Intelligent Cutting and Cutting System for Steel Plate Shearing Based on Data Analysis

Abstract

The present invention discloses an intelligent cutting system for steel plate shearing based on data analysis, belongs to the field of steel plate shearing, and solves the problem of how to perform real-time monitoring and early warning on the steel plate shearing process based on the intelligent cutting of steel plate shearing, so as to avoid the problem of excessive or inaccurate early warning due to the limitations of the intelligent cutting algorithm and the single parameter early warning method; the present invention firstly preliminarily analyzes whether there are abnormal risks and hidden dangers in the target equipment during the shearing process, and if there are abnormal risks and hidden dangers, further analyzes the equipment status and the real-time processing quality of the steel plate, and then combines the equipment status and the real-time processing quality of the steel plate to analyze whether an early warning is needed, to ensure that an early warning signal is issued only when there is a real problem, thereby reducing production interruptions caused by false alarms, improving the reliability of the early warning system, being able to extend the service life of the equipment, and improving product quality.

Description

Technical Field

[0001] The invention belongs to the field of steel plate shearing processing, and in particular relates to an intelligent cutting system for steel plate shearing processing based on data analysis. Background Art

[0002] Intelligent cutting for steel plate shearing is an automated system based on advanced technology. It aims to improve production efficiency and processing accuracy by automatically adjusting the cutting path and process parameters through real-time monitoring and analysis of the shearing conditions of the steel plate.

[0003] However, although the current intelligent cutting technology has achieved a high degree of automation and reduced manual intervention, there are still some limitations in practical applications. As a result, abnormal situations still exist in the actual shearing process, and the system may not be able to take corrective measures in time, which may lead to quality problems or equipment loss. In this case, manual intervention is still required to solve the problem. In addition, traditional monitoring and early warning systems usually rely on a single parameter for early warning, which may lead to excessive or inaccurate early warning. Summary of the invention

[0004] The present invention aims to solve at least one of the technical problems existing in the prior art. To this end, the present invention proposes an intelligent cutting system for steel plate shearing based on data analysis, which solves how to monitor and warn the steel plate shearing process in real time based on the intelligent cutting of steel plate shearing, avoiding the problem of excessive or inaccurate warning due to the limitations of the intelligent cutting algorithm and the single parameter warning method.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] Intelligent cutting system for steel plate shearing based on data analysis, including:

[0007] The preliminary analysis module is used to obtain the production data of steel plate products sheared by the target equipment in the previous monitoring time interval, and evaluate whether the target equipment currently has abnormal risk of steel plate shearing based on the production data of steel plate products sheared by the target equipment in the previous monitoring time interval;

[0008] The device status detection and analysis module is used to obtain the real-time device status data of the target device and evaluate the operating status of the target device based on the real-time device status data of the target device;

[0009] A quality inspection and analysis module, used to obtain real-time quality inspection data of the steel plate products sheared by the target equipment, and to evaluate the shearing quality of the corresponding steel plate products based on the obtained real-time quality inspection data of the steel plate products sheared by the target equipment;

[0010] The comprehensive analysis and early warning module is used to analyze and determine whether it is necessary to issue a fault warning for the steel plate shearing processing of the target equipment, when it is determined that the target equipment currently has an abnormal risk of steel plate shearing processing, based on the current operating status of the target equipment and the shearing processing quality of the steel plate products sheared by the target equipment.

[0011] Further, the evaluating whether the target device currently has a risk of abnormal steel plate shearing based on the steel plate product production data sheared by the target device in the previous monitoring time interval includes:

[0012] Acquire the steel plate product production data of the target equipment in the last monitoring time interval, wherein the steel plate product production data includes the steel plate product model and the production quantity, the number of qualified products and the total production time of the corresponding steel plate product model in the last monitoring time interval;

[0013] According to the steel plate product model, the standard qualified rate and standard production rate of the steel plate product corresponding to the corresponding steel plate product model are obtained from the database;

[0014] All steel plate product models of the target equipment in the last monitoring time interval are arranged in ascending order of time and numbered, and the production quantity, qualified product quantity and total production time of the corresponding steel plate product model are marked as NFm, NFBm and TFm respectively; and m = 1, 2...M; where M represents the total number of steel plate product models in the last monitoring time interval;

[0015] According to the calculation formula Calculate and obtain the quality efficiency coefficient QE of the steel plate product shearing processing of the target equipment in the last monitoring time interval; where QFm and PFm represent the standard qualified rate and standard productivity of the steel plate product corresponding to the corresponding steel plate product model respectively; am represents the preset proportional coefficient corresponding to the steel plate product model numbered m; and 0<am≤1,

[0016] Compare the calculated quality efficiency coefficient of the target equipment for steel plate product shearing in the previous monitoring time interval with the preset quality efficiency coefficient threshold, analyze and determine whether the target equipment currently has a risk of steel plate shearing, and send the generated signal to the comprehensive analysis and early warning module;

[0017] If the calculated quality efficiency coefficient of the steel plate product shearing process of the target equipment in the previous monitoring time interval is greater than or equal to the preset quality efficiency coefficient threshold, a normal signal is generated;

[0018] If the calculated quality efficiency coefficient of the steel plate product shearing processing of the target equipment in the previous monitoring time interval is less than the preset quality efficiency coefficient threshold, an abnormal signal is generated.

[0019] Furthermore, the value of am is divided into three cases:

[0020] If the number m=M=1, and the steel plate product model corresponding to the only shearing process in the previous monitoring time interval is the same as the steel plate product model corresponding to the current shearing process, then am=aM=1;

[0021] If the number m < M, and the product models of all steel plates sheared in the previous monitoring time interval are different from the product models of steel plates corresponding to the current shearing process, then

[0022] If the number m < M, and the last steel plate product model in ascending order in the previous monitoring time interval is the same as the steel plate product model corresponding to the current shearing process, and the other steel plate product models are different from the steel plate product models corresponding to the current shearing process, then aM = 0.5,

[0023] Furthermore, the device status data of the target device includes shear force, vibration speed, shear velocity and device temperature of the target device.

[0024] Further, the evaluating the operating status of the target device based on the real-time device status data of the target device includes: numbering a plurality of device status parameters included in the acquired real-time device status data of the target device;

[0025] According to the calculation formula Calculate and obtain the current device state abnormality assessment value SR of the target device; where Ci represents the value of the device state parameter numbered i; Ci,min and Ci,max represent the minimum and maximum values ​​of the standard range of the device state parameter numbered i, respectively; ki represents the preset proportional coefficient of the device state parameter numbered i; and ki>0,

[0026] A preset device state abnormality assessment value threshold is set. If the calculated current device state abnormality assessment value of the target device is greater than the preset device state abnormality assessment value threshold, it indicates that the current target device is in an abnormal operating state; if the calculated current device state abnormality assessment value of the target device is less than or equal to the preset device state abnormality assessment value threshold, it indicates that the current target device is in a normal operating state.

[0027] Furthermore, the real-time quality inspection data of the steel plate product sheared by the target equipment refers to a real-time high-definition image when the target equipment shears the steel plate product.

[0028] Furthermore, the shearing quality of the corresponding steel plate product is evaluated based on the real-time quality detection data of the steel plate product sheared by the target equipment, including:

[0029] A high-definition camera is used to capture a high-definition image of a steel plate product that has been sheared by the target device, and a shearing contour line of the complete steel plate product after the steel plate has been sheared and the model of the corresponding steel plate product are obtained through computer vision technology processing;

[0030] According to the model of the steel plate product in the high-definition image currently captured, a preset steel plate cutting route corresponding to the corresponding steel plate product model is obtained from the database;

[0031] Convert the pixel coordinates of the shearing contour line of the corresponding steel plate product in the high-definition image currently captured into a machine coordinate system consistent with the preset steel plate shearing route of the corresponding steel plate product model;

[0032] The preset steel plate shearing route of the corresponding steel plate product model is evenly divided into n sub-route segments, and the central machine coordinates of each sub-route segment are obtained to generate a first coordinate sequence data set; and the corresponding shearing contour line converted into a machine coordinate system consistent with the preset steel plate shearing route of the corresponding steel plate product model is evenly divided into n sub-contour line segments, and the central machine coordinates of each sub-contour line segment are obtained to generate a second coordinate sequence data set;

[0033] Compare all the coordinates contained in the generated first coordinate sequence data set with all the coordinates contained in the second coordinate sequence data set, count the number of coordinates contained in the first coordinate sequence data set that are the same as the coordinates contained in the second coordinate sequence data set and mark them as nx, and calculate according to the formula Get the shear contour match of the steel plate product that has been sheared by the target equipment.

[0034] Assign PR and stamp the shooting time;

[0035] A cutting contour matching rate threshold is preset. If the calculated cutting contour matching rate of the steel plate product that has been cut by the target device is greater than or equal to the preset cutting contour matching rate threshold, it indicates that the quality of the steel plate product that has been cut by the target device is good; if the calculated cutting contour matching rate of the steel plate product that has been cut by the target device is less than the preset cutting contour matching rate threshold, it indicates that the quality of the steel plate product that has been cut by the target device is poor.

[0036] Furthermore, the specific analysis process of the comprehensive analysis and early warning module includes:

[0037] Obtain information sent by the preliminary analysis module on whether the target device currently has abnormal risk of steel plate shearing processing;

[0038] If the signal obtained is normal, no further processing is required;

[0039] If an abnormal signal is obtained, the current operating status of the target equipment and the shearing quality of the steel plate products sheared by the target equipment are combined to analyze and determine whether a fault warning is needed for the steel plate shearing of the target equipment, including:

[0040] Send a trigger signal of equipment status detection and analysis to the equipment status detection and analysis module, and send a trigger signal of steel plate quality detection and analysis to the quality detection and analysis module; after receiving the trigger signal of equipment status detection and analysis, the equipment status detection and analysis module acquires and analyzes the real-time equipment status data of the target equipment; after receiving the trigger signal of steel plate quality detection and analysis, the quality detection and analysis module acquires and analyzes the real-time quality detection data of the steel plate product sheared by the target equipment; and then acquires the current equipment status abnormality evaluation value SR of the target equipment and the shearing contour matching rate PR of the current steel plate product;

[0041] According to the calculation formula Calculate and obtain the fault warning indication value YZ of the current shearing process of the target equipment; where SR0 and PR0 are the equipment status abnormality assessment value threshold and the shearing contour matching rate threshold respectively;

[0042] Perform numerical analysis on the calculated fault warning indication value YZ of the current shearing process of the target device; if YZ=0, a warning signal for the shearing process of the target device is generated, and relevant personnel need to handle it in time; if YZ≥1, a normal signal for the shearing process of the target device is generated, and the shearing process is kept running normally without any processing.

[0043] Compared with the prior art, the present invention has the following beneficial effects:

[0044] In the present invention, by preliminarily analyzing whether the equipment has abnormal risk hazards, potential problems can be identified in advance, avoiding over-reliance on the alarm mechanism of a single parameter, thereby reducing the possibility of false alarms. This hierarchical analysis method can more accurately evaluate the actual state of the equipment and avoid unnecessary interference with the normal operation of the equipment. After confirming the existence of abnormal risk hazards, further analysis is performed in combination with the equipment state and the real-time processing quality of the steel plate, so that the triggering of the early warning is more accurate, ensuring that the early warning signal will only be issued when there is a problem, reducing production interruptions caused by false alarms and improving the reliability of the early warning system. By pre-identifying and analyzing potential abnormalities, preventive measures can be taken before the problem expands or causes production failures, reducing the unexpected downtime of the equipment, reducing equipment losses, and improving the overall production efficiency of steel plate products. The present invention combines the data of equipment status and real-time processing quality for comprehensive analysis, providing a more comprehensive basis for equipment operation and maintenance decisions. This data-driven approach helps to optimize the production process, extend the service life of the equipment, and improve product quality. The analysis process can adapt to different production conditions and equipment status changes, avoiding the limitations of the intelligent matching algorithm, and ensuring that effective monitoring and early warning can be provided under various working conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 It is a structural schematic diagram of the intelligent cutting system for steel plate shearing based on data analysis of the present invention. DETAILED DESCRIPTION

[0046] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than 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.

[0047] like Figure 1 As shown, the intelligent cutting system for steel plate shearing based on data analysis includes: a preliminary cutting analysis module, an equipment status detection and analysis module, a quality detection and analysis module, and a comprehensive analysis and early warning module;

[0048] The preliminary analysis module is used to obtain the production data of steel plate products sheared by the target equipment in the previous monitoring time interval, and evaluate whether the target equipment currently has abnormal risk of steel plate shearing based on the production data of steel plate products sheared by the target equipment in the previous monitoring time interval;

[0049] When the steel plate is sheared, several sensors are arranged on or around the shearing equipment to detect various parameters. In the prior art, generally, an alarm is issued as long as one of the parameters is abnormal. However, in many cases, the abnormality of some parameters will not affect the overall shearing process, resulting in excessive alarms. In addition, the shearing equipment will have regular maintenance and hidden danger inspections, which can avoid equipment failures during operation to a certain extent. In order to prevent excessive alarms and interfere with the normal operation of the equipment, the present invention first preliminarily analyzes whether there are abnormal risks and hidden dangers in the shearing process of the target equipment. If there are abnormal risks and hidden dangers, the equipment status and the real-time processing quality of the steel plate are further analyzed, and then combined with the equipment status and the real-time processing quality of the steel plate to analyze whether an early warning is needed; in order to facilitate the description of the scheme, the equipment for shearing the steel plate to be studied is marked as the target equipment;

[0050] It can be understood that in the embodiment of the present invention, the steel plate product production data of the target equipment is acquired and analyzed every other monitoring time interval, and the previous monitoring time interval refers to the time period that has just ended, and the steel plate product production data of the target equipment in this time period is acquired and analyzed; the duration of the monitoring time interval can be set to 1 hour;

[0051] It should be noted that the target equipment may change the steel plate product model at any time during the steel plate shearing process. If the steel plate product model sheared by the target equipment in the previous monitoring time interval is exactly the same as the steel plate product model currently being sheared, then the qualification rate and production efficiency of the corresponding steel plate product model in the previous monitoring time interval, or whether the specification parameters set by the target equipment for the model are accurate, will have an important impact on the effect of the current steel plate shearing process; if the steel plate product model sheared in the previous monitoring time interval is completely different from the current shearing model, then the qualification rate and production efficiency in the previous monitoring time interval will be different. Or the specification parameters set for other models of the target equipment have little impact on the current shearing process. However, it should be noted that if there are many defective products and low production efficiency for other models of steel plate products in the previous monitoring time interval, it indicates that there may be problems with the equipment status of the target equipment itself, which is likely to affect the current steel plate shearing process; if the models of steel plate products sheared in the previous monitoring time interval are both the same and different from the current processing models, it is necessary to comprehensively consider the quality and production efficiency performance of the same and different models of steel plates in the previous monitoring time interval to comprehensively evaluate their potential impact on the current steel plate shearing process;

[0052] The evaluation of whether the target device currently has a risk of abnormal steel plate shearing based on the steel plate product production data of the target device shearing in the previous monitoring time interval includes:

[0053] Acquire the steel plate product production data of the target equipment in the last monitoring time interval, wherein the steel plate product production data includes the steel plate product model and the production quantity, the number of qualified products and the total production time of the corresponding steel plate product model in the last monitoring time interval; it is understandable that due to the high production speed of the equipment or the small production of some steel plate product models, it is very likely that multiple types of steel plate products are produced in one monitoring time interval;

[0054] According to the steel plate product model, the standard qualified rate and standard production rate of the steel plate product corresponding to the corresponding steel plate product model are obtained from the database;

[0055] All steel plate product models of the target equipment in the last monitoring time interval are arranged in ascending order of time and numbered, and the production quantity, qualified product quantity and total production time of the corresponding steel plate product model are marked as NFm, NFBm and TFm respectively; and m = 1, 2...M; where M represents the total number of steel plate product models in the last monitoring time interval;

[0056] According to the calculation formula Calculate and obtain the quality efficiency coefficient QE of the steel plate product shearing processing of the target equipment in the last monitoring time interval; where QFm and PFm represent the standard qualified rate and standard productivity of the steel plate product corresponding to the corresponding steel plate product model respectively; am represents the preset proportional coefficient corresponding to the steel plate product model numbered m; and 0<am≤1,

[0057] The value of am can be divided into three cases:

[0058] If the number m=M=1, and the steel plate product model corresponding to the only shearing process in the previous monitoring time interval is the same as the steel plate product model corresponding to the current shearing process, then am=aM=1;

[0059] If the number m < M, and the product models of all steel plates sheared in the previous monitoring time interval are different from the product models of steel plates corresponding to the current shearing process, then

[0060] If the number m < M, and the last steel plate product model in ascending order in the previous monitoring time interval is the same as the steel plate product model corresponding to the current shearing process, and the other steel plate product models are different from the steel plate product models corresponding to the current shearing process, then aM = 0.5,

[0061] It is understandable that when the relative pass rate The larger the value, the higher the relative productivity. The larger the value, the larger the QE value, indicating that the shearing production of the target equipment was good during the previous monitoring time interval;

[0062] Compare the calculated quality efficiency coefficient of the target equipment for steel plate product shearing in the previous monitoring time interval with the preset quality efficiency coefficient threshold, analyze and determine whether the target equipment currently has a risk of steel plate shearing, and send the generated signal to the comprehensive analysis and early warning module;

[0063] The preset quality efficiency coefficient threshold is obtained by analyzing a large number of quality efficiency coefficients calculated within the monitoring time interval and the actual quality of the steel plate products or the actual status of the equipment when the equipment that is the same as the target equipment is used to shear and process various types of steel plate products in the early stage;

[0064] If the calculated quality efficiency coefficient of the steel plate product shearing process of the target equipment in the previous monitoring time interval is greater than or equal to the preset quality efficiency coefficient threshold, a normal signal is generated;

[0065] If the calculated quality efficiency coefficient of the steel plate product shearing process of the target equipment in the previous monitoring time interval is less than the preset quality efficiency coefficient threshold, an abnormal signal is generated;

[0066] In the present invention, by analyzing the production data of steel plate products sheared by the target equipment in the previous monitoring time interval to obtain the quality efficiency coefficient, the production status and equipment status of the target equipment in the monitoring time interval can be evaluated, so as to determine whether there is an abnormal risk in the current steel plate shearing process; this method can perform a pre-analysis of abnormalities before a single parameter frequently triggers an alarm, reduce unnecessary alarms and maintenance, and thus improve the reliability and stability of the production process; in addition, through a preliminary analysis of the shearing risk, it provides the basic conditions for subsequent equipment status monitoring and quality inspection, and further supports in-depth analysis and judgment of the equipment operation status;

[0067] The device status detection and analysis module is used to obtain the real-time device status data of the target device and evaluate the operating status of the target device based on the real-time device status data of the target device;

[0068] The device status data of the target device includes device status parameters such as shear force, vibration speed, shear velocity and device temperature of the target device;

[0069] It is understandable that the comprehensive analysis of parameters such as shear force, vibration velocity, shear speed and equipment temperature can comprehensively evaluate the operating status of the equipment. Among them, shear force and shear speed can provide information on processing load and efficiency, vibration velocity reflects the mechanical health of the equipment, and equipment temperature can reveal whether the equipment has overheating problems. By monitoring and analyzing these parameters, it is possible to effectively determine whether the equipment is in normal operating condition, and to promptly discover potential faults or performance problems, thereby improving the stability and reliability of the production process;

[0070] The evaluating the operating status of the target device based on the real-time device status data of the target device includes:

[0071] Numbering a plurality of device status parameters included in the acquired real-time device status data of the target device;

[0072] According to the calculation formula Calculate and obtain the current device state abnormality assessment value SR of the target device; where Ci represents the value of the device state parameter numbered i; Ci,min and Ci,max represent the minimum and maximum values ​​of the standard range of the device state parameter numbered i, respectively; ki represents the preset proportional coefficient of the device state parameter numbered i; and ki>0, The value of ki is set by the technician according to the importance of the corresponding equipment status parameter;

[0073] Understandably, Refers to the deviation index of the corresponding device status parameter. When the deviation index of the corresponding device status parameter is equal to 0, it means that the value of the corresponding device status parameter is located at the center of the corresponding standard range. When the deviation index of the corresponding device status parameter is equal to 1, it means that the value of the corresponding device status parameter is equal to the maximum value of the corresponding standard range. When the deviation index of the corresponding device status parameter is equal to -1, it means that the value of the corresponding device status parameter is equal to the minimum value of the corresponding standard range. When the absolute value of the deviation index of the corresponding device status parameter is greater than 1, it means that the value of the corresponding device status parameter is not within the corresponding standard range. Therefore, by calculating the absolute value of the deviation index of each device status parameter and multiplying it by the corresponding preset proportional coefficient, the abnormal condition of the target device can be comprehensively evaluated from the perspective of multi-dimensional data. In this way, the gap between each device status parameter and the standard range can be directly quantified, so as to calculate and obtain the abnormal evaluation value of the device status. If the value of the abnormal evaluation value of the device status is larger, it means that the possibility of abnormality in the operation state of the target device is higher.

[0074] A preset device state abnormality assessment value threshold value is set. If the calculated current device state abnormality assessment value of the target device is greater than the preset device state abnormality assessment value threshold value, it indicates that the current target device is in an abnormal operation state; if the calculated current device state abnormality assessment value of the target device is less than or equal to the preset device state abnormality assessment value threshold value, it indicates that the current target device is in a normal operation state;

[0075] The preset device status abnormality assessment value threshold is obtained by the technicians' previous calculation of a large number of device status abnormality assessment values ​​for the same type of equipment and the corresponding analysis of whether the actual device status is abnormal;

[0076] A quality inspection and analysis module, used to obtain real-time quality inspection data of the steel plate products sheared by the target equipment, and to evaluate the shearing quality of the corresponding steel plate products based on the obtained real-time quality inspection data of the steel plate products sheared by the target equipment;

[0077] In an embodiment of the present invention, the real-time quality detection data of the steel plate product sheared by the target device refers to a real-time high-definition image when the target device shears the steel plate product;

[0078] Based on the real-time quality inspection data of the steel plate products sheared by the target equipment, the shearing quality of the corresponding steel plate products is evaluated, including:

[0079] A high-definition camera is used to capture a high-definition image of a steel plate product that has been sheared by a target device, and a shearing contour line of a complete steel plate product after the steel plate has been sheared and a model of the corresponding steel plate product in the high-definition image are obtained by computer vision technology processing; the computer vision technology is a prior art and will not be described in detail here; it should be noted that when photographing a steel plate product, it is necessary to photograph the steel plate product just after the shearing process is completed. In this embodiment, the quality inspection and analysis module receives a trigger signal of steel plate quality inspection and analysis sent by the comprehensive analysis and early warning module, and immediately sends a shooting instruction to the high-definition camera after completing a steel plate shearing process, and the high-definition camera photographs the complete steel plate product that has just been sheared;

[0080] According to the model of the steel plate product in the high-definition image currently captured, a preset steel plate cutting route corresponding to the corresponding steel plate product model is obtained from the database;

[0081] Convert the pixel coordinates of the shearing contour line of the corresponding steel plate product in the high-definition image currently captured into a machine coordinate system consistent with the preset steel plate shearing route of the corresponding steel plate product model;

[0082] The preset steel plate shearing route of the corresponding steel plate product model is evenly divided into n sub-route segments, and the central machine coordinates of each sub-route segment are obtained to generate a first coordinate sequence data set; and the corresponding shearing contour line converted into a machine coordinate system consistent with the preset steel plate shearing route of the corresponding steel plate product model is evenly divided into n sub-contour line segments, and the central machine coordinates of each sub-contour line segment are obtained to generate a second coordinate sequence data set;

[0083] Compare all the coordinates contained in the generated first coordinate sequence data set with all the coordinates contained in the second coordinate sequence data set, count the number of coordinates contained in the first coordinate sequence data set that are the same as the coordinates contained in the second coordinate sequence data set and mark them as nx, and calculate according to the formula Get the shear contour match of the steel plate product that has been sheared by the target equipment.

[0084] Assign PR and stamp the shooting time;

[0085] It can be understood that when the shear profile matching rate is greater, it means that the shearing effect or quality of the steel plate product currently sheared by the target equipment is better;

[0086] A shearing contour matching rate threshold is preset. If the calculated shearing contour matching rate of the steel plate product that has been sheared by the target device is greater than or equal to the preset shearing contour matching rate threshold, it indicates that the quality of the steel plate product that has been sheared by the target device is good; if the calculated shearing contour matching rate of the steel plate product that has been sheared by the target device is less than the preset shearing contour matching rate threshold, it indicates that the quality of the steel plate product that has been sheared by the target device is poor.

[0087] Among them, the cutting contour matching rate threshold is obtained by the technicians through analysis of a large number of cutting contour matching rate values ​​and actual quality inspection sample data after the equipment and high-definition camera are calibrated in the early stage;

[0088] By evenly dividing the preset shearing route and the actual shearing contour into n segments and comparing their center coordinates in detail, the solution can more comprehensively analyze the accuracy and consistency of the shearing process, which helps to discover potential processing deviations and quality problems. In addition, in the process of calculating the shearing contour matching rate, the shooting timestamp is recorded at the same time, which is convenient for tracing and associating the processing time point with the quality inspection results, thereby enhancing the integrity and reliability of data management. Due to the comparison method between the preset shearing route and the actual shearing contour, the solution can meet the processing and inspection requirements of different steel plate product models and has a wide range of applications. By quickly detecting and analyzing the shearing quality of steel plate products, there is no need to wait for the subsequent comprehensive quality inspection of the cutting edge quality and the size and shape of the steel plate, thereby greatly improving the inspection efficiency.

[0089] The comprehensive analysis and early warning module is used to analyze and determine whether it is necessary to issue a fault early warning for the steel plate shearing process of the target equipment, when it is determined that the target equipment currently has an abnormal risk of steel plate shearing process, based on the current operating status of the target equipment and the shearing process quality of the steel plate products sheared by the target equipment;

[0090] Specifically, the target device sent by the preliminary analysis module for matching and cutting is obtained to determine whether there is a risk of abnormal steel plate shearing processing; if a normal signal is obtained, no further processing is required;

[0091] If an abnormal signal is obtained, the current operating status of the target equipment and the shearing quality of the steel plate products sheared by the target equipment are combined to analyze and determine whether a fault warning is needed for the steel plate shearing of the target equipment, including:

[0092] Send a trigger signal of equipment status detection and analysis to the equipment status detection and analysis module, and send a trigger signal of steel plate quality detection and analysis to the quality detection and analysis module; after receiving the trigger signal of equipment status detection and analysis, the equipment status detection and analysis module acquires and analyzes the real-time equipment status data of the target equipment; after receiving the trigger signal of steel plate quality detection and analysis, the quality detection and analysis module acquires and analyzes the real-time quality detection data of the steel plate product sheared by the target equipment; and then acquires the current equipment status abnormality evaluation value SR of the target equipment and the shearing contour matching rate PR of the current steel plate product;

[0093] According to the calculation formula Calculate and obtain the fault warning indication value YZ of the current shearing process of the target equipment; where SR0 and PR0 are the equipment status abnormality assessment value threshold and the shearing contour matching rate threshold respectively;

[0094] Perform numerical analysis on the fault warning indication value YZ of the current shearing process of the target equipment obtained by calculation; if YZ=0, a warning signal of the shearing process of the target equipment is generated, and relevant personnel need to handle it in time, such as starting and stopping the target equipment to check whether there is a problem with the shearing equipment or whether there is a problem with the steel plate shearing setting; if YZ≥1, a normal shearing process signal of the target equipment is generated, and the shearing process is kept running normally without any processing;

[0095] Among them, the current equipment status abnormality assessment value SR and the current steel plate product shear contour matching rate PR are processed through the maximum value selection function to generate an early warning signal for the shearing processing of the target equipment, so that preventive measures can be taken before a serious failure of the equipment occurs, thereby reducing downtime, reducing production risks, and improving overall production efficiency and product quality; in addition, this solution can dynamically adapt to changes in equipment status under different production conditions, ensure real-time monitoring and timely response to various potential abnormalities, and provide reliable protection for the stable operation of the target equipment.

[0096] The above formulas are all calculated by removing dimensions and taking numerical values. The formula is a formula that is closest to the actual situation obtained by collecting a large amount of data and performing software simulation. The preset parameters and preset thresholds in the formula are set by technical personnel in this field according to actual conditions or obtained by simulating a large amount of data.

[0097] In the embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the modules is only a logical function division, and there may be other division methods in actual implementation; the modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the method of this embodiment.

[0098] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. Intelligent cutting system for steel plate shearing based on data analysis, characterized by: include: The preliminary analysis module is used to obtain the production data of steel plate products sheared by the target equipment in the previous monitoring time interval, and evaluate whether the target equipment currently has abnormal risk of steel plate shearing based on the production data of steel plate products sheared by the target equipment in the previous monitoring time interval; The device status detection and analysis module is used to obtain the real-time device status data of the target device and evaluate the operating status of the target device based on the real-time device status data of the target device; A quality inspection and analysis module, used to obtain real-time quality inspection data of the steel plate products sheared by the target equipment, and to evaluate the shearing quality of the corresponding steel plate products based on the obtained real-time quality inspection data of the steel plate products sheared by the target equipment; The comprehensive analysis and early warning module is used to analyze and determine whether it is necessary to issue a fault early warning for the steel plate shearing process of the target equipment, when it is determined that the target equipment currently has an abnormal risk of steel plate shearing process, based on the current operating status of the target equipment and the shearing process quality of the steel plate products sheared by the target equipment; The evaluation of whether the target device currently has a risk of abnormal steel plate shearing based on the steel plate product production data of the target device shearing in the previous monitoring time interval includes: Acquire the steel plate product production data of the target equipment in the last monitoring time interval, wherein the steel plate product production data includes the steel plate product model and the production quantity, the number of qualified products and the total production time of the corresponding steel plate product model in the last monitoring time interval; According to the steel plate product model, the standard qualified rate and standard production rate of the steel plate product corresponding to the corresponding steel plate product model are obtained from the database; All steel plate product models of the target equipment in the last monitoring time interval are arranged and numbered in ascending order of time, and the production quantity, qualified product quantity and total production time of the corresponding steel plate product model are marked as , as well as ; and m=1,2…M; Where M represents the total number of steel plate product models in the last monitoring time interval; According to the calculation formula , calculate and obtain the quality efficiency coefficient QE of the steel plate product shearing processing of the target equipment in the previous monitoring time interval; where, and Respectively represent the standard qualified rate and standard production rate of the steel plate products corresponding to the corresponding steel plate product models; represents the preset proportionality factor corresponding to the steel plate product model numbered m; and , ; Compare the calculated quality efficiency coefficient of the steel plate product shearing process of the target equipment in the previous monitoring time interval with the preset quality efficiency coefficient threshold, analyze and determine whether the target equipment currently has an abnormal risk of steel plate shearing process, and send the generated signal to the comprehensive analysis and early warning module; If the calculated quality efficiency coefficient of the steel plate product shearing process of the target equipment in the previous monitoring time interval is greater than or equal to the preset quality efficiency coefficient threshold, a normal signal is generated; If the calculated quality efficiency coefficient of the steel plate product shearing processing of the target equipment in the previous monitoring time interval is less than the preset quality efficiency coefficient threshold, an abnormal signal is generated.

2. According to the data analysis-based intelligent cutting system for steel plate shearing processing according to claim 1, it is characterized by: There are three cases for the value of : If the number m=M=1, and the steel plate product model corresponding to the only shearing process in the previous monitoring time interval is the same as the steel plate product model corresponding to the current shearing process, then ; If the number m < M, and the product models of all steel plates sheared in the previous monitoring time interval are different from the product models of steel plates corresponding to the current shearing process, then ; If the number m < M, and the last steel plate product model in ascending order in the previous monitoring time interval is the same as the steel plate product model corresponding to the current shearing process, and the other steel plate product models are different from the steel plate product models corresponding to the current shearing process, then , .

3. The intelligent cutting system for steel plate shearing based on data analysis according to claim 1 is characterized in that: The device status data of the target device includes shear force, vibration speed, shear velocity and device temperature of the target device.

4. The intelligent cutting system for steel plate shearing based on data analysis according to claim 3 is characterized in that: The evaluating the operating status of the target device based on the real-time device status data of the target device includes: numbering a plurality of device status parameters included in the acquired real-time device status data of the target device; According to the calculation formula , calculate and obtain the current device status abnormality assessment value SR of the target device; where, Indicates the status parameter value of the device numbered i; and They respectively represent the minimum and maximum values ​​of the standard range of the equipment status parameter numbered i; represents a preset proportionality factor of the device state parameter numbered i; and , ; A preset device state abnormality assessment value threshold is set. If the calculated current device state abnormality assessment value of the target device is greater than the preset device state abnormality assessment value threshold, it indicates that the current target device is in an abnormal operating state; if the calculated current device state abnormality assessment value of the target device is less than or equal to the preset device state abnormality assessment value threshold, it indicates that the current target device is in a normal operating state.

5. The intelligent cutting system for steel plate shearing based on data analysis according to claim 1 is characterized in that: The real-time quality inspection data of the steel plate product sheared by the target equipment refers to the real-time high-definition image when the target equipment shears the steel plate product.

6. The intelligent cutting system for steel plate shearing based on data analysis according to claim 5 is characterized in that: Based on the real-time quality inspection data of the steel plate products sheared by the target equipment, the shearing quality of the corresponding steel plate products is evaluated, including: A high-definition camera is used to capture a high-definition image of a steel plate product that has been sheared by the target device, and a shearing contour line of the complete steel plate product after the steel plate has been sheared and the model of the corresponding steel plate product are obtained through computer vision technology processing; According to the model of the steel plate product in the high-definition image currently captured, a preset steel plate cutting route corresponding to the corresponding steel plate product model is obtained from the database; Convert the pixel coordinates of the shearing contour line of the corresponding steel plate product in the high-definition image currently captured into a machine coordinate system consistent with the preset steel plate shearing route of the corresponding steel plate product model; The preset steel plate shearing route of the corresponding steel plate product model is evenly divided into n sub-route segments, and the central machine coordinates of each sub-route segment are obtained to generate a first coordinate sequence data set; and the corresponding shearing contour line converted into a machine coordinate system consistent with the preset steel plate shearing route of the corresponding steel plate product model is evenly divided into n sub-contour line segments, and the central machine coordinates of each sub-contour line segment are obtained to generate a second coordinate sequence data set; Compare all the coordinates contained in the generated first coordinate sequence data set with all the coordinates contained in the second coordinate sequence data set, count the number of coordinates contained in the first coordinate sequence data set that are the same as the coordinates contained in the second coordinate sequence data set and mark them as nx, and calculate according to the formula , obtain the shear contour matching rate PR of the steel plate product that has been sheared by the target equipment, and add the shooting timestamp; A cutting contour matching rate threshold is preset. If the calculated cutting contour matching rate of the steel plate product that has been cut by the target device is greater than or equal to the preset cutting contour matching rate threshold, it indicates that the quality of the steel plate product that has been cut by the target device is good; if the calculated cutting contour matching rate of the steel plate product that has been cut by the target device is less than the preset cutting contour matching rate threshold, it indicates that the quality of the steel plate product that has been cut by the target device is poor.

7. The intelligent cutting system for steel plate shearing based on data analysis according to claim 1 is characterized in that: The specific analysis process of the comprehensive analysis and early warning module includes: Obtain information sent by the preliminary analysis module on whether the target device currently has abnormal risk of steel plate shearing processing; If the signal obtained is normal, no further processing is required; If an abnormal signal is obtained, the current operating status of the target equipment and the shearing quality of the steel plate products sheared by the target equipment are combined to analyze and determine whether a fault warning is needed for the steel plate shearing of the target equipment, including: Send a trigger signal of equipment status detection and analysis to the equipment status detection and analysis module, and send a trigger signal of steel plate quality detection and analysis to the quality detection and analysis module; after receiving the trigger signal of equipment status detection and analysis, the equipment status detection and analysis module acquires and analyzes the real-time equipment status data of the target equipment; after receiving the trigger signal of steel plate quality detection and analysis, the quality detection and analysis module acquires and analyzes the real-time quality detection data of the steel plate product sheared by the target equipment; and then acquires the current equipment status abnormality evaluation value SR of the target equipment and the shearing contour matching rate PR of the current steel plate product; According to the calculation formula Calculate and obtain the fault warning indication value YZ of the current shearing process of the target equipment; where, and They are the device status abnormality assessment value threshold and the cutting contour matching rate threshold respectively; Perform numerical analysis on the calculated fault warning indication value YZ of the current shearing process of the target device; if YZ=0, a warning signal for the shearing process of the target device is generated, and relevant personnel need to handle it in time; if YZ≥1, a normal shearing process signal of the target device is generated, and the shearing process is kept running normally without any processing.

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