CN112288175A - Production line real-time optimization method and device - Google Patents
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Abstract
The invention provides a method and a device for optimizing a production line in real time, and the method for optimizing the production line in real time provided by the embodiment comprises the following steps: determining a real-time beat of the first equipment according to a real-time running state of the first equipment, real-time load data of the first equipment and a preset load change rule, wherein the first equipment is any one of target production lines; determining a theoretical beat of the first device according to the real-time beat and a preset beat judgment rule; determining real-time bottleneck equipment of the target production line according to the theoretical beats of each equipment in the target production line and a preset association relation; and optimizing the target production line in real time according to the theoretical beats of each device in the target production line and the real-time bottleneck device. The real-time optimization method for the production line provided by the embodiment of the invention can be used for more accurately analyzing and judging the dynamic rhythm and dynamic bottleneck of the production line, and provides an important data basis for the prediction of subsequent production energy and the reasonable arrangement of a production plan.
Description
Technical Field
The invention relates to the field of production line data analysis, in particular to a production line real-time optimization method and device.
Background
With the development of science and technology, the living standard of people is gradually improved, private cars are gradually popularized, and vehicle manufacturers and enterprises pay more attention to whether produced vehicles can meet continuously changing market demands while processing and producing the vehicles, so that the productivity prediction is an important analysis index. At present, the theoretical beat and the Equipment integrated efficiency (OEE) are generally adopted in the processing process of automobile parts to predict the productivity and bottleneck of the part production, and the prediction is used as the basis for subsequent production arrangement and optimization.
This prior art treatment method has the following problems: 1) more and more manufacturers take production lines as units of production organization forms, the theoretical beats and the comprehensive efficiency of the existing single equipment are influenced by the previous and subsequent processes, so that the data of the single equipment cannot reflect the real state of a production system, and the decision and prediction of production are influenced; 2) even if the existing manufacturer enterprises use the production line modeling in the production process execution management system of the manufacturing enterprises or some equipment acquisition software to obtain the theoretical beat of the production line, the data presentation of the theoretical beat is often the average value of the beat, the average value covers the fluctuation of the production line to a great extent, and the deviation of the capacity prediction and the subsequent production arrangement and the actual situation is easy to cause.
Therefore, how to perform real-time production line optimization according to the real-time beat of production line equipment is an urgent problem to be solved.
Disclosure of Invention
The invention provides a real-time optimization method for a production line, which can more accurately analyze and judge the dynamic rhythm and dynamic bottleneck of the production line according to the real-time rhythm of production equipment and provide an important data base for the prediction of subsequent production energy and the reasonable arrangement of a production plan.
In a first aspect, the present disclosure provides a method for real-time optimizing a production line, including:
determining a real-time beat of the first equipment according to a real-time running state of the first equipment, real-time load data of the first equipment and a preset load change rule, wherein the first equipment is any one of target production lines;
determining a theoretical beat of the first device according to the real-time beat and a preset beat judgment rule;
determining real-time bottleneck equipment of the target production line according to the theoretical beats of each equipment in the target production line and a preset incidence relation, wherein the preset incidence relation is a matching relation between the target production line and each equipment;
and optimizing the target production line in real time according to the theoretical beats of each device in the target production line and the real-time bottleneck device.
In one possible design, determining the real-time beat of the first device according to the real-time operating state of the first device, the real-time load data of the first device, and a preset load change rule includes:
when a first real-time load in the real-time load data is greater than a preset first load threshold value and a load change rate at the first real-time load is greater than a preset target load change rate for the first time, determining a first time point of the first real-time load as the beat starting point;
after the first time point, when the load change rate at a second real-time load in the real-time load data is greater than the target load change rate for the first time, determining that a second time point at which the second real-time load is located is the beat termination point;
and determining the real-time beat according to the beat starting point, the beat ending point and the real-time running state.
In one possible design, determining the theoretical tempo of the first device according to the real-time tempo and a preset tempo decision rule includes:
when the relative error between the real-time beat and the current theoretical beat exceeds a preset first beat threshold, if the time continuously exceeding the first beat threshold is longer than a first preset time length and the times continuously exceeding the first beat threshold is longer than a first preset time length, updating the theoretical beat to the average value of all real-time beats in the first preset time length.
In one possible design, determining a theoretical beat of the first device according to the real-time beat and a preset beat determination rule further includes:
and when the real-time beat is larger than the current theoretical beat, the relative error between the real-time beat and the current theoretical beat exceeds a preset second beat threshold value, and the real-time load data is smaller than the preset second load threshold value, removing the real-time beat, wherein the second beat threshold value is larger than the first beat threshold value.
In one possible design, determining a theoretical beat of the first device according to the real-time beat and a preset beat determination rule further includes:
and when the real-time beat is smaller than the current theoretical beat and the relative error between the real-time beat and the current theoretical beat exceeds a preset third beat threshold value, removing the real-time beat, wherein the third beat threshold value is larger than the second beat threshold value.
In one possible design, determining a real-time bottleneck device of a target production line according to a theoretical beat and a preset association relationship of each device in the target production line includes:
when the relative error between the real-time beat of the first device and the current theoretical beat exceeds a preset first beat threshold, if the time exceeding the first beat threshold is longer than a second preset time length, and the real-time beat time length of the first device is longer than the real-time beat time lengths of other devices in the target production line, determining the first device as a real-time bottleneck device, wherein the second preset time length is longer than or equal to the first preset time length.
In one possible design, the preset association relationship is determined according to a preset production line model, and the model parameters of the preset production line model include: the device information of each device, the association sequence between the devices and the theoretical beat of each device.
In one possible design, determining a real-time beat of the first device according to a real-time operating state of the first device, real-time load data of the first device, and a preset load change rule, further includes:
when the first equipment is the repeated processing type equipment, when the first real-time load in the real-time load data is larger than a preset first load threshold value and the load change rate at the first real-time load is larger than a preset target load change rate for the first time, determining a first time point where the first real-time load is located as a beat starting point; wherein, the repeated addition type equipment means that the same characteristics at different positions of the same product need to be processed for M times, and M is an integer larger than 0;
after the first time point, when the first load change rate at the second real-time load in the real-time load data is greater than the target load change rate for the first time, determining a second time point corresponding to the second real-time load;
determining the recording time to be t1 according to the first time point and the second time point; t1 is an integer greater than 0;
after the second time point, when the first load change rate is greater than the target load change rate for the second time, determining a third time point corresponding to the second real-time load;
determining the recording time to be t2 according to the second time point and the third time point; t2 is an integer greater than 0;
determining a real-time beat t, which is t1+ t2+ ·+ tm, until the Mth time of the first load change rate is greater than the target load change rate; and the time recorded by tm is the difference between the time point corresponding to the second real-time load when the Mth time of the first load change rate is greater than the target load change rate and the time point corresponding to the second real-time load when the Mth time of the first load change rate is greater than the target load change rate.
In a second aspect, the present disclosure further provides a real-time production line optimizing apparatus, including:
the first determining module is used for determining the real-time beat of the first equipment according to the real-time running state of the first equipment, the real-time load data of the first equipment and a preset load change rule, wherein the first equipment is any one of the target production lines;
the second determining module is used for determining the theoretical beat of the first equipment according to the real-time beat and a preset beat judging rule;
the third determining module is used for determining real-time bottleneck equipment of the target production line according to the theoretical beats of each equipment in the target production line and a preset incidence relation, wherein the preset incidence relation is a matching relation between the target production line and each equipment;
and the processing module is used for carrying out real-time production line optimization on the target production line according to the theoretical beats of each device in the target production line and the real-time bottleneck device.
In one possible design, the first determining module is configured to:
when a first real-time load in the real-time load data is greater than a preset first load threshold value and the load change rate at the first real-time load is greater than a preset target load change rate for the first time, determining a first time point where the first real-time load is located as a beat starting point;
after the first time point, when the load change rate of a second real-time load in the real-time load data is greater than the load change rate for the first time, determining that the second time point of the second real-time load is a beat termination point; and determining the real-time beat according to the beat starting point, the beat ending point and the real-time running state.
In one possible design, the second determining module is configured to:
when the relative error between the real-time beat and the current theoretical beat exceeds a preset first beat threshold, if the time continuously exceeding the first beat threshold is longer than a first preset time length and the times continuously exceeding the first beat threshold is longer than a first preset time length, updating the theoretical beat to the average value of all real-time beats in the first preset time length.
In one possible design, the second determining module is further configured to:
and when the real-time beat is larger than the current theoretical beat, the relative error between the real-time beat and the current theoretical beat exceeds a preset second beat threshold value, and the real-time load data is smaller than the preset second load threshold value, removing the real-time beat, wherein the second beat threshold value is larger than the first beat threshold value.
In one possible design, the second determining module is further configured to:
and when the real-time beat is smaller than the current theoretical beat and the relative error between the real-time beat and the current theoretical beat exceeds a preset third beat threshold value, removing the real-time beat, wherein the third beat threshold value is larger than the second beat threshold value.
In one possible design, the processing module is to:
when the relative error between the real-time beat of the first device and the current theoretical beat exceeds a preset first beat threshold, if the time exceeding the first beat threshold is longer than a second preset time length, and the real-time beat time length of the first device is longer than the real-time beat time lengths of other devices in the target production line, determining the first device as a real-time bottleneck device, wherein the second preset time length is longer than or equal to the first preset time length.
In one possible design, the preset association relationship is determined according to a preset production line model, and the model parameters of the preset production line model include: the device information of each device, the association sequence between the devices and the theoretical beat of each device.
In one possible design, the first determining module is further configured to:
when the first equipment is the repeated processing type equipment, when the first real-time load in the real-time load data is larger than a preset first load threshold value and the load change rate at the first real-time load is larger than a preset target load change rate for the first time, determining a first time point where the first real-time load is located as a beat starting point; wherein, the repeated addition type equipment means that the same characteristics at different positions of the same product need to be processed for M times, and M is an integer larger than 0;
after the first time point, when the first load change rate at the second real-time load in the real-time load data is greater than the target load change rate for the first time, determining a second time point corresponding to the second real-time load;
determining the recording time to be t1 according to the first time point and the second time point; t1 is an integer greater than 0;
after the second time point, when the first load change rate is greater than the target load change rate for the second time, determining a third time point corresponding to the second real-time load;
determining the recording time to be t2 according to the second time point and the third time point; t2 is an integer greater than 0;
determining a real-time beat t, which is t1+ t2+ ·+ tm, until the Mth time of the first load change rate is greater than the target load change rate; and the time recorded by tm is the difference between the time point corresponding to the second real-time load when the Mth time of the first load change rate is greater than the target load change rate and the time point corresponding to the second real-time load when the Mth time of the first load change rate is greater than the target load change rate.
In a third aspect, the present disclosure further provides a production line data system, including:
a processor; and the number of the first and second groups,
a memory for storing executable instructions of the processor;
wherein the processor is configured to execute any one of the production line real-time optimization methods of the first aspect via execution of the executable instructions.
In a fourth aspect, an embodiment of the present disclosure further provides a storage medium, where a computer program is stored, and when the program is executed by a processor, the method for real-time optimization of a production line in any one of the first aspect is implemented.
The present disclosure provides a method and a device for real-time optimization of a production line, wherein a real-time beat of a first device is determined according to a real-time operating state of the first device, real-time load data of the first device and a preset load change rule, wherein the first device is any one device in a target production line; determining a theoretical beat of the first device according to the real-time beat and a preset beat judgment rule; determining real-time bottleneck equipment of the target production line according to the theoretical beats of each equipment in the target production line and a preset incidence relation, wherein the preset incidence relation is a matching relation between the target production line and each equipment; and (3) carrying out real-time optimization on the target production line according to the theoretical beats of each device in the target production line and the real-time bottleneck device so as to realize more accurate analysis and judgment of the dynamic beats and the dynamic bottleneck of the production line and provide an important data basis for the prediction of subsequent production energy and the reasonable arrangement of a production plan.
Drawings
In order to more clearly illustrate the embodiments of the present disclosure or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present disclosure, and for those skilled in the art, other drawings can be obtained according to the drawings without inventive exercise.
FIG. 1 is an application scenario diagram illustrating a production line real-time optimization method according to an example embodiment of the present disclosure;
FIG. 2 is a schematic flow diagram illustrating a method for real-time optimization of a production line according to an example embodiment of the present disclosure;
FIG. 3 is an actual scenario application outflow diagram of a production line real-time optimization method shown in the present disclosure according to an example embodiment;
FIG. 4 is a schematic diagram illustrating the architecture of a production line real-time optimization apparatus according to an example embodiment of the present disclosure;
FIG. 5 is a schematic diagram of the structure of a production line data system shown in accordance with an example embodiment of the present disclosure.
Detailed Description
To make the objects, technical solutions and advantages of the embodiments of the present disclosure more clear, the technical solutions of the embodiments of the present disclosure will be described clearly and completely with reference to the drawings in the embodiments of the present disclosure, and it is obvious that the described embodiments are some, but not all embodiments of the present disclosure. All other embodiments, which can be derived by a person skilled in the art from the embodiments disclosed herein without making any creative effort, shall fall within the protection scope of the present disclosure.
The terms "first," "second," "third," "fourth," and the like in the description and in the claims of the present disclosure and in the drawings described above, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the disclosure described herein are, for example, capable of operation in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
The following describes the technical solutions of the present disclosure and how to solve the above technical problems with specific embodiments. Embodiments of the present disclosure will be described below with reference to the accompanying drawings.
Fig. 1 is an application scenario diagram of a real-time production line optimization method according to an example embodiment of the present disclosure, as shown in fig. 1, a production line 101 includes a plurality of devices, a device 102, a device 103, and a device 104, and a model of the production line model is performed in advance on the plurality of devices, where model parameters of the production line model include: the device information of each device, the association sequence between the devices, and the theoretical beat of each device, and the data output by the model is the association relationship between the production line 101 and the device 102, the device 103, and the device 104. Acquiring real-time load data of the equipment 102, the equipment 103 and the equipment 104 in the production and processing process of the production line 101, and respectively determining real-time beats of the equipment 102, the equipment 103 and the equipment 104 according to the real-time load data and a preset load change rule; respectively determining theoretical beats of the equipment 102, the equipment 103 and the equipment 104 according to the real-time beat and a preset beat judgment rule; meanwhile, determining which of the equipment 102, the equipment 103 and the equipment 104 the real-time bottleneck equipment of the production line is according to the theoretical beats of all the equipment and the pre-obtained association relationship between the production line 101 and the equipment 102, the equipment 103 and the equipment 104; the production line can be optimized in real time according to the determined real-time bottleneck equipment and the theoretical beats of each equipment, the dynamic beats and the dynamic bottlenecks of the production line can be analyzed and judged more accurately by the processing method, and an important data basis is provided for the prediction of subsequent production energy and the reasonable arrangement of a production plan.
FIG. 2 is a schematic flow diagram illustrating a method for real-time optimization of a production line according to an example embodiment of the present disclosure; as shown in fig. 2, the method for real-time optimizing a production line provided in this embodiment includes:
specifically, real-time data are acquired according to a production line data acquisition system, the real-time data comprise real-time running states and real-time load data of first equipment, and the real-time running states of the first equipment are acquired through the production line data acquisition system according to motion positions of the equipment, program circulation or an additional sensor and the like. The method includes that a plurality of devices are arranged in a target production line, a first device is any one of the target production lines, in the production process of the target production line, the real-time load of the first device is continuously changed along with time, a first load threshold value is preset, for example, the first load threshold value is 1000 watts, when the first real-time load in real-time load data of the first device is larger than the preset first load threshold value, the change condition of the real-time load data is continuously observed, generally speaking, the presentation form of the real-time load data is in a curve shape, a curve slope is obtained for the real-time load of each time point in the curve, a curve slope value is preset as a target load change rate, and when the load change rate at the first real-time load is larger than the preset target load change rate for the first time, the first time point where the first real-time load is located is determined as a beat starting point; after the first time point, when the load change rate of a second real-time load in the real-time load data is greater than the target load change rate for the first time, determining that the second time point of the second real-time load is a beat termination point; and when the beat starting point and the beat ending point are determined, the real-time beat value is correspondingly obtained according to the two time points and the real-time running state. The real-time beat judging method for the first equipment is suitable for the condition that the first equipment in a production line only processes one time when processing the same product; when the first device in the preset production line needs to perform repeated processing for multiple times in the process of processing the same product, for example, a product has 4 ends, the first device needs to process 2 ends of the product, but only one end of the product can be processed at a time, and after the end is processed, the first device re-processes the other end in a rotating manner, in which case the real-time beat is determined as follows.
In a possible implementation manner, the first device is a repeated processing type device, where the repeated processing type device refers to that the same characteristics at different positions of the same product need to be processed M times, and when a first real-time load in the real-time load data is greater than a preset first load threshold and a load change rate at the first real-time load is first greater than a preset target load change rate, a first time point at which the first real-time load is located is determined as a beat starting point; after the first time point, when the first load change rate at the second real-time load in the real-time load data is greater than the target load change rate for the first time, determining a second time point corresponding to the second real-time load; determining the recording time to be t1 according to the first time point and the second time point; t1 is an integer greater than 0; after the second time point, when the first load change rate is greater than the target load change rate for the second time, determining a third time point corresponding to the second real-time load; determining the recording time to be t2 according to the second time point and the third time point; t2 is an integer greater than 0; determining a real-time beat t, which is t1+ t2+ ·+ tm, until the Mth time of the first load change rate is greater than the target load change rate; and the time recorded by tm is the difference between the time point corresponding to the second real-time load when the Mth time of the first load change rate is greater than the target load change rate and the time point corresponding to the second real-time load when the Mth time of the first load change rate is greater than the target load change rate.
Step 202, determining a theoretical beat of the first device according to the real-time beat and a preset beat judgment rule;
specifically, after the real-time beat of the first device is obtained in step 201, the real-time beat is compared with the current theoretical beat, and when a relative error between the real-time beat and the current theoretical beat exceeds a preset first beat threshold, if a time of continuously exceeding the first beat threshold is greater than a first preset time and a frequency of continuously exceeding the first beat threshold is greater than a first preset frequency, all the real-time beats within the first preset time are summed and averaged, the obtained average value is used as the theoretical beat of the first device, and the theoretical beat of the first device in the system is updated. For example, the current theoretical beat is set to 21 seconds, the obtained real-time beat is 24 seconds, the relative error between the two is 14.3%, the preset first beat threshold is 10%, the first preset duration is 4 hours, and the first preset number of times is 10 times, then the relative error exceeds the preset first beat threshold, when it is observed that the time continuously exceeding the first beat threshold is 4.5 hours and the number of times is far greater than the first preset number of times by 10 times, the sum of all real-time beats within 4.5 hours is averaged, the average value is used as the theoretical beat of the first device, and the theoretical beat of the first device in the system is updated.
Further specifically, because the equipment can have the situations of idling, auxiliary feeding and the like in the production running process, when the situations occur, the real-time beat is an abnormal value and needs to be eliminated, and therefore, on the premise that the relative error between the real-time beat and the current theoretical beat exceeds a preset first beat threshold value, 2 judgment conditions are newly added to ensure the accuracy of the real-time beat.
1) And when the real-time beat is larger than the current theoretical beat, the relative error between the real-time beat and the current theoretical beat exceeds a preset second beat threshold value, and the real-time load data is smaller than the preset second load threshold value, removing the real-time beat, wherein the second beat threshold value is larger than the first beat threshold value. The second load threshold is, for example, half of the maximum load value, and the specific value is flexibly set according to the actual situation, which is not described herein again.
2) And when the real-time beat is smaller than the current theoretical beat and the relative error between the real-time beat and the current theoretical beat exceeds a preset third beat threshold value, removing the real-time beat, wherein the third beat threshold value is larger than the second beat threshold value.
In a possible implementation manner, when the relative error between the real-time beat and the current theoretical beat exceeds a preset first beat threshold, if the time continuously exceeding the first beat threshold is less than a first preset time length, the system gives an alarm to prompt so as to pay attention to the change dynamics of the real-time beat.
In a possible implementation manner, when the relative error between the real-time beat of the first device and the current theoretical beat is within a preset first beat threshold, and meanwhile, the real-time beat duration of the first device does not exceed the real-time beat duration of other devices in the target production line, neither the theoretical beat of the first device nor the real-time bottleneck device of the target production line is changed.
specifically, when the relative error between the real-time beat of the first device and the current theoretical beat exceeds a preset first beat threshold, if the time exceeding the first beat threshold is longer than a second preset duration, for example, the second preset duration is 3 hours, and the real-time beat duration of the first device is longer than the real-time beat durations of other devices in the target production line, the first device is determined as a real-time bottleneck device, where the second preset duration is longer than or equal to the first preset duration. For example, there are 3 devices in the target production line, which are a first device, a second device and a third device, where the 3 devices are arranged in association with each other according to a process sequence of the target production line, and a real-time beat of the first device obtained through collection and judgment is 24 seconds, a real-time beat of the second device is 22 seconds, a real-time beat of the third device is 20 seconds, a current theoretical beat of the first device is 21 seconds, a beat threshold value is 10%, and a second preset time is 5 hours; therefore, the relative error between the real-time beat of the first equipment and the current theoretical beat exceeds a preset first beat threshold value, the real-time beat duration of the first equipment is greater than the real-time beat duration of other equipment in the target production line, and when the time when the relative error between the real-time beat of the first equipment and the current theoretical beat exceeds the first beat threshold value is observed to be 6 hours, the alarm prompts state information; according to the alarm prompt, a production line manager can timely confirm and update the first equipment into the system as real-time bottleneck equipment, wherein the real-time bottleneck equipment represents the bottleneck of a target production line.
Further specifically, when the relative error between the real-time beat of the first device and the current theoretical beat exceeds a preset first beat threshold, if the time exceeding the first beat threshold is less than a second preset duration, for example, the second preset duration is 3 hours, but the real-time beat duration of the first device is greater than the real-time beat duration of other devices in the target production line, the state information is prompted through a system alarm, and the state information includes: a time length value exceeding a first beat threshold value, a real-time beat of the current first device, and a change condition of the real-time bottleneck device.
In a possible implementation manner, the preset association relationship between the target production line and each device therein is determined according to a preset production line model, and the model parameters of the preset production line model include: the device information of each device, the association sequence between the devices and the theoretical beat of each device.
And 204, determining the predicted capacity of the target production line according to the theoretical beats of each device in the target production line and the real-time bottleneck device.
Specifically, the theoretical beats of each device in the target production line and the real-time bottleneck device of the target production line are obtained according to the step 201 and the step 203, so that the time for producing a product by the target production line, which link takes the longest time, can be predicted, and how much the production line can be optimized in real time in one month, half year or all year, and meanwhile, the production plan and the preparation of goods and money such as orders, inventory and the like between upstream and downstream suppliers can be reasonably arranged according to the data.
Fig. 3 is a schematic view illustrating an actual scene application outflow of a real-time production line optimization method according to an exemplary embodiment of the present disclosure, as shown in fig. 3, and as can be known by combining step 201 and step 204, a real-time beat of a first device is determined according to a real-time operating state of the first device, real-time load data of the first device, and a preset load change rule, where the first device is any device in a target production line; determining a theoretical beat of the first device according to the real-time beat and a preset beat judgment rule; determining real-time bottleneck equipment of the target production line according to the theoretical beats of each equipment in the target production line and a preset incidence relation, wherein the preset incidence relation is a matching relation between the target production line and each equipment; performing real-time optimization on the target production line according to the theoretical beats of each device in the target production line and the real-time bottleneck device, for example, when the real-time bottleneck beat of the production line is judged to be reduced, no operation change of the production line exists; when the real-time bottleneck beat of the production line is judged to rise, the optimization suggestion of the production line is more accurately analyzed and determined according to the historical data such as the historical beat, the process parameters and the like, and the comparison between the real-time load change curve and the historical load change curve, so that an important data basis is provided for the prediction of the subsequent capacity and the reasonable arrangement of the production plan.
Fig. 4 is a schematic structural diagram of a real-time production line optimization apparatus according to an example embodiment of the present disclosure. As shown in fig. 4, the real-time production line optimizing apparatus 40 provided in this embodiment includes:
a first determining module 401, configured to determine a real-time beat of a first device according to a real-time operating state of the first device, real-time load data of the first device, and a preset load change rule, where the first device is any one of target production lines;
a second determining module 402, configured to determine a theoretical beat of the first device according to the real-time beat and a preset beat determination rule;
a third determining module 403, configured to determine a real-time bottleneck device of the target production line according to the theoretical beat of each device in the target production line and a preset association relationship, where the preset association relationship is a matching relationship between the target production line and each device;
and the processing module 404 is configured to perform real-time production line optimization on the target production line according to the theoretical beats of each device in the target production line and the real-time bottleneck device.
In one possible design, the first determining module 401 is configured to:
when a first real-time load in the real-time load data is greater than a preset first load threshold value and the load change rate at the first real-time load is greater than a preset target load change rate for the first time, determining a first time point where the first real-time load is located as a beat starting point;
after the first time point, when the load change rate of a second real-time load in the real-time load data is greater than the target load change rate for the first time, determining that the second time point of the second real-time load is a beat termination point;
and determining the real-time beat according to the beat starting point, the beat ending point and the real-time running state.
In one possible design, the second determining module 402 is configured to:
when the relative error between the real-time beat and the current theoretical beat exceeds a preset first beat threshold, if the time continuously exceeding the first beat threshold is longer than a first preset time length and the times continuously exceeding the first beat threshold is longer than a first preset time length, updating the theoretical beat to the average value of all real-time beats in the first preset time length.
In one possible design, the second determining module 402 is further configured to:
and when the real-time beat is larger than the current theoretical beat, the relative error between the real-time beat and the current theoretical beat exceeds a preset second beat threshold value, and the real-time load data is smaller than the preset second load threshold value, removing the real-time beat, wherein the second beat threshold value is larger than the first beat threshold value.
In one possible design, the second determining module 402 is further configured to:
and when the real-time beat is smaller than the current theoretical beat and the relative error between the real-time beat and the current theoretical beat exceeds a preset third beat threshold value, removing the real-time beat, wherein the third beat threshold value is larger than the second beat threshold value.
In one possible design, the processing module 404 is configured to:
when the relative error between the real-time beat of the first device and the current theoretical beat exceeds a preset first beat threshold, if the time exceeding the first beat threshold is longer than a second preset time length, and the real-time beat time length of the first device is longer than the real-time beat time lengths of other devices in the target production line, determining the first device as a real-time bottleneck device, wherein the second preset time length is longer than or equal to the first preset time length.
In one possible design, the preset association relationship is determined according to a preset production line model, and the model parameters of the preset production line model include: the device information of each device, the association sequence between the devices and the theoretical beat of each device.
In one possible design, the first determining module 401 is further configured to:
when the first equipment is the repeated processing type equipment, when the first real-time load in the real-time load data is larger than a preset first load threshold value and the load change rate at the first real-time load is larger than a preset target load change rate for the first time, determining a first time point where the first real-time load is located as a beat starting point; wherein, the repeated addition type equipment means that the same characteristics at different positions of the same product need to be processed for M times, and M is an integer larger than 0;
after the first time point, when the first load change rate at the second real-time load in the real-time load data is greater than the target load change rate for the first time, determining a second time point corresponding to the second real-time load;
determining the recording time to be t1 according to the first time point and the second time point; t1 is an integer greater than 0;
after the second time point, when the first load change rate is greater than the target load change rate for the second time, determining a third time point corresponding to the second real-time load;
determining the recording time to be t2 according to the second time point and the third time point; t2 is an integer greater than 0;
determining a real-time beat t, which is t1+ t2+ ·+ tm, until the Mth time of the first load change rate is greater than the target load change rate; and the time recorded by tm is the difference between the time point corresponding to the second real-time load when the Mth time of the first load change rate is greater than the target load change rate and the time point corresponding to the second real-time load when the Mth time of the first load change rate is greater than the target load change rate.
FIG. 5 is a schematic diagram of the structure of a production line data system shown in accordance with an example embodiment of the present disclosure. As shown in fig. 5, the production line data system 50 provided in this embodiment includes:
a processor 501; and the number of the first and second groups,
a memory 502 for storing executable instructions of the processor, which may also be a flash (flash memory);
wherein the processor 501 is configured to perform the various steps of the above-described method via execution of executable instructions. Reference may be made in particular to the description relating to the preceding method embodiment.
Alternatively, the memory 502 may be separate or integrated with the processor 501.
When the memory 502 is a device separate from the processor 501, the in-line data system 50 may further include:
a bus 503 for connecting the processor 501 and the memory 502.
In addition, embodiments of the present application further provide a computer-readable storage medium, in which computer-executable instructions are stored, and when at least one processor of the user equipment executes the computer-executable instructions, the user equipment performs the above-mentioned various possible methods.
Computer-readable media includes both computer storage media and communication media including any medium that facilitates transfer of a computer program from one place to another. A storage media may be any available media that can be accessed by a general purpose or special purpose computer. An exemplary storage medium is coupled to the processor such the processor can read information from, and write information to, the storage medium. Of course, the storage medium may also be integral to the processor. The processor and the storage medium may reside in an ASIC. Additionally, the ASIC may reside in user equipment. Of course, the processor and the storage medium may reside as discrete components in a communication device.
Those of ordinary skill in the art will understand that: all or a portion of the steps of implementing the above-described method embodiments may be performed by hardware associated with program instructions. The program may be stored in a computer-readable storage medium. When executed, the program performs steps comprising the method embodiments described above; and the aforementioned storage medium includes: various media that can store program codes, such as ROM, RAM, magnetic or optical disks.
Finally, it should be noted that: the above embodiments are only used for illustrating the technical solutions of the present disclosure, and not for limiting the same; while the present disclosure has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art will understand that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present disclosure.
Claims (11)
1. A real-time production line optimization method is characterized by comprising the following steps:
determining a real-time beat of a first device according to a real-time running state of the first device, real-time load data of the first device and a preset load change rule, wherein the first device is any one device in a target production line;
determining a theoretical beat of the first device according to the real-time beat and a preset beat judgment rule;
determining real-time bottleneck equipment of the target production line according to the theoretical beats of each equipment in the target production line and a preset association relation, wherein the preset association relation is a matching relation between the target production line and each equipment;
and carrying out real-time optimization on the target production line according to the theoretical beats of each device in the target production line and the real-time bottleneck device.
2. The method of claim 1, wherein determining the real-time beat of the first device according to the real-time operating state of the first device, the real-time load data of the first device and a preset load change rule comprises:
when a first real-time load in the real-time load data is greater than a preset first load threshold value and a load change rate at the first real-time load is greater than a preset target load change rate for the first time, determining a first time point of the first real-time load as the beat starting point;
after the first time point, when the load change rate at a second real-time load in the real-time load data is greater than the target load change rate for the first time, determining that a second time point at which the second real-time load is located is the beat termination point;
and determining the real-time beat according to the beat starting point, the beat ending point and the real-time running state.
3. The method of claim 1, wherein determining the theoretical tempo of the first device according to the real-time tempo and preset tempo decision rules comprises:
when the relative error between the real-time beat and the current theoretical beat exceeds a preset first beat threshold, if the time continuously exceeding the first beat threshold is greater than a first preset time length and the frequency continuously exceeding the first beat threshold is greater than a first preset frequency, updating the theoretical beat to the average value of all real-time beats in the first preset time length.
4. The method of claim 3, wherein determining the theoretical tempo of the first device according to the real-time tempo and preset tempo decision rules further comprises:
and when the real-time beat is larger than the current theoretical beat, the relative error between the real-time beat and the current theoretical beat exceeds a preset second beat threshold value, and the real-time load data is smaller than the preset second load threshold value, removing the real-time beat, wherein the second beat threshold value is larger than the first beat threshold value.
5. The method of claim 3, wherein determining the theoretical tempo of the first device according to the real-time tempo and preset tempo decision rules further comprises:
and when the real-time beat is smaller than the current theoretical beat and the relative error between the real-time beat and the current theoretical beat exceeds a preset third beat threshold value, removing the real-time beat, wherein the third beat threshold value is larger than the second beat threshold value.
6. The method according to claim 3, wherein the determining the real-time bottleneck device of the target production line according to the theoretical takt time of each device in the target production line and the preset association relationship comprises:
when the relative error between the real-time beat and the current theoretical beat of the first equipment exceeds a preset first beat threshold, if the time exceeding the first beat threshold is longer than a second preset time length, and the real-time beat time length of the first equipment is longer than the real-time beat time lengths of other equipment in a target production line, determining the first equipment as the real-time bottleneck equipment, wherein the second preset time length is longer than or equal to the first preset time length.
7. The method according to any one of claims 1 to 6, wherein the predetermined association is determined according to a predetermined production line model, and the model parameters of the predetermined production line model include: the device information of each device, the association sequence between the devices and the theoretical beat of each device.
8. The method of claim 2, wherein determining the real-time tempo of the first device according to the real-time operating status of the first device, the real-time load data of the first device, and a preset load change rule, further comprises:
when the first equipment is a repeated processing type equipment, when a first real-time load in the real-time load data is greater than a preset first load threshold value and a load change rate at the first real-time load is greater than a preset target load change rate for the first time, determining a first time point of the first real-time load as a beat starting point; the repeated processing type equipment is used for processing the same characteristics of the same product at different positions M times, wherein M is an integer larger than 0;
after the first time point, when a first load change rate at a second real-time load in the real-time load data is greater than the target load change rate for the first time, determining a second time point corresponding to the second real-time load;
determining the recording time to be t1 according to the first time point and the second time point; t1 is an integer greater than 0;
after the second time point, when the first load change rate is greater than the target load change rate for the second time, determining a third time point corresponding to the second real-time load;
determining the recording time to be t2 according to the second time point and the third time point; t2 is an integer greater than 0;
determining the real-time beat t ═ t1+ t2+ ·+ tm until the mth time of the first load change rate is greater than the target load change rate; and the time recorded by tm is the difference between the time point corresponding to the second real-time load when the Mth time of the first load change rate is greater than the target load change rate and the time point corresponding to the second real-time load when the Mth-1 time of the first load change rate is greater than the target load change rate.
9. The utility model provides a produce real-time optimizing apparatus of line which characterized in that includes:
the system comprises a first determining module, a second determining module and a third determining module, wherein the first determining module is used for determining the real-time beat of first equipment according to the real-time running state of the first equipment, the real-time load data of the first equipment and a preset load change rule, and the first equipment is any one of target production lines;
the second determining module is used for determining the theoretical beat of the first equipment according to the real-time beat and a preset beat judging rule;
a third determining module, configured to determine a real-time bottleneck device of the target production line according to a theoretical beat of each device in the target production line and a preset association relationship, where the preset association relationship is a matching relationship between the target production line and each device;
and the processing module is used for carrying out production line real-time optimization on the target production line according to the theoretical beats of each device in the target production line and the real-time bottleneck device.
10. A production line data system, comprising:
a processor; and the number of the first and second groups,
a memory for storing executable instructions of the processor;
wherein the processor is configured to perform the production line real-time optimization method of any one of claims 1 to 8 via execution of the executable instructions.
11. A storage medium having a computer program stored thereon, wherein the program, when executed by a processor, implements the method for real-time optimization of a production line according to any one of claims 1 to 8.
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