Intelligent matching and optimized discharging system for wood processing

Abstract

The invention discloses an intelligent matching and optimized discharging system for wood processing, and relates to the technical field of wood data processing. The intelligent matching and optimized discharging system for wood processing comprises a data extraction module, a preset wood analysis module and an intelligent matching module. The preset wood data of each kind of preset wood in the preset wood group is analyzed to obtain the corresponding wood matching coefficient, the wood matching coefficients of the preset wood in the preset wood group are compared to obtain the maximum wood matching coefficient, and finally the preset wood corresponding to the maximum wood matching coefficient is recorded as the storage cabinet wood. The effect of improving the wood matching accuracy is achieved, and the problem that in the prior art, the wood matching accuracy is low is solved.

Description

Technical Field

[0001] The invention relates to the technical field of wood data processing, and in particular to an intelligent matching and optimized nesting system for wood processing. Background Art

[0002] With the advancement of technology and the development of industrial automation, the wood processing industry has gradually introduced intelligent matching and optimized nesting systems to deal with the problems of low efficiency and waste of resources in traditional methods. Wood processing refers to the process of converting raw wood into finished or semi-finished products that meet specific use requirements through a series of processing processes. In traditional wood processing, the nesting process is a crucial link. Nesting refers to how to effectively use the original wood boards, minimize waste and improve wood utilization through reasonable cutting or placement methods. Traditional nesting usually relies on experience and manual operations, with poor efficiency and accuracy. The intelligent matching system is a relatively new technology application in industrial production. It is based on computer vision, laser scanning, sensor technology, etc., and uses digital and automated means to achieve accurate matching of raw material characteristics and production needs. In wood processing, the intelligent matching system can automatically select the most suitable processing plan or production needs based on the size, shape, quality and texture characteristics of each wood board. Optimized nesting refers to the optimal cutting or placement of wood boards to maximize the utilization and production efficiency of wood. The problems of traditional nesting methods include a lot of waste and low efficiency, while optimized nesting technology effectively solves these problems through algorithm optimization, improving the economic benefits and environmental friendliness of wood processing. The intelligent matching and optimized nesting system combines intelligent matching technology and optimized nesting technology, which is an important development trend in the modern wood processing industry, realizing the refined management and optimization of the nesting link in the wood processing process.

[0003] In the prior art, each wood board is scanned by using a high-precision laser scanner, and the acquired data is transmitted to a computer system. The system generates a digital model of each wood board by analyzing and processing the data. These models include the three-dimensional shape and surface features of the wood board. Based on the digital model, the system uses an algorithm to extract key features, such as the length, width, thickness and surface texture information of the wood board. Finally, based on the extracted features, the system uses a pattern recognition algorithm to compare the features of each wood board with the preset production requirements, thereby improving the efficiency of production planning and material arrangement.

[0004] For example, the invention patent with announcement number: CN109711611B discloses a wood cutting yield recognition method, device, electronic device and storage medium, including: collecting first data of the wood before the current cutting process through at least one sensor; identifying the first data through a pre-trained first neural network to obtain first state information of the wood and the optimal cutting plan for the current cutting process; controlling the output to execute the current cutting process on the wood according to the optimal cutting plan; collecting second data of the wood after the current cutting process through at least one sensor; identifying the second data through a pre-trained second neural network to obtain second state information of the wood; wherein the second state information includes at least the yield and total yield of the current cutting process.

[0005] For example, the invention patent with announcement number: CN112232588B discloses a wood cutting planning method based on accessory priority, including: according to the requirements of accessories, the accessories are divided into indefinite-length accessories, fixed-length accessories with no position requirements for surface quality, and fixed-length accessories with position requirements for surface quality; according to the preset accessory priority, the accessories to be planned on the wood raw materials are screened out in order from the accessory list; according to the type of the screened accessories, the corresponding planning method is adopted to plan on the wood raw materials according to the wood raw material information; after all the accessories in the accessory list are planned on the raw materials, the cutting plan is output.

[0006] However, in the process of implementing the technical solution of the invention in the embodiments of the present application, the present application found that the above technology has at least the following technical problems: In the prior art, a feature extraction algorithm is used to extract key information from wood features to generate an initial matching solution, and then the matching solution is continuously adjusted for iterative optimization according to a preset objective function and constraints. After multiple iterations, the optimal matching solution is obtained. However, errors may exist in the feature extraction algorithm during the design process, resulting in inaccurate matching results and the problem of low wood matching accuracy. Summary of the invention

[0007] The embodiment of the present application solves the problem of low wood matching accuracy in the prior art by providing an intelligent matching and optimized nesting system for wood processing, thereby improving the accuracy of wood matching.

[0008] An embodiment of the present application provides an intelligent matching and optimized nesting system for wood processing, including a data extraction module, a preset wood analysis module and an intelligent matching module; wherein the data extraction module is used to obtain preset wood data corresponding to each preset wood in a preset wood group; the preset wood analysis module is used to analyze the preset wood data to obtain a corresponding wood matching coefficient, and the wood matching coefficient is used to measure the degree of conformity between the preset wood type and the locker wood; the intelligent matching module is used to compare the wood matching coefficients of the preset wood in the preset wood group to obtain a maximum wood matching coefficient, and at the same time, the preset wood corresponding to the maximum wood matching coefficient is recorded as the locker wood.

[0009] Furthermore, the method for obtaining the wood matching coefficient is as follows: obtaining the wood property weight and the wood moisture-proof weight from a preset database, and calculating the wood matching coefficient in combination with the wood property index and the wood moisture-proof index, wherein the wood property index is used to measure the quality level of the preset wood, and the wood moisture-proof index is used to measure the moisture-proof ability of the preset wood. The wood matching coefficient is specifically calculated using the following formula: ; In the formula, Indicates the type number of the preset wood. , Indicates the total number of preset wood species. Indicates The wood matching coefficient of the preset wood, Indicates Wood property index for a preset wood type, Indicates The wood moisture resistance index of the preset wood, represents the wood property weight, Indicates the moisture resistance weight of wood.

[0010] Furthermore, the specific method for obtaining the wood property index is as follows: the formaldehyde emission of the preset wood is obtained by measuring the formaldehyde concentration in the wood sample extract corresponding to each preset wood in the preset wood group, and the formaldehyde emission is used to measure the formaldehyde content level of the preset wood; the corresponding wood density value is obtained by measuring the volume and weight of the wood sample, and the wood density value is used to measure the density level of the preset wood; the compressive strength value of the preset wood is obtained by applying pressure to the wood sample at a preset rate using a universal testing machine, and the compressive strength value is used to measure the compressive resistance level of the preset wood; the formaldehyde emission reference value, the wood density reference value and the compressive strength reference value are obtained from a preset database, and it is determined whether the formaldehyde emission reference value is less than the formaldehyde emission of the preset wood, and whether the wood density reference value and the compressive strength reference value are both greater than the corresponding wood density value and compressive strength value. If so, the corresponding wood property index is set to the preset wood property index, otherwise, the corresponding wood property index is obtained by combining the formaldehyde emission, the wood density value and the compressive strength value.

[0011] Furthermore, the wood property index is specifically calculated using the following formula: ; In the formula, Indicates the type number of the preset wood. , Indicates the total number of preset wood species. Indicates Wood property index for a preset wood type, represents a natural constant, Indicates Formaldehyde emission of the preset wood, Indicates The wood density value of the preset wood type, Indicates Preset compressive strength values ​​for wood, Indicates The formaldehyde emission reference value of the preset wood is Indicates The wood density reference value of the preset wood type, Indicates A reference value for the compressive strength of a preset wood.

[0012] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. By analyzing the preset wood data of each preset wood in the preset wood group to obtain the corresponding wood matching coefficient, and comparing the wood matching coefficients of the preset wood in the preset wood group to obtain the maximum wood matching coefficient, finally the preset wood corresponding to the maximum wood matching coefficient is recorded as the locker wood, thereby achieving the improvement of wood matching accuracy, and effectively solving the problem of low wood matching accuracy in the prior art.

[0013] 2. The formaldehyde release of the preset wood is obtained by measuring the formaldehyde concentration in the wood sample extract corresponding to each preset wood in the preset wood group, and then the corresponding wood density value is obtained by measuring the volume and weight of the wood sample. Then, the compressive strength value of the preset wood is obtained by applying pressure to the wood sample at a preset rate through a universal testing machine. Finally, the corresponding wood characteristic index is obtained according to the relationship between the obtained formaldehyde release reference value, wood density reference value and compressive strength reference value and the corresponding formaldehyde release amount, wood density value and compressive strength value, thereby realizing the numerical evaluation of wood characteristics, and then realizing a more accurate evaluation of wood characteristics.

[0014] 3. Measure the resistance of the wood sample corresponding to each preset wood in the preset wood group through a wood resistance meter, and obtain the initial moisture content of the wood sample corresponding to the preset wood in combination with the constructed preset water-electricity standard curve. Finally, determine whether the obtained initial moisture reference value is greater than the corresponding initial moisture content. If so, set the corresponding wood moisture resistance index to the preset wood moisture resistance index. Otherwise, obtain the corresponding wood moisture resistance index in combination with the wood density value and the initial moisture content, thereby realizing the numerical evaluation of the moisture resistance of the wood, and then realizing a more accurate evaluation of the moisture resistance of the wood. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 A schematic diagram of the structure of an intelligent matching and optimized nesting system for wood processing provided in an embodiment of the present application; Figure 2 A statistical chart showing changes in the wood moisture resistance index provided in the embodiments of the present application; Figure 3 A flow chart of a method for constructing a preset hydropower standard curve provided in an embodiment of the present application. DETAILED DESCRIPTION

[0016] The embodiment of the present application solves the problem of low wood matching accuracy in the prior art by providing an intelligent matching and optimized nesting system for wood processing. The system obtains preset wood data corresponding to each preset wood in a preset wood group, analyzes the preset wood data to obtain a corresponding wood matching coefficient, compares the wood matching coefficients of the preset wood in the preset wood group to obtain a maximum wood matching coefficient, and finally records the preset wood corresponding to the maximum wood matching coefficient as locker wood, thereby improving the accuracy of wood matching.

[0017] The technical solution in the embodiment of the present application is to solve the problem of low wood matching accuracy, and the overall idea is as follows: The obtained preset wood data is analyzed to obtain the corresponding wood matching coefficient, and then the locker wood is obtained by comparing the wood matching coefficients of the preset wood in the preset wood group, thereby achieving the effect of improving the accuracy of wood matching.

[0018] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0019] like Figure 1 As shown, it is a structural schematic diagram of an intelligent matching and optimized nesting system for wood processing provided by an embodiment of the present application. The embodiment of the present application provides an intelligent matching and optimized nesting system for wood processing, including a data extraction module, a preset wood analysis module and an intelligent matching module; wherein the data extraction module is used to obtain preset wood data corresponding to each preset wood in a preset wood group, the preset wood group includes all preset woods, and the preset wood data includes size information and quantity information of each preset wood; the preset wood analysis module is used to analyze the preset wood data to obtain a corresponding wood matching coefficient, the wood matching coefficient is used to measure the degree of conformity between the preset wood type and the locker wood, and the locker wood is used for wood processing; the intelligent matching module is used to compare the wood matching coefficients of the preset wood in the preset wood group to obtain the maximum wood matching coefficient, and at the same time, the preset wood corresponding to the maximum wood matching coefficient is recorded as the locker wood.

[0020] In this embodiment, the intelligent matching and optimized nesting system not only improves the production efficiency and quality of wood processing, but also helps to reduce production costs and resource waste. It is an important technical support for the wood processing industry to move towards intelligence and sustainable development. The wood processing intelligent matching and optimized nesting system can significantly improve the production efficiency and resource utilization of wood processing, reduce production costs and environmental pollution, and bring greater economic and social benefits to the enterprise; it realizes the improvement of wood processing efficiency.

[0021] Furthermore, the specific steps for obtaining the preset wood data are as follows: obtaining the size information of the preset wood through a laser ranging sensor, the size information including length, width and height, the laser ranging sensor being installed on the production line; counting the number of scanned wood barcodes to obtain quantity information, the number of wood barcodes being obtained by scanning the barcode affixed to the preset wood with a barcode scanner installed on the production line; integrating the size information and the quantity information to obtain the preset wood data, the data integration being used to comprehensively analyze the preset wood, the data integration being realized through a preset database table, the database table being a basic structural unit in the database for organizing and storing the preset wood data.

[0022] In this embodiment, in order to integrate the preset wood data, the preset database table can be divided into three columns, the wood type column, the wood size column and the inventory quantity column. The wood type column lists all available wood types, such as oak, pine, etc., and each type has a unique number and name; the wood size column records the different sizes of each wood, such as width, height and length, and each size is related to a specific wood type; the inventory quantity column records the current inventory quantity of each size of wood. Each size corresponds to a specific inventory quantity; once the data is integrated, various data analysis tools and techniques can be used to comprehensively analyze the preset wood data; at the same time, the database needs to be regularly maintained and updated to ensure the accuracy and integrity of the data, including deleting duplicate or invalid records, ensuring that the newly entered data complies with business rules and logic, regularly checking and optimizing query performance, ensuring that the database can respond to requests quickly, and implementing appropriate security measures, such as data encryption and access control, to protect sensitive data; improving the data integration of size information and quantity information, and realizing the improvement of the integrity of the preset wood data.

[0023] Furthermore, the method for obtaining the wood matching coefficient is as follows: obtain the wood property weight and the wood moisture resistance weight from the preset database, and calculate the wood matching coefficient in combination with the wood property index and the wood moisture resistance index. The wood property weight is used to measure the influence of the wood property index on the wood matching coefficient. The wood moisture resistance weight is used to measure the influence of the wood moisture resistance index on the wood matching coefficient. The wood property index is used to measure the quality level of the preset wood. The wood moisture resistance index is used to measure the moisture resistance of the preset wood. The wood matching coefficient is specifically calculated using the following formula: ; In the formula, Indicates the type number of the preset wood. , Indicates the total number of preset wood species. Indicates The wood matching coefficient of the preset wood, Indicates Wood property index for a preset wood type, Indicates The wood moisture resistance index of the preset wood, represents the wood property weight, Indicates the moisture resistance weight of wood.

[0024] In this embodiment, the algorithm of this embodiment combines the wood characteristic coefficient and the wood moisture resistance coefficient, and comprehensively analyzes to obtain the wood matching coefficient. In this formula, the wood characteristic coefficient and the wood moisture resistance coefficient influence each other. The wood moisture resistance coefficient measures the ability of wood to resist the influence of moisture and water, including its hygroscopicity and resistance to swelling. Wood with a higher wood characteristic coefficient usually has better structural stability and durability, making it less likely to deform or damage after being damp, thereby indirectly improving the moisture resistance, indicating the degree of conformity between the preset wood type and the wood of the locker. Therefore, by establishing a mathematical form, precise analysis can be performed.

[0025] Specifically, the wood property weights are obtained from a preset database. In a specific embodiment, a mapping set of corresponding weights is constructed based on the relationship between historical compressive strength, wood density, formaldehyde emission and wood matching coefficient, and the real-time compressive strength, wood density and formaldehyde emission are input into the constructed mapping set to obtain the corresponding wood property weights.

[0026] Specifically, the wood moisture-proof weight is obtained from a preset database. In a specific embodiment, a mapping set of corresponding weights is constructed based on the relationship between historical moisture content and wood matching coefficient, and the real-time moisture content is input into the constructed mapping set to obtain the corresponding wood moisture-proof weight.

[0027] To facilitate analysis, we define , is the wood property matching coefficient, defined , is the wood moisture-proof matching coefficient, and the calculation formula of the wood matching coefficient is , the statistical table of wood matching coefficient changes is shown in Table 1: Table 1 Statistics of changes in wood matching coefficient

[0028] Among them, the wood property matching coefficient is positively correlated with the wood matching coefficient, and the wood moisture-proof matching coefficient is positively correlated with the wood matching coefficient; a good wood property matching coefficient means that the preset wood and the locker wood are relatively consistent in all aspects of characteristics, and a good wood moisture-proof coefficient can ensure the stability of the wood in different humidity environments. By combining the wood property coefficient and the wood moisture-proof coefficient to obtain the wood matching coefficient, the locker wood suitable for making lockers in the preset wood can be screened out, thereby ensuring the quality, stability and durability of the final locker product; thereby achieving a comprehensive evaluation of the wood matching coefficient, and effectively solving the problem of low accuracy in the analysis of the degree of conformity between the preset wood types and the locker wood.

[0029] Furthermore, a specific method for obtaining the wood characteristic index is as follows: the formaldehyde emission of the preset wood is obtained by measuring the formaldehyde concentration in the wood sample extract corresponding to each preset wood in the preset wood group. The formaldehyde emission is used to measure the formaldehyde content level of the preset wood. The formaldehyde emission is used to measure the formaldehyde emission level of the preset wood. The wood sample extract is used to extract the wood sample by toluene solvent. The wood sample is a standard cube processed using the preset wood. The standard cube is generally a cube of 20×20×20 mm; the corresponding wood density value is obtained by measuring the volume and weight of the wood sample. The wood density value is used to measure the density level of the preset wood; the compressive strength value of the preset wood is obtained by applying pressure to the wood sample at a preset rate using a universal testing machine. The compressive strength value is used to measure the compressive resistance level of the preset wood. The compressive strength value is the maximum pressure value when the wood sample is destroyed. The preset rate is generally 3 mm / min; obtain the formaldehyde emission reference value, wood density reference value and compressive strength reference value from the preset database, and judge whether the formaldehyde emission reference value is less than the formaldehyde emission of the preset wood, and whether the wood density reference value and the compressive strength reference value are both greater than the corresponding wood density value and compressive strength value; if so, set the corresponding wood property index to the preset wood property index; otherwise, obtain the corresponding wood property index in combination with the formaldehyde emission, wood density value and compressive strength value.

[0030] In this embodiment, the preset wood property index is generally set to 0; the wood sample extract is used to extract the wood sample through toluene solvent. Specifically, the wood sample placed in boiling toluene solvent is first boiled to separate free formaldehyde from the board and dissolve it in the toluene solvent. Then, a perforator is used to perform liquid-liquid extraction between the solvent containing formaldehyde and water to dissolve the formaldehyde in water. Then, the formaldehyde aqueous solution is quantitatively analyzed by iodine titration. More accurate acquisition of formaldehyde release is achieved.

[0031] Furthermore, the wood property index is calculated using the following formula: ; In the formula, Indicates the type number of the preset wood. , Indicates the total number of preset wood species. Indicates Wood property index for a preset wood type, represents a natural constant, Indicates Formaldehyde emission of the preset wood, Indicates The wood density value of the preset wood type, Indicates Preset compressive strength values ​​for wood, Indicates The formaldehyde emission reference value of the preset wood is Indicates The wood density reference value of the preset wood type, Indicates A reference value for the compressive strength of a preset wood.

[0032] In this embodiment, the algorithm of this embodiment combines formaldehyde emission, wood density value and compressive strength value, and comprehensively analyzes to obtain the wood property index. In this formula, the wood density value and the compressive strength value affect each other. Generally, a higher wood density will bring higher compressive strength. Generally speaking, there is a positive correlation between the density and compressive strength of wood. Wood with higher density usually has higher compressive strength. This is because wood with higher density contains more lignin and cellulose, which are helpful to improve the compressive strength of wood. Therefore, by establishing a mathematical form, accurate analysis is performed; wherein the formaldehyde emission is negatively correlated with the wood property index, the wood density value is positively correlated with the wood property index, and the compressive strength value is positively correlated with the wood property index; wood with low formaldehyde emission, high density and high compressive strength will have a higher property index, indicating that its environmental performance, physical properties and structural safety are relatively superior. Such wood is more suitable for high-end furniture manufacturing, building decoration and special engineering fields, and can meet higher use requirements and standards, thereby realizing a comprehensive evaluation of the wood property index and effectively solving the problem of low accuracy in the quality level analysis of preset wood.

[0033] Furthermore, a specific method for obtaining the wood moisture resistance index is as follows: using a wood resistance meter to measure the resistance of a wood sample corresponding to each preset wood in a preset wood group, and combining the constructed preset water-electricity standard curve to obtain the initial moisture content of the wood sample corresponding to the preset wood, the initial moisture content is used to measure the moisture content of the preset wood before processing, the preset water-electricity standard curve is a curve drawn based on the resistance value of a known sample, and the preset water-electricity standard curve is used to measure the relationship between the moisture content and resistivity of the wood sample; obtaining an initial moisture reference value from a preset database, and determining whether the initial moisture reference value is less than the corresponding initial moisture content and whether the wood density reference value is greater than the wood density value, if so, setting the corresponding wood moisture resistance index to the preset wood moisture resistance index, otherwise obtaining the corresponding wood moisture resistance index in combination with the wood density value and the initial moisture content.

[0034] In this embodiment, the wood moisture resistance index is specifically calculated using the following formula: ; In the formula, Indicates the type number of the preset wood. , Indicates the total number of preset wood species. Indicates The wood moisture resistance index of the preset wood, represents a natural constant, Indicates The wood density value of the preset wood type, Indicates The initial moisture content of the wood is preset. Indicates The wood density reference value of the preset wood type, Indicates The initial moisture reference value of the preset wood; the algorithm of this embodiment combines the wood density value and the initial moisture content, and comprehensively analyzes to obtain the wood moisture resistance index. In this formula, the wood density value and the initial moisture content influence each other. High-density wood can usually absorb and retain more moisture, showing a higher initial moisture content, which indicates the moisture resistance of the preset wood. Therefore, by establishing a mathematical form, accurate analysis is performed.

[0035] To facilitate analysis, we define , is the moisture coefficient of wood, defined , is the wood density coefficient, then The calculation formula for the wood moisture resistance index of the preset wood is ,like Figure 2 As shown, it is a statistical graph of the changes in the moisture resistance index of wood provided in the example of the present application. It can be seen from the graph that the moisture coefficient of wood is positively correlated with the moisture resistance index of wood, and the density coefficient of wood is positively correlated with the moisture resistance index of wood; high-density wood usually has a smaller porosity and a tighter structure. This kind of wood has a lower permeability to moisture. Therefore, under the same environmental conditions, it has a stronger moisture resistance. High-density wood can better resist the invasion of moisture and water, thereby reducing the risk of expansion and deformation of wood; if high initial moisture content wood has a high initial moisture content during processing or use, its moisture resistance will be affected. High moisture content wood is more likely to absorb and release moisture when the ambient humidity changes, which may lead to dimensional changes and instability of physical properties of the wood; the preset wood moisture resistance index is generally 0; the wood moisture resistance index is obtained by combining the wood density value and initial moisture content of the preset wood, thereby achieving a comprehensive evaluation of the wood moisture resistance index, and effectively solving the problem of low accuracy in moisture resistance analysis of the preset wood.

[0036] Furthermore, the specific method for obtaining the initial moisture content is as follows: using a wood resistance meter to measure the wood resistance value of the wood sample, the wood resistance value is used to measure the resistance level of the preset wood sample; using the constructed preset water-electricity standard curve to match the wood resistance value of the wood sample to obtain the corresponding initial moisture content.

[0037] In this embodiment, the resistance value of the wood is inversely proportional to the preset initial moisture content of the wood, that is, the higher the moisture content, the lower the resistance value of the wood; wherein the corresponding initial moisture content can be obtained by digitally storing the preset water and electricity standard curve in the preset water and electricity database, and the query function of the water and electricity database can be used to directly input the resistance value of the wood to obtain the corresponding moisture content, wherein the digitization is achieved by automatically extracting the curve data through image recognition technology; thereby improving the accuracy of the initial moisture content.

[0038] Further, such as Figure 3 As shown, it is a flow chart of the method for constructing the preset water-electricity standard curve provided in the embodiment of the present application. The specific method for constructing the preset water-electricity standard curve is as follows: a preset experimental environment is built by a constant temperature and humidity device, and known samples are randomly divided into drawing samples and verification samples according to a preset ratio. The known samples are wood samples with known moisture content. The known samples are used to weigh the resistivity corresponding to wood samples with different moisture contents. The preset ratio is generally ; Use a wood resistance meter to measure the wood resistance values ​​of different drawing samples in a preset experimental environment during a preset time period, and obtain a preset water-electricity standard curve based on the known moisture content and wood resistance value of the drawing samples.

[0039] In this embodiment, the horizontal axis and vertical axis of the preset water-electricity standard curve are the known moisture content and wood resistance value of the drawing sample, respectively. It is necessary to perform correlation analysis between the wood resistance value of each sample and its corresponding moisture content. The wood resistance value and the corresponding moisture content are input into a spreadsheet software to perform regression analysis and fitting to obtain the water-electricity standard curve. The water-electricity standard curve can reflect the changing trend of the wood resistance value with the moisture content. The preset water-electricity standard curve is used to predict the moisture content of the verification sample, and the predicted value is compared with the known moisture content of the verification sample to obtain the deviation value. The deviation value is used to measure the drawing accuracy of the preset water-electricity standard curve. More accurate drawing of the water-electricity standard curve is achieved.

[0040] Furthermore, the preset wood corresponding to the maximum wood matching coefficient is recorded as the locker wood, and then the speed evaluation value is obtained to adjust the cutting speed. The speed evaluation value is used to measure the cutting speed level of the locker wood. The speed evaluation value is the maximum speed that the cutting tool can withstand. The specific method for obtaining the speed evaluation value is as follows: the cutting speed value is obtained according to the rotation speed and diameter of the cutting tool. The cutting speed value is used to measure the cutting speed level of the preset wood intelligent matching nesting system; the tool sharpness value is obtained by measuring the cutting edge angle of the cutting tool. The tool sharpness value is used to measure the sharpness of the cutting tool of the preset wood intelligent matching nesting system. The cutting angle is the angle between the cutting surface and the back surface of the cutting edge of the cutting tool; the tool hardness value is obtained by applying a preset pressure to the cutting tool and measuring the size of the impression formed by the cutting tool, and the tool hardness value is used to measure the hardness level of the preset tool; the cutting speed reference value, the tool sharpness reference value and the tool hardness reference value are obtained from the preset database, and it is determined whether the cutting speed reference value, the tool sharpness reference value and the tool hardness reference value are all greater than the corresponding cutting speed value, the tool sharpness value and the tool hardness value. If so, the corresponding speed evaluation value is set to the preset speed evaluation value, otherwise the speed evaluation value is obtained by combining the cutting speed value, the tool sharpness value and the tool hardness value.

[0041] In this embodiment, the speed evaluation value is specifically calculated using the following formula: ; In the formula, represents the speed evaluation value, Indicates the cutting speed value, Indicates the tool sharpness value, Indicates the tool hardness value, Indicates the reference value of cutting speed, Indicates the reference value of tool sharpness. Represents the reference value of tool hardness; the algorithm of this embodiment combines the cutting speed value, the tool sharpness value and the tool hardness value, and comprehensively analyzes to obtain the speed evaluation value. In this formula, the cutting speed value and the tool sharpness value affect each other, and the tool sharpness value directly affects the cutting efficiency. A sharp tool can cut into the wood more smoothly, reduce cutting resistance, and thus allow the use of a higher cutting speed value. On the contrary, a blunt tool will encounter greater resistance during the cutting process, which will not only reduce the cutting efficiency, but may also force a reduction in the cutting speed to avoid excessive wear or damage to the tool, indicating the cutting speed level of the locker wood. Therefore, by establishing a mathematical form, a precise analysis is performed; wherein the cutting speed value is positively correlated with the speed evaluation value, the tool sharpness value is positively correlated with the speed evaluation value, and the tool hardness value is positively correlated with the speed evaluation value; wherein the preset speed evaluation value is generally 0; by obtaining the speed evaluation value to adjust the cutting speed, the cutting accuracy of the locker wood is improved.

[0042] Furthermore, the specific process of cutting speed adjustment is: determine whether the cutting speed is less than the speed evaluation value, if so, continue cutting, otherwise reduce the cutting speed parameter until the cutting speed parameter is not greater than the speed evaluation value, and the cutting speed parameter is used to adjust the cutting speed of the cutting tool.

[0043] In this embodiment, the speed evaluation value is the maximum speed that the cutting tool can withstand. By controlling the cutting speed within the speed evaluation value, it can be ensured that the cutting speed remains within a safe or desired range, thereby avoiding problems such as tool wear, reduced processing quality or machine overload caused by excessive cutting; achieving improved stability and efficiency of the cutting process.

[0044] It should be understood that the cutting speed parameters generally include spindle speed, feed speed, cutting depth and cutting angle. Specifically, spindle speed is the most directly related parameter, which indicates the number of rotations per minute of the cutting tool (such as saw blades, cutters, etc.). The high or low spindle speed directly affects the frequency of contact between the cutting edge and the wood, and thus affects the cutting speed and efficiency; feed speed refers to the speed at which the cutting tool moves on the wood, usually measured in terms of the distance moved per unit time, such as millimeters per minute (mm / min) or inches per minute (in / min). The speed of the feed speed directly affects the cutting depth, cutting force and surface quality of the processed material; cutting depth refers to the depth of the cutting tool's cut into the wood each time, and this parameter also affects the cutting speed and processing quality; although the cutting angle does not directly determine the cutting speed, it has an important influence on the cutting effect and cutting speed. Different cutting angles will lead to changes in cutting force, which in turn affects cutting stability and speed.

[0045] In summary, the embodiment of the present application obtains corresponding preset wood data through the characteristic information of each preset wood in the preset wood group, and then analyzes the preset wood data to obtain the corresponding wood matching coefficient, and compares the wood matching coefficients of the preset wood in the preset wood group to obtain the maximum wood matching coefficient, and finally records the preset wood corresponding to the maximum wood matching coefficient as the locker wood, thereby achieving an improvement in the accuracy of wood matching and effectively solving the problem of low wood matching accuracy in the prior art.

[0046] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0047] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0048] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0049] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0050] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0051] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. An intelligent matching and optimized nesting system for wood processing, characterized in that: It includes data extraction module, preset wood analysis module and intelligent matching module; Wherein, the data extraction module is used to obtain preset wood data corresponding to each preset wood in the preset wood group; The preset wood analysis module is used to analyze the preset wood data to obtain a corresponding wood matching coefficient, and the wood matching coefficient is used to measure the degree of conformity between the preset wood type and the wood of the locker; The intelligent matching module is used to compare the wood matching coefficients of the preset wood in the preset wood group to obtain the maximum wood matching coefficient, and at the same time record the preset wood corresponding to the maximum wood matching coefficient as the locker wood.

2. The intelligent matching and optimized arrangement system for wood processing as claimed in claim 1, characterized in that: The specific steps for obtaining the preset wood data are as follows: Acquire the size information of the preset wood by means of a laser ranging sensor, wherein the size information includes length, width and height; Counting the number of scanned wood barcodes to obtain quantity information, wherein the number of wood barcodes is obtained by scanning a barcode attached to a preset wood by a barcode scanner installed on the production line; The size information and quantity information are integrated to obtain preset wood data.

3. The intelligent matching and optimized arrangement system for wood processing as claimed in claim 1, characterized in that: The method for obtaining the wood matching coefficient is as follows: The wood property weight and the wood moisture-proof weight are obtained from the preset database, and the wood matching coefficient is calculated by combining the wood property index and the wood moisture-proof index. The wood property index is used to measure the quality level of the preset wood, and the wood moisture-proof index is used to measure the moisture-proof ability of the preset wood. The wood matching coefficient is specifically calculated using the following formula: ； In the formula, Indicates the type number of the preset wood. , Indicates the total number of preset wood species. Indicates The wood matching coefficient of the preset wood, Indicates Wood property index for a preset wood type, Indicates The wood moisture resistance index of the preset wood, represents the wood property weight, Indicates the moisture resistance weight of wood.

4. The intelligent matching and optimized arrangement system for wood processing as claimed in claim 3, characterized in that: The specific method for obtaining the wood property index is as follows: The formaldehyde emission of the preset wood is obtained by measuring the formaldehyde concentration in the wood sample extract corresponding to each preset wood in the preset wood group, wherein the formaldehyde emission is used to measure the formaldehyde content level of the preset wood; Obtaining a corresponding wood density value by measuring the volume and weight of the wood sample, wherein the wood density value is used to measure the density level of the preset wood; Applying pressure to the wood sample at a preset rate using a universal testing machine to obtain a preset compressive strength value of the wood, wherein the compressive strength value is used to measure the compressive resistance level of the preset wood; The formaldehyde emission reference value, wood density reference value and compressive strength reference value are obtained from the preset database, and it is determined whether the formaldehyde emission reference value is less than the formaldehyde emission of the preset wood, and whether the wood density reference value and the compressive strength reference value are both greater than the corresponding wood density value and compressive strength value. If so, the corresponding wood property index is set to the preset wood property index, otherwise the corresponding wood property index is obtained by combining the formaldehyde emission, wood density value and compressive strength value.

5. The intelligent matching and optimized arrangement system for wood processing as claimed in claim 4, characterized in that: The wood property index is specifically calculated using the following formula: ； In the formula, Indicates the type number of the preset wood. , Indicates the total number of preset wood species. Indicates Wood property index for a preset wood type, represents a natural constant, Indicates Formaldehyde emission of the preset wood, Indicates The wood density value of the preset wood type, Indicates Preset compressive strength values ​​for wood, Indicates The formaldehyde emission reference value of the preset wood is Indicates The wood density reference value of the preset wood type, Indicates A reference value for the compressive strength of a preset wood.

6. The intelligent matching and optimized arrangement system for wood processing as claimed in claim 3, characterized in that: The specific method for obtaining the wood moisture resistance index is as follows: Using a wood resistance meter to measure the resistance of a wood sample corresponding to each preset wood in the preset wood group, and combining the constructed preset water-electricity standard curve to obtain the initial moisture content of the wood sample corresponding to the preset wood; An initial moisture reference value is obtained from a preset database, and it is determined whether the initial moisture reference value is less than the corresponding initial moisture content and whether the wood density reference value is greater than the wood density value. If so, the corresponding wood moisture resistance index is set to the preset wood moisture resistance index. Otherwise, the corresponding wood moisture resistance index is obtained by combining the wood density value and the initial moisture content.

7. The intelligent matching and optimized arrangement system for wood processing as claimed in claim 6, characterized in that: The specific method for obtaining the initial moisture content is as follows: Measuring the wood resistance value of the wood sample by using a wood resistance meter, wherein the wood resistance value is used to measure the resistance level of the preset wood sample; The wood resistance value of the wood sample was matched using the constructed preset water-resistance standard curve to obtain the corresponding initial moisture content.

8. The intelligent matching and optimized arrangement system for wood processing as claimed in claim 7, characterized in that: The specific construction method of the preset hydropower standard curve is as follows: A preset experimental environment is built by constant temperature and humidity equipment, and known samples are randomly divided into drawing samples and verification samples according to a preset ratio. The known samples are wood samples with known moisture content, and the known samples are used to weigh the resistivity corresponding to wood samples with different moisture contents; The wood resistance meter is used to measure the wood resistance values ​​of different drawing samples in a preset experimental environment during a preset time period, and a preset water-electricity standard curve is obtained according to the known moisture content and wood resistance value of the drawing samples.

9. The intelligent matching and optimized arrangement system for wood processing as claimed in claim 1, characterized in that: The preset wood corresponding to the maximum wood matching coefficient is recorded as the locker wood, and then the speed evaluation value is obtained to adjust the cutting speed. The specific method for obtaining the speed evaluation value is as follows: Obtaining a cutting speed value according to the rotation speed and diameter of the cutting tool, wherein the cutting speed value is used to measure the cutting speed level of the preset wood intelligent matching nesting system; The tool sharpness value is obtained by measuring the cutting edge angle of the cutting tool, and the tool sharpness value is used to measure the sharpness of the cutting tool of the preset wood intelligent matching nesting system; Obtaining a tool hardness value by applying a preset pressure to the cutting tool and measuring the size of an impression formed by the cutting tool, wherein the tool hardness value is used to measure the hardness level of the preset tool; Obtain the cutting speed reference value, tool sharpness reference value and tool hardness reference value from the preset database, and determine whether the cutting speed reference value, tool sharpness reference value and tool hardness reference value are all greater than the corresponding cutting speed value, tool sharpness value and tool hardness value. If so, set the corresponding speed evaluation value to the preset speed evaluation value, otherwise, combine the cutting speed value, tool sharpness value and tool hardness value to obtain the speed evaluation value.

10. The intelligent matching and optimized arrangement system for wood processing as claimed in claim 9, characterized in that: The specific process of cutting speed adjustment is as follows: Determine whether the cutting speed is less than the speed evaluation value. If so, continue cutting. Otherwise, reduce the cutting speed parameter until the cutting speed parameter is not greater than the speed evaluation value. The cutting speed parameter is used to adjust the cutting speed of the cutting tool.

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