CN113935711A - Metallurgical production method and device, electronic equipment and storage medium - Google Patents

Metallurgical production method and device, electronic equipment and storage medium Download PDF

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CN113935711A
CN113935711A CN202111207193.4A CN202111207193A CN113935711A CN 113935711 A CN113935711 A CN 113935711A CN 202111207193 A CN202111207193 A CN 202111207193A CN 113935711 A CN113935711 A CN 113935711A
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丁宏翔
顾锡昌
沙周凤
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Suzhou Fangxing Information Technology Co Ltd
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Abstract

The application provides a metallurgical production method, a metallurgical production device, electronic equipment and a storage medium, and belongs to the technical field of metallurgy. The method comprises the following steps: inputting live-action data into a twin data module to obtain derivative data output by the twin data module; inputting the live-action data and the derivative data into a twin scene module to obtain deduction data output by the twin scene module; intelligently analyzing the live-action data, the derivative data and the deduction data through an AI processing module to obtain control data; and controlling the metallurgical device to perform metallurgical production according to the control data, wherein the AI processing module is connected with each interface of the metallurgical device. This application has improved metallurgical production efficiency.

Description

Metallurgical production method and device, electronic equipment and storage medium
Technical Field
The present application relates to the field of metallurgical technology, and in particular, to a metallurgical production method, apparatus, electronic device, and storage medium.
Background
In the metallurgical industry, the metallurgical production process is complex, various mechanical equipment and raw material substances are used, the quality of metallurgical products is influenced by production factors such as equipment parameters, raw material categories and raw material proportions and environmental factors such as temperature, humidity and weather conditions, the quality of the current metallurgical products is mainly determined by manually operating a quality detector by technicians, a large amount of manpower and financial resources are consumed, and the metallurgical production efficiency is reduced. In addition, technicians can approach and operate metallurgical equipment with unknown safety risks.
At present, no good solution is available for the conditions of low metallurgical production efficiency and personnel safety risk.
Disclosure of Invention
An object of the embodiments of the present application is to provide a metallurgical production method, apparatus, electronic device, and storage medium, so as to solve the problems of low metallurgical production efficiency and personnel safety risk. The specific technical scheme is as follows:
in a first aspect, there is provided a metallurgical production process, the process comprising:
inputting live-action data into a twin data module to obtain derivative data output by the twin data module, wherein the live-action data comprises environmental data of an environment where a metallurgical device is located and production data required by the metallurgical device for executing steps, the derivative data is obtained according to the production data, the metallurgical device comprises at least one piece of metallurgical equipment, and the environmental data comprises a personnel space position and an equipment space position;
inputting the live-action data and the derivative data into a twin scene module to obtain deduction data output by the twin scene module, wherein the twin scene module comprises a twin scene constructed based on the live-action data and the derivative data, and the deduction data comprises personnel safety data and virtual product data;
intelligently analyzing the live-action data, the derivative data and the deduction data through an AI processing module to obtain control data;
and controlling the metallurgical device to perform metallurgical production according to the control data, wherein the AI processing module is connected with each interface of the metallurgical device.
Optionally, the intelligently analyzing the live-action data, the derived data, and the derived data by the AI processing module to obtain the operation data includes:
inputting the live-action data, the derived data and the derived data into the AI processing module;
determining that the metallurgical device is currently in a normal production mode under the condition that the deduction data is determined to be within a preset data range through the AI processing module;
and adjusting the live-action data through the AI processing module to obtain an optimal production scheme, and taking the live-action data corresponding to the optimal production scheme and the derivative data corresponding to the optimal production scheme as the control data, wherein different live-action data correspond to different simulation production schemes.
Optionally, the adjusting the live-action data by the AI processing module to obtain an optimal production scheme includes:
performing change simulation according to the environment data and the production data to obtain a plurality of simulated production schemes, wherein the change simulation comprises at least one of time lapse simulation and scene change simulation, and the simulated production schemes comprise equipment capacity schemes or equipment utilization schemes;
and taking the simulation production scheme with the highest equipment capacity or equipment utilization rate as the optimal generation scheme.
Optionally, after inputting the live-action data, the derived data and the derived data into the AI processing module, the method further comprises, by the AI processing module:
determining that the metallurgical device is currently in an abnormal production mode under the condition that the deduction data is determined to be beyond the preset data range;
determining the influence range and the influence depth of the abnormal production mode;
determining an anomaly level based on the impact range and the impact depth;
determining an abnormal reason generated by the abnormal production mode under the condition that the abnormal grade is smaller than a preset grade threshold;
and searching an abnormal solution corresponding to the abnormal reason from a database, and switching the metallurgical device to a normal production mode based on the abnormal solution, wherein the abnormal solution comprises control data, and the control data comprises adjusted live-action data and adjusted derivative data.
Optionally, the determining the influence range and the influence depth of the abnormal production mode includes:
performing a time lapse simulation according to the equipment space position and the personnel space position of the at least one metallurgical equipment;
acquiring a dangerous event in the time lapse simulation process, wherein the dangerous event is that the distance between the personnel space position and the equipment space position is smaller than a safety distance threshold value;
determining an impact range and an impact depth of the hazardous event.
Optionally, the determining the influence range and the influence depth of the abnormal production mode includes:
carrying out time lapse simulation according to the environment data and the production data to obtain simulated production schemes corresponding to different moments, wherein the simulated production schemes comprise equipment capacity or equipment utilization rate;
and determining the influence range and the influence depth of the equipment capacity or the equipment utilization rate.
Optionally, before the inputting the live-action data and the derivative data into the twin scene module, the method further comprises:
adopting three-dimensional modeling software to construct a three-dimensional model corresponding to the metallurgical scene;
processing the live-action data, the derived data and the three-dimensional model based on a digital twinning technology to obtain a unidirectional twinning scene;
and connecting the unidirectional twin scene with each operation interface of the metallurgical device to obtain a bidirectional interactive twin scene module.
In a second aspect, there is provided a metallurgical production system, the system comprising:
the twin data module is used for obtaining derived data according to live-action data, wherein the live-action data comprises environmental data of the environment where the metallurgical device is located and production data required by the step to be executed of the metallurgical device, the derived data is obtained according to the production data, the metallurgical device comprises at least one piece of metallurgical equipment, and the environmental data comprises a personnel space position and an equipment space position;
the twin scene module is used for obtaining deduction data according to the live-action data and the derivative data, wherein the twin scene module comprises a twin scene constructed based on the live-action data and the derivative data, and the deduction data comprises personnel safety data and virtual product data;
the AI processing module is used for carrying out intelligent analysis according to the live-action data, the derivative data and the deduction data to obtain control data;
and the live-action control module is used for controlling the metallurgical device to perform metallurgical production according to the control data, wherein the AI processing module is connected with each interface of the metallurgical device.
In a third aspect, there is provided a metallurgical production apparatus, the apparatus comprising:
the first input and output module is used for inputting live-action data into the twin data module to obtain derivative data output by the twin data module, wherein the live-action data comprises environmental data of an environment where a metallurgical device is located and production data required by the metallurgical device for executing steps, the derivative data is obtained according to the production data, the metallurgical device comprises at least one piece of metallurgical equipment, and the environmental data comprises a personnel space position and an equipment space position;
the second input and output module is used for inputting the live-action data and the derivative data into the twin scene module to obtain deduction data output by the twin scene module, wherein the twin scene module comprises a twin scene constructed based on the live-action data and the derivative data, and the deduction data comprises personnel safety data and virtual product data;
the analysis module is used for intelligently analyzing the live-action data, the derivative data and the deduction data through the AI processing module to obtain control data;
and the control module is used for controlling the metallurgical device to perform metallurgical production according to the control data, wherein the AI processing module is connected with each interface of the metallurgical device.
In a fourth aspect, an electronic device is provided, which includes a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory complete communication with each other through the communication bus;
a memory for storing a computer program;
and the processor is used for realizing any step of the metallurgical production method when executing the program stored in the memory.
In a fifth aspect, a computer-readable storage medium is provided, having a computer program stored therein, which computer program, when being executed by a processor, carries out any of the metallurgical production method steps.
The embodiment of the application has the following beneficial effects:
the embodiment of the application provides a metallurgical production method, which comprises the following steps: inputting live-action data into the twin data module to obtain derivative data output by the twin data module, inputting the live-action data and the derivative data into the twin scene module to obtain derivative data output by the twin scene module, intelligently analyzing the live-action data, the derivative data and the derivative data through the AI processing module to obtain control data, and controlling the metallurgical device to perform metallurgical production according to the control data, wherein the AI processing module is connected with each interface of the metallurgical device.
According to the method and the device, on one hand, a metallurgical production scheme is automatically optimized, a metallurgical result does not need to be manually evaluated, the metallurgical production efficiency is improved, on the other hand, the AI processing module uses environmental data which comprises a personnel space position and an equipment space position, and the AI processing module can analyze according to the distance between the personnel space position and the equipment space position, so that the personnel safety risk is reduced.
Of course, not all of the above advantages need be achieved in the practice of any one product or method of the present application.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly described below, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
FIG. 1 is a schematic diagram of a hardware environment of a metallurgical production method provided in an embodiment of the present application;
FIG. 2 is a flow chart of a method of metallurgical production provided by an embodiment of the present application;
FIG. 3 is a schematic structural diagram of a metallurgical production apparatus provided in an embodiment of the present application;
fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
In the following description, suffixes such as "module", "component", or "unit" used to denote elements are used only for the convenience of description of the present application, and have no specific meaning in themselves. Thus, "module" and "component" may be used in a mixture.
To address the problems noted in the background, according to an aspect of embodiments of the present application, embodiments of a metallurgical production method are provided.
Alternatively, in the embodiment of the present application, the above-described metallurgical production method may be applied to a hardware environment composed of the metallurgical device 101 and the server 103 as shown in fig. 1. As shown in fig. 1, the server 103 is connected to the metallurgical device 101 via a network, which may be used to provide services for the metallurgical device, and a database 105 may be provided on or separate from the server for providing data storage services for the server 103, including but not limited to: a wide area network, a metropolitan area network, or a local area network.
The metallurgical production method in the embodiment of the application can be executed by the server 103 and is used for controlling the metallurgical production and improving the efficiency and the safety of the metallurgical production.
The application also provides a metallurgical production system, which comprises a twin data module, a twin scene module, an AI processing module and a live-action control module which are sequentially connected, wherein the twin data module is used for obtaining derived data according to the live-action data, the live-action data comprises environmental data of the environment where the metallurgical device is located and production data required by the step to be executed of the metallurgical device, the derived data is obtained according to the production data, the metallurgical device comprises at least one piece of metallurgical equipment, and the environmental data comprises a personnel space position and an equipment space position; the twin scene module is used for obtaining deduction data according to the live-action data and the derivative data, wherein the twin scene module comprises a twin scene constructed based on the live-action data and the derivative data, and the deduction data comprises personnel safety data and virtual product data; the AI processing module is used for carrying out intelligent analysis according to the live-action data, the derivative data and the deduction data to obtain control data; and the live-action control module is used for controlling the metallurgical device to perform metallurgical production according to the control data, wherein the AI processing module is connected with each interface of the metallurgical device.
A metallurgical production method provided in the examples of the present application will be described in detail below with reference to specific embodiments, as shown in fig. 2, the specific steps are as follows:
step 201: and inputting the live-action data into the twin data module to obtain the derivative data output by the twin data module.
The real-world data comprises environmental data of the environment where the metallurgical device is located and production data required by the metallurgical device to be executed, the derived data is obtained according to the production data, the metallurgical device comprises at least one piece of metallurgical equipment, and the environmental data comprises personnel space positions and equipment space positions.
In the embodiment of the present application, the metallurgical device includes at least one metallurgical equipment, and exemplarily, the metallurgical device includes a feeding device (a bridge ore storage tank, a coke bin, a weighing car), a loading device (a skip, a skew bridge and a winch), a loading device (a hopper, a rotary distributor, a large and small bell hopper, a large and small bell balance rod, a pressure equalizing valve, a pressure equalizing relief valve, a transmission system and an airtight box), an auxiliary device (a gas dust removal device and an air supply device), and the like.
Environmental data of the environment in which the metallurgical equipment is located, including but not limited to temperature, humidity, wind, personnel space location, equipment space location, etc., can affect the quality of the metallurgical product and personnel safety. The temperature, humidity and wind power can affect the quality of metallurgical products, and the spatial position of personnel and the spatial position of equipment can affect the safety of personnel. Production data of metallurgical equipment, including but not limited to ignition temperature, sintering speed, sintering negative pressure, mineral composition, mineral mix ratio, equipment parameters, equipment operating conditions, etc., also affect product quality.
The method comprises the steps that a server obtains real-scene data of the metallurgical device, wherein the real-scene data comprises environment data of the environment where the metallurgical device is located and production data required by steps to be executed of the metallurgical device, the steps to be executed can be the next step corresponding to the current execution step, or all steps after the current execution step of equipment, or the whole steps to be executed after the equipment is started.
The server inputs the live-action data into the twin data module to obtain derivative data output by the twin data module, wherein the twin data is data which cannot be directly obtained by the server but can affect metallurgy, and the twin data comprises but is not limited to equipment loss degree, sinter alkalinity and liquid phase fluidity. Wherein the twin data module may be a deep neural network.
Step 202: and inputting the live-action data and the derivative data into the twin scene module to obtain deduction data output by the twin scene module.
The twin scene module comprises a twin scene constructed based on live-action data and derivative data, and the deduction data comprises personnel safety data and virtual product data.
After the server obtains the derived data, a twin scene is constructed based on the live-action data and the derived data, wherein the twin scene is a metallurgical scene which is carried out by the steps of 1: 1, a virtual scene is restored, a twin scene changes in real time along with the change of the live-action data and the derivative data, and the twin scene simulates a metallurgical scene under a real condition to carry out metallurgical production. And the server inputs the live-action data and the derivative data into the twin scene module to obtain deduction data output by the twin scene module, wherein the deduction data comprises personnel safety data and virtual product data obtained after the simulated metallurgical production.
Step 203: and intelligently analyzing the live-action data, the derivative data and the deduction data through the AI processing module to obtain the control data.
And the server inputs the live-action data, the derivative data and the deduction data into the AI processing module, and performs multi-dimensional intelligent analysis through the AI processing module to obtain control data, wherein the control data can be data for optimizing the production scheme in a normal production mode or data for solving an abnormal scheme in an abnormal production module.
Step 204: controlling the metallurgical device to perform metallurgical production according to the control data, wherein the AI processing module is connected with each interface of the metallurgical device.
The live-action control module is connected with each interface in the metallurgical device, acquires control data and sends the control data to the corresponding interface, so that the metallurgical device performs metallurgical production according to the control data.
According to the method and the device, the production data, the environment data and the derivative data are adopted for constructing the twin scene, the richness of the construction factors of the twin scene is increased, the reduction degree of the twin scene is higher, and the precision of the deduction data output by the twin scene is improved. Therefore, the control data output by the AI processing module is more accurate, the live-action control module automatically controls the metallurgical production according to the control data, and the metallurgical production scheme is optimized.
Compared with the prior art, the automatic optimization metallurgy production scheme on the one hand of this application need not the metallurgical result of artifical appraisal, has improved metallurgical production efficiency, and on the other hand, the AI processing module has used environmental data, and environmental data includes personnel spatial position and equipment spatial position, and the AI processing module can carry out the analysis according to the distance between personnel spatial position and the equipment spatial position, has reduced personnel's safety risk like this. On the other hand, the live-action control module controls the metallurgical production in real time according to the control data, twin simulation and actual control are integrated, and control has high real-time performance.
As an optional implementation manner, the intelligent analysis is performed according to the live-action data, the derivative data and the deduction data, and the operation and control data obtained include two situations, one is that the metallurgical device is currently in a normal production mode, the production scheme is optimized according to the operation and control data, the other is that the metallurgical device is currently in an abnormal production mode, and the metallurgical device is switched from the abnormal production mode to the normal production mode according to the operation and control data.
The first condition is as follows:
the server inputs the live-action data, the derived data and the deduction data into the AI processing module, the deduction data comprises personnel safety data and output virtual product data, and if the AI processing module determines that the deduction data is located in a preset data range, the situation that personnel have no safety risk and metallurgical products have no quality problem is shown. The virtual product data includes, but is not limited to, product purity, product hardness, and by-product ratio.
The AI processing module continuously adjusts live-action data, namely sintering negative pressure, mineral components, mineral proportion, equipment parameters, equipment running state, temperature, humidity, wind power and the like, so that a plurality of simulated production schemes can be obtained, the server selects an optimal production scheme from the plurality of simulated production schemes, live-action data corresponding to the optimal production scheme and derivative data corresponding to the optimal production scheme are used as control data, and then the live-action control module controls metallurgical production based on the control data. The adjustment of the live-action data corresponds to the adjustment of the simulation production scheme, and the adjustment of the simulation production scheme can be based on the adjustment of the live-action data category and can also be based on the adjustment of the live-action data size.
The optimal production scheme can be the highest equipment capacity, the highest equipment utilization rate, the highest product quality or the largest product quantity, and the optimal production scheme is not particularly limited and can be adjusted according to metallurgical requirements.
As an alternative embodiment, the obtaining of the optimal production scheme by adjusting the live-action data includes: performing change simulation according to the environmental data and the production data to obtain a plurality of simulated production schemes, wherein the change simulation comprises at least one of time lapse simulation and scene change simulation, and the simulated production schemes comprise equipment capacity schemes or equipment utilization schemes; and taking the simulation production scheme with the highest equipment capacity or equipment utilization rate as the optimal generation scheme.
In this embodiment of the application, if the AI processing module determines that the derived data is within the preset data range, the AI processing module performs change simulation according to the environmental data and the production data, where the change simulation includes at least one of time-lapse simulation and scene-change simulation, that is, the AI processing module determines a simulated production scenario at different time, or determines a simulated production scenario in different scenes, or determines a simulated production scenario at different time and in different scenes.
At different times, environmental data (temperature, humidity, wind power, distance between a personnel space position and an equipment space position) and production data (sintering negative pressure and equipment running state) can be changed, and corresponding simulation production schemes can be changed.
In different scenes, production data (equipment quantity, equipment parameters, mineral components, mineral proportion, ignition temperature and sintering speed) can be changed, and corresponding simulation production schemes can be changed.
In the application, if the AI processing module determines that the personnel have no safety risk and the metallurgical product has no quality problem, at least one of time lapse simulation and scene change simulation is carried out on the basis, the simulation production scheme is continuously optimized, and then the optimal production scheme is selected out, so that the accuracy of the output control data can be improved, and the metallurgical production quality, quantity or speed can be improved.
Case two:
the server inputs the live-action data, the derived data and the derived data into the AI processing module, and if the AI processing module determines that the derived data exceeds the preset data range, the AI processing module determines that the metallurgical device is currently in an abnormal production mode, namely at least one abnormal condition of personnel safety risk or unqualified metallurgical product quality exists. The AI processing module determines an impact range and an impact depth of the abnormal production pattern and then determines an abnormality level based on the impact range and the impact depth.
If the AI processing module determines that the abnormal level is smaller than the preset level threshold value and indicates that the current abnormal condition is not serious, determining an abnormal reason generated in the abnormal production mode, searching an abnormal solution corresponding to the abnormal reason from the database, and switching the metallurgical device from the abnormal production mode to the normal production mode based on the abnormal solution. Wherein the exception solution includes manipulation data, the manipulation data including adjusted live-action data and adjusted derivative data.
If the AI processing module determines that the abnormal level is not less than the preset level threshold value, the current abnormal condition is serious, the distance between the personnel space position and the equipment space position is too small, and personnel safety risks can exist, the AI processing module controls the metallurgical device to stop emergently by controlling data, personnel are prevented from being injured, or controls a warning device on the metallurgical equipment to send out safety warning by controlling the data, and early warning of personnel safety is realized. The current abnormal condition is serious, the quality of the metallurgical product is too low, the AI processing module controls the metallurgical device to stop through controlling data, or controls a warning indicator on the metallurgical equipment to send out a fault warning through controlling data, and the detection and early warning of the production of the metallurgical product are realized.
As an alternative embodiment, the metallurgical plant includes at least one metallurgical equipment, the environmental data includes spatial location of personnel, and the determining the impact range and the impact depth of the abnormal production mode by the AI processing module includes: performing a time lapse simulation based on the equipment spatial position and the personnel spatial position of the at least one metallurgical equipment; acquiring a dangerous event in a time lapse simulation process, wherein the dangerous event is that the distance between a personnel space position and an equipment space position is smaller than a safety distance threshold; the impact range and impact depth of the hazardous event are determined.
In an embodiment of the present application, the abnormal production mode may be a safety risk for personnel. The metallurgical device comprises at least one metallurgical device, some metallurgical devices (material trucks) can move, some personnel in a metallurgical scene also continuously move, the AI processing module carries out time lapse simulation according to the device space position and the personnel space position of the at least one metallurgical device, namely, the distance between the device space position and the personnel space position at different times is obtained, if the AI processing module determines that the distance between the personnel space position and the device space position is smaller than a safety distance threshold value, namely, a dangerous event exists, the AI processing module determines the influence range and the influence depth of the dangerous event, and the influence range and the influence depth comprise but are not limited to the injury severity of personnel and the number of injured personnel.
As an alternative embodiment, the determining the influence range and the influence depth of the abnormal production mode by the AI processing module includes: performing change simulation according to the environmental data and the production data to obtain a plurality of simulated production schemes, wherein the change simulation comprises at least one of time lapse simulation and scene change simulation, and the simulated production schemes comprise equipment capacity schemes or equipment utilization schemes; and determining the influence range and the influence depth of the equipment capacity or the equipment utilization rate through the AI processing module.
In the present example, the abnormal production mode may be that the metallurgical solution is problematic. If the AI processing module determines that the deduction data exceeds the preset data range, change simulation is carried out according to the environment data and the production data, wherein the change simulation comprises at least one of time lapse simulation and scene change simulation, namely the AI processing module determines simulated production schemes at different moments, or determines simulated production schemes in different scenes, or determines simulated production schemes at different moments and under different scenes. The simulation production scheme comprises an equipment capacity scheme or an equipment utilization scheme, and the AI processing module determines the influence range and the influence depth of low equipment capacity or low equipment utilization.
As an alternative embodiment, before inputting the live-action data and the derived data into the twin scene module, the method further comprises: adopting three-dimensional modeling software to construct a three-dimensional model corresponding to the metallurgical scene; processing the live-action data, the derivative data and the three-dimensional model based on a digital twinning technology to obtain a unidirectional twinning scene; and connecting the unidirectional twin scene with each operation interface of the metallurgical device to obtain a bidirectional interactive twin scene module.
After the server obtains the derived data, a three-dimensional model corresponding to the metallurgical scene is built by adopting three-dimensional modeling software, then live-action data and the derived data are processed based on a digital twin technology, a unidirectional twin scene is built, and then the unidirectional twin scene is connected with each operation interface of the metallurgical device to obtain a bidirectional interactive twin scene module. Therefore, the construction process of the twin scene module not only adopts the live-action data in the metallurgical scene, but also comprises the derivative data corresponding to the live-action data, so that the richness of the twin scene construction factor is improved, and the accuracy of the subsequent twin scene module for outputting the deduction data is improved.
The one-way twin module obtained by adopting the digital twin technology has the following advantages with the actual metallurgical scene that 1: 1, a bidirectional interactive twin scene module is further constructed, data mirroring and information interaction between a digital twin body and an actual metallurgical scene are achieved, object twinning, process twinning and performance twinning of a physical space metallurgical scene and a virtual space twinning scene are achieved, real-time synchronization of data driving and a real environment is achieved, and real-time synchronization of metallurgical product generation is achieved.
Optionally, the embodiment of the present application further provides a processing flow of the metallurgical production method, and the specific steps are as follows.
Step 1: and inputting the live-action data into the twin data module to obtain the derivative data output by the twin data module.
Step 2: and constructing a twin scene according to the live-action data and the derivative data.
And step 3: and inputting the live-action data and the derivative data into the twin scene module to obtain deduction data output by the twin scene module.
And 4, step 4: and (4) judging whether the metallurgical device is in a normal production mode currently or not through the AI processing module, if so, entering a step 5, and if not, entering a step 6.
And 5: and the AI processing module obtains the control data corresponding to the optimal production scheme by adjusting the live-action data.
Step 6: the AI processing module determines a range of influence and a depth of influence for an abnormal production mode (the abnormal production mode including a personnel safety hazard event determined from a time lapse simulation or a metallurgical plan problem determined from at least one of a time lapse simulation and a scenario transition simulation)
And 7: an anomaly level is determined based on the extent of influence and the depth of influence.
And 8: and judging whether the abnormal grade is not less than a preset grade threshold value, if not, entering a step 9, and if so, entering a step 10.
And step 9: and controlling an alarm on the metallurgical equipment to give an alarm by controlling the data.
Step 10: and searching an abnormal solution corresponding to the abnormal reason from the database, and switching the metallurgical device from the abnormal production mode to the normal production mode based on the abnormal solution.
Based on the same technical concept, the embodiment of the present application further provides a metallurgical production apparatus, as shown in fig. 3, the apparatus includes:
the first input and output module 301 is configured to input live-action data into the twin data module to obtain derivative data output by the twin data module, where the live-action data includes environmental data of an environment where the metallurgical device is located and production data required by a step to be executed of the metallurgical device, the derivative data is obtained according to the production data, the metallurgical device includes at least one piece of metallurgical equipment, and the environmental data includes a personnel space position and an equipment space position;
the second input and output module 302 is configured to input the live-action data and the derivative data into the twin scene module to obtain deduction data output by the twin scene module, where the twin scene module includes a twin scene constructed based on the live-action data and the derivative data, and the deduction data includes personal safety data and virtual product data;
the analysis module 303 is configured to perform intelligent analysis on the live-action data, the derivative data, and the deduction data through the AI processing module to obtain control data;
and the control module 304 is used for controlling the metallurgical device to perform metallurgical production according to the control data, wherein the AI processing module is connected with each interface of the metallurgical device.
Optionally, the analysis module 303 is configured to:
inputting the live-action data, the derived data and the deduction data into an AI processing module;
determining that the metallurgical device is currently in a normal production mode under the condition that the deduction data is determined to be in the preset data range through the AI processing module;
and adjusting the live-action data through the AI processing module to obtain an optimal production scheme, and taking the live-action data corresponding to the optimal production scheme and the derivative data corresponding to the optimal production scheme as control data, wherein different live-action data correspond to different simulation production schemes.
Optionally, the analysis module 303 is further configured to:
performing change simulation according to the environmental data and the production data to obtain a plurality of simulated production schemes, wherein the change simulation comprises at least one of time lapse simulation and scene change simulation, and the simulated production schemes comprise equipment capacity schemes or equipment utilization schemes;
and taking the simulation production scheme with the highest equipment capacity or equipment utilization rate as the optimal generation scheme.
Optionally, the analysis module 303 is further configured to:
determining that the metallurgical device is currently in an abnormal production mode under the condition that the deduction data exceeds the preset data range;
determining the influence range and the influence depth of the abnormal production mode;
determining an anomaly level based on the influence range and the influence depth;
determining an abnormal reason generated by the abnormal production mode under the condition that the abnormal grade is smaller than a preset grade threshold;
and searching an abnormal solution corresponding to the abnormal reason from the database, and switching the metallurgical device to a normal production mode based on the abnormal solution, wherein the abnormal solution comprises control data, and the control data comprises adjusted live-action data and adjusted derivative data.
Optionally, the analysis module 303 is further configured to:
performing a time lapse simulation based on the equipment spatial position and the personnel spatial position of the at least one metallurgical equipment;
acquiring a dangerous event in a time lapse simulation process, wherein the dangerous event is that the distance between a personnel space position and an equipment space position is smaller than a safety distance threshold;
the impact range and impact depth of the hazardous event are determined.
Optionally, the analysis module 303 is further configured to:
carrying out time lapse simulation according to the environmental data and the production data to obtain simulated production schemes corresponding to different moments, wherein the simulated production schemes comprise equipment productivity or equipment utilization rate;
and determining the influence range and the influence depth of the equipment capacity or the equipment utilization rate.
Optionally, the apparatus is further configured to:
adopting three-dimensional modeling software to construct a three-dimensional model corresponding to the metallurgical scene;
processing the live-action data, the derivative data and the three-dimensional model based on a digital twinning technology to obtain a unidirectional twinning scene;
and connecting the unidirectional twin scene with each operation interface of the metallurgical device to obtain a bidirectional interactive twin scene module.
According to another aspect of the embodiments of the present application, there is provided an electronic device, as shown in fig. 4, including a memory 403, a processor 401, a communication interface 402, and a communication bus 404, where the memory 403 stores a computer program that is executable on the processor 401, the memory 403 and the processor 401 communicate through the communication interface 402 and the communication bus 404, and the processor 401 implements the steps of the method when executing the computer program.
The memory and the processor in the electronic equipment are communicated with the communication interface through a communication bus. The communication bus may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The communication bus may be divided into an address bus, a data bus, a control bus, etc.
The Memory may include a Random Access Memory (RAM) or a non-volatile Memory (non-volatile Memory), such as at least one disk Memory. Optionally, the memory may also be at least one memory device located remotely from the processor.
The Processor may be a general-purpose Processor, and includes a Central Processing Unit (CPU), a Network Processor (NP), and the like; the Integrated Circuit 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, or discrete hardware components.
There is also provided, in accordance with yet another aspect of an embodiment of the present application, a computer-readable medium having non-volatile program code executable by a processor.
Optionally, in an embodiment of the present application, a computer readable medium is configured to store program code for the processor to execute the above method.
Optionally, the specific examples in this embodiment may refer to the examples described in the above embodiments, and this embodiment is not described herein again.
When the embodiments of the present application are specifically implemented, reference may be made to the above embodiments, and corresponding technical effects are achieved.
It is to be understood that the embodiments described herein may be implemented in hardware, software, firmware, middleware, microcode, or any combination thereof. For a hardware implementation, the Processing units may be implemented within one or more Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable Logic Devices (PLDs), Field Programmable Gate Arrays (FPGAs), general purpose processors, controllers, micro-controllers, microprocessors, other electronic units configured to perform the functions described herein, or a combination thereof.
For a software implementation, the techniques described herein may be implemented by means of units performing the functions described herein. The software codes may be stored in a memory and executed by a processor. The memory may be implemented within the processor or external to the processor.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is merely a logical division, and in actual implementation, there may be other divisions, for example, multiple modules or components may be combined or integrated into another system, or some features may be omitted, or not implemented. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the embodiments of the present application may be essentially implemented or make a contribution to the prior art, or may be implemented in the form of a software product stored in a storage medium and including several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the methods described in the embodiments of the present application. And the aforementioned storage medium includes: various media capable of storing program codes, such as a U disk, a removable hard disk, a ROM, a RAM, a magnetic disk, or an optical disk. It is noted that, in this document, relational terms such as "first" and "second," and the like, may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The above description is merely exemplary of the present application and is presented to enable those skilled in the art to understand and practice the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims (10)

1. A metallurgical production process, characterized in that it comprises:
inputting live-action data into a twin data module to obtain derivative data output by the twin data module, wherein the live-action data comprises environmental data of an environment where a metallurgical device is located and production data required by the metallurgical device for executing steps, the derivative data is obtained according to the production data, the metallurgical device comprises at least one piece of metallurgical equipment, and the environmental data comprises a personnel space position and an equipment space position;
inputting the live-action data and the derivative data into a twin scene module to obtain deduction data output by the twin scene module, wherein the twin scene module comprises a twin scene constructed based on the live-action data and the derivative data, and the deduction data comprises personnel safety data and virtual product data;
intelligently analyzing the live-action data, the derivative data and the deduction data through an AI processing module to obtain control data;
and controlling the metallurgical device to perform metallurgical production according to the control data, wherein the AI processing module is connected with each interface of the metallurgical device.
2. The method of claim 1, wherein the intelligently analyzing the live-action data, the derivative data, and the derivative data by an AI processing module to obtain the manipulation data comprises:
inputting the live-action data, the derived data and the derived data into the AI processing module;
determining that the metallurgical device is currently in a normal production mode under the condition that the deduction data is determined to be within a preset data range through the AI processing module;
and adjusting the live-action data through the AI processing module to obtain an optimal production scheme, and taking the live-action data corresponding to the optimal production scheme and the derivative data corresponding to the optimal production scheme as the control data, wherein different live-action data correspond to different simulation production schemes.
3. The method of claim 2, wherein the adjusting the live-action data by the AI processing module to obtain an optimal production solution comprises:
performing change simulation according to the environment data and the production data to obtain a plurality of simulated production schemes, wherein the change simulation comprises at least one of time lapse simulation and scene change simulation, and the simulated production schemes comprise equipment capacity schemes or equipment utilization schemes;
and taking the simulation production scheme with the highest equipment capacity or equipment utilization rate as the optimal generation scheme.
4. The method of claim 2, wherein after inputting the live action data, the derivative data, and the derivative data into the AI processing module, the method further comprises, by the AI processing module:
determining that the metallurgical device is currently in an abnormal production mode under the condition that the deduction data is determined to be beyond the preset data range;
determining the influence range and the influence depth of the abnormal production mode;
determining an anomaly level based on the impact range and the impact depth;
determining an abnormal reason generated by the abnormal production mode under the condition that the abnormal grade is smaller than a preset grade threshold;
and searching an abnormal solution corresponding to the abnormal reason from a database, and switching the metallurgical device to a normal production mode based on the abnormal solution, wherein the abnormal solution comprises control data, and the control data comprises adjusted live-action data and adjusted derivative data.
5. The method of claim 4, wherein said determining a range of influence and a depth of influence of said abnormal production pattern comprises:
performing a time lapse simulation according to the equipment space position and the personnel space position of the at least one metallurgical equipment;
acquiring a dangerous event in the time lapse simulation process, wherein the dangerous event is that the distance between the personnel space position and the equipment space position is smaller than a safety distance threshold value;
determining an impact range and an impact depth of the hazardous event.
6. The method of claim 4, wherein said determining a range of influence and a depth of influence of said abnormal production pattern comprises:
carrying out time lapse simulation according to the environment data and the production data to obtain simulated production schemes corresponding to different moments, wherein the simulated production schemes comprise equipment capacity or equipment utilization rate;
and determining the influence range and the influence depth of the equipment capacity or the equipment utilization rate.
7. The method of claim 1, wherein prior to inputting the live action data and the derivative data into a twin scene module, the method further comprises:
adopting three-dimensional modeling software to construct a three-dimensional model corresponding to the metallurgical scene;
processing the live-action data, the derived data and the three-dimensional model based on a digital twinning technology to obtain a unidirectional twinning scene;
and connecting the unidirectional twin scene with each operation interface of the metallurgical device to obtain a bidirectional interactive twin scene module.
8. A metallurgical production plant, characterized in that it comprises:
the first input and output module is used for inputting live-action data into the twin data module to obtain derivative data output by the twin data module, wherein the live-action data comprises environmental data of an environment where a metallurgical device is located and production data required by the metallurgical device for executing steps, the derivative data is obtained according to the production data, the metallurgical device comprises at least one piece of metallurgical equipment, and the environmental data comprises a personnel space position and an equipment space position;
the second input and output module is used for inputting the live-action data and the derivative data into the twin scene module to obtain deduction data output by the twin scene module, wherein the twin scene module comprises a twin scene constructed based on the live-action data and the derivative data, and the deduction data comprises personnel safety data and virtual product data;
the analysis module is used for intelligently analyzing the live-action data, the derivative data and the deduction data through the AI processing module to obtain control data;
and the control module is used for controlling the metallurgical device to perform metallurgical production according to the control data, wherein the AI processing module is connected with each interface of the metallurgical device.
9. An electronic device is characterized by comprising a processor, a communication interface, a memory and a communication bus, wherein the processor and the communication interface are used for realizing mutual communication by the memory through the communication bus;
a memory for storing a computer program;
a processor for implementing the method steps of any of claims 1 to 7 when executing a program stored in the memory.
10. A computer-readable storage medium, characterized in that a computer program is stored in the computer-readable storage medium, which computer program, when being executed by a processor, carries out the method steps of any one of claims 1 to 7.
CN202111207193.4A 2021-10-15 2021-10-15 Metallurgical production method and device, electronic equipment and storage medium Pending CN113935711A (en)

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