An industrial automation equipment control system based on intelligent network

Abstract

The present invention belongs to the field of automation control technology, and specifically is an industrial automation equipment control system based on a smart network, wherein the system includes a processor, a machine tool status monitoring module, an adaptive control module, a sustainability evaluation module and an early warning terminal; the present invention uses a pre-processing analysis module to perform analysis based on a clamping judgment symbol and a tool judgment symbol, and enables a CNC machine tool to process a workpiece when a qualified preparation signal is generated, thereby ensuring the processing stability and processing effect of the CNC machine tool, and during the processing, the machine tool status monitoring module collects the working status data of the CNC machine tool in real time, the adaptive control module optimizes the CNC machine tool processing process based on the working status data, and uses a sustainability evaluation module to perform an operation sustainability analysis on the CNC machine tool, and enables the CNC machine tool to rest in time when a low sustainability signal is generated, thereby further ensuring the quality and efficiency of the processed products, and having a high level of intelligence and automation.

Description

Technical Field

[0001] The present invention relates to the field of automation control technology, and in particular to an industrial automation equipment control system based on a smart network. Background Art

[0002] The industrial automation equipment control system of the smart network is an indispensable and important part of modern industrial production. The system deeply integrates computer, communication, control and artificial intelligence technologies, and realizes the automation, intelligence and informatization of the production process by building an efficient and intelligent network system.

[0003] CNC machine tools are a specific application of industrial automation equipment control systems in the field of machine tool processing. The full name of CNC machine tools is digitally controlled machine tools. They use digital control technology to process machine tools, combining traditional mechanical processing with modern computer technology. The movement and processing of machine tools are controlled by preset programs, thereby achieving high-precision workpiece processing.

[0004] At present, when conducting CNC machine tool operation supervision, it is difficult to reasonably judge the preparation status before the CNC machine tool processes the workpiece and automatically optimize the processing process during the processing. It is also impossible to accurately assess the risk of the CNC machine tool continuing to operate, which is not conducive to ensuring the operation safety of the CNC machine tool and improving the processing quality;

[0005] In view of the above technical defects, a solution is now proposed. Summary of the invention

[0006] The purpose of the present invention is to provide an industrial automation equipment control system based on an intelligent network, which solves the problems that the prior art is difficult to reasonably judge the preparation status before the CNC machine tool processes the workpiece and automatically optimize the processing process during the processing, and is unable to accurately assess the risk of continued operation of the CNC machine tool, which is not conducive to ensuring the safety of CNC machine tool operation and improving processing quality.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] An industrial automation equipment control system based on a smart network, used for processing control of CNC machine tools, including a processor, a machine tool status monitoring module, an adaptive control module, a sustainability assessment module and an early warning terminal;

[0009] The machine tool status monitoring module integrates multiple sensors to collect the working status data of the CNC machine tool in real time, and sends the working status data of the CNC machine tool to the adaptive control module through the processor;

[0010] The adaptive control module uses deep learning algorithms to process and analyze the working status data of CNC machine tools, predict the future working status or processing effect of CNC machine tools, automatically adjust the control parameters of CNC machine tools based on the prediction results, and optimize the processing process of CNC machine tools;

[0011] The sustainability assessment module performs an operation sustainability analysis on the CNC machine tool, generates a high sustainability signal or a low sustainability signal through the operation sustainability analysis, and sends the high sustainability signal or the low sustainability signal to the early warning terminal through the processor. When the early warning terminal receives the low sustainability signal, it issues a corresponding early warning.

[0012] Furthermore, the processor is connected to the pre-processing analysis module in communication. Before processing the corresponding workpiece, the pre-processing analysis module obtains the clamping judgment symbol JP-1 or JP-2 and the tool judgment symbol DP-1 or DP-2, and performs intersection analysis on the clamping judgment symbol and the tool judgment symbol. If JP-2∩DP-2 is obtained, a qualified preparation signal is generated, and in other cases, a preparation abnormality signal is generated.

[0013] When a qualified preparation signal is generated, the processor enables the CNC machine tool to process the workpiece. When a preparation abnormality signal is generated, the preparation abnormality signal is sent to the early warning terminal through the processor. The early warning terminal issues an early warning when receiving the processing abnormality signal.

[0014] Furthermore, the pre-processing analysis module is connected to the workpiece clamping analysis module and the tool detection and evaluation module in communication. The workpiece clamping analysis module obtains the fixture on the CNC machine tool, analyzes the clamping performance of the fixture on the corresponding workpiece, assigns a clamping judgment symbol JP-1 or JP-2 through analysis, and sends the clamping judgment symbol JP-1 or JP-2 to the pre-processing analysis module;

[0015] The tool detection and evaluation module obtains the tool used for machining operations on the CNC machine tool, visually scans the tool, and analyzes the tool condition based on the visual scan image of the tool. The tool judgment symbol DP-1 or DP-2 is assigned through analysis, and the tool judgment symbol DP-1 or DP-2 is sent to the pre-machining analysis module.

[0016] Furthermore, the specific analysis process of the workpiece clamping analysis module is as follows:

[0017] When the fixture clamps the corresponding workpiece, the contact part between the fixture and the corresponding workpiece is obtained, and several detection points are set on the contact part of the fixture. The pressure applied by the corresponding detection point on the corresponding workpiece is collected and marked as clamping data, and the clamping data is numerically compared with the preset clamping data range. If the clamping data is not within the preset clamping data range, the corresponding detection point is marked as a pressure difference point; if there is no pressure difference point, the clamping judgment symbol JP-2 is assigned.

[0018] Furthermore, if there are pressure deviation points, the number of pressure deviation points is collected and the ratio is calculated with the total number of detection points to obtain the pressure deviation detection value, and the deviation value of the compression data of the corresponding pressure deviation point compared with the preset compression data range is marked as the pressure deviation amplitude measurement value, all the pressure deviation amplitude measurement values ​​are averaged to obtain the pressure deviation amplitude table value, and the pressure deviation amplitude measurement value with the largest value is marked as the pressure deviation amplitude risk value;

[0019] The clamp state value is obtained by numerically calculating the pressure difference inspection value, the pressure difference amplitude table value and the pressure difference amplitude risk value, and the clamp state value is numerically compared with the preset clamp state threshold. If the clamp state value exceeds the preset clamp state threshold, the clamping judgment symbol JP-1 is assigned; if the clamp state value does not exceed the preset clamp state threshold, the clamping judgment symbol JP-2 is assigned.

[0020] Furthermore, the specific analysis process of the tool detection and evaluation module is as follows:

[0021] Based on the visual scanning image of the tool, the wear area on the tool is captured, the area of ​​the corresponding wear area is collected and marked as the wear surface inspection value, and the average wear amount of the corresponding wear area is collected and marked as the wear detection value. The wear risk table value is obtained by numerically calculating the wear surface inspection value and the wear detection value. The wear risk table value is numerically compared with the preset wear risk table threshold. If the wear risk table value exceeds the preset wear risk table threshold, the corresponding wear area is marked as a high-risk area; if there is a high-risk area on the tool, the tool judgment symbol DP-1 is assigned.

[0022] Furthermore, if there is no high-risk area on the tool, the wear risk table values ​​of all wear areas are averaged to obtain a comprehensive wear inspection value, and the number of wear areas is marked as a wear number inspection value, and the wear risk table value with the largest value is marked as a wear amplitude inspection value;

[0023] The tool status value is obtained by numerically calculating the comprehensive wear inspection value, the wear number inspection value and the wear amplitude inspection value, and the tool status value is numerically compared with the preset tool status threshold. If the tool status value exceeds the preset tool status threshold, the tool judgment symbol DP-1 is assigned; if the tool status value does not exceed the preset tool status threshold, the tool judgment symbol DP-2 is assigned.

[0024] Furthermore, the specific analysis process of running sustainability analysis is as follows:

[0025] The start time of the CNC machine tool during this operation is collected, and the interval between the current time and the start time is marked as the start duration;

[0026] and collecting the processing time for the corresponding workpiece, comparing the processing time with the corresponding preset processing time threshold, if the processing time exceeds the corresponding preset processing time threshold, marking the corresponding processing time as high delay time, obtaining the number of high delay times within the startup duration and calculating the ratio thereof with the total number of workpieces processed within the startup duration to obtain the delay processing value;

[0027] The number of workpieces that are scrapped after being processed by the CNC machine tool within the startup duration is obtained, and the scrapped processing value is obtained by calculating the ratio of the number of scrapped workpieces to the total number of workpieces processed within the startup duration;

[0028] The sustainable detection value is obtained by numerically calculating the startup duration, the slow processing value and the scrapped processing value, and the sustainable detection value is numerically compared with the preset sustainable detection threshold. If the sustainable detection value exceeds the preset sustainable detection threshold, a low sustainability signal is generated; if the sustainable detection value does not exceed the preset sustainable detection threshold, a high sustainability signal is generated.

[0029] Furthermore, the processor is connected to the machine tool maintenance decision module in communication. Before the CNC machine tool is started, the machine tool maintenance decision module determines the maintenance urgency of the CNC machine tool through analysis. The specific analysis process is as follows:

[0030] The total operation time of the CNC machine tool in the historical stage is obtained, and the number of occurrences in which the single continuous operation time of the CNC machine tool in the historical stage exceeds the preset single continuous operation time threshold is obtained and marked as the risk operation frequency, and the number of occurrences in which the maintenance interval time in the historical stage exceeds the preset maintenance interval time threshold is marked as a high interval maintenance frequency value;

[0031] The CNC machine tool detection value is obtained by numerically calculating the total operation time, the risk operation frequency and the high interval maintenance frequency value, and several groups of preset CNC machine tool detection value ranges are set in advance, and each group of preset CNC machine tool detection value ranges corresponds to a group of preset maintenance interval time thresholds; the CNC machine tool detection value is compared with all the preset CNC machine tool detection value ranges one by one, and the preset CNC machine tool detection value range containing the corresponding CNC machine tool detection value is marked as the target threshold range;

[0032] The time of the last adjacent maintenance of the CNC machine tool is collected, the time difference between the current time and the last adjacent maintenance time is calculated to obtain the maintenance interval time, and the maintenance interval time is numerically compared with the target threshold range. If the maintenance interval time exceeds the target threshold range, an emergency maintenance signal is generated, and the emergency maintenance signal is sent to the early warning terminal via the processor. The early warning terminal issues a corresponding early warning when receiving the emergency maintenance signal.

[0033] Furthermore, the present invention also proposes an industrial automation equipment control method based on a smart network, comprising the following steps:

[0034] Step 1: Before processing the corresponding workpiece, the pre-processing analysis module performs analysis based on the clamping judgment symbol and the tool judgment symbol, and generates a qualified preparation signal or an abnormal preparation signal through analysis;

[0035] Step 2: When the qualified preparation signal is generated, the CNC machine tool processes the workpiece, and the machine tool status monitoring module collects the working status data of the CNC machine tool in real time;

[0036] Step 3: The adaptive control module uses a deep learning algorithm to process and analyze the working status data of the CNC machine tool, automatically adjusts the control parameters of the CNC machine tool, and optimizes the processing process of the CNC machine tool;

[0037] Step 4: The sustainability assessment module performs an operation sustainability analysis on the CNC machine tool to generate a high sustainability signal or a low sustainability signal through the operation sustainability analysis;

[0038] Step 5: When a preparation abnormality signal or a low sustainability signal is generated, the early warning terminal issues an early warning.

[0039] Compared with the prior art, the present invention has the following beneficial effects:

[0040] 1. In the present invention, the pre-processing analysis module performs analysis based on the clamping judgment symbol and the tool judgment symbol, and enables the CNC machine tool to process the workpiece when a qualified preparation signal is generated, thereby ensuring the processing stability and processing effect of the CNC machine tool. In the processing process, the adaptive control module performs analysis based on the working status data and optimizes the processing process of the CNC machine tool, and the sustainability evaluation module performs operation sustainability analysis on the CNC machine tool, and enables the CNC machine tool to rest in time when a low sustainability signal is generated, thereby further ensuring the quality and efficiency of the processed products.

[0041] 2. In the present invention, the machine tool maintenance decision module determines the maintenance urgency of the CNC machine tool before starting the CNC machine tool, and when an emergency maintenance signal is generated, the early warning terminal issues an early warning to remind the operator not to start the CNC machine tool temporarily and to perform maintenance and repair on the CNC machine tool, thereby ensuring the safe and stable operation of the CNC machine tool with a high level of intelligence and automation. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to facilitate understanding by those skilled in the art, the present invention is further described below in conjunction with the accompanying drawings;

[0043] Figure 1 This is a system block diagram of Embodiment 1 of the present invention;

[0044] Figure 2This is a system block diagram of Embodiment 2 of the present invention;

[0045] Figure 3 This is a flow chart of the method of Embodiment 3 of the present invention. DETAILED DESCRIPTION

[0046] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0047] Embodiment 1: Figure 1 As shown, the present invention proposes an industrial automation equipment control system based on a smart network, which is used for processing control of CNC machine tools, including a processor, a pre-processing analysis module, a machine tool status monitoring module, an adaptive control module, a sustainability assessment module and an early warning terminal;

[0048] Before processing the corresponding workpiece, the pre-processing analysis module obtains the clamping judgment symbol JP-1 or JP-2 and the tool judgment symbol DP-1 or DP-2, and performs intersection analysis on the clamping judgment symbol and the tool judgment symbol. If JP-2∩DP-2 is obtained, a qualified preparation signal is generated, and in other cases, a preparation abnormality signal is generated;

[0049] When a qualified preparation signal is generated, the processor enables the CNC machine tool to process the workpiece. When a preparation exception signal is generated, the processor sends the preparation exception signal to the early warning terminal. When the early warning terminal receives the processing exception signal, it issues an early warning to remind the operator to adjust the fixture or replace the tool, thereby ensuring the processing stability and processing effect of the CNC machine tool on the workpiece.

[0050] Furthermore, the pre-processing analysis module is connected to the workpiece clamping analysis module and the tool detection and evaluation module in communication. The workpiece clamping analysis module obtains the fixture on the CNC machine tool, analyzes the clamping performance of the fixture on the corresponding workpiece, assigns the clamping judgment symbol JP-1 or JP-2 through analysis, and sends the clamping judgment symbol JP-1 or JP-2 to the pre-processing analysis module, which can accurately feedback the clamping performance of the fixture on the workpiece, provide information support for the analysis process of the pre-processing analysis module, and ensure the accuracy of its analysis results; the specific analysis process of the workpiece clamping analysis module is as follows:

[0051] When the fixture clamps the corresponding workpiece, the contact position between the fixture and the corresponding workpiece is obtained, a number of detection points are set on the contact position of the fixture, the pressure applied by the corresponding detection points to the corresponding workpiece is collected and marked as clamping data, and the clamping data is numerically compared with a preset clamping data range;

[0052] If the clamping data is not within the preset clamping data range, it indicates that the pressure performance of the corresponding detection point does not meet the requirements, and the corresponding detection point is marked as a pressure difference point; if there is no pressure difference point, it indicates that the clamping performance of the fixture on the corresponding workpiece is good, and the clamping judgment symbol JP-2 is assigned;

[0053] If there are pressure deviation points, the number of pressure deviation points is collected and the ratio is calculated with the total number of detection points to obtain the pressure deviation detection value, and the deviation value of the compression data of the corresponding pressure deviation point compared with the preset compression data range is marked as the pressure deviation amplitude measurement value, all the pressure deviation amplitude measurement values ​​are averaged to obtain the pressure deviation amplitude table value, and the pressure deviation amplitude measurement value with the largest value is marked as the pressure deviation amplitude risk value;

[0054] The pressure difference detection value FY, the pressure difference amplitude table value FN and the pressure difference amplitude risk value FS are numerically calculated by the formula FX=c1*FY+(c2*FN+c3*FS) / 2 to obtain the fixture state value FX; wherein c1, c2 and c3 are preset proportional coefficients, c1>c2>c3>0; and the larger the value of the fixture state value FX, the worse the clamping performance of the CNC machine tool fixture on the corresponding workpiece is overall;

[0055] The fixture state value FX is numerically compared with the preset fixture state threshold. If the fixture state value FX exceeds the preset fixture state threshold, it indicates that the clamping performance of the CNC machine tool fixture for the corresponding workpiece is generally poor, and the clamping judgment symbol JP-1 is assigned; if the fixture state value FX does not exceed the preset fixture state threshold, it indicates that the clamping performance of the CNC machine tool fixture for the corresponding workpiece is generally good, and the clamping judgment symbol JP-2 is assigned.

[0056] The tool detection and evaluation module obtains the tool used for processing operations on the CNC machine tool, performs a visual scan of the tool, and analyzes the tool condition based on the visual scan image of the tool (extracts the wear area through image processing technology and measures the wear). The tool is assigned a judgment symbol DP-1 or DP-2 through analysis, and the tool judgment symbol DP-1 or DP-2 is sent to the pre-processing analysis module, which can accurately feedback the quality status of the tool, provide information support for the analysis process of the pre-processing analysis module, and further ensure the accuracy of its analysis results; the specific analysis process of the tool detection and evaluation module is as follows:

[0057] Based on the visual scanning image of the tool, the wear area on the tool is captured, the area of ​​the corresponding wear area is collected and marked as a wear surface inspection value, and the average wear amount of the corresponding wear area is collected and marked as a wear inspection value;

[0058] The wear surface inspection value MY and the wear detection value ML are numerically calculated by the formula MS=wq1*MY+wq2*ML to obtain the wear risk table value MS, wherein wq1 and wq2 are preset proportional coefficients with values ​​greater than zero, and the larger the value of the wear risk table value MS is, the greater the adverse effect of the corresponding wear area on the tool quality is;

[0059] The wear risk table value MS is numerically compared with the preset wear risk table threshold. If the wear risk table value MS exceeds the preset wear risk table threshold, it indicates that the corresponding wear area has a greater adverse effect on the tool quality, and the corresponding wear area is marked as a high-risk area; if there is a high-risk area on the tool, it indicates that the tool quality of the CNC machine tool is poor, and the tool judgment symbol DP-1 is assigned;

[0060] If there is no high-risk area on the tool, the wear risk table values ​​of all wear areas are averaged to obtain the wear comprehensive inspection value, and the number of wear areas is marked as the wear number inspection value, and the wear risk table value with the largest value is marked as the wear amplitude inspection value;

[0061] The tool status value QY is obtained by numerically calculating the wear comprehensive inspection value QW, the wear number inspection value QR and the wear amplitude inspection value QK through the formula QY=up2*QR+(up1*QW+up3*QK) / 2; wherein up1, up2 and up3 are preset proportional coefficients with values ​​greater than zero, and the larger the value of the tool status value QY is, the worse the tool quality performance of the CNC machine tool is overall;

[0062] The tool status value QY is numerically compared with the preset tool status threshold. If the tool status value QY exceeds the preset tool status threshold, it indicates that the tool quality performance of the CNC machine tool is generally poor, and the tool judgment symbol DP-1 is assigned; if the tool status value QY does not exceed the preset tool status threshold, it indicates that the tool quality performance of the CNC machine tool is generally good, and the tool judgment symbol DP-2 is assigned.

[0063] The machine tool status monitoring module integrates multiple sensors to collect the working status data of the CNC machine tool (including spindle load, tool temperature, etc.) in real time, and sends the working status data of the CNC machine tool to the adaptive control module through the processor;

[0064] The adaptive control module uses deep learning algorithms (i.e. deep learning models, such as convolutional neural networks, recurrent neural networks, long short-term memory networks, etc.) to process and analyze the working status data of CNC machine tools, predict the future working status or processing effect of CNC machine tools, and automatically adjust the control parameters of CNC machine tools based on the prediction results, such as feed rate, cutting force, etc., to optimize the CNC machine tool processing process, significantly improve processing accuracy and production efficiency, and have a high level of intelligence and automation.

[0065] The sustainability assessment module performs an operation sustainability analysis on the CNC machine tool, generates a high sustainability signal or a low sustainability signal through the operation sustainability analysis, and sends the high sustainability signal or the low sustainability signal to the early warning terminal through the processor. When the early warning terminal receives the low sustainability signal, it issues a corresponding early warning to remind the operator to suspend the operation of the CNC machine tool as needed, so that the CNC machine tool can be rested in time to avoid subsequent operation safety hazards and ensure the quality and efficiency of the processed products; the specific analysis process of the operation sustainability analysis is as follows:

[0066] The start time of the CNC machine tool during this operation is collected, and the interval between the current time and the start time is marked as the start duration;

[0067] and collecting the processing time for the corresponding workpiece, comparing the processing time with the corresponding preset processing time threshold, if the processing time exceeds the corresponding preset processing time threshold, marking the corresponding processing time as high delay time, obtaining the number of high delay times within the startup duration and calculating the ratio thereof with the total number of workpieces processed within the startup duration to obtain the delay processing value;

[0068] The number of workpieces that are scrapped after being processed by the CNC machine tool within the startup duration is obtained, and the scrapped processing value is obtained by calculating the ratio of the number of scrapped workpieces to the total number of workpieces processed within the startup duration;

[0069] By formula The startup duration TS, the delayed processing value TX and the scrapped processing value TF are numerically calculated to obtain the sustainable detection value TP; wherein hy1, hy2 and hy3 are preset proportional coefficients, hy3>hy2>hy1>0; and the larger the value of the sustainable detection value TP is, the greater the safety hazard of the CNC machine tool continuing to operate is;

[0070] The sustainable detection value TP is numerically compared with the preset sustainable detection threshold. If the sustainable detection value TP exceeds the preset sustainable detection threshold, it indicates that the safety hazard of the continued operation of the CNC machine tool is relatively large, and a low sustainability signal is generated; if the sustainable detection value TP does not exceed the preset sustainable detection threshold, it indicates that the safety hazard of the continued operation of the CNC machine tool is relatively small, and a high sustainability signal is generated.

[0071] Embodiment 2: Figure 2 As shown, the difference between this embodiment and the first embodiment is that the processor is connected to the machine tool maintenance decision module in communication. Before the CNC machine tool is started, the machine tool maintenance decision module determines the maintenance urgency of the CNC machine tool through analysis. The specific analysis process is as follows:

[0072] The total operation time of the CNC machine tool in the historical stage is obtained, and the number of occurrences in which the single continuous operation time of the CNC machine tool in the historical stage exceeds the preset single continuous operation time threshold is obtained and marked as the risk operation frequency, and the number of occurrences in which the maintenance interval time in the historical stage exceeds the preset maintenance interval time threshold is marked as a high interval maintenance frequency value;

[0073] The total operation time LZ, risk operation frequency LK and high interval maintenance frequency value LW are numerically calculated by the formula LF=e1*LZ / (e2+e3)+e2*LK+e3*LW to obtain the CNC machine tool detection value LF; wherein, e1, e2, and e3 are preset proportional coefficients, e3>e2>e1>0; and, the larger the value of the CNC machine tool detection value LF, the worse the equipment condition of the corresponding CNC machine tool is in general;

[0074] A number of groups of preset CNC machine tool detection value ranges are set in advance, and each group of preset CNC machine tool detection value ranges corresponds to a group of preset maintenance interval time thresholds; it should be noted that the larger the value of the preset CNC machine tool detection value range is, the smaller the value of the preset maintenance interval time threshold adapted thereto is; the CNC machine tool detection value LF is compared with all the preset CNC machine tool detection value ranges one by one, and the preset CNC machine tool detection value range containing the corresponding CNC machine tool detection value LF is marked as the target threshold range;

[0075] The time of the last adjacent maintenance for the CNC machine tool is collected, the time difference between the current time and the last adjacent maintenance time is calculated to obtain the maintenance interval time, and the maintenance interval time is numerically compared with the target threshold range. If the maintenance interval time exceeds the target threshold range, an emergency maintenance signal is generated, and the emergency maintenance signal is sent to the early warning terminal via the processor. When the early warning terminal receives the emergency maintenance signal, it issues a corresponding early warning to remind the operator not to start the CNC machine tool temporarily and to perform maintenance and repair on the CNC machine tool, thereby ensuring the safe and stable operation of the CNC machine tool.

[0076] Embodiment 3: Figure 3 As shown, the difference between this embodiment and the first and second embodiments is that the present invention proposes a method for controlling industrial automation equipment based on a smart network, comprising the following steps:

[0077] Step 1: Before processing the corresponding workpiece, the pre-processing analysis module performs analysis based on the clamping judgment symbol and the tool judgment symbol, and generates a qualified preparation signal or an abnormal preparation signal through analysis;

[0078] Step 2: When the qualified preparation signal is generated, the CNC machine tool processes the workpiece, and the machine tool status monitoring module collects the working status data of the CNC machine tool in real time;

[0079] Step 3: The adaptive control module uses a deep learning algorithm to process and analyze the working status data of the CNC machine tool, automatically adjusts the control parameters of the CNC machine tool, and optimizes the processing process of the CNC machine tool;

[0080] Step 4: The sustainability assessment module performs an operation sustainability analysis on the CNC machine tool to generate a high sustainability signal or a low sustainability signal through the operation sustainability analysis;

[0081] Step 5: When a preparation abnormality signal or a low sustainability signal is generated, the early warning terminal issues an early warning.

[0082] The working principle of the present invention is as follows: when in use, before processing the corresponding workpiece, the pre-processing analysis module performs analysis based on the clamping judgment symbol and the tool judgment symbol to generate a preparation qualification signal or a preparation abnormality signal. When the preparation qualification signal is generated, the CNC machine tool processes the workpiece. When the preparation abnormality signal is generated, the operator is reminded to adjust the fixture or replace the tool to ensure the processing stability and processing effect of the CNC machine tool. During the processing, the machine tool status monitoring module collects the working status data of the CNC machine tool in real time. The adaptive control module optimizes the processing process of the CNC machine tool based on the working status data, significantly improving the processing accuracy and production efficiency. The sustainability evaluation module performs an operation sustainability analysis on the CNC machine tool, generates a high sustainability signal or a low sustainability signal through the operation sustainability analysis, and makes the CNC machine tool rest in time when a low sustainability signal is generated to avoid subsequent operation safety hazards, further ensuring the quality and efficiency of the processed products, and having a high level of intelligence and automation.

[0083] The above formulas are all dimensionless and numerical calculations. The formula is a formula obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formula are set by technicians in this field according to actual conditions. The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the contents of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that technicians in the relevant technical field can understand and use the present invention well. The present invention is only limited by the claims and their full scope and equivalents.

Claims

1. An industrial automation equipment control system based on a smart network, used for processing control of CNC machine tools, characterized in that: It includes a processor, a machine tool status monitoring module, an adaptive control module, a sustainability assessment module and an early warning terminal; The machine tool status monitoring module integrates multiple sensors to collect the working status data of the CNC machine tool in real time, and sends the working status data of the CNC machine tool to the adaptive control module through the processor; The adaptive control module uses deep learning algorithms to process and analyze the working status data of CNC machine tools, predict the future working status or processing effect of CNC machine tools, automatically adjust the control parameters of CNC machine tools based on the prediction results, and optimize the processing process of CNC machine tools; The sustainability assessment module performs an operation sustainability analysis on the CNC machine tool, generates a high sustainability signal or a low sustainability signal through the operation sustainability analysis, and sends the high sustainability signal or the low sustainability signal to the early warning terminal via the processor. When the early warning terminal receives the low sustainability signal, it issues a corresponding early warning; The specific analysis process for running a sustainability analysis is as follows: The start time of the CNC machine tool during this operation is collected, and the interval between the current time and the start time is marked as the start duration; and collecting the processing time for the corresponding workpiece, comparing the processing time with the corresponding preset processing time threshold, if the processing time exceeds the corresponding preset processing time threshold, marking the corresponding processing time as high delay time, obtaining the number of high delay times within the startup duration and calculating the ratio thereof with the total number of workpieces processed within the startup duration to obtain the delay processing value; The number of workpieces that are scrapped after being processed by the CNC machine tool within the startup duration is obtained, and the scrapped processing value is obtained by calculating the ratio of the number of scrapped workpieces to the total number of workpieces processed within the startup duration; A sustainable detection value is obtained by numerically calculating the startup duration, the delayed processing value and the scrapped processing value. If the sustainable detection value exceeds a preset sustainable detection threshold, a low sustainability signal is generated; if the sustainable detection value does not exceed the preset sustainable detection threshold, a high sustainability signal is generated. The processor communicates with the machine tool maintenance decision module. Before starting the CNC machine tool, the machine tool maintenance decision module determines the maintenance urgency of the CNC machine tool through analysis. The specific analysis process is as follows: The total operation time of the CNC machine tool in the historical stage is obtained, and the number of occurrences in which the single continuous operation time of the CNC machine tool in the historical stage exceeds the preset single continuous operation time threshold is obtained and marked as the risk operation frequency, and the number of occurrences in which the maintenance interval time in the historical stage exceeds the preset maintenance interval time threshold is marked as a high interval maintenance frequency value; The total operation time LZ, risk operation frequency LK and high interval maintenance frequency value LW are numerically calculated by the formula LF=e1*LZ / (e2+e3)+e2*LK+e3*LW to obtain the CNC machine tool detection value LF; wherein, e1, e2, and e3 are preset proportional coefficients, and several groups of preset CNC machine tool detection value ranges are set in advance, and each group of preset CNC machine tool detection value ranges corresponds to a group of preset maintenance interval time thresholds; the CNC machine tool detection value is compared with all preset CNC machine tool detection value ranges one by one, and the preset CNC machine tool detection value range containing the corresponding CNC machine tool detection value is marked as the target threshold range; The time of the last adjacent maintenance of the CNC machine tool is collected, and the time difference between the current time and the last adjacent maintenance time is calculated to obtain the maintenance interval time. If the maintenance interval time exceeds the target threshold range, an emergency maintenance signal is generated, and the emergency maintenance signal is sent to the early warning terminal via the processor. The early warning terminal issues a corresponding early warning when receiving the emergency maintenance signal.

2. According to claim 1, the industrial automation equipment control system based on the smart network is characterized in that: The processor is connected to the pre-processing analysis module in communication. Before processing the corresponding workpiece, the pre-processing analysis module obtains the clamping judgment symbol JP-1 or JP-2 and the tool judgment symbol DP-1 or DP-2, and performs intersection analysis on the clamping judgment symbol and the tool judgment symbol. If JP-2∩DP-2 is obtained, a qualified preparation signal is generated, and in other cases, a preparation abnormality signal is generated; When a qualified preparation signal is generated, the processor enables the CNC machine tool to process the workpiece. When a preparation abnormality signal is generated, the preparation abnormality signal is sent to the early warning terminal through the processor. The early warning terminal issues an early warning when receiving the processing abnormality signal.

3. The industrial automation equipment control system based on the smart network according to claim 2 is characterized in that: The pre-processing analysis module is connected to the workpiece clamping analysis module and the tool detection and evaluation module in communication. The workpiece clamping analysis module obtains the fixture on the CNC machine tool, analyzes the clamping performance of the fixture on the corresponding workpiece, assigns the clamping judgment symbol JP-1 or JP-2 through analysis, and sends the clamping judgment symbol JP-1 or JP-2 to the pre-processing analysis module; The tool detection and evaluation module obtains the tool used for machining operations on the CNC machine tool, visually scans the tool, and analyzes the tool condition based on the visual scan image of the tool. The tool judgment symbol DP-1 or DP-2 is assigned through analysis, and the tool judgment symbol DP-1 or DP-2 is sent to the pre-machining analysis module.

4. The industrial automation equipment control system based on a smart network according to claim 3 is characterized in that: The specific analysis process of the workpiece clamping analysis module is as follows: When the fixture clamps the corresponding workpiece, the contact part between the fixture and the corresponding workpiece is obtained, and several detection points are set on the contact part of the fixture. The pressure applied by the corresponding detection point on the corresponding workpiece is collected and marked as clamping data. If the clamping data is not within the preset clamping data range, the corresponding detection point is marked as a pressure difference point; if there is no pressure difference point, the clamping judgment symbol JP-2 is assigned.

5. The industrial automation equipment control system based on the smart network according to claim 4 is characterized in that: If there are pressure deviation points, the number of pressure deviation points is collected and the ratio is calculated with the total number of detection points to obtain the pressure deviation detection value, and the deviation value of the compression data of the corresponding pressure deviation point compared with the preset compression data range is marked as the pressure deviation amplitude measurement value, all the pressure deviation amplitude measurement values ​​are averaged to obtain the pressure deviation amplitude table value, and the pressure deviation amplitude measurement value with the largest value is marked as the pressure deviation amplitude risk value; The clamp state value is obtained by numerically calculating the pressure difference detection value, the pressure difference amplitude table value and the pressure difference amplitude risk value. If the clamp state value exceeds the preset clamp state threshold, the clamping judgment symbol JP-1 is assigned; if the clamp state value does not exceed the preset clamp state threshold, the clamping judgment symbol JP-2 is assigned.

6. The industrial automation equipment control system based on the smart network according to claim 3 is characterized in that: The specific analysis process of the tool detection and evaluation module is as follows: Based on the visual scanning image of the tool, the wear area on the tool is captured, the area of ​​the corresponding wear area is collected and marked as the wear surface inspection value, and the average wear amount of the corresponding wear area is collected and marked as the wear detection value. The wear risk table value is obtained by numerically calculating the wear surface inspection value and the wear detection value. If the wear risk table value exceeds the preset wear risk table threshold, the corresponding wear area is marked as a high-risk area; if there is a high-risk area on the tool, the tool judgment symbol DP-1 is assigned.

7. The industrial automation equipment control system based on the smart network according to claim 6 is characterized in that: If there is no high-risk area on the tool, the wear risk table values ​​of all wear areas are averaged to obtain the wear comprehensive inspection value, and the number of wear areas is marked as the wear number inspection value, and the wear risk table value with the largest value is marked as the wear amplitude inspection value; The tool status value is obtained by numerically calculating the comprehensive wear inspection value, the wear number inspection value and the wear width inspection value. If the tool status value exceeds the preset tool status threshold, the tool judgment symbol DP-1 is assigned; if the tool status value does not exceed the preset tool status threshold, the tool judgment symbol DP-2 is assigned.

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