Furniture processing control system and control method based on artificial intelligence

Abstract

The invention discloses a furniture processing control system and control method based on artificial intelligence, and relates to the technical field of furniture processing control, the system firstly uses related data of historical drilling to generate a wood quality drilling reference table, and then according to the quality data of each marked wood, the wood quality drilling reference table is calculated. The method comprises the following steps: selecting drill bit types and drilling control parameters required during drilling of each piece of marked wood, analyzing the drilling sequence of each piece of marked wood according to the bearing capacity of the wood to a drill bit, monitoring data of holes of the wood after processing, analyzing whether the wood is subjected to negative influence of drilling equipment or not, and then performing corresponding adjustment. Intelligent monitoring and control of drilling are achieved, the accuracy and quality of wood drilling are guaranteed, meanwhile, the service life of the drill bit is prolonged, the qualification of wood quality and the drilling effect are guaranteed, and the safety of the drilling process is guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of furniture processing control, and specifically relates to an artificial intelligence-based furniture processing control system and control method. Background Art

[0002] An artificial intelligence-based furniture processing control system is a system that uses artificial intelligence technology to optimize and automate the furniture processing process. It integrates a variety of advanced technologies such as sensor technology, machine learning algorithms, and automation control to achieve precise control and optimization of all aspects of furniture processing.

[0003] The prior art, such as the invention patent with publication number CN119472513A, discloses a control method and control system for a furniture board processing device. By collecting data of the furniture board processing device and forming a knowledge vector spectrum with multiple depths based on pyramid knowledge vector mining, it can comprehensively and deeply explore the data value. Obtaining the equipment control decision features of different processing tasks from the knowledge vector spectra with different pyramid depths can accurately adapt to the requirements of linkage processing tasks and other processing tasks. For linkage processing tasks, the decision features obtained from the first pyramid depth can effectively coordinate the relationship between devices, avoid the adverse effects between devices, and improve the overall processing efficiency. Obtaining the decision features of the second processing task from the knowledge vector spectra of other depths, considering more complex processing influencing factors, can improve the processing quality. Finally, by combining the two decision features, remote control is realized, reducing the manual intervention cost, improving the timeliness and accuracy of control, and thus realizing the high-efficiency, high-quality, and intelligent furniture board processing.

[0004] The above solution specifically discloses remote control of processing tasks based on the data of processing equipment. However, there are significant differences in the quality and type of wood raw materials used for different furniture. For the necessary processing links in wood processing, such as drilling, the processing equipment in this link needs to control the corresponding tools and parameters of the equipment according to the quality of the wood, etc. However, the above solution lacks specific control analysis of the drilling process, unable to ensure the smoothness of the hole, reducing the drilling effect, and at the same time unable to reduce the amount of wood chips in the hole, thus increasing the cleaning cost of the wood chips in the hole and reducing the efficiency of drilling processing.

[0005] When drilling wood, the drilling sequence affects the processing quality. However, the above solution lacks analysis of the processing sequence, unable to flexibly adjust the processing sequence of wood according to the characteristics of the wood, unable to achieve high-efficiency and high-quality processing effects, and unable to extend the service life of the drill bit, increasing the cost of furniture processing. Summary of the Invention

[0006] Aiming at the above existing technical deficiencies, the purpose of the present invention is to provide an artificial intelligence-based furniture processing control system and control method.

[0007] To solve the above technical problems, the present invention adopts the following technical solutions: In the first aspect, the present invention provides an artificial intelligence-based furniture processing control system, including: a drilling analysis unit, a drilling control unit, and a drilling regulation unit.

[0008] The drilling analysis unit is used to obtain each piece of wood to be drilled, denoted as each marked wood, obtain the quality data of each marked wood, analyze the drilling parameters of each marked wood, and then analyze the drilling sequence of each marked wood.

[0009] The drilling control unit is used to drill each marked wood in sequence according to the drilling sequence and drilling parameters of each marked wood, monitor the drilling of each marked wood, and obtain the monitoring data of each marked wood.

[0010] The drilling regulation unit is used to analyze the drilling quality of each marked wood according to the monitoring data of each marked wood, and perform drilling regulation when the drilling quality of at least one marked wood is not good.

[0011] In the second aspect, the present invention provides an artificial intelligence-based furniture processing control method, including: S1. Drilling analysis: Obtain each piece of wood to be drilled, denoted as each marked wood, obtain the quality data of each marked wood, analyze the drilling parameters of each marked wood, and then analyze the drilling sequence of each marked wood.

[0012] S2. Drilling control: Drill each marked wood in sequence according to the drilling sequence and drilling parameters of each marked wood, monitor the drilling of each marked wood, and obtain the monitoring data of each marked wood.

[0013] S3. Drilling regulation: Analyze the drilling quality of each marked wood according to the monitoring data of each marked wood, and perform drilling regulation when the drilling quality of at least one marked wood is not good.

[0014] The beneficial effects of the present invention are as follows: The present invention provides an artificial intelligence-based furniture processing control system and control method. First, using the relevant data of historical drilling, a drilling reference table for wood quality is generated. Then, according to the quality data of each marked wood, the type of drill bit and drilling control parameters required for each marked wood during drilling are selected. At the same time, according to the bearing capacity of the wood for the drill bit, the drilling sequence of each marked wood is analyzed, and the data of the holes in the wood are monitored after processing to analyze whether the wood is negatively affected by the drilling equipment, and then corresponding adjustments are made. This application realizes intelligent monitoring and control of drilling, ensures the accuracy and quality of wood drilling, increases the service life of the drill bit at the same time, ensures the qualification of wood quality and the effect of drilling, and guarantees the safety of the drilling process. Description of the Drawings

[0015] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0016] Figure 1 This is a schematic diagram of the system structure connection of the present invention.

[0017] Figure 2 The figure is a schematic flow chart of the steps for implementing the method of the present invention. DETAILED DESCRIPTION

[0018] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0019] Example 1:

[0020] See Figure 1 As shown, a furniture processing control system based on artificial intelligence includes: a drilling analysis unit, a drilling control unit and a drilling regulation unit.

[0021] The drilling analysis unit is used to obtain each piece of wood that needs to be drilled, record it as each marked wood, obtain quality data of each marked wood, analyze the drilling parameters of each marked wood, and then analyze the drilling sequence of each marked wood.

[0022] The drilling analysis unit includes a drilling parameter analysis module and a drilling sequence analysis module.

[0023] The drilling parameter analysis module is used to obtain the quality data, drilling quality characteristic values, drilling control data, drill bit type and drill bit effect value corresponding to each historically processed wood from the data center, generate a wood quality drilling reference table, and use the quality data of each marked wood to confirm the drilling parameters of each marked wood.

[0024] Among the above, the quality data include hardness, water content and thickness, etc.; before drilling, the hardness, water content and thickness are collected by equipment such as hardness testers, moisture content meters and laser thickness gauges.

[0025] Drilling control data includes drill speed, drill feed rate and chip removal frequency.

[0026] Drill bit types include spiral drill bits and spade drill bits.

[0027] In a specific embodiment, the specific process of the drilling parameter analysis module is as follows: S11-1. According to the quality data, drilling quality characteristic values, drilling control data, drill bit types, and drill bit effect values corresponding to each historical processed wood, count the drilling quality characteristic values and drill bit effect values of the processed wood with each drill bit type using each drilling control data to process each quality data, calculate the drilling grades of the processed wood with each drill bit type using each drilling control data to process each quality data, and generate a drilling reference table for wood quality.

[0028] It should be noted that the drilling quality characteristic values and drill bit effect values of the processed wood with each drill bit type using each drilling control data to process each quality data are respectively denoted as a1 qwj and a2 qwj , where q represents the number of each drill bit type, w represents the number of each drilling control data, j represents the number of each quality data, and q, w, and j are all positive integers. According to the calculation formula: Obtain the drilling grade γ of the processed wood with the q-th drill bit type using the w-th drilling control data to process the j-th quality data qwj , where represents rounding up.

[0029] The drilling grades in the drilling reference table for wood quality are for the processed wood with each drill bit type using each drilling control data to process each quality data.

[0030] S11-2. According to the quality data of each marked wood, obtain the drilling grades of the processed wood with each drill bit type using each drilling control data for each marked wood from the drilling reference table for wood quality. When the drilling grade of a certain drill bit type using a certain drilling control data to process a certain marked wood is greater than or equal to the preset qualified drilling grade, then take this drill bit type and the drilling control data of this drill bit type as the candidate drill bit type and the drilling control data of the candidate drill bit type for this marked wood, and accordingly obtain the candidate drill bit types and the drilling control data of the candidate drill bit types for this marked wood, and then execute S11-3.

[0031] It should be noted that the qualified drilling grade is the reference value for evaluating whether the drilling quality is qualified, and the specific value is set by the engineer according to production requirements, and no specific numerical limit is given here; when the drilling grade is greater than or equal to the preset qualified drilling grade, it indicates that the wood is not damaged during drilling, the drilling quality is good, and the drill bit damage is small, otherwise it indicates that the wood is damaged during drilling, the drilling quality is poor, and the drill bit damage is large.

[0032] S11-3. Use the types of candidate drills for the marked wood and the drilling control data of each type of candidate drill to analyze the drilling characteristic values of the marked wood when using each type of candidate drill. Select the type of candidate drill with the largest drilling characteristic value as the target drill type for the marked wood, and select the drilling control data with the highest drilling grade as the target drilling control data for the target drill type.

[0033] S11-4. If the drilling grades of a certain marked wood processed by each drill type using each drilling control data are all less than the preset qualified drilling grade, mark the marked wood as high-risk wood. Based on the drilling reference table for wood quality, confirm the pretreatment parameters for the high-risk wood, and then perform pretreatment on the high-risk wood. After the pretreatment is completed, select the target drill type and the target drilling control data of the target drill type.

[0034] Preferably, select the quality data of each drill type processed by each drilling control data with a drilling grade greater than or equal to the preset qualified drilling grade from the drilling reference table for wood quality, then subtract the quality data of the high-risk wood. Select the quality data with the smallest difference from the quality data of each drill type processed by each drilling control data as the pretreatment parameters for the high-risk wood, and use the drill type and drilling control data corresponding to this quality data as the target drill type and the target drilling control data of the target drill type.

[0035] S11-5. Obtain the target drill type and the target drilling control data of the target drill type for each marked wood according to S11-2 - S11-4 as the drilling parameters for each marked wood.

[0036] The drilling sequence analysis module is used to divide each marked wood into processing groups according to the drilling parameters of each marked wood, and then analyze the drilling sequence of each marked wood.

[0037] In a specific embodiment, the specific process of the drilling sequence analysis module is: obtain the target drill type and the target drilling control data of each marked wood from the drilling parameters of each marked wood, and regard the marked woods with the same target drill type as a processing group, thereby obtaining each processing group.

[0038] Obtain the drilling grades corresponding to each marked wood in each processing group from the drilling reference table for wood quality, analyze the processing sequence of each processing group and the drilling sequence of each marked wood in each processing group. First, sort each processing group according to the processing sequence, and then sort the marked woods in each processing group according to the drilling sequence to obtain the drilling sequence of each marked wood.

[0039] Preferably, the analysis process of the processing sequence of each processing group and the drilling sequence of each marked wood in each processing group is: record the drilling grades corresponding to each marked wood in each processing group as γgf where \(g\) represents the number of each processing group, \(f\) represents the number of each marked wood, and both \(g\) and \(f\) are positive integers. Using the calculation formula: the evaluation value \(\mu\) of the drilling grade corresponding to the \(g\)th processing group is obtained g , where \(\Delta\gamma\) is the average difference of the drilling grades in the processing group, and \(\gamma\) gmax , \(\gamma\) gmin respectively represent the maximum drilling grade and the minimum drilling grade corresponding to the \(g\)th processing group, represents the average value of the maximum drilling grades, represents the average value of the differences between the maximum drilling grades of each processing group and the average value of the maximum drilling grades, represents the average drilling grade corresponding to the \(g\)th processing group, represents the average drilling grade, represents the average value of the differences between the average drilling grades of each processing group and the average drilling grade.

[0040] Among the above, where \(G\) represents the number of processing groups,

[0041]

[0042] where \(F\) represents the number of marked woods,

[0043]

[0044] Sort the evaluation values of the drilling grades corresponding to each processing group in descending order, and the sorting result is the processing order of each processing group.

[0045] Normalize the target drilling control data and drilling grades of each marked wood in each processing group, then calculate the mean value. The result is the evaluation value of the drilling order of each marked wood in each processing group. Then sort the evaluation values of the drilling order of each marked wood in each processing group in descending order, and the sorting result is the drilling order of each marked wood in each processing group.

[0046] The drilling control unit is used to drill each marked wood in sequence according to the drilling order and drilling parameters of each marked wood, and monitor the drilling of each marked wood to obtain the monitoring data of each marked wood.

[0047] In a specific embodiment, the monitoring of the drilling of each marked wood is as follows: When the drilling of each marked wood is completed, use a thermal imager to collect the thermal images of each hole in each marked wood, and obtain the temperature data of each hole in each marked wood from the thermal images; at the same time, use a roughness measuring instrument to detect the roughness of each hole in each marked wood.

[0048] Use a camera to collect the characteristic images of each hole of each marked wood and the drill bit image, and use image processing technology to obtain the damage data of each hole in each marked wood, the chip accumulation amount in each hole, and the drill bit damage data. Take the temperature data of each hole, the roughness of each hole, the damage data of each hole, the chip accumulation amount in each hole, and the drill bit damage data in each marked wood as the monitoring data of each marked wood.

[0049] Among the above, the temperature data is the temperature of each point in the hole; the damage data of each hole is the crack area, the burning area, etc.; the drill bit damage data includes the notch area in the drill bit and the wear amount of the outer diameter, etc. The wear amount of the outer diameter is the difference between the diameter of the outermost circumference of the current drill bit and the diameter of the outermost circumference of the initial drill bit. Obtain the diameter of the outermost circumference of the current drill bit from the drill bit image and obtain the diameter of the outermost circumference of the initial drill bit from the data center.

[0050] The drilling control unit is used to analyze the drilling quality of each marked wood according to the monitoring data of each marked wood, and perform drilling control when the drilling quality of at least one marked wood is poor.

[0051] The drilling control unit includes a drilling quality analysis module and a drilling control module.

[0052] The drilling quality analysis module is used to use the monitoring data of each marked wood to analyze the drilling quality characteristic value and the drill bit effect value of each marked wood, and then analyze the drilling quality of each marked wood. When the drilling quality of a certain marked wood is poor, mark the marked wood as the target wood, and then execute the drilling control module.

[0053] In a specific embodiment, the specific process of the drilling quality analysis module is as follows: use the temperature data of each hole, the roughness of each hole, and the damage data of each hole in each marked wood to calculate the drilling quality characteristic value of each marked wood, denoted as α1 f , where f represents the number of each marked wood, and f is a positive integer.

[0054] Among the above, the calculation process of the drilling quality characteristic value of each marked wood is as follows: obtain the temperature of each point in the hole from the temperature data of each hole in each marked wood, then calculate the temperature influence characteristic value of each hole in each marked wood, and then normalize the temperature influence characteristic value of each hole, the roughness of each hole, and the damage data of each hole in each marked wood. The processed values are respectively denoted as c1 fx 、c2 fx and c3 fx , where x represents the number of each hole, and x is a positive integer; α1 f The calculation formula of:

[0055] where x represents the number of holes.

[0056] It should be noted that the ignition point of each marked wood is obtained from the data center and is recorded as T f At the same time, the temperature of each point in each hole of each marked wood is recorded as T fxm , m represents the number of each point, Where, T fxmax 、T fxmin They represent the maximum temperature and minimum temperature of each point in the x-th hole in the f-th marked wood, M represents the number of points, ω1 and ω2 represent the temperature uniformity weight and temperature conformity weight, respectively.

[0057] in, ω2=1-ω1, where κ1 and κ2 represent the temperature difference change rate and the ignition point temperature difference change rate ratio respectively.

[0058] Using the chip accumulation and drill bit damage data of each hole in each marked wood, the drill bit effect value of each marked wood is calculated and recorded as α2 f .

[0059] In the above, the calculation process of the drill bit effect value of each marked wood is as follows: the chip accumulation amount and drill bit damage data of each hole in each marked wood are normalized, and the processed values are recorded as c4 fx and c5 f , α2 f The calculation formula is:

[0060] According to the analysis formula: Get the drilling quality δ of the fth marked wood f , where α1 and α2 represent the drilling quality characteristic value threshold and the drill bit effect value threshold, respectively.

[0061] When δ f = -1, indicating that the drilling quality of the fth marked wood is poor; when δ f =1, indicating that the drilling quality of the f-th marked wood is qualified.

[0062] It should be noted that α1 and α2 are the benchmark values for evaluating drilling quality and drill bit drilling effect, respectively. The specific values are set by engineers based on furniture processing requirements and drilling equipment conditions. No specific numerical restrictions are imposed here. When it is greater than or equal to the drilling quality characteristic value threshold, it indicates that the drilled hole quality is qualified. When it is greater than or equal to the drill bit effect value threshold, it indicates that the drill bit drilling effect is good.

[0063] The drilling control module is used to adjust the drilling parameters of the target wood, and at the same time obtain each marked wood of the undrilled holes in the same processing group as the target wood, which is recorded as each wood to be drilled, and adjust the drilling order of each wood to be drilled.

[0064] In a specific embodiment, the specific process of the drilling control module is as follows: obtain each marked wood of the drilled holes in the same processing group as the target wood, which is recorded as each drilled wood, obtain the monitoring data of each drilled wood, analyze the drilling change state of the processing group where the target wood is located. If the drilling change state of the processing group where the target wood is located is a gradual change type, replace the drill bit of the processing group where the target wood is located, and at the same time calibrate the drilling control data of the drilling equipment.

[0065] It should be noted that number each drilled wood according to the drilling order, then use the number of each drilled wood as the abscissa and the monitoring data as the ordinate to construct a line graph, and then obtain the slope between each adjacent point in the line graph. When the slope between each adjacent point is less than the preset slope threshold, it indicates that the drilling change state of the processing group where the target wood is located is a gradual change type. When there is at least one point whose slope with its adjacent point is greater than the preset slope threshold, it indicates that the drilling change state of the processing group where the target wood is located is a sudden change type.

[0066] It should be added that when the drilling change state of the processing group where the target wood is located is a sudden change type, the drilling equipment may malfunction. At this time, a check signal needs to be sent to the monitoring terminal to prompt the staff to perform equipment detection.

[0067] Obtain the quality data of each wood to be drilled, and obtain each historical drilled wood with the same quality data as each wood to be drilled from the data center as each associated wood of each wood to be drilled. Then obtain the monitoring data and historical drilling control data of each associated wood of each wood to be drilled from the data center, analyze the drilling priority of each wood to be drilled, and sort each wood to be drilled in descending order of drilling priority to obtain the adjusted order of each wood to be drilled.

[0068] In the above, the analysis process of the drilling priority of each wood to be drilled is as follows: based on the monitoring data and historical drilling control data of each associated wood of each wood to be drilled, regard each associated wood with the same historical drilling control data as an associated group, and thus obtain each associated group of each wood to be drilled.

[0069] According to the monitoring data of each associated wood in each associated group of each wood to be drilled, calculate the drilling quality characteristic value and drill bit effect value of each associated wood in each associated group of each wood to be drilled, and record the drilling quality characteristic value and drill bit effect value of each associated wood in each associated group of each wood to be drilled as α1 dsn and α2 dsn, d represents the number of each piece of wood to be drilled, s is the number of each associated group, n represents the number of each associated piece of wood, and d, s, and n are all positive integers; according to the calculation formula: obtain the drilling priority λ of the d-th piece of wood to be drilled d , where S and N respectively represent the number of associated groups and the number of associated pieces of wood.

[0070] It should be noted that α1 dsn and α2 dsn are calculated in the same way as α1 f and α2 f , and will not be elaborated here.

[0071] Embodiment 2:

[0072] Refer to Figure 2 shown, an artificial intelligence-based furniture processing control method includes: S1. Drilling analysis: Obtain each piece of wood that needs to be drilled, denoted as each marked piece of wood, obtain the quality data of each marked piece of wood, analyze the drilling parameters of each marked piece of wood, and then analyze the drilling order of each marked piece of wood;

[0073] S2. Drilling control: According to the drilling order and drilling parameters of each marked piece of wood, drill each marked piece of wood in sequence, and monitor the drilling of each marked piece of wood to obtain the monitoring data of each marked piece of wood;

[0074] S3. Drilling regulation: According to the monitoring data of each marked piece of wood, analyze the drilling quality of each marked piece of wood. When the drilling quality of at least one marked piece of wood is poor, perform drilling regulation.

[0075] The above content is only an example and explanation of the concept of the present invention. Those skilled in the art of this technology make various modifications or supplements to the described specific embodiments or use similar methods to replace them. As long as they do not deviate from the concept of the invention or exceed the scope defined by this specification, they should all fall within the protection scope of the present invention.

Claims

1. An artificial intelligence-based furniture processing control system, characterized in that, Including: A drilling analysis unit, a drilling control unit, and a drilling regulation unit; The drilling analysis unit is used to obtain each piece of wood that needs to be drilled, denoted as each marked wood, obtain the quality data of each marked wood, analyze the drilling parameters of each marked wood, and then analyze the drilling sequence of each marked wood; The drilling control unit is used to drill each marked wood in sequence according to the drilling sequence and drilling parameters of each marked wood, monitor the drilling of each marked wood, and obtain the monitoring data of each marked wood; The drilling regulation unit is used to analyze the drilling quality of each marked wood according to the monitoring data of each marked wood, and perform drilling regulation when the drilling quality of at least one marked wood is poor.

2. The furniture processing control system based on artificial intelligence according to claim 1, characterized in that, The drilling analysis unit includes a drilling parameter analysis module and a drilling sequence analysis module; The drilling parameter analysis module is used to obtain the quality data, drilling quality characteristic values, drilling control data, drill bit types, and drill bit effect values corresponding to each historical processed wood from the data center, generate a wood quality drilling reference table, and use the quality data of each marked wood to confirm the drilling parameters of each marked wood; The drilling sequence analysis module is used to divide each marked wood into processing groups according to the drilling parameters of each marked wood, and then analyze the drilling sequence of each marked wood.

3. An artificial intelligence-based furniture processing control system according to claim 2, characterized in that, The specific process of the drilling parameter analysis module is as follows: S11-1. According to the quality data, drilling quality characteristic values, drilling control data, drill bit types, and drill bit effect values corresponding to each historical processed wood, count the drilling quality characteristic values and drill bit effect values of the processed wood with each drill bit type using each drilling control data to process each quality data, calculate the drilling grades of the processed wood with each drill bit type using each drilling control data to process each quality data, and generate a wood quality drilling reference table; S11-2. According to the quality data of each marked wood, obtain the drilling grades of each drill bit type using each drilling control data to process each marked wood from the wood quality drilling reference table. When the drilling grade of a certain drill bit type using a certain drilling control data to process a certain marked wood is greater than or equal to the preset qualified drilling grade, then take this drill bit type and the drilling control data of this drill bit type as the candidate drill bit type and the drilling control data of the candidate drill bit type for this marked wood, and obtain the candidate drill bit types and the drilling control data of the candidate drill bit types for this marked wood accordingly, and then execute S11-3; S11-3. Use the candidate drill bit types and the drilling control data of the candidate drill bit types of this marked wood to analyze the drilling characteristic values of this marked wood using each candidate drill bit type, select the candidate drill bit type with the largest drilling characteristic value as the target drill bit type of this marked wood, and select the drilling control data with the largest drilling grade as the target drilling control data of the target drill bit type; S11-4. If the drilling grades of a certain marked wood processed with the drilling control data of each drill bit type are all less than the preset qualified drilling grade, then mark this marked wood as high-risk wood, confirm the pretreatment parameters of the high-risk wood based on the wood quality drilling reference table, and then perform pretreatment on the high-risk wood. After the pretreatment is completed, select the target drill bit type and the target drilling control data of the target drill bit type; S11-5. Obtain the target drill bit type and the target drilling control data of the target drill bit type for each marked wood according to S11-2 - S11-4 as the drilling parameters of each marked wood.

4. An artificial intelligence-based furniture processing control system according to claim 2, characterized in that, The specific process of the drilling sequence analysis module is as follows: Obtain the target drill bit type and the target drilling control data of each marked wood from the drilling parameters of each marked wood, and regard the marked woods with the same target drill bit type as a processing group, thus obtaining each processing group; Obtain the drilling grades corresponding to each marked wood in each processing group from the wood quality drilling reference table, analyze the processing sequence of each processing group and the drilling sequence of each marked wood in each processing group. First, sort each processing group according to the processing sequence, and then sort the marked woods in each processing group according to the drilling sequence to obtain the drilling sequence of each marked wood.

5. The furniture processing control system based on artificial intelligence according to claim 1, characterized in that, The monitoring of the drilling of each marked wood is specifically as follows: When the drilling of each marked wood is completed, use a thermal imager to collect the thermal images of each hole in each marked wood, and obtain the temperature data of each hole in each marked wood from the thermal images; at the same time, use a roughness measuring instrument to detect the roughness of each hole in each marked wood; Use a camera to collect the characteristic images of each hole and the drill bit images of each marked wood, and use image processing technology to obtain the damage data of each hole, the chip accumulation amount in each hole, and the drill bit damage data in each marked wood. Take the temperature data of each hole, the roughness of each hole, the damage data of each hole, the chip accumulation amount in each hole, and the drill bit damage data in each marked wood as the monitoring data of each marked wood.

6. The furniture processing control system based on artificial intelligence according to claim 1, wherein The drilling control unit includes a drilling quality analysis module and a drilling control module; The drilling quality analysis module is used to analyze the drilling quality characteristic values and drill bit effect values of each marked wood by using the monitoring data of each marked wood, and then analyze the drilling quality of each marked wood. When the drilling quality of a certain marked wood is poor, mark this marked wood as the target wood, and then execute the drilling control module; The drilling control module is used to adjust the drilling parameters of the target wood, and at the same time obtain each marked wood with undrilled holes in the same processing group as the target wood, denoted as each wood to be drilled, and adjust the drilling sequence of each wood to be drilled.

7. An artificial intelligence-based furniture processing control system according to claim 6, characterized in that, The specific process of the drilling quality analysis module is as follows: Calculate the drilling quality characteristic value of each marked wood, denoted as α1, using the temperature data of each hole, the roughness of each hole, and the damage data of each hole in each marked wood f , where f represents the number of each marked wood, and f is a positive integer Using the chip accumulation amount in each hole and the drill bit damage data in each marked wood, calculate the drill bit effect value of each marked wood, denoted as α2 f ; According to the analysis formula: the drilling quality δ of the f-th marked wood is obtained f , where α1 and α2 respectively represent the threshold value of the drilling quality characteristic value and the threshold value of the drill bit effect value; When δ f = -1, it indicates that the drilling quality of the f-th marked wood is poor; when δ f = 1, it indicates that the drilling quality of the f-th marked wood is qualified.

8. An artificial intelligence-based furniture processing control system according to claim 6, characterized in that, The specific process of the drilling control module is as follows: Obtain each marked wood with drilled holes in the same processing group as the target wood, denoted as each drilled wood, obtain the monitoring data of each drilled wood, analyze the drilling change state of the processing group where the target wood is located. If the drilling change state of the processing group where the target wood is located is a gradual change type, then replace the drill bit of the processing group where the target wood is located, and at the same time calibrate the drilling control data of the drilling equipment; Obtain the quality data of each piece of wood to be drilled, and obtain each piece of historical drilled wood in the data center that has the same quality data as each piece of wood to be drilled as the associated wood of each piece of wood to be drilled. Then, obtain the monitoring data and historical drilling control data of the associated wood of each piece of wood to be drilled from the data center, analyze the drilling priorities of each piece of wood to be drilled, and sort each piece of wood to be drilled in descending order of drilling priority to obtain the adjusted order of each piece of wood to be drilled.

9. The furniture processing control system based on artificial intelligence according to claim 8, characterized in that, The analysis process of the drilling priorities of each piece of wood to be drilled is as follows: Based on the monitoring data and historical drilling control data of the associated wood of each piece of wood to be drilled, take the associated wood with the same historical drilling control data as an associated group, and thus obtain the associated groups of each piece of wood to be drilled; Based on the monitoring data of each associated wood in each associated group of each wood to be drilled, calculate the drilling quality characteristic value and the drill bit effect value of each associated wood in each associated group of each wood to be drilled, and denote the drilling quality characteristic value and the drill bit effect value of each associated wood in each associated group of each wood to be drilled as α1 dsn and α2 dsn , d represents the number of each wood to be drilled, s is the number of each associated group, n represents the number of each associated wood, and d, s, and n are all positive integers; according to the calculation formula: Obtain the drilling priority λ of the d-th wood to be drilled d , where S and N respectively represent the number of associated groups and the number of associated woods, Indicates rounding up.

10. A furniture processing control method executed by the artificial intelligence-based furniture processing control system according to any one of claims 1-9, characterized in that, Including: S1. Drilling analysis: Obtain each piece of wood that needs to be drilled, denoted as each marked wood, obtain the quality data of each marked wood, analyze the drilling parameters of each marked wood, and then analyze the drilling order of each marked wood; S2. Drilling control: According to the drilling order and drilling parameters of each marked wood, drill each marked wood in sequence, and monitor the drilling of each marked wood to obtain the monitoring data of each marked wood; S3. Drilling regulation: According to the monitoring data of each marked wood, analyze the drilling quality of each marked wood. When the drilling quality of at least one marked wood is poor, perform drilling regulation.

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