CN113344468A - Process quality control analysis platform based on industrial big data - Google Patents
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Abstract
The invention provides a process quality control analysis platform based on industrial big data, which comprises a plurality of single-point monitoring sensors which are used for acquiring process parameters of a target product positioned in the range of a sampling cavity and can move along a target production line; the platform also comprises a data processing system which is used for receiving the process parameters acquired by the single-point monitoring sensor and executing process quality control analysis; generating a position scheduling instruction and a sampling quantity regulating instruction based on a process quality control analysis result, wherein the position scheduling instruction is used for scheduling a first quantity of single-point monitoring sensors to move according to a target strategy; the sampling number adjustment command is used for instructing the second number of sampling cavities to change the space size of the sampling cavities. The technical scheme of the invention can adjust the sampling position and the sampling number based on the analysis result of the process quality. Meanwhile, based on the characteristics of industrial big data, the invention can realize the uniform full stack processing of high-frequency data locally, avoid frequent data transmission for many times and reduce the pressure of big data transmission and processing.
Description
Technical Field
The invention belongs to the technical field of big data quality analysis, and particularly relates to a process quality control analysis platform and method based on industrial big data and a computer program instruction medium for realizing the method.
Background
Big data (big data) is a powerful means for manufacturing to improve core capability, integrate industry chain, and implement transformation from element drive to innovation drive. For a manufacturing enterprise, the big data can be used to not only improve the operation efficiency of the enterprise, but also change the business flow and business model through the capability provided by the new generation information technology such as big data.
Industrial big data (Industrial big data) refers to a large amount of data generated by Industrial equipment at a high speed, corresponds to equipment states at different times, and is information in the internet of things. The industrial big data is a general term of the product and service life cycle data in the industrial field, and comprises data generated and used by industrial enterprises in the links of research, development, manufacture, management, operation and maintenance service and the like, data in an industrial internet platform and the like. Industrial big data also means that large amounts of data produced by industrial equipment have its potential commercial value. At present, manufacturing enterprises are difficult to realize production process management and control and problem analysis under the states of mixed data types, strong association coupling, low information density and large time span based on the traditional process quality management and control modes of report analysis, statistical analysis, trend graph checking and the like of surface information, and the rules and values hidden in data are difficult to effectively explore and utilize. The big data mining model does not depend on the characteristic of accurate mathematical relation, is suitable for quality control and analysis based on statistical rules, and can reduce the dependence on the experience of people through the application of the model.
The Chinese invention patent application with the application number of CN202011336933.X provides a production full-process visual intelligent control method based on big data, which comprises the following steps: acquiring production line data of the cylinder block assembly in the manufacturing process, wherein the production line data is acquired through a plurality of data sources; performing data fusion on the production line data to obtain fused data; and generating management data according to the fusion data. According to the big data-based production full-flow visual intelligent control method, data fusion processing and intelligent analysis are carried out by collecting multi-source heterogeneous production line data, and effective management of manufacturing big data is achieved. Through the relationship between the operation data of the deep mining equipment and the product processing data, the whole process data of the product is visually displayed, auxiliary decisions are provided for enterprise managers, and the data value is fully realized.
The Chinese patent publication with publication number CN112884241A proposes a cloud-edge collaborative manufacturing task scheduling method based on an intelligent Agent, which comprises the following steps: s1: inputting a manufacturing task to be scheduled into a constructed target decision model; s2: the cloud computing module decomposes the manufacturing task into a plurality of subtasks; then, the service quality indexes of all the subtasks are restricted to obtain a preliminary scheduling scheme; s3: the edge management and control module detects the disturbance condition of each production line: if a certain production line has fault disturbance, the process goes to step S4; if no fault disturbance exists in each production line, the step S5 is carried out; s4: the cloud computing module performs secondary constraint on the service quality index of the subtask corresponding to the production line to obtain a rescheduling scheme; finally, returning to step S3; s5: scheduling of the manufacturing task is complete. According to the cloud-side collaborative manufacturing task scheduling method based on the intelligent Agent, the constraint on the service quality index and the adjustment on the disturbance can be realized.
However, the industrial big data acquisition and processing in the prior art are based on a static data acquisition mode and cannot be dynamically and intelligently adapted to change along with the change of a real-time acquisition result; meanwhile, a large amount of frequent interaction exists between field data and background processing, massive data transmission amount is caused in a big data environment, and the pressure of data transmission and processing is huge for systems and personnel.
Disclosure of Invention
In order to solve the technical problems, the invention provides a process quality control analysis platform and method based on industrial big data and a computer program instruction medium for realizing the method.
In general, the implementation principle of the technical scheme of the invention is as follows:
(1) acquiring technological parameters of a target product on a target production line;
(2) performing process quality control analysis on the obtained process parameters;
(3) generating a regulation and control and scheduling instruction based on the process quality control analysis result;
(4) and responding to the regulation and control instruction, and reacquiring the process parameters.
In order to realize the technical principle, the invention can realize the following technical schemes in three aspects:
in a first aspect of the present invention, a process quality management and control analysis platform based on industrial big data is provided, where the platform includes a plurality of single-point monitoring sensors, and the plurality of single-point monitoring sensors are used to acquire process parameters of a target product on a target production line.
More specifically, a plurality of sampling cavities are arranged on the target production line, each sampling cavity is provided with a sensing interface, and after the single-point monitoring sensor is connected to the sensing interface, the process parameters of a target product in the range of the sampling cavity are obtained;
at least some of the plurality of single point monitoring sensors are movable along the target production line;
the platform further comprises a data processing system;
the data processing system receives the process parameters acquired by the single-point monitoring sensor and executes process quality control analysis;
based on the process quality control analysis result, the data processing system generates a position scheduling instruction and a sampling quantity regulating instruction;
the position scheduling instruction is used for scheduling a first number of single-point monitoring sensors to move according to a target strategy;
the goal strategy maximizes the frequency at which the first number of single point monitoring sensors directly send process parameters to the data processing system.
The sampling number regulating instruction is used for instructing the second number of sampling cavities to change the space size of the sampling cavities.
Wherein the single point monitoring sensor configures a local data storage stack; at least one of the local data storage stacks of the single point monitoring sensor configuration to which the sensing interfaces of the second number of sampling chambers are connected is in a full stack state.
In a second aspect of the present invention, a process quality management and control analysis method based on industrial big data is provided, and the method is implemented based on the process quality management and control analysis platform based on industrial big data in the first aspect.
The method for implementing the foregoing principles generally comprises the steps of:
(1) acquiring technological parameters of a target product on a target production line;
(2) performing process quality control analysis on the obtained process parameters;
(3) generating a regulation and control and scheduling instruction based on the process quality control analysis result;
(4) and responding to the regulation and control instruction, and reacquiring the process parameters.
The method of the second aspect may be performed automatically by program instructions executed by a terminal device comprising a processor and a memory, especially an image processing terminal device, including a mobile terminal, a desktop terminal, a server cluster, and the like, and therefore, in a third aspect of the present invention, there is also provided a computer readable storage medium having computer program instructions stored thereon; the program instructions are executed by an image terminal processing device comprising a processor and a memory for implementing all or part of the steps of the method. The processor and the memory are connected through a bus to form internal communication of the terminal equipment.
Generally speaking, the technical scheme of the invention can adjust the sampling position and the sampling number based on the analysis result of the process quality. Meanwhile, based on the characteristics of industrial big data, the invention can realize the uniform full stack processing of high-frequency data locally, avoid frequent data transmission for many times and reduce the pressure of big data transmission and processing.
Further advantages of the invention will be apparent in the detailed description section in conjunction with the drawings attached hereto.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
FIG. 1 is a schematic structural diagram of a process quality control analysis platform based on industrial big data according to an embodiment of the present invention
FIG. 2 is a schematic structural diagram of a single-point monitoring sensor related to the industrial big data based process quality control analysis platform shown in FIG. 1
FIG. 3 is a schematic diagram of data interaction between a single-point monitoring sensor and a data processing system of the industrial big data based process quality control analysis platform shown in FIG. 1
FIG. 4 is a schematic diagram of the overall working principle of the industrial big data based process quality control analysis platform shown in FIG. 1
FIG. 5 is a main flow chart of a process quality control analysis method based on industrial big data implemented by the process quality control analysis platform based on industrial big data shown in FIG. 1
FIG. 6 is a schematic diagram of a computer-readable storage medium and a terminal device for implementing the method flow of FIG. 5
Detailed Description
The invention is further described with reference to the following drawings and detailed description.
Fig. 1 is a schematic structural diagram of a process quality control analysis platform based on industrial big data according to an embodiment of the present invention.
In fig. 1, a target production line on which a plurality of target products are conveyed is schematically shown; a plurality of sampling cavities are arranged on the target production line, and each sampling cavity is provided with a sensing interface.
Preferably, the sampling cavity is relatively separated from the target production line.
More specifically, the sampling chambers are arranged on two sides of the target production line, and one or more target products are grabbed or sampled from the target production line through a mechanical arm or other sampling means. The quantity of the target products to be grabbed or sampled at each time is determined by the current space size of the sampling cavity.
Preferably, in various embodiments of the present invention, the spatial size of the sampling cavity is variable.
The platform includes a plurality of single-point monitoring sensors for acquiring process parameters of a target product on a target production line.
It should be noted that the sensor is a single-point monitoring sensor, and single-point monitoring means that the sensor can only perform data acquisition at one location point at a time, and cannot simultaneously acquire sampling data of multiple locations.
And after the single-point monitoring sensor is connected to the sensing interface, acquiring the process parameters of the target product within the range of the sampling cavity.
The process parameter is a parameter related to the process quality of the target product, and is related to the type of the target product. For example, various actual parameters such as the size, shape, color, weight, and three-dimensional image of the target product may be included, which is not limited in this embodiment.
It is worth pointing out that, as an important improvement of the present invention, not only the spatial size of the sampling chamber is variable, but also at least a part of the plurality of single-point monitoring sensors can be moved along the target production line.
In fig. 1, it is also highlighted that the platform further comprises a data processing system;
the data processing system receives the process parameters acquired by the single-point monitoring sensor and executes process quality control analysis;
and based on the process quality control analysis result, the data processing system generates a position scheduling instruction and/or a quantity control instruction.
If the single-point monitoring sensors contain the position scheduling instructions, the position scheduling instructions are used for scheduling a first number of single-point monitoring sensors to move according to a target strategy;
the sampling number adjustment command is used to instruct a second number of sampling chambers to change their spatial size if included.
To further embody the platform described in fig. 1, on the basis of fig. 1, see fig. 2.
In the embodiment of fig. 2, the single point monitoring sensor also configures a local data recognition model and a local data store in which a local data storage stack may be created.
A stack (stack) is a data structure. A stack is a data structure in which data items are arranged in order, and data items can only be inserted and deleted at one end, called the top of the stack (top).
Specifically, a stack is a particular memory area or register that is fixed on one end and floating on the other. The data stored in this storage area of the heap is a special data structure. All data storage or retrieval can only be performed at one floating end (called the top of the stack), and access is strictly performed according to the principle of "first-in last-out", and elements located in the middle of the floating end must be removed one by one after elements at the upper part of the stack (last-in-stack). Opening an area in an internal memory (random access memory) as a stack called a software stack; the stack formed by registers is called a hardware stack.
The invention adopts stacks instead of arrays or queues and other structures, which can obviously reduce the pressure of big data transmission and processing, and the effect of the invention will be further described in the following embodiments.
In FIG. 2, the single point monitoring sensor has different monitoring (sampling) modes, each sampling mode collecting a different process parameter;
as mentioned above, the different process parameters include various actual parameters such as size, shape, color, weight, and three-dimensional image of the target product, and the single-point monitoring sensor can obtain different types of process parameters in different monitoring modes (also called sampling modes).
After the process parameters are obtained, the single-point monitoring sensor judges whether the process parameters obtained in the current sampling mode are abnormal or not through the local data identification model.
Then, the data transmission and transmission stage of the present invention is entered, referring to fig. 3, fig. 3 shows a data interaction diagram of a single-point monitoring sensor and a data processing system of a process quality management and control analysis platform based on industrial big data.
In fig. 3, the single-point monitoring sensor sends the process parameters obtained in the current sampling mode to the local data identification model;
the local data identification model judges whether the process parameters obtained in the current sampling mode are abnormal or not;
and if the process parameters obtained by the single-point monitoring sensor in the current mode are abnormal, directly sending the abnormal process parameters to the data processing system.
If the process parameters obtained by the single-point monitoring sensor in the current mode are not abnormal, a local data storage stack is created in the local data storage, and the process parameters without abnormality are stored to the local data storage stack.
Then, continuously judging whether the local data storage stack is full,
and if the stack is full, all the process parameters stored in the local data storage stack are sent to the data processing system at one time.
The inventor notices that, in the actual production field, most target products on the target production line are qualified products, and the technological parameters of the target products meet the preset standard, namely, no abnormity exists; in practice, only a small fraction of the target product has process parameters that are abnormal.
In the prior art, all process parameters can be monitored in real time, but various data are continuously transmitted and transmitted after being collected, so that the data transmission cost and the data transmission pressure are high.
In order to improve such a problem, in this embodiment, if the process parameter obtained by the single-point monitoring sensor in the current mode is abnormal, the abnormal process parameter is directly sent to the data processing system;
if the process parameters obtained by the single-point monitoring sensor in the current mode are not abnormal, a local data storage stack is created in the local data storage, the process parameters without abnormality are stored to the local data storage stack, and the process parameters are not sent once until the stack is full.
Due to the adoption of the stack structure for storage, the space size of the stack structure can be set in advance according to the current acquisition mode of the current single-point monitoring sensor, so that the data transmission frequency is obviously reduced, and the data transmission pressure is reduced.
More importantly, as a further preferred option, although only a small number of process parameters of the target product are actually abnormal, the inventors further found that, once the process parameters of a certain target product are monitored to be abnormal at a certain moment, it is likely that other target products at the same moment or in a related time period are also abnormal, and at this moment, the process parameters of a corresponding number of target products at the moment and corresponding positions need to be focused, that is, the working state of the monitoring sensor needs to be changed.
In the prior art, a static mode is always adopted and is unchanged, and the change cannot be adapted to.
In the invention, further creative improvements are made to the method, which is specifically embodied in that:
and based on the process quality control analysis result, the data processing system generates a position scheduling instruction and/or a quantity control instruction.
In one aspect, the location scheduling instructions are operable to schedule a first number of single point monitoring sensors to move according to a target policy.
Preferably, the targeting strategy maximizes the frequency with which the first number of single point monitoring sensors send process parameters to the data processing system.
As another preference, the target policy is such that the local data storage stacks of the first number of single point monitoring sensor configurations are not full of stacks within a target time period;
the target time period is determined based on a frequency at which the single point monitoring sensor obtains a process parameter in a current mode.
In another aspect, the sampling number adjustment instructions are operable to instruct a second number of sampling chambers to change their spatial size.
Preferably, at least one of the local data storage stacks of the single point monitoring sensor configuration to which the sensing interfaces of the second number of sampling chambers are connected is in a full stack state.
In general, the main principles of the technical solutions implemented by the embodiments shown in fig. 1 to 3 include:
acquiring process parameters, performing process quality control analysis, and generating position scheduling instructions and/or quantity control instructions, which are summarized in the flowchart of fig. 4.
In the flow of fig. 4, the flow may be further described as follows:
the method comprises the steps of obtaining technological parameters of target products on a target production line, executing technological quality control analysis on the obtained technological parameters, generating control and scheduling instructions based on the technological quality control analysis result, and responding to the control and scheduling instructions to obtain the technological parameters again.
Based on the description of fig. 4, the embodiment of fig. 5 can also be implemented as a process quality management and control analysis method based on industrial big data, where the method is implemented based on the process quality management and control analysis platform based on industrial big data as described in fig. 1.
The method illustrated in fig. 5 is divided into 10 steps, including S100-S105 and S107-S110, and each step is implemented as follows:
s100: the single-point monitoring sensor is connected to the sensing interface to acquire the process parameters of the target product within the range of the sampling cavity;
s101: sending the process parameters to a local data identification model;
s102: judging whether the process parameters are abnormal or not, and if not, entering the step S102; otherwise, go to step S107;
s103: setting the size of a stack according to the current acquisition mode of the current single-point monitoring sensor, and building a local data storage stack;
s104: sending the process parameters to a local data storage stack;
s105: judging whether the stack is full; if yes, go to step S107; otherwise, returning to the step S104;
s107: sending the process parameters to a data processing system;
s108: the data processing system receives the process parameters acquired by the single-point monitoring sensor and executes process quality control analysis;
s109: based on the process quality control analysis result, the data processing system generates a position scheduling instruction and/or a quantity control instruction;
s110: the first number of single point monitoring sensors are scheduled to move according to the target strategy and/or the second number of sampling chambers are instructed to change their spatial size, returning to step S100.
Although not shown, as a further preferred, the method includes a step S106, the step S106 is located after the step S105, and the step S106 specifically includes: the local data storage stack is reclaimed.
The method of fig. 5 may be performed automatically by a terminal device comprising a processor and a memory, in particular an image processing terminal device, including a mobile terminal, a desktop terminal, a server cluster, etc., through program instructions.
Thus, referring to fig. 6, the present embodiment also provides a computer-readable storage medium having stored thereon computer program instructions; the program instructions are executed by an image terminal processing device comprising a processor and a memory for implementing all or part of the steps of the method. The processor and the memory are connected through a bus to form internal communication of the terminal equipment.
Compared with the prior art, the invention has the advantages that:
(1) sampling a target product through a sampling cavity with changeable size, and changing the size of the sampling cavity based on a regulation instruction so as to dynamically change the sampling number;
(2) the method comprises the steps that process parameters are obtained through a single-point monitoring sensor at a movable position, and the position is changed based on a scheduling instruction, so that the sampling position is dynamically changed;
(3) the single-point monitoring sensor is provided with a local data identification model, and process parameters are sent in different modes based on a local data identification result, so that the frequency of data sending is avoided;
(4) the single-point monitoring sensor has different acquisition modes, stacks with different sizes can be newly built in the different acquisition modes, and data transmission pressure is reduced by means of uniformly sending data when the stacks are full.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (10)
1. A technology quality management and control analysis platform based on industry big data, the platform includes a plurality of single-point monitoring sensor, a plurality of single-point monitoring sensor are used for obtaining the technological parameter of target product on the target production line, its characterized in that:
a plurality of sampling cavities are arranged on the target production line, each sampling cavity is provided with a sensing interface, and after the single-point monitoring sensor is connected to the sensing interface, the process parameters of a target product within the range of the sampling cavity are obtained;
at least some of the plurality of single point monitoring sensors are movable along the target production line;
the platform further comprises a data processing system;
the data processing system receives the process parameters acquired by the single-point monitoring sensor and executes process quality control analysis;
based on the process quality control analysis result, the data processing system generates a position scheduling instruction, and the position scheduling instruction is used for scheduling a first number of single-point monitoring sensors to move according to a target strategy.
2. The industrial big data-based process quality management and control analysis platform as claimed in claim 1, wherein:
the sampling cavity is relatively separated from the target production line, and the space size of the sampling cavity is variable.
3. The industrial big data-based process quality management and control analysis platform as claimed in claim 2, wherein:
based on the process quality control analysis results, the data processing system generates a sampling number adjustment and control instruction for instructing a second number of sampling chambers to change their spatial size.
4. The industrial big data-based process quality management and control analysis platform as claimed in any one of claims 1-3, wherein:
the single-point monitoring sensor has different monitoring modes, and each sampling mode acquires different process parameters;
the single-point monitoring sensor is also provided with a local data identification model, and whether the process parameters obtained in the current sampling mode are abnormal or not is judged through the local data identification model.
5. The industrial big data-based process quality management and control analysis platform as claimed in claim 4, wherein:
and if the process parameters obtained by the single-point monitoring sensor in the current mode are abnormal, directly sending the abnormal process parameters to the data processing system.
6. The industrial big data-based process quality management and control analysis platform as claimed in claim 4, wherein:
the single-point monitoring sensor is also provided with a local data storage stack;
and if the process parameters obtained by the single-point monitoring sensor in the current mode are not abnormal, storing the process parameters without abnormality to the local data storage stack.
7. The industrial big data-based process quality management and control analysis platform as claimed in any one of claims 1-3 or 5-6, wherein:
the target strategy maximizes a frequency at which the first number of single-point monitoring sensors send process parameters to the data processing system.
8. The industrial big data-based process quality management and control analysis platform of claim 6, wherein:
the target policy causes the local data storage stacks of the first number of single point monitoring sensor configurations to be less than full within a target time period;
the target time period is determined based on a frequency at which the single point monitoring sensor obtains a process parameter in a current mode.
9. The industrial big data-based process quality management and control analysis platform as claimed in claim 3, wherein:
the single-point monitoring sensor configures a local data storage stack;
at least one of the local data storage stacks of the single point monitoring sensor configuration to which the sensing interfaces of the second number of sampling chambers are connected is in a full stack state.
10. A process quality control analysis method based on industrial big data, which is implemented based on the process quality control analysis platform based on industrial big data of any one of claims 1-9.
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Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20120311307A1 (en) * | 2011-05-31 | 2012-12-06 | Chynoweth Michael W | Method And Apparatus For Obtaining A Call Stack To An Event Of Interest And Analyzing The Same |
| CN109386839A (en) * | 2018-09-12 | 2019-02-26 | 福州众联信息科技有限公司 | A kind of online coal-fired monitoring management method and system based on technology of Internet of things |
| CN111008756A (en) * | 2019-11-06 | 2020-04-14 | 重庆仙桃前沿消费行为大数据有限公司 | Transportation scheduling method, system and computer readable storage medium |
| KR102094639B1 (en) * | 2019-05-30 | 2020-05-04 | 박종문 | Factory manufacturing line management system, and method thereof |
| CN111580487A (en) * | 2020-05-30 | 2020-08-25 | 宁波伟立机器人科技股份有限公司 | Intelligent monitoring control system for industrial production line |
| CN112541729A (en) * | 2020-11-25 | 2021-03-23 | 中国海洋大学 | Big data based visual intelligent management and control method and system for production whole process |
| CN113177698A (en) * | 2021-04-12 | 2021-07-27 | 北京科技大学 | Industrial big data analysis aid decision platform system |
-
2021
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Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20120311307A1 (en) * | 2011-05-31 | 2012-12-06 | Chynoweth Michael W | Method And Apparatus For Obtaining A Call Stack To An Event Of Interest And Analyzing The Same |
| CN109386839A (en) * | 2018-09-12 | 2019-02-26 | 福州众联信息科技有限公司 | A kind of online coal-fired monitoring management method and system based on technology of Internet of things |
| KR102094639B1 (en) * | 2019-05-30 | 2020-05-04 | 박종문 | Factory manufacturing line management system, and method thereof |
| CN111008756A (en) * | 2019-11-06 | 2020-04-14 | 重庆仙桃前沿消费行为大数据有限公司 | Transportation scheduling method, system and computer readable storage medium |
| CN111580487A (en) * | 2020-05-30 | 2020-08-25 | 宁波伟立机器人科技股份有限公司 | Intelligent monitoring control system for industrial production line |
| CN112541729A (en) * | 2020-11-25 | 2021-03-23 | 中国海洋大学 | Big data based visual intelligent management and control method and system for production whole process |
| CN113177698A (en) * | 2021-04-12 | 2021-07-27 | 北京科技大学 | Industrial big data analysis aid decision platform system |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117270480A (en) * | 2023-11-21 | 2023-12-22 | 深圳市天兴诚科技有限公司 | Production line monitoring method and system |
| CN117270480B (en) * | 2023-11-21 | 2024-02-09 | 深圳市天兴诚科技有限公司 | Production line monitoring method and system |
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