CN117195460A - Cold-rolled product sampling method and system based on dynamic programming algorithm - Google Patents
Cold-rolled product sampling method and system based on dynamic programming algorithm Download PDFInfo
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- 238000005070 sampling Methods 0.000 title claims abstract description 202
- 238000000034 method Methods 0.000 title claims abstract description 60
- 229910000831 Steel Inorganic materials 0.000 claims abstract description 202
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- 238000005457 optimization Methods 0.000 claims abstract description 67
- 238000013461 design Methods 0.000 claims abstract description 53
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- 238000012360 testing method Methods 0.000 claims abstract description 41
- 239000000463 material Substances 0.000 claims abstract description 35
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- 238000001514 detection method Methods 0.000 claims abstract description 20
- 238000005097 cold rolling Methods 0.000 claims abstract description 12
- 239000002436 steel type Substances 0.000 claims abstract description 10
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- 238000009713 electroplating Methods 0.000 description 2
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- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 201000009240 nasopharyngitis Diseases 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
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Abstract
The application provides a cold rolling product sampling method and system based on a dynamic programming algorithm, comprising the following steps: the method comprises the steps of managing grouping rules of material steel types and determining steel coils which can participate in sampling optimization; judging the manufacturing process of each single coil of the steel coil which can participate in sampling optimization, and outputting sampling data to the steel coil which is successfully sampled and optimized; matching or newly creating test lot groups to form a relation between the steel coil and the test lot, and using the relation as key index information for issuing a follow-up sampling instruction and judging the performance of the steel coil. The application realizes the automatic judgment of the material related preamble process technology, the function of dynamically optimizing the design of the sampling detection instruction based on the process technology by a single roll, and the batch sampling detection function based on different conditions.
Description
Technical Field
The application relates to a design of sampling requirements of cold-rolled products in the metallurgical steel industry, in particular to a method and a system for sampling the cold-rolled products based on a dynamic programming algorithm.
Background
The performance detection link is an important link for ensuring that the factory performance meets the use requirement of a user, the detection performance of the performance index is the digital representation of the product quality, and the performance detection link has an important position in the product quality management. But on the other hand, the quality is produced instead of detected, a large amount of equipment and manpower are required for product detection, and the quality is an important component of production cost.
In the prior art, under the current product design framework, the sampling design of the product is the unified design aiming at contracts and production lines, and because the processes of steelmaking, hot rolling, cold rolling and the like tend to have fluctuation and instability in the actual manufacturing process, in order to ensure that the performance of the whole coil meets the requirements, a plurality of positions of the same coil can be detected at the same time only according to the most severe standard, but for the steel coil with very stable whole coil process, the performance of the whole coil is very similar, and the detection of the plurality of positions is redundant. For steel coils of the same steel grade produced in batches, if the process is kept stable, the performances of a plurality of coils are very similar, and the results of detection according to the same batch can theoretically represent the performances of all batch coils on the premise of standard permission.
In these cases, the redundant sampling test brings about an increase in the cost of the test, without a significant benefit to the quality stabilization. Therefore, it is very necessary to systematically optimize the sampling design module of the product, change the single sampling instruction design aiming at the contract production line at present, but realize the personalized sampling instruction design based on materials according to the evaluation of the process stability of the material process, and realize the batch sampling design of a plurality of process same rolls at the same time, thereby minimizing the sample detection quantity and reducing the detection cost on the premise of meeting the quality requirement.
Disclosure of Invention
Aiming at the defects in the prior art, the application aims to provide a cold-rolled product sampling method and system based on a dynamic programming algorithm.
The application provides a cold rolling product sampling system based on a dynamic programming algorithm, which comprises the following components:
material steel grade grouping module M1: the method comprises the steps of managing grouping rules of material steel types and determining steel coils which can participate in sampling optimization;
and a single-roll manufacturing process judging and sampling optimizing module M2: judging the manufacturing process of each single coil of the steel coil which can participate in sampling optimization, and outputting sampling data to the steel coil which is successfully sampled and optimized;
multi-reel batch sampling design module M3: matching or newly creating test lot groups to form a relation between the steel coil and the test lot, and using the relation as key index information for issuing a follow-up sampling instruction and judging the performance of the steel coil.
Preferably, the material steel grade grouping module M1 comprises:
submodule M1.1: acquiring standard, link and actual performance data related to the steel coil indicated by the steel coil number as input parameters according to the data value logic aiming at the received steel coil number;
submodule M1.2: according to the input parameters, performing material steel grade grouping logic rules on the steel coil to obtain matched grouping numbers;
submodule M1.3: and judging whether the steel coil can participate in sampling optimization according to a logic judgment rule corresponding to the grouping number, if so, triggering a single coil manufacturing process judgment and sampling optimization module M2 to continue execution, and if not, conventionally circulating according to the original sampling design requirement of the contract.
Preferably, the single-roll manufacturing process differentiating and sampling optimizing module M2 includes:
submodule M2.1: collecting and outputting various relevant data of the steel coil which can participate in sampling optimization;
submodule M2.2: judging whether abnormality exists according to the logic rule in the group of the steel coil, and outputting the result: if the abnormal condition exists, judging that sampling optimization fails, and conventionally circulating according to the original sampling design requirement of the contract; if no abnormality exists, the sampling optimization is judged to be successful, and the sampling block number data of the head part, the middle part and the tail part of the sampling steel coil are output.
Preferably, the multi-volume batch sampling design module M3 includes:
submodule M3.1: collecting and outputting various related data used by the same batch of steel coil conditions;
submodule M3.2: calculating the same batch conditions according to the input group numbers and related various data used by the same batch conditions of the steel coil, and preferentially judging whether the steel coil can be added into the existing test batch as the represented steel coil; if the current steel coil is not matched with the representative coil, a new trial batch is created, and the current steel coil is directly used as the representative coil; if the data are matched, automatically recalculating the data of the number of the represented steel coiling sample blocks and the total sample block number in the existing test batch; forming a relation between the steel coil and the trial batch as key index information for judging the performance of the steel coil issued by a subsequent sampling instruction; the related various data comprise sampling block number data of the head part, the middle part and the tail part of the sampling steel coil output by the submodule M2.2.
Preferably, the method further comprises:
an interface module: the system is used for starting intelligent sampling design among a plurality of systems of a manufacturing management system, a manufacturing execution system and an inspection and test management system, transmitting information such as single-coil sample optimizing results, a plurality of coils of same batch relations and the like consistently, linking an upper system and a lower system, guiding on-site sampling sample feeding and laboratory template registration management, and collecting and judging performance data to process the follow-up procedures; the calculation result of the product performance prediction model and the online detection recommendation result are supported to be used as judging conditions for participating in single coiled sample optimization design, so that seamless integration is realized.
The application provides a cold rolling product sampling method based on a dynamic programming algorithm, which comprises the following steps:
material steel grade grouping step S1: the method comprises the steps of managing grouping rules of material steel types and determining steel coils which can participate in sampling optimization;
judging and sampling optimization step S2 of single-roll manufacturing process: judging the manufacturing process of each single coil of the steel coil which can participate in sampling optimization, and outputting sampling data to the steel coil which is successfully sampled and optimized;
step S3 of multi-roll batch sampling design: matching or newly creating test lot groups to form a relation between the steel coil and the test lot, and using the relation as key index information for issuing a follow-up sampling instruction and judging the performance of the steel coil.
Preferably, the grouping step S1 of the steel grades of materials includes:
substep S1.1: acquiring standard, link and actual performance data related to the steel coil indicated by the steel coil number as input parameters according to the data value logic aiming at the received steel coil number;
substep S1.2: according to the input parameters, performing material steel grade grouping logic rules on the steel coil to obtain matched grouping numbers;
substep S1.3: and judging whether the steel coil can participate in sampling optimization according to a logic judgment rule corresponding to the grouping number, if so, triggering the judging and sampling optimization step S2 of the single coil manufacturing process to continue execution, and if not, requiring routine circulation according to the original sampling design of the contract.
Preferably, the single-roll manufacturing process differentiating and sampling optimizing step S2 includes:
substep S2.1: collecting and outputting various relevant data of the steel coil which can participate in sampling optimization;
substep S2.2: judging whether abnormality exists according to the logic rule in the group of the steel coil, and outputting the result: if the abnormal condition exists, judging that sampling optimization fails, and conventionally circulating according to the original sampling design requirement of the contract; if no abnormality exists, the sampling optimization is judged to be successful, and the sampling block number data of the head part, the middle part and the tail part of the sampling steel coil are output.
Preferably, the multi-volume batch sampling design step S3 includes:
substep S3.1: collecting and outputting various related data used by the same batch of steel coil conditions;
substep S3.2: calculating the same batch conditions according to the input group numbers and related various data used by the same batch conditions of the steel coil, and preferentially judging whether the steel coil can be added into the existing test batch as the represented steel coil; if the current steel coil is not matched with the representative coil, a new trial batch is created, and the current steel coil is directly used as the representative coil; if the data are matched, automatically recalculating the data of the number of the represented steel coiling sample blocks and the total sample block number in the existing test batch; forming a relation between the steel coil and the trial batch as key index information for judging the performance of the steel coil issued by a subsequent sampling instruction; the related various data comprise sampling block number data of the head part, the middle part and the tail part of the sampling steel coil output in the substep S2.2.
Preferably, the method further comprises:
an interface step: the system is used for starting intelligent sampling design among a plurality of systems of a manufacturing management system, a manufacturing execution system and an inspection and test management system, transmitting information such as single-coil sample optimizing results, a plurality of coils of same batch relations and the like consistently, linking an upper system and a lower system, guiding on-site sampling sample feeding and laboratory template registration management, and collecting and judging performance data to process the follow-up procedures; the calculation result of the product performance prediction model and the online detection recommendation result are supported to be used as judging conditions for participating in single coiled sample optimization design, so that seamless integration is realized.
Compared with the prior art, the application has the following beneficial effects:
1. the application realizes the automatic judgment of the material related preamble process technology by establishing the judging sampling optimizing grouping rule of the whole process consistent manufacturing process of the cold rolled product.
2. The application establishes process technology distinguishing rules of cold rolling different steel grades, and further realizes the function of dynamically optimizing the design of the sampling detection instruction based on the process technology by a single coil through a system on the basis.
3. According to the application, the batch sampling detection function based on different conditions is further realized through the system on the basis of establishing a sample group generation rule for sampling a plurality of rolls of cold rolled different steel grades according to batches.
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Other features, objects and advantages of the present application will become more apparent upon reading of the detailed description of non-limiting embodiments, given with reference to the accompanying drawings in which:
FIG. 1 is a schematic diagram of a cold rolled product intelligent sampling flow.
Detailed Description
The present application will be described in detail with reference to specific examples. The following examples will assist those skilled in the art in further understanding the present application, but are not intended to limit the application in any way. It should be noted that variations and modifications could be made by those skilled in the art without departing from the inventive concept. These are all within the scope of the present application.
The application provides a cold rolling product sampling system based on a dynamic programming algorithm, which comprises the following components: the system comprises a material steel grade grouping module M1, a single-roll manufacturing process distinguishing and sampling optimizing module M2 and a multi-roll batch sampling design module M3.
The material steel grade grouping module M1: and managing grouping rules of the material steel types. Specifically, the material steel grade grouping module M1 includes:
submodule M1.1: according to the received steel coil number and the data value logic, acquiring the data of the standard, the link and the actual performance related to the steel coil indicated by the steel coil number as the input parameters;
submodule M1.2: according to the input parameters, performing material steel grade grouping logic rules on the steel coil to obtain matched grouping numbers;
submodule M1.3: and judging whether the steel coil can participate in sampling optimization according to a logic judgment rule corresponding to the grouping number, if so, triggering a single coil manufacturing process judgment and sampling optimization module M2 to continue execution, and if not, conventionally circulating according to the original sampling design requirement of the contract.
A key item list and a data value logic of each key item are established in advance, and cold rolling products such as ordinary cooling, hot plating, electroplating, tinning, chromeplating, acid washing and the like are managed in groups; defining a packet number of a packet and packet key tag contents; compiling logic judgment rules for each group number; and automatically judging whether each steel coil can participate in sampling optimization or not and belonging grouping numbers according to the characteristic values of the key items.
For a hot dip coating and continuous annealing unit, a starting point is arranged after a steel coil is produced; for other cold rolling units, the start point is set after the production schedule is determined. The data of the standard, the link and the actual performance related to the steel coil in the manufacturing management system are acquired by a submodule M1.1 and are used as the input parameters; and starting the submodules M1.2 and M1.3, executing a material steel grade grouping logic rule, obtaining a matching grouping number result, triggering the single-coil manufacturing process judging and sampling optimizing module M2 to perform the next processing if the effective grouping number is obtained, and otherwise, exiting the process and the process according to the normal circulation required by the original sampling design of the contract.
The single-roll manufacturing process judging and sampling optimizing module M2: and judging the manufacturing process of each single roll by constructing a single roll process judging rule and a sampling optimizing algorithm, and determining whether the sampling design can be optimized or not and an optimizing algorithm of the sampling block number. Specifically, the single-roll manufacturing process differentiating and sampling optimizing module M2 includes:
submodule M2.1: collecting and outputting various relevant data of the steel coil which can participate in sampling optimization;
submodule M2.2: judging whether abnormality exists according to the logic rule in the group of the steel coil, and outputting the result: if the abnormal condition exists, judging that sampling optimization fails, and conventionally circulating according to the original sampling design requirement of the contract; if no abnormality exists, judging that sampling optimization is successful, and outputting sampling block number data of the head part, the middle part and the tail part of the sampled steel coil;
and pre-establishing a key item list and a data value logic of each key item, and compiling a single volume process judgment logic judgment rule for each group number. Business personnel fully verify and analyze based on abundant experience accumulation and a large amount of historical data to form a set of mature logic judgment rules, so as to judge whether the technical process data of each steel coil are abnormal one by one, determine whether the single coil meets the condition of sampling optimization, and recommend the optimal sampling block number of each single coil.
After the starting point setting material steel grade grouping module M1 is executed, if the grouping number is successfully acquired according to the output result, the steel coil number and the grouping number are transmitted, the submodule M2.1 collects and outputs various data related to the steel coil, the submodule M2.2 judges whether abnormality exists according to the logic rule in the group, and the output result is that: if the abnormal condition exists, judging that sampling optimization is NG, and conventionally circulating according to the original sampling design requirement of the contract; if no abnormality exists, judging that the sampling optimization is OK, starting a sampling block number optimization algorithm, and outputting data such as the number of sampling blocks at the head, the middle and the tail of the optimized sampling steel coil; and continuously triggering the multi-volume batch sampling design module M3 to carry out the next processing.
The multi-volume batch sampling design module M3: the method is used for constructing an automatic batch integration rule base of sample groups of typical steel types. Specifically, the multi-volume batch sampling design module M3 includes:
submodule M3.1: collecting and outputting various related data used by the same batch of steel coil conditions;
submodule M3.2: according to the input packet numbers and various related data used by the same batch condition of the steel coil, wherein the various related data comprise sampling block number data of the head part, the middle part and the tail part of the sampled steel coil output by the submodule M2.2, the same batch condition is calculated, whether the steel coil can be added into the existing test batch as the represented steel coil is preferentially judged, if the steel coil cannot be matched, a new test batch is created, and the current steel coil is directly taken as the representative coil; if the data are matched, automatically recalculating the data of the number of the represented steel coiling sample blocks and the total sample block number in the existing test batch; and forming a relation between the steel coil and the trial batch as key index information for judging the performance of the steel coil, wherein the key index information is issued by a subsequent sampling instruction.
And establishing a key item list and data value logic of each key item in advance, and compiling logic judgment rules of whether each grouping number can be grouped in the same trial batch.
After the starting point is set in the single-coil manufacturing process distinguishing and sampling optimizing module M2, a single-coil sample design result is obtained according to the output result, the submodule M3.1 collects and outputs various related data used by the same lot condition of the steel coil, the submodule M3.2 calculates the same lot condition according to the input group number and various related data of the steel coil, and preferentially judges whether the steel coil can be taken as a represented steel coil to be added into an existing test lot, if the steel coil cannot be matched, a new test lot is created, and the current steel coil is directly taken as a representative coil; if the number of the representative steel coiling sample blocks in the existing test batch is matched, automatically recalculating the data such as the number of the representative steel coiling sample blocks, the total sample block number and the like; and forming a relation between the steel coil and the trial batch as key index information for judging the performance of the steel coil, wherein the key index information is issued by a subsequent sampling instruction.
The interface module is used for starting intelligent sampling design among a manufacturing management system, a manufacturing execution system, an inspection and test management system, consistent information transmission of single-coiled sample optimizing results, multi-coiled same-batch relations and the like, and linkage of an upper system and a lower system, and is used for guiding subsequent flow processes such as on-site sampling and sample feeding, laboratory template registration management, performance data collection and judgment and the like. The calculation result of the product performance prediction model and the online detection recommendation result are supported to be used as judging conditions for participating in single coiled sample optimization design, so that seamless integration is realized.
The present application will be described in more detail below.
In the material steel grade grouping module M1:
the list of key items and the data value logic of each key item, such as steel grade, standard, internal steel grade, end user, end use, specification, sorting degree, supply source, passing unit, ra, PPI, WCA performance standard requirement, etc., are used for carrying out grouping management on cold-rolled products such as common cooling, hot-dip plating, electroplating, tinning, chrome plating, acid washing, etc.
The definition of the grouping number and the grouping key label content, such as a common cold manganese carbon steel related machine set, a steel grade and the like.
The logic decision rule is formulated for each packet number. For example, the characteristic values of common cold carbon manganese steel.27 CM welding wire steel relate to steel grade codes, tapping marks, product specification codes, annealing/hot dip galvanizing passing units and the like.
The established and defined rules and other information can be audited and released to take effect after being fully simulated and verified by the online historical data, and are used for online automatic steel coil sampling optimization judgment.
In the single-roll manufacturing process differentiation and sampling optimization module M2:
the key item list and the data value logic of each key item are established, for example, contract design standard class, heat composition standard, hot rolling key temperature standard, flatness target, annealing key temperature/speed standard, steel coil pre-process production actual results, slab positions, steel types in the furnace, front/rear furnace inner steel types, important chemical composition actual values, hot rolling various key temperature mean values/curves/extreme values, flatness mean values/curves/extreme values, annealing key temperature mean values/curves/extreme values and the like are formed to form a parameter pool, and secondary calculation of partial parameter items is also supported.
The logic judgment rules for judging the single coil process of each grouping number, such as the actual result value of important elements of steel-making heat of 45LA of reinforced steel of general Leng Xichu, the core processes such as hot rolling, continuous annealing and the like, must be in a standard range, and the like.
The established and defined rules and other information can be audited and released to take effect after being fully simulated and verified by the online historical data, and are used for online automatic steel coil sampling optimization judgment.
In the multi-volume batch sampling design module M3:
the key item list and the data value logic of each key item are established, such as standard, steel grade, product major class, furnace number, upper ton of trial batch, cumulative ton, composition difference, etc.
The logic judgment rules of whether each grouping number can be assembled in the same test batch are compiled, for example, the chromium plating DR material, the same standard, the same steel grade, the same specification, the same sampling requirement, the same test standard requirement, the difference value of various elements or the actual performance difference value of the process are in a controllable range, and the like, of a plurality of steel coils in the same test batch of the TF8 MCM.
The established and defined rules and other information can be audited and released to take effect after being fully simulated and verified by the online historical data, and are used for online automatic steel coil sampling optimization judgment.
In the interface module:
after the unit plans such as cold rolling pickling/finishing and the like are determined or the output actual results of the continuous annealing/hot galvanizing unit are received, an intelligent sampling optimization interface function is called;
sending a sampling requirement and a same batch relation after sampling optimization to a cold rolling manufacturing execution system;
the manufacturing management system sends sampling requirements and the same batch relation after sampling optimization to the inspection and test management system;
sampling according to the sampling instruction on site and sending the sample to a laboratory;
template registration and performance detection actual results uploading;
the steel coil performance judging calls a sampling optimization interface function to acquire the comparison relation between the test lot and the steel coil, and the performance actual results of the representative coil are collected and judged; the subsequent flow is unchanged.
The application is further described below.
The application can reduce the sampling amount of steel materials with stable process. Through tracking statistics, 6000 steel coils in each month belong to steel products with stable process, and after strict logic judgment and optimization calculation, about 2000 steel coils are successfully optimized, 2100 pieces of sampling blocks are reduced, the optimization proportion accounts for about 35%, and the detection cost of the cold-rolled products is greatly reduced.
Those skilled in the art will appreciate that the systems, apparatus, and their respective modules provided herein may be implemented entirely by logic programming of method steps such that the systems, apparatus, and their respective modules are implemented as logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers, etc., in addition to the systems, apparatus, and their respective modules being implemented as pure computer readable program code. Therefore, the system, the apparatus, and the respective modules thereof provided by the present application may be regarded as one hardware component, and the modules included therein for implementing various programs may also be regarded as structures within the hardware component; modules for implementing various functions may also be regarded as being either software programs for implementing the methods or structures within hardware components.
The application also provides a cold-rolled product sampling method based on the dynamic programming algorithm, and a person skilled in the art can realize the cold-rolled product sampling system based on the dynamic programming algorithm by executing the step flow of the cold-rolled product sampling method based on the dynamic programming algorithm, namely the cold-rolled product sampling method based on the dynamic programming algorithm can be understood as a preferred implementation mode of the cold-rolled product sampling system based on the dynamic programming algorithm.
The application provides a cold rolling product sampling method based on a dynamic programming algorithm, which comprises the following steps:
material steel grade grouping step S1: the method comprises the steps of managing grouping rules of material steel types and determining steel coils which can participate in sampling optimization;
judging and sampling optimization step S2 of single-roll manufacturing process: judging the manufacturing process of each single coil of the steel coil which can participate in sampling optimization, and outputting sampling data to the steel coil which is successfully sampled and optimized;
step S3 of multi-roll batch sampling design: matching or newly creating test lot groups to form a relation between the steel coil and the test lot, and using the relation as key index information for issuing a follow-up sampling instruction and judging the performance of the steel coil.
An interface step: the system is used for starting intelligent sampling design among a plurality of systems of a manufacturing management system, a manufacturing execution system and an inspection and test management system, transmitting information such as single-coil sample optimizing results, a plurality of coils of same batch relations and the like consistently, linking an upper system and a lower system, guiding on-site sampling sample feeding and laboratory template registration management, and collecting and judging performance data to process the follow-up procedures; the calculation result of the product performance prediction model and the online detection recommendation result are supported to be used as judging conditions for participating in single coiled sample optimization design, so that seamless integration is realized.
The material steel grade grouping step S1 comprises the following steps:
substep S1.1: acquiring standard, link and actual performance data related to the steel coil indicated by the steel coil number as input parameters according to the data value logic aiming at the received steel coil number;
substep S1.2: according to the input parameters, performing material steel grade grouping logic rules on the steel coil to obtain matched grouping numbers;
substep S1.3: and judging whether the steel coil can participate in sampling optimization according to a logic judgment rule corresponding to the grouping number, if so, triggering the judging and sampling optimization step S2 of the single coil manufacturing process to continue execution, and if not, requiring routine circulation according to the original sampling design of the contract.
The step S2 of judging and sampling optimization in the single-roll manufacturing process includes:
substep S2.1: collecting and outputting various relevant data of the steel coil which can participate in sampling optimization;
substep S2.2: judging whether abnormality exists according to the logic rule in the group of the steel coil, and outputting the result: if the abnormal condition exists, judging that sampling optimization fails, and conventionally circulating according to the original sampling design requirement of the contract; if no abnormality exists, the sampling optimization is judged to be successful, and the sampling block number data of the head part, the middle part and the tail part of the sampling steel coil are output.
The multi-volume batch sampling design step S3 comprises the following steps:
substep S3.1: collecting and outputting various related data used by the same batch of steel coil conditions;
substep S3.2: calculating the same batch conditions according to the input group numbers and related various data used by the same batch conditions of the steel coil, and preferentially judging whether the steel coil can be added into the existing test batch as the represented steel coil; if the current steel coil is not matched with the representative coil, a new trial batch is created, and the current steel coil is directly used as the representative coil; if the data are matched, automatically recalculating the data of the number of the represented steel coiling sample blocks and the total sample block number in the existing test batch; forming a relation between the steel coil and the trial batch as key index information for judging the performance of the steel coil issued by a subsequent sampling instruction; the related various data comprise sampling block number data of the head part, the middle part and the tail part of the sampling steel coil output in the substep S2.2.
The foregoing describes specific embodiments of the present application. It is to be understood that the application is not limited to the particular embodiments described above, and that various changes or modifications may be made by those skilled in the art within the scope of the appended claims without affecting the spirit of the application. The embodiments of the application and the features of the embodiments may be combined with each other arbitrarily without conflict.
Claims (10)
1. A cold rolled product sampling system based on a dynamic programming algorithm, comprising:
material steel grade grouping module M1: the method comprises the steps of managing grouping rules of material steel types and determining steel coils which can participate in sampling optimization;
and a single-roll manufacturing process judging and sampling optimizing module M2: judging the manufacturing process of each single coil of the steel coil which can participate in sampling optimization, and outputting sampling data to the steel coil which is successfully sampled and optimized;
multi-reel batch sampling design module M3: matching or newly creating test lot groups to form a relation between the steel coil and the test lot, and using the relation as key index information for issuing a follow-up sampling instruction and judging the performance of the steel coil.
2. The cold rolled product sampling system based on a dynamic programming algorithm according to claim 1, wherein the material steel grade grouping module M1 comprises:
submodule M1.1: acquiring standard, link and actual performance data related to the steel coil indicated by the steel coil number as input parameters according to the data value logic aiming at the received steel coil number;
submodule M1.2: according to the input parameters, performing material steel grade grouping logic rules on the steel coil to obtain matched grouping numbers;
submodule M1.3: and judging whether the steel coil can participate in sampling optimization according to a logic judgment rule corresponding to the grouping number, if so, triggering a single coil manufacturing process judgment and sampling optimization module M2 to continue execution, and if not, conventionally circulating according to the original sampling design requirement of the contract.
3. The cold rolled product sampling system based on the dynamic programming algorithm according to claim 2, wherein the single coil manufacturing process differentiating and sampling optimizing module M2 comprises:
submodule M2.1: collecting and outputting various relevant data of the steel coil which can participate in sampling optimization;
submodule M2.2: judging whether abnormality exists according to the logic rule in the group of the steel coil, and outputting the result: if the abnormal condition exists, judging that sampling optimization fails, and conventionally circulating according to the original sampling design requirement of the contract; if no abnormality exists, the sampling optimization is judged to be successful, and the sampling block number data of the head part, the middle part and the tail part of the sampling steel coil are output.
4. The cold rolled product sampling system based on a dynamic programming algorithm as claimed in claim 3, wherein the multi-coil batch sampling design module M3 comprises:
submodule M3.1: collecting and outputting various related data used by the same batch of steel coil conditions;
submodule M3.2: calculating the same batch conditions according to the input group numbers and related various data used by the same batch conditions of the steel coil, and preferentially judging whether the steel coil can be added into the existing test batch as the represented steel coil; if the current steel coil is not matched with the representative coil, a new trial batch is created, and the current steel coil is directly used as the representative coil; if the data are matched, automatically recalculating the data of the number of the represented steel coiling sample blocks and the total sample block number in the existing test batch; forming a relation between the steel coil and the trial batch as key index information for judging the performance of the steel coil issued by a subsequent sampling instruction; the related various data comprise sampling block number data of the head part, the middle part and the tail part of the sampling steel coil output by the submodule M2.2.
5. The cold rolled product sampling system based on the dynamic programming algorithm of claim 4, further comprising:
an interface module: the system is used for starting intelligent sampling design among a plurality of systems of a manufacturing management system, a manufacturing execution system and an inspection and test management system, transmitting information such as single-coil sample optimizing results, a plurality of coils of same batch relations and the like consistently, linking an upper system and a lower system, guiding on-site sampling sample feeding and laboratory template registration management, and collecting and judging performance data to process the follow-up procedures; the calculation result of the product performance prediction model and the online detection recommendation result are supported to be used as judging conditions for participating in single coiled sample optimization design, so that seamless integration is realized.
6. The cold rolling product sampling method based on the dynamic programming algorithm is characterized by comprising the following steps of:
material steel grade grouping step S1: the method comprises the steps of managing grouping rules of material steel types and determining steel coils which can participate in sampling optimization;
judging and sampling optimization step S2 of single-roll manufacturing process: judging the manufacturing process of each single coil of the steel coil which can participate in sampling optimization, and outputting sampling data to the steel coil which is successfully sampled and optimized;
step S3 of multi-roll batch sampling design: matching or newly creating test lot groups to form a relation between the steel coil and the test lot, and using the relation as key index information for issuing a follow-up sampling instruction and judging the performance of the steel coil.
7. The cold rolled product sampling method based on the dynamic programming algorithm as claimed in claim 6, wherein the grouping step S1 of the material steel grade comprises:
substep S1.1: acquiring standard, link and actual performance data related to the steel coil indicated by the steel coil number as input parameters according to the data value logic aiming at the received steel coil number;
substep S1.2: according to the input parameters, performing material steel grade grouping logic rules on the steel coil to obtain matched grouping numbers;
substep S1.3: and judging whether the steel coil can participate in sampling optimization according to a logic judgment rule corresponding to the grouping number, if so, triggering the judging and sampling optimization step S2 of the single coil manufacturing process to continue execution, and if not, requiring routine circulation according to the original sampling design of the contract.
8. The method according to claim 7, wherein the single coil manufacturing process differentiating and sampling optimizing step S2 comprises:
substep S2.1: collecting and outputting various relevant data of the steel coil which can participate in sampling optimization;
substep S2.2: judging whether abnormality exists according to the logic rule in the group of the steel coil, and outputting the result: if the abnormal condition exists, judging that sampling optimization fails, and conventionally circulating according to the original sampling design requirement of the contract; if no abnormality exists, the sampling optimization is judged to be successful, and the sampling block number data of the head part, the middle part and the tail part of the sampling steel coil are output.
9. The method for sampling cold rolled products based on a dynamic programming algorithm according to claim 8, wherein the step S3 of designing the multi-coil batch sampling comprises:
substep S3.1: collecting and outputting various related data used by the same batch of steel coil conditions;
substep S3.2: calculating the same batch conditions according to the input group numbers and related various data used by the same batch conditions of the steel coil, and preferentially judging whether the steel coil can be added into the existing test batch as the represented steel coil; if the current steel coil is not matched with the representative coil, a new trial batch is created, and the current steel coil is directly used as the representative coil; if the data are matched, automatically recalculating the data of the number of the represented steel coiling sample blocks and the total sample block number in the existing test batch; forming a relation between the steel coil and the trial batch as key index information for judging the performance of the steel coil issued by a subsequent sampling instruction; the related various data comprise sampling block number data of the head part, the middle part and the tail part of the sampling steel coil output in the substep S2.2.
10. The method for sampling cold rolled products based on a dynamic programming algorithm according to claim 9, further comprising:
an interface step: the system is used for starting intelligent sampling design among a plurality of systems of a manufacturing management system, a manufacturing execution system and an inspection and test management system, transmitting information such as single-coil sample optimizing results, a plurality of coils of same batch relations and the like consistently, linking an upper system and a lower system, guiding on-site sampling sample feeding and laboratory template registration management, and collecting and judging performance data to process the follow-up procedures; the calculation result of the product performance prediction model and the online detection recommendation result are supported to be used as judging conditions for participating in single coiled sample optimization design, so that seamless integration is realized.
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