CN116500983A

CN116500983A - Method and device for processing production bottleneck factors, electronic equipment and storage medium

Info

Publication number: CN116500983A
Application number: CN202310352001.1A
Authority: CN
Inventors: 请求不公布姓名
Original assignee: Huizhou Haikui Information Technology Co ltd; Shenzhen Haikui Information Technology Co ltd; Guangdong Lyric Robot Intelligent Automation Co Ltd
Current assignee: Huizhou Haikui Information Technology Co ltd; Shenzhen Haikui Information Technology Co ltd; Guangdong Lyric Robot Automation Co Ltd
Priority date: 2023-04-03
Filing date: 2023-04-03
Publication date: 2023-07-28

Abstract

The utility model provides a production bottleneck factor processing method, device, electronic equipment and storage medium, this scheme confirms bottleneck production equipment based on the operation data of each production equipment on the production line to combine equipment panorama state diagram algorithm to confirm that each bottleneck production equipment corresponds stifled, lacks material duration critical value and whole line shut down duration critical value, and then divide all alarm record of bottleneck production equipment, obtain the bottleneck data collection that contains alarm record that has different processing priorities, thereby can carry out alarm event processing based on processing priority. According to the scheme, bottleneck factor processing based on priority can be achieved, the processing mode is more scientific, and production efficiency can be improved greatly.

Description

Method and device for processing production bottleneck factors, electronic equipment and storage medium

Technical Field

The application relates to the technical field of automation, in particular to a method and a device for processing production bottleneck factors, electronic equipment and a storage medium.

Background

At present, a flow type production mode consisting of production equipment is mostly adopted in a production scene, the efficiency of a flow type production line is high, but all the production equipment has obvious equipment mutual restriction and wood barrel effect, so that the highest efficiency of the whole line is influenced by all the production equipment. Therefore, the identification process of the bottleneck factor of the overall line production efficiency existing in the flow-type production line is important for improving the production efficiency.

The traditional bottleneck analysis mode of production efficiency mostly adopts the alarm times, or determines bottleneck factors based on the accumulated statistics of alarm time length. The traditional analysis mode has the defects of incomplete analysis dimension, insufficient analysis depth and the like, and cannot show fundamental or foremost bottleneck factors, so that the process production line does not put improved resources into the real key bottleneck factors in the production climbing stage, and the help to improve the production efficiency is not great.

Disclosure of Invention

The object of the present application includes, for example, providing a method, an apparatus, an electronic device, and a storage medium for processing bottleneck factors, which can implement processing of bottleneck factors based on priority, and greatly contribute to improvement of production efficiency.

Embodiments of the present application may be implemented as follows:

in a first aspect, the present application provides a method for processing a bottleneck factor, the method comprising:

acquiring operation data of each production device aiming at each production device on a production line;

determining bottleneck production equipment according to the operation data of each production equipment;

calculating a critical value of the blocking and unfilled time length and a critical value of the whole line shutdown time length corresponding to the bottleneck production equipment by utilizing an equipment panoramic state diagram algorithm aiming at each bottleneck production equipment;

And obtaining all alarm records of the bottleneck production equipment, dividing all alarm records into a plurality of bottleneck data sets by combining the blocking and unfilled time critical values and the whole line shutdown time critical values, and setting corresponding processing priorities for the bottleneck data sets.

In an optional embodiment, the step of calculating the critical value of the blocking and unfilled duration and the critical value of the whole line shutdown duration corresponding to the bottleneck production equipment by using an equipment panoramic state diagram algorithm includes:

aiming at the bottleneck production equipment, obtaining a whole line shutdown time point when the whole line shutdown occurs;

searching forwards from the whole line shutdown moment point on a time axis to obtain a first single machine alarm trigger point;

traversing from the first single machine alarm trigger point to obtain the operation state of each production device at each moment point until the whole line stop moment point;

and obtaining a critical value of the blocking and material shortage duration and a critical value of the whole line stop duration according to the operation state of each production device at each time point obtained by traversing.

In an optional embodiment, the step of obtaining the critical value of the blocking and unfilled duration and the critical value of the whole line shutdown duration according to the operation state of each production device at each time point obtained by traversing includes:

Taking the production equipment with the alarm at the first single machine alarm trigger point as reference production equipment, and obtaining the operation states of each front-stage process production equipment and each back-stage process production equipment of the reference production equipment at each moment;

obtaining a first moment point when the operation states of the front-stage process production equipment or the back-stage process production equipment are abnormal, a second moment point when the operation states of the front-stage process production equipment and the back-stage process production equipment are abnormal, and a third moment point when the operation states of all production equipment in the whole line are abnormal;

and obtaining a blocking and material shortage duration critical value and a line-finishing stop duration critical value according to the first time point, the second time point and the third time point.

In an alternative embodiment, the critical values of the blocking and starving time period comprise a first critical value for causing the abnormal operation state of the production equipment of the front-stage process or the production equipment of the back-stage process and a second critical value for causing the abnormal operation state of the production equipment of the front-stage process and the production equipment of the back-stage process;

the step of obtaining the critical value of the blocking and unfilling duration and the critical value of the whole line stop duration according to the first time point, the second time point and the third time point comprises the following steps:

Taking a first time length from the first single machine alarm trigger point to the first time point as a first time length critical value, taking a second time length from the first single machine alarm trigger point to the second time point as a second time length critical value, and taking a third time length from the first single machine alarm trigger point to the third time point as an integral line stop time length critical value.

In an alternative embodiment, each production facility has a facility number and numeric status information arranged in sequence;

the step of obtaining a first time point when the operation states of the front-stage process production equipment or the back-stage process production equipment are abnormal, a second time point when the operation states of the front-stage process production equipment and the back-stage process production equipment are abnormal, and a third time point when the operation states of all the production equipment in the whole line are abnormal, comprises the following steps:

for the numerical state information of each preceding process production device and the numerical state information of each subsequent process production device, processing one type into a decimal form and the other type into an integer form;

accumulating the numerical state information of all production equipment at each moment point according to each moment point;

Obtaining a first moment point when the operation state of the front-stage process production equipment or the back-stage process production equipment is abnormal, and a second moment point when the operation state of the front-stage process production equipment and the back-stage process production equipment is abnormal according to the decimal part information and the integer part information in the accumulated result;

and restoring the decimal part information in the accumulated result into an integer form, and determining a third moment when the operation state of all production equipment of the whole line is abnormal by combining the integer part information in the accumulated result and the total amount of the production equipment on the production line.

In an optional embodiment, the step of obtaining the critical value of the blocking and unfilled duration and the critical value of the whole line shutdown duration according to the operation state of each production device at each time point obtained by traversing further includes:

and eliminating production equipment which is abnormal in operation state at the moment and is not adjacent to the production equipment which is abnormal in operation state and is adjacent to the reference production equipment for each moment.

In an alternative embodiment, the job data includes an average inter-fault time per unit time;

the step of determining the bottleneck production equipment according to the operation data of the production equipment comprises the following steps:

Counting the average fault interval time in unit time of each production device;

the production equipment with the smallest average fault interval time in unit time is determined as bottleneck production equipment.

In an alternative embodiment, the job data includes a material shortage duration and/or a material blockage duration in unit time;

counting the material shortage duration and/or the material blocking duration in a plurality of unit time of each production device;

according to the material shortage duration and/or the material blocking duration in a plurality of unit time, calculating to obtain the average material shortage duration and/or the material blocking duration in the unit time;

and determining the production equipment with the maximum average material shortage duration and/or material blocking duration in unit time as bottleneck production equipment.

In an alternative embodiment, the method further comprises:

determining alarm records with causal relation in a plurality of alarm records in the bottleneck data sets;

deleting other alarm records except the first alarm record in the alarm records with causal relation.

In a second aspect, the present application provides a production bottleneck factor handling apparatus, comprising:

The acquisition module is used for acquiring the operation data of each production device aiming at each production device on the production line;

the determining module is used for determining bottleneck production equipment according to the operation data of the production equipment;

the calculation module is used for calculating the critical value of the blocking and unfilled duration and the critical value of the whole line stop duration corresponding to the bottleneck production equipment by utilizing an equipment panoramic state diagram algorithm aiming at each bottleneck production equipment;

the setting module is used for obtaining all alarm records of the bottleneck production equipment, dividing all alarm records into a plurality of bottleneck data sets by combining the blocking and unfilled duration critical values and the whole line shutdown duration critical values, and setting corresponding processing priorities for the bottleneck data sets.

In a third aspect, the present application provides an electronic device, comprising:

at least one processor;

and a memory coupled to the at least one processor; wherein the memory has stored thereon instructions executable by the at least one processor to enable the at least one processor, when executed, to perform the method steps of any one of the previous embodiments.

In a fourth aspect, the present application provides a computer-readable storage medium having stored thereon executable instructions that when executed by a processor implement the method steps of any of the preceding embodiments.

The beneficial effects of the embodiment of the application include, for example:

the utility model provides a production bottleneck factor processing method, device, electronic equipment and storage medium, this case is through confirming bottleneck production facility to combine equipment panorama state drawing algorithm to confirm corresponding stifled, lack material duration critical value and whole line shut down duration critical value, and then divide bottleneck production facility's alarm record, thereby carry out the setting of priority. The bottleneck factor processing based on the priority can be realized, the processing mode is more scientific, and the production efficiency can be greatly improved.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments will be briefly described below, it being understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered limiting the scope, and that other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

FIG. 1 is a flowchart of a method for processing bottleneck factors in a production process according to an embodiment of the present disclosure;

FIG. 2 is a flow chart of sub-steps included in step S12 of FIG. 1;

FIG. 3 is another flow chart of sub-steps included in step S12 of FIG. 1;

FIG. 4 is a flow chart of sub-steps included in step S13 of FIG. 1;

FIG. 5 is a flow chart of sub-steps included in step S134 of FIG. 4;

fig. 6 is a schematic view of a panoramic status of an apparatus according to an embodiment of the present application;

FIG. 7 is a flow chart of sub-steps included in step S1342 of FIG. 5;

FIG. 8 is a flowchart of a deletion method in the processing method of bottleneck factors in the production provided in the embodiment of the present application;

FIG. 9 is a functional block diagram of a device for processing bottleneck factors in a production process according to an embodiment of the present application;

fig. 10 is a schematic structural diagram of an electronic device according to an embodiment of the present application.

Icon: 100-a production bottleneck factor processing device; 110-an acquisition module; 120-determining a module; 130-a calculation module; 140-setting up a module; 210-a processor; 220-memory; 230-a communication bus; 240-computer program.

Detailed Description

For the purposes of making the objects, technical solutions and advantages of the embodiments of the present application more clear, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments. The components of the embodiments of the present application, which are generally described and illustrated in the figures herein, may be arranged and designed in a wide variety of different configurations.

Thus, the following detailed description of the embodiments of the present application, as provided in the accompanying drawings, is not intended to limit the scope of the application, as claimed, but is merely representative of selected embodiments of the application. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments herein without making any inventive effort, are intended to be within the scope of the present application.

It should be noted that: like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further definition or explanation thereof is necessary in the following figures.

Furthermore, the terms "first," "second," "third," and the like, if any, are used merely for distinguishing between descriptions and not for indicating or implying a relative importance. And as used in the claims and specification of this application, the term "plurality" means at least two.

It should be noted that, without conflict, features in embodiments of the present application may be combined with each other.

Referring to fig. 1, an embodiment of the present application provides a method for processing a bottleneck factor, including the following steps:

S11, acquiring operation data of each production device aiming at each production device on a production line.

S12, determining bottleneck production equipment according to the operation data of the production equipment.

S13, calculating a critical value of the blocking and unfilled time length and a critical value of the whole line stop time length corresponding to the bottleneck production equipment by using an equipment panoramic state diagram algorithm according to each bottleneck production equipment.

S14, obtaining all alarm records of the bottleneck production equipment, dividing all alarm records into a plurality of bottleneck data sets by combining the blocking and unfilled time critical values and the whole line shutdown time critical values, and setting corresponding processing priorities for the bottleneck data sets.

In this embodiment, a flow-type production line has a plurality of stations, and each station may have a production facility accordingly, so that a flow-type production line has a plurality of production facilities. The plurality of production devices are sequentially arranged on the flow-type production line according to the operation sequence.

During the production operation, each production device will generate a plurality of operation data. In this embodiment, each production device may be connected to a gateway device, and the gateway device may obtain operation data of each production device during operation. In order to enable the final production bottleneck factors to be accurately identified, in this embodiment, operation data of each production device may be acquired under certain limiting conditions. The limiting condition is a normal stage of the whole line climbing stage of the production line, and ensures that the feeding of the front-stage working procedure of the whole line is sufficient and the discharging of the rear-stage working procedure is smooth, thereby avoiding the adverse effect on the bottle neck factor identification caused by the inaccuracy of the acquired data under the conditions such as non-stop line rectification, air-engine trial run stage and the like.

The job data obtained for each production facility may include, but is not limited to, real-time throughput, facility status, alarm records, actual minute throughput, and action beat data. Wherein, the real-time yield refers to the shift yield at the current moment. The equipment state can be divided into normal production, shutdown alarm, material blocking in the lower working procedure and material shortage in the upper working procedure. The alarm records may include shutdown alarm prompts, process records, and the like. The actual minute capacity refers to the number of products produced in a single minute, and the beat data refers to time-consuming data for each action beat.

In the production process, all production actions of finishing one working procedure are generally called an action beat. Each action beat may consist of one action group or a plurality of action groups, and each action group consists of a plurality of production actions.

On the whole flow-type production line, factors which have major influence on production efficiency are the occurrence of blockage and shortage and the occurrence of faults. For a plurality of production devices, a situation of blocking, material shortage and a situation of failure may occur on each production device, wherein the production device with serious blocking, material shortage or failure has a great influence on the overall production efficiency, and in this embodiment, such production device is referred to as a bottleneck production device.

In this embodiment, the analysis is focused on the related information of the bottleneck production equipment, so as to analyze and process the whole production bottleneck factors.

The equipment panoramic state diagram algorithm can analyze and process based on the information of each production equipment in the flow-type production line and the operation state of each production equipment at each moment, so as to obtain the critical value of the blocking and unfilled time length and the critical value of the whole line stop time length of each bottleneck production equipment correspondingly. The critical value of the blocking and material shortage duration can represent the minimum duration of blocking and material shortage of adjacent production equipment of the production equipment, and the critical value of the whole line shutdown duration can represent the minimum duration of whole line shutdown of the production equipment.

Multiple alarms may occur in the production process of each bottleneck production device, and some alarms have long duration, which may affect the operation state of the production device in the previous process or the operation state of the production device in the subsequent process, and may also cause abnormal operation states of the production devices in the whole production line. Or the number of occurrences is large but the recovery time is fast, and the influence of stopping production on other production equipment is probably avoided.

On the basis of determining the critical value of the blocking and unfilled time length and the critical value of the whole line stop time length, each alarm record of the bottleneck production equipment can be marked into a corresponding bottleneck data set so as to acquire which type the alarm record belongs to. It can be seen that the importance degrees of alarm records contained in the plurality of bottleneck data sets obtained by division are different, and therefore, corresponding processing priorities can be set for the respective bottleneck data sets.

For example, the highest processing priority may be set for the bottleneck data set corresponding to the abnormal operation state of the production equipment that may cause the whole production line, the next highest processing priority may be set for the bottleneck data set corresponding to the influence of both the former-stage production equipment and the latter-stage production equipment, and the lowest processing priority may be set for the bottleneck data set corresponding to the influence of either the former-stage production equipment or the latter-stage production equipment.

Therefore, the bottleneck production equipment is determined, and the corresponding critical value of the blocking and material shortage duration and the critical value of the whole line downtime duration are determined by combining the equipment panoramic state diagram algorithm, so that the alarm records of the bottleneck production equipment are divided, the priority is set, the bottleneck factor processing based on the priority can be realized, the processing mode is more scientific, and the production efficiency can be greatly improved.

As can be seen from the foregoing, one of the main factors causing the bottleneck to the improvement of the production efficiency is the occurrence of the fault, including, for example, the frequency and the duration, so, referring to fig. 2, in one implementation manner, the bottleneck production device may be determined from the dimension where the fault occurs, and the following manner may be adopted:

S121A, counting average fault interval time in unit time of each production equipment.

And S122A, determining the production equipment with the minimum average fault interval time in the unit time as bottleneck production equipment.

In this embodiment, the obtained job data of each production apparatus includes an average failure interval time per unit time, which may be, for example, unit hour. For each production device, average value calculation can be performed based on the obtained average fault interval time in a plurality of unit time, and the average fault interval time is obtained. And the production equipment with the smallest average fault interval time shows that the frequency of faults is the most frequent and the time of the faults is the longest, so that the production equipment can be determined as bottleneck production equipment.

In addition, the blockage and the lack of materials are factors that greatly affect the production efficiency, so in this embodiment, referring to fig. 3, in a possible implementation manner, the bottleneck production device may be determined based on the dimensions of the blockage and the lack of materials, and the following manner may be adopted:

S121B, counting the material shortage duration and/or the material blocking duration of each production equipment in a plurality of unit time.

S122B, calculating to obtain average material shortage duration and/or material blocking duration in unit time according to the material shortage duration and/or material blocking duration in unit time.

And S123B, determining the production equipment with the maximum average material shortage duration and/or material blocking duration in unit time as bottleneck production equipment.

In this embodiment, the obtained operation data of each production device includes a material shortage duration and/or a material blocking duration in a unit time, and the unit time may be a unit hour. When the material shortage duration of the production equipment is overlong, the working state of the production equipment in the previous working procedure is abnormal, the feeding condition is not satisfied, and the production equipment in the previous working procedure is not processed and is in a stop waiting state. When the production equipment is blocked for too long, the next discharging condition is not satisfied, and the working state of the production equipment in the subsequent process is abnormal.

Therefore, the material shortage duration and the material blocking duration are too long to affect other production devices, and in this embodiment, for each production device, after obtaining the material blocking duration or the material shortage duration in a plurality of unit time, the average value may be calculated. The production equipment with the largest average material shortage duration or average material blocking duration can be the production equipment which is very easy to influence the operation of other production equipment, and the production equipment is determined to be the bottleneck production equipment.

On the basis, the critical value of the blocking and unfilling time length and the critical value of the whole line stop time length can be calculated for bottle neck production equipment in a targeted manner. Referring to fig. 4, in one possible implementation manner, the critical value of the blocking and starving duration and the critical value of the whole line shutdown duration may be obtained as follows:

s131, obtaining a whole line stop time point when the whole line stop occurs for the bottleneck production equipment.

And S132, searching forwards from the whole line shutdown time point on a time axis to obtain a first single machine alarm trigger point.

S133, traversing from the first single machine alarm trigger point to the back to obtain the operation state of each production device at each moment point until the whole line stop moment point.

S134, obtaining a critical value of the blocking and material shortage duration and a critical value of the whole line stop duration according to the operation state of each production device at each time point obtained through traversing.

In this embodiment, the production conditions within the statistical period may be analyzed, for example, within 24 hours or within 48 hours. The gateway device may record the job status of each production device at each point in time within the statistical time period. The time point may be a sampling point, and the interval duration of the sampling point may be set according to the requirement, which is not particularly limited.

The situation that one or more whole line stops can occur in the statistical time period can obtain the whole line stop time point when the whole line stops occur each time. The whole line shutdown time point is the time point when all production equipment on the production line has abnormal operation state, such as shutdown. For each whole line shutdown moment point, a first single machine alarm trigger point is arranged in front of the whole line shutdown moment point, wherein the first single machine alarm trigger point is the moment point when the production equipment gives an alarm for the first time, and in the embodiment, the production equipment giving the alarm for the first time is bottleneck production equipment. There should be a period of time between the first single machine alarm trigger point to the full line shutdown time point.

The production equipment operation state of each moment point up to the whole line stop moment point can be obtained by traversing from the first single machine alarm trigger point to the back. The time points are the first single machine alarm trigger point, the whole line stop time point and each time point between the two.

By the method, the critical value of the blocking and unfilling time length and the critical value of the whole line stop time length, which are caused by the first front and back working procedure production equipment and the whole line production equipment working state abnormality of the bottleneck production equipment, can be calculated based on the working states of all production equipment from the first occurrence of the alarm to the whole line stop.

In this embodiment, referring to fig. 5, in one possible implementation manner, the blocking and starving duration critical value and the whole line shutdown duration critical value may be obtained based on the operation states of the production devices at each time point by:

s1341, taking the production equipment with the alarm at the first single machine alarm trigger point as reference production equipment, and obtaining the operation states of each front-stage process production equipment and each back-stage process production equipment of the reference production equipment at each moment point.

S1342, obtaining a first moment point when the operation states of the front-stage process production equipment or the back-stage process production equipment are abnormal, a second moment point when the operation states of the front-stage process production equipment and the back-stage process production equipment are abnormal, and a third moment point when the operation states of all production equipment in the whole line are abnormal.

S1343, obtaining a critical value of the blocking and shortage duration and a critical value of the whole line stop duration according to the first time point, the second time point and the third time point.

Referring to fig. 6 in combination, the horizontal axis in fig. 6 may be a time axis, the vertical axis may be a number of each production device on the production line, and the information in the box may represent the operation state corresponding to the production device. Wherein, the blank represents normal operation state, and the other boxes except the blank represent abnormal operation state.

As shown in fig. 6, it can be seen that the production apparatus in which the alarm is given at the time of the first single machine alarm trigger point is the production apparatus numbered M14 (marked J at the time of the abnormality of the operation state), that is, the production apparatus can be regarded as the reference production apparatus (the essence, i.e., bottleneck production apparatus). In this way, the production apparatuses numbered M1 to M13 are the former process production apparatuses (the operation state abnormality is marked with X) of the reference production apparatus, and the production apparatuses numbered M15 to M26 are the latter process production apparatuses (the operation state abnormality is marked with H) of the reference production apparatus.

After the reference production equipment gives an alarm, the subsequent production equipment of the front-stage working procedure causes the abnormal working state of the production equipment of the rear-stage working procedure. It is possible that the former-stage process production equipment will be caused to be abnormal, and it is also possible that the latter-stage process production equipment will be caused to be abnormal. For example, as shown in fig. 6, the reference production apparatus M14 will first cause the subsequent process production apparatus to have an abnormal operation state at the time point 9, and then move backward, causing its previous process production apparatus to have an abnormal operation state at the time point 13. Starting from the time point 13, that is, the operation states of the front-stage process production equipment and the back-stage process production equipment are abnormal. And up to point in time 29, all production equipment on the production line is out of order.

As shown in fig. 6, the first time point obtained may be time point 9, the second time point may be time point 13, and the third time point may be time point 29. In the case of obtaining the critical time point of the production equipment state change on the production line, the critical value of the blocking and material shortage time length and the critical value of the whole line stop time length can be obtained.

In this embodiment, specifically, the blocking and starving duration threshold includes a first duration threshold that causes an abnormality in the operation state of the former-stage process production equipment or the latter-stage process production equipment, and a second duration threshold that causes an abnormality in the operation state of the former-stage process production equipment and the latter-stage process production equipment.

The first time length from the first single machine alarm trigger point to the first time point can be used as a first time length critical value, the second time length from the first single machine alarm trigger point to the second time point can be used as a second time length critical value, and the third time length from the first single machine alarm trigger point to the third time point can be used as an integral line stop time length critical value.

In the process of processing the data information by the electronic equipment, the numerical information processing is more convenient and effective. Therefore, in this embodiment, each production device on the production line has a device number and numerical status information, which characterize the operation status, set in sequence. For example, assuming 5 production devices on a production line, the device number may be 1-5, and the numerical status information may be 0 or 1, where 1 indicates that the working status is abnormal and 0 indicates that the working status is normal. At some point in time, the information about the 5 production apparatuses may be written as { (1, 1), (2, 0), (3, 0), (4, 0), (5, 0) }, indicating that the states of the other production apparatuses are normal except for the state abnormality of the first production apparatus.

In this way, the first time point, the second time point and the third time point can be obtained based on the digitized information, specifically, referring to fig. 7, the following manner may be implemented:

s13421, processing one type of the numeric state information of each preceding process production facility and the numeric state information of each subsequent process production facility into a decimal form and processing the other type of the numeric state information into an integer form.

S13422, accumulating, for each time point, the numerical state information of all production apparatuses except the reference production apparatus at the time point.

S13423, obtaining a first moment point when the operation state of the front-stage process production equipment or the back-stage process production equipment is abnormal and a second moment point when the operation state of the front-stage process production equipment and the back-stage process production equipment is abnormal according to the decimal part information and the integer part information in the accumulated result.

S13424, restoring the decimal part information in the accumulated result into an integer form, and determining a third moment when the operation state of all production equipment of the whole line is abnormal by combining the integer part information in the accumulated result and the total amount of the production equipment on the production line.

In this embodiment, in order to distinguish the state changes of the former-stage process production equipment and the latter-stage process production equipment, one type of the numerical state information may be processed into a decimal form, and the other type of the numerical state information may be retained in an integer form. For example, the numerical state information of each preceding process production facility may be divided by 100, and if the numerical state information is 1, the number of steps after the processing is 0.01. And if the numerical state information of the post-process production equipment is 1, the numerical state information is kept to be 1 in an integer form. Of course, the numerical state information of each subsequent process production apparatus may be processed into a decimal form, and the numerical state information of each preceding process production apparatus may be kept in an integer form, and the embodiment is not particularly limited.

Thus, after the numerical state information of all production equipment except the reference production equipment at a certain moment is accumulated, the decimal part information in the accumulated result can represent the integral state of the production equipment of the front-stage working procedure, the integral part information can represent the integral state of the production equipment of the rear-stage working procedure, or the integral part information in the accumulated result represents the integral state of the production equipment of the rear-stage working procedure, and the decimal part information represents the integral state of the production equipment of the front-stage working procedure.

Therefore, once the working state of the front-stage working procedure production equipment or the back-stage working procedure production equipment is abnormal, corresponding changes of the decimal part information or the integral part information are characterized, and therefore the first moment point and the second moment point can be determined.

The state of each production device can be represented by the decimal part information and the integral part information, and by combining the total amount of the production devices, whether all the production devices are abnormal can be determined, and then the third moment can be determined.

In one possible implementation, if there are 5 production devices on the production line, numbered 1-5, a working state anomaly is indicated by 1, and a working state normal is indicated by 0. The 3 rd production equipment is the reference production equipment, the numerical state information of the production equipment of the previous step is processed into a decimal form, and the numerical state information of the production equipment of the subsequent step is reserved into an integer form.

If the integral part is greater than 0 or the decimal part is greater than 0 in the accumulation result at a certain moment, the state abnormality of the production equipment of the later process or the production equipment of the earlier process is indicated, and the moment point can be marked as a first moment point.

If the first time point is recorded, whether the integral part is larger than 0 and the decimal part is larger than 0 in the accumulated result can be judged, if so, the time point can be marked as a second time point, wherein the abnormal state of the production equipment of the later-stage process and the production equipment of the earlier-stage process is indicated.

If the second time point is recorded, the decimal part in the accumulation result can be restored to an integer form, the restored integer and the integer part in the accumulation result are added, if the total quantity of the production equipment is equal to 5, the condition that all the production equipment on the production line is abnormal at the moment is indicated, and the time point can be recorded as a third time point.

In this embodiment, in addition to the abnormality caused by the influence of the adjacent production equipment during the production process, there may be some alarm, shutdown caused by other reasons, and such alarm shutdown is called noise. At a certain point in time, such noise points are often not adjacent to production equipment with abnormal operation states, but are spaced by production equipment with normal operation states.

In order to avoid the influence of the noise points on analysis, the noise points can be eliminated in the process of calculating the critical value of the blocking and unfilled time length and the critical value of the whole line stop time length.

Specifically, for each time point, production equipment whose operation state at the time point is abnormal and production equipment whose operation state adjacent to the reference production equipment is abnormal is not adjacent to the reference production equipment is removed.

For example, as shown in fig. 6, at the time point 11, an abnormality occurs in the production apparatus numbered M6, but the abnormality is an abnormality caused by other causes, not an abnormality caused by the reference production apparatus. The production equipment is not adjacent to the reference production equipment, is not adjacent to the production equipment with abnormal operation state adjacent to the reference production equipment, can be determined as noise points, and can be removed.

As another example, as shown in fig. 6, at the time point 16, the production apparatus numbered M12 is abnormal, but the production apparatus M12 is adjacent to the production apparatus numbered M13, and the production apparatus numbered M13 is abnormal in state and adjacent to the reference production apparatus, so it is seen that the abnormality of the production apparatus M12 may be caused after the abnormality of other production apparatuses, not so-called noise, without performing culling.

Under the condition of noise point elimination, determining a first time point, a second time point and a third time point based on the numerical processing mode, and further determining a first time critical value, a second time critical value and a whole line stop time critical value contained in the blocking and shortage time critical values.

On this basis, when a plurality of alarm records are obtained for a period of time of the bottleneck production facility, then the plurality of alarm records may be divided into a plurality of bottleneck data sets.

The method comprises the steps that the method is divided into three bottleneck data sets, and if the alarm duration in each alarm record is between a first duration critical value and a second duration critical value, the first bottleneck data set is divided. And if the alarm time is between the second time critical value and the whole line stop time critical value, dividing the alarm time into a second bottleneck data set. If the alarm time length is greater than the whole line stop time length critical value, dividing into a third bottleneck data set. And if the alarm time length is smaller than the first time length critical value, the alarm time length is not processed.

Thus, the processing priority of the third bottleneck data set to the first bottleneck data set can be sequentially reduced. When the alarm is processed, the alarm in the third bottleneck data set can be processed preferentially, and then the alarm in the second bottleneck data set and the alarm in the first bottleneck data set are processed sequentially.

In this embodiment, considering that there may be a correlation between alarm events, for example, the alarm event a will cause the alarm event B to appear after the alarm event a appears, when processing, if the alarm event a is processed, the alarm event B will be automatically released. Therefore, in order to avoid some unnecessary processing work, after dividing into multiple bottleneck data sets, deletion processing may be performed in the following manner, please refer to fig. 8 in combination:

S15, determining alarm records with causal relation in a plurality of alarm records in the bottleneck data sets.

S16, deleting other alarm records except the first alarm record in the alarm records with causal relation.

In this embodiment, the alarm records with causal relationship can be determined by manual calibration, or the processing model can be obtained by training by taking the alarm records with causal relationship as samples in advance, and when the processing model is applied, the alarm records with causal relationship can be directly screened out by using the processing model, so that the embodiment is not particularly limited.

For alarm records with causal relation, each alarm record has a corresponding alarm time point, and the alarm record with the alarm time point which is not the forefront can be deleted. For such alarm records, the alarm records can be left unprocessed, and after the alarm records with causality and the first alarm records are processed and solved, the alarm event can be automatically relieved, so that the processing workload can be saved.

Referring to fig. 9, an embodiment of the present application further provides a device 100 for processing a bottleneck factor, where the device includes an obtaining module 110, a determining module 120, a calculating module 130, and a setting module 140. The functions of the respective functional blocks of the production bottleneck factor processing apparatus 100 are described in detail below.

An obtaining module 110, configured to obtain, for each production device on a production line, operation data of each production device;

it will be appreciated that the acquisition module 110 may be configured to perform step S11 described above, and reference may be made to the details of step S11 regarding the implementation of the acquisition module 110.

A determining module 120, configured to determine a bottleneck production device according to the job data of each production device;

it will be appreciated that the determination module 120 may be used to perform step S12 described above, and reference may be made to the details of implementation of the determination module 120 as described above with respect to step S12.

The calculating module 130 is configured to calculate, for each of the bottleneck production devices, a critical value of a blocking and starving duration and a critical value of a line stop duration corresponding to the bottleneck production device by using a device panoramic state diagram algorithm;

it will be appreciated that the computing module 130 may be configured to perform step S13 described above, and reference may be made to the details of step S13 regarding the implementation of the computing module 130.

The setting module 140 is configured to obtain all alarm records of the bottleneck production equipment, divide all alarm records into a plurality of bottleneck data sets by combining the critical values of the blocking and unfilling time periods and the critical value of the whole line shutdown time period, and set corresponding processing priorities for the bottleneck data sets.

It will be appreciated that the setup module 140 may be used to perform step S14 described above, and reference may be made to the details of the implementation of the setup module 140 as described above with respect to step S14.

In one possible implementation, the job data includes an average failure interval time per unit time, and the determining module 120 may be configured to:

counting the average fault interval time in unit time of each production device; the production equipment with the smallest average fault interval time in unit time is determined as bottleneck production equipment.

In one possible implementation, the job data includes a material shortage duration and/or a material blockage duration in a unit time, and the determining module 120 may be configured to:

counting the material shortage duration and/or the material blocking duration in a plurality of unit time of each production device; according to the material shortage duration and/or the material blocking duration in a plurality of unit time, calculating to obtain the average material shortage duration and/or the material blocking duration in the unit time; and determining the production equipment with the maximum average material shortage duration and/or material blocking duration in unit time as bottleneck production equipment.

In one possible embodiment, the production bottleneck factor processing device 100 further includes a deletion module that can be used to:

Determining alarm records with causal relation in a plurality of alarm records in the bottleneck data sets; deleting other alarm records except the first alarm record in the alarm records with causal relation.

In one possible implementation, the computing module 130 may be configured to:

aiming at the bottleneck production equipment, obtaining a whole line shutdown time point when the whole line shutdown occurs; searching forwards from the whole line shutdown moment point on a time axis to obtain a first single machine alarm trigger point; traversing from the first single machine alarm trigger point to obtain the operation state of each production device at each moment point until the whole line stop moment point; and obtaining a critical value of the blocking and material shortage duration and a critical value of the whole line stop duration according to the operation state of each production device at each time point obtained by traversing.

In one possible implementation, the computing module 130 may be configured to:

taking the production equipment with the alarm at the first single machine alarm trigger point as reference production equipment, and obtaining the operation states of each front-stage process production equipment and each back-stage process production equipment of the reference production equipment at each moment; obtaining a first moment point when the operation states of the front-stage process production equipment or the back-stage process production equipment are abnormal, a second moment point when the operation states of the front-stage process production equipment and the back-stage process production equipment are abnormal, and a third moment point when the operation states of all production equipment in the whole line are abnormal; and obtaining a blocking and material shortage duration critical value and a line-finishing stop duration critical value according to the first time point, the second time point and the third time point.

In one possible implementation manner, the blocking and starving duration threshold includes a first duration threshold that causes an abnormality in the operation state of the former-stage process production equipment or the latter-stage process production equipment, and a second duration threshold that causes an abnormality in the operation state of the former-stage process production equipment and the latter-stage process production equipment, and the calculation module 130 may specifically be configured to:

In one possible implementation, each of the production devices has a device number and numeric status information that are sequentially arranged, and the computing module 130 may be configured to:

for the numerical state information of each preceding process production device and the numerical state information of each subsequent process production device, processing one type into a decimal form and the other type into an integer form; accumulating the numerical state information of all production equipment except the reference production equipment at each moment point; obtaining a first moment point when the operation state of the front-stage process production equipment or the back-stage process production equipment is abnormal, and a second moment point when the operation state of the front-stage process production equipment and the back-stage process production equipment is abnormal according to the decimal part information and the integer part information in the accumulated result; and restoring the decimal part information in the accumulated result into an integer form, and determining a third moment when the operation state of all production equipment of the whole line is abnormal by combining the integer part information in the accumulated result and the total amount of the production equipment on the production line.

In one possible implementation, the computing module 130 may be further configured to:

Referring to fig. 10, an embodiment of the present application further provides an electronic device. The electronic device includes: at least one processor 210; and a memory 220 coupled to the at least one processor 210. The processor 210 and the memory 220 are connected by a communication bus 230.

Wherein the memory 220 stores instructions executable by the at least one processor 210, i.e., the computer program 240, which are executed by the at least one processor 210 to enable the at least one processor 210, when executed, to implement the steps of the method for processing a bottleneck factor as described in any one of the embodiments above.

The embodiment of the application also provides a computer storage medium, and executable instructions are stored on the computer storage medium, and when the executable instructions are executed by a processor, the steps of the method for processing the bottleneck factor according to any one of the embodiments are realized.

In summary, the method, the device, the electronic device and the storage medium for processing the bottleneck factors in production provided by the embodiments of the present application obtain, for each production device on a production line, operation data of each production device, and determine the bottleneck production device according to the operation data of each production device. And calculating the critical value of the blocking and unfilling duration and the critical value of the whole line shutdown duration corresponding to the bottleneck production equipment by utilizing an equipment panoramic state diagram algorithm aiming at each bottleneck production equipment. And obtaining all alarm records of bottleneck production equipment, dividing all alarm records into a plurality of bottleneck data sets by combining the blocking and unfilled time critical values and the whole line shutdown time critical value, and setting corresponding processing priorities for all bottleneck data sets.

According to the scheme, the bottleneck production equipment is determined, the corresponding blocking and unfilled duration critical value and the whole line downtime critical value are determined by combining the equipment panoramic state diagram algorithm, and then the alarm records of the bottleneck production equipment are divided, so that the priority is set. The bottleneck factor processing based on the priority can be realized, the processing mode is more scientific, and the production efficiency can be greatly improved.

The foregoing is merely specific embodiments of the present application, but the scope of the present application is not limited thereto, and any changes or substitutions easily conceivable by those skilled in the art within the technical scope of the present application should be covered in the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims

1. A method of processing a production bottleneck factor, the method comprising:

2. The method for processing the bottleneck factors according to claim 1, wherein the step of calculating the critical value of the blocking and starving duration and the critical value of the whole line shutdown duration corresponding to the bottleneck production equipment by using an equipment panoramic state diagram algorithm comprises the following steps:

3. The method for processing the production bottleneck factors according to claim 2, wherein the step of obtaining the critical value of the blocking and starving duration and the critical value of the whole line shutdown duration according to the operation state of each production device at each time point obtained by traversing comprises the following steps:

4. The method according to claim 3, wherein the critical values of the time length of the material blockage and shortage include a first critical value of time length causing abnormality of the operation state of the former process production equipment or the latter process production equipment, and a second critical value of time length causing abnormality of the operation state of the former process production equipment and the latter process production equipment;

5. A production bottleneck factor processing method as set forth in claim 3, wherein each of said production apparatuses has an apparatus number and numerical status information set in sequence;

6. The method for processing a production bottleneck factor according to claim 3, wherein the step of obtaining the critical value of the blocking and starving duration and the critical value of the whole line shutdown duration according to the operation state of each production device at each time point obtained by traversing further comprises:

7. The production bottleneck factor processing method of claim 1, wherein the job data includes an average failure interval time per unit time;

8. The production bottleneck factor processing method according to claim 1, wherein the job data includes a shortage duration and/or a blockage duration per unit time;

according to the material shortage duration and/or the material blocking duration in the unit time, calculating to obtain average material shortage duration and/or material blocking duration in the unit time;

9. The method of claim 1, further comprising:

10. A production bottleneck factor handling apparatus, the apparatus comprising:

11. An electronic device, comprising:

at least one processor; the method comprises the steps of,

a memory coupled to the at least one processor; wherein the memory has stored thereon instructions executable by at least one processor to enable the at least one processor, when executed, to perform the method steps of any one of claims 1 to 9.

12. A computer-readable storage medium, having stored thereon executable instructions which, when executed by a processor, implement the method steps of any of claims 1 to 9.