Wood component density prediction method and device, storage medium and program product

Abstract

The invention provides a wood component density prediction method and device, a storage medium and a program product. The method comprises the steps that sample wood is determined; respectively carrying out a resistance test and a density test on the sample wood to obtain a resistance value and a density value of the sample wood, and determining a regression equation between the resistance value and the density value of the sample wood; obtaining the decay depth of the to-be-measured wood component; under the condition that the decay depth is smaller than or equal to the upper limit value of the detection depth of the micro-drill resistance meter, the resistance value of the wood component to be detected is obtained; based on the resistance value of the to-be-measured wood component, the density value of the to-be-measured wood component is obtained through the regression equation. According to the method, a large amount of test data is obtained from the sample wood by using the decay information through a specific test method, and an accurate empirical formula is obtained, so that the density value of the wood component can be predicted more accurately, a scientific and reliable basis is provided for performance evaluation of the wood component, unnecessary replacement is reduced, and the cost is reduced. And the integrity of the historical building is protected.

Description

Technical Field

[0001] The present invention relates to the technical field of building engineering, and in particular to a method, device, storage medium and program product for predicting density of a wood component. Background Art

[0002] Due to their age, wooden components in historical buildings often have problems such as decay and decay. These problems will weaken the overall performance of wooden components to a certain extent, including density, strength, elastic modulus, etc. However, it is difficult to fit the degree of weakening of wooden component performance with simple models, so the results obtained by existing estimation methods often deviate from the actual situation. In the maintenance of ancient buildings, the original shape, structure, materials, etc. should be maintained as much as possible to comply with the principle of the original state of cultural relics. For conservative reasons, in order to make the maintenance effect meet the building maintenance standards, wooden components with sufficient performance are often replaced, thereby destroying the historical value of ancient buildings. Therefore, in order to protect the integrity of historical buildings, the maintenance and replacement of historical wooden components needs to be carried out on the basis of obtaining accurate performance data to reduce unnecessary replacements. Summary of the invention

[0003] In view of the shortcomings of the prior art, the present invention provides a method, device, storage medium and program product for predicting the density of wooden components of historical buildings, aiming to provide a non-destructive, scientific and reliable measurement method for the density of wooden components of historical buildings, which can accurately predict the density of wooden components affected by wood decay and provide a basis for the protection and restoration of historical buildings.

[0004] In order to achieve the above purpose and other advantages, the present invention adopts the following technical solutions:

[0005] In a first aspect, the present invention provides a method for predicting density of a wood component, comprising:

[0006] Identify the sample wood;

[0007] Performing a resistance test and a density test on the sample wood respectively to obtain a resistance value and a density value of the sample wood, and determining a regression equation between the resistance value and the density value of the sample wood;

[0008] Obtain the corrosion depth of the wood component to be tested;

[0009] When the decay depth is less than or equal to the upper limit of the detection depth of the micro-drill resistance meter, obtaining the resistance value of the wood component to be tested;

[0010] Based on the resistance value of the wood component to be tested, the density value of the wood component to be tested is obtained through the regression equation.

[0011] According to a method for predicting density of a wood component provided by the present invention, the steps of respectively performing a resistance test and a density test on the sample wood to obtain a resistance value and a density value of the sample wood, and determining a regression equation between the resistance value and the density value of the sample wood include:

[0012] Dividing the sample wood into a plurality of measurement units according to a preset first length unit, measuring the resistance of each measurement unit, and obtaining the resistance value of each measurement unit;

[0013] Performing density measurement on each of the measurement units to obtain a density value of each of the measurement units;

[0014] Based on the resistance value and density value of the measuring unit, a regression equation between the resistance value and the density value of the sample wood is established through a linear regression method.

[0015] According to a method for predicting density of a wood component provided by the present invention, the step of dividing the sample wood into a plurality of measurement units according to a preset first length unit, measuring the resistance of each of the measurement units, and obtaining the resistance value of each of the measurement units comprises:

[0016] Dividing each of the measurement units into a plurality of measurement subunits according to a preset second length unit, wherein the first length unit is an integer multiple of the second length unit;

[0017] A plurality of first measuring points are respectively set on at least one surface of the measuring subunit, a resistance value of each of the first measuring points is obtained by using a micro-drill resistance meter, and an average value of the measured multiple resistance values ​​is taken as the resistance value of the measuring subunit;

[0018] Taking an average value of the resistance values ​​of a plurality of the measuring subunits under the same measuring unit as the resistance value of the measuring unit;

[0019] Accordingly, the step of performing density measurement on each of the measurement units to obtain the density value of each of the measurement units includes:

[0020] cutting each of the measuring units according to the second length unit;

[0021] The density value of each of the measuring subunits is measured, and the density values ​​of a plurality of the measuring subunits under the same measuring unit are averaged to serve as the density value of the measuring unit.

[0022] According to a method for predicting density of a wood component provided by the present invention, the step of establishing a regression equation between the resistance value and the density value of the sample wood by a linear regression method based on the resistance value and the density value of the measuring unit comprises:

[0023] Inputting the resistance values ​​and density values ​​of the plurality of said measuring units into a regression equation generating model;

[0024] In the regression equation generation model, a regression equation between the resistance value and the density value of the sample wood is established, and the degree of fit of the regression equation is obtained, wherein the degree of fit represents the proportion of the independent variable explaining the variation of the dependent variable in the regression equation;

[0025] Compare whether the degree of fit is less than a preset degree of fit threshold. If the degree of fit is less than the degree of fit threshold, it means that the regression equation is not credible, and the number of samples of the sample wood should be increased, or the sample wood should be reselected, and the resistance value and density value of the measuring unit should be remeasured until the regression equation with the degree of fit greater than or equal to the degree of fit threshold is established.

[0026] According to a method for predicting density of a wood component provided by the present invention, the step of obtaining the decay depth of the wood component to be measured comprises:

[0027] Selecting a middle test area of ​​the wood component to be tested, wherein the middle test area is a middle area at a midpoint position in the length direction;

[0028] Selecting a plurality of second measurement points on the adjacent surface of the middle test area, and obtaining a resistance curve of each of the second measurement points;

[0029] Connect the mutation points on the resistance curve using a smooth curve;

[0030] Based on the connected curves, the undamaged parts are filled with shadows to distinguish the damaged and undamaged areas, and a cross-sectional schematic diagram of the damage inside the wood is drawn;

[0031] The deepest depth of the wood component to be tested is obtained according to the cross-sectional schematic diagram to serve as the depth of the wood component to be tested.

[0032] According to a method for predicting density of a wood component provided by the present invention, the step of determining the sample wood comprises:

[0033] The sample wood is selected to be of the same type as the wooden component to be tested. The length of the sample wood should be greater than or equal to a preset length, and the degree of damage to the sample wood should be moderate to deep.

[0034] A method for predicting density of a wood component provided by the present invention further includes:

[0035] Based on the degree of wood infestation of the sample wood, the sample wood is divided into moderately infested and deeply infested;

[0036] For the moderate borer and the deep borer, corresponding regression equations are established respectively;

[0037] Accordingly, while obtaining the corrosion depth of the wood component to be measured, the corrosion degree of the wood component to be measured is also obtained;

[0038] Based on the degree of decay of the wood component to be tested, a corresponding regression equation is selected to obtain the density value of the wood component to be tested.

[0039] In a second aspect, the present invention provides an electronic device, the electronic device comprising:

[0040] One or more processors; and a memory storing computer program instructions, wherein when the computer program instructions are executed, the processor executes the steps of any one of the above-mentioned methods for predicting the density of a wood component.

[0041] In a third aspect, the present invention provides a computer-readable storage medium having a computer program / instruction stored thereon, wherein the computer program / instruction, when executed by a processor, implements the steps of any of the above-described methods for predicting the density of wooden components.

[0042] In a fourth aspect, the present invention provides a computer program product, comprising a computer program / instruction, which, when executed by a processor, implements the steps of any of the above-mentioned methods for predicting the density of wooden components.

[0043] The present invention provides a method, device, storage medium and program product for predicting the density of a wood component. The method comprises the following steps: determining a sample wood; performing a resistance test and a density test on the sample wood respectively to obtain the resistance value and the density value of the sample wood, and determining a regression equation between the resistance value and the density value of the sample wood; obtaining the decay depth of the wood component to be tested; obtaining the resistance value of the wood component to be tested when the decay depth is less than or equal to the upper limit of the detection depth of the micro-drill resistance meter; and obtaining the density value of the wood component to be tested through a regression equation based on the resistance value of the wood component to be tested. The present invention uses the decay information to obtain a large amount of test data using a specific test method for the sample wood, and obtains an accurate empirical formula, so that the density value of the wood component can be predicted more accurately. The present invention provides a new technical means for non-destructive testing of wood components of historical buildings. By predicting the density value of wood components, a scientific and reliable basis is provided for the performance evaluation of wood components, and corresponding data support is provided for the protection and restoration of historical buildings, thereby reducing unnecessary replacements and protecting the integrity of historical buildings. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other implementation methods can be obtained based on these drawings without paying any creative work.

[0045] Figure 1 is a schematic flow chart of a method for predicting density of a wood component provided by an embodiment of the present invention;

[0046] Figure 2 is a schematic diagram of arranging measurement points on the surface of each measurement subunit of a sample wood provided by an embodiment of the present invention;

[0047] Figure 3 is a schematic diagram of arranging measurement points on the surface of a wood component to be measured provided by an embodiment of the present invention;

[0048] Figure 4 is a schematic diagram of drawing the internal decay condition of a wood component to be tested provided by an embodiment of the present invention;

[0049] Figure 5 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0050] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the following specifically cites a preferred embodiment and describes it in detail with the accompanying drawings as follows.

[0051] It should be noted that it is explicitly and implicitly understood by those of ordinary skill in the art that the embodiments described in the present invention can be combined with other embodiments without conflict. Unless otherwise defined, the technical terms or scientific terms involved in the present invention should be the usual meanings understood by people with ordinary skills in the technical field to which the present invention belongs. The words "one", "a", "a", "the" and the like involved in the present invention do not represent quantity restrictions and may represent the singular or plural. The terms "including", "comprising", "having" and any of their variations involved in the present invention are intended to cover non-exclusive inclusions; the terms "first", "second", "third", etc. involved in the present invention are merely to distinguish similar objects and do not represent a specific ordering of objects.

[0052] As a non-destructive testing instrument, the micro-drill resistance meter works by drilling tiny holes inside the wood and measuring the resistance changes during the drilling process to obtain information about the inside of the wood. The instrument can penetrate the entire cross section of the wood, obtain information such as the density and hardness of the wood, and then evaluate the properties of the wood.

[0053] Micro-drill resistance meters are widely used in the study of wood property detection, but due to the diversity of wood types, intact conditions, test methods, etc., the application of resistance meter tests in engineering practice is limited. The applicant has found that based on a large amount of research data, using specific test methods using decay information can improve the accuracy of micro-drill resistance meter prediction of wood density, thereby providing a new set of technical solutions for the actual engineering application of micro-drill resistance meters. Therefore, the embodiments of the present invention provide a method, device, storage medium and program product for predicting the density of wooden components of historical buildings, aiming to provide a non-destructive, scientific and reliable measurement method for the density of wooden components of historical buildings, which can accurately predict the density of wooden components affected by decay, and provide a basis for the protection and restoration of historical buildings.

[0054] Reference Figure 1 As shown, an embodiment of the present invention provides a method for predicting density of a wood component, comprising:

[0055] Step S1: Determine the sample wood.

[0056] In this embodiment, step S1 specifically includes:

[0057] Select sample wood of the same type as the wood component to be tested. The length of the sample wood should be greater than or equal to the preset length, and the degree of wood damage to the sample wood should be moderate to deep.

[0058] First, select the same type of wood as the wood component to be tested from the building or surrounding environment to which the wood component to be tested belongs as the sample wood. Preferably, select the minor part of the wood from the same building (same origin, same age) as the wood specimen; secondly, the same type of wood can also be used as the sample wood after being decayed by artificial experiments.

[0059] In order to meet the test requirements, ensure that the length of the selected sample wood is not less than the preset length (such as 3m). Sufficiently long sample wood can ensure that enough data points can be obtained during the test to establish an accurate prediction model. At the same time, through observation, testing or professional evaluation, determine the degree of wood infestation of the sample wood, and select moderate to deep wood infestation as the test object, which can more effectively reflect the impact of wood infestation on wood performance.

[0060] Step S2: Perform a resistance test and a density test on the wood sample, respectively, to obtain a resistance value and a density value of the wood sample, and determine a regression equation between the resistance value and the density value of the wood sample.

[0061] In this embodiment, step S2 specifically includes:

[0062] Step S201: Divide the sample wood into a plurality of measurement units according to a preset first length unit, measure the resistance of each measurement unit, and obtain the resistance value of each measurement unit.

[0063] Specifically, after the sample wood is selected, the sample wood is divided into a number of measurement units with a length of 75 cm as a unit, and the resistance of each measurement unit is measured to obtain the average resistance value Pr of each measurement unit.

[0064] In this embodiment, step S201 specifically includes:

[0065] Step S2011: Divide each measurement unit into a plurality of measurement subunits according to a preset second length unit, where the first length unit is an integer multiple of the second length unit;

[0066] Step S2012: setting a plurality of first measuring points on at least one surface of the measuring subunit, obtaining a resistance value of each first measuring point using a micro-drill resistance meter, and taking an average value of the measured multiple resistance values ​​as the resistance value of the measuring subunit;

[0067] Step S2013: taking an average value of the resistance values ​​of multiple measuring sub-units under the same measuring unit as the resistance value of the measuring unit;

[0068] Specifically, in this embodiment, the sample wood is divided into a plurality of measurement units by a first length unit, and each measurement unit is divided into a plurality of measurement subunits (segments) by a second length unit, and the first length unit is an integer multiple of the second length unit. For example, if the first length unit is set to 75 cm and the second length unit is set to 25 cm, each measurement unit will be subdivided into three measurement subunits (segment 1, 2, 3). In this embodiment, the values ​​of the first length unit and the second length unit are not specifically limited and can be adjusted according to specific circumstances.

[0069] It should be noted that since the degree and distribution of wood decay may vary in different areas, by subdividing the measurement units, the wood decay situation can be captured more accurately, reducing the difference in decay within each unit, thereby improving the accuracy of the measurement results.

[0070] After determining the measuring subunit according to the second length unit, four measuring points (a, b, c, d) can be set on each surface of each measuring subunit (upper surface, lower surface, front surface and rear surface), and the resistance value of each measuring point can be obtained by using a micro-drill resistance meter. In order to reduce the edge effect and improve the accuracy of the measurement results, the measurement area formed by the measuring points a, b, c, d is usually located at the center of the wood cross section or near the center, such as Figure 2 shown. Figure 2 It is only an example to set four measurement points on a surface, where measurement points a and c are located on the same horizontal line, and the distance between them is 12.5 cm. Measurement points a and b are located on the same vertical line, and the distance between them is one third of the surface height h. The resistance measured by all measurement points on the measurement subunit is averaged, that is, the average resistance value Pr of each 75 cm measurement unit.

[0071] It should be noted that the settings and distances of these measuring points are only examples and should be adjusted according to the specific conditions of the wood specimens and the purpose of measurement during actual measurement.

[0072] Step S202: performing density measurement on each measurement unit to obtain the density value of each measurement unit, specifically including:

[0073] Accordingly, the step of performing density measurement on each measurement unit to obtain the density value of each measurement unit includes:

[0074] Step S2021: cutting each measurement unit according to the second length unit;

[0075] Step S2022: measuring the density value of each measuring subunit, and taking the average value of the density values ​​of multiple measuring subunits under the same measuring unit as the density value of the measuring unit.

[0076] Since the density of wood may vary depending on factors such as location, annual rings, moisture content, etc., by dividing it into smaller measurement sub-units, the influence of these factors can be reduced to improve the accuracy of density measurement. In the density measurement step, each measurement unit (usually a larger wood sample) needs to be cut into multiple smaller measurement sub-units. The basis for cutting is the second length unit, such as 25cm. Its value is not specifically limited and can be adjusted according to the specific situation. However, it must be cut in the second length unit set for resistance measurement to ensure the consistency of the measurement data.

[0077] Since the sample wood is processed into a regular geometric shape (such as a cube or a cuboid) in advance, its size is measured and the volume is calculated using tools such as a micrometer or a vernier caliper. Use a high-precision balance to weigh the processed sample and record the mass of the sample. According to the density formula (density = mass / volume), substitute the measured mass and volume into the formula to calculate the density of the measuring subunit.

[0078] Alternatively, a density meter or other measuring tool may be used to measure the density of each measuring subunit. After all measuring subunits are measured, the density values ​​of multiple measuring subunits under the same measuring unit are averaged to obtain the density value ρ of the measuring unit. Since the density of the wood sample may have local differences, the influence of these differences on the final measurement result can be reduced by calculating the average value. In this embodiment, the density values ​​of the three measuring subunits under the same measuring unit are averaged as the density value of the measuring unit.

[0079] Step S203: Based on the resistance value and density value of the measuring unit, a regression equation between the resistance value and the density value of the sample wood is established by a linear regression method.

[0080] In this embodiment, step S203 specifically includes:

[0081] Step S2031: inputting the resistance values ​​and density values ​​of the plurality of measurement units into a regression equation generation model;

[0082] Step S2032: in the regression equation generation model, a regression equation between the resistance value and the density value of the sample wood is established, and the degree of fit of the regression equation is obtained, where the degree of fit indicates the proportion of the independent variable explaining the variation of the dependent variable in the regression equation;

[0083] Step S2033: Compare whether the degree of fit is less than a preset degree of fit threshold. If the degree of fit is less than the degree of fit threshold, it means that the regression equation is not credible, and the number of sample wood samples should be increased, or the sample wood should be reselected, and the resistance value and density value of the measuring unit should be remeasured until a regression equation with a degree of fit greater than or equal to the degree of fit threshold is established.

[0084] Specifically, the regression equation generation model can be a statistical software specifically used for regression analysis. Linear regression analysis is a statistical method used to study the linear relationship between one or more independent variables (explanatory variables) and a dependent variable (response variable). In the regression equation generation model, the input resistance value and density value data are used for regression analysis. Regression analysis will calculate the correlation coefficient between the independent variable (resistance value Pr) and the dependent variable (density value ρ), and establish a univariate regression equation based on this correlation coefficient. For example, Pr = β × ρ + β 0, where the coefficient β indicates how much the dependent variable is expected to change for every unit change in the independent variable. A positive coefficient indicates a positive correlation, and a negative coefficient indicates a negative correlation. 0 It represents the expected value of the dependent variable when all independent variables are zero.

[0085] At the same time, the model will also give the goodness of fit R2 of the regression equation, that is, the proportion of the independent variable explaining the variation of the dependent variable. By comparing the goodness of fit R2 and the corresponding goodness of fit threshold, it can be determined whether the regression equation meets the requirements. In this embodiment, the goodness of fit threshold of the regression equation is set to 0.6. When the goodness of fit R2 is lower than 0.6, it means that the credibility of the regression equation is low and cannot accurately describe the relationship between the resistance value and the density value. At this time, corresponding decisions should be made to improve the equation, such as increasing the number of samples of the sample wood, or reselecting the sample wood, and re-measuring the resistance value and density value using the above steps. Then, regression analysis is performed again until a regression equation with a goodness of fit greater than or equal to the goodness of fit threshold is established. An accurate regression equation can be better applied to the subsequent estimation of the density value of the wood components to be tested, providing a scientific basis for the performance evaluation and utilization of wood. In practical applications, there is no specific limitation on the goodness of fit threshold. The quality and sample size of the data will directly affect the accuracy of the goodness of fit. If the data quality is high and the sample size is large enough, then the goodness of fit threshold may be set higher, R 2 The closer the value is to 1, the stronger the explanatory power of the model. At the same time, the opinions and suggestions of domain experts can also provide useful guidance for setting the threshold.

[0086] In addition, we can also understand which factors affect the density of wood. For example, according to the type of wood, we can collect relevant data such as the decay rate and generate a multiple regression equation to predict the density value of the wood component.

[0087] Step S3: Obtain the corrosion depth of the wood component to be tested.

[0088] In this embodiment, step S3 specifically includes:

[0089] Step S301: Selecting a middle test area of ​​the wood component to be tested, the middle test area being the middle area at the midpoint position in the length direction;

[0090] Step S302: selecting a plurality of second measurement points on the adjacent surfaces of the middle test area, and obtaining a resistance curve of each second measurement point;

[0091] Step S303: Use a smooth curve to connect the mutation points on the resistance curve;

[0092] Step S304: Based on the connected curves, fill the uncorroded part with a shadow to distinguish the corroded area from the uncorroded area, and draw a cross-sectional schematic diagram of the internal corrosion of the wood;

[0093] Step S305: Obtain the deepest depth of the wood component according to the cross-sectional schematic diagram to serve as the depth of the wood component to be tested.

[0094] It should be noted that in order to ensure the accuracy of the density prediction results, the corrosion depth of the wood component to be tested must be predicted to ensure that the corrosion depth is within the upper limit of the detection depth of the micro-drill resistance meter. Among them, the prediction of the corrosion depth can be achieved by the following two methods:

[0095] Empirical method: By determining the species of bark borers and relying on experts’ experience with their habits, the approximate range of the depth of infestation can be obtained.

[0096] Resistance meter test method: Use a micro-drill resistance meter to select the middle test area for a two-way resistance meter test to obtain a schematic diagram of the affected cross-section, thereby obtaining the maximum corrosion depth.

[0097] Specifically, find the midpoint in the length direction of the wood component to be tested, and use this as the center to delineate a middle section area as the middle section test area. The edge part may have different physical properties due to processing, wear or environmental factors. The middle section area avoids the edge part of the wood component, and this area is less affected by the outside world. It is usually more representative of its overall physical and chemical properties and better reflects the natural state of the wood.

[0098] On the adjacent surfaces of the middle test area, multiple second measurement points are evenly selected. Figure 3 As shown, the upper surface and the front surface are selected as two adjacent surfaces, and two measuring points are set on each surface. The measuring points are located on the same horizontal line, and the distance between the two is one-third of the height h of the surface. These measuring points should cover the entire test area to ensure the comprehensiveness of the measurement. Use professional measuring equipment (such as a micro-drill resistance meter) to measure each second measuring point and obtain its resistance curve. The resistance curve reflects the change in resistance of wood at different depths and is an important basis for judging the internal decay of wood. Figure 4As shown in the figure, the leftmost side of the figure is a photo of the wood component to be tested, and the middle side of the figure is the resistance curve of a certain measuring point. The mutation point on the resistance curve is the deepest point of the corrosion on the measuring line. The four resistance curves are connected with the mutation points by smooth curves. The schematic diagram of the cross-section corrosion situation shown on the right side of the figure is drawn using graphics processing software. The uncorroded part is filled with the shaded part, and the area outside the shaded part is the area with corrosion, which is set as a blank area to clearly distinguish the corroded and uncorroded areas. On the schematic diagram of the cross-section corrosion situation drawn, find the deepest position in the corroded area and measure its depth. This depth value is used as the corrosion depth of the wood component to be tested, and is recorded and analyzed. Before density estimation, by introducing corrosion information to the wood component to be tested, the measurement depth of the micro-drill resistance meter can be determined according to the corrosion depth information, avoiding the distortion of the measurement result caused by the drill bit entering the severely corroded area, thereby improving the accuracy of the micro-drill resistance meter measurement.

[0099] Step S4: when the decay depth is less than or equal to the upper limit of the detection depth of the micro-drill resistance meter, obtaining the resistance value of the wood component to be tested;

[0100] Step S5: Based on the resistance value of the wood component to be tested, the density value of the wood component to be tested is obtained through a regression equation.

[0101] Make sure that the corrosion depth of the wood component to be tested meets the upper limit detection requirements of the micro-drill resistance meter. If the corrosion depth exceeds the upper limit of its detection depth, the instrument may not be able to accurately measure the resistance value inside the wood, thus affecting the accuracy of the subsequent density value prediction.

[0102] During the measurement, a micro-drill resistance meter is used to drill holes at predetermined locations of the wood component to be tested, and the resistance value during the drilling process is recorded. Using a regression equation previously established using a large number of sample wood (such as a univariate regression equation or a multivariate regression equation), the resistance value of the wood component to be tested is substituted into the equation to calculate its density value. Therefore, using a regression equation to predict density values ​​is a non-destructive testing method that does not cause additional damage to the wood. Moreover, the regression equation established using the test data of a large number of sample wood has a high prediction accuracy and can accurately reflect the actual density of the wood.

[0103] In this embodiment, the prediction method further includes:

[0104] Based on the degree of wood infestation, the wood samples are divided into moderately infested and deeply infested.

[0105] For moderate borer damage and deep borer damage, the corresponding regression equations were established respectively;

[0106] Accordingly, the corrosion depth of the wood component to be tested is obtained while also obtaining the degree of corrosion of the wood component to be tested;

[0107] Based on the degree of decay of the wood component to be tested, a corresponding regression equation is selected to obtain the density value of the wood component to be tested.

[0108] It should be noted that the internal structure and properties of wood with different degrees of infestation may be significantly different. Therefore, the use of a unified regression equation may not accurately predict its density value. By dividing the sample wood into two categories, moderately infested and deeply infested, regression equations can be established for wood with different degrees of infestation, thereby improving the accuracy and reliability of the prediction. The standard for the classification of the degree of infestation can be determined according to the actual situation and test requirements. For example, it can be classified according to the proportion of the infestation area to the total cross-sectional area of ​​the wood, the depth of infestation, or the degree of influence of infestation on the mechanical properties of the wood.

[0109] After the classification of the degree of decay is completed, a large number of moderately and deeply decayed wood samples are selected for resistance meter tests and density tests, respectively. Then, linear regression or other statistical methods are used to establish regression equations between the resistance value and density value of the two types of wood. These equations will be used to predict the density of the wood components to be tested with different degrees of decay in the future. Specifically, after obtaining the degree of decay and resistance value of the wood component to be tested, the corresponding regression equation is selected according to the degree of decay. Then, the resistance value is substituted into the selected regression equation to calculate the density value of the wood component to be tested. This further improves the prediction accuracy of the density and enhances the applicability of the regression equation.

[0110] In summary, a method for predicting the density of a wood component provided by an embodiment of the present invention is to determine the sample wood; perform resistance test and density test on the sample wood respectively to obtain the resistance value and density value of the sample wood, and determine the regression equation between the resistance value and density value of the sample wood; obtain the decay depth of the wood component to be tested; obtain the resistance value of the wood component to be tested when the decay depth is less than or equal to the upper limit of the detection depth of the micro-drill resistance meter; and obtain the density value of the wood component to be tested through a regression equation based on the resistance value of the wood component to be tested. The present invention uses the decay information to obtain a large amount of test data using a specific test method for the sample wood, and obtains an accurate empirical formula, so that the density value of the wood component can be predicted more accurately. The present invention provides a new technical means for non-destructive testing of wood components of historical buildings. By predicting the density value of wood components, a scientific and reliable basis is provided for the performance evaluation of wood components, and corresponding data support is provided for the protection and restoration of historical buildings, thereby reducing unnecessary replacements and protecting the integrity of historical buildings.

[0111] Those skilled in the art will appreciate that, in the above method of specific implementation, the order in which the steps are written does not imply a strict execution order and does not constitute any limitation on the implementation process. The specific execution order of the steps should be determined by their functions and possible internal logic.

[0112] In addition, some embodiments of the present application also provide an electronic device. The electronic device may be a digital computer in various forms, such as a laptop computer, a desktop computer, a workbench, a personal digital assistant, a server, a blade server, a mainframe computer, etc. The electronic device may also be a mobile device in various forms, such as a personal digital processing, a cellular phone, a smart phone, a wearable device, and other similar computing devices.

[0113] The electronic device comprises: one or more processors; and a memory storing computer program instructions, wherein when the computer program instructions are executed, the processor executes the steps of the method for predicting the density of a wood component provided in any one or more of the above embodiments. Figure 5 An exemplary structural diagram of the electronic device is disclosed. Figure 5 As shown, the electronic device includes: one or more processors 1101, memory 1102, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. The various components are connected to each other using different buses and can be installed on a common mainboard or installed in other ways as needed. The processor can process instructions executed in the electronic device, including instructions stored in or on the memory to display graphical information of the GUI on an external input / output device (such as a display device coupled to the interface). In some other embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple electronic devices can be connected, and each device provides some necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Among them, the components shown in this article, their connections and relationships, and their functions are only used as examples, and are not intended to limit the implementation of the present application described and / or required herein.

[0114] The electronic device may further include: an input device 1103 and an output device 1104. The processor 1101, the memory 1102, the input device 1103 and the output device 1104 may be connected via a bus or other means. Figure 5 The example of connecting through bus is taken in the following.

[0115] The input device 1103 can receive input digital or character information, and generate key signal input related to the user settings and function control of the electronic device, such as a touch screen, a keypad, a mouse, a track pad, a touch pad, an indicator bar, one or more mouse buttons, a trackball, a joystick and other input devices. The output device 1104 may include a display device, an auxiliary lighting device (e.g., an LED) and a tactile feedback device (e.g., a vibration motor), etc. The display device may include, but is not limited to, a liquid crystal display (LCD), a light emitting diode (LED) display and a plasma display. In some embodiments, the display device may be a touch screen.

[0116] To provide interaction with a user, the electronic device may be a computer. The computer has: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or a trackball), through which the user can provide input to the computer. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0117] In the embodiments of the present application, a computer program / instruction is stored on a computer-readable medium, and when the computer program / instruction is executed by a processor, the steps of the method for predicting the density of a wood component provided in any one or more of the above embodiments are implemented. The computer-readable medium may be included in the electronic device described in the above embodiments; or it may exist independently without being assembled into the device. The above computer-readable medium carries one or more computer-readable instructions.

[0118] The memory 1102 can be used as a non-transient computer-readable storage medium, which can be used to store non-transient software programs, non-transient computer executable programs and modules. The processor 1101 executes various functional applications and data processing of the server by running the non-transient software programs, instructions and modules stored in the memory 1102, so as to implement the program instructions / modules corresponding to the method provided by any one or more embodiments in the embodiments of the present application.

[0119] The memory 1102 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created according to the use of the electronic device, etc. In addition, the memory 1102 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory 1102 may optionally include a memory remotely arranged relative to the processor 1101, and these remote memories may be connected to the electronic device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0120] It should be noted that more specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present application, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, apparatus, or device.

[0121] Computer readable storage media include permanent and non-permanent, removable and non-removable media, and can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, modules of programs or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, read-only compact disk (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices or any other non-transmission medium that can be used to store information that can be accessed by a computing device.

[0122] Computer program code for performing the operations of the present application may be written in one or more programming languages, or a combination thereof, including object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as the "C" language or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0123] In the above-described embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. For example, it can be implemented by using an application specific integrated circuit (ASIC), a general-purpose computer or any other similar hardware device. In certain embodiments, the software program of the present application can be executed by a processor to implement the above steps or functions. Similarly, the software program of the present application (including relevant data structures) can be stored in a computer-readable recording medium, for example, a RAM memory, a magnetic or optical drive or a floppy disk and similar devices. In addition, some steps or functions of the present application can be implemented by hardware, for example, as a circuit that cooperates with a processor to perform each step or function.

[0124] The computer program product provided in the embodiment of the present application includes one or more computer programs / instructions, and when the computer program / instructions are executed by the processor, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions may be stored in a computer-readable storage medium, or transmitted from a computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from a website site, a computer, a server or a data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or a data center that includes one or more available media integrations. The available medium may be a magnetic medium, (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid state disk (SSD)), etc.

[0125] The flow chart or block diagram in the accompanying drawings shows the possible architecture, function and operation of the equipment, method and computer program product according to various embodiments of the present application. In this regard, each box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented with a dedicated system for hardware that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0126] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily mention changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims, and the above embodiments should be regarded as exemplary and non-restrictive.

Claims

1. A method for predicting density of a wood component, characterized in that: include: Identify the sample wood; Performing a resistance test and a density test on the sample wood respectively to obtain a resistance value and a density value of the sample wood, and determining a regression equation between the resistance value and the density value of the sample wood; Obtain the corrosion depth of the wood component to be tested; When the decay depth is less than or equal to the upper limit of the detection depth of the micro-drill resistance meter, obtaining the resistance value of the wood component to be tested; Based on the resistance value of the wood component to be tested, the density value of the wood component to be tested is obtained through the regression equation.

2. The method for predicting the density of a wood component according to claim 1, characterized in that: The steps of respectively performing a resistance test and a density test on the sample wood to obtain a resistance value and a density value of the sample wood, and determining a regression equation between the resistance value and the density value of the sample wood include: Dividing the sample wood into a plurality of measurement units according to a preset first length unit, measuring the resistance of each measurement unit, and obtaining the resistance value of each measurement unit; Performing density measurement on each of the measurement units to obtain a density value of each of the measurement units; Based on the resistance value and density value of the measuring unit, a regression equation between the resistance value and the density value of the sample wood is established through a linear regression method.

3. The method for predicting the density of a wood component according to claim 2, characterized in that: The step of dividing the sample wood into a plurality of measurement units according to a preset first length unit, measuring the resistance of each measurement unit, and obtaining the resistance value of each measurement unit includes: Dividing each of the measurement units into a plurality of measurement subunits according to a preset second length unit, wherein the first length unit is an integer multiple of the second length unit; A plurality of first measuring points are respectively set on at least one surface of the measuring subunit, a resistance value of each of the first measuring points is obtained by using a micro-drill resistance meter, and an average value of the measured multiple resistance values ​​is taken as the resistance value of the measuring subunit; Taking an average value of the resistance values ​​of a plurality of the measuring sub-units under the same measuring unit as the resistance value of the measuring unit; Accordingly, the step of performing density measurement on each of the measurement units to obtain the density value of each of the measurement units includes: cutting each of the measuring units according to the second length unit; The density value of each measuring subunit is measured, and the density values ​​of a plurality of measuring subunits under the same measuring unit are averaged to serve as the density value of the measuring unit.

4. The method for predicting the density of a wood component according to claim 2, characterized in that: The step of establishing a regression equation between the resistance value and the density value of the sample wood by a linear regression method based on the resistance value and the density value of the measuring unit comprises: Inputting the resistance values ​​and density values ​​of the plurality of said measuring units into a regression equation generating model; In the regression equation generation model, a regression equation between the resistance value and the density value of the sample wood is established, and the degree of fit of the regression equation is obtained, wherein the degree of fit represents the proportion of the independent variable explaining the variation of the dependent variable in the regression equation; Compare whether the degree of fit is less than a preset degree of fit threshold. If the degree of fit is less than the degree of fit threshold, it means that the regression equation is not credible, and the number of samples of the sample wood should be increased, or the sample wood should be reselected, and the resistance value and density value of the measuring unit should be remeasured until the regression equation with the degree of fit greater than or equal to the degree of fit threshold is established.

5. The method for predicting density of a wood component according to claim 1, characterized in that: The step of obtaining the corrosion depth of the wood component to be tested includes: Selecting a middle test area of ​​the wood component to be tested, wherein the middle test area is a middle area at a midpoint position in the length direction; Selecting a plurality of second measurement points on the adjacent surface of the middle test area, and obtaining a resistance curve of each of the second measurement points; Using a smooth curve to connect the mutation points on the resistance curve; Based on the connected curves, the undamaged parts are filled with shadows to distinguish the damaged and undamaged areas, and a cross-sectional schematic diagram of the damage inside the wood is drawn; The deepest depth of the wood component to be tested is obtained according to the cross-sectional schematic diagram to serve as the depth of the wood component to be tested.

6. The method for predicting density of a wood component according to claim 1, characterized in that: The step of determining the sample wood comprises: The sample wood is selected to be of the same type as the wooden component to be tested. The length of the sample wood should be greater than or equal to a preset length, and the degree of damage to the sample wood should be moderate to deep.

7. The method for predicting density of a wood component according to claim 1 or 6, characterized in that: Also includes: Based on the degree of wood damage of the sample wood, the sample wood is divided into moderately damaged wood and deeply damaged wood; For the moderate borer and the deep borer, corresponding regression equations are established respectively; Accordingly, while obtaining the corrosion depth of the wood component to be measured, the corrosion degree of the wood component to be measured is also obtained; Based on the degree of decay of the wood component to be tested, a corresponding regression equation is selected to obtain the density value of the wood component to be tested.

8. An electronic device, characterized in that: The electronic device comprises: One or more processors; and a memory storing computer program instructions, wherein when the computer program instructions are executed, the processor executes the steps of the method for predicting the density of a wood member according to any one of claims 1 to 7.

9. A computer-readable storage medium having a computer program / instruction stored thereon, characterized in that: When the computer program / instructions are executed by a processor, the steps of the method for predicting density of a wood component according to any one of claims 1 to 7 are implemented.

10. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instructions are executed by a processor, the steps of the method for predicting density of a wood component according to any one of claims 1 to 7 are implemented.

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