CN111966045A - Machine tool crash monitoring method and device, terminal equipment and storage medium - Google Patents
Machine tool crash monitoring method and device, terminal equipment and storage medium Download PDFInfo
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- CN111966045A CN111966045A CN202010653840.3A CN202010653840A CN111966045A CN 111966045 A CN111966045 A CN 111966045A CN 202010653840 A CN202010653840 A CN 202010653840A CN 111966045 A CN111966045 A CN 111966045A
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/18—Numerical control [NC], i.e. automatically operating machines, in particular machine tools, e.g. in a manufacturing environment, so as to execute positioning, movement or co-ordinated operations by means of programme data in numerical form
- G05B19/406—Numerical control [NC], i.e. automatically operating machines, in particular machine tools, e.g. in a manufacturing environment, so as to execute positioning, movement or co-ordinated operations by means of programme data in numerical form characterised by monitoring or safety
- G05B19/4065—Monitoring tool breakage, life or condition
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/30—Nc systems
- G05B2219/37—Measurements
- G05B2219/37624—Detect collision, blocking by measuring change of velocity or torque
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Abstract
The present disclosure relates to the field of machine tool crash monitoring technologies, and in particular, to a machine tool crash monitoring method, device, terminal device, and storage medium. The method comprises the following steps: acquiring control data and operation data corresponding to the machine tool; respectively preprocessing the control data and the operation data to obtain a preprocessed target data group; and inputting the target data set into a trained crash monitoring model for processing to obtain a crash monitoring result output by the crash monitoring model, wherein the crash monitoring model is a crash monitoring model corresponding to the machine tool. The technical problem that the prior art lacks the machine tool collision prediction scheme with high accuracy is solved.
Description
Technical Field
The application relates to the technical field of machine tool crash monitoring, in particular to a machine tool crash monitoring method, a machine tool crash monitoring device, terminal equipment and a storage medium.
Background
In the process of machine tool operation, when the machine tool has problems such as program error, workpiece error, operation error and clamping error, the moving part of the machine tool may collide with the processing spindle or the processing tool. If the machine tool collides, the machine tool is failed to process, functional components are damaged or the machine tool is damaged, so that the machine tool needs to be monitored for collision in real time to ensure the safe operation of the machine tool.
The first monitoring method is to set a protection threshold value according to the load of the machine tool, and trigger system protection when the monitored load of the machine tool exceeds the set protection threshold value. However, the protection threshold in this monitoring method is greatly affected by the processing object, and it is difficult to set a relatively reasonable protection threshold, when the protection threshold is too small, the problem of over-protection occurs during heavy load processing, which affects production, and when the protection threshold is too large, the problem of too long protection response time occurs, which results in poor protection effect. The second monitoring method is to arrange a vibration sensor on the machine tool, and trigger the protection action of the machine tool when the vibration value of the machine tool exceeds a preset vibration value. A third monitoring method is to provide a strain sensor on the machine tool and trigger the protection when the machine tool encounters a collision at low speed. The second and third monitoring methods are greatly influenced by the model of the machine tool, a large amount of configuration work needs to be carried out according to the model of the machine tool before use, the configured parts comprise a sensor, a signal amplifier, a processor and the like corresponding to the sensor, the acquired data is single, the judgment standard is single, and when the machine tools of different models are judged, the condition of misjudgment and misjudgment can occur, so that the machine tool is stopped to operate wrongly, and the normal production of the machine tool is influenced. Therefore, the prior art lacks a machine tool crash monitoring scheme with high accuracy.
Disclosure of Invention
In view of this, embodiments of the present application provide a method and an apparatus for monitoring a machine crash, a terminal device, and a storage medium, so as to provide a machine crash monitoring scheme with high accuracy.
In a first aspect, an embodiment of the present application provides a machine tool crash monitoring method, including:
acquiring control data and operation data corresponding to the machine tool;
respectively preprocessing the control data and the operation data to obtain a preprocessed target data group;
and inputting the target data set into a trained crash monitoring model for processing to obtain a crash monitoring result output by the crash monitoring model, wherein the crash monitoring model is a crash monitoring model corresponding to the machine tool.
In one example, the crash monitoring model is trained by:
acquiring training data sets, wherein each training data set comprises historical control data and historical operating data corresponding to the machine tool;
respectively preprocessing historical control data and historical operating data corresponding to each training data set to obtain a target training data set corresponding to each training data set;
marking the target crash result corresponding to each target training data set;
and training a crash monitoring model by using each target training data set and the corresponding target crash result to obtain the trained crash monitoring model.
In one example, after obtaining the crash monitoring result output by the crash monitoring model, the method includes:
and if the crash monitoring result indicates that a crash behavior exists, sending control information to the machine tool, wherein the control information is used for controlling the machine tool to execute a target action.
In one example, if the crash monitoring result indicates that crash behavior exists, sending control information to the machine tool includes:
if the crash monitoring result indicates that a crash behavior exists, determining a behavior type corresponding to the crash behavior;
and acquiring control information corresponding to the behavior type, and sending the control information to the machine tool.
In one example, the behavior category corresponding to the crash behavior includes any one of: an abnormal rapid impact machine, an abnormal manual impact machine, an abnormal workpiece impact machine or an abnormal tool impact machine.
In one example, the pre-processing the control data and the operation data respectively to obtain a pre-processed target data set includes:
and respectively carrying out alignment treatment, cleaning treatment, exception treatment and type conversion treatment on the control data and the operation data to obtain preprocessed control data and preprocessed operation data, and forming the preprocessed control data and the preprocessed operation data into the target data group.
In a second aspect, an embodiment of the present application provides a machine tool crash monitoring device, including:
the data acquisition module is used for acquiring control data and operation data corresponding to the machine tool;
the data preprocessing module is used for respectively preprocessing the control data and the operation data to obtain a preprocessed target data group;
and the result acquisition module is used for inputting the target data set to a trained crash monitoring model for processing to obtain a crash monitoring result output by the crash monitoring model, wherein the crash monitoring model is a crash monitoring model corresponding to the machine tool.
In one example, the apparatus further comprises:
the training data set acquisition module is used for acquiring training data sets, and each training data set comprises historical control data and historical operating data corresponding to the machine tool;
the target training data set acquisition module is used for respectively preprocessing the historical control data and the historical operating data corresponding to each training data set to obtain a target training data set corresponding to each training data set;
a target crash result marking module for marking the target crash result corresponding to each target training data set;
and the crash monitoring module training module is used for training a crash monitoring model by utilizing each target training data set and the corresponding target crash result to obtain the trained crash monitoring model.
In a third aspect, an embodiment of the present application provides a terminal device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the machine crash monitoring method according to any one of the first aspect when executing the computer program.
In a fourth aspect, the present application provides a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, the computer program implements the machine crash monitoring method according to any one of the first aspect.
According to the machine tool crash monitoring method provided by the embodiment of the application, when the machine tool is started, the control data and the operation data corresponding to the machine tool are obtained, and after the control data and the operation data are respectively preprocessed, a target data group comprising the preprocessed data of the control data and the preprocessed data is obtained. Because the signal loss of the target data group obtained after preprocessing is small, the carried data distortion degree is low, the data is preprocessed and then transmitted, the loss in the data transmission process can be reduced, and the accuracy of behavior judgment on the machine tool in the follow-up process is improved. And then, inputting the target data set acquired in real time into a pre-trained crash monitoring model for processing, wherein the crash monitoring model is a crash monitoring model corresponding to the machine tool to obtain a crash monitoring result corresponding to the target data set, and performing crash behavior monitoring on the machine tool to perform crash behavior monitoring on the machine tool through the pre-trained crash monitoring model corresponding to the machine tool, so that the monitoring result is more objective and accurate, and the condition of misjudgment is reduced. According to the method and the device, the collision monitoring model corresponding to the machine tool processes the target data set with small data loss to judge the behavior of the machine tool, and the accuracy of machine tool collision monitoring can be improved.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
Fig. 1 is a schematic flow chart of a machine tool crash monitoring method according to an embodiment of the present disclosure;
fig. 2 is a schematic flow chart of a machine tool crash monitoring method according to a second embodiment of the present application;
fig. 3 is a schematic structural diagram of a machine tool crash monitoring device provided in the third embodiment of the present application;
fig. 4 is a schematic diagram of a terminal device according to a fourth embodiment of the present application.
Detailed Description
In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all 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 application.
The terms "comprises" and "comprising," and any variations thereof, in the description and claims of this application and the drawings described above, are intended to cover non-exclusive inclusions. For example, a process, method, or system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements listed, but may alternatively include other steps or elements not listed, or inherent to such process, method, article, or apparatus. Furthermore, the terms "first," "second," and "third," etc. are used to distinguish between different objects and are not used to describe a particular order.
Example one
Fig. 1 is a schematic flow chart of a machine tool crash monitoring method according to an embodiment of the present disclosure. The embodiment can be suitable for the working scene that the numerical control machining equipment is likely to have abnormal collision in the running process. The execution subject of the present embodiment may be a terminal device. As shown in fig. 1, the method specifically includes the following steps:
s110, acquiring control data and operation data corresponding to the machine tool;
specifically, before the machine tool to be monitored is started, a user may install a sensor for acquiring operation data according to the model of the machine tool, and the sensor may be installed on a spindle of the machine tool. Operational data includes, but is not limited to, acceleration data, velocity data, and gravity data. The sensors can comprise acceleration sensors, speed sensors and gravity sensors, and the sensors are configured to collect operation data such as acceleration data, speed data and gravity data of the machine tool respectively when the machine tool runs, and can send the collected operation data such as acceleration data, speed data and gravity data to the terminal equipment.
In this embodiment, the terminal device may be in communication connection with a controller or a control panel of the machine tool to acquire control data of the machine tool. In particular, the terminal device may be communicatively connected to a controller or control panel of the machine tool via a network cable or a machine tool bus. The machine bus may be a can bus, an automation bus profinet or a field bus profibus.
It should be understood that the operation panel of the machine tool may also be equipped with a display device, and the display device may display the control data corresponding to the machine tool. The control data may include, but is not limited to, machine tool start-stop information, program call information, and axis motion information.
It should be noted that, when the acceleration sensor collects acceleration data, the detected signal may be converted into a digital signal after being subjected to jitter elimination, filtering, protection, level conversion and isolation, and finally the obtained digital signal is sent to the terminal device as acceleration data, so as to reduce loss of the acceleration data during data transmission. Similarly, when the speed sensor collects speed data, the detected signal can be converted into a digital signal after jitter elimination, filtering, protection, level conversion and isolation, and finally the obtained digital signal is used as the speed data and sent to the terminal equipment, so that the loss of the speed data in data transmission is reduced. Similarly, when the gravity sensor collects the gravity data, the detected signal can be converted into a digital signal after shaking elimination, filtering, protection, level conversion and isolation, and finally the obtained digital signal is sent to the terminal device as the gravity data so as to reduce the loss of the gravity data during data transmission.
S120, respectively preprocessing the control data and the operation data to obtain a preprocessed target data group;
after the terminal device obtains the operation data and the control data, the terminal device can respectively carry out alignment, cleaning, abnormal value processing and type conversion on the operation data and the control data to obtain preprocessed operation data and preprocessed control data, and the preprocessed operation data and the preprocessed control data form a target data set.
And S130, inputting the target data set into a trained crash monitoring model for processing to obtain a crash monitoring result output by the crash monitoring model, wherein the crash monitoring model is a crash monitoring model corresponding to the machine tool.
Specifically, after the terminal device obtains the target data set, the target data set can be input into a crash monitoring model which is trained in advance and corresponds to the machine tool for processing. The crash monitoring model can judge the behavior of the machine tool to be tested through the target data set and output the crash monitoring result of the machine tool.
It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.
According to the machine tool crash monitoring method provided by the embodiment of the application, when the machine tool is started, the control data and the operation data corresponding to the machine tool are obtained, and after the control data and the operation data are respectively preprocessed, a target data group comprising the preprocessed data of the control data and the preprocessed data is obtained. Because the signal loss of the target data group obtained after preprocessing is small, the carried data distortion degree is low, the data is preprocessed and then transmitted, the loss in the data transmission process can be reduced, and the accuracy of behavior judgment on the machine tool in the follow-up process is improved. And then, inputting the target data set acquired in real time into a pre-trained crash monitoring model for processing, wherein the crash monitoring model is a crash monitoring model corresponding to the machine tool to obtain a crash monitoring result corresponding to the target data set, and performing crash behavior monitoring on the machine tool to perform crash behavior monitoring on the machine tool through the pre-trained crash monitoring model corresponding to the machine tool, so that the monitoring result is more objective and accurate, and the condition of misjudgment is reduced. According to the method and the device, the collision monitoring model corresponding to the machine tool processes the target data set with small data loss to judge the behavior of the machine tool, and the accuracy of machine tool collision monitoring can be improved.
Example two
Fig. 2 is a schematic flow chart of a machine tool crash monitoring method according to a second embodiment of the present application. On the basis of the first embodiment, the embodiment further provides a training process of the crash monitoring model corresponding to the machine tool, so that the crash monitoring model corresponding to the machine tool is obtained through one-to-one training to judge the behavior of the machine tool, and therefore the accuracy of a behavior judgment result is ensured.
In one embodiment, the crash monitoring model is trained by:
s210, acquiring training data sets, wherein each training data set comprises historical control data and historical operating data corresponding to the machine tool;
s211, respectively preprocessing historical control data and historical operating data corresponding to each training data set to obtain a target training data set corresponding to each training data set;
s212, marking a target crash result corresponding to each target training data set;
and S213, training a crash monitoring model by using each target training data set and the corresponding target crash result to obtain the trained crash monitoring model.
Specifically, before the behavior of the machine tool is judged, the machine tool can be trained to establish a corresponding crash monitoring model. The training process of the crash monitoring model is as follows: historical operating data and historical control data corresponding to the machine tool are obtained first. Wherein the historical operating data includes, without limitation, historical acceleration data, historical speed data, and historical gravity data of the machine tool, and the historical control data includes, without limitation, start-stop information, program call information, and axis motion information of the machine tool. The target collision result may be a result of occurrence of a collision behavior of the machine tool when the machine tool has the historical operation data and the historical control data, or a result of non-occurrence of the collision behavior of the machine tool. And respectively aligning, cleaning, processing abnormal values and converting types of the marked historical operating data and the corresponding historical control data to obtain corresponding data after preprocessing of the historical operating data and the corresponding historical control data, and forming a target training data set corresponding to a training data set. And constructing an initial crash monitoring model according to a preset algorithm, and then training the initial crash monitoring model by utilizing a target training data set so as to obtain a trained crash monitoring model. The predetermined algorithm may be any one of a support vector machine (support vector machine), an extreme gradient boost (xgboost), a punctuation mark (lightGBM), a gradient boost (catboost), a logistic regression (logistic regression), an isolated forest (isolation forest) and a random forest (random forest).
In one example, after obtaining the crash monitoring result output by the crash monitoring model, the method includes:
and if the crash monitoring result indicates that a crash behavior exists, sending control information to the machine tool, wherein the control information is used for controlling the machine tool to execute a target action.
Specifically, because the crash monitoring model is generated by historical operating data and historical control data of various working conditions, during monitoring, the terminal device can input operating data and control data corresponding to the machine tool during operation into the crash monitoring model for processing, and a crash monitoring result output by the crash monitoring model is obtained. When the crash monitoring result indicates that the machine tool has abnormal crash behavior, the terminal device can generate a control signal and send the control signal to the machine tool, so that the machine tool can make a corresponding target action according to the control signal. Target actions include, but are not limited to, programmed shut down, emergency shut down, and emergency lift of the shaft, among others.
In one example, if the crash monitoring result indicates that crash behavior exists, sending control information to the machine tool includes:
if the crash monitoring result indicates that a crash behavior exists, determining a behavior type corresponding to the crash behavior;
and acquiring control information corresponding to the behavior type, and sending the control information to the machine tool.
In this embodiment, when the crash monitoring model is trained according to the target data set, the operating condition type corresponding to the target data set may be further labeled according to the operating condition information corresponding to the historical operating data and the historical control data, and therefore, the crash monitoring result output by the crash monitoring model obtained through training may further include the operating condition type.
Specifically, during monitoring, the terminal device may input the operation data and the control data corresponding to the machine tool during operation into the crash monitoring model for processing, so as to obtain a crash result of the machine tool output by the crash monitoring model. When the machine tool crashes, as a result, the machine tool has abnormal machine tool crashes, the terminal device can determine the behavior category of the crashed behavior according to the working condition category in the crash monitoring result, and can generate a control signal corresponding to the behavior category and send the control signal to the machine tool, so that the machine tool can make a corresponding target action according to the control signal. The abnormal crash behavior category of the machine tool can be an abnormal quick crash, an abnormal manual crash, an abnormal workpiece crash or an abnormal tool crash. Target actions include, without limitation, program stops, emergency stops, and emergency lift shafts.
For example, when the behavior type of the crash behavior is determined to be an abnormal rapid crash according to the working condition type in the crash monitoring result, a control signal corresponding to the behavior type may be generated and sent to the machine tool, so that the machine tool makes a corresponding target action according to the control signal, where the target action may be a program shutdown operation. The machine tool can execute program stop operation according to the control signal so as to protect the machine tool. When the behavior type of the crash behavior is determined to be abnormal manual crash according to the working condition type in the crash monitoring result, a control signal corresponding to the behavior type can be generated and sent to the machine tool, so that the machine tool can make a corresponding target action according to the control signal, wherein the target action can be emergency shutdown operation. The machine tool can perform an emergency stop operation according to the control signal to protect the machine tool. When the behavior type of the crash behavior is determined to be abnormal crash of the workpiece according to the working condition type in the crash monitoring result, a control signal corresponding to the behavior type can be generated and sent to the machine tool, so that the machine tool can make a corresponding target action according to the control signal, wherein the target action can be emergency shaft lifting operation. The machine tool can execute emergency shaft lifting operation according to the control signal so as to protect the machine tool. It should be noted that the correspondence between the target action and the crash behavior may be set manually, rather than fixed.
EXAMPLE III
Fig. 3 is a schematic structural diagram of a monitoring device for a machine tool crash according to a third embodiment of the present application. On the basis of the first embodiment or the second embodiment, the embodiment of the present application further provides a machine tool crash monitoring device, which may include:
the data acquisition module 301 is used for acquiring control data and operation data corresponding to the machine tool;
a data preprocessing module 302, configured to preprocess the control data and the operation data, respectively, to obtain a preprocessed target data set;
and the result acquisition module 303 is configured to input the target data set to a trained crash monitoring model for processing, and obtain a crash monitoring result output by the crash monitoring model, where the crash monitoring model is a crash monitoring model corresponding to the machine tool.
In one example, the device for monitoring crash of machine tool may further include:
the training data set acquisition module is used for acquiring training data sets, and each training data set comprises historical control data and historical operating data corresponding to one machine tool;
the target training data set acquisition module is used for respectively preprocessing the historical control data and the historical operating data corresponding to each training data set to obtain a target training data set corresponding to each training data set;
a target crash result marking module for marking the target crash result corresponding to each target training data set;
and the crash monitoring module training module is used for training a crash monitoring model by utilizing each target training data set and the corresponding target crash result to obtain the trained crash monitoring model.
In one example, the device for monitoring crash of machine tool may further include:
and the target action execution module is used for sending control information to the machine tool if the crash monitoring result indicates that a crash behavior exists, wherein the control information is used for controlling the machine tool to execute a target action.
In one example, the target action execution module may include:
a behavior type determining unit, configured to determine a behavior type corresponding to the crash behavior if the crash monitoring result indicates that the crash behavior exists;
and the control information sending unit is used for acquiring the control information corresponding to the behavior type and sending the control information to the machine tool.
In one example, the crash behavior that the enforcement behavior class determination unit may determine includes any one of: an abnormal rapid impact machine, an abnormal manual impact machine, an abnormal workpiece impact machine or an abnormal tool impact machine.
In one example, the data pre-processing module may be to:
and respectively carrying out alignment treatment, cleaning treatment, exception treatment and type conversion treatment on the control data and the operation data to obtain preprocessed control data and preprocessed operation data, and forming the preprocessed control data and the preprocessed operation data into the target data group.
Example four
Fig. 4 is a schematic diagram of a terminal device according to a fourth embodiment of the present application. The terminal device of this embodiment includes: a processor 40, a memory 41 and a computer program 42, such as a crash monitor program, stored in said memory 41 and executable on said processor 40. The processor 40 implements the steps in the above embodiments when executing the computer program 42, such as steps S110 to S130 shown in fig. 1 or steps S210 to S213 shown in fig. 2.
Illustratively, the computer program 42 may be partitioned into one or more modules that are stored in the memory 41 and executed by the processor 40 to accomplish the present application. The one or more modules may be a series of computer program instruction segments capable of performing specific functions, which are used to describe the execution of the computer program 42 in the terminal device 4. For example, the computer program 42 may be divided into a data acquisition module, a data preprocessing module, and a result acquisition module, and the specific functions of each module are as follows:
the data acquisition module is used for acquiring control data and operation data corresponding to the machine tool;
the data preprocessing module is used for preprocessing the control data and the operation data to obtain a preprocessed target data group;
and the result acquisition module is used for inputting the target data to the trained crash monitoring model for processing to obtain a crash monitoring result output by the crash monitoring model.
The terminal device 4 may be a desktop computer, a notebook, a palm computer, a cloud server, or other computing devices. The terminal device may include, but is not limited to, a processor 40, a memory 41, and a memory 41. It will be understood by those skilled in the art that fig. 4 is only an example of the terminal device 4, and does not constitute a limitation to the terminal device 4, and may include more or less components than those shown, or combine some components, or different components, for example, the control device may further include an input-output device, a network access device, a bus, etc.
The Processor 40 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 41 may be an internal memory unit of the crash monitoring device, such as a hard disk or a memory of the terminal device 4. The memory 41 may also be an external storage device of the terminal device 4, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card) and the like provided on the terminal device 4. Further, the memory 41 may also include both an internal storage unit of the terminal device 4 and an external storage device. The memory 41 is used to store the computer program and other programs and data required by the crash monitoring device 4. The memory 41 may also be used to temporarily store data that has been output or is to be output.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-mentioned functions. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working processes of the units and modules in the system may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus/terminal device and method may be implemented in other ways. For example, the above-described embodiments of the apparatus/terminal device are merely illustrative, and for example, the division of the modules or units is only one logical division, and there may be other divisions when actually implemented, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated modules/units, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. Based on such understanding, all or part of the flow in the method of the embodiments described above can be realized by a computer program, which can be stored in a computer-readable storage medium and can realize the steps of the embodiments of the methods described above when the computer program is executed by a processor. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable storage medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like. It should be noted that the computer readable storage medium may contain content that is subject to appropriate increase or decrease as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable storage media that does not include electrical carrier signals and telecommunications signals in accordance with legislation and patent practice.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present application and are intended to be included within the scope of the present application.
Claims (10)
1. A machine tool crash monitoring method is characterized by comprising the following steps:
acquiring control data and operation data corresponding to the machine tool;
respectively preprocessing the control data and the operation data to obtain a preprocessed target data group;
and inputting the target data set into a trained crash monitoring model for processing to obtain a crash monitoring result output by the crash monitoring model, wherein the crash monitoring model is a crash monitoring model corresponding to the machine tool.
2. The machine crash monitoring method of claim 1 wherein said crash monitoring model is trained by:
acquiring training data sets, wherein each training data set comprises historical control data and historical operating data corresponding to the machine tool;
respectively preprocessing historical control data and historical operating data corresponding to each training data set to obtain a target training data set corresponding to each training data set;
marking the target crash result corresponding to each target training data set;
and training a crash monitoring model by using each target training data set and the corresponding target crash result to obtain the trained crash monitoring model.
3. The machine tool crash monitoring method according to claim 1, after said obtaining of the crash monitoring result output by said crash monitoring model, comprising:
and if the crash monitoring result indicates that a crash behavior exists, sending control information to the machine tool, wherein the control information is used for controlling the machine tool to execute a target action.
4. The machine crash monitoring method of claim 3, wherein if the crash monitoring result is that there is a crash behavior, sending control information to the machine tool comprises:
if the crash monitoring result indicates that a crash behavior exists, determining a behavior type corresponding to the crash behavior;
and acquiring control information corresponding to the behavior type, and sending the control information to the machine tool.
5. The machine crash monitoring method according to claim 4, wherein the behavior category corresponding to the crash behavior comprises any one of: an abnormal rapid impact machine, an abnormal manual impact machine, an abnormal workpiece impact machine or an abnormal tool impact machine.
6. The machine crash monitoring method of any one of claims 1-5, wherein said pre-processing said control data and said operational data, respectively, to obtain a pre-processed target data set, comprises:
and respectively carrying out alignment treatment, cleaning treatment, exception treatment and type conversion treatment on the control data and the operation data to obtain preprocessed control data and preprocessed operation data, and forming the preprocessed control data and the preprocessed operation data into the target data group.
7. A machine tool crash monitoring device, comprising:
the data acquisition module is used for acquiring control data and operation data corresponding to the machine tool;
the data preprocessing module is used for respectively preprocessing the control data and the operation data to obtain a preprocessed target data group;
and the result acquisition module is used for inputting the target data set to a trained crash monitoring model for processing to obtain a crash monitoring result output by the crash monitoring model, wherein the crash monitoring model is a crash monitoring model corresponding to the machine tool.
8. The crash monitor device according to claim 7, further comprising:
the training data set acquisition module is used for acquiring training data sets, and each training data set comprises historical control data and historical operating data corresponding to the machine tool;
the target training data set acquisition module is used for respectively preprocessing the historical control data and the historical operating data corresponding to each training data set to obtain a target training data set corresponding to each training data set;
a target crash result marking module for marking the target crash result corresponding to each target training data set;
and the crash monitoring module training module is used for training a crash monitoring model by utilizing each target training data set and the corresponding target crash result to obtain the trained crash monitoring model.
9. A terminal device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the method of machine crash monitoring according to any one of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out a method of machine crash monitoring according to any one of claims 1 to 7.
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