A task processing method and product applied to materials science

Abstract

The embodiment of the present application provides a task processing method and product applied to materials science, belonging to the technical field of data processing, wherein the task processing method includes responding to a task setting operation of any user, the central processing unit executing the target calculation strategy corresponding to the current material task, obtaining the calculation result of the current material task, the target calculation strategy including at least one target calculation tool, when the target calculation strategy includes multiple target calculation tools, the target calculation strategy also includes the execution order of the multiple target calculation tools; when the current material task is a preset experimental task type, the central processing unit determines the experimental execution strategy corresponding to the current material task, and generates an experimental operation instruction set of the current material task; and executes the experimental process of the current material task according to the experimental operation instruction set. The embodiment of the present application aims to improve the efficiency and accuracy of materials science research.

Description

Technical Field

[0001] The embodiments of the present application relate to the technical field of data processing, and in particular, to a task processing method and product applied to materials science. Background Art

[0002] Materials science is one of the important fields of contemporary scientific and technological development. Its research objects include various materials from nanoscale to macroscale, involving many aspects such as the structure, performance, synthesis and application of materials. With the rapid development of computing technology and information technology, computational materials science has gradually become an important direction in materials science research. Through computational simulation and data analysis, it reveals the microscopic mechanism of materials, predicts the macroscopic properties of materials, and provides a scientific basis for material design and optimization.

[0003] Currently, computational tools commonly used in materials science research, such as VASP and Quantum ESPRESSO, can provide comprehensive functions from electronic structure calculation to material property prediction. However, in the process of performing tasks on any material, not only a separate computing tool is required, but the input must be manually input into multiple computing tools in sequence for calculation. This results in a lot of time spent in the calculation of materials or the processing of material tasks, and large errors in human operations, which reduces the efficiency and accuracy of materials science research. Summary of the invention

[0004] The embodiments of the present application provide a task processing method and product applied to materials science, aiming to improve the efficiency and accuracy of materials science research.

[0005] In a first aspect, an embodiment of the present application provides a task processing method applied to materials science, the method comprising:

[0006] In response to a task setting operation of any user, the central processing unit executes a target calculation strategy corresponding to the current material task to obtain a calculation result of the current material task, wherein the target calculation strategy includes at least one target calculation tool. When the target calculation strategy includes multiple target calculation tools, the target calculation strategy also includes an execution order of the multiple target calculation tools.

[0007] When the current material task is a preset experimental task type, the central processing unit determines the experimental execution strategy corresponding to the current material task, and generates an experimental operation instruction set for the current material task, wherein the experimental operation instruction set includes setting operation instructions for target parameters of any experimental equipment;

[0008] The experimental process of the current material task is executed according to the experimental operation instruction set, and any experimental data generated during the experimental process is acquired.

[0009] Optionally, in response to a task setting operation by any user, the central processing unit executes a target calculation strategy corresponding to the current material task, including:

[0010] In response to a task setting operation of any user, the central processor obtains the current material task;

[0011] Matching the target calculation strategy corresponding to the current material task in a preset calculation strategy database, wherein the calculation strategy database includes target calculation strategies corresponding to different material tasks;

[0012] When the target calculation strategy corresponding to the current material task includes a target calculation tool, calling the target calculation tool to calculate and obtain the calculation result of the current material task;

[0013] When the target calculation strategy corresponding to the current material task includes multiple target calculation tools, the multiple target calculation tools are called in sequence according to the execution order of the multiple target calculation tools to calculate the calculation result of the current material task.

[0014] Optionally, in response to a task setting operation of any user, the central processing unit executes a target calculation strategy corresponding to the current material task, and after obtaining a calculation result of the current material task, the method further includes:

[0015] The central processing unit matches whether the current material task belongs to an experimental task type in the experimental task database, wherein the experimental task data stores material tasks of multiple experimental task types and an experimental execution strategy corresponding to any material task, and the experimental execution strategy includes at least one of the experimental material object, quantity, experimental temperature, experimental pressure, experimental humidity and experimental application environment.

[0016] Optionally, the method further comprises:

[0017] In response to an experiment task setting operation by any user, the central processing unit acquires the experiment operation task;

[0018] Determine, in the experimental task database, an experimental execution strategy corresponding to the experimental operation task, wherein the experimental execution strategy includes at least one of an experimental material object, quantity, experimental temperature, experimental pressure, experimental humidity, and an experimental application environment;

[0019] The central processing unit generates a set of experimental operation instructions for the experimental operation task according to the experimental execution strategy corresponding to the experimental operation task;

[0020] Execute the experimental process of the experimental operation task according to the experimental operation instruction set, and obtain any experimental data generated during the experimental process;

[0021] The experimental operation task, the experimental execution strategy corresponding to the experimental operation task, and any experimental data generated during the experimental process of the experimental operation task are stored.

[0022] Optionally, the central processing unit determines the experimental execution strategy corresponding to the current material task and generates the experimental operation instruction set of the current material task, or the central processing unit generates the experimental operation instruction set of the experimental operation task according to the experimental execution strategy corresponding to the experimental operation task, including:

[0023] The central processor configures at least one of the following experimental operation instructions according to the experimental execution strategy corresponding to the current material task or according to the experimental execution strategy corresponding to the experimental operation task to construct an experimental operation instruction set:

[0024] Generate an experimental material object selection instruction according to the experimental operation task or the experimental material object in the experimental execution strategy;

[0025] Generate an instruction for selecting the number of experimental material objects according to the number of experimental materials corresponding to the experimental operation task or the experimental execution strategy;

[0026] Generate a temperature control instruction according to the experimental operation task or the experimental temperature in the experimental execution strategy;

[0027] Generate a pressure control instruction according to the experimental operation task or the experimental pressure in the experimental execution strategy;

[0028] Generate a humidity control instruction according to the experimental operation task or the experimental humidity in the experimental execution strategy;

[0029] An application environment selection instruction is generated according to the experimental application environment in the experimental operation task or the experimental execution strategy.

[0030] Optionally, the experimental process of executing the current material task according to the experimental operation instruction set includes:

[0031] Responding in sequence to each instruction in the experimental operation instruction set generated by the central processor, configuring target parameters of any experimental equipment;

[0032] When each instruction in the experimental operation instruction set is configured, the experimental process is triggered to execute.

[0033] Optionally, during the experimental process of performing the current material task, the method further includes:

[0034] Monitoring any monitoring information during the experiment of the current material task, wherein the monitoring information includes humidity information, temperature information and pressure information;

[0035] When any monitoring information is larger than the configuration range of the monitoring information in the experiment execution strategy of the current material task, an error prompt of the monitoring information is displayed.

[0036] Optionally, during the experimental process of performing the current material task, the method further includes:

[0037] Monitoring abnormal conditions during the experiment of the current material task;

[0038] When any abnormal state exists in the experimental process of the current material task, the safety processing strategy corresponding to the abnormal state is executed and an abnormal prompt is displayed.

[0039] Optionally, during the experimental process of performing the current material task, the method further includes:

[0040] According to any experimental data generated during the experiment of the current material task, the experimental operation instruction set is optimized and the parameter setting of any experimental equipment is updated.

[0041] Optionally, the method further comprises:

[0042] In the storage database, according to different material tasks, any current material task, the calculation result of the current material task, the experimental execution strategy of the current material task and any experimental data generated during the experiment of the current material task are classified and stored.

[0043] Optionally, the method further comprises:

[0044] In the storage database, the calculation results and experimental data corresponding to the same material are obtained for data statistics to obtain the data statistical results of the material;

[0045] In response to a material data acquisition operation by any user, the data statistics corresponding to the target material are acquired in the storage database and returned.

[0046] Optionally, the method further comprises:

[0047] In response to a user's simulation task setting operation, storing a plurality of simulation calculation tasks and a plurality of simulation experiment tasks;

[0048] When there is no current material task set by the user, obtain any simulation calculation task, execute the target calculation strategy corresponding to the simulation calculation task, obtain the calculation result of the simulation calculation task and store it;

[0049] Obtain any simulation experiment task, execute the experiment execution strategy corresponding to the simulation experiment task, and generate a set of experimental operation instructions for the simulation experiment task, execute the experimental process of the simulation experiment task according to the experimental operation instruction set of the simulation experiment task, and store any experimental data generated during the experimental process of the simulation experiment task.

[0050] Optionally, the method further comprises:

[0051] In response to a new operation of adding a computing tool, a calling interface of any computing tool is added.

[0052] In a second aspect, an embodiment of the present application further provides a non-volatile readable storage medium, wherein the non-volatile readable storage medium stores a computer program, wherein when the computer program is executed by a processor, the task processing method applied to materials science described in the first aspect of the embodiment is executed.

[0053] In a third aspect, an embodiment of the present application further provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements the task processing method applied to materials science described in the first aspect of the embodiment.

[0054] Beneficial effects:

[0055] In response to a task setting operation by any user, the central processing unit executes the target calculation strategy corresponding to the current material task to obtain the calculation result of the current material task, wherein the target calculation strategy includes at least one target calculation tool. When the target calculation strategy includes multiple target calculation tools, the target calculation strategy also includes the execution order of the multiple target calculation tools. When the current material task is a preset experimental task type, the central processing unit determines the experimental execution strategy corresponding to the current material task and generates an experimental operation instruction set for the current material task, wherein the experimental operation instruction set includes setting operation instructions for target parameters of any experimental equipment. The experimental process of the current material task is executed according to the experimental operation instruction set, and any experimental data generated during the experimental process is obtained.

[0056] In the task processing method provided in this embodiment, in response to the current material task set by the user, a target calculation strategy for the current material task is executed. The target calculation strategy includes one or more calculation tools required to execute the current material task and the execution order of the calculation tools. By executing the target calculation strategy, the calculation result of the current material task can be calculated based on one or more calculation tools in sequence, without the need for manual input of data into multiple calculation tools for calculation, thereby improving the efficiency and accuracy of material science research.

[0057] Moreover, for the current material task of the experimental task type, after obtaining the calculation results, the experimental execution strategy of the current material task can also be generated, and the calculation results can be verified through the experimental execution strategy, further improving the efficiency and accuracy of material science research. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following is a brief introduction to the drawings required for use in the embodiments or related technical descriptions.

[0059] Figure 1 A flowchart showing the steps of a task processing method applied to materials science provided by an embodiment of the present application is shown;

[0060] Figure 2 A schematic diagram of the structure of a computing system applied to materials science provided by an embodiment of the present application is shown;

[0061] Figure 3 A schematic diagram of a computing module provided in an embodiment of the present application is shown;

[0062] Figure 4 The functional module diagram of the intelligent control module provided in the embodiment of the present application is shown;

[0063] Figure 5 A schematic diagram of an experimental module provided in an embodiment of the present application is shown;

[0064] Figure 6 A schematic diagram of a non-volatile readable storage medium provided in an embodiment of the present application is shown;

[0065] Figure 7 A schematic diagram of a computer program product provided by an embodiment of the present application is shown. DETAILED DESCRIPTION

[0066] The technical solutions in the embodiments of the present application will be described below in conjunction with the drawings in the embodiments of the present application.

[0067] To make the purpose, technical scheme and advantages of the embodiments of the present application clearer, each embodiment of the present application will be described in detail below in conjunction with the accompanying drawings. However, it will be appreciated by those skilled in the art that in each embodiment of the present application, many technical details are proposed in order to enable the reader to better understand the present application. However, even without these technical details and various changes and modifications based on the following embodiments, the technical scheme claimed in the present application can also be implemented. The division of the following embodiments is for the convenience of description, and the specific implementation of the present application should not constitute any limitation, and the various embodiments can be combined with each other and referenced to each other without contradiction.

[0068] VASP: Vienna Ab-initio Simulation Package, electronic structure calculation and quantum mechanics-molecular dynamics simulation software package;

[0069] Quantum ESPRESSO is an open source software with multiple modules, based on density functional theory, plane waves and pseudopotentials, which can be used for material modeling and electronic structure calculation at the nanoscale.

[0070] CPU: Central Processing Unit, central processing unit;

[0071] GPU: graphics processing unit, graphics processor;

[0072] PCIe: Peripheral Component Interconnect Express, a high-speed serial computer expansion bus standard.

[0073] Materials science is one of the important areas of contemporary scientific and technological development. Its research objects include various materials from nanoscale to macroscale, involving many aspects such as the structure, performance, synthesis and application of materials.

[0074] With the rapid development of computing technology and information technology, computational materials science has gradually become an important direction in materials science research. Through computational simulation and data analysis, it reveals the microscopic mechanism of materials, predicts the macroscopic properties of materials, and provides a scientific basis for material design and optimization.

[0075] Currently, computational tools commonly used in materials science research, such as VASP and Quantum ESPRESSO, can provide comprehensive functions from electronic structure calculation to material property prediction. However, these tools face several challenges in use, including high computing resource requirements, complex input file formats, inefficient user interfaces, and compatibility issues between different computational tools. These problems cause materials science researchers to spend a lot of time on data processing and format conversion during the calculation process, reducing the efficiency and accuracy of materials science research.

[0076] Therefore, there is an urgent need for a solution that can improve the efficiency and accuracy of material science research. In order to improve the efficiency and accuracy of material science research, this embodiment provides a task processing method applied to material science.

[0077] Reference Figure 1 , shows a flowchart of the steps of a task processing method applied to materials science provided by an embodiment of the present application, the task processing method comprising:

[0078] S101: In response to a task setting operation by any user, the central processing unit executes a target calculation strategy corresponding to a current material task to obtain a calculation result of the current material task. The target calculation strategy includes at least one target calculation tool. When the target calculation strategy includes multiple target calculation tools, the target calculation strategy also includes an execution order of the multiple target calculation tools.

[0079] Specifically, in response to any user's task setting operation, the central processing unit (CPU) executes the target calculation strategy corresponding to the current material task, including the following steps:

[0080] A1: In response to a task setting operation of any user, the current material task is obtained.

[0081] For example, material tasks can be the task of simulating the physical and chemical properties of any material, the business of calculating the electronic structure of any material, the task of performing material screening, and the task of predicting the performance of any material under different conditions, such as the conductivity performance of material A at temperature 1 and the conductivity performance at temperature 2, etc.

[0082] A2: Matching the target calculation strategy corresponding to the current material task in a preset calculation strategy database, wherein the calculation strategy database includes target calculation strategies corresponding to different material tasks.

[0083] In the actual implementation process, target calculation strategies corresponding to different material tasks can be preset in the calculation strategy database, and the preset calculation strategy data can be matched according to the current material task to determine the target calculation strategy corresponding to the current material task.

[0084] For example, the relationship between existing material tasks and target computing strategies can also be learned based on the artificial intelligence model, so as to generate more correspondences between material tasks and target computing strategies and store them in the storage module. For example, the correspondence between material tasks and target computing strategies can be learned and generated based on a large model.

[0085] A3: When the target calculation strategy corresponding to the current material task includes a target calculation tool, the target calculation tool is called to calculate the calculation result of the current material task.

[0086] A4: When the target calculation strategy corresponding to the current material task includes multiple target calculation tools, the multiple target calculation tools are called in sequence according to the execution order of the multiple target calculation tools to calculate the calculation result of the current material task.

[0087] In the research process of materials science, different computing tools are needed for different materials and processing directions. In the actual implementation process, the computing tools can include computing tools in various fields such as physics, electricity, magnetism, biology and chemistry, such as numerical simulation tools, material property analysis tools and magnetic anisotropy calculation tools. Multiple computing tool calling interfaces are configured in the computing module.

[0088] In a feasible implementation, it also includes adding a calling interface of any computing tool in response to a new operation of the computing tool, thereby expanding different computing tools to provide richer computing functions, so as to calculate various different material tasks.

[0089] The computational tools used in the target computational strategy of any material task can be interdisciplinary computational tools, thus supporting comprehensive computations of multiple scales and multiple physical fields, such as electronic structure calculations at the atomic scale and material performance simulations at the macroscopic scale, providing comprehensive material information, supporting coupled calculations of multiple physical fields such as thermodynamics, electricity, and magnetism, and enabling comprehensive predictions of material performance under complex physical conditions.

[0090] S102: When the current material task is a preset experimental task type, the central processing unit determines the experimental execution strategy corresponding to the current material task, and generates an experimental operation instruction set for the current material task, wherein the experimental operation instruction set includes setting operation instructions for target parameters of any experimental equipment.

[0091] After executing the target calculation strategy for the current material task and obtaining the theoretical calculation value, experiments can also be carried out to verify it.

[0092] In a feasible implementation manner, after executing the target calculation strategy corresponding to the current material task and obtaining the calculation result of the current material task, the method further includes:

[0093] The central processing unit matches whether the current material task belongs to an experimental task type in the experimental task database, wherein the experimental task data stores material tasks of multiple experimental task types and an experimental execution strategy corresponding to any material task, and the experimental execution strategy includes at least one of the experimental material object, quantity, experimental temperature, experimental pressure, experimental humidity and experimental application environment.

[0094] If the current material task is a preset experimental task type, the experimental execution strategy corresponding to the current material task can be further determined in the experimental task database, and a set of experimental operation instructions for the current material task can be generated.

[0095] In addition to performing calculations according to the current material task or further verifying through experiments, the user may be allowed to individually set experimental operation tasks. Specifically, in response to any user's experimental task setting operation, the experimental operation task is acquired.

[0096] In the experimental task database, determine the experimental execution strategy corresponding to the experimental operation task, the experimental execution strategy includes at least one of the experimental material object, quantity, experimental temperature, experimental pressure, experimental humidity and experimental application environment, and then the central processing unit generates an experimental operation instruction set of the experimental operation task according to the experimental execution strategy corresponding to the experimental operation task; execute the experimental process of the experimental operation task according to the experimental operation instruction set, and obtain any experimental data generated during the experimental process; store the experimental operation task, the experimental execution strategy corresponding to the experimental operation task and any experimental data generated during the experimental process of the experimental operation task.

[0097] In a feasible implementation manner, the central processing unit generates the experimental operation instruction set of the current material task, or the central processing unit generates the experimental operation instruction set of the experimental operation task according to the experimental execution strategy corresponding to the experimental operation task, including: configuring at least one of the following experimental operation instructions according to the experimental execution strategy corresponding to the current material task to construct the experimental operation instruction set:

[0098] Generate an experimental material object selection instruction according to the experimental operation task or the experimental material object in the experimental execution strategy, that is, configure the material object required for the experiment module to conduct the experiment;

[0099] Generate an instruction for selecting the number of experimental material objects according to the number of experimental materials in the experimental operation task or the experimental execution strategy, that is, configure the number of material objects required for the experiment module to conduct the experiment;

[0100] Generate a temperature control instruction according to the experimental operation task or the experimental temperature in the experimental execution strategy, that is, configure the temperature value required for the experimental module to perform the experiment;

[0101] Generate a pressure control instruction according to the experimental operation task or the experimental pressure in the experimental execution strategy, that is, configure the pressure value required for the experimental module to perform the experiment;

[0102] Generate a humidity control instruction according to the experimental operation task or the experimental humidity in the experimental execution strategy, that is, configure the humidity value required for the experimental module to conduct the experiment;

[0103] According to the experimental application environment in the experimental operation task or the experimental execution strategy, an application environment selection instruction is generated, that is, the application environment required for the experiment module to conduct the experiment, such as the application environment for performance loss.

[0104] The values ​​of the various setting objects in the above experimental operation tasks or the experimental execution strategies can be fixed values ​​specified in the experimental operation tasks or any value in a range of values; or they can be fixed values ​​or ranges of values ​​set in the experimental execution strategies, which can be customized according to the needs of the actual application process, and this embodiment does not impose any restrictions.

[0105] S103: Execute the experimental process of the current material task according to the experimental operation instruction set, and obtain any experimental data generated during the experimental process.

[0106] Optionally, after the central processor generates the experimental operation instruction set, the experimental operation instruction set can be sent to a control module for setting parameters of the experimental equipment, such as an electronic hand, or the central processor can send any instruction to the corresponding experimental equipment.

[0107] The experimental process of executing the current material task according to the experimental operation instruction set includes: responding to each instruction in the experimental operation instruction set in turn, and configuring the target parameters of any experimental equipment; when each instruction in the experimental operation instruction set is configured, triggering the execution of the experimental process.

[0108] In a feasible implementation manner, during the experimental process of performing the current material task, the method further includes:

[0109] Monitor any monitoring information during the experiment of the current material task, the monitoring information including humidity information, temperature information and pressure information; when any monitoring information is greater than the configuration range of the monitoring information in the experiment execution strategy of the current material task, display an error prompt for the monitoring information.

[0110] In another feasible implementation manner, during the experimental process of performing the current material task, the method further includes:

[0111] Monitor abnormal conditions during the experiment of the current material task; when any abnormal condition exists during the experiment of the current material task, execute the safety handling strategy corresponding to the abnormal condition and display an abnormal prompt. For example, the safety handling strategy may be an emergency shutdown or other safety measures.

[0112] Optionally, during the experimental process of performing the current material task, the method further includes:

[0113] According to any experimental data generated during the experiment of the current material task, the experimental operation instruction set is optimized, and the parameter settings of any experimental equipment are updated; for example, during the experiment, when the initially set temperature value causes a large difference between the current experimental data and the process data included in the calculation results, the initially set temperature value can be changed, and an optimization prompt can be displayed in a pop-up window during the optimization process, allowing the user to input the changed parameter settings of the experimental equipment through a manual interaction interface. For example, the user is allowed to modify the temperature value, and the temperature control instructions in the experimental operation instruction set are optimized according to the changed temperature value; optimization rules can also be preset according to any material task, such as the parameter optimization direction of any experimental equipment corresponding to material task A and the parameter change value of each optimization, the parameter optimization direction such as increasing the temperature value, and the temperature value increased each time is D, etc. Other optimization methods can also be set during the actual implementation, and this embodiment does not impose any restrictions.

[0114] In a feasible implementation, the method further includes: storing the current material task, the calculation result of the current material task obtained by the target calculation strategy, the experiment execution strategy, and any experimental data generated during the experiment.

[0115] Specifically, the process of storing the current material task, the calculation result of the current material task obtained by the target calculation strategy, the experimental execution strategy, and any experimental data generated during the experiment can be stored in a storage database. In the storage database, according to different material tasks, any current material task, the calculation result of the current material task, the experimental execution strategy of the current material task, and any experimental data generated during the experiment of the current material task are classified and stored. After the classified storage, the processing process of the current material task can be summarized, so that the user can view the calculation process and calculation data related to the current material task, as well as the experimental process and experimental data.

[0116] Furthermore, for the security of data, a redundant database may be set up to back up and store any data in the storage database to ensure the security of any data.

[0117] In the actual implementation process, in addition to classifying and storing data according to material tasks, all tasks for the same material can also be classified. Specifically, in the storage database, the calculation results and experimental data corresponding to the same material are obtained for data statistics to obtain the data statistical results of the material.

[0118] In response to any user's material data acquisition operation, the data statistics corresponding to the target material are acquired in the storage database and returned, thereby providing the user with rich calculation data and experimental data for the same material.

[0119] In a feasible implementation, in order to effectively utilize computing resources, multiple simulation computing tasks and multiple simulation experiment tasks can also be stored in response to the user's simulation task setting operation; when there is no current material task set by the user, any simulation computing task is obtained, and the target computing strategy corresponding to the simulation computing task is executed, and the calculation result of the simulation computing task is obtained and stored.

[0120] Obtain any simulation experiment task, execute the experiment execution strategy corresponding to the simulation experiment task, and generate a set of experimental operation instructions for the simulation experiment task, execute the experimental process of the simulation experiment task according to the experimental operation instruction set of the simulation experiment task, and store any experimental data generated during the experimental process of the simulation experiment task.

[0121] Reference Figure 2 , showing a schematic diagram of the structure of a computing system applied to materials science provided in an embodiment of the present application, the computing system includes a computing module, an intelligent control module, an experimental module and a storage module, and the computing system executes the task processing method applied to materials science described in this embodiment.

[0122] The calculation module is used to respond to any user's task setting operation, execute the target calculation strategy corresponding to the current material task, obtain the calculation result of the current material task, or when the current material task is a preset experimental task type, determine the experimental execution strategy corresponding to the current material task.

[0123] Reference Figure 3 , showing a schematic diagram of a computing module provided in an embodiment of the present application, the functions of the computing module can be implemented by a server, the central processing unit, i.e., CPU, and the graphics processing unit, i.e., GPU, in the server can provide computing power at the same time, the CPU and GPU can exchange data based on the PCIe protocol, and the memory in the server can store intermediate data in the calculation process of any current material task.

[0124] The rich calling interfaces of computing tools included in the computing module can enable multiple computing tools to be coupled for calculation to obtain rich computing data. For example, the computing module includes interfaces for the first-principles computing tool VASP, the difference computing tool wannier90, and the interface between wannier90 and the magnetic material transport computing tool, thereby realizing simulation from the microscopic to the macroscopic level. The data required for the next computing tool to calculate can be sent directly to the next computing tool, without the need for frequent manual input of data between different computing tools.

[0125] For example, one of the output files of the difference calculation tool wannier90 is a Hamiltonian matrix that needs to be used by the magnetic material transport calculation tool. By setting the execution order of the calculation tool, the intermediate calculation result Hamiltonian matrix is ​​temporarily stored. Then, when the magnetic material transport calculation tool performs the calculation, the intermediate calculation result, that is, the Hamiltonian matrix, can be directly obtained to continue the calculation related to magnetic transport, such as:

[0126] ;

[0127] in, is the Hamiltonian matrix element. The symbols are the expressions of left and right arrows respectively, is the Hamiltonian operator, is the Hamiltonian matrix element in the Wannier basis representation, represents the phase factor and R is the position variable.

[0128] In the actual implementation process, the system management user can add a computing tool that can be called in the computing module. For example, the computing module is used to respond to the computing tool addition operation and add a calling interface of the newly added computing tool in the computing module.

[0129] In the actual implementation process, the storage module includes a calculation strategy database, which includes target calculation strategies corresponding to different material tasks; when responding to a task setting operation of any user, the calculation module can search the storage module for a target calculation strategy corresponding to the current material task, and call one or more target calculation tools in sequence according to one or more target calculation tools contained in the target calculation strategy and the execution order of multiple target calculation tools, calculate the calculation result of the current material task, and store the current task material and the corresponding calculation result in the storage module.

[0130] After executing the target calculation strategy for the current material task and obtaining the theoretical calculation value, experiments can also be carried out to verify it.

[0131] Specifically, the storage module includes an experimental task database, which stores material tasks of multiple experimental task types and experimental execution strategies corresponding to any material task. The calculation module can first determine whether the current material task belongs to the experimental task type in the experimental task database of the storage module. When the current material task is a preset experimental task type, the calculation module further determines the experimental execution strategy corresponding to the current material task.

[0132] After determining the target calculation strategy corresponding to the current material task, the calculation module matches the experiment execution strategy corresponding to the current material task in the storage module, and sends the experiment execution strategy to the intelligent control module.

[0133] The intelligent control module can directly respond to any user's experimental operation task to control the experimental module to conduct experiments, and can also automatically generate a set of experimental operation instructions for the current material task according to the experimental execution strategy corresponding to the current material task. The experimental operation instruction set includes setting operation instructions for target parameters of any experimental equipment. By way of example, the experimental operation instruction set may include experimental material object selection instructions, experimental material object quantity selection instructions, temperature control instructions, pressure control instructions, humidity control instructions, and application environment selection instructions, etc.

[0134] Reference Figure 4 , showing a functional module diagram of the intelligent control module provided by the implementation of the present application. Specifically, the intelligent control module includes an electronic hand control unit, which is used to respond to each instruction in the experimental operation instruction set in turn and configure the target parameters of any experimental equipment in the experimental module; when each instruction in the experimental operation instruction set is configured, the execution of the experimental process is triggered.

[0135] Reference Figure 5 , showing a schematic diagram of the experimental module provided in an embodiment of the present application, the experimental module may include local experimental equipment, and the experimental equipment may include material preparation equipment, testing equipment, and application equipment, etc. The material preparation equipment can be used to synthesize and process materials, such as material growth devices, temperature control devices, and pressure regulating devices, etc.; the testing equipment is used to measure the physical and chemical properties of materials, such as microscopes and spectrometers, etc.; the application equipment is used to simulate the performance of any material in actual applications, such as durability testing equipment, etc.

[0136] For any intelligent device in the experimental module, the experimental parameters can be configured through the electronic hand control unit of the intelligent control module, such as controlling the temperature control device in response to the temperature control instruction through the electronic hand control unit of the intelligent control module to set the temperature value during the experiment; this system allows the connection of any new experimental equipment.

[0137] After the experimental module performs adaptive configuration of the experimental parameters of each device, such as the experimental material object and quantity, temperature value, humidity value, pressure value and experimental application environment, in response to the electronic hand control unit of the intelligent control module, the experimental module can execute the experimental process.

[0138] In the actual implementation process, intelligent equipment can automatically perform its experimental process during the experiment, and for non-intelligent equipment, manual operation can be used to assist in completing the experimental process.

[0139] In a feasible implementation, the intelligent control module also includes an early warning processing unit, which can detect any monitoring information during the experiment, and the monitoring information includes humidity information, temperature information and pressure information; when any monitoring information is greater than the configuration range of the monitoring information in the experimental operation task or the experimental execution strategy, an error prompt of the monitoring information is displayed.

[0140] In another feasible implementation, an abnormal state is pre-configured in the early warning processing unit; when the early warning processing unit detects the existence of any pre-configured abnormal state during the experiment, an abnormal prompt is displayed or a safety processing strategy is executed. For example, the safety processing strategy may be an emergency shutdown or other safety measures.

[0141] During the experiment of the experimental module, the intelligent control module obtains any experimental data generated by the experimental module during the experiment and sends it to the storage module for storage.

[0142] The intelligent control module is also used to optimize the experimental operation instruction set and update the setting of target parameters of any experimental equipment according to any experimental data generated by the experimental module during the experiment. For example, during the experiment, when the initially set temperature value causes a large difference between the current experimental data and the process data included in the calculation results, the initially set temperature value can be changed.

[0143] The storage database in the storage module is used to classify and store any current material task, the calculation result of the current material task obtained by the target calculation strategy, the experimental execution strategy, and any experimental data generated during the experiment according to the material task, and the redundant database in the storage module can back up and store any data stored in the storage database to ensure the security of any data.

[0144] In the actual implementation process, in addition to classifying and storing the data in the storage module according to material tasks or experimental operation tasks, all tasks for the same material can also be classified. The storage module also includes a data analysis unit, which is used to perform data statistics based on the calculation results for the same material to obtain the data statistical results of the material.

[0145] The storage module supports automatic archiving and classification of data, thereby ensuring the integrity and traceability of the data. It also performs data statistics on the calculation results of the same material based on the data analysis unit, and can also discover the potential rules in the calculation results of the material.

[0146] In a feasible implementation, the storage module is also used to: store multiple simulation computing tasks and multiple simulation experiment tasks in response to the user's simulation task setting operation; when the computing system is currently in an idle state, the computing module is used to obtain any simulation computing task in the storage module for calculation, and store the calculation results of the simulation computing task in the storage module.

[0147] The intelligent control module is used to obtain any simulated experimental task in the storage module and generate a set of experimental operation instructions for the simulated experimental task. The experimental module executes the experimental process in response to the set of experimental operation instructions. The intelligent control module monitors any experimental data generated by the experimental module during the experimental process of the simulated experimental task and stores them in the storage module.

[0148] The idle time of the system can be utilized, and the computing system can process the preset simulation computing tasks and simulation experiment tasks, so that the computing system can automatically perform the simulation process and exploration process of any material, and generate more data related to the material and store them in the storage module.

[0149] The computing system also includes a user interaction module, which can be a human-computer interaction interface. The user interaction module is used to detect any user's task setting operation or experimental operation task, and in response to any user's target data acquisition operation, search and return the target data in the storage module, and can also allow users to download calculation results, experimental results and other files of the current material task.

[0150] In a feasible implementation, in order to facilitate the research and development of materials science, the system can also build a database, which stores the calculation results and experimental results of any material in the storage module, and the database also stores historical calculation documents and historical experimental results corresponding to different materials. Different preparation uses and application scenarios can be extracted for any material. Based on different preparation users and application scenarios, more target calculation strategies and experimental execution strategies for any material can be configured and stored in the storage module, thereby realizing the research expansion of any material.

[0151] In a feasible implementation, taking the current material task of "developing new magnetic materials" as an example, the calculation module first executes the target calculation strategy corresponding to the current material task, determines the calculation result of the current material task, such as the calculation result includes the temperature T1 and magnetic field strength H1 corresponding to the development of new magnetic materials, and generates the experimental execution strategy corresponding to the current material task, such as: "Experiment with magnetic material A at temperature T1 and magnetic field strength H1, and perform performance tests", and sends it to the intelligent control module.

[0152] The intelligent control module generates a set of experimental operation instructions according to the experimental execution strategy, which includes experimental material object selection instructions corresponding to magnetic material A, temperature control instructions corresponding to temperature T1, and magnetic field strength control instructions corresponding to magnetic field strength H1; the magnetic field strength of the magnetic field generator and the temperature of the heating furnace in the experimental module are set through the electronic hand control unit of the intelligent control module. The experimental module prepares the material, and after obtaining magnetic material A, it continues to use a spectrometer to test the material's performance, and generates experimental data such as the hysteresis curve and Curie temperature of the measured material.

[0153] The intelligent control device detects the experimental data in the experimental equipment in real time. When the early warning processing unit of the intelligent control device detects that the settings of the temperature parameters or magnetic field strength parameters are deviated or abnormal, the early warning processing unit will immediately identify and trigger an alarm, and suspend the experiment if necessary for early warning processing.

[0154] At the same time, intelligent control equipment can also automatically adjust experimental conditions based on feedback from experimental data, such as changing the magnetic field strength or adjusting the cooling rate, to ensure the optimal performance conditions and extreme service conditions of the material.

[0155] All data during the experiment, including material preparation conditions, physical properties test results, etc., will be uploaded to the storage device in real time through the intelligent control device. The storage device will automatically archive and classify the data for subsequent analysis and retrieval.

[0156] In the actual implementation process, the computing system can be implemented based on a server cluster, which includes a client node, multiple computing nodes and a storage server.

[0157] The client node is used to implement the functions of the user interaction module in this system and provide data interaction between this computing system and the user.

[0158] Multiple computing nodes are used to implement the functions of the computing module in this computing system. In the actual implementation process, computing nodes can also be allocated according to the current material tasks, so as to balance the task processing process of the computing system and avoid high delays in the task processing process.

[0159] The storage server is used to implement the functions of the storage module in this computing system and is dedicated to storing data. The storage server can perform processes such as cleaning, data classification, and data analysis on the stored data.

[0160] The computing system provided by this embodiment has at least the following beneficial effects:

[0161] 1. Different computing tools can be integrated. For any material task, the execution order of multiple target computing tools can be determined. There is no need to manually input data into multiple computing tools for calculation, which improves the efficiency and accuracy of material science research.

[0162] 2. After obtaining the calculation results for the target calculation strategy of the calculation module, the calculation module can also generate the experimental execution strategy for the current material task, and verify the calculation results through the experimental execution strategy, further improving the efficiency and accuracy of material science research.

[0163] 3. The present invention supports comprehensive calculations of multiple scales and multiple physical fields, such as calculations of electronic structures at the atomic scale and simulations of material properties at the macroscopic scale, providing comprehensive material information;

[0164] 4. The computing system automates the calculation process, which can reduce human intervention, realize automated processing, and further improve the efficiency of materials science research.

[0165] Reference Figure 6 , shows a schematic diagram of a non-volatile readable storage medium provided in an embodiment of the present application, wherein the non-volatile readable storage medium 600 stores a computer program 601, wherein the computer program 601, when executed by a processor, performs the task processing method applied to materials science described in the embodiment.

[0166] Reference Figure 7 , showing a schematic diagram of a computer program product provided in an embodiment of the present application, wherein the computer program product 700 includes a computer program / instruction 701, which, when executed by a processor, implements the task processing method applied to materials science described in the embodiment.

[0167] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0168] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, devices, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0169] The embodiments of the present application are described with reference to the flowcharts and / or block diagrams of the methods, terminal devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing terminal device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing terminal device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0170] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing terminal device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0171] These computer program instructions can also be loaded onto a computer or other programmable data processing terminal device so that a series of operating steps are executed on the computer or other programmable terminal device to produce a computer-implemented process, thereby providing instructions for executing on the computer or other programmable terminal device to implement the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0172] Although the preferred embodiments of the present application have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the embodiments of the present application.

[0173] Finally, it should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or terminal device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or terminal device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or terminal device including the elements.

[0174] Specific examples are used herein to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method of the present application and its core idea. At the same time, for those skilled in the art, according to the idea of ​​the present application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A task processing method applied to materials science, characterized in that: The method comprises: In response to a task setting operation of any user, the central processing unit executes a target calculation strategy corresponding to the current material task to obtain a calculation result of the current material task, wherein the target calculation strategy includes at least one target calculation tool. When the target calculation strategy includes multiple target calculation tools, the target calculation strategy also includes an execution order of the multiple target calculation tools. The current material task includes a task of simulating the physical and chemical properties of any material, a task of calculating the electronic structure of any material, a task of performing material screening, and a task of predicting the performance of any material under different conditions. When the current material task is a preset experimental task type, the central processing unit determines the experimental execution strategy corresponding to the current material task, and generates an experimental operation instruction set for the current material task, wherein the corresponding experimental operation instruction is generated according to at least one of the experimental material object, quantity, experimental temperature, experimental pressure, experimental humidity and experimental application environment included in the experimental execution strategy corresponding to the current material task, and the experimental operation instruction set is constructed, and the experimental operation instruction set includes setting operation instructions for target parameters of any experimental equipment; The experimental process of the current material task is executed according to the experimental operation instruction set, and any experimental data generated during the experimental process is acquired.

2. The task processing method according to claim 1, characterized in that: In response to any user's task setting operation, the central processor executes the target calculation strategy corresponding to the current material task, including: In response to a task setting operation of any user, the central processor obtains the current material task; Matching the target calculation strategy corresponding to the current material task in a preset calculation strategy database, wherein the calculation strategy database includes target calculation strategies corresponding to different material tasks; When the target calculation strategy corresponding to the current material task includes a target calculation tool, calling the target calculation tool to calculate and obtain the calculation result of the current material task; When the target calculation strategy corresponding to the current material task includes multiple target calculation tools, the multiple target calculation tools are called in sequence according to the execution order of the multiple target calculation tools to calculate the calculation result of the current material task.

3. The task processing method according to claim 1, characterized in that: In response to a task setting operation of any user, the central processing unit executes the target calculation strategy corresponding to the current material task, and after obtaining the calculation result of the current material task, the method further includes: The central processing unit matches whether the current material task belongs to an experimental task type in the experimental task database, wherein the experimental task data stores material tasks of multiple experimental task types and an experimental execution strategy corresponding to any material task, and the experimental execution strategy includes at least one of the experimental material object, quantity, experimental temperature, experimental pressure, experimental humidity and experimental application environment.

4. The task processing method according to claim 1, characterized in that: The method further comprises: In response to an experiment task setting operation by any user, the central processing unit acquires the experiment operation task; Determine, in the experimental task database, an experimental execution strategy corresponding to the experimental operation task, wherein the experimental execution strategy includes at least one of an experimental material object, quantity, experimental temperature, experimental pressure, experimental humidity, and an experimental application environment; The central processing unit generates a set of experimental operation instructions for the experimental operation task according to the experimental execution strategy corresponding to the experimental operation task; Execute the experimental process of the experimental operation task according to the experimental operation instruction set, and obtain any experimental data generated during the experimental process; The experimental operation task, the experimental execution strategy corresponding to the experimental operation task, and any experimental data generated during the experimental process of the experimental operation task are stored.

5. The task processing method according to claim 4, characterized in that: The central processing unit determines the experimental execution strategy corresponding to the current material task and generates the experimental operation instruction set of the current material task, or the central processing unit generates the experimental operation instruction set of the experimental operation task according to the experimental execution strategy corresponding to the experimental operation task, including: The central processor configures at least one of the following experimental operation instructions according to the experimental execution strategy corresponding to the current material task or according to the experimental execution strategy corresponding to the experimental operation task to construct an experimental operation instruction set: Generate an experimental material object selection instruction according to the experimental operation task or the experimental material object in the experimental execution strategy; Generate an instruction for selecting the number of experimental material objects according to the number of experimental materials corresponding to the experimental operation task or the experimental execution strategy; Generate a temperature control instruction according to the experimental operation task or the experimental temperature in the experimental execution strategy; Generate a pressure control instruction according to the experimental operation task or the experimental pressure in the experimental execution strategy; Generate a humidity control instruction according to the experimental operation task or the experimental humidity in the experimental execution strategy; An application environment selection instruction is generated according to the experimental application environment in the experimental operation task or the experimental execution strategy.

6. The task processing method according to claim 5, characterized in that: The experimental process of executing the current material task according to the experimental operation instruction set includes: Responding in sequence to each instruction in the experimental operation instruction set generated by the central processor, configuring target parameters of any experimental equipment; When each instruction in the experimental operation instruction set is configured, the experimental process is triggered to execute.

7. The task processing method according to claim 1, characterized in that: During the experimental process of performing the current material task, the method further includes: Monitoring any monitoring information during the experiment of the current material task, wherein the monitoring information includes humidity information, temperature information and pressure information; When any monitoring information is larger than the configuration range of the monitoring information in the experiment execution strategy of the current material task, an error prompt of the monitoring information is displayed.

8. The task processing method according to claim 7, characterized in that: During the experimental process of performing the current material task, the method further includes: Monitoring abnormal conditions during the experiment of the current material task; When any abnormal state exists in the experimental process of the current material task, the safety processing strategy corresponding to the abnormal state is executed and an abnormal prompt is displayed.

9. The task processing method according to claim 1, characterized in that: During the experimental process of performing the current material task, the method further includes: According to any experimental data generated during the experiment of the current material task, the experimental operation instruction set is optimized and the parameter setting of any experimental equipment is updated.

10. The task processing method according to claim 1, characterized in that: The method further comprises: In the storage database, according to different material tasks, any current material task, the calculation result of the current material task, the experimental execution strategy of the current material task and any experimental data generated during the experiment of the current material task are classified and stored.

11. The task processing method according to claim 10, characterized in that: The method further comprises: In the storage database, the calculation results and experimental data corresponding to the same material are obtained for data statistics to obtain the data statistical results of the material; In response to a material data acquisition operation by any user, the data statistics corresponding to the target material are acquired in the storage database and returned.

12. The task processing method according to claim 11, characterized in that: The method further comprises: In response to a user's simulation task setting operation, storing a plurality of simulation calculation tasks and a plurality of simulation experiment tasks; When there is no current material task set by the user, obtain any simulation calculation task, execute the target calculation strategy corresponding to the simulation calculation task, obtain the calculation result of the simulation calculation task and store it; Obtain any simulation experiment task, execute the experiment execution strategy corresponding to the simulation experiment task, and generate a set of experimental operation instructions for the simulation experiment task, execute the experimental process of the simulation experiment task according to the experimental operation instruction set of the simulation experiment task, and store any experimental data generated during the experimental process of the simulation experiment task.

13. The task processing method according to claim 1, characterized in that: The method further comprises: In response to a new operation of adding a computing tool, a calling interface of any computing tool is added.

14. A non-volatile readable storage medium, characterized in that: The non-volatile readable storage medium stores a computer program, wherein the computer program, when executed by a processor, performs the task processing method applied to materials science as described in any one of claims 1 to 13.

15. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instruction is executed by a processor, the task processing method applied to materials science described in any one of claims 1 to 13 is implemented.

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