CN117380376A - Pressure correction method, device, equipment and storage medium - Google Patents
Pressure correction method, device, equipment and storage medium Download PDFInfo
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B02—CRUSHING, PULVERISING, OR DISINTEGRATING; PREPARATORY TREATMENT OF GRAIN FOR MILLING
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Abstract
The invention discloses a pressure correction method, a pressure correction device, pressure correction equipment and a storage medium. Comprising the following steps: acquiring historical material data of the roller press, and establishing a pressure prediction model according to the historical material data; acquiring material data to be detected, and determining predicted steady-state pressure according to the material data to be detected and a pressure prediction model; and carrying out pressure correction of the roller press according to the predicted steady-state pressure. By acquiring historical material data of the roller press and establishing a pressure prediction model based on a big data statistical regression modeling method, the steady-state pressure of the next batch can be predicted according to the steady-state pressure of the previous batch, then the corresponding target pressure increment is determined according to the target steady-state speed set by a user, and finally the difference value between the predicted steady-state pressure and the target pressure increment is taken as the target correction pressure, so that the pressure correction is realized, the problem of material rejection caused by too slow pressure adjustment is solved, the pressure can be effectively and quickly adjusted, the pressure adjustment efficiency is improved, the occurrence of oversized materials is reduced, and the production quality is ensured.
Description
Technical Field
The present invention relates to the field of data processing technologies, and in particular, to a pressure correction method, apparatus, device, and storage medium.
Background
The roll squeezer, also called calender, twin-roll mill, rolling mill, extrusion mill and roll mill can replace a ball mill pre-grinding system with high energy consumption and low efficiency, and has the functions of reducing steel consumption and noise.
In the production process of the rolling technology, materials are required to be extruded and ground to specified specifications by controlling a roller press. Because the characteristics of materials in different batches are different, the pressure requirements of the materials in different batches are different. Before entering the roll squeezer, the condition that the incoming material characteristics cannot be directly measured exists, important feedforward information is lost at the moment, and if the initial pressure of the material head during production is inaccurate, the condition that the pressure adjustment is too slow easily occurs only by extruding feedback information of the specification of the ground material, so that more materials exceed the specification and are scrapped.
Disclosure of Invention
The invention provides a pressure correction method, a device, equipment and a storage medium, which are used for rapidly correcting the initial pressure of a roller press during grinding materials and ensuring the working efficiency of the roller press.
According to an aspect of the present invention, there is provided a pressure correction method, the method comprising:
acquiring historical material data of a roller press, and establishing a pressure prediction model according to the historical material data, wherein the pressure prediction model comprises a corresponding relation between the material data and steady-state pressure;
acquiring material data to be detected, and determining predicted steady-state pressure according to the material data to be detected and a pressure prediction model;
and carrying out pressure correction of the roller press according to the predicted steady-state pressure.
Optionally, acquiring historical material data of the roller press includes: acquiring production related data in a specified time range of the roller press, wherein the production related data comprises steady-state pressure, stub bar specification, target specification and initial pressure; determining production batches corresponding to the production related data, and generating a data group according to the production batches and the production related data, wherein the data group comprises the production related data of the same production batch; each data packet is used as historical material data.
Optionally, building a pressure prediction model according to the historical material data includes: constructing a network structure of a multiple regression model, and determining model parameters corresponding to the network structure, wherein the model parameters comprise intercept items, thickness deviation change amounts corresponding to unit steady-state pressure differences and thickness deviation change amounts corresponding to unit pressure release; training model parameters according to historical material data, and determining a training error function by adopting a least square method; when the training error function is smaller than a preset threshold value, taking the corresponding model parameter as a target model parameter; and establishing a pressure prediction model according to the target model parameters.
Optionally, acquiring data of the material to be measured includes: determining a current production batch, and acquiring a current stub bar specification and a target stub bar specification corresponding to the current production batch; acquiring a last steady-state pressure and a last initial pressure corresponding to a last batch according to a current production batch; and taking the current material head specification, the target material head specification, the last steady-state pressure and the last initial pressure as material data to be measured.
Optionally, determining the predicted steady-state pressure according to the material data to be measured and the pressure prediction model includes: calculating the difference between the last steady-state pressure and the last initial pressure to determine a pressure relief change value; and inputting the current stub bar specification, the target stub bar specification, the last steady-state pressure and the pressure relief change value into a pressure prediction model to obtain the output predicted steady-state pressure.
Optionally, the pressure correction of the roller press is performed according to the predicted steady-state pressure, including: acquiring a target steady-state speed, and determining a target pressure increment according to the target steady-state speed; taking the difference between the predicted steady-state pressure and the target pressure increment as the target correction pressure; and carrying out pressure correction of the roller press according to the target correction pressure.
Optionally, determining the target pressure increase based on the target steady state speed includes: acquiring a pressure increment list, wherein the pressure increment list comprises pressure increments corresponding to the speed variation; acquiring the current speed of the roller press, and calculating the difference between the current speed and the target steady-state speed as a target speed variation; and matching the target speed variation through the pressure increment list to obtain a target pressure increment corresponding to the target speed variation.
According to another aspect of the present invention, there is provided a pressure correction device, the device comprising:
the pressure prediction model building module is used for obtaining historical material data of the roller press and building a pressure prediction model according to the historical material data, wherein the pressure prediction model comprises a corresponding relation between the material data and steady-state pressure;
the predicted steady-state pressure determining module is used for acquiring the data of the material to be detected and determining the predicted steady-state pressure according to the data of the material to be detected and the pressure predicting model;
and the roller press pressure correction module is used for correcting the pressure of the roller press according to the predicted steady-state pressure.
According to another aspect of the present invention, there is provided an electronic apparatus including:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform a pressure correction method according to any one of the embodiments of the present invention.
According to another aspect of the present invention, there is provided a computer readable storage medium storing computer instructions for causing a processor to execute a pressure correction method according to any one of the embodiments of the present invention.
According to the technical scheme, the pressure prediction model is established by acquiring historical material data of the roller press based on a big data statistical regression modeling method, the steady-state pressure of the next batch can be predicted according to the steady-state pressure of the previous batch, then the corresponding target pressure increment is determined according to the target steady-state speed set by a user, and finally the difference value between the predicted steady-state pressure and the target pressure increment is taken as the target correction pressure, so that the pressure correction is realized, the problem of material rejection caused by too slow pressure adjustment is solved, the pressure can be effectively and quickly adjusted, the pressure adjustment efficiency is improved, the occurrence of materials with excessive specifications is reduced, and the production quality is ensured.
It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the invention or to delineate the scope of the invention. Other features of the present invention will become apparent from the description that follows.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a pressure correction method according to a first embodiment of the present invention;
FIG. 2 is a flow chart of another pressure correction method according to a second embodiment of the present invention;
fig. 3 is a schematic structural view of a pressure correction device according to a third embodiment of the present invention;
fig. 4 is a schematic structural diagram of an electronic device implementing a pressure correction method according to an embodiment of the present invention.
Detailed Description
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present invention and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Example 1
Fig. 1 is a flowchart of a pressure correction method according to an embodiment of the present invention, which is applicable to a case where a roll squeezer squeezes a ground material, and the method may be performed by a pressure correction device, which may be implemented in hardware and/or software, and the pressure correction device may be configured in a computer controller. As shown in fig. 1, the method includes:
s110, acquiring historical material data of the roller press, and establishing a pressure prediction model according to the historical material data.
The roller press is grinding equipment for extruding and grinding materials to specified specifications, and is also called a calender, a twin-roll machine, a rolling mill, an extrusion mill and a rolling mill. The roll squeezer comprises two squeeze rolls which rotate synchronously in opposite directions, and when the roll squeezer works, materials are fed from the upper parts of the two squeeze rolls and are continuously taken into the space between the squeeze rolls, so that the materials are crushed under the action of high pressure. In the grinding process of the roller press, equipment parameters such as pressure are preset according to the physical properties of the raw materials, and when the physical properties of the raw materials change, the equipment parameters of the roller press are required to be adjusted in time so as to ensure the stability of production and the safety of equipment. The historical material data refers to the historical data of the materials ground by the roller press, and comprises steady-state pressure of each batch of materials in stable production in continuous production, first specification data of each batch of materials in total material head production and initial pressure at the moment. The extrusion grinding effect of the same pressure on the materials is different at different speeds; the faster the speed, the more pressure is required to achieve the same squeeze grinding effect; therefore, after the production of one batch of materials is finished, the pressure is required to be relieved, so that the low-speed production of the material head stage of the next batch of materials can be met.
Specifically, a pressure prediction model can be established according to the acquired historical material data, the pressure prediction model comprises a corresponding relation between the material data and the steady-state pressure, and the pressure prediction model can be used for predicting the next steady-state pressure according to the last steady-state pressure, so that pressure correction is realized.
Optionally, acquiring historical material data of the roller press includes: acquiring production related data in a specified time range of the roller press, wherein the production related data comprises steady-state pressure, stub bar specification, target specification and initial pressure; determining production batches corresponding to the production related data, and generating a data group according to the production batches and the production related data, wherein the data group comprises the production related data of the same production batch; each data packet is used as historical material data.
Specifically, the specified time range can be set according to the needs of a user, and when data are acquired, the controller can acquire continuous production related data of a plurality of batches of the roller press according to the specified time range, wherein the controller refers to a computer controller for pressure correction, and the production related data comprise steady-state pressure, stub bar specification, target specification and initial pressure. It should be noted that the pressure is an important parameter for the stable operation of the roll squeezer, and has a direct influence on the extrusion effect of the roll squeezer. However, the extrusion force of the roller press cannot be too large or too small, the roller surface is greatly abraded due to the fact that the extrusion effect is affected due to the fact that the extrusion force is too small. The steady-state pressure is the pressure corresponding to the stable operation of the roller press, the material head specification is the initial size information of the material, the target specification is the grinding size information set by a user, and the roller press can grind the material according to the target specification set by the user so that the material head specification reaches the target specification. The initial pressure is the corresponding pressure of the material just entering the roll press. Further, the controller determines production lots corresponding to the production related data, and then divides the production related data of the same production lot into the same data group so as to establish a pressure prediction model in the form of the data group. Grouping by production lot is to better establish the relationship between the previous lot data and the next lot data when model training is performed, so as to further perform pressure prediction.
Optionally, building a pressure prediction model according to the historical material data includes: constructing a network structure of a multiple regression model, and determining model parameters corresponding to the network structure, wherein the model parameters comprise intercept items, thickness deviation change amounts corresponding to unit steady-state pressure differences and thickness deviation change amounts corresponding to unit pressure release; training model parameters according to historical material data, and determining a training error function by adopting a least square method; when the training error function is smaller than a preset threshold value, taking the corresponding model parameter as a target model parameter; and establishing a pressure prediction model according to the target model parameters.
Specifically, the network structure of the multiple regression model is represented by the following formula (1):
Y stub bar -Y Target object ＝k 0 +k 1 *(P The next steady state pressure -P Last steady state pressure )+k 2 *ΔP Pressure relief (1)
Wherein Y is Stub bar Indicating the specification of the stub bar, Y Target object Representing the target specification, P Last steady state pressure Represents the steady state pressure of the previous batch, P The next steady state pressure Represents the steady state pressure of the next batch, ΔP Pressure relief Indicating the amount of pressure relief, i.e., the amount of change in the initial pressure and the steady state pressure. k (k) 0 、k 1 And k 2 As model parameters, k 0 Represents the intercept term, k 1 Representing the thickness deviation change amount corresponding to the unit steady-state pressure difference, namely when the steady-state pressure difference of the front roll and the back roll is 1 unit, the change amount of the thickness deviation，k 2 The change amount of the thickness deviation corresponding to the unit pressure relief, namely, the change amount of the thickness deviation when 1 unit pressure is released is shown. According to the historical material data, model parameters can be trained, a least square method is adopted for fitting in the training process, a training error function is determined, when the training error function is smaller than a preset threshold value, model training is completed, and at the moment, a pressure prediction model can be established according to the corresponding target model parameters.
S120, acquiring data of the material to be measured, and determining predicted steady-state pressure according to the data of the material to be measured and the pressure prediction model.
Optionally, acquiring data of the material to be measured includes: determining a current production batch, and acquiring a current stub bar specification and a target stub bar specification corresponding to the current production batch; acquiring a last steady-state pressure and a last initial pressure corresponding to a last batch according to a current production batch; and taking the current material head specification, the target material head specification, the last steady-state pressure and the last initial pressure as material data to be measured.
Specifically, since the pressure prediction model predicts the next batch of data according to the previous batch of data, when steady-state pressure prediction is performed, the current production batch needs to be determined, then the current stub bar specification and the target stub bar specification corresponding to the current production batch are obtained, the last steady-state pressure and the last initial pressure corresponding to the last batch are obtained, and then the current stub bar specification, the target stub bar specification, the last steady-state pressure and the last initial pressure are used as the material data to be measured.
Optionally, determining the predicted steady-state pressure according to the material data to be measured and the pressure prediction model includes: calculating the difference between the last steady-state pressure and the last initial pressure to determine a pressure relief change value; and inputting the current stub bar specification, the target stub bar specification, the last steady-state pressure and the pressure relief change value into a pressure prediction model to obtain the output predicted steady-state pressure.
Specifically, the pressure relief change value is the difference between the last steady-state pressure and the last initial pressure, and after the pressure relief change value is determined, the current stub bar specification, the target stub bar specification, the last steady-state pressure and the pressure relief change value are input into a pressure prediction model, so that the predicted steady-state pressure output by the model can be obtained.
And S130, performing pressure correction of the roller press according to the predicted steady-state pressure.
Specifically, the predicted steady-state pressure output by the model is the predicted value of the steady-state pressure of the next batch, the initial pressure is adjusted according to the predicted steady-state pressure, and the initial pressure is quickly corrected by a roll squeezer control method of big data statistical regression modeling.
According to the technical scheme, the pressure prediction model is established by acquiring historical material data of the roller press based on a big data statistical regression modeling method, the steady-state pressure of the next batch can be predicted according to the steady-state pressure of the previous batch, then the corresponding target pressure increment is determined according to the target steady-state speed set by a user, and finally the difference value between the predicted steady-state pressure and the target pressure increment is taken as the target correction pressure, so that the pressure correction is realized, the problem of material rejection caused by too slow pressure adjustment is solved, the pressure can be effectively and quickly adjusted, the pressure adjustment efficiency is improved, the occurrence of materials with excessive specifications is reduced, and the production quality is ensured.
Example two
Fig. 2 is a flowchart of a pressure correction method according to a second embodiment of the present invention, in which a specific process of performing pressure correction of a roll squeezer according to a predicted steady-state pressure is added on the basis of the first embodiment. As shown in fig. 2, the method includes:
s210, acquiring historical material data of the roller press, and establishing a pressure prediction model according to the historical material data, wherein the pressure prediction model comprises a corresponding relation between the material data and steady-state pressure.
Optionally, acquiring historical material data of the roller press includes: acquiring production related data in a specified time range of the roller press, wherein the production related data comprises steady-state pressure, stub bar specification, target specification and initial pressure; determining production batches corresponding to the production related data, and generating a data group according to the production batches and the production related data, wherein the data group comprises the production related data of the same production batch; each data packet is used as historical material data.
Optionally, building a pressure prediction model according to the historical material data includes: constructing a network structure of a multiple regression model, and determining model parameters corresponding to the network structure, wherein the model parameters comprise intercept items, thickness deviation change amounts corresponding to unit steady-state pressure differences and thickness deviation change amounts corresponding to unit pressure release; training model parameters according to historical material data, and determining a training error function by adopting a least square method; when the training error function is smaller than a preset threshold value, taking the corresponding model parameter as a target model parameter; and establishing a pressure prediction model according to the target model parameters.
S220, acquiring data of the material to be measured, and determining predicted steady-state pressure according to the data of the material to be measured and the pressure prediction model.
Optionally, acquiring data of the material to be measured includes: determining a current production batch, and acquiring a current stub bar specification and a target stub bar specification corresponding to the current production batch; acquiring a last steady-state pressure and a last initial pressure corresponding to a last batch according to a current production batch; and taking the current material head specification, the target material head specification, the last steady-state pressure and the last initial pressure as material data to be measured.
Optionally, determining the predicted steady-state pressure according to the material data to be measured and the pressure prediction model includes: calculating the difference between the last steady-state pressure and the last initial pressure to determine a pressure relief change value; and inputting the current stub bar specification, the target stub bar specification, the last steady-state pressure and the pressure relief change value into a pressure prediction model to obtain the output predicted steady-state pressure.
S230, acquiring a target steady-state speed, and determining a target pressure increment according to the target steady-state speed.
Optionally, determining the target pressure increase based on the target steady state speed includes: acquiring a pressure increment list, wherein the pressure increment list comprises pressure increments corresponding to the speed variation; acquiring the current speed of the roller press, and calculating the difference between the current speed and the target steady-state speed as a target speed variation; and matching the target speed variation through the pressure increment list to obtain a target pressure increment corresponding to the target speed variation.
Specifically, the pressure increment list is a list set by a user according to manual experience and historical numerical values, wherein the list comprises pressure increments corresponding to various speed variation amounts, and the pressure increments can be positive values or negative values. After finishing a batch of materials, the roller press also needs to determine the current speed of the roller press because the rotating speed of the motor is not necessarily returned to 0 due to the inertia effect, then the difference between the current speed and the target steady-state speed is calculated to determine the target speed variation, and the target speed variation can be matched through the pressure increment list, so that the increment of pressure, namely the target pressure increment, when the speed is from the current speed to the target steady-state speed can be determined.
S240, taking the difference value between the predicted steady-state pressure and the target pressure increment as the target correction pressure.
S250, performing pressure correction of the roller press according to the target correction pressure.
Specifically, according to the predicted steady-state pressure predicted by the model, the initial pressure during the production of the material head can be quickly corrected by utilizing the pressure increment in the speed-up process, specifically, the target correction pressure can be determined by calculating the difference between the predicted steady-state pressure and the target pressure increment, the target correction pressure is the corrected pressure, and the controller finally realizes pressure correction through the target correction pressure.
According to the technical scheme, the pressure prediction model is established by acquiring historical material data of the roller press based on a big data statistical regression modeling method, the steady-state pressure of the next batch can be predicted according to the steady-state pressure of the previous batch, then the corresponding target pressure increment is determined according to the target steady-state speed set by a user, and finally the difference value between the predicted steady-state pressure and the target pressure increment is taken as the target correction pressure, so that the pressure correction is realized, the problem of material rejection caused by too slow pressure adjustment is solved, the pressure can be effectively and quickly adjusted, the pressure adjustment efficiency is improved, the occurrence of materials with excessive specifications is reduced, and the production quality is ensured.
Example III
Fig. 3 is a schematic structural diagram of a pressure correction device according to a third embodiment of the present invention. As shown in fig. 3, the apparatus includes:
the pressure prediction model building module 310 is configured to obtain historical material data of the roller press, and build a pressure prediction model according to the historical material data, where the pressure prediction model includes a correspondence between material data and steady-state pressure;
the predicted steady-state pressure determining module 320 is configured to obtain data of a material to be measured, and determine a predicted steady-state pressure according to the data of the material to be measured and the pressure prediction model;
the roll squeezer pressure correction module 330 is used for correcting the roll squeezer pressure according to the predicted steady-state pressure.
Optionally, the pressure prediction model building module 310 specifically includes: the historical material data acquisition unit is used for: acquiring production related data in a specified time range of the roller press, wherein the production related data comprises steady-state pressure, stub bar specification, target specification and initial pressure; determining production batches corresponding to the production related data, and generating a data group according to the production batches and the production related data, wherein the data group comprises the production related data of the same production batch; each data packet is used as historical material data.
Optionally, the pressure prediction model building module 310 specifically includes: the pressure prediction model building unit is used for: constructing a network structure of a multiple regression model, and determining model parameters corresponding to the network structure, wherein the model parameters comprise intercept items, thickness deviation change amounts corresponding to unit steady-state pressure differences and thickness deviation change amounts corresponding to unit pressure release; training model parameters according to historical material data, and determining a training error function by adopting a least square method; when the training error function is smaller than a preset threshold value, taking the corresponding model parameter as a target model parameter; and establishing a pressure prediction model according to the target model parameters.
Optionally, the predicted steady-state pressure determining module 320 specifically includes: the material data acquisition unit to be measured is used for: determining a current production batch, and acquiring a current stub bar specification and a target stub bar specification corresponding to the current production batch; acquiring a last steady-state pressure and a last initial pressure corresponding to a last batch according to a current production batch; and taking the current material head specification, the target material head specification, the last steady-state pressure and the last initial pressure as material data to be measured.
Optionally, the predicted steady-state pressure determining module 320 specifically includes: a predicted steady-state pressure determination unit configured to: calculating the difference between the last steady-state pressure and the last initial pressure to determine a pressure relief change value; and inputting the current stub bar specification, the target stub bar specification, the last steady-state pressure and the pressure relief change value into a pressure prediction model to obtain the output predicted steady-state pressure.
Optionally, the roll squeezer pressure correction module 330 specifically includes: a target pressure increase determining unit for: acquiring a target steady-state speed, and determining a target pressure increment according to the target steady-state speed; a target corrected pressure calculation unit configured to: taking the difference between the predicted steady-state pressure and the target pressure increment as the target correction pressure; a pressure correction unit for: and carrying out pressure correction of the roller press according to the target correction pressure.
Optionally, the target pressure increase determining unit is specifically configured to: acquiring a pressure increment list, wherein the pressure increment list comprises pressure increments corresponding to steady-state speeds; and matching the target steady-state speed through the pressure increment list to obtain a target pressure increment corresponding to the target steady-state speed.
According to the technical scheme, the pressure prediction model is established by acquiring historical material data of the roller press based on a big data statistical regression modeling method, the steady-state pressure of the next batch can be predicted according to the steady-state pressure of the previous batch, then the corresponding target pressure increment is determined according to the target steady-state speed set by a user, and finally the difference value between the predicted steady-state pressure and the target pressure increment is taken as the target correction pressure, so that the pressure correction is realized, the problem of material rejection caused by too slow pressure adjustment is solved, the pressure can be effectively and quickly adjusted, the pressure adjustment efficiency is improved, the occurrence of materials with excessive specifications is reduced, and the production quality is ensured.
The pressure correction device provided by the embodiment of the invention can execute the pressure correction method provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.
Example IV
Fig. 4 shows a schematic diagram of the structure of an electronic device 10 that may be used to implement an embodiment of the invention. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. Electronic equipment may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.
As shown in fig. 4, the electronic device 10 includes at least one processor 11, and a memory, such as a Read Only Memory (ROM) 12, a Random Access Memory (RAM) 13, etc., communicatively connected to the at least one processor 11, in which the memory stores a computer program executable by the at least one processor, and the processor 11 may perform various appropriate actions and processes according to the computer program stored in the Read Only Memory (ROM) 12 or the computer program loaded from the storage unit 18 into the Random Access Memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 may also be stored. The processor 11, the ROM 12 and the RAM 13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to bus 14.
Various components in the electronic device 10 are connected to the I/O interface 15, including: an input unit 16 such as a keyboard, a mouse, etc.; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, an optical disk, or the like; and a communication unit 19 such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.
The processor 11 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, digital Signal Processors (DSPs), and any suitable processor, controller, microcontroller, etc. The processor 11 performs the various methods and processes described above, such as a pressure correction method. Namely: acquiring historical material data of a roller press, and establishing a pressure prediction model according to the historical material data, wherein the pressure prediction model comprises a corresponding relation between the material data and steady-state pressure; acquiring material data to be detected, and determining predicted steady-state pressure according to the material data to be detected and a pressure prediction model; and carrying out pressure correction of the roller press according to the predicted steady-state pressure.
In some embodiments, a pressure correction method may be implemented as a computer program tangibly embodied on a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 10 via the ROM 12 and/or the communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of a pressure correction method as described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform a pressure correction method in any other suitable manner (e.g., by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
A computer program for carrying out methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be implemented. The computer program may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) through which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), blockchain networks, and the internet.
The computing system may include clients and servers. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical hosts and VPS service are overcome.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps described in the present invention may be performed in parallel, sequentially, or in a different order, so long as the desired results of the technical solution of the present invention are achieved, and the present invention is not limited herein.
The above embodiments do not limit the scope of the present invention. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of the present invention.
Claims (10)
1. A method of pressure correction, comprising:
acquiring historical material data of a roller press, and establishing a pressure prediction model according to the historical material data, wherein the pressure prediction model comprises a corresponding relation between material data and steady-state pressure;
acquiring material data to be detected, and determining predicted steady-state pressure according to the material data to be detected and the pressure prediction model;
and carrying out pressure correction on the roller press according to the predicted steady-state pressure.
2. The method of claim 1, wherein the acquiring historical material data for the roller press comprises:
acquiring production related data in a specified time range of a roller press, wherein the production related data comprises steady-state pressure, stub bar specification, target specification and initial pressure;
determining production batches corresponding to the production related data, and generating a data group according to the production batches and the production related data, wherein the data group comprises production related data of the same production batch;
and taking each data packet as the historical material data.
3. The method of claim 1, wherein said building a pressure prediction model from said historical material data comprises:
building a network structure of a multiple regression model, and determining model parameters corresponding to the network structure, wherein the model parameters comprise intercept items, thickness deviation change amounts corresponding to unit steady-state pressure differences and thickness deviation change amounts corresponding to unit pressure release;
training the model parameters according to the historical material data, and determining a training error function by adopting a least square method;
when the training error function is smaller than a preset threshold value, taking the corresponding model parameter as a target model parameter;
and establishing a pressure prediction model according to the target model parameters.
4. The method of claim 1, wherein the acquiring the data of the material to be measured comprises:
determining a current production batch, and acquiring a current stub bar specification and a target stub bar specification corresponding to the current production batch;
acquiring a last steady-state pressure and a last initial pressure corresponding to a last batch according to the current production batch;
and taking the current material head specification, the target material head specification, the last steady-state pressure and the last initial pressure as the material data to be measured.
5. The method of claim 4, wherein said determining a predicted steady state pressure from said material under test data and said pressure prediction model comprises:
calculating the difference between the last steady-state pressure and the last initial pressure to determine a pressure relief change value;
and inputting the current stub bar specification, the target stub bar specification, the last steady-state pressure and the pressure relief change value into the pressure prediction model to obtain the output predicted steady-state pressure.
6. The method of claim 1, wherein said performing pressure correction of the roller press based on said predicted steady state pressure comprises:
obtaining a target steady-state speed, and determining a target pressure increment according to the target steady-state speed;
taking the difference between the predicted steady-state pressure and the target pressure increment as a target correction pressure;
and carrying out pressure correction of the roller press according to the target correction pressure.
7. The method of claim 6, wherein said determining a target pressure increase from said target steady state speed comprises:
acquiring a pressure increment list, wherein the pressure increment list comprises pressure increments corresponding to speed variation;
acquiring the current speed of a roller press, and calculating the difference between the current speed and the target steady-state speed as a target speed variation;
and matching the target speed variation through the pressure increment list to obtain a target pressure increment corresponding to the target speed variation.
8. A pressure correction device, comprising:
the pressure prediction model building module is used for obtaining historical material data of the roller press and building a pressure prediction model according to the historical material data, wherein the pressure prediction model comprises a corresponding relation between material data and steady-state pressure;
the predicted steady-state pressure determining module is used for acquiring material data to be detected and determining predicted steady-state pressure according to the material data to be detected and the pressure prediction model;
and the roller press pressure correction module is used for correcting the pressure of the roller press according to the predicted steady-state pressure.
9. An electronic device, the electronic device comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-7.
10. A computer storage medium storing computer instructions for causing a processor to perform the method of any one of claims 1-7 when executed.
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| CN117619903A (en) * | 2024-01-25 | 2024-03-01 | 邢台纳科诺尔精轧科技股份有限公司 | Rolling control method and device |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117619903A (en) * | 2024-01-25 | 2024-03-01 | 邢台纳科诺尔精轧科技股份有限公司 | Rolling control method and device |
| CN117619903B (en) * | 2024-01-25 | 2024-03-29 | 邢台纳科诺尔精轧科技股份有限公司 | Rolling control method and device |
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