Intelligent production control method and system for furniture plates

Abstract

The invention discloses an intelligent production control method and system for furniture plates, and relates to the technical field of production control, and the method comprises the steps: obtaining an actual cutting path track, and judging whether deviation occurs or not; if the cutting equipment deviates, the time point at which deviation starts serves as an initial time point, and equipment state data of the cutting equipment in a first preset observation time window starting from the initial time point is recorded; recording the equipment state data and the offset data of plate cutting as judgment data, and judging whether a production stop alarm needs to be given or not according to the judgment data; if the production stop alarm does not need to be emitted, setting a second observation time window according to the judgment data of the first preset observation time window, and judging whether the production stop alarm is emitted or not according to the judgment data of the first observation time window and the second observation time window; therefore, judgment can be made according to actual conditions, whether cutting production of the furniture plates needs to be stopped or not is determined, and the production efficiency and the product quality are balanced through the reasonable alarm mechanism.

Description

Technical Field

[0001] The present invention relates to the technical field of production control, and in particular to an intelligent production control method and system for furniture panels. Background Art

[0002] In the production of furniture panels, precise production control is key to ensuring product quality and efficiency. This process involves the selection and management of raw materials, the optimal scheduling of production equipment, meticulous control of processing techniques, and final product quality inspection. With the widespread adoption of automation technology and intelligent equipment, modern furniture panel production is gradually moving towards high precision, low error, and low energy consumption. During the production process, the operating status and accuracy of the equipment directly affect the quality of the panels. This is especially true in processes such as fine cutting, edge banding, and punching, where even the slightest deviation can result in a substandard finished product. Therefore, traditional production control requires not only precise equipment adjustments but also real-time monitoring of the production process to ensure that each step is executed according to strict quality standards.

[0003] However, in the actual furniture panel production process, especially during the fine-cutting phase, equipment can occasionally malfunction or deviate from the preset cutting path. While these deviations may be minor in some cases, they can lead to serious quality issues if not promptly identified and addressed. To prevent this, many production lines have alarm systems in place that immediately shut down the machine for inspection if the cutting deviation exceeds a preset range.

[0004] However, frequent alarm downtime can significantly reduce production efficiency and may cause unnecessary downtime due to false alarms. Therefore, how to properly set the alarm mechanism to balance production efficiency and product quality has become a pressing problem in the current furniture panel production control. Summary of the Invention

[0005] The purpose of the present invention is to solve the above-mentioned problems and provide an intelligent production control method and system for furniture panels.

[0006] In a first aspect of the present invention, a method for intelligent production control of furniture panels is first proposed, the method comprising:

[0007] During the plate cutting process, the actual cutting path trajectory is obtained in real time, and combined with the preset cutting path trajectory to determine whether the plate cutting is offset;

[0008] If an offset occurs, the time point at which the offset begins is taken as the initial time point, and the device status data of the cutting device within the first preset observation time window starting from the initial time point is recorded;

[0009] Recording the equipment status data and the offset data of the plate cutting as judgment data, and determining whether to issue a production stop alarm based on the judgment data in a first preset observation time window; the first preset observation time window is set based on historical plate cutting data;

[0010] If a production stop alarm does not need to be issued, a second observation time window is set according to the judgment data of the first preset observation time window, and whether to issue a production stop alarm is determined according to the judgment data of the first observation time window and the second observation time window.

[0011] Optionally, the device status data of the cutting device includes a cutting force abnormality value and a vibration abnormality value of the cutting device, and a comprehensive abnormality value of the cutting device is obtained according to the cutting force abnormality value and the vibration abnormality value of the cutting device, wherein the step of calculating the cutting force abnormality value is:

[0012] Obtain the cutting trajectory of the furniture board corresponding to the first time window, and record the cutting depth at each time point to obtain the original cutting depth sequence based on time sequence;

[0013] The original cutting depth sequence is smoothed by a Gaussian filter to obtain the target cutting depth sequence;

[0014] Calculate the first-order difference of the target cutting depth sequence, and calculate the second-order difference based on the first-order difference to obtain the acceleration sequence of the cutting depth;

[0015] The cutting dynamics residual sequence is constructed based on the original cutting depth sequence, the target cutting depth sequence and the acceleration sequence of the cutting depth. The constructed formula is:

[0016]

[0017] Where R t The dynamic residual at time t, d raw t The original cutting depth at time t, d filtered t is the target cutting depth after smooth filtering at time t, σ d is the standard deviation of the target cutting depth sequence, Δ 2 d t is the acceleration of the cutting depth at the tth moment, EΔ 2 d is the mean absolute deviation of the cutting depth acceleration sequence;

[0018] Calculate the cutting force abnormal value, the calculation formula is: Where DF is the abnormal value of cutting force, μ(R t ) is the mean of the cutting dynamics residual sequence, and n is the total number of moments.

[0019] Optionally, the steps for calculating the vibration abnormal value and the abnormal comprehensive value are:

[0020] Acquire a vibration signal in a first time window, perform a fast Fourier transform on the vibration signal, convert the vibration signal from the time domain to the frequency domain, and obtain a spectrum of the vibration signal;

[0021] Set a frequency threshold, record the frequency range in the spectrum that is not less than the threshold as the high-frequency range, and calculate the energy corresponding to the high-frequency range as the vibration abnormality energy;

[0022] Calculate the energy of all regions of the spectrum and record it as the total vibration energy. Divide the vibration abnormality energy by the total vibration energy to obtain the vibration abnormality value.

[0023] The cutting force outliers and vibration outliers are normalized and mapped to the interval of [0-1]. The weights of the normalized cutting force outliers and vibration outliers are set to 0.5. The normalized cutting force outliers and vibration outliers are multiplied by the corresponding weights respectively, and the multiplication results are added to obtain the comprehensive abnormal value.

[0024] Optionally, the cutting path offset value is calculated based on the offset data of the plate cutting, and the calculation steps are as follows:

[0025] Obtain the trajectory of the furniture board being cut corresponding to the first time window, map it to a three-dimensional coordinate system, and calculate the actual path direction vector and actual path coordinates at each time point;

[0026] Obtaining a preset target trajectory corresponding to the trajectory of the furniture board being cut corresponding to the first time window, and calculating the target path direction vector and target path coordinates at each corresponding time point;

[0027] Calculate the angle θ between the actual path direction vector and the target path direction vector at each time point i , calculate the distance D between the actual path coordinates and the target path coordinates at each time point i , and according to D i θ i Calculate the path offset value at each time point using the following formula: Where W i is the path offset value at the i-th time point, λ is the attenuation factor, which controls the influence of direction difference on the offset, and its value range is 0.1-1;

[0028] Add up the path offset values ​​at all time points to get the cutting path offset value.

[0029] Optionally, the step of determining whether to issue a production stop alarm according to the determination data of the first preset observation time window is:

[0030] The judgment data is an abnormal comprehensive value of the cutting equipment and a cutting path offset value, the abnormal comprehensive value of the equipment is compared with a preset abnormal comprehensive value threshold, and the cutting path offset value is compared with a preset cutting path offset value threshold;

[0031] If the abnormal comprehensive value is not less than the preset abnormal comprehensive value threshold or the cutting path deviation value is not less than the preset cutting path deviation value threshold, it means that the probability of the subsequent furniture board cutting path returning to the preset cutting path is low. At this time, a production stop alarm is immediately issued to stop the cutting of the current furniture board;

[0032] If the abnormal comprehensive value is less than the preset abnormal comprehensive value threshold and the cutting path offset value is less than the preset cutting path offset value threshold, it means that the probability of the subsequent furniture panel cutting path returning to the preset cutting path is low. At this time, the production stop alarm will not be issued temporarily, and the current furniture panel cutting production will continue.

[0033] Optionally, the first preset observation time window is set based on historical plate cutting data, and the specific setting steps are:

[0034] The minimum value of the duration from the initial displacement to the complete displacement of the plate is obtained from the historical data, and the duration corresponding to the minimum value is used as the first preset observation time window.

[0035] Optionally, the step of setting the second observation time window according to the judgment data of the first preset observation time window is:

[0036] The abnormal comprehensive value and cutting path offset value of the first preset observation time window are normalized and added together to obtain a value in the range of [0-1] as the adjustment value. The adjustment value is multiplied by the first preset observation time window to obtain the second observation time window, and the abnormal comprehensive value and cutting path offset value of the second observation time window are used to determine whether to issue a production stop alarm.

[0037] In a second aspect of the present invention, an intelligent production control system for furniture panels is provided, the system comprising:

[0038] Judgment module: During the plate cutting process, the actual cutting path trajectory is obtained in real time, and combined with the preset cutting path trajectory to determine whether the plate cutting is offset;

[0039] Equipment status data module: If an offset occurs, the time point when the offset starts is used as the initial time point, and the equipment status data of the cutting equipment within the first preset observation time window starting from the initial time point is recorded;

[0040] A first judgment control module records the equipment status data and the offset data of the plate cutting as judgment data, and determines whether to issue a production stop alarm based on the judgment data in a first preset observation time window; the first preset observation time window is set based on historical plate cutting data;

[0041] The second judgment control module: If it is not necessary to issue a production stop alarm, a second observation time window is set according to the judgment data of the first preset observation time window, and whether to issue a production stop alarm is determined according to the judgment data of the first observation time window and the second observation time window.

[0042] Beneficial effects of the present invention:

[0043] The present invention proposes an intelligent production control method and system for furniture panels, which obtains the actual cutting path trajectory in real time during the panel cutting process, and determines whether the panel cutting is offset in combination with the preset cutting path trajectory; if an offset occurs, the time point when the offset starts is used as the initial time point, and the equipment status data of the cutting equipment within the first preset observation time window starting from the initial time point is recorded; the equipment status data and the offset data of the panel cutting are recorded as judgment data, and whether a production stop alarm is to be issued is determined based on the judgment data of the first preset observation time window; if a production stop alarm does not need to be issued, a second observation time window is set based on the judgment data of the first preset observation time window, and whether a production stop alarm is to be issued is determined based on the judgment data of the first observation time window and the second observation time window; in this way, a judgment can be made based on the actual situation, and it can be determined whether the furniture panel cutting production needs to be stopped, and through this reasonable alarm mechanism, production efficiency and product quality can be balanced. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] The present invention will be further described below with reference to the accompanying drawings.

[0045] Figure 1 The figure is a flow chart of an intelligent production control method for furniture panels;

[0046] Figure 2 This is a framework diagram of an intelligent production control system for furniture panels. DETAILED DESCRIPTION

[0047] 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.

[0048] The embodiment of the present invention provides an intelligent production control method for furniture panels. Figure 1 , Figure 1 A flowchart of an intelligent production control method for furniture panels provided by an embodiment of the present invention. The method comprises the following steps:

[0049] During the plate cutting process, the actual cutting path trajectory is obtained in real time, and combined with the preset cutting path trajectory to determine whether the plate cutting is offset;

[0050] If an offset occurs, the time point at which the offset begins is taken as the initial time point, and the device status data of the cutting device within the first preset observation time window starting from the initial time point is recorded;

[0051] Recording the equipment status data and the offset data of the plate cutting as judgment data, and judging whether to issue a production stop alarm according to the judgment data in a first preset observation time window; the first preset observation time window is set based on historical plate cutting data;

[0052] If a production stop alarm does not need to be issued, a second observation time window is set according to the judgment data of the first preset observation time window, and whether to issue a production stop alarm is determined according to the judgment data of the first observation time window and the second observation time window.

[0053] Based on the intelligent production control method of furniture panels provided by the embodiment of the present invention, through the above steps, it is possible to make a judgment based on the actual situation and determine whether it is necessary to stop the cutting production of furniture panels. Through this reasonable alarm mechanism, a balance can be achieved between production efficiency and product quality.

[0054] In one embodiment, during the plate cutting process, the actual cutting path trajectory is obtained in real time, and combined with the preset cutting path trajectory, it is determined whether the plate cutting is offset;

[0055] It should be noted that during the sheet metal cutting process, the actual cutting path trajectory is captured in real time and compared with the preset cutting path, typically achieved through high-precision sensors and automated control systems. Specifically, sensors such as laser scanners, visual sensors (e.g., cameras), or lidar are installed on the cutting equipment. These sensors can capture the position of the cutting head and the actual cutting path of the sheet metal in real time during the cutting process. These sensors then compare the collected data with the preset cutting path. The preset path is typically an ideal trajectory generated by computer numerical control (CNC) system programming. Using software algorithms (such as path comparison algorithms or image processing algorithms), the system compares the actual trajectory with the preset path in real time and calculates the deviation between the two. If the deviation exceeds the set tolerance, the system deems the cutting to have deviated and conducts subsequent analysis to determine whether to stop cutting the furniture sheet metal. For example, on a laser cutting machine, a laser scanner captures the laser head's motion trajectory in real time and compares this trajectory data with the programmed path. If the deviation exceeds the tolerance, the system performs an analysis to determine whether to stop cutting the furniture sheet metal.

[0056] Specifically: In one embodiment, if an offset occurs, the time point at which the offset starts is used as the initial time point, and the device status data of the cutting device within a first preset observation time window starting from the initial time point is recorded;

[0057] Specifically, the device status data of the cutting device includes a cutting force abnormality value and a vibration abnormality value of the cutting device. The abnormal comprehensive value of the cutting device is obtained according to the cutting force abnormality value and the vibration abnormality value of the cutting device. The steps for calculating the cutting force abnormality value are as follows:

[0058] Obtain the cutting trajectory of the furniture board corresponding to the first time window, and record the cutting depth at each time point to obtain the original cutting depth sequence based on time sequence;

[0059] The original cutting depth sequence is smoothed by a Gaussian filter to obtain the target cutting depth sequence;

[0060] Calculate the first-order difference of the target cutting depth sequence, and calculate the second-order difference based on the first-order difference to obtain the acceleration sequence of the cutting depth;

[0061] The cutting dynamics residual sequence is constructed based on the original cutting depth sequence, the target cutting depth sequence and the acceleration sequence of the cutting depth. The constructed formula is:

[0062]

[0063] Where R t The dynamic residual at time t, d rawt The original cutting depth at time t, d filtered t is the target cutting depth after smooth filtering at time t, σ d is the standard deviation of the target cutting depth sequence, Δ 2 d t is the acceleration of the cutting depth at the tth moment, EΔ 2 d is the mean absolute deviation of the cutting depth acceleration sequence;

[0064] Calculate the cutting force abnormal value, the calculation formula is: Where DF is the abnormal value of cutting force, μ(R t ) is the mean of the cutting dynamics residual sequence, and n is the total number of moments.

[0065] It should be noted that the cutting force outlier refers to the difference between the dynamic residuals during the cutting process and the expected state of the equipment. It reflects the deviation of the equipment from the preset cutting trajectory during the process. Specifically, the cutting force outlier is calculated by calculating the mean of the cutting dynamic residual sequence. A larger mean indicates more unstable equipment operation, more severe deviation, and lower cutting accuracy during the cutting process. A smaller mean indicates less deviation from the preset cutting trajectory during the cutting process, indicating more stable operation. In the actual furniture panel cutting process, smaller cutting force outliers indicate a smoother cutting process and more regular changes in cutting depth. This indicates that even if a deviation occurs at a certain moment, the equipment is likely to quickly return to the preset cutting trajectory. For example, if the cutting force of the cutting equipment does not show obvious abnormal fluctuations in the initial stage of the deviation and the residual is small, it can be inferred that the deviation is accidental and temporary. In this case, the cutting equipment state has not changed drastically. Although the cutting path has deviated, this deviation is unlikely to last long. The equipment is likely to automatically adjust and return to the preset cutting trajectory during the subsequent operation. For example, if a machine's cutting force anomaly is low during a cutting process, it may indicate stable operation and that the path deviation occurred within a short time window. Assuming the deviation is caused by minor unevenness in the plate or slight vibration in the machine, this deviation is likely a rare occurrence and not a sign of long-term machine instability. Since the cutting force anomaly is small, it indicates that the machine's dynamic residuals and cutting depth variations are within reasonable limits and have not experienced drastic changes. Therefore, it can be inferred that the cutting machine will recover quickly and will not significantly impact the overall cutting process. Conversely, a large cutting force anomaly indicates significant deviations in the machine's operating state. The cutting path deviation may not be a rare occurrence, significantly impacting the machine's accuracy and stability. Even if the initial deviation is small, it may indicate that the machine is experiencing a sustained, abnormal operating state. The deviation may gradually increase, potentially leading to a significant decline in cutting quality. Therefore, in this case, the system is more likely to issue an alarm and consider whether to shut down the machine for inspection to prevent further quality issues.

[0066] In one implementation, calculating cutting force anomalies allows for a more accurate assessment of whether the cutting equipment maintains its intended operating state and accuracy during actual operation. This calculation method helps identify abnormal fluctuations in the cutting process, particularly in dynamic parameters such as cutting depth and acceleration. By smoothing and filtering the original cutting depth sequence to eliminate short-term fluctuations caused by external disturbances or minor errors, the target cutting depth sequence provides a more stable and accurate reference. This allows the system to focus on larger abnormal fluctuations in equipment operation, more effectively detecting equipment failures or deviations. By further calculating the difference between the cutting dynamics residual and acceleration, combined with the standard deviation and mean absolute deviation, the degree of equipment instability or anomaly during the cutting process can be quantified. Large cutting force anomalies indicate a significant equipment failure or deviation from the normal trajectory. Timely detection and intervention can avoid the risk of quality failure and ensure efficient and stable production processes. Therefore, this calculation method not only provides accurate monitoring of equipment status but also significantly improves the efficiency of anomaly identification during production, reducing unnecessary downtime and false alarms, and ensuring overall production quality and efficiency.

[0067] In one embodiment, the steps for calculating the vibration abnormal value and the abnormal comprehensive value are as follows:

[0068] Acquire a vibration signal in a first time window, perform a fast Fourier transform on the vibration signal, convert the vibration signal from the time domain to the frequency domain, and obtain a spectrum of the vibration signal;

[0069] Set a frequency threshold, record the frequency range in the spectrum that is not less than the threshold as the high-frequency range, and calculate the energy corresponding to the high-frequency range as the vibration abnormality energy;

[0070] Calculate the energy of all regions of the spectrum and record it as the total vibration energy. Divide the vibration abnormality energy by the total vibration energy to obtain the vibration abnormality value.

[0071] The outliers for cutting force and vibration are normalized and mapped to the range of [0-1]. The weights for the normalized outliers are set to 0.5. The normalized outliers for cutting force and vibration are multiplied by their corresponding weights, and the sum of the multiplication results is used to obtain the combined anomaly value. The calculation formula is: HJ = 0.5 × nb + 0.5 × nv, where HJ is the combined anomaly value, nb is the normalized outlier for cutting force, and nv is the normalized outlier for vibration.

[0072] It's important to note that the vibration anomaly value reflects the degree of abnormal vibration generated by the cutting equipment during operation, particularly in the high-frequency region. If the equipment experiences an abnormality or malfunction during the cutting process, such as tool wear or loose components, this typically results in an increase in high-frequency vibration components. By calculating the ratio of the vibration anomaly energy to the total energy, the vibration anomaly value can be calculated to quantify the degree of vibration anomaly. A smaller vibration anomaly value indicates more stable vibration during operation and a lower probability of anomalies. This means that even if the cutting path deviates, the deviation is likely accidental and temporary, and the equipment will likely recover quickly and return to the intended cutting path. For example, if the equipment experiences a slight deviation while cutting furniture panels, a small vibration anomaly value indicates that the overall equipment operation is very stable, with minimal vibration fluctuations. This indicates that the deviation was likely caused by a minor disturbance or accidental factors (such as a brief period of uneven load or transient increase in tool friction). In this case, the equipment will likely self-correct and return to the correct cutting path within a short period of time, preventing long-term deviation. However, if the vibration anomaly value is large, it may mean that the equipment has a more serious fault (such as loose mechanical parts, power system problems, etc.), resulting in large vibration fluctuations. The deviation of the cutting path is not only accidental, but may also be persistent, and the equipment needs to be shut down for inspection and repair. This calculation of the vibration anomaly value helps to determine the nature of the deviation, whether it is a small, accidental deviation or a persistent problem caused by equipment failure, so as to make more reasonable production decisions, avoid false alarms and ensure product quality.

[0073] In one implementation, calculating vibration anomaly values ​​allows for a more accurate assessment of the vibration state during equipment operation, particularly energy variations in the high-frequency region. High-frequency vibration is often associated with mechanical failure or abnormal behavior (such as loose components, tool wear, or unbalanced loads). Therefore, by setting a frequency threshold and calculating the energy of vibration anomalies in the high-frequency region, we can effectively identify potential equipment issues. If the equipment's high-frequency vibration is low and stable during normal operation, the vibration anomaly value will also be small, indicating smooth operation. Deviations are likely occasional or temporary and will not significantly impact cutting quality. Conversely, larger vibration anomaly values ​​indicate a more serious equipment failure, with unstable vibration. In this case, even if a cutting path deviation occurs, it is likely caused by a problem with the equipment, and recovery may be slow, potentially leading to further deviations. Therefore, vibration anomaly values ​​not only help monitor equipment operating conditions but also play a vital role in early warning systems, identifying potential failures before they occur, thereby avoiding unnecessary production downtime and quality issues.

[0074] In one implementation, a combined abnormality value effectively combines cutting force and vibration abnormalities to more accurately reflect the overall equipment status and potential risks. Considering cutting force and vibration separately can miss some hidden issues, as these two factors are closely related and jointly affect cutting accuracy and equipment health. By normalizing them and then weighting their summation, the importance of each factor is balanced, preventing any single factor from overly influencing the results. This approach helps detect abnormalities promptly during the cutting process, not only accurately determining whether the equipment is faulty but also effectively distinguishing short-term deviations caused by accidental factors from long-term or persistent issues. For example, if the cutting force abnormality value is small but the vibration abnormality value is large, this may indicate a potential structural problem in the equipment that cannot be identified through cutting force data alone. The combined value provides a more comprehensive perspective, enabling operators to take timely repair or adjustment measures, reduce the probability of equipment failure, ensure production process stability and product quality, thereby improving production efficiency, reducing downtime, and lowering maintenance costs.

[0075] In one embodiment, the cutting path offset value is calculated based on the offset data of the plate cutting, and the calculation steps are as follows:

[0076] Obtain the trajectory of the furniture board being cut corresponding to the first time window, map it to a three-dimensional coordinate system, and calculate the actual path direction vector and actual path coordinates at each time point;

[0077] Obtaining a preset target trajectory corresponding to the trajectory of the furniture board being cut corresponding to the first time window, and calculating the target path direction vector and target path coordinates at each corresponding time point;

[0078] Calculate the angle θ between the actual path direction vector and the target path direction vector at each time point i , calculate the distance D between the actual path coordinates and the target path coordinates at each time point i , and according to D i θ i Calculate the path offset value at each time point using the following formula: Where W i is the path offset value at the i-th time point, λ is the attenuation factor, which controls the influence of direction difference on the offset, and its value range is 0.1-1;

[0079] Add up the path offset values ​​at all time points to get the cutting path offset value.

[0080] In one implementation method, the main purpose of calculating the path offset value through the above steps is to accurately monitor and evaluate the deviation in the cutting process, and ensure that the difference between the actual cutting path and the preset path is captured and adjusted in a timely manner. The calculation of the path offset value can reflect the degree of deviation of the equipment in actual operation and help the operator understand whether there are potential cutting quality problems. By calculating the angle and distance between the actual path and the target path, each deviation that occurs in the cutting process can be quantified in detail, especially in scenarios where high accuracy of direction changes and spatial positions is required. Using this data, small deviations in equipment operation can be discovered and corrected in a timely manner, thereby reducing unnecessary material waste, improving production efficiency, and ensuring the stability of cutting accuracy. In addition, the step of calculating the path offset value can also provide data support for equipment maintenance and optimization. Through long-term accumulation of offset data, possible equipment failures or links that need to be optimized can be identified, ultimately improving the reliability and consistency of the entire cutting process.

[0081] In one embodiment, the equipment status data and the offset data of the plate cutting are recorded as the judgment data, and whether to issue a production stop alarm is determined based on the judgment data in a first preset observation time window; the first preset observation time window is set based on historical plate cutting data;

[0082] Specifically, the steps of determining whether to issue a production stop alarm based on the judgment data of the first preset observation time window are:

[0083] The judgment data is an abnormal comprehensive value of the cutting equipment and a cutting path offset value, the abnormal comprehensive value of the equipment is compared with a preset abnormal comprehensive value threshold, and the cutting path offset value is compared with a preset cutting path offset value threshold;

[0084] If the abnormal comprehensive value is not less than the preset abnormal comprehensive value threshold or the cutting path deviation value is not less than the preset cutting path deviation value threshold, it means that the probability of the subsequent furniture board cutting path returning to the preset cutting path is low. At this time, a production stop alarm is immediately issued to stop the cutting of the current furniture board;

[0085] If the abnormal comprehensive value is less than the preset abnormal comprehensive value threshold and the cutting path offset value is less than the preset cutting path offset value threshold, it means that the probability of the subsequent furniture panel cutting path returning to the preset cutting path is low. At this time, the production stop alarm will not be issued temporarily, and the current furniture panel cutting production will continue.

[0086] It should be noted that the preset abnormal comprehensive value threshold and the preset cutting path offset value threshold are set by professionals based on actual conditions and are not specifically limited or elaborated on.

[0087] It should be noted that the purpose of this judgment step is to determine whether the current production status is normal by analyzing the device's combined abnormality value and cutting path deviation value. When an abnormality occurs, a production stop alarm is issued promptly, thereby avoiding quality issues or material waste caused by equipment failure or path deviation. Specifically, the combined abnormality value reflects the combined performance of multiple abnormal indicators (such as cutting force and vibration) during the device's operation, while the cutting path deviation value measures the difference between the actual cutting path and the preset path. If either value exceeds a preset threshold, it indicates that the device or cutting path has significantly deviated, and there is a high probability that the device will not be able to return to normal. In this case, a production stop alarm should be issued immediately to prevent further abnormalities from spreading during production, thereby affecting production quality and preventing material waste. For example, if the cutting path deviation value is large and the combined abnormality value exceeds the threshold, it may indicate a mechanical failure or positioning error in the device, preventing it from completing subsequent cutting tasks. In this case, stopping production promptly can avoid greater losses. Conversely, if neither of these indicators reaches the preset threshold, it indicates that the device is operating within the normal range, the cutting path is likely to return to the preset path in the short term, and continuing production does not pose a significant risk. Therefore, the stop alarm can be omitted to maintain production progress. The advantage of this judgment mechanism is that it can ensure production safety while avoiding frequent shutdown operations and maximizing production efficiency.

[0088] In one embodiment, the first preset observation time window is set based on historical plate cutting data, and the specific setting steps are:

[0089] The minimum value of the duration from the initial displacement to the complete displacement of the plate is obtained from the historical data, and the duration corresponding to the minimum value is used as the first preset observation time window.

[0090] It should be noted that the purpose of setting the first preset observation time window is to use historical data to determine a reasonable observation period, so as to promptly detect any deviation that may occur during the cutting process. By obtaining the minimum duration from the start of plate deviation to its complete deflection, the observation time window is ensured to be neither too long nor too short, avoiding unnecessary waiting time due to a too long duration, nor insufficient data capture due to a too short duration. For example, suppose historical data shows that the minimum time from the start of deviation to the complete return to the preset cutting path for some plates during cutting is 60 seconds. This means that if the observation window is set to less than 60 seconds, the deviation signal may not be effectively captured, which may lead to misjudgment or missed early signs of anomaly. On the other hand, if the time window is set too long, it may delay production judgment and cause unnecessary production stoppages, affecting efficiency. By selecting the observation window corresponding to the minimum duration, the system can more sensitively respond to deviation changes during the cutting process, ensuring that problems can be detected and corrected soon after the deviation begins. This ensures smooth production while avoiding excessive intervention, effectively improving production efficiency and product quality stability.

[0091] In one embodiment, if a production stop alarm does not need to be issued, a second observation time window is set according to the judgment data of the first preset observation time window, and whether to issue a production stop alarm is determined according to the judgment data of the first observation time window and the second observation time window.

[0092] Specifically, the steps of setting the second observation time window according to the judgment data of the first preset observation time window are:

[0093] The abnormal comprehensive value and cutting path offset value of the first preset observation time window are normalized and added together to obtain a value in the range of [0-1] as the adjustment value. The adjustment value is multiplied by the first preset observation time window to obtain the second observation time window, and the abnormal comprehensive value and cutting path offset value of the second observation time window are used to determine whether to issue a production stop alarm.

[0094] It should be noted that the purpose of setting the second observation time window is to dynamically adjust the subsequent observation windows based on the data of the first preset observation time window, so as to ensure that the real-time response of the system during the cutting process is more flexible and accurate. When the abnormal comprehensive value of the first time window and the cutting path offset value are normalized and added together to obtain the adjustment value, and this adjustment value is multiplied by the length of the first observation time window to set the second observation time window, such a setting helps to dynamically adjust the observation time according to the actual production situation. If the abnormal value of the first time window is high, it means that there may be a large offset or abnormality in the cutting process. At this time, the system will shorten the second observation time window, making subsequent judgments faster and avoiding long production delays; on the contrary, if the abnormal value of the first time window is low, it means that the cutting process is relatively stable, and the second observation time window can be appropriately extended to more comprehensively observe and judge the cutting status. For example, if the outlier value of a cutting device is high within the first preset time window, indicating a possible equipment problem or significant deviation, the system will shorten the second observation time window to ensure rapid action, such as immediately issuing a production stop alarm. If the outlier value is low, indicating a relatively normal cutting process, the second time window can be appropriately extended to give the system more time to monitor the subsequent cutting process and avoid premature and unnecessary alarms. This dynamic adjustment enables more precise and efficient response to varying production conditions, improving production efficiency and equipment reliability.

[0095] Based on the same inventive concept, the present invention also provides an intelligent production control system for furniture panels. Figure 2 , Figure 2 This is a framework diagram of an intelligent production control system for furniture panels provided by an embodiment of the present invention. The system includes:

[0096] Judgment module: During the plate cutting process, the actual cutting path trajectory is obtained in real time, and combined with the preset cutting path trajectory to determine whether the plate cutting is offset;

[0097] Equipment status data module: If an offset occurs, the time point when the offset starts is used as the initial time point, and the equipment status data of the cutting equipment within the first preset observation time window starting from the initial time point is recorded;

[0098] A first judgment control module records the equipment status data and the offset data of the plate cutting as judgment data, and determines whether to issue a production stop alarm based on the judgment data in a first preset observation time window; the first preset observation time window is set based on historical plate cutting data;

[0099] The second judgment control module: If it is not necessary to issue a production stop alarm, a second observation time window is set according to the judgment data of the first preset observation time window, and whether to issue a production stop alarm is determined according to the judgment data of the first observation time window and the second observation time window.

[0100] An intelligent production control system for furniture panels provided in an embodiment of the present invention can, through the above steps, make judgments based on the actual situation and determine whether it is necessary to stop the cutting production of furniture panels. Through this reasonable alarm mechanism, a balance can be achieved between production efficiency and product quality.

[0101] 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 should not be considered to limit the scope 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. An intelligent production control method for furniture panels, characterized in that: The following steps are involved: During the plate cutting process, the actual cutting path trajectory is obtained in real time, and combined with the preset cutting path trajectory to determine whether the plate cutting is offset; If an offset occurs, the time point at which the offset begins is taken as the initial time point, and the device status data of the cutting device within the first preset observation time window starting from the initial time point is recorded; Recording the equipment status data and the offset data of the plate cutting as judgment data, and determining whether to issue a production stop alarm based on the judgment data in a first preset observation time window; the first preset observation time window is set based on historical plate cutting data; If a production stop alarm does not need to be issued, a second observation time window is set according to the judgment data of the first preset observation time window, and whether to issue a production stop alarm is determined according to the judgment data of the first observation time window and the second observation time window.

2. The intelligent production control method for furniture panels according to claim 1 is characterized in that: The device status data of the cutting device includes a cutting force abnormality value and a vibration abnormality value of the cutting device. A comprehensive abnormality value of the cutting device is obtained based on the cutting force abnormality value and the vibration abnormality value of the cutting device. The steps for calculating the cutting force abnormality value are as follows: Obtain the cutting trajectory of the furniture board corresponding to the first time window, and record the cutting depth at each time point to obtain the original cutting depth sequence based on time sequence; The original cutting depth sequence is smoothed by a Gaussian filter to obtain the target cutting depth sequence; Calculate the first-order difference of the target cutting depth sequence, and calculate the second-order difference based on the first-order difference to obtain the acceleration sequence of the cutting depth; The cutting dynamics residual sequence is constructed based on the original cutting depth sequence, the target cutting depth sequence and the acceleration sequence of the cutting depth. The constructed formula is: Where R t The dynamic residual at time t, d raw t The original cutting depth at time t, d filtered t is the target cutting depth after smooth filtering at time t, σ d is the standard deviation of the target cutting depth sequence, Δ 2 d t is the acceleration of the cutting depth at the tth moment, EΔ 2 d is the mean absolute deviation of the cutting depth acceleration sequence; Calculate the cutting force abnormal value, the calculation formula is: Where DF is the abnormal value of cutting force, μ(R t ) is the mean of the cutting dynamics residual sequence, and n is the total number of moments.

3. The intelligent production control method of furniture board according to claim 2 is characterized in that: The calculation steps for the vibration abnormal value and the abnormal comprehensive value are as follows: Acquire a vibration signal in a first time window, perform a fast Fourier transform on the vibration signal, convert the vibration signal from the time domain to the frequency domain, and obtain a spectrum of the vibration signal; Set a frequency threshold, record the frequency range in the spectrum that is not less than the threshold as the high-frequency range, and calculate the energy corresponding to the high-frequency range as the vibration abnormality energy; Calculate the energy of all regions of the spectrum and record it as the total vibration energy. Divide the vibration abnormality energy by the total vibration energy to obtain the vibration abnormality value. The cutting force outliers and vibration outliers are normalized and mapped to the interval of [0-1]. The weights of the normalized cutting force outliers and vibration outliers are set to 0.

5. The normalized cutting force outliers and vibration outliers are multiplied by the corresponding weights respectively, and the multiplication results are added to obtain the comprehensive abnormal value.

4. The intelligent production control method for furniture panels according to claim 3 is characterized in that: Calculate the cutting path offset value based on the offset data of the plate cutting. The calculation steps are as follows: Obtain the trajectory of the furniture board being cut corresponding to the first time window, map it to a three-dimensional coordinate system, and calculate the actual path direction vector and actual path coordinates at each time point; Obtaining a preset target trajectory corresponding to the trajectory of the furniture board being cut corresponding to the first time window, and calculating the target path direction vector and target path coordinates at each corresponding time point; Calculate the angle θ between the actual path direction vector and the target path direction vector at each time point i , calculate the distance D between the actual path coordinates and the target path coordinates at each time point i , and according to D i θ i Calculate the path offset value at each time point using the following formula: Where W i is the path offset value at the i-th time point, λ is the attenuation factor, which controls the influence of direction difference on the offset, and its value range is 0.1-1; Add up the path offset values ​​at all time points to get the cutting path offset value.

5. The intelligent production control method of furniture board according to claim 4 is characterized in that: The steps of determining whether to issue a production stop alarm according to the judgment data of the first preset observation time window are: The judgment data is an abnormal comprehensive value of the cutting equipment and a cutting path offset value, the abnormal comprehensive value of the equipment is compared with a preset abnormal comprehensive value threshold, and the cutting path offset value is compared with a preset cutting path offset value threshold; If the abnormal comprehensive value is not less than the preset abnormal comprehensive value threshold or the cutting path deviation value is not less than the preset cutting path deviation value threshold, it means that the probability of the subsequent furniture board cutting path returning to the preset cutting path is low. At this time, a production stop alarm is immediately issued to stop the cutting of the current furniture board; If the abnormal comprehensive value is less than the preset abnormal comprehensive value threshold and the cutting path offset value is less than the preset cutting path offset value threshold, it means that the probability of the subsequent furniture panel cutting path returning to the preset cutting path is low. At this time, the production stop alarm will not be issued temporarily, and the current furniture panel cutting production will continue.

6. The intelligent production control method for furniture panels according to claim 1 is characterized in that: The first preset observation time window is set based on historical plate cutting data, and the specific setting steps are: The minimum value of the duration from the initial displacement to the complete displacement of the plate is obtained from the historical data, and the duration corresponding to the minimum value is used as the first preset observation time window.

7. The intelligent production control method for furniture panels according to claim 1 is characterized in that: The steps of setting the second observation time window according to the judgment data of the first preset observation time window are: The abnormal comprehensive value and cutting path offset value of the first preset observation time window are normalized and added together to obtain a value in the range of [0-1] as the adjustment value. The adjustment value is multiplied by the first preset observation time window to obtain the second observation time window, and the abnormal comprehensive value and cutting path offset value of the second observation time window are used to determine whether to issue a production stop alarm.

8. An intelligent production control system for furniture panels, used to implement the intelligent production control method for furniture panels as described in any one of claims 1 to 7, characterized in that: The system comprises: Judgment module: During the plate cutting process, the actual cutting path trajectory is obtained in real time, and combined with the preset cutting path trajectory to determine whether the plate cutting is offset; Equipment status data module: If an offset occurs, the time point when the offset starts is used as the initial time point, and the equipment status data of the cutting equipment within the first preset observation time window starting from the initial time point is recorded; A first judgment control module records the equipment status data and the offset data of the plate cutting as judgment data, and determines whether to issue a production stop alarm based on the judgment data in a first preset observation time window; the first preset observation time window is set based on historical plate cutting data; The second judgment control module: If it is not necessary to issue a production stop alarm, a second observation time window is set according to the judgment data of the first preset observation time window, and whether to issue a production stop alarm is determined according to the judgment data of the first observation time window and the second observation time window.

Images