CN110000705B - Detection method and system for abnormal machining operation of mill and related components - Google Patents
Detection method and system for abnormal machining operation of mill and related components Download PDFInfo
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- CN110000705B CN110000705B CN201910414769.0A CN201910414769A CN110000705B CN 110000705 B CN110000705 B CN 110000705B CN 201910414769 A CN201910414769 A CN 201910414769A CN 110000705 B CN110000705 B CN 110000705B
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B24—GRINDING; POLISHING
- B24B—MACHINES, DEVICES, OR PROCESSES FOR GRINDING OR POLISHING; DRESSING OR CONDITIONING OF ABRADING SURFACES; FEEDING OF GRINDING, POLISHING, OR LAPPING AGENTS
- B24B19/00—Single-purpose machines or devices for particular grinding operations not covered by any other main group
- B24B19/22—Single-purpose machines or devices for particular grinding operations not covered by any other main group characterised by a special design with respect to properties of the material of non-metallic articles to be ground
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B24—GRINDING; POLISHING
- B24B—MACHINES, DEVICES, OR PROCESSES FOR GRINDING OR POLISHING; DRESSING OR CONDITIONING OF ABRADING SURFACES; FEEDING OF GRINDING, POLISHING, OR LAPPING AGENTS
- B24B49/00—Measuring or gauging equipment for controlling the feed movement of the grinding tool or work; Arrangements of indicating or measuring equipment, e.g. for indicating the start of the grinding operation
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B24—GRINDING; POLISHING
- B24B—MACHINES, DEVICES, OR PROCESSES FOR GRINDING OR POLISHING; DRESSING OR CONDITIONING OF ABRADING SURFACES; FEEDING OF GRINDING, POLISHING, OR LAPPING AGENTS
- B24B55/00—Safety devices for grinding or polishing machines; Accessories fitted to grinding or polishing machines for keeping tools or parts of the machine in good working condition
Abstract
The application discloses a detection method for abnormal machining operation of a grinding machine, which comprises the steps of obtaining to-be-detected operation parameters of grinding machine equipment in a preset time period, and performing data slicing operation on the to-be-detected operation parameters to obtain a plurality of subdata; judging whether the class operation behavior corresponding to the subdata is a preset operation behavior; if yes, obtaining all historical operating parameters of the mill equipment, and judging whether abnormal machining operation of the mill exists according to the historical operating parameters. The abnormal machining operation of the grinding machine can be accurately detected, and the condition that the abnormal machining operation of the grinding machine is missed or hidden is avoided. The application also discloses a detection system for abnormal machining operation of the mill, a computer readable storage medium and an electronic device, which have the beneficial effects.
Description
Technical Field
The invention relates to the field of glass production and manufacturing, in particular to a method and a system for detecting abnormal processing operation of a grinding machine, a computer readable storage medium and electronic equipment.
Background
With the rapid development of the glass manufacturing industry, mobile phone production parties have more and more requirements on mobile phone screens, and the requirements on the screen quality are more and more strict. The market competition is global, so that the cost of the glass is reduced on the premise of ensuring the quality, and the improvement of the yield becomes more important. However, the frying phenomenon (glass fragmentation abnormality in the process of glass double-side polishing) caused by human factors in the glass production process is more and more, so that the production cost is greatly increased, and the yield is lower and lower.
In the related technology, no corresponding technology or method exists to avoid or prevent the abnormal processing phenomenon of the frying machine, and in order to reduce the probability of the frying machine caused by the artificial reason, a production factory mainly restricts the behavior of staff by manually inspecting records and combining certain punishment and incentive policies, so that the frying probability is reduced. However, the above method has a case where an operator or a patrol person withholds the report and the report, and thus the intended effect cannot be achieved.
Therefore, how to accurately detect the abnormal machining operation of the mill and avoid the report missing or the report concealing of the abnormal machining operation of the mill is a technical problem which needs to be solved by the technical personnel in the field at present.
Disclosure of Invention
An object of the present application is to provide a method and a system for detecting abnormal machining operation of a grinding machine, a computer-readable storage medium, and an electronic device, which can accurately detect abnormal machining operation of a grinding machine and avoid the abnormal machining operation of the grinding machine from being missed or hidden.
In order to solve the technical problem, the present application provides a method for detecting abnormal machining operation of a mill, which includes:
acquiring to-be-detected operating parameters of mill equipment in a preset time period, and executing data slicing operation on the to-be-detected operating parameters to obtain a plurality of subdata;
judging whether the class operation behavior corresponding to the subdata is a preset operation behavior;
if yes, obtaining all historical operating parameters of the mill equipment, and judging whether abnormal machining operation of the mill exists according to the historical operating parameters.
Optionally, the determining whether the class operation behavior corresponding to the child data is a preset operation behavior includes:
extracting target subdata corresponding to the abnormal processing time period from all the subdata;
and judging whether the class operation behavior corresponding to the target subdata is a preset operation behavior.
Optionally, the determining whether the class operation behavior corresponding to the child data is a preset operation behavior includes:
performing class operation behavior characterization conversion according to the to-be-detected operation parameters corresponding to the subdata to obtain target behavior characteristics; the operation parameters to be detected comprise equipment state parameters, the current upper disc real-time rotating speed, the current lower disc real-time rotating speed, the current real-time operation pressure, disc repairing mark parameters, disc repairing pressure and a lower disc state;
performing behavior characteristic serialization operation on the target behavior characteristics to obtain the class operation behaviors;
and judging whether the class operation behavior is the preset operation behavior.
Optionally, the step of judging whether the abnormal machining operation of the mill exists according to the historical operating parameters comprises the following steps:
determining an actual operation flow corresponding to the subdata according to the operation category and the target behavior characteristics of the preset operation behavior;
judging whether the historical operating parameters meet operating parameter requirements corresponding to the actual operating process;
and if not, judging that the abnormal machining operation of the mill exists.
Optionally, before acquiring the to-be-detected operating parameter of the mill equipment within the preset time period, the method further includes:
acquiring historical operating parameters of all mill equipment by using an internet of things data access system, and storing the historical operating parameters of all mill equipment to a MongoDB database;
correspondingly, the operation parameters to be detected of the mill equipment within the preset time period are acquired by the method comprising the following steps:
and acquiring the to-be-detected operation parameters of the mill equipment in the preset time period from the MongoDB database.
Optionally, the abnormal processing operation of the mill is specifically a processing behavior causing glass fracture during the operation of the mill equipment.
Optionally, the method further includes:
and storing the detection result of the operation parameter to be detected into a result database.
The present application further provides a detection system for abnormal machining operation of a mill, the detection system comprising:
the data extraction module is used for acquiring to-be-detected operation parameters of the grinding machine equipment in a preset time period and performing data slicing operation on the to-be-detected operation parameters to obtain a plurality of subdata;
the behavior detection module is used for judging whether the class operation behavior corresponding to the subdata is a preset operation behavior;
and the abnormal detection module is used for acquiring all historical operating parameters of the mill equipment when the class operating behavior corresponding to the subdata is a preset operating behavior, and judging whether abnormal processing operation of the mill exists or not according to the historical operating parameters.
The present application also provides a computer-readable storage medium having stored thereon a computer program which, when executed, implements the steps performed by the above-described method for detecting abnormal machining operation of a mill.
The application also provides electronic equipment which comprises a memory and a processor, wherein the memory stores a computer program, and the processor calls the computer program in the memory to realize the steps executed by the detection method for the abnormal machining operation of the grinding machine.
The invention provides a method for detecting abnormal processing operation of a grinding machine, which comprises the steps of obtaining to-be-detected operation parameters of grinding machine equipment in a preset time period, and executing data slicing operation on the to-be-detected operation parameters to obtain a plurality of subdata; judging whether the class operation behavior corresponding to the subdata is a preset operation behavior or not; if yes, obtaining all historical operating parameters of the mill equipment, and judging whether abnormal machining operation of the mill exists according to the historical operating parameters.
The method and the device for processing the data in the data slicing operation are characterized in that the operation parameters to be detected in the preset time period are firstly obtained, the data slicing operation can be performed on the operation parameters to be detected according to the operation parameters to be detected, and the data in the time period can be customized into a certain corresponding operation behavior, namely the data operation behavior. When there is a worker using the mill apparatus to perform an abnormal mill machining operation, the mill apparatus performs certain specific and regular operation steps, and the preset operation behavior in this application is certain specific and regular operation steps in the abnormal mill machining operation. If the detected similar operation behavior is the preset operation behavior, the abnormal processing operation of the mill possibly exists in the mill equipment, so that whether the abnormal processing operation of the mill exists or not is detected by combining all historical operation parameters of the mill equipment. The abnormal machining operation of the grinding machine can be accurately detected, and the condition that the abnormal machining operation of the grinding machine is missed or hidden is avoided. The application also provides a detection system for abnormal machining operation of the grinding machine, a computer readable storage medium and electronic equipment, which have the beneficial effects and are not repeated herein.
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In order to more clearly illustrate the embodiments of the present application, the drawings needed for the embodiments 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 that other drawings can be obtained by those skilled in the art without inventive effort.
FIG. 1 is a flow chart of a method for detecting abnormal operation of a mill according to an embodiment of the present application;
FIG. 2 is a flow chart of another method for detecting abnormal operation of a mill provided in an embodiment of the present application;
fig. 3 is a schematic structural diagram of a system for detecting abnormal machining operation of a mill according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely 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.
With the rapid development of the glass manufacturing industry, the requirements for glass processing in the industry are more and more strict. Glass processing may include cutting, edging, chamfering, polishing, etc., where there are a number of operations that require manual manipulation of the equipment by a worker to perform a series of processing operations on the glass. In the process of processing glass by workers using equipment, phenomena such as glass breakage, cutting size error and the like caused by personal reasons of the workers often exist, and the operation causing the phenomena is called abnormal processing operation of a grinding machine. In the related art, the mode of detecting the abnormal machining operation of the mill mainly depends on manual inspection record, but the manual inspection mode has the conditions of reporting and missing, and the accurate detection result of the abnormal machining operation of the mill cannot be obtained. In view of the above drawbacks of the related art, the present application provides a new detection scheme for abnormal machining operation of a mill by using the following embodiments, which can accurately detect abnormal machining operation of the mill, and avoid the abnormal machining operation of the mill from being missed or being hidden.
Referring to fig. 1, fig. 1 is a flowchart illustrating a method for detecting abnormal machining operation of a grinding machine according to an embodiment of the present disclosure.
The specific steps may include:
s101: acquiring to-be-detected operating parameters of mill equipment within a preset time period, and executing data slicing operation on the to-be-detected operating parameters to obtain a plurality of subdata;
the abnormal processing behavior of the mill mentioned here can specifically refer to the processing behavior (i.e., the frying machine) that the glass is cracked during the process that the worker uses the mill device to polish the double sides of the glass. Further, the number of the mill devices is not limited in the embodiment, and the embodiment can detect abnormal machining operation of the mill for a plurality of mill devices at the same time.
The present embodiment is not limited to the preset time period, and may be any time period before the current time, and as an optional implementation manner, when the ending time point of the preset time period is the current time, it may be detected whether there is an abnormal machining operation of the mill within the latest time period. Of course, it is also possible to detect only abnormal machining operations of the mill for a fixed period of time, which may be implementation dependent. The specific time length of the preset time period is also not limited in this embodiment, and the time length of the preset time period may be selected according to the specific application scenario of this embodiment. For example, if the mill device is in a working state 24 hours a day, the length of the preset time period may be set to be detected every 2 hours or 4 hours; if the mill device is kept in the working state only from eight am to sixteen pm, the detection can be set only in the time interval. In another possible scenario, because the operating time of the mill equipment may be long and the abnormal mill machining operation may occur in the glass machining process according to a certain specific probability, if the analysis of the whole machining process of the equipment needs to consume too many computing resources, a preset time period may be selected from the operating time period of the mill equipment as a spot check object, and it is detected whether there is a suspicion of the abnormal mill machining operation in the preset time period.
It should be noted that the working time period of the worker operating the mill can be divided into a normal working time period and an abnormal working time period, the normal working time period refers to a time period when the mill is in a workpiece grinding state, the abnormal working time period refers to all time periods except the normal time period, and the abnormal working time period exists in the abnormal processing operation of the mill. It can be understood that the preset time period mentioned in this step may include a normal time period or an abnormal time period, so that all the sub-data obtained after performing the data and slicing operation on the to-be-detected operating parameter in the preset time period may be data corresponding to the normal operating time period, may be data corresponding to the abnormal operating time period, and may be data corresponding to the normal operating time period and the abnormal operating time period, which is not limited herein. Since the abnormal operation of the mill exists only in the abnormal working period, the related operations of S102 and S103 may be performed by extracting only the sub data corresponding to the abnormal working period after the present step, as a possible embodiment.
S102: judging whether the class operation behavior corresponding to the subdata is a preset operation behavior or not; if yes, entering S103; if not, ending the flow;
in this embodiment, based on S101 that the to-be-detected operation parameter is obtained and the data segmentation operation is performed on the to-be-detected operation parameter, the operation executed by the operator to control the device may be determined according to the operation parameter of the device, and the class processing behavior executed in the preset time period may be determined and converted by presetting the corresponding relationship between the device operation parameter and the class processing behavior, which is the step of characterizing the class operation behavior. For example, the class operation behavior may be determined according to the upper and lower disk rotation speeds and the disk repair parameters corresponding to the sub-data.
The abnormal mill machining operation may include one or a combination of sub-steps, and the step may be preceded by an operation of setting a preset operation behavior, which refers to a sub-step corresponding to the abnormal mill machining operation. It can be understood that when the sub-step exists in the mill equipment, which is a necessary and insufficient condition for judging that the abnormal processing operation of the mill is detected, when the class operation behavior of the sub-data is monitored to be the preset operation behavior, the suspicion that the abnormal processing operation of the mill exists in the mill equipment can be judged. However, during normal operation of the device, there may be an operation of performing a specific sub-step, so that the detection operation of S103 on all the historical operating parameters may be performed on the basis of determining that the class operating behavior corresponding to the sub-data is the preset operating behavior in S102.
To illustrate the above process, for example, the abnormal operation behavior is sequentially executed by A, B, C three class operation behaviors, a class operation behavior corresponding to the detected sub data is B within a preset time period, which indicates that there is a possibility of the abnormal operation behavior, and it may be determined that the abnormal operation behavior is detected if the class a operation behavior is executed before the preset time period and the class C operation behavior is executed before the preset time period. If the class operation behavior corresponding to the subdata is detected to be D in the preset time period, it is shown that the abnormal operation behavior cannot be determined according to the to-be-detected operation parameters in the preset time period.
It can be understood that, in this step, when it is determined that the class operation behavior is not the preset operation behavior, the current sub data detection process may be ended. As a feasible implementation manner, after the current sub-data detection process is finished, the operation process of S102 may be performed on the next sub-data, so as to implement the loop detection of multiple sub-data until all sub-data are detected.
S103: and acquiring all historical operating parameters of the mill equipment, and judging whether abnormal processing operation of the mill exists or not according to the historical operating parameters. If yes, entering S104; if not, ending the flow;
when the class operation behavior in the preset time period is detected to be the substep of the abnormal processing operation of the grinding machine, the target processing equipment is indicated to have the possibility of executing the abnormal processing operation of the grinding machine, and whether the abnormal processing operation of the grinding machine exists or not is judged through the historical operating parameters. For example, the following steps are carried out: the operation of causing the glass to chip during polishing may include: when the rotating speed of the upper and lower discs is kept at 20 rpm/s and is increased to 150 rpm/s within 3 seconds, and the rotating speed of the upper and lower discs is detected to be increased from 100 to 150 rpm/s within 2 seconds to 3 seconds, the situation that whether glass is broken or not cannot be judged only according to the operating parameters within 2 seconds to 3 seconds (namely the preset time period) at the moment, so that the situation that the rotating speed of the upper and lower discs is increased from 20 rpm/s to 150 rpm/s needs to be judged by combining the historical operating parameters within 0 second to 3 seconds.
It is understood that the time period corresponding to the historical operating parameter may be longer than the preset time period. On the basis of obtaining the historical operating parameters, the embodiment can determine the operation of the mill equipment in the time period corresponding to the historical operating parameters, and further judge whether the abnormal processing operation of the mill exists in the operation. In the step, when the abnormal machining operation of the mill does not exist according to the historical operating parameters, the current subdata detection process can be ended. As a possible implementation manner, after the current sub-data detection process is finished, the operation processes of S102 and S103 may be performed on the next sub-data, so as to implement the loop detection of multiple sub-data until all sub-data are detected.
The embodiment firstly obtains the to-be-detected operation parameters in a preset time period, and according to the to-be-detected operation parameters, data slicing operation can be performed on the to-be-detected operation parameters, and data in a period of time can be customized into a certain corresponding operation behavior, namely data operation behavior. When there is a worker using the mill apparatus to perform an abnormal mill machining operation, the mill apparatus performs certain specific and regular operation steps, and the preset operation behavior in this embodiment is certain specific and regular operation steps in the abnormal mill machining operation. If the detected similar operation behavior is the preset operation behavior, the abnormal processing operation of the mill possibly exists in the mill equipment, so that whether the abnormal processing operation of the mill exists or not is detected by combining all historical operation parameters of the mill equipment. The embodiment can accurately detect the abnormal machining operation of the mill, and avoids the report missing or the report concealing of the abnormal machining operation of the mill.
As a possible implementation manner, after the embodiment corresponding to fig. 1 finishes detecting all the sub-data, all the detection results may be saved in the result database. The detection result can be classified into three types, namely, abnormal machining operation of the grinding machine, suspected abnormal machining operation of the grinding machine and abnormal behavior of illegal operation. Confirming that the abnormal machining operation of the mill meets the judgment condition of abnormal machining; abnormal machining operation of the suspected mill is that the historical data does not meet judgment conditions of abnormal machining, but abnormal shutdown operation occurs in the machining process of staff, and abnormal machining is performed when the abnormal machining operation is performed with a certain probability (such as about 80%) through historical data analysis; the illegal operation abnormal behavior is an operation behavior that the judgment condition of abnormal processing is not satisfied, but abnormal shutdown does not occur, but the product is not processed according to the set time, and abnormal shutdown occurs. Besides the classification, the detection result also includes a plurality of remark information such as the starting time, the ending time, the equipment mark and the like of the abnormal processing, and the remark information can be used for front-end display and subsequent other analysis.
As a further supplement to the embodiment corresponding to fig. 1, the step of determining whether the class operation behavior corresponding to the sub data is the preset operation behavior in S102 may include the following steps:
step 1, extracting target subdata corresponding to an abnormal processing time period from all subdata;
and 2, judging whether the class operation behavior corresponding to the target subdata is a preset operation behavior.
In the above embodiment, the operation of the mill apparatus is divided into the normal processing time period and the abnormal processing time period, and since the abnormal processing operation of the ground glass exists in the abnormal processing time period, only the class operation behavior corresponding to the target sub-data during the non-ground glass operation can be regarded as the object of the evaluation. An abnormal processing operation of a mill is in particular a processing behavior which leads to glass breakage during operation of the mill equipment (also known as a stir-frying machine).
As a further supplement to the embodiment corresponding to fig. 1, the step of determining whether the class operation behavior corresponding to the sub data is the preset operation behavior in S102 may include the following steps:
step 1, performing class operation behavior characterization conversion according to the to-be-detected operation parameters corresponding to the subdata to obtain target behavior characteristics;
the operation parameters to be detected can comprise equipment state parameters, the current upper disc real-time rotating speed, the current lower disc real-time rotating speed, the current real-time operation pressure, disc repairing mark parameters, disc repairing pressure and a lower disc state. The above-mentioned device status parameter, also called device operation parameter, is a parameter for describing whether the device is in an operation state. As a possible embodiment, the operation parameters to be checked may further include auxiliary judgment parameters, such as real-time operation time, set time, emergency stop signal, start signal, jog signal, stop signal, and the like.
Step 2, executing behavior characteristic serialization operation on the target behavior characteristics to obtain the class operation behaviors;
specifically, the to-be-detected operation parameters can be converted into the operation behavior feature table of the sequence mode from the change rule of the to-be-detected operation parameters corresponding to the sub data and the actual corresponding relation of the processing steps, so as to obtain the similar operation behaviors.
And 3, judging whether the class operation behavior is the preset operation behavior.
Further, on the basis of the corresponding embodiment of fig. 1 and the above supplement, the step of determining whether the abnormal machining operation of the mill exists in S103 according to the historical operating parameters may include the following steps:
step 1, determining an actual operation flow corresponding to the subdata according to the operation category and the target behavior characteristics of the preset operation behavior;
step 2, judging whether the historical operating parameters meet operating parameter requirements corresponding to the actual operating process; if yes, judging that abnormal processing operation of the mill does not exist; if not, judging that abnormal machining operation of the mill exists.
And in the step, the historical operating parameters are used as auxiliary reference items for judging whether abnormal machining operation of the grinding machine exists or not. The step of judging whether the abnormal machining operation of the mill exists or not according to the historical operating parameters is judged based on a decision rule, the classification of the preset operating behaviors is important to distinguish, the stronger the auxiliary condition required by the characteristics of the preset operating behaviors is, the weaker the auxiliary condition required by the characteristics of the weaker preset operating behaviors is, and the stronger the auxiliary condition judgment is required by the categories of the weaker preset operating behaviors. For example, when the preset operation behavior is the operation steps of spin-drying, slag picking, disc repairing and spin-drying again, the required auxiliary condition only needs to judge whether abnormal shutdown exists or not; if the preset operation behavior is spin-drying, slag picking and spin-drying again, the required auxiliary conditions are judged to be the existence of an emergency stop signal and the residual time of advanced stop for joint judgment.
Referring to fig. 2, fig. 2 is a flowchart of another method for detecting abnormal machining operation of a mill according to an embodiment of the present application, and the specific steps may include:
s201: and acquiring historical operating parameters of all the grinding machines by using the data access system of the Internet of things, and storing the historical operating parameters of all the grinding machines into a MongoDB database.
S202: and acquiring the to-be-detected operating parameters of all the mill equipment in the preset time period from the MongoDB database.
S203: performing data slicing operation on the to-be-detected operation parameters to obtain a plurality of subdata, and extracting target subdata corresponding to the abnormal processing time period from all the subdata;
s204: performing class operation behavior characterization conversion according to the to-be-detected operation parameters corresponding to the target subdata to obtain target behavior characteristics, and performing behavior characteristic serialization operation on the target behavior characteristics to obtain the class operation behaviors;
s205: judging whether the class operation behavior is a preset operation behavior or not; if yes, entering S206; if not, the process goes to S209;
s206: determining an actual operation flow corresponding to the subdata according to the operation category and the target behavior characteristics of the preset operation behavior;
s207: judging whether the historical operating parameters meet operating parameter requirements corresponding to the actual operating process; if yes, entering S208; if not, the process goes to S209;
the method comprises the following steps of firstly, obtaining all historical operating parameters of mill equipment;
s208: judging that abnormal machining operation of the mill exists, generating prompt information, and storing the detection result of the to-be-detected operation parameter into a result database.
S209: judging whether all the target subdata are completely detected; if yes, ending the process; if not, the process goes to step S204 to determine the next undetected target sub-data.
It can be understood that, the present embodiment may perform detection according to the sequence of the mill equipment, and after all the target subdata of the current mill equipment is detected, detect the next mill equipment; of course, the determination may also be performed according to a time sequence, for example, the detection may be performed sequentially according to the occurrence time of the target sub-data, and the detection order is not limited in this embodiment, as long as all the target sub-data of all the mill devices can be detected.
In the embodiment, the operation parameter information of each glass processing device is collected through the data access system of the internet of things, and the operation parameter information is stored in the MongoDB database. The operations in steps S202 to S206 may be implemented by a Python-based algorithm model, which extracts the operating parameters within a period of time from the MongoDB database for analysis and recognition, and finally stores the recognition result in the result database, so that the result database displays the recognition result on the interface in the form of a report. Specifically, in this embodiment, the operation parameter information of the device in a certain time period is obtained from the MongoDB database, data of the abnormal glass grinding process is extracted after data slicing processing is performed on the operation parameter information, and the real-time upper and lower disc rotating speeds are converted into the actual operation process according to the upper and lower disc rotating speeds and the disc repairing parameters. And extracting characteristics from the data after the operation behavior is changed, and if the characteristics are one of the characteristics of the abnormal processing operation of the grinding machine, judging whether the operation corresponding to the historical operation parameters has the characteristics of all the abnormal processing operations of the grinding machine by the aid of all the historical operation parameters. Specifically, after the actual workflow of the target glass processing device is determined according to the determined historical operating parameters, a plurality of operating characteristics in the actual workflow can be acquired, and whether abnormal processing operation of the grinding machine exists or not can be judged based on the classification rules.
Whether this embodiment has taken place the machine of frying through big data analysis discernment operator historical moment to can restrain staff's action, reduce the probability that the unusual processing operation of mill appears, the waste of the reduction glass cost of certain degree, thereby output also can obtain improving.
Referring to fig. 3, fig. 3 is a schematic structural diagram of a system for detecting abnormal machining operation of a grinding machine according to an embodiment of the present disclosure;
the system may include:
the data extraction module 100 is configured to obtain to-be-detected operation parameters of the mill equipment within a preset time period, and perform data slicing operation on the to-be-detected operation parameters to obtain a plurality of sub-data;
the behavior detection module 200 is configured to determine whether a class operation behavior corresponding to the sub data is a preset operation behavior;
and the anomaly detection module 300 is configured to, when the class operation behavior corresponding to the sub-data is a preset operation behavior, acquire all historical operation parameters of the mill equipment, and determine whether abnormal machining operation of the mill exists according to the historical operation parameters.
The embodiment firstly obtains the to-be-detected operation parameters in a preset time period, and according to the to-be-detected operation parameters, data slicing operation can be performed on the to-be-detected operation parameters, and data in a period of time can be customized into a certain corresponding operation behavior, namely data operation behavior. When there is a worker using the mill apparatus to perform an abnormal mill machining operation, the mill apparatus performs certain specific and regular operation steps, and the preset operation behavior in this embodiment is certain specific and regular operation steps in the abnormal mill machining operation. If the detected similar operation behavior is the preset operation behavior, the abnormal processing operation of the mill possibly exists in the mill equipment, so that whether the abnormal processing operation of the mill exists or not is detected by combining all historical operation parameters of the mill equipment. The embodiment can accurately detect the abnormal machining operation of the mill, and avoids the report missing or the report concealing of the abnormal machining operation of the mill.
Further, the behavior detection module 200 includes:
the data extraction unit is used for extracting target subdata corresponding to the abnormal processing time period from all the subdata;
the first judging unit is used for judging whether the class operation behavior corresponding to the target subdata is a preset operation behavior.
Further, the behavior detection module 200 includes:
the behavior determining unit is used for performing characteristic conversion of similar operation behaviors according to the to-be-detected operation parameters corresponding to the subdata to obtain target behavior characteristics; the system is also used for executing behavior feature serialization operation on the target behavior feature to obtain the class operation behavior; the operation parameters to be detected comprise equipment state parameters, the current upper disc real-time rotating speed, the current lower disc real-time rotating speed, the current real-time operation pressure, disc repairing mark parameters, disc repairing pressure and a lower disc state;
and the second judging unit is used for judging whether the class operation behavior is the preset operation behavior.
Further, the anomaly detection module 300 includes:
the historical parameter acquisition unit is used for acquiring all historical operating parameters of the mill equipment when the class operating behavior corresponding to the sub-data is a preset operating behavior;
an actual flow determining unit, configured to determine an actual operation flow corresponding to the sub-data according to the operation category and the target behavior feature of the preset operation behavior;
a third judging unit, configured to judge whether the historical operating parameter meets an operating parameter requirement corresponding to the actual operating process; if not, judging that abnormal machining operation of the mill exists.
Further, the detection system further comprises:
the system comprises a parameter acquisition module, a MongoDB database and a data processing module, wherein the parameter acquisition module is used for acquiring historical operating parameters of all grinding machines by using an internet of things data access system before acquiring to-be-detected operating parameters of the grinding machines in a preset time period, and storing the historical operating parameters of all glass grinding machines in the MongoDB database;
correspondingly, the data extraction module 100 is specifically a module for obtaining the to-be-detected operation parameters of the mill equipment within the preset time period from the MongoDB database, and performing a data slicing operation on the to-be-detected operation parameters to obtain a plurality of sub-data.
Further, the abnormal processing operation of the mill is specifically the processing behavior which causes the glass to be cracked during the operation of the mill equipment.
Further, the detection system further comprises:
and the result storage module is used for storing the detection result of the to-be-detected operation parameter into a result database.
Since the embodiment of the system part corresponds to the embodiment of the method part, the embodiment of the system part is described with reference to the embodiment of the method part, and is not repeated here.
The present application also provides a computer readable storage medium having stored thereon a computer program which, when executed, may implement the steps provided by the above-described embodiments. The storage medium may include: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
The application further provides an electronic device, which may include a memory and a processor, where the memory stores a computer program, and the processor may implement the steps provided by the foregoing embodiments when calling the computer program in the memory. Of course, the electronic device may also include various network interfaces, power supplies, and the like. Alternatively, the electronic device may be a server.
The embodiments are described in a progressive manner in the specification, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. For the system disclosed by the embodiment, the description is relatively simple because the system corresponds to the method disclosed by the embodiment, and the relevant points can be referred to the method part for description. It should be noted that, for those skilled in the art, it is possible to make several improvements and modifications to the present application without departing from the principle of the present application, and such improvements and modifications also fall within the scope of the claims of the present application.
It is further noted that, in the present specification, relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
Claims (10)
1. A method of detecting abnormal machining operation of a mill, comprising:
acquiring to-be-detected operating parameters of mill equipment in a preset time period, and executing data slicing operation on the to-be-detected operating parameters to obtain a plurality of subdata;
judging whether the class operation behavior corresponding to the subdata is a preset operation behavior;
if yes, acquiring all historical operating parameters of the mill equipment, and judging whether abnormal processing operation of the mill exists or not according to the historical operating parameters;
judging whether the class operation behavior corresponding to the subdata is a preset operation behavior comprises the following steps:
performing class operation behavior characterization conversion according to the to-be-detected operation parameters corresponding to the subdata to obtain target behavior characteristics;
performing behavior characteristic serialization operation on the target behavior characteristics to obtain the class operation behaviors;
and judging whether the class operation behavior is the preset operation behavior.
2. The detection method of claim 1, wherein determining whether the class operation behavior corresponding to the sub-data is a preset operation behavior comprises:
extracting target subdata corresponding to the abnormal processing time period from all the subdata;
and judging whether the class operation behavior corresponding to the target subdata is a preset operation behavior.
3. The detection method according to claim 1, wherein the operation parameters to be detected comprise equipment state parameters, current upper disc real-time rotating speed, current lower disc real-time rotating speed, current real-time operation pressure, disc repairing mark parameters, disc repairing pressure and lower disc state.
4. The detection method of claim 3, wherein determining whether a mill abnormal machining operation exists based on the historical operating parameters comprises:
determining an actual operation flow corresponding to the subdata according to the operation category and the target behavior characteristics of the preset operation behavior;
judging whether the historical operating parameters meet operating parameter requirements corresponding to the actual operating process;
and if not, judging that the abnormal machining operation of the mill exists.
5. The inspection method as set forth in claim 1, further comprising, before acquiring the parameter of operation of the mill apparatus to be inspected for a predetermined period of time:
acquiring historical operating parameters of all the grinding machine equipment by using an internet of things data access system, and storing the historical operating parameters of all the grinding machine equipment to a MongoDB database;
correspondingly, the operation parameters to be detected of the mill equipment within the preset time period are acquired by the method comprising the following steps:
and acquiring the to-be-detected operation parameters of the mill equipment in the preset time period from the MongoDB database.
6. The detection method according to claim 1, wherein the abnormal processing operation of the mill is specifically a processing behavior causing glass breakage during operation of mill equipment.
7. The detection method according to any one of claims 1 to 6, further comprising:
and storing the detection result of the operation parameter to be detected into a result database.
8. A system for detecting abnormal machining operation of a grinding machine, comprising:
the data extraction module is used for acquiring to-be-detected operation parameters of the grinding machine equipment in a preset time period and performing data slicing operation on the to-be-detected operation parameters to obtain a plurality of subdata;
the behavior detection module is used for judging whether the class operation behavior corresponding to the subdata is a preset operation behavior;
the abnormal detection module is used for acquiring all historical operating parameters of the mill equipment when the class operating behavior corresponding to the subdata is a preset operating behavior, and judging whether abnormal processing operation of the mill exists or not according to the historical operating parameters;
the process of the behavior detection module judging whether the class operation behavior corresponding to the subdata is a preset operation behavior comprises the following steps:
performing class operation behavior characterization conversion according to the to-be-detected operation parameters corresponding to the subdata to obtain target behavior characteristics;
performing behavior characteristic serialization operation on the target behavior characteristics to obtain the class operation behaviors;
and judging whether the class operation behavior is the preset operation behavior.
9. A computer-readable storage medium, characterized in that it has stored thereon a computer program which, when being executed by a processor, carries out the steps of a method of detecting abnormal machining operations of a mill according to any one of claims 1 to 7.
10. An electronic device, comprising:
a memory for storing a computer program;
a processor for implementing the steps of the method of detecting abnormal machining operation of a mill as claimed in any one of claims 1 to 7 when said computer program is executed.
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| CN112008543B (en) * | 2020-07-20 | 2022-11-01 | 上海大制科技有限公司 | Abnormal grinding diagnosis method for electrode cap of welding gun |
| CN112766059A (en) * | 2020-12-30 | 2021-05-07 | 深圳市裕展精密科技有限公司 | Method and device for detecting product processing quality |
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