CN112288175B - Real-time optimization method and device for production line - Google Patents
Real-time optimization method and device for production line Download PDFInfo
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Abstract
The invention provides a real-time production line optimization method and device, and the real-time production line optimization method provided by the embodiment comprises the following steps: 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 equipment 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 all the equipment in the target production line and preset association relations; 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 of the production line provided by the embodiment of the invention can be used for more accurately analyzing and judging the dynamic beat and dynamic bottleneck of the production line, and providing an important data basis for the prediction of the subsequent productivity and the reasonable arrangement of the production plan.
Description
Technical Field
The invention relates to the field of production line data analysis, in particular to a real-time production line optimization method and device.
Background
Along with the development of science and technology, the living standard of people is gradually improved, private cars are gradually popular, and manufacturers enterprises of vehicles pay more attention to whether the produced vehicles can meet the continuously-changing market demands while processing and producing the vehicles, so that the productivity prediction is an important analysis index. At present, theoretical beats and comprehensive equipment efficiency (Overall Equipment Effectiveness, OEE) are generally adopted in the processing process of automobile parts to predict the productivity and bottleneck of the production of the parts, and the productivity and bottleneck are used as the basis for the subsequent production arrangement and optimization.
This treatment method of the prior art has the following problems: 1) More and more manufacturers enterprises produce organization forms which take production lines as units, and the theoretical beats and the comprehensive efficiency of the existing single equipment can be influenced by the previous and subsequent procedures, so that the data of the single equipment often 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 manufacturing enterprise production process execution management system or some equipment acquisition software to obtain the theoretical beats of the production line, the data of the theoretical beats are often average values of the beats, the average values mask the fluctuation of the production line to a great extent, and the deviation of productivity prediction, subsequent production arrangement and actual conditions is easy to cause.
Therefore, how to optimize the production line in real time according to the real-time beat of the production line equipment is a problem to be solved.
Disclosure of Invention
The real-time optimization method for the production line provided by the disclosure can be used for more accurately analyzing and judging the dynamic beat and the dynamic bottleneck of the production line according to the real-time beat of production equipment, and providing an important data basis for the prediction of subsequent productivity and the reasonable arrangement of production plans.
In a first aspect, the present disclosure provides a real-time production line optimization method, including:
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 equipment 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 all the equipment in the target production line and a preset association relationship, wherein the preset association relationship is a matching relationship between the target production line and all the 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 operation state of the first device, the real-time load data of the first device, and the preset load change rule includes:
When a 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 at which the first real-time load is located as the 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 larger than the target load change rate for the first time, determining a second time point at which the second real-time load is located as 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 of the real-time beat and the current theoretical beat exceeds a preset first beat threshold value, if the time of continuously exceeding the first beat threshold value is longer than a first preset duration and the number of times of continuously exceeding the first beat threshold value is longer than a first preset number of times, updating the theoretical beat to be the average value of all the real-time beats in the first preset duration.
In one possible design, determining the theoretical tempo of the first device according to the real-time tempo and a preset tempo determination rule further includes:
And when the real-time beat is larger than the current theoretical beat, the relative error of 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 the theoretical tempo of the first device according to the real-time tempo and a preset tempo determination rule further includes:
And when the real-time beat is smaller than the current theoretical beat and the relative error of 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 of each device in the target production line and a preset association relationship includes:
when the relative error of the real-time beat of the first device and the current theoretical beat exceeds a preset first beat threshold value, if the time exceeding the first beat threshold value 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, the first device is determined to be 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 line model, and model parameters of the preset line model include: device information of each device, association order between devices, and theoretical beats of each device.
In one possible design, determining the real-time beat of the first device according to the real-time operation state of the first device, the real-time load data of the first device, and the preset load change rule further includes:
When the first equipment is a repeated processing type equipment, and when a first real-time load in real-time load data is larger than a preset first load threshold value, determining a first time point at which the first real-time load is located as a beat starting point, wherein the load change rate at the first real-time load is larger than a preset target load change rate for the first time; wherein, the repeated processing type equipment means that the same characteristics at different positions of the same product need to be processed M times, M is an integer greater than 0;
after the first time point, when the first load change rate of the second real-time load in the real-time load data is larger 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 as 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 as 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=t1+t2+ & gt tm until the mth time of the first load change rate is greater than the target load change rate; the tm records the difference between the time point corresponding to the second real-time load when the first load change rate is M-1 times greater than the target load change rate and the time point corresponding to the second real-time load when the first load change rate is M-1 times greater than the target load change rate.
In a second aspect, the present disclosure further provides a real-time production line optimization 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 equipment in the target production line;
the second determining module is used for determining the theoretical beat of the first device 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 all the equipment in the target production line and a preset association relationship, wherein the preset association relationship is a matching relationship between the target production line and all the 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 all the devices 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 larger than a preset first load threshold value, determining a first time point at which the first real-time load is located as a beat starting point when the load change rate at the first real-time load is larger than a preset target load change rate for the first time;
After the first time point, when the load change rate of the second real-time load in the real-time load data is larger than the load change rate for the first time, determining that the second time point where the second real-time load is located 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 of the real-time beat and the current theoretical beat exceeds a preset first beat threshold value, if the time of continuously exceeding the first beat threshold value is longer than a first preset duration and the number of times of continuously exceeding the first beat threshold value is longer than a first preset number of times, updating the theoretical beat to be the average value of all the real-time beats in the first preset duration.
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 of 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 of 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 configured to:
when the relative error of the real-time beat of the first device and the current theoretical beat exceeds a preset first beat threshold value, if the time exceeding the first beat threshold value 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, the first device is determined to be 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 line model, and model parameters of the preset line model include: device information of each device, association order between devices, and theoretical beats of each device.
In one possible design, the first determining module is further configured to:
When the first equipment is a repeated processing type equipment, and when a first real-time load in real-time load data is larger than a preset first load threshold value, determining a first time point at which the first real-time load is located as a beat starting point, wherein the load change rate at the first real-time load is larger than a preset target load change rate for the first time; wherein, the repeated processing type equipment means that the same characteristics at different positions of the same product need to be processed M times, M is an integer greater than 0;
after the first time point, when the first load change rate of the second real-time load in the real-time load data is larger 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 as 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 as 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=t1+t2+ & gt tm until the mth time of the first load change rate is greater than the target load change rate; the tm records the difference between the time point corresponding to the second real-time load when the first load change rate is M-1 times greater than the target load change rate and the time point corresponding to the second real-time load when the first load change rate is M-1 times greater than the target load change rate.
In a third aspect, the present disclosure also provides a production line data system, comprising:
a processor; and
A memory for storing executable instructions of the processor;
wherein the processor is configured to perform any of the 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 having stored thereon a computer program that, when executed by a processor, implements any one of the production line real-time optimization methods of the first aspect.
The present disclosure provides a real-time optimization method and apparatus for a production line, by 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, 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 all the equipment in the target production line and a preset association relationship, wherein the preset association relationship is a matching relationship between the target production line and all the equipment; the target production line is optimized in real time according to the theoretical beats of each device in the target production line and the real-time bottleneck device, so that the dynamic beats and the dynamic bottlenecks of the production line are analyzed and judged more accurately, and an important data basis is provided for the prediction of subsequent productivity and the reasonable arrangement of production plans.
Drawings
In order to more clearly illustrate the embodiments of the present disclosure or the solutions in the prior art, a brief description will be given below of the drawings that are needed in the embodiments or the description of the prior art, it being obvious that the drawings in the following description are some embodiments of the present disclosure, and that other drawings may be obtained from these drawings without inventive effort to a person of ordinary skill in the art.
FIG. 1 is an application scenario diagram of a production line real-time optimization method according to an example embodiment of the present disclosure;
FIG. 2 is a flow diagram of a production line real-time optimization method according to an example embodiment of the present disclosure;
FIG. 3 is a schematic illustration of a real-time scenario application outflow of the production line real-time optimization method according to an example embodiment of the present disclosure;
FIG. 4 is a schematic diagram of a real-time production line optimization device according to an example embodiment of the present disclosure;
fig. 5 is a schematic diagram of a production line data system according to an example embodiment of the present disclosure.
Detailed Description
For the purposes of making the objects, technical solutions and advantages of the embodiments of the present disclosure more apparent, the technical solutions of the embodiments of the present disclosure will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present disclosure, and it is apparent that the described embodiments are some embodiments of the present disclosure, but not all embodiments. Based on the embodiments in this disclosure, all other embodiments that a person of ordinary skill in the art would obtain without making any inventive effort are within the scope of protection of this disclosure.
The terms "first," "second," "third," "fourth" and the like in the description and in the claims and in the above-described figures, if any, are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the disclosure described herein may be capable of operation in sequences other than those illustrated or described herein, for example. 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 scheme of the present disclosure and how the technical scheme of the present disclosure solves the above technical problems in detail 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 optimization method of a production line according to an exemplary embodiment of the present disclosure, where, as shown in fig. 1, there are a plurality of devices in a production line 101, a device 102, a device 103, and a device 104, and a production line is modeled in advance on the plurality of devices, where model parameters of the production line model include: the data output by the model are the association relationship between the production line 101 and the equipment 102, between the equipment 103 and between the equipment 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; determining theoretical beats of the device 102, the device 103 and the device 104 according to the real-time beats and preset beat judgment rules; meanwhile, determining which of the devices 102, 103 and 104 is the real-time bottleneck device of the production line according to the theoretical beats of all the devices and the association relation among the production line 101, the devices 102, the devices 103 and the devices 104 which are obtained in advance; according to the determined real-time bottleneck equipment and the theoretical beats of each equipment, the production line can be optimized in real time, the dynamic beats and the dynamic bottlenecks of the production line can be analyzed and judged more accurately through the processing method, and an important data basis is provided for the prediction of subsequent productivity and the reasonable arrangement of production plans.
FIG. 2 is a flow diagram of a production line real-time optimization method according to an example embodiment of the present disclosure; as shown in fig. 2, the real-time production line optimization method provided in this embodiment includes:
Step 201, 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 device in a target production line;
Specifically, real-time data is collected according to the production line data collection system, the real-time data comprises real-time running state and real-time load data of the first equipment, and the real-time running state of the first equipment is obtained through the production line data collection system according to the movement position of the equipment, program circulation or a mode of adding a sensor and the like. The method comprises the steps that a plurality of devices are arranged in a target production line, a first device is any device in the target production line, real-time load of the first device is continuously changed along with time in the production and processing process of the target production line, a first load threshold is preset, for example, the first load threshold is 1000 watts, when the first real-time load of the first device in real-time load data is larger than the preset first load threshold, the change condition of the real-time load data is continuously observed, generally, the real-time load data is in a curve shape, the 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 point, the first time point of the beat of the first real-time load is determined as a starting point; after the first time point, when the load change rate of the second real-time load in the real-time load data is larger than the target load change rate for the first time, determining the second time point where the second real-time load is located as a beat termination point; when determining the beat starting point and the beat ending point, the numerical value of the real-time beat is correspondingly obtained according to the two time points and the real-time running state. The method for judging the real-time beat of the first equipment is suitable for the condition that the first equipment in the production line only processes once when processing the same product; when the process of processing the same product by the first device in the preset production line needs to be repeated for a plurality of times, for example, a piece of product has 4 ends, the first device needs to process 2 ends of the product, but only one end can be processed at a time, and when the end is processed, the first device reprocesses the other end by rotating, and the real-time beat in this case is determined as follows.
In a possible implementation manner, the first device is a repetitive processing type device, where the repetitive processing type device is to process the same feature M times at different positions of the same product, and when a first real-time load in the real-time load data is greater than a preset first load threshold value, a first time point at which the first real-time load is located is determined to be a beat start point when a load change rate at the first real-time load is greater than a preset target load change rate for the first time; after the first time point, when the first load change rate of the second real-time load in the real-time load data is larger 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 as 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 as 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=t1+t2+ & gt tm until the mth time of the first load change rate is greater than the target load change rate; the tm records the difference between the time point corresponding to the second real-time load when the first load change rate is M-1 times greater than the target load change rate and the time point corresponding to the second real-time load when the first load change rate is M-1 times greater than the target load change rate.
Step 202, determining a theoretical beat of the first device according to a 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, when the relative error between the real-time beat and the current theoretical beat exceeds a preset first beat threshold, if the time of continuously exceeding the first beat threshold is longer than a first preset duration and the number of times of continuously exceeding the first beat threshold is longer than the first preset number of times, summing all the real-time beats within the first preset duration, averaging, and using the obtained average value as the theoretical beat of the first device, and updating the theoretical beat of the first device in the system. 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 value is 10%, the first preset duration is 4 hours, the first preset times are 10 times, the relative error exceeds the preset first beat threshold value, when the time continuously exceeding the first beat threshold value is observed to be 4.5 hours and the times are far greater than the first preset times by 10 times, all the real-time beats within 4.5 hours are summed and averaged, the average value is taken as the theoretical beat of the first equipment, and the theoretical beat of the first equipment in the system is updated.
Further, since the equipment can idle, feed auxiliary and other conditions in the production and operation process, when the conditions occur, the real-time beat is an abnormal value and needs to be removed, 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 of 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.
2) And when the real-time beat is smaller than the current theoretical beat and the relative error of 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 implementation, when the relative error of the real-time beat and the current theoretical beat exceeds a preset first beat threshold, if the time of continuously exceeding the first beat threshold is less than a first preset duration, the system alarms to prompt to pay attention to the change dynamics of the real-time beat.
In one possible implementation manner, when the relative error between the real-time beat of the first device and the current theoretical beat is within the preset first beat threshold, and the real-time beat time length of the first device is not compared with the real-time beat time lengths 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 changes.
Step 203, determining real-time bottleneck equipment of the target production line according to the theoretical beats of all the equipment in the target production line and a preset association relationship, wherein the preset association relationship is a matching relationship between the target production line and all the equipment;
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 time length, for example, the second preset time length is 3 hours, 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, the first device is determined to be a real-time bottleneck device, wherein the second preset time length is longer than or equal to the first preset time length. For example, there are 3 devices in the target production line, namely a first device, a second device and a third device, where the 3 devices are arranged in an associated manner according to the process sequence of the target production line, the real-time beat of the first device obtained by collection and judgment is 24 seconds, the real-time beat of the second device is 22 seconds, the real-time beat of the third device is 20 seconds, the current theoretical beat of the first device is 21 seconds, the beat threshold is 10%, and the second preset duration is 5 hours; the relative error of the real-time beat of the first equipment and the current theoretical beat exceeds a preset first beat threshold value, the real-time beat time of the first equipment is longer than the real-time beat time of other equipment in the target production line, and when the time for observing that the relative error of the real-time beat of the first equipment and the current theoretical beat exceeds the first beat threshold value is 6 hours, the state information is warned; according to the alarm prompt, the line production manager can timely confirm and update the first device to the system as a real-time bottleneck device, wherein the real-time bottleneck device represents the bottleneck of the target line production.
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 time length, for example, the second preset time length is 3 hours, but the real-time beat time of the first device is longer than the real-time beat time length of other devices in the target production line, the state information is prompted by a system alarm, and the state information includes: the time length value exceeding the first beat threshold value, the current real-time beat of the first device and the change condition of the real-time bottleneck device.
In one possible implementation manner, the preset association relationship between the target production line and each device is determined according to a preset production line model, and model parameters of the preset production line model include: device information of each device, association order between devices, and theoretical beats of each device.
And 204, determining the predicted capacity of the target production line according to the theoretical beats of all the devices 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 steps 201-203, so that the time of producing a product in the target production line, which link consumes the longest time, and how much real-time production line optimization can reach in one month, half year or whole year can be predicted, and meanwhile, the production plan, orders, inventory and other supplies and money preparation between upstream and downstream suppliers can be reasonably arranged according to the data.
Fig. 3 is an actual scenario application outflow schematic diagram of a real-time optimization method of a production line according to an example embodiment of the present disclosure, as shown in fig. 3, in combination with steps 201 to 204, it can be known that, according to a real-time operation state of a first device, real-time load data of the first device, and a preset load change rule, a real-time beat of the first device is determined, 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 all the equipment in the target production line and a preset association relationship, wherein the preset association relationship is a matching relationship between the target production line and all the equipment; 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, for example, when judging that the real-time bottleneck beat of the production line is reduced, no production line operation changes; when the real-time bottleneck beat of the production line is judged to be increased, the optimization proposal of the production line is more accurately analyzed and determined according to the comparison of the historical data such as the historical beat, the technological parameters and the like, the real-time load change curve and the historical load change curve, and an important data basis is provided for the prediction of the subsequent productivity and the reasonable arrangement of the production plan.
Fig. 4 is a schematic structural diagram of a real-time production line optimizing apparatus according to an exemplary embodiment of the present disclosure. As shown in fig. 4, the real-time production line optimizing apparatus 40 provided in this embodiment includes:
The first determining module 401 is configured to determine 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, where the first device is any device in a target production line;
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 determining rule;
a third determining module 403, 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 404 is used for optimizing the production line of 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, the first determining module 401 is configured to:
when a first real-time load in the real-time load data is larger than a preset first load threshold value, determining a first time point at which the first real-time load is located as a beat starting point when the load change rate at the first real-time load is larger than a preset target load change rate for the first time;
After the first time point, when the load change rate of the second real-time load in the real-time load data is larger than the target load change rate for the first time, determining the second time point where the second real-time load is located as 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 of the real-time beat and the current theoretical beat exceeds a preset first beat threshold value, if the time of continuously exceeding the first beat threshold value is longer than a first preset duration and the number of times of continuously exceeding the first beat threshold value is longer than a first preset number of times, updating the theoretical beat to be the average value of all the real-time beats in the first preset duration.
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 of 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 of 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 of the real-time beat of the first device and the current theoretical beat exceeds a preset first beat threshold value, if the time exceeding the first beat threshold value 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, the first device is determined to be 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 line model, and model parameters of the preset line model include: device information of each device, association order between devices, and theoretical beats of each device.
In one possible design, the first determining module 401 is further configured to:
When the first equipment is a repeated processing type equipment, and when a first real-time load in real-time load data is larger than a preset first load threshold value, determining a first time point at which the first real-time load is located as a beat starting point, wherein the load change rate at the first real-time load is larger than a preset target load change rate for the first time; wherein, the repeated processing type equipment means that the same characteristics at different positions of the same product need to be processed M times, M is an integer greater than 0;
after the first time point, when the first load change rate of the second real-time load in the real-time load data is larger 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 as 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 as 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=t1+t2+ & gt tm until the mth time of the first load change rate is greater than the target load change rate; the tm records the difference between the time point corresponding to the second real-time load when the first load change rate is M-1 times greater than the target load change rate and the time point corresponding to the second real-time load when the first load change rate is M-1 times greater than the target load change rate.
Fig. 5 is a schematic diagram of a production line data system according to an example embodiment of the present disclosure. As shown in fig. 5, a production line data system 50 provided in this embodiment includes:
a processor 501; and
A memory 502 for storing executable instructions of the processor, which may also be a flash memory;
wherein the processor 501 is configured to perform the steps of the above-described method via execution of executable instructions. Reference may be made in particular to the description of the embodiments of the method described above.
Alternatively, the memory 502 may be separate or integrated with the processor 501.
When memory 502 is a device separate from processor 501, production line data system 50 may further include:
A bus 503 for connecting the processor 501 and the memory 502.
In addition, the embodiment of the application further provides a computer-readable storage medium, wherein computer-executable instructions are stored in the computer-readable storage medium, and when at least one processor of the user equipment executes the computer-executable instructions, the user equipment executes the various possible methods.
Among them, computer-readable media include 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. In the alternative, the storage medium may be integral to the processor. The processor and the storage medium may reside in an ASIC. In addition, the ASIC may reside in a user device. The processor and the storage medium may reside as discrete components in a communication device.
Those of ordinary skill in the art will appreciate that: all or part of the steps for implementing the method embodiments described above may be performed by hardware associated with program instructions. The foregoing program may be stored in a computer readable storage medium. The program, when executed, performs steps including the method embodiments described above; and the aforementioned storage medium includes: various media that can store program code, such as ROM, RAM, magnetic or optical disks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present disclosure, and not for limiting the same; although the present disclosure has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present disclosure.
Claims (10)
1. The real-time optimization method of the production line is characterized by comprising the following steps of:
Determining the real-time beat of a first device according to the real-time running state of the first device, the real-time load data of the first device and a preset load change rule, wherein 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 all the equipment in the target production line and a preset association relationship, wherein the preset association relationship is a matching relationship between the target production line and all the equipment;
optimizing the target production line in real time according to the theoretical beats of all the devices in the target production line and the real-time bottleneck device;
The determining the real-time beat of the first device according to the real-time running state of the first device, the real-time load data of the first device and the preset load change rule comprises the following steps:
When a 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 at which 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 larger than the target load change rate for the first time, determining a second time point at which the second real-time load is located as 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.
2. The method of claim 1, wherein the determining the theoretical beat of the first device according to the real-time beat and a preset beat determination rule comprises:
When the relative error of the real-time beat and the current theoretical beat exceeds a preset first beat threshold value, if the time of continuously exceeding the first beat threshold value is longer than a first preset duration and the number of times of continuously exceeding the first beat threshold value is longer than a first preset number of times, updating the theoretical beat to be the average value of all the real-time beats in the first preset duration.
3. The method of claim 2, wherein the determining the theoretical beat of the first device according to the real-time beat and a preset beat determination rule further comprises:
And when the real-time beat is larger than the current theoretical beat, and the relative error of 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 a preset second load threshold value, removing the real-time beat, wherein the second beat threshold value is larger than the first beat threshold value.
4. The method of claim 2, wherein the determining the theoretical beat of the first device according to the real-time beat and a preset beat determination rule further comprises:
and when the real-time beat is smaller than the current theoretical beat and the relative error of the real-time beat and the current theoretical beat exceeds a preset third beat threshold, removing the real-time beat, wherein the third beat threshold is larger than a second beat threshold.
5. The method according to claim 2, wherein the determining the real-time bottleneck device of the target production line according to the theoretical beats of each device in the target production line and the preset association relation 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 of the first device is longer than the real-time beat time of other devices in the target production line, determining the first device as the real-time bottleneck device, wherein the second preset time length is longer than or equal to the first preset time length.
6. The method according to any one of claims 1-5, wherein the preset association relation is determined according to a preset line model, and model parameters of the preset line model include: device information of each device, association order between devices, and theoretical beats of each device.
7. The method of claim 1, wherein determining the real-time beat of the first device based on the real-time operational 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, and when a 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 at which the first real-time load is located as a beat starting point; wherein the repeated processing equipment is used for processing the same characteristic M times at different positions of the same product, wherein M is an integer greater 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 larger than the target load change rate for the first time, determining a second time point corresponding to the second real-time load;
determining a recording time as 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 a recording time as 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+ & gt tm until the first load change rate mth is greater than the target load change rate; the tm records the time that is the difference between the time point corresponding to the second real-time load when the first load change rate is M-th time greater than the target load change rate and the time point corresponding to the second real-time load when the first load change rate is M-1 th time greater than the target load change rate.
8. Real-time optimizing device of production line, characterized by comprising:
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 equipment in a target production line;
the second determining module is used for determining the theoretical beat of the first device 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 all the equipment in the target production line and a preset association relationship, wherein the preset association relationship is a matching relationship between the target production line and all the equipment;
the processing module is used for carrying out real-time optimization on the production line of the target production line according to the theoretical beats of all the devices in the target production line and the real-time bottleneck device;
the first determining module is specifically configured to:
When a 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 at which 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 larger than the target load change rate for the first time, determining a second time point at which the second real-time load is located as 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.
9. A production line data system, comprising:
a processor; and
A memory for storing executable instructions of the processor;
Wherein the processor is configured to perform the line real-time optimization method of any one of claims 1 to 7 via execution of the executable instructions.
10. A storage medium having stored thereon a computer program, which when executed by a processor, implements the production line real-time optimization method of any one of claims 1 to 7.
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