A furniture door panel production method and system based on intelligent manufacturing

Abstract

The present invention discloses a furniture door panel production method and system based on intelligent manufacturing, and specifically relates to the technical field of furniture manufacturing. An operation state instability coefficient is obtained by acquiring an alarm error coefficient, an encryption leakage coefficient and a current instability coefficient, and a set is established. All abnormal coefficients in the set are screened out, and the standard deviation of all abnormal coefficients is calculated. The standard deviation is compared with a preset standard deviation value, and whether to issue a special alarm signal is determined according to the comparison result. The operation state of a furniture door panel production system based on intelligent manufacturing can be monitored. Once a problem occurs in the operation state of the furniture door panel production system based on intelligent manufacturing, an urgent alarm can be issued in time to remind staff that a problem occurs in the operation state of the furniture door panel production system based on intelligent manufacturing, and corresponding measures need to be taken and the manufacture of the furniture door panels should be stopped in time, thereby reducing the impact on the production quality of the furniture door panels and ensuring the safety of production personnel.

Description

Technical Field

[0001] The present invention relates to the technical field of furniture manufacturing, and in particular to a method and system for producing furniture door panels based on intelligent manufacturing. Background Art

[0002] The furniture door panel production system based on intelligent manufacturing is a production system that integrates advanced technology and intelligent management, aiming to improve production efficiency, reduce costs, and enhance product quality and flexibility. Its main functions include: design (designing door panels through CAD software, generating 3D models and processing drawings), production planning (generating production plans and material requirements plans based on order requirements and inventory conditions), material management (managing raw material inventory, automatic procurement and inventory warning), processing (using CNC machine tools, laser cutting machines, spraying equipment, etc. to carry out door panel cutting, punching, assembly, spraying and other processes), quality inspection (real-time monitoring of door panels through sensors and visual inspection systems to ensure the quality of each door panel) and data collection and analysis (collecting and analyzing data generated during the production process to optimize the production process), etc., and after each step is completed, a corresponding alarm will be issued to remind the staff and proceed to the next step. Through the above steps, the furniture door panel production system based on intelligent manufacturing can fully and intelligently realize the intelligent management of the entire process from design to production.

[0003] When the above-mentioned furniture door panel production system based on smart manufacturing is managed intelligently, it is almost always assumed that the operating status of the furniture door panel production system based on smart manufacturing is accurate, without any operating errors, and there is a lack of monitoring of the operating status of the furniture door panel production system based on smart manufacturing. Once there is a problem with the operating status of the furniture door panel production system based on smart manufacturing and it is not perceived in time, the production of furniture door panels will still be managed by the furniture door panel production system based on smart manufacturing, which may cause problems with the production quality of the furniture door panels and may even affect the safety of production personnel. Summary of the invention

[0004] The purpose of the present invention is to solve the problem of lack of monitoring of the operating status of a furniture door panel production system based on intelligent manufacturing. Once a problem occurs in the operating status of a furniture door panel production system based on intelligent manufacturing and is not perceived in a timely manner, the production of furniture door panels will still be managed by using the furniture door panel production system based on intelligent manufacturing. This may lead to problems with the production quality of the furniture door panels and may even affect the safety of production personnel. A method and system for producing furniture door panels based on intelligent manufacturing are proposed.

[0005] In a first aspect of the present invention, a method and system for producing furniture door panels based on intelligent manufacturing are first proposed, the method comprising:

[0006] The acquisition system obtains the target time interval based on the start time of the target furniture door panel production project to the current running time, and divides the target time interval according to the preset window to obtain several sub-intervals;

[0007] Obtain the time data of the alarm issued by the system in each sub-interval, and obtain the alarm error coefficient of the sub-interval according to the time data;

[0008] Obtain the relevant data initially encrypted by the system and the relevant data last encrypted in each sub-interval, and obtain the encryption leakage coefficient of the sub-interval;

[0009] Acquire actual current data of the system during operation in each sub-interval, and obtain the current instability coefficient of the sub-interval according to the actual current data;

[0010] The operating state instability coefficient of each sub-interval is obtained according to the alarm error coefficient, the encryption leakage coefficient and the current instability coefficient in each sub-interval;

[0011] An unstable operating state coefficient set is established based on the unstable operating state coefficients in all sub-intervals, and all abnormal coefficients in the unstable operating state coefficient set are screened out, and an abnormal set is established for all abnormal coefficients, and the standard deviation in the abnormal set is calculated, and the standard deviation is compared with the preset standard deviation value, and it is determined whether to issue a special alarm signal based on the comparison result.

[0012] Optionally, obtaining the time data of the alarm issued by the system in each sub-interval, and obtaining the alarm error coefficient of the sub-interval according to the time data includes:

[0013] In each sub-interval, the actual time when the system issues an alarm and the corresponding preset time when the system issues an alarm are collected in real time at different times, and the actual time when the system issues an alarm and the corresponding preset time when the system issues an alarm are marked as P 实 y and P 预 y , y represents the number of the actual time when the system issues an alarm at different times and the corresponding preset time when the system issues an alarm, y=1, 2, 3, 4, ..., m, m is the number of numbers and is a positive integer;

[0014] Calculate the alarm error coefficient, the calculation expression is:

[0015] Where RF is the alarm error coefficient.

[0016] Optionally, obtaining the relevant data initially encrypted by the system and the relevant data last encrypted in each sub-interval, and obtaining the encryption leakage coefficient of the sub-interval includes:

[0017] Acquire the data that needs to be encrypted in the target furniture door panel production project, and record the data that has not been encrypted as original data;

[0018] Calculate the information entropy of the original data. The calculation formula is:

[0019]

[0020] In the formula, H(X) represents the information entropy of the original data X, p(x i ) represents the probability of the i-th possible value in the original data X; n represents the number of data;

[0021] Calculate the information entropy of the encrypted data using the following formula:

[0022]

[0023] In the formula, H(Y) represents the information entropy of the encrypted data Y, p(y i ) represents the probability of the i-th possible value in the encrypted data Y;

[0024] Calculate the difference in information entropy between the data before and after encryption. The calculation expression is:

[0025] Gain=H(X)-H(Y)

[0026] In the formula, Gain represents information gain, that is, the difference in information entropy between data before and after encryption;

[0027] Calculate the encryption leakage coefficient, the calculation expression is: WD = Gain / H (X);

[0028] Where WD is the encryption leakage coefficient.

[0029] Optionally, obtaining actual current data of the system during operation in each sub-interval, and obtaining the current instability coefficient of the sub-interval according to the actual current data includes:

[0030] Acquire actual current data of the system during operation in each sub-interval, and obtain a current sequence in the sub-interval based on the acquired sequence;

[0031] The data in the current sequence within this subinterval is marked as A 流 w , w represents the number of data in the current sequence, w = 1, 2, 3, 4, ..., f, f is a positive integer;

[0032] Calculate the data A in the current series流 w The average The calculation formula is:

[0033] The average Calculate the current instability coefficient with the preset minimum current, and the calculation formula is: Where WS is the current instability coefficient and Wh is the preset minimum current.

[0034] Optionally, obtaining the operating state instability coefficient of each sub-interval according to the alarm error coefficient, the encryption leakage coefficient and the current instability coefficient in the sub-interval includes:

[0035] The alarm error coefficient, encryption leakage coefficient and current instability coefficient are weighted and summed to obtain the operating state instability coefficient. The calculation formula is:

[0036]

[0037] Wherein, Lgt is the operating state instability coefficient, RF, WD and WS are the alarm error coefficient, encryption leakage coefficient and current instability coefficient respectively, α, β, γ are the preset proportional coefficients of the alarm error coefficient, encryption leakage coefficient and current instability coefficient respectively, and α, β, γ are all greater than 0.

[0038] Optionally, a set of unstable operating state coefficients is established according to the unstable operating state coefficients in all subintervals, and all abnormal coefficients in the set of unstable operating state coefficients are screened out, including:

[0039] When the running state instability coefficient is not less than a preset running state instability coefficient reference threshold, marking the running state instability coefficient as an abnormal coefficient;

[0040] When the running state unstable coefficient is less than a preset running state unstable coefficient reference threshold, the running state unstable coefficient is marked as a normal coefficient.

[0041] Optionally, all abnormal coefficients are used to establish an abnormal set, and the standard deviation in the abnormal set is calculated, and the standard deviation is compared with a preset standard deviation value, and judging whether to issue a special alarm signal according to the comparison result includes:

[0042] A number of abnormal coefficients are used to establish a data set and calibrated as K, then K = {LP d}, where d = {1, 2, 3…r}, r is a positive integer;

[0043] And calculate the standard deviation of several abnormal coefficients in the data set, and calibrate the standard deviation of several abnormal coefficients in the data set as PU, and compare the abnormal coefficient standard deviation PU with the preset abnormal coefficient standard deviation threshold BH, and choose whether to issue a special alarm signal according to the comparison result of the abnormal coefficient standard deviation and the preset abnormal coefficient standard deviation threshold;

[0044] If the abnormal coefficient standard deviation PU is not less than the preset abnormal coefficient standard deviation threshold BH, a special alarm signal is issued;

[0045] If the abnormal coefficient standard deviation PU is less than the preset abnormal coefficient standard deviation threshold BH, no special alarm signal is issued.

[0046] In a second aspect of the present invention, a furniture door panel production system based on intelligent manufacturing is proposed, comprising: a division module: obtaining the time from when the system starts to run the target furniture door panel production project to the current running time, obtaining a target time interval, and dividing the target time interval according to a preset window to obtain a plurality of sub-intervals;

[0047] Alarm error module: obtains the time data of the alarm issued by the system in each sub-interval, and obtains the alarm error coefficient of the sub-interval according to the time data;

[0048] Encryption leakage module: obtain the relevant data initially encrypted by the system and the relevant data last encrypted in each sub-interval, and obtain the encryption leakage coefficient of the sub-interval;

[0049] Current instability module: obtains the actual current data of the system during operation in each sub-interval, and obtains the current instability coefficient of the sub-interval based on the actual current data;

[0050] Operation status module: obtains the operation status instability coefficient of each sub-interval according to the alarm error coefficient, encryption leakage coefficient and current instability coefficient in each sub-interval;

[0051] Evaluation and judgment module: establish an unstable operating state coefficient set according to the unstable operating state coefficients in all sub-intervals, and filter out all abnormal coefficients in the unstable operating state coefficient set, and establish an abnormal set for all abnormal coefficients, and calculate the standard deviation in the abnormal set, and compare the standard deviation with the preset standard deviation value, and determine whether to issue a special alarm signal based on the comparison result.

[0052] Beneficial effects of the present invention:

[0053] The present invention proposes a furniture door panel production method and system based on intelligent manufacturing. The operating state instability coefficient of the sub-interval is obtained by obtaining the alarm error coefficient, the encryption leakage coefficient and the current instability coefficient, and an operating state instability coefficient set is established according to the operating state instability coefficients in all sub-intervals, and all abnormal coefficients in the operating state instability coefficient set are screened out, and an abnormal set is established for all abnormal coefficients, and the standard deviation in the abnormal set is calculated, and the standard deviation is compared with a preset standard deviation value, and it is determined whether to issue a special alarm signal according to the comparison result. In this way, the operating state of the furniture door panel production system based on intelligent manufacturing can be monitored. Once there is a problem with the operating state of the furniture door panel production system based on intelligent manufacturing, an urgent alarm can be issued in time to remind the staff that there is a problem with the operating state of the furniture door panel production system based on intelligent manufacturing, and corresponding measures need to be taken and the manufacture of the furniture door panels should be stopped in time, thereby reducing the impact on the production quality of the furniture door panels and ensuring the safety of the production personnel. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] The present invention will be further described below in conjunction with the accompanying drawings.

[0055] Figure 1 A flowchart of a furniture door panel production method and system based on intelligent manufacturing provided by an embodiment of the present invention;

[0056] Figure 2 A framework diagram of a furniture door panel production system based on intelligent manufacturing provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0057] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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 creative work are within the scope of protection of the present invention.

[0058] Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in the field without making any creative work shall fall within the scope of protection of the present invention.

[0059] The embodiment of the present invention provides a method and system for producing furniture door panels based on intelligent manufacturing. Figure 1 , Figure 1 A flowchart of a furniture door panel production method and system based on intelligent manufacturing provided by an embodiment of the present invention. The method comprises the following steps:

[0060] The acquisition system obtains the target time interval based on the start time of the target furniture door panel production project to the current running time, and divides the target time interval according to the preset window to obtain several sub-intervals;

[0061] Obtain the time data of the alarm issued by the system in each sub-interval, and obtain the alarm error coefficient of the sub-interval according to the time data;

[0062] Obtain the relevant data initially encrypted by the system and the relevant data last encrypted in each sub-interval, and obtain the encryption leakage coefficient of the sub-interval;

[0063] Acquire actual current data of the system during operation in each sub-interval, and obtain the current instability coefficient of the sub-interval according to the actual current data;

[0064] The operating state instability coefficient of each sub-interval is obtained according to the alarm error coefficient, the encryption leakage coefficient and the current instability coefficient in each sub-interval;

[0065] An unstable operating state coefficient set is established based on the unstable operating state coefficients in all sub-intervals, and all abnormal coefficients in the unstable operating state coefficient set are screened out, and an abnormal set is established for all abnormal coefficients, and the standard deviation in the abnormal set is calculated, and the standard deviation is compared with the preset standard deviation value, and it is determined whether to issue a special alarm signal based on the comparison result.

[0066] A method for producing furniture door panels based on intelligent manufacturing is provided based on an embodiment of the present invention. The operating state instability coefficient of the sub-interval is obtained by obtaining the alarm error coefficient, the encryption leakage coefficient and the current instability coefficient, and an operating state instability coefficient set is established according to the operating state instability coefficients in all sub-intervals, and all abnormal coefficients in the operating state instability coefficient set are screened out, and an abnormal set is established for all abnormal coefficients, and the standard deviation in the abnormal set is calculated, and the standard deviation is compared with a preset standard deviation value, and it is determined whether to issue a special alarm signal according to the comparison result. In this way, the operating state of the furniture door panel production system based on intelligent manufacturing can be monitored. Once there is a problem with the operating state of the furniture door panel production system based on intelligent manufacturing, an urgent alarm can be issued in time to remind the staff that there is a problem with the operating state of the furniture door panel production system based on intelligent manufacturing, and corresponding measures need to be taken and the manufacture of furniture door panels should be stopped in time, thereby reducing the impact on the production quality of furniture door panels and ensuring the safety of production personnel.

[0067] In one embodiment, obtaining the time data of the alarm issued by the system in each sub-interval and obtaining the alarm error coefficient of the sub-interval according to the time data includes:

[0068] In each sub-interval, the actual time when the system issues an alarm and the corresponding preset time when the system issues an alarm are collected in real time at different times, and the actual time when the system issues an alarm and the corresponding preset time when the system issues an alarm are marked as P 实 y and P 预 y , y represents the number of the actual time when the system issues an alarm at different times and the corresponding preset time when the system issues an alarm, y=1, 2, 3, 4, ..., m, m is the number of numbers and is a positive integer;

[0069] Calculate the alarm error coefficient, the calculation expression is:

[0070] Where RF is the alarm error coefficient.

[0071] In one implementation method, the alarm error coefficient can be accurately obtained through the above method. The calculated alarm error coefficient can more truly reflect the operating status of the entire furniture door panel production system based on intelligent manufacturing, which is convenient for subsequent analysis.

[0072] It should be noted that the alarm error coefficient refers to the difference between the time when the actual system issues a corresponding alarm and the time when the corresponding preset system issues an alarm after each step of the entire furniture door panel production system based on intelligent manufacturing is completed; if the difference between the time when the actual system issues a corresponding alarm and the time when the corresponding preset system issues an alarm is greater, it means that there is a problem with the operating status of the furniture door panel production system based on intelligent manufacturing, which may cause problems with the production quality of the furniture door panels, and may even affect the safety of production personnel, because:

[0073] Abnormalities or failures in the production process: Larger alarm time differences may indicate abnormalities or failures in the production process. For example, equipment failure, material supply problems, or processing program errors may cause deviations in alarm time. Such abnormalities may affect the processing quality and production efficiency of furniture door panels.

[0074] Quality control issues: If the actual system generates alerts primarily related to quality issues, such as defects or substandard products found by quality inspection equipment, then large alert time differences may indicate a quality control problem that results in poor or unstable product quality.

[0075] Safety hazards: Large differences in the time it takes for an actual system to issue an alarm may indicate safety hazards in the production environment. For example, if equipment operates beyond the safe range or fails to detect a fault, it may lead to an accident and threaten the safety of production personnel.

[0076] Reduced production efficiency: If the difference in alarm time is mainly caused by low production efficiency or problems in the execution of production plan, it may affect production efficiency and production cycle, resulting in order delays and increased costs.

[0077] To sum up, the greater the difference between the time when the actual system issues an alarm and the time when the preset system issues an alarm, the more likely it is that there are abnormalities or problems in the system operation, which may affect product quality, production safety and production efficiency, and thus have a negative impact on the entire production process.

[0078] It should be noted that the actual time when the system issues an alarm and the corresponding preset time when the system issues an alarm can be obtained through the monitoring system in the entire furniture door panel production system based on intelligent manufacturing, and the details will not be elaborated here.

[0079] In one embodiment, obtaining the relevant data initially encrypted by the system and the relevant data last encrypted in each sub-interval, and obtaining the encryption leakage coefficient of the sub-interval includes:

[0080] Acquire the data that needs to be encrypted in the target furniture door panel production project, and record the data that has not been encrypted as original data;

[0081] Calculate the information entropy of the original data. The calculation formula is:

[0082]

[0083] In the formula, H(X) represents the information entropy of the original data X, p(x i ) represents the probability of the i-th possible value in the original data X; n represents the number of data;

[0084] Calculate the information entropy of the encrypted data using the following formula:

[0085]

[0086] In the formula, H(Y) represents the information entropy of the encrypted data Y, p(y i ) represents the probability of the i-th possible value in the encrypted data Y;

[0087] Calculate the difference in information entropy between the data before and after encryption. The calculation expression is:

[0088] Gain=H(X)-H(Y)

[0089] In the formula, Gain represents information gain, that is, the difference in information entropy between data before and after encryption;

[0090] Calculate the encryption leakage coefficient, the calculation expression is: WD = Gain / H (X);

[0091] Where WD is the encryption leakage coefficient.

[0092] In one implementation, through the above method, the calculated encryption leakage coefficient is more accurate and can more truly reflect the operating status of the entire furniture door panel production system based on intelligent manufacturing, which is convenient for subsequent analysis.

[0093] It should be noted that the encryption leakage coefficient refers to the degree of leakage of data that needs to be encrypted in the intelligent manufacturing process during the operation of the entire furniture door panel production system based on intelligent manufacturing; if the leakage degree is greater, it means that there is a problem with the operation status of the furniture door panel production system based on intelligent manufacturing, which may cause problems with the production quality of the furniture door panels, and may even affect the safety of production personnel, because:

[0094] Quality control failure: Tampering or theft of encrypted data may lead to quality control failure in the production process. Since key parameters or production processes in the production process are affected, the product may not meet the design requirements for size, appearance and materials, resulting in unstable product quality or non-compliance with standards.

[0095] Product quality degradation: Leakage and tampering of encrypted data may lead to deviations in key parameters during the production process, which in turn affects the quality stability and consistency of the product. This may result in defects in the furniture door panels produced, such as inaccurate dimensions, poor appearance or material problems, affecting the reliability and quality of the product.

[0096] Safety of production personnel is threatened: If the leakage and tampering of encrypted data leads to serious quality problems in the production process, it may pose a threat to the safety of production personnel. For example, if the product size is inaccurate or there is a problem with the material, it may cause abnormal operation of production equipment or accidents, endangering the safety of production personnel.

[0097] In summary, the leakage of encrypted data may lead to the failure of quality control, the decline of product quality and the threat to the safety of production personnel.

[0098] It should be noted that the data that needs to be encrypted involved in the target furniture door panel production project can be obtained through the specific target furniture door panel production project, and the details will not be repeated here.

[0099] In one embodiment, obtaining actual current data of the system during operation in each sub-interval, and obtaining the current instability coefficient of the sub-interval according to the actual current data includes:

[0100] Acquire actual current data of the system during operation in each sub-interval, and obtain a current sequence in the sub-interval based on the acquired sequence;

[0101] The data in the current sequence within this subinterval is marked as A 流 w , w represents the number of data in the current sequence, w = 1, 2, 3, 4, ..., f, f is a positive integer;

[0102] Calculate the data A in the current series 流 w The average The calculation formula is:

[0103] The average Calculate the current instability coefficient with the preset minimum current, and the calculation formula is: Where WS is the current instability coefficient and Wh is the preset minimum current.

[0104] In one implementation method, through the above method, the calculated current instability coefficient is more accurate and can more truly reflect the operating status of the entire furniture door panel production system based on intelligent manufacturing, which is convenient for subsequent analysis.

[0105] It should be noted that the current instability coefficient refers to the instability of the current during the operation of the entire furniture door panel production system based on intelligent manufacturing; if the current instability is greater, it means that there is a problem with the operation status of the furniture door panel production system based on intelligent manufacturing, which may cause problems in the production quality of the furniture door panels, and may even affect the safety of production personnel, because:

[0106] Production quality issues: The instability of current may cause abnormal operation of production equipment and affect the production quality of furniture door panels. For example, a sudden increase or decrease in current may cause errors in the processing process, resulting in inconsistencies in product size, shape or surface quality.

[0107] Reduced production efficiency: The instability of current may cause shutdown or failure of production equipment, thereby reducing production efficiency. Unstable current during production may trigger the safety mechanism of the equipment, causing the production line to stop or slow down, increasing production time and cost.

[0108] Safety hazards: Unstable current may cause equipment overload or short circuit, resulting in fire, electric shock or other safety accidents, threatening the safety of production personnel.

[0109] Therefore, monitoring and controlling the stability of current is crucial for furniture door panel production systems based on intelligent manufacturing.

[0110] It should be noted that the actual current data can be obtained through the monitoring system in the entire furniture door panel production system based on intelligent manufacturing, and the details are not described here.

[0111] In one embodiment, the operating state instability coefficient of each sub-interval is obtained according to the alarm error coefficient, the encryption leakage coefficient and the current instability coefficient in each sub-interval, including:

[0112] The alarm error coefficient, encryption leakage coefficient and current instability coefficient are weighted and summed to obtain the operating state instability coefficient. The calculation formula is:

[0113]

[0114] Wherein, Lgt is the operating state instability coefficient, RF, WD and WS are the alarm error coefficient, encryption leakage coefficient and current instability coefficient respectively, α, β, γ are the preset proportional coefficients of the alarm error coefficient, encryption leakage coefficient and current instability coefficient respectively, and α, β, γ are all greater than 0.

[0115] In one implementation, the operating state instability coefficient obtained by the above method is more accurate.

[0116] In one embodiment, a set of unstable operating state coefficients is established according to the unstable operating state coefficients in all subintervals, and all abnormal coefficients in the set of unstable operating state coefficients are screened out, including:

[0117] When the operating state instability coefficient is not less than the preset operating state instability coefficient reference threshold, it means that the possibility of problems in the operating state of the furniture door panel production system based on intelligent manufacturing is greater, and the operating state instability coefficient is marked as an abnormal coefficient;

[0118] When the operating state instability coefficient is less than the preset operating state instability coefficient reference threshold, it means that the possibility of problems in the operating state of the furniture door panel production system based on intelligent manufacturing is smaller. At this time, the operating state instability coefficient is marked as a normal coefficient.

[0119] In one implementation, the above-mentioned preset operating state instability coefficient reference threshold is based on the actual situation

[0120] In one embodiment, all abnormal coefficients are used to establish an abnormal set, and the standard deviation in the abnormal set is calculated, and the standard deviation is compared with a preset standard deviation value. According to the comparison result, determining whether to issue a special alarm signal includes:

[0121] A number of abnormal coefficients are used to establish a data set and calibrated as K, then K = {LP d}, where d = {1, 2, 3…r}, r is a positive integer;

[0122] And calculate the standard deviation of several abnormal coefficients in the data set, and calibrate the standard deviation of several abnormal coefficients in the data set as PU, and compare the abnormal coefficient standard deviation PU with the preset abnormal coefficient standard deviation threshold BH, and choose whether to issue a special alarm signal according to the comparison result of the abnormal coefficient standard deviation and the preset abnormal coefficient standard deviation threshold;

[0123] If the abnormal coefficient standard deviation PU is not less than the preset abnormal coefficient standard deviation threshold BH, a special alarm signal is issued;

[0124] If the abnormal coefficient standard deviation PU is less than the preset abnormal coefficient standard deviation threshold BH, no special alarm signal is issued.

[0125] In one implementation method, when a special alarm signal is issued, it indicates that there is a problem with the operating status of the furniture door panel production system based on intelligent manufacturing, and corresponding measures need to be taken and the manufacture of furniture door panels needs to be stopped in time, thereby reducing the impact on the production quality of furniture door panels and ensuring the safety of production personnel.

[0126] Based on the same inventive concept, the embodiment of the present invention also provides a furniture door panel production system based on intelligent manufacturing. Figure 2 , Figure 2 A structural schematic diagram of a furniture door panel production system based on intelligent manufacturing provided by an embodiment of the present invention includes:

[0127] Division module: obtain the time from the start of the target furniture door panel production project to the current running time, obtain the target time interval, and divide the target time interval according to the preset window to obtain several sub-intervals;

[0128] Alarm error module: obtains the time data of the alarm issued by the system in each sub-interval, and obtains the alarm error coefficient of the sub-interval according to the time data;

[0129] Encryption leakage module: obtain the relevant data initially encrypted by the system and the relevant data last encrypted in each sub-interval, and obtain the encryption leakage coefficient of the sub-interval;

[0130] Current instability module: obtains the actual current data of the system during operation in each sub-interval, and obtains the current instability coefficient of the sub-interval based on the actual current data;

[0131] Operation status module: obtains the operation status instability coefficient of each sub-interval according to the alarm error coefficient, encryption leakage coefficient and current instability coefficient in each sub-interval;

[0132] Evaluation and judgment module: establish an unstable operating state coefficient set according to the unstable operating state coefficients in all sub-intervals, and filter out all abnormal coefficients in the unstable operating state coefficient set, and establish an abnormal set for all abnormal coefficients, and calculate the standard deviation in the abnormal set, and compare the standard deviation with the preset standard deviation value, and determine whether to issue a special alarm signal based on the comparison result.

[0133] A furniture door panel production system based on intelligent manufacturing provided by an embodiment of the present invention obtains the operating state instability coefficient of the sub-interval by acquiring the alarm error coefficient, the encryption leakage coefficient and the current instability coefficient, and establishes an operating state instability coefficient set according to the operating state instability coefficients in all sub-intervals, and screens out all abnormal coefficients in the operating state instability coefficient set, and establishes an abnormal set for all abnormal coefficients, and calculates the standard deviation in the abnormal set, and compares the standard deviation with the preset standard deviation value, and determines whether to issue a special alarm signal according to the comparison result. In this way, the operating state of the furniture door panel production system based on intelligent manufacturing can be monitored, and once there is a problem with the operating state of the furniture door panel production system based on intelligent manufacturing, an urgent alarm can be issued in time to remind the staff that there is a problem with the operating state of the furniture door panel production system based on intelligent manufacturing, and corresponding measures need to be taken and the manufacture of furniture door panels should be stopped in time, thereby reducing the impact on the production quality of furniture door panels and ensuring the safety of production personnel.

[0134] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A method for producing furniture door panels based on intelligent manufacturing, characterized in that: The following steps are involved: The acquisition system obtains the target time interval based on the start time of the target furniture door panel production project to the current running time, and divides the target time interval according to the preset window to obtain several sub-intervals; Obtain the time data of the alarm issued by the system in each sub-interval, and obtain the alarm error coefficient of the sub-interval according to the time data; including: In each sub-interval, the actual time when the system issues an alarm and the corresponding preset time when the system issues an alarm are collected in real time at different times, and the actual time when the system issues an alarm and the corresponding preset time when the system issues an alarm are marked as P 实 y and P 预 y , y represents the number of the actual time when the system issues an alarm at different times and the corresponding preset time when the system issues an alarm, y=1, 2, 3, 4, ..., m, m is the number of numbers and is a positive integer; Calculate the alarm error coefficient, the calculation expression is: Where RF is the alarm error coefficient; Obtain the relevant data initially encrypted by the system and the relevant data last encrypted in each sub-interval, and obtain the encryption leakage coefficient of the sub-interval; including: Acquire the data that needs to be encrypted in the target furniture door panel production project, and record the data that has not been encrypted as original data; Calculate the information entropy of the original data. The calculation formula is: In the formula, H(X) represents the information entropy of the original data X, p(x i ) represents the probability of the i-th possible value in the original data X; n represents the number of data; Calculate the information entropy of the encrypted data using the following formula: In the formula, H(Y) represents the information entropy of the encrypted data Y, p(y j ) represents the probability of the i-th possible value in the encrypted data Y; Calculate the difference in information entropy between the data before and after encryption. The calculation expression is: Gain=H(X)-H(Y) In the formula, Gain represents information gain, that is, the difference in information entropy between data before and after encryption; Calculate the encryption leakage coefficient, the calculation expression is: WD = Gain / H (X); In the formula, WD is the encryption leakage coefficient; Acquire the actual current data of the system during operation in each sub-interval, and obtain the current instability coefficient of the sub-interval according to the actual current data; including: Acquire actual current data of the system during operation in each sub-interval, and obtain a current sequence in the sub-interval based on the acquired sequence; The data in the current sequence within this subinterval is marked as A 流 w, w represents the number of the data in the current sequence, w=1, 2, 3, 4, ..., f, f is a positive integer; Calculate the data A in the current series 流 The average value of w The calculation formula is: The average Calculate the current instability coefficient with the preset minimum current, and the calculation formula is: In the formula, WS is the current instability coefficient, and Wh is the preset minimum current; The operating state instability coefficient of each sub-interval is obtained according to the alarm error coefficient, the encryption leakage coefficient and the current instability coefficient in each sub-interval; An unstable operating state coefficient set is established based on the unstable operating state coefficients in all sub-intervals, and all abnormal coefficients in the unstable operating state coefficient set are screened out, and an abnormal set is established for all abnormal coefficients, and the standard deviation in the abnormal set is calculated, and the standard deviation is compared with the preset standard deviation value, and it is determined whether to issue a special alarm signal based on the comparison result.

2. A method for producing furniture door panels based on intelligent manufacturing according to claim 1, characterized in that: According to the alarm error coefficient, encryption leakage coefficient and current instability coefficient in each sub-interval, the operating state instability coefficient of the sub-interval is obtained, including: The alarm error coefficient, encryption leakage coefficient and current instability coefficient are weighted and summed to obtain the operating state instability coefficient. The calculation formula is: Wherein, Lgt is the operating state instability coefficient, RF, WD and WS are the alarm error coefficient, encryption leakage coefficient and current instability coefficient respectively, α, β, γ are the preset proportional coefficients of the alarm error coefficient, encryption leakage coefficient and current instability coefficient respectively, and α, β, γ are all greater than 0.

3. The method for producing furniture door panels based on intelligent manufacturing according to claim 1, characterized in that: A set of unstable operating state coefficients is established according to the unstable operating state coefficients in all subintervals, and all abnormal coefficients in the unstable operating state coefficient set are screened out, including: When the running state instability coefficient is not less than a preset running state instability coefficient reference threshold, marking the running state instability coefficient as an abnormal coefficient; When the running state unstable coefficient is less than a preset running state unstable coefficient reference threshold, the running state unstable coefficient is marked as a normal coefficient.

4. The method for producing furniture door panels based on intelligent manufacturing according to claim 1, characterized in that: All abnormal coefficients are used to establish an abnormal set, and the standard deviation in the abnormal set is calculated. The standard deviation is compared with the preset standard deviation value, and whether to issue a special alarm signal is determined based on the comparison results, including: A number of abnormal coefficients are used to establish a data set and calibrated as K, then K = {LP d }, where d = 1, 2, 3…r}, r is a positive integer; And calculate the standard deviation of several abnormal coefficients in the data set, and calibrate the standard deviation of several abnormal coefficients in the data set as PU, and compare the abnormal coefficient standard deviation PU with the preset abnormal coefficient standard deviation threshold BH, and choose whether to issue a special alarm signal according to the comparison result of the abnormal coefficient standard deviation and the preset abnormal coefficient standard deviation threshold; If the abnormal coefficient standard deviation PU is not less than the preset abnormal coefficient standard deviation threshold BH, a special alarm signal is issued; If the abnormal coefficient standard deviation PU is less than the preset abnormal coefficient standard deviation threshold BH, no special alarm signal is issued.

5. A furniture door panel production system based on intelligent manufacturing, used to implement a furniture door panel production method based on intelligent manufacturing as described in any one of claims 1 to 4, characterized in that: include: Division module: obtain the time from the start of the target furniture door panel production project to the current running time, obtain the target time interval, and divide the target time interval according to the preset window to obtain several sub-intervals; Alarm error module: obtains the time data of the alarm issued by the system in each sub-interval, and obtains the alarm error coefficient of the sub-interval according to the time data; Encryption leakage module: obtain the relevant data initially encrypted by the system and the relevant data last encrypted in each sub-interval, and obtain the encryption leakage coefficient of the sub-interval; Current instability module: obtains the actual current data of the system during operation in each sub-interval, and obtains the current instability coefficient of the sub-interval based on the actual current data; Operation status module: obtains the operation status instability coefficient of each sub-interval according to the alarm error coefficient, encryption leakage coefficient and current instability coefficient in each sub-interval; Evaluation and judgment module: establish an unstable operating state coefficient set according to the unstable operating state coefficients in all sub-intervals, and filter out all abnormal coefficients in the unstable operating state coefficient set, and establish an abnormal set for all abnormal coefficients, and calculate the standard deviation in the abnormal set, and compare the standard deviation with the preset standard deviation value, and determine whether to issue a special alarm signal based on the comparison result.

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