pw=NUMp×M+(RSSI1+RSSI2+......)×N, where NUMp is the thickness distribution index of the target paper, RSSI is the surface roughness distribution index of several stylus-type detection units, and M and N are specified weight values ​​that sum to 1. The formula for calculating the lower reliability Pg is Pg=CN1×Q+Ω×S, where CN1 is the environmental impact index, Ω is the prediction deviation parameter, and Q and S are specified weight values ​​that sum to 1. If both the upper confidence level Pw and the lower confidence level Pg satisfy the verification formula, then the confidence level Ep is finally determined according to the formula Ep=Pg / (Pg+Pw).

[0037] After obtaining the correction parameters using an adaptive optimization algorithm, the thickness detection results of the target paper are adjusted based on these parameters. For example, when the detection results show that the thickness distribution of the target paper is uneven, the correction parameters will combine thickness distribution uniformity parameters, surface roughness distribution parameters, optical interference distribution parameters, and environmental impact parameters to recalculate and adjust the detection results. Similarly, for surface roughness detection results, the correction parameters will also be adjusted by comprehensively considering the above indicators, thereby improving the accuracy and reliability of the detection results.

[0038] In practical applications, this method effectively avoids detection biases caused by changes in environmental temperature and humidity or differences in paper material properties. For example, in high-temperature and high-humidity environments, paper may expand due to moisture absorption, and traditional detection methods struggle to fully reflect the impact of this change on thickness and surface roughness. This invention, however, uses a dynamic compensation model to collect and analyze future environmental parameters, combined with iterative optimization of an adaptive optimization algorithm, to predict and correct the impact of these changes on the detection results in advance, thereby significantly improving detection efficiency and product quality. This method is not only suitable for detecting various types of paper but can also be widely applied in other fields requiring precise measurement of material thickness and surface properties.

[0039] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for detecting paper thickness and surface roughness, characterized in that: Includes the following steps: S1: Collect the thickness characteristic parameters of the target paper; S2: Collect the surface roughness characteristic parameters of the target paper; S3: Collect environmental parameters that affect the properties of the target paper; S4: Extract the parameter data collected in S1 to S3 and process it to obtain the indicator parameter data; S5: Construct a dynamic compensation model; S6: Obtain the prediction deviation parameter Ω within the future time threshold based on the constructed dynamic compensation model; S7: Construct an adaptive optimization algorithm using the prediction deviation parameter Ω within the future time threshold and the parameter data of each indicator; S8: Obtain the correction parameter index through an adaptive optimization algorithm; S9: Adjust the thickness and surface roughness test results of the target paper by correcting the parameter index.

2. The method for detecting paper thickness and surface roughness according to claim 1, characterized in that: In S1, the acquisition of the thickness characteristic parameters specifically includes the thickness distribution uniformity parameter of the target paper, the maximum thickness value of the target paper, and the minimum thickness value of the target paper.

3. The method for detecting paper thickness and surface roughness according to claim 1, characterized in that: In step S2, the acquisition of the surface roughness characteristic parameters includes at least a stylus-type detection unit, an optical interference detection unit, and a pressure feedback unit; the parameter data of the stylus-type detection unit includes the maximum and minimum probe displacement of all stylus-type detection units; the parameter data of the optical interference detection unit includes the maximum and minimum optical path difference of all optical interference detection units.

4. The method for detecting paper thickness and surface roughness according to claim 1, characterized in that: In step S3, the collection of environmental parameters includes at least the ambient temperature, ambient humidity, and material density distribution data of the detection area where the target paper is located.

5. The method for detecting paper thickness and surface roughness according to claim 1, characterized in that: In step S4, the specific steps for obtaining the data of each indicator parameter are as follows: S4.1: Extract the thickness characteristic parameters of the target paper; S4.2: Combine the maximum and minimum thickness values ​​of the target paper and add a relative floating range to obtain the thickness distribution index; S4.3: Extract the surface roughness characteristic parameters of the target paper; S4.4: Collect the maximum and minimum probe displacements of all stylus-type detection units and add a relative floating range to obtain the surface roughness distribution index. S4.5: Collect the maximum and minimum optical path difference values ​​of all optical interference detection units and add a relative floating range to obtain the optical interference distribution index; S4.6: Extract environmental parameters of the target paper; S4.7: Set several time period thresholds to extract data on ambient temperature, ambient humidity, and material density distribution based on the set time period thresholds; S4.8: By aggregating environmental parameter data within at least three time thresholds, environmental impact indicators within each time threshold are obtained.

6. The method for detecting paper thickness and surface roughness according to claim 1, characterized in that: In S5, the dynamic compensation model includes a future environmental parameter acquisition module, a future environmental parameter impact analysis module, and a future environmental parameter impact verification module; the future environmental parameter acquisition module and the future environmental parameter impact analysis module are electrically connected; the future environmental parameter impact analysis module and the future environmental parameter impact verification module are electrically connected.

7. The method for detecting paper thickness and surface roughness according to claim 6, characterized in that: The function of the future environmental parameter acquisition module is to collect parameter data on environmental temperature change range, environmental humidity change range, and material density distribution change within the future time threshold. The function of the future environmental parameter impact analysis module is to analyze the impact of each parameter data collected by the future environmental parameter acquisition module on the thickness and surface roughness of the target paper, and obtain the prediction deviation parameter Ω. The function of the future environmental parameter impact verification module is to verify the prediction deviation parameter Ω obtained by the future environmental parameter impact analysis module. The verification formula is Ωmax·C ≤ Ω ≤ Ωmax·D, where C is the minimum lower threshold and D is the maximum upper threshold. If the verification formula is true, the prediction deviation parameter Ω is confirmed; otherwise, the prediction deviation parameter Ω is invalid.

8. The method for detecting paper thickness and surface roughness according to claim 1, characterized in that: In step S7, the specific steps for constructing the adaptive optimization algorithm include: S7.1: First, receive the prediction deviation parameter Ω, as well as the thickness characteristic parameter, surface roughness characteristic parameter, and environmental parameter of the target paper, and substitute them into the adaptive optimization algorithm; S7.2: Extract thickness and surface roughness detection results within at least five time thresholds as a benchmark; S7.3: Substitute the extracted thickness and surface roughness detection results into the adaptive optimization algorithm; S7.4: Confirm the required parameter data, substitute it into the adaptive optimization algorithm, and perform iterative optimization; S7.5: Data analysis and parameter adjustment are performed through adaptive optimization algorithms to obtain correction parameter indices; S7.6: Confirm the reliability of the obtained correction parameter indicators.

9. The method for detecting paper thickness and surface roughness according to claim 8, characterized in that: In confirming the reliability of the correction parameter index, two types of data are combined and analyzed to confirm the upper reliability Pw and the lower reliability Pg. The upper reliability Pw is calculated as Pw = NUMp×M + (RSSI1 + RSSI2 +......)×N, where NUMp represents the thickness distribution index of the target paper, RSSI represents the surface roughness distribution index of several stylus-type detection units, and M and N are both specified weight values ​​with a sum of 1. The lower reliability Pg is calculated as Pg = CN1×Q + Ω×S, where CN1 represents the environmental impact index, Ω represents the prediction deviation parameter, and Q and S are both specified weight values ​​with a sum of 1. If the verification formulas for the upper confidence level Pw and the lower confidence level Pg both hold true, then the confidence level Ep is finally determined based on the first confidence level Pw and the second confidence level Pg. The specific formula is Ep = Pg / (Pg + Pw).

10. The method for detecting paper thickness and surface roughness according to claim 1, characterized in that: In step S9, after obtaining the correction parameter index through the adaptive optimization algorithm, the thickness detection result of the target paper is adjusted according to the obtained correction parameter index, and the surface roughness detection result of the target paper is adjusted according to the correction parameter index.

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