阅读说明：本技术 一种五轴加工中心的智能监控系统 (Intelligent monitoring system of five-axis machining center ) 是由 刘振华 朱帅 于 2021-09-06 设计创作，主要内容包括：本发明涉及五轴加工监控技术领域,具体为一种五轴加工中心的智能监控系统,包括监工单元、分监单元、处理器、析分单元、核安单元、智能设备和警报单元；监工单元用于采集五轴加工设备在此刻时间点之前的加工状况,并将五轴加工设备在此刻时间点之前的加工状况标定为完工信息；本发明是通过采集五轴加工中心以往的加工记录,来分析处理出相同的物件所需要加工的顺序,以及对应加工所需要用到的加工刀头,对刀头进行依次排序,并且在排序的同时,提取对应刀头加工所消耗的时间,从而确保每个相同的物件加工的时间一致,保证物件加工的一致性,避免物件在加工过程中出现加工错误,造成材料的浪费。(The invention relates to the technical field of five-axis machining monitoring, in particular to an intelligent monitoring system of a five-axis machining center, which comprises a monitoring unit, a sub-monitoring unit, a processor, an analysis unit, a nuclear safety unit, intelligent equipment and an alarm unit, wherein the monitoring unit is used for monitoring the five-axis machining center; the monitoring unit is used for acquiring the machining condition of the five-axis machining equipment before the time point at the moment and marking the machining condition of the five-axis machining equipment before the time point at the moment as finishing information; the method comprises the steps of analyzing and processing the sequence of the same objects to be processed and the processing tool bits required by the corresponding processing by collecting the previous processing records of a five-axis processing center, sequencing the tool bits in sequence, and extracting the time consumed by the processing of the corresponding tool bits while sequencing, thereby ensuring the processing time consistency of each same object, ensuring the processing consistency of the objects and avoiding the waste of materials caused by processing errors of the objects in the processing process.)

1. An intelligent monitoring system of a five-axis machining center is characterized by comprising a monitoring unit, an analysis monitoring unit, a processor, an analysis unit, a nuclear safety unit, intelligent equipment and an alarm unit;

the monitoring unit is used for acquiring the machining condition of the five-axis machining equipment before the time point at the moment, marking the machining condition of the five-axis machining equipment before the time point at the moment as finishing information, and transmitting the finishing information to the sub-monitoring unit;

the sub-monitoring unit is used for sub-monitoring and identifying the completion information, sub-monitoring and identifying processing result data, and transmitting the processing result data to the processor;

the processor receives and stores the processing result data, and simultaneously carries out further processing analysis on the processing result data so as to obtain a corresponding surface floating value and a corresponding conversion floating value;

the monitoring unit is also used for monitoring real-time machining information of the five-axis machining center in real time and transmitting the real-time machining information to the nuclear safety unit, image information related to the five-axis machining center is stored in the processor, the nuclear safety unit acquires the image information from the processor, verifies the image information with the real-time machining information, the surface floating value and the rotating floating value, processes the image information to obtain an early warning signal and an adjusting value, transmits the early warning signal to the alarm unit and transmits the adjusting value to the intelligent equipment;

the alarm unit is used for receiving the early warning signal, converting the early warning signal into an early warning alarm, sending the early warning alarm to the intelligent equipment and simultaneously sending out the alarm;

the intelligent device receives the early warning alarm and the adjusting value, displays the early warning alarm and the adjusting value and simultaneously reminds a manager.

2. The intelligent monitoring system of the five-axis machining center according to claim 1, wherein the specific process of sub-monitoring identification is as follows:

acquiring completion information, and marking the completion information as production name data, dough adding data, timing data, part temperature data, cutter rotation data, external temperature data, part rotation data, running water data, cutter cutting data and time data;

acquiring production name data, extracting corresponding plus data, receiving data, piece temperature data, external temperature data, piece rotation data, running water data, cutter data and time data according to the production name data, matching the cutter data with the time data, identifying the time for replacing cutter bits each time, namely the switching interval time between every two cutter bits, marking the interval time point between every two cutter bits as the switching time, marking the model of the cutter bits in the cutter data as cutter rule data, and sequencing the cutter rule data in sequence to obtain cutter rule sequencing data;

substituting the added surface data and the contact time data corresponding to each identical processed object into an area calculation formula: adding surface data, contact time data u1, wherein u1 represents a deviation influence factor of the total surface value, the total surface value represents a total contact area between the tool bit and the object in the machining process, the total surface value corresponding to each same machined object is substituted into a summation calculation formula, a total surface average value is calculated, and the total surface average value is marked;

and (3) bringing the piece rotation data and the cutter rotation data into a rotating speed calculation formula: the method comprises the following steps that (workpiece rotation data + tool rotation data) u2 is a rotation counting value, wherein u2 is a calculation adjusting factor of the workpiece rotation data and the tool rotation data, the rotation counting value is a sum calculation rotating speed of the workpiece rotation data and the tool rotation data, the rotation counting value is taken into an average value calculation formula, and a rotation counting average value is calculated;

sequentially selecting the element temperature data and the external temperature data, the element temperature data and the running water data, the total surface mean value and the element temperature data, the transfer mean value and the element temperature data, processing the influence factors to obtain the influence factors of the external temperature data, the running water data, the total surface mean value and the transfer mean value on the element temperature data, and sequentially marking the influence factors as u3, u4, u5 and u 6;

the switching time, rule sort data, u1, total joint value, total face mean, u2, revolution value, revolution mean, u3, u4, u5, and u6 were calibrated as processing result data.

3. The intelligent monitoring system of the five-axis machining center according to claim 2, wherein the specific processing procedure of the influence factor processing is as follows:

selecting workpiece temperature data and external temperature data, selecting different time periods, changing the workpiece temperature data of the same object under different external temperature conditions, and performing line graph processing on different temperature changes, wherein the line graph processing refers to that the external temperature data and the workpiece temperature data corresponding to the same object at different time points are subjected to position marking in a plane rectangular coordinate system, and the specific marking method comprises the following steps: taking the piece temperature data as a substitute numerical value of a Y axis and taking the external temperature data as a substitute numerical value of an X axis, carrying out coordinate marking, selecting a temperature numerical value with the piece temperature data kept unchanged, and calibrating the temperature numerical value as initial piece temperature data; selecting piece temperature data corresponding to different external temperature data, and bringing the piece temperature data into a temperature influence calculation formula: the external temperature data u3+ initial piece temperature data is piece temperature data, wherein u3 is an influence factor of the external temperature data on the piece temperature data, and the initial piece temperature data refers to the initial temperature of the object;

selecting piece temperature data and running water data, and calculating an influence factor u4 of the running water data on the piece temperature data according to an influence factor u3 of the external temperature data on the piece temperature data;

selecting total surface mean value and piece temperature data, and calculating an influence factor u5 of the total surface mean value on the piece temperature according to the method for calculating the influence factor of the flowing water data on the piece temperature data;

and selecting the measurement mean value and the piece temperature data, and calculating the influence factor u6 of the measurement mean value on the piece temperature according to the calculation method of the influence factor of the flowing water data on the piece temperature data.

4. The intelligent monitoring system of the five-axis machining center according to claim 3, wherein the specific process of processing and analyzing is as follows:

selecting a plurality of corresponding total junction values and total surface mean values, calculating the area difference values of the total junction values and the total surface mean values, calculating the area difference values, bringing the area difference values into a mean value calculation formula, and calculating the surface difference mean values, wherein the area difference value calculation formula is as follows: the total surface mean value-total junction surface value is equal to the area difference value, and the calculation process of the surface difference mean value is as follows: summing the plurality of area difference values, calibrating the obtained sum into a total area value, counting the number of the area difference values, and dividing the total area value by the number of the area difference values to obtain a mean area difference value;

selecting a plurality of corresponding rotation values and rotation average values, and calculating a rotation average value according to a calculation method of a surface difference average value;

extracting a surface difference mean value and a slip mean value, and carrying out influence floating calculation on the surface difference mean value and the slip mean value with u1 and u2, wherein the specific calculation formula is as follows: a plane floating value is a plane difference mean value/u 1, wherein the plane floating value represents a floating magnitude as a numerical value;

and calculating the transfer floating value according to the calculation mode of the surface floating value by using the transfer difference mean value and u 2.

5. The intelligent monitoring system of a five-axis machining center according to claim 4, wherein the specific operation process of the verification operation is as follows:

the method comprises the following steps of acquiring real-time processing information, marking a real-time image in the real-time processing information as real image data, marking a time point in the real-time processing information as real data, judging, identifying and matching the real image data according to object name data, object image data, knife image data and knife name data, and specifically:

matching the product name data with the object name data, selecting corresponding object shadow data according to the object name data, matching the object shadow data with corresponding real shadow data, generating an object error signal if the matching result is inconsistent, otherwise not generating a signal, selecting corresponding knife shadow data and knife name data according to the knife rule sequencing data, identifying the real shadow data according to the knife shadow data and the knife name data, identifying the corresponding real time knife shadow and the real time knife name, calibrating the time point of occurrence of the real time knife shadow and the real time knife name, sequentially matching the corresponding time point with the knife rule sequencing data, if the matching result is inconsistent, generating a sequential error signal, otherwise, not generating a signal, extracting the time point of occurrence of the real time knife shadow, performing difference calculation on the time points of two adjacent real time knife shadows, calculating the occurrence difference, and matching the occurrence difference with the switching time, when the matching results are inconsistent, generating a processing time error signal, otherwise, generating no signal, and transmitting the object error signal, the sequence error signal and the processing time error signal to an alarm unit;

and identifying the signals in the judging and identifying matching, and sending the signals to an alarm unit when identifying an object error signal, a sequence error signal and a processing time error signal, otherwise, carrying out safety calculation operation to obtain an adjusting value and an alarm signal.

6. The intelligent monitoring system of the five-axis machining center according to claim 5, wherein the specific operation process of the safety calculation operation is as follows:

calibrating the real-time processing information into real-time dough adding data, real-time timing data, real-time workpiece temperature data, real-time cutter rotation data, real-time external temperature data, real-time workpiece rotation data, real-time flow data and real-time values;

bringing real-time dough adding data, real-time receiving data, real-time cutter rotation data, real-time external temperature data, real-time piece rotation data and real-time running water data into a safety calculation formula together with u1, u2, u3, u4, u5 and u6, and calculating a temperature calculation value;

presetting a temperature safety value, comparing the temperature safety value with a temperature calculation value, judging that the temperature exceeds the safety value after the object is processed when the temperature safety value is greater than the temperature calculation value, generating an early warning signal, processing and adjusting according to the difference value between the temperature safety value and the temperature calculation value, namely calculating a relevant numerical value corresponding to adjustment by reversely deducing a safety calculation formula, calibrating the relevant numerical value as an adjustment value, transmitting the early warning signal to an alarm unit, and transmitting the adjustment value to intelligent equipment.

7. The intelligent monitoring system of a five-axis machining center according to claim 6, wherein the specific calculation process of the safety calculation formula is as follows:

wherein, W_{Fruit of Chinese wolfberry}The data are expressed as calculated temperature values, namely temperature calculation values, JM is expressed as real-time surfacing data, JS is expressed as real-time timing data, MF is expressed as surfacing floating values, DZ is expressed as real-time cutter rotation data, JZ is expressed as real-time workpiece rotation data, ZF is expressed as rotation floating values, LS real-time flow data, WW is expressed as real-time external temperature data, t is expressed as the time required by an object to be processed, and e is expressed as influence calculation deviation correction factors of the real-time surfacing data, the real-time timing data, the real-time cutter rotation data, the real-time external temperature data, the real-time workpiece rotation data and the real-time flow data.

Technical Field

The invention relates to the technical field of five-axis machining monitoring, in particular to an intelligent monitoring system of a five-axis machining center.

Background

The five-axis linkage machining center is also called as a five-axis machining center, is a machining center which is high in technological content and precision and is specially used for machining complex curved surfaces, the machining center system has a very important influence on the industries of aviation, aerospace, military, scientific research, precision instruments, high-precision medical equipment and the like in one country, and the five-axis machining center needs to judge the quality of an object after the object is machined;

currently, the processing analysis of a five-axis processing center is distinguished by experienced technicians, and after a plurality of processing tests are carried out by the technicians, relevant processing elements are summarized, a large amount of human resources are consumed, each step of processing an object cannot be monitored in real time through the human resources, the processing procedure of the object in the processing process cannot be automatically monitored, and therefore the processing procedure or sequence of the object cannot be monitored;

meanwhile, for controlling the temperature of the object, a technician usually uses water to reduce the temperature, and applies the same water consumption regardless of the generated temperature, thereby causing resource waste, failing to accurately analyze data according to operation data related to processing, and failing to control the temperature of the object in the processing process.

Therefore, an intelligent monitoring system of the five-axis machining center is provided.

Disclosure of Invention

The invention aims to provide an intelligent monitoring system of a five-axis machining center, which analyzes and processes the sequence of the same objects to be machined and machining tool bits required to be used for corresponding machining by acquiring the past machining records of the five-axis machining center, sequences the tool bits in sequence, and extracts the time consumed by the machining of the corresponding tool bits while sequencing, thereby ensuring the consistent machining time of each same object, ensuring the machining consistency of the objects and avoiding the waste of materials caused by the occurrence of machining errors in the machining process of the objects; the method comprises the steps of carrying out data analysis on related data in the processing process to process and obtain the influence of the related data on the temperature of an object in the processing process of the object, carrying out prediction calculation on the temperature of the object being processed according to the related data obtained by analysis, calculating the temperature of the object when the processing is finished, judging the temperature, and carrying out reverse calculation on the data according to the related influence value when the judgment result is negative and does not meet the standard, thereby calculating the required regulated value, increasing the accuracy of data analysis, increasing the safety of object processing, saving time and improving the working efficiency.

The purpose of the invention can be realized by the following technical scheme:

an intelligent monitoring system of a five-axis machining center comprises a monitoring unit, a sub-monitoring unit, a processor, an analysis unit, a nuclear safety unit, intelligent equipment and an alarm unit;

the monitoring unit is used for acquiring the machining condition of the five-axis machining equipment before the time point at the moment, marking the machining condition of the five-axis machining equipment before the time point at the moment as finishing information, and transmitting the finishing information to the sub-monitoring unit;

the sub-monitoring unit is used for sub-monitoring and identifying the completion information, sub-monitoring and identifying processing result data, and transmitting the processing result data to the processor;

the processor receives and stores the processing result data, and simultaneously carries out further processing analysis on the processing result data so as to obtain a corresponding surface floating value and a corresponding conversion floating value;

the monitoring unit is also used for monitoring real-time machining information of the five-axis machining center in real time and transmitting the real-time machining information to the nuclear safety unit, image information related to the five-axis machining center is stored in the processor, the nuclear safety unit acquires the image information from the processor, verifies the image information with the real-time machining information, the surface floating value and the rotating floating value, processes the image information to obtain an early warning signal and an adjusting value, transmits the early warning signal to the alarm unit and transmits the adjusting value to the intelligent equipment;

the alarm unit is used for receiving the early warning signal, converting the early warning signal into an early warning alarm, sending the early warning alarm to the intelligent equipment and simultaneously sending out the alarm;

the intelligent device receives the early warning alarm and the adjusting value, displays the early warning alarm and the adjusting value and simultaneously reminds a manager.

Further, the specific process of sub-monitoring identification is as follows:

acquiring completion information, and marking the completion information as production name data, dough adding data, timing data, part temperature data, cutter rotation data, external temperature data, part rotation data, running water data, cutter cutting data and time data;

acquiring production name data, extracting corresponding plus data, receiving data, piece temperature data, external temperature data, piece rotation data, running water data, cutter data and time data according to the production name data, matching the cutter data with the time data, identifying the time for replacing cutter bits each time, namely the switching interval time between every two cutter bits, marking the interval time point between every two cutter bits as the switching time, marking the model of the cutter bits in the cutter data as cutter rule data, and sequencing the cutter rule data in sequence to obtain cutter rule sequencing data;

substituting the added surface data and the contact time data corresponding to each identical processed object into an area calculation formula: adding surface data, contact time data u1, wherein u1 represents a deviation influence factor of the total surface value, the total surface value represents a total contact area between the tool bit and the object in the machining process, the total surface value corresponding to each same machined object is substituted into a summation calculation formula, a total surface average value is calculated, and the total surface average value is marked;

and (3) bringing the piece rotation data and the cutter rotation data into a rotating speed calculation formula: the method comprises the following steps that (workpiece rotation data + tool rotation data) u2 is a rotation counting value, wherein u2 is a calculation adjusting factor of the workpiece rotation data and the tool rotation data, the rotation counting value is a sum calculation rotating speed of the workpiece rotation data and the tool rotation data, the rotation counting value is taken into an average value calculation formula, and a rotation counting average value is calculated;

sequentially selecting the element temperature data and the external temperature data, the element temperature data and the running water data, the total surface mean value and the element temperature data, the transfer mean value and the element temperature data, processing the influence factors to obtain the influence factors of the external temperature data, the running water data, the total surface mean value and the transfer mean value on the element temperature data, and sequentially marking the influence factors as u3, u4, u5 and u 6;

the switching time, rule sort data, u1, total joint value, total face mean, u2, revolution value, revolution mean, u3, u4, u5, and u6 were calibrated as processing result data.

Further, the specific processing procedure of the influence factor processing is as follows:

selecting workpiece temperature data and external temperature data, selecting different time periods, changing the workpiece temperature data of the same object under different external temperature conditions, and performing line graph processing on different temperature changes, wherein the line graph processing refers to that the external temperature data and the workpiece temperature data corresponding to the same object at different time points are subjected to position marking in a plane rectangular coordinate system, and the specific marking method comprises the following steps: taking the piece temperature data as a substitute numerical value of a Y axis and taking the external temperature data as a substitute numerical value of an X axis, carrying out coordinate marking, selecting a temperature numerical value with the piece temperature data kept unchanged, and calibrating the temperature numerical value as initial piece temperature data; selecting piece temperature data corresponding to different external temperature data, and bringing the piece temperature data into a temperature influence calculation formula: the external temperature data u3+ initial piece temperature data is piece temperature data, wherein u3 is an influence factor of the external temperature data on the piece temperature data, and the initial piece temperature data refers to the initial temperature of the object;

selecting piece temperature data and running water data, and calculating an influence factor u4 of the running water data on the piece temperature data according to an influence factor u3 of the external temperature data on the piece temperature data;

selecting total surface mean value and piece temperature data, and calculating an influence factor u5 of the total surface mean value on the piece temperature according to the method for calculating the influence factor of the flowing water data on the piece temperature data;

and selecting the measurement mean value and the piece temperature data, and calculating the influence factor u6 of the measurement mean value on the piece temperature according to the calculation method of the influence factor of the flowing water data on the piece temperature data.

Further, the specific process of processing and analyzing is as follows:

selecting a plurality of corresponding total junction values and total surface mean values, calculating the area difference values of the total junction values and the total surface mean values, calculating the area difference values, bringing the area difference values into a mean value calculation formula, and calculating the surface difference mean values, wherein the area difference value calculation formula is as follows: the total surface mean value-total junction surface value is equal to the area difference value, and the calculation process of the surface difference mean value is as follows: summing the plurality of area difference values, calibrating the obtained sum into a total area value, counting the number of the area difference values, and dividing the total area value by the number of the area difference values to obtain a mean area difference value;

selecting a plurality of corresponding rotation values and rotation average values, and calculating a rotation average value according to a calculation method of a surface difference average value;

extracting a surface difference mean value and a slip mean value, and carrying out influence floating calculation on the surface difference mean value and the slip mean value with u1 and u2, wherein the specific calculation formula is as follows: a plane floating value is a plane difference mean value/u 1, wherein the plane floating value represents a floating magnitude as a numerical value;

and calculating the transfer floating value according to the calculation mode of the surface floating value by using the transfer difference mean value and u 2.

Further, the specific operation process of the verification operation is as follows:

the method comprises the following steps of acquiring real-time processing information, marking a real-time image in the real-time processing information as real image data, marking a time point in the real-time processing information as real data, judging, identifying and matching the real image data according to object name data, object image data, knife image data and knife name data, and specifically:

matching the product name data with the object name data, selecting corresponding object shadow data according to the object name data, matching the object shadow data with corresponding real shadow data, generating an object error signal if the matching result is inconsistent, otherwise not generating a signal, selecting corresponding knife shadow data and knife name data according to the knife rule sequencing data, identifying the real shadow data according to the knife shadow data and the knife name data, identifying the corresponding real time knife shadow and the real time knife name, calibrating the time point of occurrence of the real time knife shadow and the real time knife name, sequentially matching the corresponding time point with the knife rule sequencing data, if the matching result is inconsistent, generating a sequential error signal, otherwise, not generating a signal, extracting the time point of occurrence of the real time knife shadow, performing difference calculation on the time points of two adjacent real time knife shadows, calculating the occurrence difference, and matching the occurrence difference with the switching time, when the matching results are inconsistent, generating a processing time error signal, otherwise, generating no signal, and transmitting the object error signal, the sequence error signal and the processing time error signal to an alarm unit;

and identifying the signals in the judging and identifying matching, and sending the signals to an alarm unit when identifying an object error signal, a sequence error signal and a processing time error signal, otherwise, carrying out safety calculation operation to obtain an adjusting value and an alarm signal.

Further, the specific operation process of the secure computing operation is as follows:

calibrating the real-time processing information into real-time dough adding data, real-time timing data, real-time workpiece temperature data, real-time cutter rotation data, real-time external temperature data, real-time workpiece rotation data, real-time flow data and real-time values;

bringing real-time dough adding data, real-time receiving data, real-time cutter rotation data, real-time external temperature data, real-time piece rotation data and real-time running water data into a safety calculation formula together with u1, u2, u3, u4, u5 and u6, and calculating a temperature calculation value;

presetting a temperature safety value, comparing the temperature safety value with a temperature calculation value, judging that the temperature exceeds the safety value after the object is processed when the temperature safety value is greater than the temperature calculation value, generating an early warning signal, processing and adjusting according to the difference value between the temperature safety value and the temperature calculation value, namely calculating a relevant numerical value corresponding to adjustment by reversely deducing a safety calculation formula, calibrating the relevant numerical value as an adjustment value, transmitting the early warning signal to an alarm unit, and transmitting the adjustment value to intelligent equipment.

Further, the specific calculation process of the safety calculation formula is as follows:

wherein, W_{Fruit of Chinese wolfberry}The data are expressed as calculated temperature values, namely temperature calculation values, JM is expressed as real-time surfacing data, JS is expressed as real-time timing data, MF is expressed as surfacing floating values, DZ is expressed as real-time cutter rotation data, JZ is expressed as real-time workpiece rotation data, ZF is expressed as rotation floating values, LS real-time flow data, WW is expressed as real-time external temperature data, t is expressed as the time required by an object to be processed, and e is expressed as influence calculation deviation correction factors of the real-time surfacing data, the real-time timing data, the real-time cutter rotation data, the real-time external temperature data, the real-time workpiece rotation data and the real-time flow data.

The invention has the beneficial effects that:

(1) the method comprises the steps of analyzing and processing the sequence of the same objects to be processed and the processing tool bits required to be used for corresponding processing by collecting the previous processing records of a five-axis processing center, sequencing the tool bits in sequence, and extracting the time consumed by the processing of the corresponding tool bits while sequencing, thereby ensuring the processing time consistency of each same object, ensuring the processing consistency of the objects and avoiding the waste of materials caused by the processing error of the objects in the processing process;

(2) the method comprises the steps of carrying out data analysis on related data in the processing process to process and obtain the influence of the related data on the temperature of an object in the processing process of the object, carrying out prediction calculation on the temperature of the object being processed according to the related data obtained by analysis, calculating the temperature of the object when the processing is finished, judging the temperature, and carrying out reverse calculation on the data according to the related influence value when the judgment result is negative and does not meet the standard, thereby calculating the required regulated value, increasing the accuracy of data analysis, increasing the safety of object processing, saving time and improving the working efficiency.

Drawings

The invention is further described below with reference to the accompanying drawings.

FIG. 1 is a system block diagram of the present invention.

Detailed Description

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

Referring to fig. 1, the present invention is an intelligent monitoring system of a five-axis machining center, including a monitoring unit, a sub-monitoring unit, a processor, an analysis unit, a nuclear safety unit, a security unit, an execution unit, an intelligent device and an alarm unit;

the monitoring unit is used for collecting the processing condition of the five-axis processing equipment before the time point at the moment, the processing condition of the five-axis processing equipment before the time point at the moment is marked as finishing information, the finishing information comprises production name data, facing data, timing data, part temperature data, cutter rotation data, external temperature data, part rotation data, flow data, cutter cutting data and time data, the production name data refers to the name and the model of a product at the position where a user needs to process the five-axis processing center, the facing data refers to the area size of the contact between the equipment of the five-axis processing center and the product during processing of the product, the timing data refers to the time size of the contact between the equipment of the five-axis processing center and the product during processing of the product, the part temperature data refers to the temperature size of the product at each time point during processing of the equipment of the five-axis processing center, and the external temperature data refers to the temperature size outside the equipment at each time point during processing of the product by the equipment of the five-axis processing center, the workpiece rotation data refers to the rotating speed of an object when the five-axis machining center machines, the cutter rotation data refers to the rotating speed of a cutter head when the five-axis machining center machines, the flowing data refers to the added water flow of the five-axis machining center machines, the cutter cutting data refers to the switching sequence and the cutter head model of the cutter head when the five-axis machining center machines the same object, the time data refers to the corresponding time point of the five-axis machining center in the machining process, and the finished work information is transmitted to the sub-monitoring unit;

the sub-monitoring unit is used for sub-monitoring and identifying name data, face data, timing data, part temperature data, external temperature data, part rotation data, cutter rotation data, running water data, cutter cutting data and time data, and the specific process of sub-monitoring and identifying is as follows:

acquiring production name data, extracting corresponding plus data, receiving data, piece temperature data, external temperature data, piece rotation data, running water data, cutter data and time data according to the production name data, matching the cutter data with the time data, identifying the time for replacing cutter bits each time, namely the switching interval time between every two cutter bits, marking the interval time point between every two cutter bits as the switching time, marking the model of the cutter bits in the cutter data as cutter rule data, and sequencing the cutter rule data in sequence to obtain cutter rule sequencing data;

substituting the added surface data and the contact time data corresponding to each identical processed object into an area calculation formula: adding surface data, contact time data u1, wherein u1 represents a deviation influence factor of the total surface value, the total surface value represents a total contact area between the tool bit and the object in the machining process, the total surface value corresponding to each same machined object is substituted into a summation calculation formula, a total surface average value is calculated, and the total surface average value is marked;

and (3) bringing the piece rotation data and the cutter rotation data into a rotating speed calculation formula: the method comprises the following steps that (workpiece rotation data + tool rotation data) u2 is a rotation counting value, wherein u2 is a calculation adjusting factor of the workpiece rotation data and the tool rotation data, the rotation counting value is a sum calculation rotating speed of the workpiece rotation data and the tool rotation data, the rotation counting value is taken into an average value calculation formula, and a rotation counting average value is calculated;

selecting workpiece temperature data and external temperature data, selecting different time periods, changing the workpiece temperature data of the same object under different external temperature conditions, and performing line graph processing on different temperature changes, wherein the line graph processing refers to that the external temperature data and the workpiece temperature data corresponding to the same object at different time points are subjected to position marking in a plane rectangular coordinate system, and the specific marking method comprises the following steps: taking the piece temperature data as a substitute numerical value of a Y axis and taking the external temperature data as a substitute numerical value of an X axis, carrying out coordinate marking, selecting a temperature numerical value with the piece temperature data kept unchanged, and calibrating the temperature numerical value as initial piece temperature data; selecting piece temperature data corresponding to different external temperature data, and bringing the piece temperature data into a temperature influence calculation formula: the external temperature data u3+ initial piece temperature data is piece temperature data, wherein u3 is an influence factor of the external temperature data on the piece temperature data, and the initial piece temperature data refers to the initial temperature of the object;

selecting piece temperature data and flow data, selecting piece temperature data changes of the same object under different flow conditions in different time periods, and performing line graph processing on the temperature changes corresponding to different flow data, wherein the line graph processing refers to position marking of the flow data and the piece temperature data corresponding to the same object at different time points in a plane rectangular coordinate system, and the specific marking method comprises the following steps: taking the piece temperature data as a substitute numerical value of a Y axis and the flowing water data as a substitute numerical value of an X axis, carrying out coordinate marking, selecting a temperature numerical value with the piece temperature data kept unchanged, and calibrating the temperature numerical value as initial piece temperature data; selecting piece temperature data corresponding to different flowing water data, and bringing the piece temperature data into a temperature influence calculation formula: the running water data u4+ initial piece temperature data is piece temperature data, wherein u4 is expressed as an influence factor of the running water data on the piece temperature data;

selecting total surface mean value and piece temperature data, calculating an influence factor u5 of the total surface mean value on the piece temperature according to a calculation method of the influence factor of the flowing water data on the piece temperature data, selecting transfer mean value and piece temperature data, and calculating an influence factor u6 of the transfer mean value on the piece temperature according to a calculation method of the influence factor of the flowing water data on the piece temperature data;

calibrating the switching time, the rule sequencing data, u1, the total interface value, the total surface mean value, u2, the rotation value, the rotation mean value, u3, u4, u5 and u6 into processing result data, and transmitting the analysis result data to the processor;

the processor is used for receiving the switching time, the rule sequencing data, u1, the total interface value, the total surface mean value, u2, the rotation value, the rotation mean value, u3, u4, u5 and u6 and storing the data;

the analysis unit acquires the total interface value, the total interface mean value, u3, u4, the rotation value and the rotation mean value from the processor, and carries out processing analysis according to the total interface value, the total interface mean value, u1, u2, the rotation value and the rotation mean value, wherein the specific process of the processing analysis is as follows:

selecting a plurality of corresponding total junction values and total surface mean values, calculating the area difference values of the total junction values and the total surface mean values, calculating the area difference values, bringing the area difference values into a mean value calculation formula, and calculating the surface difference mean values, wherein the area difference value calculation formula is as follows: the total surface mean value-total junction surface value is equal to the area difference value, and the calculation process of the surface difference mean value is as follows: summing the plurality of area difference values, calibrating the obtained sum into a total area value, counting the number of the area difference values, and dividing the total area value by the number of the area difference values to obtain a mean area difference value;

selecting a plurality of corresponding rotation value and rotation mean value, rotating the rotation value and the rotation mean value to calculate a rotation difference value, calculating a rotation difference value, bringing the rotation difference value into a mean value calculation formula, and calculating the rotation mean value, wherein the rotation difference value calculation formula is as follows: the rotation average value-rotation value is equal to the rotation difference value, and the calculation process of the rotation average value is as follows: summing the rotation difference values, calibrating the obtained sum to be a rotation total value, counting the number of the rotation difference values, and dividing the rotation total value by the number of the rotation difference values to obtain a rotation difference mean value;

extracting a surface difference mean value and a slip mean value, and carrying out influence floating calculation on the surface difference mean value and the slip mean value with u1 and u2, wherein the specific calculation formula is as follows: a plane floating value is a plane difference mean value/u 1, wherein the plane floating value represents a floating magnitude as a numerical value;

calculating a transfer floating value according to the transfer average value and the u2 in a calculation mode of the surface floating value;

extracting a surface floating value and a conversion floating value, and transmitting the surface floating value and the conversion floating value to a nuclear safety unit together with switching time, cutting rule sequencing data, a total interface value, a total surface mean value, a conversion mean value, u1, u2, u3, u4, u5 and u 6;

the monitoring unit is also used for monitoring the real-time machining information of the five-axis machining center in real time and transmitting the real-time machining information to the nuclear safety unit;

image information related to a five-axis machining center is stored in the processor, wherein the image information comprises object name data, object shadow data, cutter shadow data and cutter name data, the object name data refers to the name of a machined object, the object shadow data refers to the image of the machined object, the cutter shadow data refers to the image of a cutter head during machining of the object, and the cutter name data refers to the name model of the cutter head during machining of the object;

the core security unit acquires image information from the processor, and performs a verification operation on the real-time processing information according to the image information, the surface floating value, the conversion floating value, the production name data, the switching time, the rule sorting data, the total interface value, the total surface mean value, the conversion mean value, u1, u2, u3, u4, u5 and u6, wherein the specific operation process of the verification operation is as follows:

the method comprises the following steps of acquiring real-time processing information, marking a real-time image in the real-time processing information as real image data, marking a time point in the real-time processing information as real data, judging, identifying and matching the real image data according to object name data, object image data, knife image data and knife name data, and specifically:

matching the product name data with the object name data, selecting corresponding object shadow data according to the object name data, matching the object shadow data with corresponding real shadow data, generating an object error signal if the matching result is inconsistent, otherwise not generating a signal, selecting corresponding knife shadow data and knife name data according to the knife rule sequencing data, identifying the real shadow data according to the knife shadow data and the knife name data, identifying the corresponding real time knife shadow and the real time knife name, calibrating the time point of occurrence of the real time knife shadow and the real time knife name, sequentially matching the corresponding time point with the knife rule sequencing data, if the matching result is inconsistent, generating a sequential error signal, otherwise, not generating a signal, extracting the time point of occurrence of the real time knife shadow, performing difference calculation on the time points of two adjacent real time knife shadows, calculating the occurrence difference, and matching the occurrence difference with the switching time, when the matching results are inconsistent, generating a processing time error signal, otherwise, generating no signal, and transmitting the object error signal, the sequence error signal and the processing time error signal to an alarm unit;

and identifying the signals in the judging and identifying matching, when identifying an object error signal, a sequence error signal and a processing time error signal, sending the signals to an alarm unit, otherwise, carrying out safety calculation operation, wherein the specific operation process of the safety calculation operation is as follows:

the real-time processing information is calibrated into real-time surfacing data, real-time receiving data, real-time workpiece temperature data, real-time cutter rotation data, real-time external temperature data, real-time workpiece rotation data, real-time flow data and real time values, and the definitions of the real-time surfacing data, the real-time receiving data, the real-time cutter rotation data, the real-time external temperature data, the real-time workpiece rotation data, the real-time flow data and the real time values are the same as the real-time surfacing data, the receiving data, the cutter rotation data, the external temperature data, the workpiece rotation data, the flow data and the time data;

and (3) bringing real-time dough adding data, real-time receiving data, real-time cutter rotation data, real-time external temperature data, real-time piece rotation data and real-time running water data into safety calculation formulas with u1, u2, u3, u4, u5 and u 6:

wherein, W_{Fruit of Chinese wolfberry}The real-time surface heating data are expressed as calculated temperature values, namely temperature calculation values, JM is expressed as real-time surface heating data, JS is expressed as real-time receiving data, MF is expressed as surface floating values, DZ is expressed as real-time cutter rotation data, JZ is expressed as real-time workpiece rotation data, ZF is expressed as rotation floating values, LS real-time flow data, WW is expressed as real-time external temperature data, t is expressed as the time required by an object to be processed, and e is expressed as influence calculation deviation correction factors of the real-time surface heating data, the real-time receiving data, the real-time cutter rotation data, the real-time external temperature data, the real-time workpiece rotation data and the real-time flow data;

presetting a temperature safety value, comparing the temperature safety value with a temperature calculation value, judging that the temperature exceeds the safety value after the object is processed when the temperature safety value is greater than the temperature calculation value, generating an early warning signal, carrying out processing adjustment according to the difference value between the temperature safety value and the temperature calculation value, calculating a relevant value corresponding to adjustment by reversely deducing a safety calculation formula, calibrating the relevant value as an adjustment value, transmitting the early warning signal to an alarm unit, and transmitting the adjustment value to intelligent equipment;

the alarm unit receives the object error signal, the sequence error signal, the processing time error signal and the early warning signal, performs signal conversion, sequentially converts the object error signal, the sequence error signal and the processing time error signal into an object error alarm, a sequence error alarm and a processing time error alarm when the object error signal, the sequence error signal and the processing time error signal are identified, transmits the object error alarm, the sequence error alarm and the processing time error alarm to the intelligent equipment, converts the object error alarm, the sequence error alarm and the processing time error alarm into an early warning alarm when the early warning signal is identified, and sends out an alarm sound when the early warning signal is sent to the intelligent equipment;

the intelligent equipment is used for receiving object error alarms, sequence error alarms, processing time error alarms and early warning alarms, displaying and reminding managers;

the safety prevention unit is used for carrying out safety judgment on potential safety hazards in the five-axis machining center, and specifically comprises the following steps:

the safety protection unit acquires image information from the processing unit, and the image information further comprises a tooling image, a safety image and a safety data, wherein the tooling image refers to images related to the work of the five-axis machining center, such as scrap iron, water and the like of object machining, the safety image refers to images unrelated to the five-axis machining center, such as hand, stone, plastic and the like, and the safety data refers to names corresponding to the tooling image and the safety image;

the safety prevention unit acquires real-time processing information from the monitoring unit, marks images of five-axis processing in the real-time processing information as time image data, wherein the time image data refer to images of a processing table and the periphery of a five-axis processing center during processing, carries out potential safety hazard analysis on the images, a tooling recording image and a tooling recording data, judges that the processing is normal when the tooling recording image is identified in the time image data, automatically extracts the corresponding tooling recording data when the tooling recording image is identified, generates a potential safety hazard alarm, and transmits the potential safety hazard alarm and the corresponding tooling recording data to the execution unit;

and the execution unit receives the hidden danger alarm and the corresponding nomination data, saves the working progress of the five-axis machining center, stops machining and prompts a manager to process.

When the five-axis machining equipment works, the machining condition of the five-axis machining equipment before the time point at the moment is collected through the monitoring unit, the machining condition of the five-axis machining equipment before the time point at the moment is marked as completion information, the completion information is transmitted to the sub-monitoring unit, the sub-monitoring unit carries out sub-monitoring and identification on production name data, facing data, timing data, workpiece temperature data, external temperature data, workpiece rotation data, cutter rotation data, running water data, cutter cutting data and time data to obtain processing result data, the analysis unit obtains a total joint value, a total surface mean value, u3, u4, a rotation value and a rotation mean value from the processor, carries out processing analysis according to the total joint value, the total surface mean value, u1, u2, the rotation value and the rotation mean value to obtain a surface floating value and a rotation floating value, transmits the surface floating value and the rotation floating value to the processor, and obtains image information from the processor through the nuclear safety unit, and according to the image information, the surface floating value, the conversion floating value, the name data, the switching time, the rule sorting data, the total interface value, the total surface mean value, the conversion mean value, u1, u2, u3, u4, u5 and u6, checking the real-time processing information to obtain a regulating value and an alarm signal, transmitting the alarm signal to an alarm unit, transmitting the regulating value to the intelligent equipment, receiving the object error signal, the sequence error signal, the processing time error signal and the alarm signal by the alarm unit, performing signal conversion, sequentially converting the signals into an object error alarm, a sequence error alarm, a processing time error alarm and an early warning alarm, transmitting the object error alarm, the sequence error alarm, the processing time error alarm and the early warning alarm to the intelligent equipment, and displaying and reminding a manager.

The foregoing is merely exemplary and illustrative of the present invention and various modifications, additions and substitutions may be made by those skilled in the art to the specific embodiments described without departing from the scope of the invention as defined in the following claims.