Production line processing control method, device, system and computer equipment

Abstract

The present application relates to a production line processing control method, device, computer equipment, storage medium and computer program product. The method comprises: obtaining the predicted processing time and the corresponding standard processing time of the processing station on the production line, and determining the target processing station from each of the processing stations; obtaining the operating parameters and processing parameters of the target processing station, and determining the event category corresponding to the target processing station according to the operating parameters and the processing parameters; determining the target control terminal that matches the event category, and sending the event category of the target processing station to the target control terminal. The disclosed embodiment can improve the processing efficiency of the production line.

Description

Technical Field

[0001] The present application relates to the field of industrial control technology, and in particular to a production line processing control method, device, computer equipment, storage medium and computer program product. Background Art

[0002] The production line processing cycle refers to the time required for the entire process from the raw materials entering the production line to the finished products being output from the production line. During this process, the delay of any processing station on the production line will affect the processing efficiency of the entire production line, resulting in a longer production line processing cycle.

[0003] In the related art, the processing work of the production line is manually monitored. When the processing efficiency is found to be reduced, the delay problem is manually discovered, and the parameters of the processing station are adjusted based on experience. In another related art, when a delay is detected in a processing station, a simple automatic parameter adjustment is performed on the processing station. However, the above methods cannot effectively guarantee the processing efficiency of the production line. Summary of the invention

[0004] Based on this, it is necessary to provide a production line processing control method, device, computer equipment, computer readable storage medium and computer program product that can improve production efficiency in response to the above technical problems.

[0005] In a first aspect, the present application provides a production line processing control method, the method comprising:

[0006] Obtaining the predicted processing time and the corresponding standard processing time of the processing stations on the production line, and determining the target processing station from each of the processing stations; wherein the deviation between the predicted processing time and the standard processing time of the target processing station is greater than a preset threshold;

[0007] Acquire the operating parameters and processing parameters of the target processing station, and determine the event category corresponding to the target processing station according to the operating parameters and the processing parameters;

[0008] A target control terminal matching the event category is determined, and the event category of the target processing station is sent to the target control terminal.

[0009] In one embodiment, obtaining the predicted processing time of a processing station on a production line includes:

[0010] Obtain the operating parameters and processing parameters of the processing stations on the production line;

[0011] According to a preset processing time evaluation strategy, the operation parameter and the processing parameter are algorithmically processed to obtain a first time;

[0012] Obtaining a second duration according to a preset time prediction model, the operating parameters, and the processing parameters;

[0013] The first duration and the second duration are weightedly fused to obtain a predicted processing duration of the processing station.

[0014] In one embodiment, performing algorithmic processing on the operating parameters and the processing parameters according to a preset processing time strategy to obtain a first time includes:

[0015] Determining a gain weight according to the operating parameter and the processing parameter;

[0016] Based on the gain weight and a preset reference processing time, a first time is obtained.

[0017] In one embodiment, obtaining the second duration according to the preset time prediction model, the operating parameters and the processing parameters includes:

[0018] The operating parameters and processing parameters are input into the corresponding time prediction model, and the predicted processing time of the processing station is output; wherein the time prediction model is obtained by training according to the corresponding relationship between sample operating parameters, sample processing parameters and processing time.

[0019] In one embodiment, determining the event category corresponding to the target processing station according to the operating parameter and the processing parameter includes:

[0020] The operating parameters and the processing parameters are input into an event category prediction model, and the event category corresponding to the target processing station is output, wherein the event category prediction model is obtained by training according to the correspondence between sample operating parameters, sample processing parameters and event categories.

[0021] In one embodiment, the event category includes at least one of component wear, operation abnormality, and other categories, and sending the event category of the target processing station to the target control terminal includes:

[0022] In the case where the event category includes component wear, sending identification information of the target processing station and the component wear event to the visualization control terminal;

[0023] In the case where the event category includes an operation abnormality, the identification information of the target processing station and the operation abnormality event are sent to the PID control terminal;

[0024] In the case where the event category includes other categories, the identification information of the target processing station and other category events are sent to the planning control end, and the planning control end is used to control the product processing trajectory.

[0025] In one embodiment, the planning control end is also used to determine the processing rate of other processing stations on the production line and the transportation rate of the transportation end according to the position information of the target processing station on the production line and the deviation, and send the processing rate to the corresponding other processing stations and send the transportation rate to the corresponding transportation end.

[0026] In a second aspect, the present application further provides a production line processing control device, the device comprising:

[0027] An acquisition module, used to acquire the predicted processing time and the corresponding standard processing time of the processing stations on the production line, and determine the target processing station from each of the processing stations; wherein the deviation between the predicted processing time and the standard processing time of the target processing station is greater than a preset threshold;

[0028] A determination module, used for obtaining 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;

[0029] The control module is used 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.

[0030] In one embodiment, the acquisition module includes:

[0031] The acquisition submodule is used to obtain the operating parameters and processing parameters of the processing stations on the production line;

[0032] A first processing submodule, configured to perform algorithmic processing on the operating parameters and the processing parameters according to a preset processing time evaluation strategy to obtain a first time;

[0033] A prediction submodule, used for obtaining a second duration according to a preset time prediction model, the operating parameters and the processing parameters;

[0034] The second processing submodule is used to perform weighted fusion processing on the first duration and the second duration to obtain the predicted processing duration of the processing station.

[0035] In one embodiment, the first processing submodule includes:

[0036] A determination unit, configured to determine a gain weight according to the operating parameter and the processing parameter;

[0037] A generating unit is used to obtain a first duration based on the gain weight and a preset benchmark processing duration.

[0038] In one embodiment, the prediction submodule includes:

[0039] A prediction unit is used to input the operating parameters and processing parameters into a corresponding time prediction model and output the predicted processing time of the processing station; wherein the time prediction model is obtained by training based on the corresponding relationship between sample operating parameters, sample processing parameters and processing time.

[0040] In one embodiment, the determining module includes:

[0041] A determination submodule is used to input the operating parameters and the processing parameters into an event category prediction model, and output the event category corresponding to the target processing station, wherein the event category prediction model is obtained by training based on the correspondence between sample operating parameters, sample processing parameters and event categories.

[0042] In one embodiment, the event category includes at least one of component wear, operation abnormality, and other categories, and the control module is further configured to:

[0043] In the case where the event category includes component wear, sending identification information of the target processing station and the component wear event to the visualization control terminal;

[0044] In the case where the event category includes an operation abnormality, the identification information of the target processing station and the operation abnormality event are sent to the PID control terminal;

[0045] In the case where the event category includes other categories, the identification information of the target processing station and other category events are sent to the planning control end, and the planning control end is used to control the product processing trajectory.

[0046] In one embodiment, the planning control end is also used to determine the processing rate of other processing stations on the production line and the transportation rate of the transportation end according to the position information of the target processing station on the production line and the deviation, and send the processing rate to the corresponding other processing stations and send the transportation rate to the corresponding transportation end.

[0047] In a third aspect, the present application also provides a production line processing control system, the system comprising:

[0048] Multiple processing stations; wherein the processing stations are used to process materials, and a transportation channel is provided between two processing stations;

[0049] The control end includes a memory and a processor, wherein the memory stores a computer program, and is characterized in that when the processor executes the computer program, the steps of the method described in any one of the embodiments of the present disclosure are implemented.

[0050] In a fourth aspect, the present application further provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of any one of the methods described in the embodiments of the present disclosure when executing the computer program.

[0051] In a fifth aspect, the present application further provides a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, the steps of the method described in any one of the embodiments of the present disclosure are implemented.

[0052] In a sixth aspect, the present application further provides a computer program product, including a computer program, which, when executed by a processor, implements the steps of the method described in any one of the embodiments of the present disclosure.

[0053] The above-mentioned production line processing control method, device, computer equipment, storage medium and computer program product obtain the operating parameters and processing parameters of the processing station on the production line to determine the predicted processing time of the processing station. When the deviation between the predicted processing time and the standard processing time is greater than a preset threshold, the event category of the target processing station is determined, and the corresponding target control terminal is notified according to the event category. Compared with the prior art, when the delay occurs, the corresponding processing station is processed. The present application can predict the processing time and determine the target processing station where the delay may occur, and then notify the corresponding control terminal according to the event category 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 can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related technologies, the drawings required for use in the embodiments or the related technical descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0055] FIG1 is a schematic flow chart of a production line processing control method in one embodiment;

[0056] Figure 2 A schematic diagram of a process flow of a production line processing control method in one embodiment;

[0057] Figure 3 A schematic diagram of a process flow of a production line processing control method in one embodiment;

[0058] Figure 4 It is a structural block diagram of a production line processing control device in one embodiment;

[0059] FIG5 is a diagram showing the internal structure of a computer device in one embodiment;

[0060] FIG. 6 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0061] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0062] In one embodiment, as shown in FIG1 , a production line processing control method is provided. This embodiment is illustrated by applying the method to a terminal. It is understandable that the method can also be applied to a server, and can also be applied to a system including a terminal and a server, and is implemented through the interaction between the terminal and the server. In this embodiment, the method includes the following steps:

[0063] Step S101, obtaining the predicted processing time and the corresponding standard processing time of the processing stations on the production line, and determining the target processing station from each of the processing stations; wherein the deviation between the predicted processing time and the standard processing time of the target processing station is greater than a preset threshold.

[0064] The production line may include production lines for various application scenarios. For example, in a specific embodiment, the production line may include mechanical processing equipment, assembly equipment, and testing equipment, etc. The processing station may include mechanical processing equipment and assembly equipment, etc. on the production line.

[0065] Wherein, the predicted processing time may include the processing time obtained according to the operating parameters and processing parameters of the processing station. In an exemplary embodiment, the operating parameters and the processing parameters may be algorithmically processed according to a preset processing time evaluation strategy to obtain the predicted processing time of the processing station. In another exemplary embodiment, the processing time of the processing station may also be predicted according to a preset time prediction model and the operating parameters and the processing parameters to obtain the predicted processing time of the processing station. Optionally, when the predicted processing time calculated by the processing time evaluation strategy is inconsistent with the processing time predicted by the event prediction model, a weighted algorithm may be used to combine the two predicted processing times to obtain the final predicted processing time. In addition, the predicted processing time of the processing station may also be manually evaluated, and the processing time may be evaluated based on experience or process knowledge.

[0066] Among them, the standard processing time of the processing station is a preset parameter used as a reference.

[0067] In an exemplary embodiment, when the deviation between the actual processing time of a processing station and the standard processing time is greater than a preset threshold, the processing station is used as a target processing station. The target processing station may include multiple processing stations. For example, when the deviation between the actual processing time of processing station A and processing station B and the standard processing time is greater than the corresponding preset threshold, processing station A and processing station B can both be used as target processing stations.

[0068] In an exemplary embodiment, it is possible to determine whether a processing station meets the delay event based on the operating parameters and processing parameters of the processing station on the production line. For example, if the wear of a processing tool or abnormal equipment oscillation amplitude is detected, and the condition of "processing time deviation" is met, the processing station is marked. Subsequently, the processing time and the corresponding standard processing time are predicted for the processing station, instead of obtaining the predicted processing time for all processing stations on the entire production line, which can reduce the amount of data processing and improve control efficiency.

[0069] Step S103, obtaining the operating parameters and processing parameters of the target processing station, and determining the event category corresponding to the target processing station according to the operating parameters and the processing parameters.

[0070] Among them, the event may include the processing time of the processing station, causing the deviation between the predicted processing time and the standard processing time to be greater than a preset threshold. For example, the event may include equipment failure, tool wear, operating error, improper process parameter setting, etc.

[0071] The operating parameters may include parameters generated when the processing station is running, such as operating status, temperature, vibration, current, voltage, rotation speed, noise, etc. The operating status may include running, stopping and fault, etc. The processing parameters may include tool wear, cutting parameters, welding parameters, etc., wherein the cutting parameters may include cutting speed, feed speed, cutting depth, tool type. The welding parameters may include welding current, welding speed, welding material, etc. In an exemplary embodiment, the operating parameters and processing parameters may be input into the event category prediction model, and the event category may be output. Optionally, the event category prediction model may correspond to the type of processing station, and different processing stations correspond to different event category prediction models.

[0072] Step S105, determining a target control terminal that matches the event category, and sending the event category of the target processing station to the target control terminal.

[0073] Specifically, an association relationship between the time category and the control terminal can be established in advance. In an exemplary embodiment, when the event category is component wear, the corresponding target control terminal includes a visualization control terminal to remind the operator to perform corresponding maintenance or replacement processing. In another exemplary embodiment, when the event category is an operational abnormality, such as abnormal changes in the vibration frequency and amplitude of the equipment, the corresponding target terminal may include a PID control terminal. In another exemplary embodiment, when the time category is other categories, such as changes in processing time caused by other reasons, the time category of the target processing station is sent to the planning control terminal, wherein the planning control terminal is used to adjust parameters of other processing stations or transportation terminals on the processing line, for example, adjusting the processing rate of other processing stations or the transportation rate of the transportation terminal.

[0074] In the above embodiment, the predicted processing time of the processing station is determined by obtaining the operating parameters and processing parameters of the processing station on the production line. When the deviation between the predicted processing time and the standard processing time is greater than a preset threshold, the event category of the target processing station is determined, and the corresponding target control terminal is notified according to the event category. Compared with the prior art, when the delay occurs, the corresponding processing station is processed. The present application can predict the processing time and determine the target processing station where the delay may occur, and then notify the corresponding control terminal according to the event category 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 can be improved.

[0075] In one embodiment, reference Figure 2 As shown, the predicted processing time of the processing station on the production line is obtained, including:

[0076] Step S201, obtaining the operating parameters and processing parameters of the processing station on the production line.

[0077] Specifically, the operating parameters may include parameters generated when the processing station is running, such as operating status, temperature, vibration, current and voltage. Among them, the vibration may include vibration frequency and amplitude, and the frequency and amplitude of the vibration can reflect the operating status of the processing station. If the vibration frequency and amplitude exceed a certain threshold, it may indicate that the processing station is faulty or worn. The processing parameters may include tool wear, cutting parameters, and the rate of change of processing time. Among them, the wear state of the equipment can be evaluated by measuring the wear of the tool, and the tool wear can be calculated by measuring the changes in parameters such as the thickness, length, and diameter of the tool. Among them, the operating status of the processing station can be analyzed by monitoring the stability of the processing time. If the rate of change of the processing time is large, it may indicate that the processing station is faulty or worn.

[0078] Step S203, according to a preset processing time evaluation strategy, algorithm processing is performed on the operating parameters and the processing parameters to obtain a first time.

[0079] In the disclosed embodiment, the evaluation strategy of the processing time can be determined in the following manner: in an exemplary embodiment, a gain weight is determined according to the operating parameters and the processing parameters; based on the gain weight and the preset benchmark processing time, a first time is obtained, for example, the gain weight is multiplied by the preset benchmark processing time to obtain the first time. In another exemplary embodiment, a relationship curve between the processing time and the operating parameters and the processing parameters can also be fitted by a statistical method, and then the first time is predicted using the relationship curve.

[0080] Step S205, obtaining a second duration according to a preset time prediction model, the operating parameters and the processing parameters.

[0081] In the disclosed embodiment, the operating parameters and processing parameters of the processing station can also be input into a preset time prediction model, and the second duration can be output through the time prediction model. The time prediction model can be obtained by training sample operating parameters and sample processing parameters through machine learning.

[0082] Step S207, performing weighted fusion processing on the first duration and the second duration to obtain the predicted processing duration of the processing station.

[0083] In the above embodiment, when the first duration is inconsistent with the second duration, a weighted algorithm can be used for fusion processing. For example, the result of the processing time evaluation strategy is assigned a certain weight a, and the result of the time prediction model is assigned a certain weight b. Then, the predicted processing time t = (a×first duration+b×second duration) / 2.

[0084] In the above embodiment, a relatively accurate prediction of the processing time can be obtained by performing weighted fusion processing on the prediction result of the processing time evaluation strategy and the prediction result of the time prediction model.

[0085] In one embodiment, the algorithm processing the operating parameters and the processing parameters according to the preset processing time strategy to obtain the first time includes:

[0086] Determining a gain weight according to the operating parameter and the processing parameter;

[0087] Based on the gain weight and a preset reference processing time, a first time is obtained.

[0088] The operating parameters and processing parameters in the embodiment of the present disclosure are the same as those described in the above embodiment, and will not be repeated here. In a specific embodiment, taking a robotic arm processing station as an example, the tool wear usually refers to the end effector (such as a fixture, a tool, etc.), and the wear of these tools will directly affect the processing accuracy and efficiency. The reference range of the wear amount depends on factors such as tool material, processing material, processing time and processing conditions. For a tool, its wear amount can range from a few microns to a few millimeters. For a fixture, its wear can be manifested as a decrease in positioning accuracy as the clamping force decreases. The vibration frequency of the robotic arm refers to the mechanical vibration frequency generated by the robotic arm during operation. These vibrations may come from the structure of the robotic arm itself, the motor drive, the transmission mechanism, etc. Among them, the reference range of the vibration size of the robotic arm depends on the design, manufacturing process and operating environment of the robotic arm. Generally speaking, the vibration frequency can range from a few hertz to hundreds of hertz. In some high-precision applications, the vibration frequency may need to be controlled within a lower range to ensure processing accuracy. The reference range of the amplitude size of the robotic arm is also affected by factors such as the design, manufacturing process and operating environment of the robotic arm. For most robotic arms, the vibration amplitude is usually controlled within a few microns to a few millimeters. In some high-precision applications, the vibration amplitude may need to be controlled at a lower level. Therefore, based on the above factors, different processing time strategies are formulated for different types of processing stations, process requirements and monitoring systems.

[0089] In a specific implementation, the operating parameters may be selected from the vibration frequency and amplitude of the processing station, and the processing parameters may be selected from the tool wear amount and the rate of change of the processing time, and the first duration is determined based on the following formula:

[0090] First duration = (1+ tool wear) × (1+ equipment vibration frequency) × (1+ equipment amplitude) × reference processing duration. Among them, (1+ tool wear) × (1+ equipment vibration frequency) × (1+ equipment amplitude) represents the gain weight.

[0091] The above embodiment determines the gain weight through operating parameters and processing parameters, and integrates the gain weight into the benchmark processing time, which can quantitatively describe the change in the processing time of the processing station during the working process and accurately estimate the predicted processing time of the processing station.

[0092] In one embodiment, obtaining the second duration according to the preset time prediction model, the operating parameters and the processing parameters includes:

[0093] The operating parameters and processing parameters are input into the corresponding time prediction model, and the predicted processing time of the processing station is output; wherein the time prediction model is obtained by training according to the corresponding relationship between sample operating parameters, sample processing parameters and processing time.

[0094] Specifically, the time prediction model may include a linear regression model or a machine learning model. For example, historical data of operating parameters and processing parameters or difference data of historical data are used as training sets, and processing time is used as labels. The sample operating parameters and sample processing parameters in the training set are input into the initial time prediction model, and the predicted processing time is output. According to the difference between the predicted processing time and the processing time marked by the label, the initial time prediction model is iteratively adjusted to obtain the time prediction model. In the application stage, the operating parameters and processing parameters are input into the corresponding time prediction model, and the predicted processing time of the processing station is output.

[0095] In the above embodiment, the time prediction model is trained to predict the processing time of the processing station through the time prediction model, which has a high accuracy.

[0096] In one embodiment, determining the event category corresponding to the target processing station according to the operating parameters and the processing parameters includes:

[0097] The operating parameters and the processing parameters are input into an event category prediction model, and the event category corresponding to the target processing station is output, wherein the event category prediction model is obtained by training according to the correspondence between sample operating parameters, sample processing parameters and event categories.

[0098] In the embodiments of the present disclosure, the operating parameters and processing parameters are the same as those in the above embodiments, and the present disclosure will not repeat them here. After the raw data corresponding to the operating parameters and processing parameters are obtained, the collected data can be cleaned, denoised, normalized, and the like. The data is converted into a data format that is convenient for analysis, such as a time series, a matrix, and the like. Key features, such as vibration frequency, vibration amplitude, and temperature, are extracted from the above processed data to obtain sample operating parameters and sample processing parameters as a training set. In an exemplary implementation, the sample operating parameters and sample processing parameters are input into an initial event category prediction model, and a predicted event category is output. Based on the difference between the predicted event category and the actual event category, the initial event category prediction model is iteratively adjusted to obtain an event category prediction model. In a specific application process, the operating parameters and processing parameters are input into an event category prediction model, and the event category corresponding to the target processing station is output.

[0099] In the above embodiment, an event category prediction model is obtained by training the correspondence between sample operation parameters, sample processing parameters and event categories, and the event category prediction model is used to predict the cause of the time deviation, which is then sent to the corresponding control terminal. This can coordinate multiple events, comprehensively solve the problems that cause time deviations, and improve production line efficiency.

[0100] In one embodiment, the event category includes at least one of component wear, operation abnormality, and other categories, and sending the event category of the target processing station to the target control terminal includes:

[0101] In the case where the event category includes component wear, the identification information of the target processing station and the component wear event are sent to the visualization control terminal.

[0102] In the case where the event category includes an operational abnormality, the identification information of the target processing station and the operational abnormality event are sent to the PID control terminal.

[0103] In the case where the event category includes other categories, the identification information of the target processing station and other category events are sent to the planning control end, and the planning control end is used to control the product processing trajectory.

[0104] Specifically, when the event category includes component wear, such as tool wear, the identification information of the target processing station and the component wear event can be sent to the visualization control end. Among them, the visualization control end may include a display interface for displaying and notifying the operator to perform corresponding processing. When the event category includes operation abnormality, the identification information of the target processing station and the operation abnormality event are sent to the PID control end. For example, when the operation abnormality includes an abnormality in one of the vibration frequency and amplitude, the relevant information is sent to the PID control end, and the PID control end is used to adjust the PID parameters to suppress vibration. In addition, when the target processing station is not detected, a normal signal can be sent.

[0105] In the above embodiment, the event categories are sent to the corresponding control end, and the control end can make corresponding processing strategies according to the event categories to ensure the stability and efficient operation of the production line.

[0106] In one embodiment, the planning control end is also used to determine the processing rate of other processing stations on the production line and the transportation rate of the transportation end according to the position information of the target processing station on the production line and the deviation amount, and send the processing rate to the corresponding other processing stations and send the transportation rate to the corresponding transportation end.

[0107] Specifically, the planning and 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 and the standard processing time. Optionally, the transportation rate of the transportation end can be adjusted. Through synchronous control or trajectory planning, the coordination between processing stations and the smooth flow of materials can be ensured, thereby improving the processing efficiency of the production line.

[0108] In a specific implementation, refer to Figure 3As shown, first, it is possible to determine whether the processing station meets the delay event based on the operating parameters and processing parameters of the processing station on the production line. For example, if a certain processing tool is worn, the equipment oscillation amplitude is abnormal, etc., and the condition of "processing time deviation" is met, then the processing station is marked. Subsequently, the predicted processing time and the corresponding standard processing time of the processing station are obtained. When the deviation between the predicted processing time and the standard processing time is greater than the preset threshold, the event category corresponding to the target processing station is determined based on the operating parameters and the processing parameters. According to the corresponding relationship between the event category and the control end, the identification information and event of the target processing station are sent to the corresponding control end. The control end can make overall planning for other processing stations and transportation ends to determine the processing rate of the processing station and the transportation rate of the transportation end. The transportation end may include a conveyor belt, a track, a vehicle or a magnetic levitation transportation device.

[0109] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0110] Based on the same inventive concept, the embodiment of the present application also provides a production line processing control device for implementing the production line processing control method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more production line processing control device embodiments provided below can refer to the limitations of the production line processing control method above, and will not be repeated here.

[0111] In an exemplary embodiment, Figure 4 As shown, a production line processing control device is provided, comprising:

[0112] The acquisition module 401 is used to acquire the predicted processing time and the corresponding standard processing time of the processing stations on the production line, and determine the target processing station from each of the processing stations; wherein the deviation between the predicted processing time and the standard processing time of the target processing station is greater than a preset threshold;

[0113] A determination module 403 is used to obtain the operation parameters and processing parameters of the target processing station, and determine the event category corresponding to the target processing station according to the operation parameters and the processing parameters;

[0114] The control module 405 is used to determine a target control terminal that matches the event category and send the event category of the target processing station to the target control terminal.

[0115] In one embodiment, the acquisition module includes:

[0116] The acquisition submodule is used to obtain the operating parameters and processing parameters of the processing stations on the production line;

[0117] A first processing submodule, configured to perform algorithmic processing on the operating parameters and the processing parameters according to a preset processing time evaluation strategy to obtain a first time;

[0118] A prediction submodule, used for obtaining a second duration according to a preset time prediction model, the operating parameters and the processing parameters;

[0119] The second processing submodule is used to perform weighted fusion processing on the first duration and the second duration to obtain the predicted processing duration of the processing station.

[0120] In one embodiment, the first processing submodule includes:

[0121] A determination unit, configured to determine a gain weight according to the operating parameter and the processing parameter;

[0122] A generating unit is used to obtain a first duration based on the gain weight and a preset benchmark processing duration.

[0123] In one embodiment, the prediction submodule includes:

[0124] A prediction unit is used to input the operating parameters and processing parameters into a corresponding time prediction model and output the predicted processing time of the processing station; wherein the time prediction model is obtained by training based on the corresponding relationship between sample operating parameters, sample processing parameters and processing time.

[0125] In one embodiment, the determining module includes:

[0126] A determination submodule is used to input the operating parameters and the processing parameters into an event category prediction model, and output the event category corresponding to the target processing station, wherein the event category prediction model is obtained by training based on the correspondence between sample operating parameters, sample processing parameters and event categories.

[0127] In one embodiment, the event category includes at least one of component wear, operation abnormality, and other categories, and the control module is further configured to:

[0128] In the case where the event category includes component wear, sending identification information of the target processing station and the component wear event to the visualization control terminal;

[0129] In the case where the event category includes an operation abnormality, the identification information of the target processing station and the operation abnormality event are sent to the PID control terminal;

[0130] In the case where the event category includes other categories, the identification information of the target processing station and other category events are sent to the planning control end, and the planning control end is used to control the product processing trajectory.

[0131] In one embodiment, the planning control end is also used to determine the processing rate of other processing stations on the production line and the transportation rate of the transportation end according to the position information of the target processing station on the production line and the deviation, and send the processing rate to the corresponding other processing stations and send the transportation rate to the corresponding transportation end.

[0132] Each module in the above production line processing control device can be implemented in whole or in part by software, hardware and their combination. Each module can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the corresponding operations of each module.

[0133] In an embodiment of an embodiment, a production line processing control system is provided, characterized in that the system comprises:

[0134] Multiple processing stations; wherein the processing stations are used to process materials, and a transportation channel is provided between two processing stations;

[0135] The control end includes a memory and a processor, wherein the memory stores a computer program, and is characterized in that when the processor executes the computer program, the steps of the method described in any one of the embodiments of the present disclosure are implemented.

[0136] Specifically, the system can include a variety of interfaces to realize information transmission with the processing station and the transmission end, for example: TimeControl interface: an interface in the processing station control system, used to detect processing time deviations and predict processing time. The interface contains the following methods: DelayPredict interface: used to detect delays and predict delays as needed. DataInfo interface: used for event communication to transmit processing time deviation information to other units. The interface contains the following methods: MsgUpdate: receives the information object generated by the processing station, which contains the identifier and time delay of the processing station that triggered the event. MsgUpdateID interface: used for units that need to adjust the speed in response to events (such as transportation systems). The interface contains the following methods: sgNotify: used to notify the unit and forward the information object to the unit that needs to adjust the speed.

[0137] The present invention can monitor the processing time of each processing station in real time, predict its changes, and promptly transmit relevant information to relevant parties for adjustment. This can not only improve the stability of the production line, but also greatly improve production efficiency.

[0138] In an exemplary embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be shown in FIG5. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. The processor, the memory and the input / output interface are connected via a system bus, and the communication interface is connected to the system bus via the input / output interface. The processor of the computer device is used 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, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store production line processing control data. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a production line processing control method is implemented.

[0139] In an exemplary embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be shown in FIG6. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit, and an input device. The processor, the memory, and the input / output interface are connected via a system bus, and the communication interface, the display unit, and the input device are connected to the system bus via the input / output interface. The processor of the computer device is used 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 the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner may be implemented through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a production line processing control method is implemented. The display unit of the computer device is used to form a visually visible picture, which may 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 device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device shell, or an external keyboard, touchpad or mouse.

[0140] Those skilled in the art will understand that the structure shown in FIG. 6 is merely a block diagram of a partial structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different arrangement of components.

[0141] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0142] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0143] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be construed as limiting the scope of the present application. It should be noted that, for a person of ordinary skill in the art, several modifications and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A production line processing control method, characterized in that: The method comprises: Obtain the operating parameters and processing parameters of the processing stations on the production line; According to a preset processing time evaluation strategy, the operation parameters and the processing parameters are algorithmically processed to obtain a first time; wherein the processing time evaluation strategy is obtained by fitting a relationship curve between the processing time and the operation parameters and the processing parameters in a statistical manner; Obtaining a second duration according to a preset time prediction model, the operating parameters, and the processing parameters; Performing weighted fusion processing on the first duration and the second duration to obtain a predicted processing duration of the processing station; Obtaining a standard processing time corresponding to the predicted processing time of the processing station, and determining a target processing station from each of the processing stations; wherein a deviation between the predicted processing time of the target processing station and the standard processing time is greater than a preset threshold; Acquire the operating parameters and processing parameters of the target processing station, and determine the event category corresponding to the target processing station according to the operating parameters and the processing parameters; wherein the event category includes at least one of the following: equipment failure, operation error, and improper process parameter setting; A target control terminal matching the event category is determined, and the event category of the target processing station is sent to the target control terminal.

2. The method according to claim 1, characterized in that The step of performing algorithm processing on the operating parameters and the processing parameters according to a preset processing time strategy to obtain a first time includes: Determining a gain weight according to the operating parameter and the processing parameter; Based on the gain weight and a preset reference processing time, a first time is obtained.

3. The method according to claim 1, characterized in that The obtaining of the second duration according to the preset time prediction model, the operating parameters and the processing parameters comprises: The operating parameters and processing parameters are input into the corresponding time prediction model, and the predicted processing time of the processing station is output; wherein the time prediction model is obtained by training according to the corresponding relationship between sample operating parameters, sample processing parameters and processing time.

4. The method according to claim 1, characterized in that: The step of determining the event category corresponding to the target processing station according to the operating parameter and the processing parameter includes: The operating parameters and the processing parameters are input into an event category prediction model, and the event category corresponding to the target processing station is output, wherein the event category prediction model is obtained by training according to the correspondence between sample operating parameters, sample processing parameters and event categories.

5. The method according to claim 1, characterized in that The event category includes at least one of component wear, operation abnormality, and other categories, and sending the event category of the target processing station to the target control terminal includes: In the case where the event category includes component wear, sending identification information of the target processing station and the component wear event to the visualization control terminal; In the case where the event category includes an operation abnormality, the identification information of the target processing station and the operation abnormality event are sent to the PID control terminal; In the case where the event category includes other categories, the identification information of the target processing station and other category events are sent to the planning control end, and the planning control end is used to control the product processing trajectory.

6. The method according to claim 5, characterized in that The planning control end is also used to determine the processing rate of other processing stations on the production line and the transportation rate of the transportation end according to the position information of the target processing station on the production line and the deviation, and send the processing rate to the corresponding other processing stations and the transportation rate to the corresponding transportation end.

7. A production line processing control device, characterized in that: The device comprises: An acquisition module is used to acquire the operating parameters and processing parameters of a processing station on a production line; according to a preset processing time evaluation strategy, the operating parameters and the processing parameters are algorithmically processed to obtain a first time; wherein the processing time evaluation strategy is obtained by fitting a relationship curve between processing time and operating parameters and processing parameters in a statistical manner; according to a preset time prediction model and the operating parameters and the processing parameters, a second time is obtained; a weighted fusion process is performed on the first time and the second time to obtain a predicted processing time of the processing station; a standard processing time corresponding to the predicted processing time of the processing station is obtained, and a target processing station is determined from each of the processing stations; wherein a deviation between the predicted processing time of the target processing station and the standard processing time is greater than a preset threshold; A determination module, used to obtain the operating parameters and processing parameters of the target processing station, and determine the event category corresponding to the target processing station according to the operating parameters and the processing parameters; wherein the event category includes at least one of the following: equipment failure, operation error, improper process parameter setting; The control module is used 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.

8. A production line processing control system, characterized in that: The system comprises: Multiple processing stations; wherein the processing stations are used to process materials, and a transportation channel is provided between two processing stations; A control end, comprising a memory and a processor, wherein the memory stores a computer program, and wherein the processor implements the steps of the method according to any one of claims 1 to 6 when executing the computer program.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

11. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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