Reinforcing steel bar quality management system and method
阅读说明：本技术 一种钢筋质量管理系统和方法 (Reinforcing steel bar quality management system and method ) 是由 邝昌云 皮坤 杨伟 许斌 贾鹏杰 王化 李平 范心怡 于 2021-06-29 设计创作，主要内容包括：本发明涉及一种钢筋质量管理系统和方法,通过测温仪采集钢坯出钢温度、钢坯开扎温度、钢筋上冷床温度,通过力学性能测试智能设备采集钢筋的屈服强度、抗拉强度、断后伸长率,并将这些数据通过物联网数据采集协议转换模块实时接入物联网边缘平台,在边缘平台的模型算法层调用边缘计算节点模块进行大数据分析,根据算法模型的实时边缘计算和钢筋力学性能的关联分析结果,实时给出温度控制方案,通过上述系统的研发和应用,有效地在钢筋生产工艺方面通过应用数据采集技术、边缘算法技术、大数据技术、数据模型等技术实现生产工艺质量寻优,有效地提升了钢筋产品质量。(The invention relates to a system and a method for managing the quality of steel bars, which collect the steel tapping temperature of a steel billet, the steel billet rolling temperature and the temperature of a steel bar cooling bed through a thermodetector, the yield strength, the tensile strength and the elongation after fracture of the steel bar are collected by intelligent mechanical property testing equipment, and the data are accessed to the Internet of things edge platform in real time through the Internet of things data acquisition protocol conversion module, calling an edge computing node module at a model algorithm layer of an edge platform to perform big data analysis, giving a temperature control scheme in real time according to the real-time edge computing of an algorithm model and the correlation analysis result of the mechanical property of the reinforcing steel bar, through the research and the application of the system, the quality optimization of the production process is effectively realized in the aspect of the steel bar production process by applying the data acquisition technology, the edge algorithm technology, the big data technology, the data model and other technologies, and the quality of steel bar products is effectively improved.)
1. A reinforcing bar quality management system which characterized in that: the system comprises a collector and a processor, wherein the collector comprises a first data acquisition module, a second data acquisition module and a third data acquisition module; the processor comprises an equipment protocol conversion module, a data model training module, an edge calculation module and a temperature control module;
the first data acquisition module acquires data of the second data module and the third data module, and the second data module acquires steel billet tapping temperature, steel billet rolling temperature and steel bar cold bed temperature data; the third data acquisition module acquires the yield strength, the tensile strength and the elongation after fracture of the steel bar;
the device protocol conversion module converts the protocol of the acquisition device for the data model training module and the edge calculation module to use;
the data model training module performs characteristic value extraction and effective combination training on temperature parameters and mechanical property parameters which affect the quality of the steel bar, and performs model training through massive sample data stored in a large data platform to obtain the optimal mechanical property and temperature control combination of the steel bar;
the edge calculation module carries out edge calculation on the edge platform and provides reference for temperature control in the steel bar production process;
and the temperature control module is used for displaying the temperature state of the current steel bar production process and the quality requirement to be met by the mechanical property of the steel bar in a visual mode based on the result of the edge calculation module and controlling the temperature.
2. The rebar quality management system of claim 1, wherein: the data model training comprises training the range and the target of the temperature-equalizing hot section of the hot billet steel tapping temperature heating furnace, the range and the target of the hot billet head, middle and tail rolling temperatures, the values of the strength-to-yield ratio, the yield-to-yield ratio, the tensile strength, the yield strength and the like of the steel bar, and obtaining the optimal mechanical property and temperature control combination of the steel bar.
3. The rebar quality management system of claim 1, wherein: the device protocol converted by the protocol conversion module comprises MQTT, EMQ and EMQTT device protocols.
4. The rebar quality management system of claim 1, wherein: the data model training module is specifically performed as follows:
obtaining standard values of core parameters of a data model influencing the quality of the steel bars, including yield strength, tensile strength, Poisson ratio and elongation, through an RESTFUL API (representational state transfer language API) interface provided by a big data platform;
collecting core parameters at different speeds and different heating furnace temperatures; calculating the real-time yield strength, the tensile strength, the elongation and the Poisson ratio of the steel bar at the current temperature through a heat transmission equation, comparing the mechanical properties of the steel bar under the same temperature theoretical condition with those of the steel bar under the service condition, and searching the numerical difference under the two conditions, wherein the heat transmission equation is as follows:
wherein:
is the vector: the heat flux passing through a certain point in the temperature field and the temperature gradient of the point are combined;
λ (T) is the thermal conductivity: taking the temperature of 36 multiplied by 1.163W/m per hour to 40 multiplied by 1.163W/m per hour;
is the heat transfer rate: 66 Wxm-1 XK-1 to 406 Wxm-1 XK-1;
the temperature value is as follows: corresponding to the temperature of the billet or the steel bar when the billet or the steel bar is taken out of the heating furnace, the rolling mill and the cooling bed.
Is based on temperature and thermal coefficient and rate heat transfer;
the digital frequency acquisition and the correction amplification factor are controlled through a temperature control mathematical model, and the temperature control mathematical model formula is as follows:
wherein:
Tds +1 is a time constant, the value is 0.3-400 seconds, and the up-and-down floating time is 1 second;
Kde-τsis the amplification factor, and the value is between 38 and 62.
G(s) is a temperature control mathematical model based on temperature amplification coefficients in corresponding time intervals under a certain temperature;
the optimal mechanical property and temperature control combination of the alloy steel is obtained by continuously adjusting the influence of the speed and the temperature of the steel billet out of the heating furnace, the rolling mill and the cooling bed on the quality of the steel bar under the service condition, and a control decision support is provided for the optimization of the production process.
5. The rebar quality management system of claim 4, wherein: and the edge calculation module specifically calculates according to a heat transfer equation and a temperature control model formula.
6. A reinforcing steel bar quality management method is characterized in that: the method comprises the following steps:
collecting steel billet tapping temperature, steel billet rolling temperature and steel bar cold bed temperature data; collecting yield strength, tensile strength and elongation after fracture data of the steel bar;
protocol conversion is used by the data model training module and the edge calculation module;
carrying out characteristic value extraction and effective combination training on temperature parameters and mechanical property parameters influencing the quality of the steel bar, and carrying out model training through mass sample data stored in a large data platform to obtain the optimal mechanical property and temperature control combination of the steel bar;
performing edge calculation on the edge platform to provide reference for temperature control in the steel bar production process;
based on the result of the edge calculation module, the temperature state of the current steel bar production process and the quality requirement to be met by the mechanical property of the steel bar are displayed in a visual mode, and the temperature is controlled.
7. The reinforcing bar quality management method according to claim 6, wherein: the data model training is specifically performed as follows:
obtaining standard values of core parameters of a data model influencing the quality of the steel bars, including yield strength, tensile strength, Poisson ratio and elongation, through an RESTFUL API (representational state transfer language API) interface provided by a big data platform;
collecting core parameters at different speeds and different heating furnace temperatures; calculating the real-time yield strength, the tensile strength, the elongation and the Poisson ratio of the steel bar at the current temperature through a heat transmission equation, comparing the mechanical properties of the steel bar under the same temperature theoretical condition with those of the steel bar under the service condition, and searching the numerical difference under the two conditions, wherein the heat transmission equation is as follows:
wherein:
is the vector: the heat flux passing through a certain point in the temperature field and the temperature gradient of the point are combined;
λ (T) is the thermal conductivity: taking the temperature of 36 multiplied by 1.163W/m DEG C to 40 multiplied by 1.163W/m DEG C;
is the heat transfer rate: 66 Wxm-1 XK-1 to 406 Wxm-1 XK-1;
the temperature value is as follows: corresponding to the temperature of the billet or the steel bar when the billet or the steel bar is taken out of the heating furnace, the rolling mill and the cooling bed.
Is based on temperature and thermal coefficient and rate heat transfer;
the digital frequency acquisition and the correction amplification factor are controlled through a temperature control mathematical model, and the temperature control mathematical model formula is as follows:
wherein:
Tds +1 is a time constant, the value is 0.3-400 seconds, and the up-and-down floating time is 1 second;
Kde-τsis the amplification factor, and the value is between 38 and 62.
G(s) is a temperature control mathematical model based on temperature amplification coefficients in corresponding time intervals under a certain temperature;
the optimal mechanical property and temperature control combination of the alloy steel is obtained by continuously adjusting the influence of the speed and the temperature of the steel billet out of the heating furnace, the rolling mill and the cooling bed on the quality of the steel bar under the service condition, and a control decision support is provided for the optimization of the production process.
8. The reinforcing bar quality management method according to claim 7, wherein: and the edge calculation module specifically calculates according to a heat transfer equation and a temperature control model formula.
9. An electronic device comprising a memory, a processor, and a computer program that is executable on the memory and on the processor, wherein: the processor, when executing the computer program, realizes the steps of the method of any of the preceding claims 6 to 8.
10. A non-transitory computer-readable storage medium having stored thereon a computer program, characterized in that: the computer program, when executed by a processor, implementing the steps of the method as claimed in any one of claims 6 to 8.
Technical Field
The invention belongs to the field of steel bar production, and particularly relates to a steel bar quality management system and a steel bar quality management method.
Background
In the production process of the steel bars, the specification or type of the steel bars is determined by the chemical components of steel, such as C, Si, Mn, S and the like, and the temperature and the process mechanical property control in the production process of the steel bars are usually closely related to the quality of steel bar products. How to realize the optimization of the quality of the steel bar production process and further improve the quality of steel bar products by analyzing and effectively controlling the big data of the steel bar tapping temperature, the steel bar rolling temperature, the temperature of a cooling bed on the steel bar, the yield strength, the tensile strength, the elongation after fracture and other mechanical properties of the steel bar in the production process of the steel bar is worthy of research.
Disclosure of Invention
In order to solve the problems, the invention provides a reinforcing steel bar quality management system and a reinforcing steel bar quality management method, which belong to the technical field of product quality optimization in the reinforcing steel bar production process by using a data acquisition technology, an edge calculation technology, a big data technology and a data model algorithm technology, and improve the quality of a reinforcing steel bar product by analyzing and effectively controlling the large data of the mechanical property of the reinforcing steel bar process and the temperature of the reinforcing steel bar.
The technical scheme of the invention is as follows:
a steel bar quality management system comprises a collector and a processor, wherein the collector comprises a first data acquisition module, a second data acquisition module and a third data acquisition module; the processor comprises an equipment protocol conversion module, a data model training module, an edge calculation module and a temperature control module;
the first data acquisition module acquires data of the second data module and the third data module, and the second data module acquires steel billet tapping temperature, steel billet rolling temperature and steel bar cold bed temperature data; the third data acquisition module acquires the yield strength, the tensile strength and the elongation after fracture of the steel bar;
the device protocol conversion module converts the protocol of the acquisition device for the data model training module and the edge calculation module to use;
the data model training module performs characteristic value extraction and effective combination training on temperature parameters and mechanical property parameters which affect the quality of the steel bar, and performs model training through massive sample data stored in a large data platform to obtain the optimal mechanical property and temperature control combination of the steel bar;
the edge calculation module carries out edge calculation on the edge platform and provides reference for temperature control in the steel bar production process;
and the temperature control module is used for displaying the temperature state of the current steel bar production process and the quality requirement to be met by the mechanical property of the steel bar in a visual mode based on the result of the edge calculation module and controlling the temperature.
Further, the training of the data model comprises training of the range and the target of the temperature-equalizing hot section of the hot billet steel tapping temperature heating furnace, the range and the target of the hot billet head, middle and tail opening temperature, the values of the steel bar yield ratio, the tensile strength and the yield strength, and the like, so as to obtain the optimal mechanical property and temperature control combination of the steel bar.
Further, the device protocols converted by the protocol conversion module include MQTT, EMQ, and EMQTT device protocols.
Further, the data model training module specifically performs the following:
obtaining standard values of core parameters of a data model influencing the quality of the steel bars, including yield strength, tensile strength, Poisson ratio and elongation, through an RESTFULAPI interface provided by a big data platform;
collecting core parameters at different speeds and different heating furnace temperatures; calculating the real-time yield strength, the tensile strength, the elongation and the Poisson ratio of the steel bar at the current temperature through a heat transmission equation, comparing the mechanical properties of the steel bar under the same temperature theoretical condition with those of the steel bar under the service condition, and searching the numerical difference under the two conditions, wherein the heat transmission equation is as follows:
wherein:
is the vector: the heat flux passing through a certain point in the temperature field and the temperature gradient of the point are combined;
λ (T) ″ is the thermal conductivity: taking the temperature of 36 multiplied by 1.163W/m DEG C to 40 multiplied by 1.163W/m DEG C;
is the heat transfer rate: 66 Wxm-1 XK-1 to 406 Wxm-1 XK-1;
the temperature value is as follows: corresponding to the temperature of the billet or the steel bar when the billet or the steel bar is taken out of the heating furnace, the rolling mill and the cooling bed.
Is based on temperature and thermal coefficient and rate heat transfer;
the digital frequency acquisition and the correction amplification factor are controlled through a temperature control mathematical model, and the temperature control mathematical model formula is as follows:
wherein:
Tds +1 is a time constant, the value is 0.3-400 seconds, and the up-and-down floating time is 1 second;
Kde-isis the amplification factor, and the value is between 38 and 62.
G(s) is a temperature control mathematical model based on temperature amplification coefficients in corresponding time intervals under a certain temperature;
the optimal mechanical property and temperature control combination of the alloy steel is obtained by continuously adjusting the influence of the speed and the temperature of the steel billet out of the heating furnace, the rolling mill and the cooling bed on the quality of the steel bar under the service condition, and a control decision support is provided for the optimization of the production process.
Further, the edge calculation module specifically performs calculation according to a heat transfer equation and a temperature control model formula.
The module is an edge calculation algorithm box, and speed and temperature algorithm models which influence the quality of steel bars and are trained by a data model training module in a steel production process quality optimizing management system are arranged in the box.
The model provides the data of different data sources, such as flash, Kafka, TCP and the like, to a corresponding database or algorithm model after performing M/R, Join and other complex operations on the data by edge calculation of an edge platform, such as Spark Streaming, and guides the effective control of the temperature in the production process of the steel bars.
The invention also relates to a reinforcing steel bar quality management method, which is carried out as follows:
collecting steel billet tapping temperature, steel billet rolling temperature and steel bar cold bed temperature data; collecting yield strength, tensile strength and elongation after fracture data of the steel bar;
protocol conversion is used by the data model training module and the edge calculation module;
carrying out characteristic value extraction and effective combination training on temperature parameters and mechanical property parameters influencing the quality of the steel bar, and carrying out model training through mass sample data stored in a large data platform to obtain the optimal mechanical property and temperature control combination of the steel bar;
performing edge calculation on the edge platform to provide reference for temperature control in the steel bar production process;
based on the result of the edge calculation module, the temperature state of the current steel bar production process and the quality requirement to be met by the mechanical property of the steel bar are displayed in a visual mode, and the temperature is controlled.
Further, the data model training is specifically performed as follows:
obtaining standard values of core parameters of a data model influencing the quality of the steel bars, including yield strength, tensile strength, Poisson ratio and elongation, through an RESTFULAPI interface provided by a big data platform;
collecting core parameters at different speeds and different heating furnace temperatures; calculating the real-time yield strength, the tensile strength, the elongation and the Poisson ratio of the steel bar at the current temperature through a heat transmission equation, comparing the mechanical properties of the steel bar under the same temperature theoretical condition with those of the steel bar under the service condition, and searching the numerical difference under the two conditions, wherein the heat transmission equation is as follows:
wherein:
is the vector: the heat flux passing through a certain point in the temperature field and the temperature gradient of the point are combined;
λ (T) ″ is the thermal conductivity: taking the temperature of 36 multiplied by 1.163W/m per hour to 40 multiplied by 1.163W/m per hour;
is the heat transfer rate: 66 Wxm-1 XK-1 to 406 Wxm-1 XK-1;
the temperature value is as follows: corresponding to the temperature of the billet or the steel bar when the billet or the steel bar is taken out of the heating furnace, the rolling mill and the cooling bed.
Is based on temperature and thermal coefficient and rate heat transfer;
the digital frequency acquisition and the correction amplification factor are controlled through a temperature control mathematical model, and the temperature control mathematical model formula is as follows:
wherein:
Tds +1 is a time constant, the value is 0.3-400 seconds, and the up-and-down floating time is 1 second;
Kde-isis the amplification factor, and the value is between 38 and 62.
G(s) is a temperature control mathematical model based on temperature amplification coefficients in corresponding time intervals under a certain temperature;
the optimal mechanical property and temperature control combination of the alloy steel is obtained by continuously adjusting the influence of the speed and the temperature of the steel billet out of the heating furnace, the rolling mill and the cooling bed on the quality of the steel bar under the service condition, and a control decision support is provided for the optimization of the production process.
Further, the edge calculation module specifically performs calculation according to a heat transfer equation and a temperature control model formula.
The invention also relates to an electronic device comprising a memory, a processor and a computer program that is executable on the memory and on the processor, the processor implementing the steps of the method described above when executing the computer program.
The invention also relates to a non-transitory computer-readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the above-described method.
Compared with the prior art, the invention has the following beneficial effects:
according to the invention, the steel billet tapping temperature, the steel billet piercing temperature and the steel bar cooling bed temperature are collected by a thermodetector, the yield strength, the tensile strength and the elongation after fracture of the steel bar are collected by an intelligent device for testing mechanical properties, the data are accessed to an Internet of things edge platform in real time through an Internet of things data collection protocol conversion module, an edge calculation node module is called at a model algorithm layer of the edge platform to carry out big data analysis, a temperature control scheme is given in real time according to the real-time edge calculation of an algorithm model and the correlation analysis result of the mechanical properties of the steel bar, and through the research and development and application of the system, the quality of a steel bar product is effectively optimized through the production process by applying the technologies such as a data collection technology, an edge algorithm technology, a big data technology and a data model in the aspect of a steel bar production process.
Drawings
Fig. 1 is a block diagram of the system of the present invention.
Detailed Description
The technical solutions in the embodiments will be described clearly and completely with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the examples without making any creative effort, shall fall within the protection scope of the present invention.
Unless otherwise defined, technical or scientific terms used in the embodiments of the present application should have the ordinary meaning as understood by those having ordinary skill in the art. The use of "first," "second," and similar terms in the present embodiments does not denote any order, quantity, or importance, but rather the terms are used to distinguish one element from another. The word "comprising" or "comprises", and the like, means that the element or item listed before the word covers the element or item listed after the word and its equivalents, but does not exclude other elements or items. "mounted," "connected," and "coupled" are to be construed broadly and may, for example, be fixedly coupled, detachably coupled, or integrally coupled; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. "Upper," "lower," "left," "right," "lateral," "vertical," and the like are used solely in relation to the orientation of the components in the figures, and these directional terms are relative terms that are used for descriptive and clarity purposes and that can vary accordingly depending on the orientation in which the components in the figures are placed.
As shown in fig. 1, the reinforcing steel bar quality management system of the embodiment includes a collector and a processor, wherein the collector includes a first data acquisition module, a second data acquisition module and a third data acquisition module; the processor comprises a device protocol conversion module, a data model training module, an edge calculation module and a temperature control module.
The first data acquisition module acquires data of the second data module and the third data module, and the second data module acquires steel billet tapping temperature, steel billet rolling temperature and steel bar cold bed temperature data; and the third data acquisition module acquires the yield strength, the tensile strength and the elongation after fracture of the steel bar.
And the equipment protocol conversion module converts the protocol of the acquisition equipment for the data model training module and the edge calculation module to use.
The data model training module performs characteristic value extraction and effective combination training on temperature parameters and mechanical property parameters which affect the quality of the steel bar, and performs model training through massive sample data stored in a large data platform to obtain the optimal mechanical property and temperature control combination of the steel bar.
The edge calculation module carries out edge calculation on the edge platform and provides reference for temperature control in the steel bar production process.
And the temperature control module is used for displaying the temperature state of the current steel bar production process and the quality requirement to be met by the mechanical property of the steel bar in a visual mode based on the result of the edge calculation module and controlling the temperature.
During data acquisition, equipment in steel production is regarded as one 'end' of the system, steel billet tapping temperature, steel billet rolling temperature and steel bar cooling bed temperature data are acquired according to the 'end' such as a temperature measuring instrument, yield strength, tensile strength and post-fracture elongation data of the steel bars are acquired through the intelligent equipment for mechanical property testing, and the acquired data are transmitted back in a unified mode.
The second data acquisition module calls the temperature measuring instrument to acquire steel billet tapping temperature, steel billet rolling temperature and steel bar upper cooling bed temperature data, and transmits the acquired real-time data to the first data acquisition module in a unified manner.
The third data acquisition module acquires the yield strength, the tensile strength and the elongation after fracture of the steel bar through the intelligent mechanical property test equipment, and transmits the acquired real-time data back to the first data acquisition module in a unified manner.
A data model training module: the method is used for performing characteristic value extraction and effective combination training on temperature parameters and mechanical property parameters influencing the quality of the steel bars, acquiring standard values of core parameters of a data model influencing the quality of the steel bars through massive sample data (characteristic values) stored in a large data platform, such as yield strength (elastic limit stress value, proportional limit stress value and residual deformation stress value), pull-up strength (pull-up rate), Poisson's ratio (GT/T3623-. The conditions of the mechanical properties (yield strength, tensile strength, elongation and Poisson ratio) of the steel bar are influenced by collecting different speeds and different heating furnace temperatures such as 1050 ℃. Calculating the real-time yield strength, the tensile strength, the elongation and the Poisson ratio of the steel bar at the current temperature through a heat transmission equation, comparing the mechanical properties of the steel bar under the same temperature theoretical condition with the mechanical properties of the steel bar under the service condition, and searching the numerical difference between the two conditions. Heat transfer equation:
wherein:
is the vector: the heat flux in the temperature field across a point coincides with the temperature gradient at that point.
λ (T) ″ is the thermal conductivity: the temperature is 36 multiplied by 1.163W/m DEG C to 40 multiplied by 1.163W/m DEG C.
Is the rate of heat transfer: 66 Wxm-1 XK-1 to 406 Wxm-1 XK-1.
The temperature value is as follows: corresponding to the temperature of the billet or the steel bar when the billet or the steel bar is taken out of the heating furnace, the rolling mill and the cooling bed.
Is based on temperature and thermal coefficient and rate of heat transfer.
The digital frequency and the proofreading amplification factor are controlled by a temperature control mathematical model. Temperature control mathematical model formula:
wherein:
Tds +1 is a time constant, the value is 0.3-400 seconds, and the up-and-down floating time is 1 second;
Kde-isis the amplification factor, and the value is between 38 and 62.
G(s) is a mathematical model of temperature control based on temperature amplification factors in corresponding time intervals at a certain temperature.
The optimal mechanical property and temperature control combination of the alloy steel is obtained by continuously adjusting the influence of the speed and the temperature of the steel billet out of the heating furnace, the rolling mill and the cooling bed on the quality of the steel bar under the service condition, and a control decision support is provided for the optimization of the production process.
An edge calculation module: the module is an edge calculation algorithm box, speed and temperature algorithm models which influence the quality of steel bars and are trained by a data model training module in a steel production process quality optimizing management system are built in the box, and the models are provided for corresponding databases or algorithm models after performing M/R and Join complex operations on data of different data sources, such as flash, Kafka and TCP and the like through edge calculation of an edge platform, such as Spark Streaming, so as to guide the effective control of temperature in the steel bar production process.
A temperature control module: the module provides a visual and control suggestion for the effective control of the quality optimizing temperature of the steel bar product, and the temperature state of the current steel bar production process and the quality requirement to be met by the mechanical property of the steel bar are displayed in a visual mode through the calculation result and the guidance suggestion of the edge calculation module on the edge platform, so that a production manager provides decision support or guides a production operator to directly control the temperature.
In this embodiment, the data model training is specifically performed as follows:
acquiring data model core parameter standard values influencing the quality of the steel bars through massive sample data (characteristic values) stored in a large data platform, such as yield strength (elastic limit stress value, proportion limit stress value and residual deformation stress value), pull-up strength (pull-up rate), Poisson ratio (GT/T3623-;
the method comprises the steps of collecting alloy steel in real time at different process links such as a heating furnace, a roughing mill, a middle rolling mill, a finishing mill, a steel bar cooling bed and the like through intelligent mechanical property testing equipment, OPC, PLC and the like, and accessing the data to an Internet of things edge platform such as Hbase and the like in real time for real-time data storage through an Internet of things data collection protocol such as Modbus RTU conversion Modbus TCP, AUTOMDI/MDIX and other protocol conversion modules;
calculating the real-time yield strength, the tensile strength, the elongation and the Poisson ratio of the steel bar at the current temperature through a heat transmission equation, comparing the mechanical properties of the steel bar under the same temperature theoretical condition with the mechanical properties of the steel bar under the service condition, and searching the numerical difference between the two conditions.
The digital frequency and the proofreading amplification factor are controlled by a temperature control mathematical model.
The optimal mechanical property and temperature control combination of the alloy steel is obtained by continuously adjusting the influence of the speed and the temperature of the steel billet out of the heating furnace, the rolling mill and the cooling bed on the quality of the steel bar under the service condition, and a control decision support is provided for the optimization of the production process.
In this embodiment, the edge calculation module specifically performs the following steps:
the collector obtains the real-time speed and temperature of the billet or the steel bar when the billet or the steel bar is discharged from the heating furnace, the rolling mill and the cooling bed in real time in the production field according to a certain frequency, the data are provided to an edge calculation algorithm box after protocol conversion or data processing, and a mathematical model of the speed and the temperature of the billet or the steel bar discharged from the heating furnace, the rolling mill and the cooling bed steel bar which influence the quality of the steel bar and are trained by a data model training module in the steel production process quality optimizing management system is built in the box.
The model is provided for an edge calculation module after performing M/R and Join complex operations on data of different data sources, such as flash, Kafka, TCP and the like through edge calculation of an edge platform, such as Spark Streaming, the edge calculation module returns a calculation result and a guidance suggestion to a central control machine for displaying in a visual form, a production manager provides decision support or guides a production operator to directly control the temperature, and if a linear non-time-varying PID algorithm can be issued to a heating furnace through I/A control for temperature control, the effective control on the temperature in the steel bar production process is guided.
The Processor may be a general-purpose Processor, including a Central Processing Unit (CPU), a Network Processor (NP), and the like; but may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component.
Optionally, an embodiment of the present application further provides a storage medium, where instructions are stored, and when the storage medium is run on a computer, the storage medium causes the computer to execute the method according to the embodiment described above.
Optionally, an embodiment of the present application further provides a chip for executing the instruction, where the chip is configured to execute the method in the foregoing illustrated embodiment.
The embodiments of the present application also provide a program product, where the program product includes a computer program, where the computer program is stored in a storage medium, and at least one processor can read the computer program from the storage medium, and when the at least one processor executes the computer program, the at least one processor can implement the method of the above-mentioned embodiments.
In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loaded and executed on a computer, cause the processes or functions described in accordance with the embodiments of the application to occur, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored in a readable storage medium or transmitted from one readable storage medium to another readable storage medium, for example, the computer instructions may be transmitted from one website site, computer, server, or data center to another website site, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The readable storage medium may be any available medium that can be accessed by a computer or a data storage device including one or more available media integrated servers, data centers, and the like. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., Solid State Disk (SSD)), among others.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
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