CN111949941A - Equipment fault detection method, system, device and storage medium - Google Patents
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Abstract
The invention discloses a method, a system, a device and a storage medium for detecting equipment faults, wherein the method comprises the following steps: acquiring a plurality of first action duration data of the equipment; calculating a first average value according to the first action duration data; screening the first action duration data according to the first average value to obtain second action duration data; calculating a second average value according to the second action duration data; calculating the motion stability of the equipment according to the second motion duration data and the second average value; and acquiring the fault information of the equipment according to the action stability. The invention provides an equipment fault detection method based on action stability, which realizes the detection of equipment faults; compared with the existing fault detection method, the fault detection method provided by the embodiment of the invention has the advantages that the fault detection is carried out through the action stability, the universality is stronger, and the efficiency is higher. The invention can be widely applied to the field of fault detection.
Description
Technical Field
The present invention relates to the field of fault detection technologies, and in particular, to a method, a system, an apparatus, and a storage medium for detecting a device fault.
Background
In the global trend of industrial informatization and digitization, the real-time acquisition, storage analysis and diagnosis application of production equipment data in manufacturing workshops are developed unprecedentedly. The stability of the line body action is an important part in line body diagnosis and analysis, the beat and the action stability directly reflect whether abnormal beat and action exist, and indirectly reflect the stability of line body equipment and the productivity stability. Most of the existing line equipment fault detection methods diagnose equipment based on electrical parameters, different electrical parameters need to be acquired for different equipment by the fault detection methods based on the electrical parameters, and some equipment do not send real-time electrical parameters to a data acquisition unit, so that the applicability of the existing detection methods is greatly limited.
Disclosure of Invention
In view of the above, the present invention provides a method, system, apparatus and storage medium for detecting device failure, so as to improve the applicability of failure detection.
The first technical scheme adopted by the invention is as follows:
an equipment fault detection method, comprising:
acquiring a plurality of first action duration data of the equipment;
calculating a first average value according to the first action duration data;
screening the first action duration data according to the first average value to obtain second action duration data;
calculating a second average value according to the second action duration data;
calculating the motion stability of the equipment according to the second motion duration data and the second average value;
and acquiring the fault information of the equipment according to the action stability.
Further, the screening the first action duration data according to the first average value to obtain second action duration data specifically includes:
dividing the first action duration data into a plurality of sets with the same interval size according to the first average value;
acquiring a set with the maximum data volume in the first action duration data as a central set;
and taking the action duration data in the central set and the adjacent sets thereof as second action duration data.
Further, the taking the action duration data in the center set and the plurality of sets adjacent to the center set as second action duration data specifically includes:
and taking the action duration data in the center set, the front set of the center set and the rear set of the center set as second action duration data, wherein the front set and the rear set are centrosymmetric based on the center set.
Further, the screening the first action duration data according to the first average value to obtain second action duration data specifically includes:
calculating a standard deviation according to the first action duration data;
calculating a first interval range according to the first average value and the standard deviation;
and taking the data in the first interval range in the first action data as second action duration data.
Further, the calculating the motion stability of the device according to the second motion duration data and the second average value specifically includes:
calculating a second interval range according to the second average value;
and calculating the motion stability of the equipment according to the proportion of the second motion duration data in the second interval range.
Further, the acquiring fault information of the device according to the action stability specifically includes:
determining that the motion stability is greater than a threshold value, and the device is free of faults;
determining that the action stability is smaller than a threshold value, and the equipment has a fault;
wherein the threshold is the motion stability of the standard equipment.
Further, the acquiring the fault information of the device according to the action stability further includes the following steps:
and determining that the motion stability is equal to the threshold, reducing the range of the second interval, and recalculating the motion stability and the threshold.
The second technical scheme adopted by the invention is as follows:
an equipment fault detection system comprising:
the timing module is used for acquiring a plurality of first action duration data of the equipment;
the screening module is used for calculating a first average value according to the first action duration data; screening the first action duration data according to the first average value to obtain second action duration data;
the calculating module is used for calculating a second average value according to the second action duration data; calculating the motion stability of the equipment according to the second motion duration data and the second average value;
and the detection module is used for acquiring the fault information of the equipment according to the action stability.
The third technical scheme adopted by the invention is as follows:
an equipment fault detection device comprising:
the timer is used for acquiring a plurality of first action duration data of the equipment;
the processor is used for calculating a first average value according to the first action duration data; screening the first action duration data according to the first average value to obtain second action duration data; calculating a second average value according to the second action duration data; calculating the motion stability of the equipment according to the second motion duration data and the second average value; and acquiring the fault information of the equipment according to the action stability.
The fourth technical scheme adopted by the invention is as follows:
a computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out a method of device failure detection as set forth.
Compared with the prior art, the embodiment of the invention provides an equipment fault detection method based on motion stability, which comprises the steps of obtaining first motion duration data of equipment, screening according to a first average value of the first motion duration data to obtain second motion duration data, and calculating the motion stability of the equipment by using the second motion duration data, so that the detection of equipment faults is realized; compared with the existing fault detection method, the fault detection method provided by the embodiment of the invention has the advantage that the fault detection is carried out through the action stability, so that the applicability is stronger.
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Fig. 1 is a flowchart of an apparatus fault detection method according to an embodiment of the present invention.
Detailed Description
The conception, the specific structure and the technical effects of the present invention will be clearly and completely described in conjunction with the embodiments and the accompanying drawings to fully understand the objects, the schemes and the effects of the present invention.
The invention is described in further detail below with reference to the figures and the specific embodiments. The step numbers in the following embodiments are provided only for convenience of illustration, the order between the steps is not limited at all, and the execution order of each step in the embodiments can be adapted according to the understanding of those skilled in the art. Further, for several described in the following embodiments, it is denoted as at least one.
An embodiment of the present invention provides an apparatus fault detection method, referring to fig. 1, including:
s100, acquiring a plurality of first action duration data of the equipment;
s200, calculating a first average value according to the first action duration data;
s300, screening the first action duration data according to the first average value to obtain second action duration data;
s400, calculating a second average value according to the second action duration data;
s500, calculating the motion stability of the equipment according to the second motion duration data and the second average value;
s600, acquiring fault information of the equipment according to the action stability.
Specifically, the embodiment of the present invention provides an apparatus fault detection method, which is used for detecting an apparatus fault by calculating stability of data of an action duration of an apparatus operation process.
The first action duration data is derived from the duration of a single process action in a product cycle during process monitoring, and the first action duration data corresponds to the duration of a single process action in different product cycles. Generally, the first action duration data is more than 200.
The first average value is obtained by averaging all the action durations in the first action duration data, and is used for selecting or rejecting some data in a targeted manner when selecting the data so as to improve the usability of the data.
The second action duration data is data obtained after the first action duration data is screened according to the first average value, edge data with a large difference with most data can be screened in the screening process, and the data are often generated due to special conditions and are not caused by the performance of the equipment.
The second average value is obtained by averaging all the action durations in the second action duration data, and the second average value is used for calculating the concentration degree of the second action data. The higher the concentration degree of the second action time length data is, the higher the action stability of the equipment is, and the equipment runs normally.
The action stability reflects whether the action time tends to a determined value under the condition that the action occurs for many times, and a large number of actual time samples of each action are calculated through a certain algorithm and are used for analyzing the actual process, so that the line rhythm and the action stability are diagnosed.
The fault information comprises two conditions of equipment fault and equipment normal, and is used for judging the running state of the equipment.
In some embodiments, the screening the first action duration data according to the first average value to obtain the second action duration data specifically includes:
dividing the first action duration data into a plurality of sets with the same interval size according to the first average value;
acquiring a set with the maximum data volume in the first action duration data as a central set;
and taking the action duration data in the central set and the adjacent sets thereof as second action duration data.
Specifically, 1/N of the first average value is used as the interval size, a plurality of intervals with the same size are obtained by combining the maximum value and the minimum value of the action duration in the first action duration data, and the first action duration data are classified according to the obtained intervals to obtain a plurality of sets with the same interval size. And taking a plurality of sets with similar central set interval ranges as second action duration data on the basis of the central set by taking the set with the largest data volume as the central set. The set with large data size is obtained, and the influence of a small part of abnormal data on the motion stability calculation can be filtered.
Intervals generally refer to a set of real numbers of the type: if x and y are two numbers in a set, then any number between x and y also belongs to the set. For example, a set of real numbers that satisfy 0 ≦ x ≦ 1 is an interval that contains 0, 1, and also the total real numbers between 0 and 1. By acquiring a plurality of intervals with the same size, classification of the first action duration data can be realized.
A collection is a collection of concrete or abstract objects with certain properties. The objects that make up a collection are referred to as elements of the collection. By dividing the first action duration data into a plurality of sets with the same interval size, the first action duration data can be subjected to next statistical analysis.
The data amount refers to the number of data, and in the present embodiment, refers to the number of first action duration data.
The central set refers to a set with the largest data amount, and more concentrated data in the first action duration data can be obtained by acquiring the set with the largest data amount and an adjacent set thereof, namely, the abnormal data is removed, and the action duration data of the equipment under normal conditions is obtained.
In some embodiments, the taking the action duration data in the central set and the several sets adjacent to the central set as the second action duration data specifically includes:
and taking the action duration data in the center set, the front set of the center set and the rear set of the center set as second action duration data, wherein the front set and the rear set are centrosymmetric based on the center set.
Specifically, the center set and the symmetric set adjacent to the center set are taken as the second action duration data, so that the calculation effect is better, the center set is the set in the first action duration data set, the center set is taken as the center, the front K sets, the rear K sets and the middle set are taken as the second action duration data, the first action duration data, the rear K sets and the middle set accord with the normal distribution rule of the data, and the screening effect is better.
In some embodiments, the screening the first action duration data according to the first average value to obtain the second action duration data specifically includes:
calculating a standard deviation according to the first action duration data;
calculating a first interval range according to the first average value and the standard deviation;
and taking the data in the first interval range in the first action data as second action duration data.
Specifically, in the process of screening the first action duration data, the data can be screened according to the discrete condition of the data, so that unreasonable data far away from the center can be eliminated. And screening data through the standard deviation obtained by calculation to obtain second action duration data.
The standard deviation, also commonly referred to as mean square error in the Chinese context, is the square root of the arithmetic mean squared off mean, expressed as σ. Most often used in probability statistics as a measure of the degree of statistical distribution. The standard deviation is the arithmetic square root of the variance. The standard deviation can reflect the degree of dispersion of a data set. The standard deviation is not necessarily the same for two sets of data with the same mean. The dispersion degree of the first action duration data can be obtained through the standard deviation, and most normal data can be reserved by screening the data according to the dispersion degree.
The first interval range is obtained by a first average value and a standard deviation, and a plus-minus triple standard deviation range of the average value can be used as the first interval range.
In some embodiments, the calculating the motion stability of the device according to the second motion duration data and the second average value specifically includes:
calculating a second interval range according to the second average value;
and calculating the motion stability of the equipment according to the proportion of the second motion duration data in the second interval range.
Specifically, the second interval range is used for calibrating the rationality of the second action duration data, and when the second action duration data is within the second interval range, the action duration data is judged to be rational; when the second action duration data is out of the second interval range, the action duration data is judged to be unreasonable. By calculating the proportion of the second action duration data in the second interval range, the action stability of the equipment can be obtained.
In some embodiments, the obtaining fault information of the device according to the motion stability specifically includes:
determining that the motion stability is greater than a threshold value, and the device is free of faults;
determining that the action stability is smaller than a threshold value, and the equipment has a fault;
wherein the threshold is the motion stability of the standard equipment.
The standard equipment, which is referred to as a calibrated equipment herein, can determine whether the current equipment has a fault by calculating the motion stability of the standard equipment and combining the motion stability of the current equipment.
In some embodiments, the obtaining the fault information of the device according to the motion stability further includes:
and determining that the motion stability is equal to the threshold, reducing the range of the second interval, and recalculating the motion stability and the threshold.
Specifically, when the motion stability of the standard device is the same as that of the current device, the second range interval can be reduced, and the value of the motion stability with a higher identification degree is continuously obtained, so that the fault of the current device is detected.
The embodiment of the present invention further provides an apparatus fault detection system, including:
the timing module is used for acquiring a plurality of first action duration data of the equipment;
the screening module is used for calculating a first average value according to the first action duration data; screening the first action duration data according to the first average value to obtain second action duration data;
the calculating module is used for calculating a second average value according to the second action duration data; calculating the motion stability of the equipment according to the second motion duration data and the second average value;
and the detection module is used for acquiring the fault information of the equipment according to the action stability.
Specifically, the contents in the method embodiments are all applicable to the system embodiments, the functions specifically implemented by the system embodiments are the same as the method embodiments, and the beneficial effects achieved by the system embodiments are also the same as the beneficial effects achieved by the method embodiments.
The embodiment of the present invention further provides an apparatus for detecting an equipment fault, including:
the timer is used for acquiring a plurality of first action duration data of the equipment;
the processor is used for calculating a first average value according to the first action duration data; screening the first action duration data according to the first average value to obtain second action duration data; calculating a second average value according to the second action duration data; calculating the motion stability of the equipment according to the second motion duration data and the second average value; and acquiring the fault information of the equipment according to the action stability.
Specifically, the contents in the method embodiments are all applicable to the system embodiments, the functions specifically implemented by the system embodiments are the same as the method embodiments, and the beneficial effects achieved by the system embodiments are also the same as the beneficial effects achieved by the method embodiments.
The layers, modules, units, platforms, and/or the like included in the system may be implemented or embodied by computer hardware, a combination of hardware and software, or by computer instructions stored in a non-transitory computer readable memory. The methods may be implemented in a computer program using standard programming techniques, including a non-transitory computer-readable storage medium configured with the computer program, where the storage medium so configured causes a computer to operate in a specific and predefined manner, according to the methods and figures described in the detailed description. Each program may be implemented in a high level procedural or object oriented programming language to communicate with a computer system. However, the program(s) can be implemented in assembly or machine language, if desired. In any case, the language may be a compiled or interpreted language. Furthermore, the program can be run on a programmed application specific integrated circuit for this purpose.
The data processing flows performed by the layers, modules, units, and/or platforms included in the system may be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The data processing flows correspondingly performed by the layers, modules, units and/or platforms included in the system of embodiments of the invention may be performed under the control of one or more computer systems configured with executable instructions, and may be implemented as code (e.g., executable instructions, one or more computer programs, or one or more applications) collectively executed on one or more processors, by hardware, or a combination thereof. The computer program includes a plurality of instructions executable by one or more processors.
The present invention also provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements a device failure detection method as described.
Specifically, the storage medium stores processor-executable instructions, and the processor-executable instructions are configured to, when executed by the processor, perform the steps of the method for processing mutual information according to any one of the above-mentioned method embodiments. For the storage medium, it may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid state storage device. It can be seen that the contents in the foregoing method embodiments are all applicable to this storage medium embodiment, the functions specifically implemented by this storage medium embodiment are the same as those in the foregoing method embodiments, and the advantageous effects achieved by this storage medium embodiment are also the same as those achieved by the foregoing method embodiments.
In industrial production, abnormal actions which are difficult to observe by naked eyes often occur, and the stability and the production efficiency of a production line body are influenced. Digital factories, transparent production, large data platforms, etc. have all been developed to solve this problem. The method generally has the function of detecting the stability of the equipment, if the stability of the equipment action is too low and even lower than the stability percentage of manual action, the equipment is likely to have faults or places with unreasonable design, important analysis is needed, and the method can also be used for line body diagnosis.
The above description is only a preferred embodiment of the present invention, and the present invention is not limited to the above embodiment, and any modifications, equivalent substitutions, improvements, etc. within the spirit and principle of the present invention should be included in the protection scope of the present invention as long as the technical effects of the present invention are achieved by the same means. The invention is capable of other modifications and variations in its technical solution and/or its implementation, within the scope of protection of the invention.
Claims (10)
1. An equipment fault detection method, comprising:
acquiring a plurality of first action duration data of the equipment;
calculating a first average value according to the first action duration data;
screening the first action duration data according to the first average value to obtain second action duration data;
calculating a second average value according to the second action duration data;
calculating the motion stability of the equipment according to the second motion duration data and the second average value;
and acquiring the fault information of the equipment according to the action stability.
2. The method according to claim 1, wherein the screening the first action duration data according to the first average value to obtain second action duration data specifically includes:
dividing the first action duration data into a plurality of sets with the same interval size according to the first average value;
acquiring a set with the maximum data volume in the first action duration data as a central set;
and taking the action duration data in the central set and the adjacent sets thereof as second action duration data.
3. The method according to claim 2, wherein the step of using the action duration data in the central set and the sets adjacent to the central set as second action duration data specifically includes:
and taking the action duration data in the center set, the front set of the center set and the rear set of the center set as second action duration data, wherein the front set and the rear set are centrosymmetric based on the center set.
4. The method according to claim 1, wherein the screening the first action duration data according to the first average value to obtain second action duration data specifically includes:
calculating a standard deviation according to the first action duration data;
calculating a first interval range according to the first average value and the standard deviation;
and taking the data in the first interval range in the first action data as second action duration data.
5. The method according to claim 1, wherein the calculating the motion stability of the device according to the second motion duration data and the second average value specifically includes:
calculating a second interval range according to the second average value;
and calculating the motion stability of the equipment according to the proportion of the second motion duration data in the second interval range.
6. The method according to claim 5, wherein the obtaining fault information of the device according to the motion stability specifically includes:
determining that the motion stability is greater than a threshold value, and the device is free of faults;
determining that the action stability is smaller than a threshold value, and the equipment has a fault;
wherein the threshold is the motion stability of the standard equipment.
7. The method according to claim 6, wherein the step of obtaining the fault information of the device according to the motion stability further comprises the steps of:
and determining that the motion stability is equal to the threshold, reducing the range of the second interval, and recalculating the motion stability and the threshold.
8. An equipment fault detection system, comprising:
the timing module is used for acquiring a plurality of first action duration data of the equipment;
the screening module is used for calculating a first average value according to the first action duration data; screening the first action duration data according to the first average value to obtain second action duration data;
the calculating module is used for calculating a second average value according to the second action duration data; calculating the motion stability of the equipment according to the second motion duration data and the second average value;
and the detection module is used for acquiring the fault information of the equipment according to the action stability.
9. An apparatus for detecting a failure of a device, comprising:
the timer is used for acquiring a plurality of first action duration data of the equipment;
the processor is used for calculating a first average value according to the first action duration data; screening the first action duration data according to the first average value to obtain second action duration data; calculating a second average value according to the second action duration data; calculating the motion stability of the equipment according to the second motion duration data and the second average value; and acquiring the fault information of the equipment according to the action stability.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out a method of device failure detection according to any one of claims 1-7.
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| CN202010635093.0A CN111949941B (en) | 2020-07-03 | 2020-07-03 | Equipment fault detection method, system, device and storage medium |
| PCT/CN2020/140970 WO2022001060A1 (en) | 2020-07-03 | 2020-12-29 | Device fault detection method, system and apparatus, and storage medium |
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| CN202010635093.0A CN111949941B (en) | 2020-07-03 | 2020-07-03 | Equipment fault detection method, system, device and storage medium |
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| WO2022001060A1 (en) * | 2020-07-03 | 2022-01-06 | 广州明珞装备股份有限公司 | Device fault detection method, system and apparatus, and storage medium |
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| CN114637264A (en) * | 2022-03-15 | 2022-06-17 | 广州明珞装备股份有限公司 | Motion stability analysis method, system, device and storage medium |
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Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105894329A (en) * | 2016-03-31 | 2016-08-24 | 乐视控股（北京）有限公司 | Data analysis method and device |
| CN108509634A (en) * | 2018-04-10 | 2018-09-07 | 深信服科技股份有限公司 | Jitterbug monitoring method, monitoring device and computer readable storage medium |
| CN109711570A (en) * | 2018-12-26 | 2019-05-03 | 中国移动通信集团江苏有限公司 | Method, device, device and medium for equipment operation detection |
| CN110334308A (en) * | 2019-04-23 | 2019-10-15 | 中国电子科技集团公司第二十四研究所 | A kind of calculation method optimizing bias condition in voltage sweep parameter testing |
| CN110458380A (en) * | 2019-06-18 | 2019-11-15 | 广州明珞汽车装备有限公司 | A kind of calculation method, system and the storage medium of production technology temporal reference value |
| CN111078974A (en) * | 2019-12-10 | 2020-04-28 | 苏州朗动网络科技有限公司 | Method, device and storage medium for detecting abnormal news volume in real time |
| US20200201311A1 (en) * | 2018-12-19 | 2020-06-25 | Shenzhen Jingjiang Yunchuang Technology Co., Ltd. | Equipment maintenance method, equipment maintenance device, and storage medium for the same |
Family Cites Families (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20110145811A1 (en) * | 2010-06-29 | 2011-06-16 | Joerg Middendorf | Method and system for software update of wind energy hardware components |
| CN104793270B (en) * | 2015-04-14 | 2017-06-13 | 中国气象局气象探测中心 | A kind of synthetic determination method of equipment running status in Integrated Meteorological Observation net |
| CN108319568B (en) * | 2018-03-06 | 2021-08-06 | 江苏中科瀚星数据科技有限公司 | A method for locating abnormal equipment status |
| CN108764498B (en) * | 2018-05-25 | 2020-12-29 | 安徽领云物联科技有限公司 | Method and device for acquiring health index of equipment |
| CN111949941B (en) * | 2020-07-03 | 2023-03-03 | 广州明珞汽车装备有限公司 | Equipment fault detection method, system, device and storage medium |
-
2020
- 2020-07-03 CN CN202010635093.0A patent/CN111949941B/en active Active
- 2020-12-29 WO PCT/CN2020/140970 patent/WO2022001060A1/en active Application Filing
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105894329A (en) * | 2016-03-31 | 2016-08-24 | 乐视控股（北京）有限公司 | Data analysis method and device |
| CN108509634A (en) * | 2018-04-10 | 2018-09-07 | 深信服科技股份有限公司 | Jitterbug monitoring method, monitoring device and computer readable storage medium |
| US20200201311A1 (en) * | 2018-12-19 | 2020-06-25 | Shenzhen Jingjiang Yunchuang Technology Co., Ltd. | Equipment maintenance method, equipment maintenance device, and storage medium for the same |
| CN109711570A (en) * | 2018-12-26 | 2019-05-03 | 中国移动通信集团江苏有限公司 | Method, device, device and medium for equipment operation detection |
| CN110334308A (en) * | 2019-04-23 | 2019-10-15 | 中国电子科技集团公司第二十四研究所 | A kind of calculation method optimizing bias condition in voltage sweep parameter testing |
| CN110458380A (en) * | 2019-06-18 | 2019-11-15 | 广州明珞汽车装备有限公司 | A kind of calculation method, system and the storage medium of production technology temporal reference value |
| CN111078974A (en) * | 2019-12-10 | 2020-04-28 | 苏州朗动网络科技有限公司 | Method, device and storage medium for detecting abnormal news volume in real time |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2022001060A1 (en) * | 2020-07-03 | 2022-01-06 | 广州明珞装备股份有限公司 | Device fault detection method, system and apparatus, and storage medium |
| CN113065979A (en) * | 2021-03-22 | 2021-07-02 | 贵州电网有限责任公司 | Load report improving and self-checking method for dispatching automation system |
| CN114625088A (en) * | 2022-03-15 | 2022-06-14 | 广州明珞装备股份有限公司 | Efficiency optimization method, system, equipment and storage medium of automatic production line |
| CN114637264A (en) * | 2022-03-15 | 2022-06-17 | 广州明珞装备股份有限公司 | Motion stability analysis method, system, device and storage medium |
| CN114625088B (en) * | 2022-03-15 | 2024-02-02 | 广州明珞装备股份有限公司 | Efficiency optimization method, system, equipment and storage medium for automatic production line |
| CN114637264B (en) * | 2022-03-15 | 2024-05-17 | 广州明珞装备股份有限公司 | Motion stability analysis method, system, device and storage medium |
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