An artificial intelligence-based furniture processing control system and control method

Abstract

The application discloses a furniture processing control system and method based on artificial intelligence, and relates to the technical field of furniture processing control.The system firstly generates a wood quality drilling reference table by using relevant data of historical drilling, then selects the drill bit type and drilling control parameters required by each marked wood during drilling according to the quality data of each marked wood, analyzes the drilling sequence of each marked wood according to the strength of the wood to the drill bit bearing capacity, monitors the data of the wood hole after processing, analyzes whether the wood is affected by the negative impact of the drilling equipment, and then makes corresponding adjustment.The application realizes intelligent monitoring and control of drilling, ensures the accuracy and quality of wood drilling, increases the service life of the drill bit, ensures the eligibility of wood quality and the effect of drilling, and guarantees the safety of the drilling process.

Description

Technical Field

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

[0002] An AI-based furniture manufacturing control system uses artificial intelligence to optimize and automate the furniture manufacturing process. It integrates advanced technologies such as sensors, machine learning algorithms, and automated control to achieve precise control and optimization of every aspect of furniture manufacturing.

[0003] Existing technologies include a control method and control system for furniture panel processing equipment, such as the invention patent disclosed in the application with publication number CN119472513A. By collecting data from furniture panel processing equipment and forming a multi-depth knowledge vector spectrum based on pyramid knowledge vector mining, the value of data can be mined comprehensively and deeply. Obtaining equipment control decision features for different processing tasks from knowledge vector spectra at different pyramid depths can accurately adapt to the needs of linked processing tasks and other processing tasks. For linked processing tasks, the decision features obtained from the first pyramid depth can effectively coordinate the relationship between equipment, avoid adverse effects between equipment, and improve overall processing efficiency. The decision features of the second processing task are obtained from knowledge vector spectra at other depths, taking into account more complex processing influencing factors and improving processing quality. Finally, by combining the two decision features, remote control can be achieved, reducing the cost of manual intervention, improving the timeliness and accuracy of control, and thus achieving efficient, high-quality, and intelligent furniture panel processing.

[0004] The above scheme specifically discloses remote control of processing tasks based on data from processing equipment, but there are large differences in the quality and type of wood raw materials used in different furniture. For processing links that are necessary for wood processing, such as drilling, the processing equipment in the link needs to control the corresponding tools and parameters of the equipment according to the quality of the wood and other conditions. However, the above scheme lacks specific control analysis of the drilling process, cannot guarantee the smoothness of the hole, reduces the drilling effect, and cannot reduce the amount of wood chips in the hole, thereby increasing the cost of cleaning the wood chips in the hole and reducing the efficiency of drilling processing.

[0005] When drilling wood, the order of drilling affects the quality of the processing. However, the above solution lacks analysis of the processing sequence, and cannot flexibly adjust the processing sequence of the wood according to the characteristics of the wood. It cannot achieve efficient and high-quality processing results, nor can it extend the service life of the drill bit, thereby increasing the cost of furniture processing. Summary of the Invention

[0006] In view of the above-mentioned technical deficiencies, the purpose of the present invention is to provide a furniture processing control system and control method based on artificial intelligence.

[0007] In order to solve the above technical problems, the present invention adopts the following technical solutions: In the first aspect, the present invention provides a furniture processing control system based on artificial intelligence, 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 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.

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

[0010] The drilling control unit is used to analyze the drilling quality of each marked timber according to the monitoring data of each marked timber, and perform drilling control when the drilling quality of at least one marked timber is poor.

[0011] In a second aspect, the present invention provides a furniture processing control method based on artificial intelligence, including: S1, drilling analysis: obtaining each piece of wood that needs to be drilled, recording it as each marked wood, obtaining the quality data of each marked wood, analyzing the drilling parameters of each marked wood, and then analyzing the drilling sequence of each marked wood.

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

[0013] S3. Drilling control: Analyze the drilling quality of each marked timber based on the monitoring data of each marked timber. If the drilling quality of at least one marked timber is poor, perform drilling control.

[0014] The beneficial effects of the present invention are: the present invention provides a furniture processing control system and control method based on artificial intelligence, firstly, using the relevant data of historical drilling to generate a wood quality drilling reference table, and then according to the quality data of each marked wood, selecting the drill bit type and drilling control parameters required for each marked wood when drilling, and at the same time analyzing the drilling sequence of each marked wood according to the wood's ability to withstand the drill bit, and monitoring the data of the wood holes after processing to analyze whether the wood is negatively affected by the drilling equipment, and then making corresponding adjustments. The present application realizes intelligent monitoring and control of drilling, ensures the accuracy and quality of wood drilling, and at the same time increases the life of the drill bit, ensures the quality of the wood and the effect of drilling, and ensures the safety of the drilling process. BRIEF 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 historically processed wood, the drilling quality characteristic values ​​and drill bit effect values ​​of each drill bit type using each drilling control data to process each quality data of the processed wood are counted, the drilling grades of each drill bit type using each drilling control data to process each quality data of the processed wood are calculated, and a wood quality drilling reference table is generated.

[0028] It should be noted that the drilling quality characteristic value and the drill effect value of the wood processed by each drill type using each drilling control data and each quality data are respectively recorded 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, and j represents the number of each quality data. q, w, and j are all positive integers. According to the calculation formula: Get the drilling grade γ of the wood processed by the qth drill type using the wth drilling control data and the jth quality data qwj ,in Indicates rounding up.

[0029] The wood quality drilling reference table shows the drilling grades for wood of various quality data processed using various drilling control data for each drill type.

[0030] S11-2. Based on the quality data of each marked wood, obtain the drilling grade 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 use the drill bit type and the drilling control data of the drill bit type as the candidate drill bit type and the drilling control data of the candidate drill bit type for the marked wood. Based on this, obtain each candidate drill bit type and the drilling control data of each candidate drill bit type for the marked wood, and then execute S11-3.

[0031] It should be noted that the qualified drilling grade is a benchmark value for evaluating whether the drilling quality is qualified. The specific value is set by engineers according to production requirements and is not limited 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 is less damaged. Otherwise, it indicates that the wood is damaged during drilling, the drilling quality is poor, and the drill bit is more damaged.

[0032] S11-3. Analyze the drilling characteristic values ​​of each candidate drill bit type for the marked wood using the candidate drill bit types and the drilling control data of each candidate drill bit type. Select the candidate drill bit type with the largest drilling characteristic value as the target drill bit type for the marked wood, and select the drilling control data with the largest drilling grade as the target drilling control data for the target drill bit type.

[0033] S11-4. If the drilling grades of a certain marked wood processed by each drill bit type using each drilling control data are all lower than the preset qualified drilling grade, the marked wood is marked as high-risk wood, and the pretreatment parameters of the high-risk wood are confirmed based on the wood quality drilling reference table. The high-risk wood is then pretreated. After the pretreatment is completed, the target drill bit type and the target drilling control data of the target drill bit type are selected.

[0034] Preferably, each quality data processed using each drilling control data for each drill bit type with a drilling grade greater than or equal to a preset qualified drilling grade is selected from the wood quality drilling reference table, and then subtracted from the quality data of high-risk wood. The quality data with the smallest difference from the quality data of high-risk wood is selected from the quality data processed using each drilling control data for each drill bit type as the preprocessing parameter for high-risk wood, and the drill bit type and drilling control data corresponding to the quality data are used as the target drill bit type and the target drilling control data of the target drill bit type.

[0035] S11-5. According to S11-2-S11-4, the target drill bit type and target drilling control data of the target drill bit type for each marked wood are obtained as drilling parameters for each marked wood.

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

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

[0038] Obtain the drilling grade 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 wood 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 as follows: the drilling grade corresponding to each marked wood in each processing group is recorded as γgf , g represents the number of each processing group, f represents the number of each marked wood, g and f are both positive integers, and the calculation formula is: Get the drilling grade evaluation value μ corresponding to the g-th processing group g , where Δγ is the average difference in drilling grade in the processing group, γ gmax , γ gmin They represent the maximum drilling level and minimum drilling level corresponding to the g-th processing group, represents the average value of the maximum drilling grade, It represents the average value of the difference between the maximum drilling grade and the average value of the maximum drilling grade of each processing group. represents the average drilling grade corresponding to the g-th processing group, Indicates the average drilling grade, It represents the average value of the difference between the average drilling grade 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 wood,

[0043]

[0044] The drilling grade evaluation values ​​corresponding to each processing group are sorted in descending order, and the sorting result is the processing order of each processing group.

[0045] The target drilling control data and drilling grade of each marked wood in each processing group are normalized, and then the mean is calculated. The result is the drilling sequence evaluation value of each marked wood in each processing group. The drilling sequence evaluation values ​​of each marked wood in each processing group are then sorted in descending order. The sorting result is the drilling sequence of each marked wood in each processing group.

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

[0047] In a specific embodiment, the drilling of each marked wood is monitored, and the specific process is as follows: after the drilling of each marked wood is completed, a thermal image of each hole in each marked wood is collected using a thermal imager, and temperature data of each hole in each marked wood is obtained from the thermal image; at the same time, a roughness measuring instrument is used to detect the roughness of each hole in each marked wood.

[0048] A camera is used to collect the characteristic images and drill bit images of each hole in each marked wood. Image processing technology is used to obtain the damage data of each hole in each marked wood, the amount of chip accumulation in each hole, and the drill bit damage data. The temperature data, roughness of each hole, damage data, chip accumulation in each hole, and drill bit damage data of each hole in each marked wood are used as monitoring data for each marked wood.

[0049] In the above, the temperature data is the temperature of each point in the hole; the damage data of each hole is the crack area and the burn area, etc.; the drill bit damage data includes the notch area in the drill bit and the wear of the outer diameter, etc., among which the wear 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. The diameter of the outermost circumference of the current drill bit is obtained from the drill bit image, and the diameter of the outermost circumference of the initial drill bit is obtained from the data center.

[0050] The drilling control unit is used to analyze the drilling quality of each marked timber according to the monitoring data of each marked timber, and perform drilling control when the drilling quality of at least one marked timber 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 drill bit effect value of each marked wood, and then analyze the drilling quality of each marked wood. When the drilling quality of a marked wood is poor, the marked wood is recorded as the target wood, and then the drilling control module is executed.

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

[0054] In the above, the calculation process of the drilling quality characteristic value of each marked wood is as follows: the temperature of each point in the hole is obtained from the temperature data of each hole in each marked wood, and then the temperature influence characteristic value of each hole in each marked wood is calculated. Then, the temperature influence characteristic value of each hole in each marked wood, the roughness of each hole and the damage data of each hole are normalized, and the processed values ​​are recorded as c1 and c2 respectively. 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 is:

[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 the marked woods that are not drilled in the same processing group as the target wood, record them as the woods to be drilled, and adjust the drilling order of the woods to be drilled.

[0064] In a specific embodiment, the specific process of the drilling control module is: obtaining each marked wood with a drilled hole in the same processing group as the target wood, recording it as each drilled wood, obtaining monitoring data of each drilled wood, analyzing the drilling change status of the processing group where the target wood is located, if the drilling change status of the processing group where the target wood is located is a gradual change, then replacing the drill bit of the processing group where the target wood is located, and calibrating the drilling control data of the drilling equipment at the same time.

[0065] It should be noted that each drilled timber is numbered according to the drilling order, and then a broken line graph is constructed with the number of each drilled timber as the horizontal axis and the monitoring data as the vertical axis, and then the slope between each adjacent point in the broken line graph is obtained. 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 timber is located is a gradual change type. When there is at least one point whose slope between it and its adjacent point is greater than the preset slope threshold, it indicates that the drilling change state of the processing group where the target timber 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 be faulty. At this time, it is necessary to send an inspection signal to the monitoring terminal to prompt the staff to perform equipment inspection.

[0067] The quality data of each timber to be drilled is obtained, and the historically drilled timbers having the same quality data as the timber to be drilled are obtained from the data center as the associated timbers of each timber to be drilled. Then, the monitoring data and the historical drilling control data of the associated timbers of each timber to be drilled are obtained from the data center, the drilling priority of each timber to be drilled is analyzed, and the timbers to be drilled are sorted in descending order of the drilling priority to obtain the adjustment order of each timber to be drilled.

[0068] In the above, the analysis process of the drilling priority of each timber to be drilled is: based on the monitoring data and historical drilling control data of each associated timber of each timber to be drilled, each associated timber with the same historical drilling control data is regarded as an associated group, thereby obtaining each associated group of each timber to be drilled.

[0069] According to the monitoring data of each associated wood in each associated group of each wood to be drilled, 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 are calculated, and 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 are respectively recorded as α1 dsn and α2 dsn, d represents the number of each wood to be drilled, s represents the number of each associated group, n represents the number of each associated wood, d, s and n are all positive integers; according to the calculation formula: Get the drilling priority λ of the dth wood to be drilled d , where S and N represent the number of associated groups and the number of associated wood, respectively.

[0070] It should be noted that α1 dsn and α2 dsn The calculation method is the same as α1 f and α2 f The calculation method is the same and will not be repeated here.

[0071] Example 2:

[0072] See Figure 2 As shown, a furniture processing control method based on artificial intelligence includes: S1, drilling analysis: obtaining each wood to be drilled, recording it as each marked wood, obtaining quality data of each marked wood, analyzing the drilling parameters of each marked wood, and then analyzing the drilling sequence of each marked wood;

[0073] S2. Drilling control: drilling each marked timber piece in sequence according to the drilling sequence and drilling parameters of each marked timber piece, monitoring the drilling of each marked timber piece, and obtaining monitoring data of each marked timber piece;

[0074] S3. Drilling control: Analyze the drilling quality of each marked timber based on the monitoring data of each marked timber. If the drilling quality of at least one marked timber is poor, perform drilling control.

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

Claims

1. A furniture processing control system based on artificial intelligence, characterized in that: include: Drilling analysis unit, drilling control unit and drilling regulation unit; A drilling analysis unit is used to obtain each wood 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; A drilling control unit is used to drill each marked timber in sequence according to the drilling sequence and drilling parameters of each marked timber, monitor the drilling of each marked timber, and obtain monitoring data of each marked timber; A drilling control unit is used to analyze the drilling quality of each marked timber according to the monitoring data of each marked timber, and perform drilling control when the drilling quality of at least one marked timber is poor; 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 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; 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 perform drilling sequence analysis on each marked wood; The specific process of the drilling parameter analysis module is as follows: S11-1. Based on the quality data, drilling quality characteristic values, drilling control data, drill bit types, and drill bit effect values ​​corresponding to each historically processed wood piece, the drilling quality characteristic values ​​and drill bit effect values ​​of each drill bit type processed with each drilling control data for each quality data are counted, and the drilling grade of each drill bit type processed with each drilling control data for each quality data is calculated to generate a wood quality drilling reference table; the drilling grade is calculated by rounding up the average of the drilling quality characteristic values ​​and the drill bit effect values; S11-2. Based on the quality data of each marked wood, obtain the drilling grade of each drill bit type using each drilling control data for processing each marked wood from a wood quality drilling reference table. When the drilling grade of a certain drill bit type using a certain drilling control data for processing a certain marked wood is greater than or equal to a preset qualified drilling grade, use the drill bit type and the drilling control data of the drill bit type as a candidate drill bit type and the drilling control data of the candidate drill bit type for the marked wood. Based on this, obtain each candidate drill bit type and the drilling control data of each candidate drill bit type for the marked wood, and then execute S11-3. S11-3. Analyze the drilling characteristic values ​​of each candidate drill bit type for the marked wood using the candidate drill bit types and the drilling control data of each candidate drill bit type. Select the candidate drill bit type with the largest drilling characteristic value as the target drill bit type for the marked wood. Select the drilling control data with the largest drilling grade as the target drilling control data for the target drill bit type. S11-4. If the drilling grade of the marked wood produced by each drill bit type using each drilling control data is less than the preset qualified drilling grade, the marked wood is marked as high-risk wood. Pretreatment parameters for the high-risk wood are determined based on the wood quality drilling reference table. The high-risk wood is then pretreated. After the pretreatment is completed, a target drill bit type and target drilling control data for the target drill bit type are selected. S11-5, obtaining the target drill bit type and target drilling control data of the target drill bit type for each marked wood according to S11-2-S11-4 as drilling parameters for each marked wood; The specific process of the drilling sequence analysis module is as follows: Obtaining target drill bit types and target drilling control data for each marked wood from the drilling parameters of each marked wood, and treating each marked wood with the same target drill bit type as a processing group, thereby obtaining each processing group; Obtain the drilling grade corresponding to each marked wood in each processing group from the wood quality drilling reference table, analyze the processing order of each processing group and the drilling order of each marked wood in each processing group, first sort each processing group according to the processing order, then sort each marked wood in each processing group according to the drilling order, and obtain the drilling order of each marked wood; Sort the drilling grade evaluation values ​​corresponding to each processing group in descending order, and the sorting result is the processing order of each processing group; The target drilling control data and drilling grade of each marked wood in each processing group are normalized, and then the mean is calculated. The result is the drilling sequence evaluation value of each marked wood in each processing group. The drilling sequence evaluation values ​​of each marked wood in each processing group are then sorted in descending order. The sorting result is the drilling sequence of each marked wood in each processing group.

2. The artificial intelligence-based furniture processing control system according to claim 1, characterized in that: The specific process of monitoring the drilling of each marked wood is as follows: After drilling is completed on each marked wood, a thermal image of each hole in each marked wood is collected using a thermal imager, and temperature data of each hole in each marked wood is obtained from the thermal image; at the same time, a roughness measuring instrument is used to detect the roughness of each hole in each marked wood; A camera is used to collect the characteristic images and drill bit images of each hole in each marked wood. Image processing technology is used to obtain the damage data of each hole in each marked wood, the amount of chip accumulation in each hole, and the drill bit damage data. The temperature data, roughness of each hole, damage data, chip accumulation in each hole, and drill bit damage data of each hole in each marked wood are used as monitoring data for each marked wood.

3. The artificial intelligence-based furniture processing control system according to claim 1, characterized in that: 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 value and drill bit effect value 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 marked wood is poor, the marked wood is recorded as the target wood, and then the drilling control module is executed; The drilling control module is used to adjust the drilling parameters of the target wood, and at the same time obtain the marked woods that are not drilled in the same processing group as the target wood, record them as the woods to be drilled, and adjust the drilling order of the woods to be drilled.

4. The artificial intelligence-based furniture processing control system according to claim 3, characterized in that: The specific process of the drilling quality analysis module is as follows: Using the temperature data, roughness data and damage data of each hole in each marked wood, the drilling quality characteristic value of each marked wood is calculated and recorded as , where f represents the number of each marked wood, and f is a positive integer; The drill bit effect value of each marked wood is calculated using the chip accumulation amount and drill bit damage data of each hole in each marked wood, which is recorded as ; According to the analysis formula: , get the drilling quality of the fth marked wood , where and They represent the drilling quality characteristic value threshold and the drill bit effect value threshold respectively; when =-1, indicating that the drilling quality of the fth marked wood is poor; when =1, indicating that the drilling quality of the f-th marked wood is qualified.

5. The artificial intelligence-based furniture processing control system according to claim 4, characterized in that: The specific process of the drilling control module is as follows: Obtain each marked timber piece that has been drilled in the same processing group as the target timber piece, record it as each drilled timber piece, obtain monitoring data for each drilled timber piece, analyze the drilling change status of the processing group where the target timber piece is located, and if the drilling change status of the processing group where the target timber piece is located is a gradual change, replace the drill bit of the processing group where the target timber piece is located, and calibrate the drilling control data of the drilling equipment; The quality data of each timber to be drilled is obtained, and the historically drilled timbers having the same quality data as the timber to be drilled are obtained from the data center as the associated timbers of each timber to be drilled. Then, the monitoring data and the historical drilling control data of the associated timbers of each timber to be drilled are obtained from the data center, the drilling priority of each timber to be drilled is analyzed, and the timbers to be drilled are sorted in descending order of the drilling priority to obtain the adjustment order of each timber to be drilled.

6. The artificial intelligence-based furniture processing control system according to claim 5, characterized in that: 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, the associated wood with the same historical drilling control data is grouped as an associated group, thereby obtaining each associated group of each wood to be drilled; According to the monitoring data of each associated wood in each associated group of each wood to be drilled, 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 are calculated, and 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 are recorded as and , d represents the number of each wood to be drilled, s represents the number of each associated group, n represents the number of each associated wood, d, s and n are all positive integers; according to the calculation formula: , get the drilling priority of the dth wood to be drilled , where S and N represent the number of associated groups and the number of associated wood, respectively, Indicates rounding up.

7. A furniture processing control method executed by the artificial intelligence-based furniture processing control system according to any one of claims 1 to 6, characterized in that: include: S1. Drilling analysis: Obtain each piece of wood to be drilled, record it 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; S2. Drilling control: drilling each marked timber piece in sequence according to the drilling sequence and drilling parameters of each marked timber piece, monitoring the drilling of each marked timber piece, and obtaining monitoring data of each marked timber piece; S3. Drilling control: Analyze the drilling quality of each marked timber based on the monitoring data of each marked timber. If the drilling quality of at least one marked timber is poor, perform drilling control.

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