CN118226814A - Production line processing control method, device, system and computer equipment - Google Patents
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Abstract
The present application relates to a production line processing control method, apparatus, computer device, storage medium and computer program product. The method comprises the following steps: obtaining predicted machining time length and corresponding standard machining time length of machining stations on a production line, and determining a target machining station from the machining stations; acquiring the operation parameters and the processing parameters of the target processing station, and determining event categories corresponding to the target processing station according to the operation parameters and the processing parameters; and determining a target control end matched with the event category, and sending the event category of the target processing station to the target control end. The embodiment of the disclosure can improve the processing efficiency of the production line.
Description
Technical Field
The present application relates to the technical field of industrial control, and in particular, to a method, an apparatus, a computer device, a storage medium and a computer program product for controlling processing of a production line.
Background
The production line processing cycle refers to the time required for the whole process from the raw material entering the production line to the finished product completing the output production line. In the process, the time delay of any processing station on the production line can affect the processing efficiency of the whole production line, so that the processing period of the production line becomes long.
In the related art, by manually monitoring the processing work of the production line, when the processing efficiency is found to be reduced, the delay problem is found manually, and the parameters of the processing station are adjusted according to experience. In another related art, when a delay condition of a processing station is detected, simple automatic parameter adjustment is performed on the processing station. However, none of the above methods can effectively guarantee the processing efficiency of the production line.
Disclosure of Invention
In view of the foregoing, it is desirable to provide a line processing control method, apparatus, computer device, computer-readable storage medium, and computer program product that can improve production efficiency.
In a first aspect, the present application provides a method for controlling processing in a production line, the method comprising:
obtaining predicted machining time length and corresponding standard machining time length of machining stations on a production line, and determining a target machining station from the machining stations; the deviation between the predicted processing time length and the standard processing time length of the target processing station is greater than a preset threshold value;
Acquiring the operation parameters and the processing parameters of the target processing station, and determining event categories corresponding to the target processing station according to the operation parameters and the processing parameters;
and determining a target control end matched with the event category, and sending the event category of the target processing station to the target control end.
In one embodiment, obtaining a predicted processing time for a processing station on a production line includes:
Acquiring operation parameters and processing parameters of a processing station on a production line;
carrying out algorithm processing on the operation parameters and the processing parameters according to a preset processing time length evaluation strategy to obtain a first time length;
obtaining a second duration according to a preset time prediction model, the operation parameters and the processing parameters;
and carrying out weighted fusion processing on the first time length and the second time length to obtain the predicted processing time length of the processing station.
In one embodiment, the performing algorithm processing on the operation parameter and the processing parameter according to a preset processing duration policy to obtain a first duration includes:
Determining a gain weight according to the operating parameter and the processing parameter;
and obtaining a first time length based on the gain weight and a preset reference processing time length.
In one embodiment, the obtaining the second duration according to the preset time prediction model, the running parameter and the processing parameter includes:
Inputting the operation parameters and the processing parameters into corresponding time prediction models, and outputting predicted processing time lengths of the processing stations; the time prediction model is obtained through training according to the corresponding relation among the sample operation parameters, the sample processing parameters and the processing time length.
In one embodiment, the determining, according to the operation parameter and the processing parameter, an event category corresponding to the target processing station includes:
And inputting the operation parameters and the processing parameters into an event type prediction model, and outputting event types corresponding to the target processing stations, wherein the event type prediction model is obtained through training according to the corresponding relation among the sample operation parameters, the sample processing parameters and the event types.
In one embodiment, the event category includes at least one of component wear, abnormal operation, and other categories, and the sending the event category of the target processing station to the target control end includes:
Transmitting the identification information of the target processing station and the component abrasion event to a visual control end under the condition that the event category comprises component abrasion;
under the condition that the event category comprises abnormal operation, the identification information of the target processing station and the abnormal operation event are sent to a PID control end;
And under the condition that the event category comprises other categories, sending the identification information of the target processing station and other category events to a planning control end, wherein the planning control end is used for controlling the processing track of the product.
In one embodiment, the planning control end is further configured to determine a processing rate of other processing stations on the production line and a transport rate of the transport end according to the position information of the target processing station on the production line and the deviation amount, send the processing rate to the corresponding other processing stations, and send the transport rate to the corresponding transport end.
In a second aspect, the present application also provides a production line processing control device, the device comprising:
The system comprises an acquisition module, a processing station processing module and a processing module, wherein the acquisition module is used for acquiring the predicted processing time length and the corresponding standard processing time length of the processing stations on the production line and determining a target processing station from the processing stations; the deviation between the predicted processing time length and the standard processing time length of the target processing station is greater than a preset threshold value;
The determining module is used for acquiring the operation parameters and the processing parameters of the target processing station and determining event categories corresponding to the target processing station according to the operation parameters and the processing parameters;
And the control module is used for determining a target control end matched with the event category and sending the event category of the target processing station to the target control end.
In one embodiment, the acquiring module includes:
the acquisition sub-module is used for acquiring the operation parameters and the processing parameters of the processing stations on the production line;
The first processing sub-module is used for carrying out algorithm processing on the operation parameters and the processing parameters according to a preset processing time length evaluation strategy to obtain a first time length;
The prediction sub-module is used for obtaining a second duration according to a preset time prediction model, the running parameters and the processing parameters;
And the second processing sub-module is used for carrying out weighted fusion processing on the first time length and the second time length to obtain the predicted processing time length of the processing station.
In one embodiment, the first processing sub-module includes:
a determining unit, configured to determine a gain weight according to the operation parameter and the processing parameter;
The generating unit is used for obtaining a first time length based on the gain weight and a preset reference processing time length.
In one embodiment, the prediction submodule includes:
The prediction unit is used for inputting the operation parameters and the processing parameters into the corresponding time prediction model and outputting the predicted processing time length of the processing station; the time prediction model is obtained through training according to the corresponding relation among the sample operation parameters, the sample processing parameters and the processing time length.
In one embodiment, the determining module includes:
The determining submodule is used for inputting the operation parameters and the processing parameters into an event type prediction model and outputting event types corresponding to the target processing stations, wherein the event type prediction model is obtained through training according to the corresponding relation among the sample operation parameters, the sample processing parameters and the event types.
In one embodiment, the event categories include at least one of component wear, operational anomalies, other categories, and the control module is further configured to:
Transmitting the identification information of the target processing station and the component abrasion event to a visual control end under the condition that the event category comprises component abrasion;
under the condition that the event category comprises abnormal operation, the identification information of the target processing station and the abnormal operation event are sent to a PID control end;
And under the condition that the event category comprises other categories, sending the identification information of the target processing station and other category events to a planning control end, wherein the planning control end is used for controlling the processing track of the product.
In one embodiment, the planning control end is further configured to determine a processing rate of other processing stations on the production line and a transport rate of the transport end according to the position information of the target processing station on the production line and the deviation amount, send the processing rate to the corresponding other processing stations, and send the transport rate to the corresponding transport end.
In a third aspect, the present application also provides a production line process control system, the system comprising:
A plurality of processing stations; the processing stations are used for processing materials, and a conveying channel is arranged between the two processing stations;
The control terminal comprises a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to realize the steps of the method according to any one of the embodiments of the disclosure.
In a fourth aspect, the present application also provides a computer device comprising a memory storing a computer program and a processor implementing the steps of the method of any of the embodiments of the present disclosure when the computer program is executed by the processor.
In a fifth aspect, the present application also provides a computer readable storage medium having stored thereon a computer program which when executed by a processor implements the steps of the method of any of the embodiments of the present disclosure.
In a sixth aspect, the application also provides a computer program product comprising a computer program which, when executed by a processor, implements the steps of the method according to any of the embodiments of the disclosure.
According to the production line processing control method, the device, the computer equipment, the storage medium and the computer program product, the predicted processing time length of the processing station is determined by acquiring the operation parameters and the processing parameters of the processing station on the production line, when the deviation between the predicted processing time length and the standard processing time length is greater than the preset threshold value, the event type of the target processing station is determined, and the corresponding target control end is notified according to the event type. Compared with the prior art, when the time delay condition occurs, the processing time is processed for the corresponding processing stations, the processing time can be predicted, the target processing station with the time delay possibility is determined, and then the corresponding control end is notified according to the event type of the target processing station, so that the processing progress of each processing station of the production line can be controlled in real time, and the processing efficiency of the production line is improved.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the related art, the drawings that are required to be used in the embodiments or the related technical descriptions will be briefly described, and it is apparent that the drawings in the following description are only some embodiments of the present application, and other drawings may be obtained according to the drawings without inventive effort for those skilled in the art.
FIG. 1 is a flow chart of a process control method of a production line in one embodiment;
FIG. 2 is a flow chart of a process control method of a production line in one embodiment;
FIG. 3 is a flow chart of a process control method of a production line in one embodiment;
FIG. 4 is a block diagram of a process control apparatus for a production line in one embodiment;
FIG. 5 is an internal block diagram of a computer device in one embodiment;
fig. 6 is an internal structural diagram of a computer device in one embodiment.
Detailed Description
The present application will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present application more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
In one embodiment, as shown in fig. 1, a method for controlling processing of a production line is provided, where the method is applied to a terminal for illustration, it is understood that the method may also be applied to a server, and may also be applied to a system including the terminal and the server, and implemented through interaction between the terminal and the server. In this embodiment, the method includes the steps of:
Step S101, obtaining predicted machining time length and corresponding standard machining time length of a machining station on a production line, and determining a target machining station from the machining stations; the deviation between the predicted processing time length and the standard processing time length of the target processing station is larger than a preset threshold value.
The production line may include a production line of various application scenarios, for example, in a specific embodiment, the production line may include a machining device, an assembling device, a detecting device, and the like. The processing stations may include, among other things, machining equipment and assembly equipment on a production line.
The predicted processing time length may include a processing time length obtained according to an operation parameter of the processing station and a processing parameter. In an exemplary embodiment, the operation parameter and the processing parameter may be processed by an algorithm according to a preset processing duration evaluation policy, so as to obtain a predicted processing duration of the processing station. In another exemplary embodiment, the processing duration of the processing station may be further predicted according to a preset time prediction model, the operation parameter and the processing parameter, so as to obtain a predicted processing duration of the processing station. Optionally, when the predicted processing duration calculated by the processing duration evaluation strategy is inconsistent with the processing duration predicted by the event prediction model, a weighting algorithm may be adopted to integrate the two predicted processing durations to obtain the final predicted processing duration. In addition, the predicted processing time of the processing station can be estimated manually, and the processing time can be estimated according to experience or process knowledge during manual estimation.
The standard machining time length of the machining station is a preset parameter serving as a reference.
In an exemplary embodiment, a certain processing station is regarded as a target processing station when the deviation amount between the actual processing time period and the standard processing time period of the processing station is greater than a preset threshold value. The target processing station may include a plurality of processing stations, for example, when the deviation amounts of the actual processing time periods and the standard processing time periods of the processing station a and the processing station B are both greater than the corresponding preset threshold values, the processing station a and the processing station B may each be a target processing station.
In an exemplary embodiment, it may be determined whether the processing station meets a delay event, for example, detecting that a certain processing tool is worn, the oscillation amplitude of the equipment is abnormal, etc., according to the operation parameters of the processing station and the processing parameters on the production line, and the processing station is marked if the condition of "the processing time is deviated" is met. The predicted processing time length and the corresponding standard processing time length of the processing stations are obtained in the follow-up process, and the predicted processing time length is not obtained for all the processing stations on the whole production line, so that the data processing amount can be reduced, and the control efficiency is improved.
Step S103, acquiring the operation parameters and the processing parameters of the target processing station, and determining the event category corresponding to the target processing station according to the operation parameters and the processing parameters.
Wherein the event may include affecting a processing duration of the processing station, resulting in a deviation of the predicted processing duration from the standard processing duration greater than a preset threshold. For example, events may include equipment failure, tool wear, operational errors, improper process parameter settings, and the like.
The operating parameters may include parameters generated during operation of the processing station, such as operating conditions, including operation, shut down, malfunction, etc., temperature, vibration, current, voltage, rotational speed, noise, etc. The machining parameters may include tool wear, cutting parameters, welding parameters, etc., wherein the cutting parameters may include cutting speed, feed speed, cutting depth, cutter type. The welding parameters may include welding current, welding speed, welding material, etc. In an exemplary embodiment, the operating parameters and the process parameters may be input to an event category prediction model, outputting event categories. Alternatively, the event category prediction model may correspond to a category of a processing station, and different processing stations correspond to different event category prediction models.
Step S105, determining a target control end matched with the event category, and sending the event category of the target processing station to the target control end.
Specifically, an association relationship between the time category and the control terminal may be established in advance. In an exemplary embodiment, when the component class is component wear, the corresponding target control end includes a visual control end that alerts the operator to perform the corresponding repair or replacement process. In another exemplary embodiment, in the case where the event class is an operation abnormality, for example, a variation in the vibration frequency and amplitude of the device is abnormal, the corresponding target terminal may include a PID control terminal. In another exemplary embodiment, the time category is another category, such as a change in the processing time length due to another reason, and the time category of the target processing station is sent to the planning control end, where the planning control end is configured to perform parameter adjustment on other processing stations or transportation ends on the processing line, such as adjusting the processing rate of the other processing stations or the transportation rate of the transportation end.
In the above embodiment, the predicted processing time length of the processing station is determined by acquiring the operation parameters and the processing parameters of the processing station on the production line, and when the deviation between the predicted processing time length and the standard processing time length is greater than the preset threshold, the event type of the target processing station is determined, and the corresponding target control end is notified according to the event type. Compared with the prior art, when the time delay condition occurs, the processing time is processed for the corresponding processing stations, the processing time can be predicted, the target processing station with the time delay possibility is determined, and then the corresponding control end is notified according to the event type of the target processing station, so that the processing progress of each processing station of the production line can be controlled in real time, and the processing efficiency of the production line is improved.
In one embodiment, referring to FIG. 2, obtaining a predicted process duration for a process station on a production line includes:
step S201, acquiring operation parameters and processing parameters of a processing station on a production line.
In particular, the operating parameters may include parameters generated by the processing station when operating, such as operating conditions, temperature, vibration, current, voltage, and the like. Wherein the vibrations may include vibration frequencies and amplitudes that are capable of reflecting the operating conditions of the processing station. If the vibration frequency and amplitude exceeds certain thresholds, it may be indicative of a malfunction or wear of the processing station. The machining parameters may include tool wear, cutting parameters, rate of change of machining time, and the like. Wherein the wear state of the device can be evaluated by measuring the wear amount of the tool, which can be calculated by measuring the variation of the thickness, length, diameter, etc. parameters of the tool. The operation state of the processing station can be analyzed by monitoring the stability of the processing time, and if the change rate of the processing time is large, the processing station may be indicated to have faults or wear.
Step S203, performing algorithm processing on the operation parameter and the processing parameter according to a preset processing duration evaluation strategy, so as to obtain a first duration.
In the embodiment of the disclosure, the evaluation strategy of the processing time length may be determined by the following manner: in an exemplary embodiment, a gain weight is determined based on the operating parameter and the process parameter; and obtaining a first time length based on the gain weight and the preset reference processing time length, for example, performing product processing on the gain weight and the preset reference processing time length to obtain the first time length. In another exemplary embodiment, the first duration may also be predicted by statistically fitting a relationship between the processing duration and the operating and processing parameters, and using the relationship.
Step S205, obtaining a second duration according to a preset time prediction model, the operation parameter and the processing parameter.
In the embodiment of the disclosure, the operation parameters and the processing parameters of the processing station may be input to a preset time prediction model, and the second duration may be output through the time prediction model. The time prediction model can be obtained by training a sample operation parameter and a sample processing parameter in a machine learning mode.
Step S207, performing weighted fusion processing on the first duration and the second duration, to obtain a predicted processing duration of the processing station.
In the above embodiment, when the first duration is inconsistent with the second duration, a weighting algorithm may be used to perform fusion processing, for example, a result of the processing duration evaluation policy is given a certain weight a, a result of the time prediction model is given a certain weight b, and then the predicted processing duration t= (a×first duration+b×second duration)/2.
According to the embodiment, the predicted processing time length can be accurately determined by carrying out weighted fusion processing on the predicted result of the processing time length evaluation strategy and the predicted result of the time prediction model.
In one embodiment, the performing algorithm processing on the operation parameter and the processing parameter according to a preset processing duration policy to obtain a first duration includes:
Determining a gain weight according to the operating parameter and the processing parameter;
and obtaining a first time length based on the gain weight and a preset reference processing time length.
The operation parameters and the processing parameters in the embodiments of the present disclosure are the same as those described in the above embodiments, and are not described herein. In particular embodiments, using a robotic arm processing station as an example, tool wear is typically referred to as end effectors (e.g., clamps, cutters, etc.), and wear of these tools directly affects processing accuracy and efficiency. The reference range of the amount of wear depends on factors such as tool material, machining duration, machining conditions, and the like. The amount of wear for a tool may vary from a few microns to a few millimeters. For a clamp, its wear may manifest itself as a decrease in positioning accuracy with a decrease in clamping force. The vibration frequency of the mechanical arm refers to the mechanical vibration frequency generated by the mechanical arm during operation. These vibrations may come from the structure of the robotic arm itself, motor drives, transmission mechanisms, etc. The reference range of the vibration of the mechanical arm depends on the design, the manufacturing process and the operation environment of the mechanical arm. In general, the vibration frequency may vary from a few hertz to a few hundred hertz. In some high precision applications, the vibration frequency may need to be controlled in a lower range to ensure machining accuracy. The reference range of the equipment amplitude of the mechanical arm is also influenced by factors such as the design, manufacturing process and operation environment of the mechanical arm. For most robotic arms, the vibration amplitude is typically controlled to be in the range of a few microns to a few millimeters, and in some high precision applications, the vibration amplitude may need to be controlled to a lower level. Therefore, based on the above factors, different process duration strategies are formulated for different types of processing stations, process requirements and monitoring systems.
In a specific implementation, the operating parameters may select a machining station vibration frequency and amplitude, the machining parameters may select a tool wear amount and a rate of change of machining time, and the first time period is determined based on the following equation:
First time period= (1+tool wear amount) × (1+equipment vibration frequency) × (1+equipment amplitude) ×reference machining time period. Wherein (1+tool wear amount) × (1+equipment vibration frequency) × (1+equipment amplitude) represents gain weight.
According to the embodiment, the gain weight is determined through the operation parameters and the processing parameters, and the gain weight is fused to the reference processing time length, so that the change amount of the processing time length of the processing station in the working process can be quantitatively described, and the predicted processing time length of the processing station can be accurately estimated.
In one embodiment, the obtaining the second duration according to the preset time prediction model, the operation parameter and the processing parameter includes:
Inputting the operation parameters and the processing parameters into corresponding time prediction models, and outputting predicted processing time lengths of the processing stations; the time prediction model is obtained through training according to the corresponding relation among the sample operation parameters, the sample processing parameters and the processing time length.
In particular, the temporal prediction model may comprise a linear regression model or a machine learning model. For example, the historical data of the operation parameter and the processing parameter or the difference data of the historical data is used as a training set, and the processing duration is used as a label. And inputting the sample operation parameters and the sample processing parameters in the training set into an initial time prediction model, outputting predicted processing time, and performing iterative adjustment on the initial time prediction model according to the difference between the predicted processing time and the processing time marked by the label to obtain a time prediction model. And in the application stage, the operation parameters and the processing parameters are input into corresponding time prediction models, and the predicted processing time length of the processing station is output.
According to the embodiment, the processing time of the processing station is predicted through the time prediction model by training the time prediction model, so that the accuracy is high.
In one embodiment, the determining the event category corresponding to the target processing station according to the operation parameter and the processing parameter includes:
And inputting the operation parameters and the processing parameters into an event type prediction model, and outputting event types corresponding to the target processing stations, wherein the event type prediction model is obtained through training according to the corresponding relation among the sample operation parameters, the sample processing parameters and the event types.
In the embodiments of the present disclosure, the operation parameters and the processing parameters are the same as those of the above embodiments, and are not described herein. After the original data corresponding to the operation parameters and the processing parameters are obtained, the collected data can be subjected to cleaning, denoising, normalization and the like. The data is converted into a data format, such as a time series, matrix, etc., that facilitates analysis. Key features such as vibration frequency, vibration amplitude, temperature and the like are extracted from the processed data, and sample operation parameters and sample processing parameters are obtained as training sets. In an exemplary implementation, sample operating parameters and sample processing parameters are input into an initial event type prediction model, a predicted event type is output, and the initial event type prediction model is iteratively adjusted based on differences between the predicted event type and an actual event type to obtain an event type prediction model. In a specific application process, the operation parameters and the processing parameters are input into an event type prediction model, and event types corresponding to the target processing stations are output.
In the above embodiment, the event type prediction model is obtained through the training of the corresponding relation between the sample operation parameters, the sample processing parameters and the event types, and the event type prediction model is used for predicting the reason of the time deviation, so that the reason of the time deviation is sent to the corresponding control end, various events can be comprehensively planned, the problem of the time deviation is comprehensively solved, and the production line efficiency is improved.
In one embodiment, the event categories include at least one of component wear, abnormal operation, and other categories, and the sending the event category of the target processing station to the target control end includes:
and under the condition that the event category comprises component abrasion, sending the identification information of the target processing station and the component abrasion event to a visual control end.
And under the condition that the event category comprises abnormal operation, sending the identification information of the target processing station and the abnormal operation event to a PID control end.
And under the condition that the event category comprises other categories, sending the identification information of the target processing station and other category events to a planning control end, wherein the planning control end is used for controlling the processing track of the product.
In particular, in the event that the event category includes component wear, such as tool wear, identification information of the target machining station may be sent to the visualization control end along with the component wear event. The visual control end can comprise a display interface for displaying and informing an operator to perform corresponding processing. When the event category includes abnormal operation, the identification information of the target machining station and the abnormal operation event are sent to the PID control end, for example, when the abnormal operation includes one of vibration frequency and amplitude, the relevant information is sent to the PID control end, and the PID control end is used for adjusting PID parameters and inhibiting vibration. Further, in the case where the target processing station is not detected, a normal signal may be transmitted.
According to the embodiment, the event types are sent to the corresponding control end, and the control end can make corresponding processing strategies according to the event types, so that the stability of the production line and the operation of a college are ensured.
In one embodiment, the planning control end is further configured to determine a processing rate of other processing stations on the production line and a transport rate of a transport end according to the position information of the target processing station on the production line and the deviation amount, send the processing rate to the corresponding other processing stations, and send the transport rate to the corresponding transport end.
Specifically, the planning control end can adjust the processing rate of other processing stations according to the position information of the target processing station on the production line and the deviation between the predicted processing time length and the standard processing time length. Alternatively, the transport rate of the transport end may be adjusted. Through synchronous control or track planning, coordination among processing stations and smoothness of materials are ensured, and further the processing efficiency of a production line is improved.
In a specific implementation, referring to fig. 3, first, it may be determined whether a processing station meets a delay event according to an operation parameter and a processing parameter of a processing station on a production line, for example, when a condition that a certain processing tool is worn, an oscillation amplitude of equipment is abnormal is detected, and a condition that a processing time is deviated is met, and then the processing station is marked. And subsequently acquiring the predicted machining time length and the corresponding standard machining time length of the machining station, and determining the event category corresponding to the target machining station according to the operation parameter and the machining parameter when the deviation between the predicted machining time length and the standard machining time length is greater than a preset threshold value. And according to the corresponding relation between the event category and the control end, the identification information of the target processing station and the event are sent to the corresponding control end. The control end can carry out overall planning on other processing stations and the transportation end, and the processing rate of the processing stations and the transportation rate of the transportation end are determined. Wherein the transport end may comprise a conveyor belt, rail, vehicle, magnetic levitation transport, or the like.
It should be understood that, although the steps in the flowcharts related to the embodiments described above are sequentially shown as indicated by arrows, these steps are not necessarily sequentially performed in the order indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in the flowcharts described in the above embodiments may include a plurality of steps or a plurality of stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of the steps or stages is not necessarily performed sequentially, but may be performed alternately or alternately with at least some of the other steps or stages.
Based on the same inventive concept, the embodiment of the application also provides a production line processing control device for realizing the production line processing control method. The implementation of the solution provided by the apparatus is similar to that described in the above method, so the specific limitations in the embodiments of the production line processing control apparatus or apparatus provided below may be referred to above for limitations of the production line processing control method, and will not be repeated here.
In one exemplary embodiment, as shown in fig. 4, there is provided a line process control apparatus comprising:
An obtaining module 401, configured to obtain a predicted processing time length and a corresponding standard processing time length of a processing station on a production line, and determine a target processing station from the processing stations; the deviation between the predicted processing time length and the standard processing time length of the target processing station is greater than a preset threshold value;
a determining module 403, configured to obtain an operation parameter and a processing parameter of the target processing station, and determine an event category corresponding to the target processing station according to the operation parameter and the processing parameter;
and the control module 405 is configured to determine a target control end that matches the event category, and send the event category of the target processing station to the target control end.
In one embodiment, the acquiring module includes:
the acquisition sub-module is used for acquiring the operation parameters and the processing parameters of the processing stations on the production line;
The first processing sub-module is used for carrying out algorithm processing on the operation parameters and the processing parameters according to a preset processing time length evaluation strategy to obtain a first time length;
The prediction sub-module is used for obtaining a second duration according to a preset time prediction model, the running parameters and the processing parameters;
And the second processing sub-module is used for carrying out weighted fusion processing on the first time length and the second time length to obtain the predicted processing time length of the processing station.
In one embodiment, the first processing sub-module includes:
a determining unit, configured to determine a gain weight according to the operation parameter and the processing parameter;
The generating unit is used for obtaining a first time length based on the gain weight and a preset reference processing time length.
In one embodiment, the prediction submodule includes:
The prediction unit is used for inputting the operation parameters and the processing parameters into the corresponding time prediction model and outputting the predicted processing time length of the processing station; the time prediction model is obtained through training according to the corresponding relation among the sample operation parameters, the sample processing parameters and the processing time length.
In one embodiment, the determining module includes:
The determining submodule is used for inputting the operation parameters and the processing parameters into an event type prediction model and outputting event types corresponding to the target processing stations, wherein the event type prediction model is obtained through training according to the corresponding relation among the sample operation parameters, the sample processing parameters and the event types.
In one embodiment, the event categories include at least one of component wear, operational anomalies, other categories, and the control module is further configured to:
Transmitting the identification information of the target processing station and the component abrasion event to a visual control end under the condition that the event category comprises component abrasion;
under the condition that the event category comprises abnormal operation, the identification information of the target processing station and the abnormal operation event are sent to a PID control end;
And under the condition that the event category comprises other categories, sending the identification information of the target processing station and other category events to a planning control end, wherein the planning control end is used for controlling the processing track of the product.
In one embodiment, the planning control end is further configured to determine a processing rate of other processing stations on the production line and a transport rate of the transport end according to the position information of the target processing station on the production line and the deviation amount, send the processing rate to the corresponding other processing stations, and send the transport rate to the corresponding transport end.
The various modules in the production line processing control device can be fully or partially implemented by software, hardware and a combination thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
In one embodiment, a process control system for a production line, the system comprising:
A plurality of processing stations; the processing stations are used for processing materials, and a conveying channel is arranged between the two processing stations;
The control terminal comprises a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to realize the steps of the method according to any one of the embodiments of the disclosure.
In particular, the system may include various interfaces for enabling information transfer with the processing station and the transfer end, such as: timeControl interface: and an interface in the processing station control system is used for detecting the processing time deviation and predicting the processing time. The interface comprises the following steps: DELAYPREDICT interface: for detecting delays and predicting delays as needed. DataInfo interface: for event communication, process time offset information is transmitted to other units. The interface comprises the following steps: msgUpdate: an information object generated by a processing station is received, which includes an identifier of the processing station and a time delay of the triggering event. MsgUpdateID interface: a unit (e.g., a transportation system) for which speed needs to be adjusted in response to an event. The interface comprises the following steps: sgNotify: for informing the units and forwarding the information object to the units requiring speed adjustment.
The invention can monitor the processing time of each processing station in real time, forecast the change of each processing station, and timely transmit the related information to the related party for adjustment. Thus, the stability of the production line can be improved, and the production efficiency can be greatly improved.
In one exemplary embodiment, a computer device is provided, which may be a server, the internal structure of which may be as shown in fig. 5. The computer device includes a processor, a memory, an Input/Output interface (I/O) and a communication interface. The processor, the memory and the input/output interface are connected through a system bus, and the communication interface is connected to the system bus through the input/output interface. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the computer device is used for storing production line processing control data. The input/output interface of the computer device is used to exchange information between the processor and the external device. The communication interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a method of line process control.
In an exemplary embodiment, a computer device, which may be a terminal, is provided, and an internal structure diagram thereof may be as shown in fig. 6. The computer device includes a processor, a memory, an input/output interface, a communication interface, a display unit, and an input means. The processor, the memory and the input/output interface are connected through a system bus, and the communication interface, the display unit and the input device are connected to the system bus through the input/output interface. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The input/output interface of the computer device is used to exchange information between the processor and the external device. The communication interface of the computer device is used for carrying out wired or wireless communication with an external terminal, and the wireless mode can be realized through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. The computer program is executed by a processor to implement a method of line process control. The display unit of the computer device is used for forming a visual picture, and can be a display screen, a projection device or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, can also be a key, a track ball or a touch pad arranged on the shell of the computer equipment, and can also be an external keyboard, a touch pad or a mouse and the like.
It will be appreciated by those skilled in the art that the structure shown in FIG. 6 is merely a block diagram of some of the structures associated with the present inventive arrangements and is not limiting of the computer device to which the present inventive arrangements may be applied, and that a particular computer device may include more or fewer components than shown, or may combine some of the components, or have a different arrangement of components.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, database, or other medium used in embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high density embedded nonvolatile Memory, resistive random access Memory (ReRAM), magneto-resistive random access Memory (Magnetoresistive Random Access Memory, MRAM), ferroelectric Memory (Ferroelectric Random Access Memory, FRAM), phase change Memory (PHASE CHANGE Memory, PCM), graphene Memory, and the like. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory, and the like. By way of illustration, and not limitation, RAM can be in various forms such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), etc. The databases referred to in the embodiments provided herein may include at least one of a relational database and a non-relational database. The non-relational database may include, but is not limited to, a blockchain-based distributed database, and the like. The processor referred to in the embodiments provided in the present application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic unit, a data processing logic unit based on quantum computing, or the like, but is not limited thereto.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The foregoing examples illustrate only a few embodiments of the application and are described in detail herein without thereby limiting the scope of the application. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the application, which are all within the scope of the application. Accordingly, the scope of the application should be assessed as that of the appended claims.
Claims (12)
1. A method of line process control, the method comprising:
obtaining predicted machining time length and corresponding standard machining time length of machining stations on a production line, and determining a target machining station from the machining stations; the deviation between the predicted processing time length and the standard processing time length of the target processing station is greater than a preset threshold value;
Acquiring the operation parameters and the processing parameters of the target processing station, and determining event categories corresponding to the target processing station according to the operation parameters and the processing parameters;
and determining a target control end matched with the event category, and sending the event category of the target processing station to the target control end.
2. The method of claim 1, wherein obtaining a predicted process duration for a process station on a production line comprises:
Acquiring operation parameters and processing parameters of a processing station on a production line;
carrying out algorithm processing on the operation parameters and the processing parameters according to a preset processing time length evaluation strategy to obtain a first time length;
obtaining a second duration according to a preset time prediction model, the operation parameters and the processing parameters;
and carrying out weighted fusion processing on the first time length and the second time length to obtain the predicted processing time length of the processing station.
3. The method of claim 2, wherein the performing an algorithm on the operating parameter and the processing parameter according to a preset processing duration policy to obtain a first duration includes:
Determining a gain weight according to the operating parameter and the processing parameter;
and obtaining a first time length based on the gain weight and a preset reference processing time length.
4. The method of claim 2, wherein the obtaining a second duration based on the predetermined time prediction model and the operating parameters and the processing parameters comprises:
Inputting the operation parameters and the processing parameters into corresponding time prediction models, and outputting predicted processing time lengths of the processing stations; the time prediction model is obtained through training according to the corresponding relation among the sample operation parameters, the sample processing parameters and the processing time length.
5. The method of claim 1, wherein determining the event category corresponding to the target processing station based on the operating parameter and the processing parameter comprises:
And inputting the operation parameters and the processing parameters into an event type prediction model, and outputting event types corresponding to the target processing stations, wherein the event type prediction model is obtained through training according to the corresponding relation among the sample operation parameters, the sample processing parameters and the event types.
6. The method of claim 1, wherein the event categories include at least one of component wear, operational anomalies, other categories, and wherein the sending the event category for the target processing station to the target control terminal comprises:
Transmitting the identification information of the target processing station and the component abrasion event to a visual control end under the condition that the event category comprises component abrasion;
under the condition that the event category comprises abnormal operation, the identification information of the target processing station and the abnormal operation event are sent to a PID control end;
And under the condition that the event category comprises other categories, sending the identification information of the target processing station and other category events to a planning control end, wherein the planning control end is used for controlling the processing track of the product.
7. The method of claim 6, wherein the planning control terminal is further configured to determine a processing rate of other processing stations on the production line and a transport rate of a transport terminal based on the positional information of the target processing station on the production line and the deviation amount, send the processing rate to the corresponding other processing stations, and send the transport rate to the corresponding transport terminal.
8. A production line process control apparatus, the apparatus comprising:
The system comprises an acquisition module, a processing station processing module and a processing module, wherein the acquisition module is used for acquiring the predicted processing time length and the corresponding standard processing time length of the processing stations on the production line and determining a target processing station from the processing stations; the deviation between the predicted processing time length and the standard processing time length of the target processing station is greater than a preset threshold value;
The determining module is used for acquiring the operation parameters and the processing parameters of the target processing station and determining event categories corresponding to the target processing station according to the operation parameters and the processing parameters;
And the control module is used for determining a target control end matched with the event category and sending the event category of the target processing station to the target control end.
9. A production line process control system, the system comprising:
A plurality of processing stations; the processing stations are used for processing materials, and a conveying channel is arranged between the two processing stations;
Control terminal comprising a memory and a processor, said memory storing a computer program, characterized in that the processor implements the steps of the method according to any one of claims 1 to 7 when executing said computer program.
10. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of claims 1 to 7 when the computer program is executed.
11. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 7.
12. A computer program product comprising a computer program, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 7.
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