CN115762683B - Method and device for processing fuel cell design data and electronic equipment - Google Patents
Method and device for processing fuel cell design data and electronic equipment Download PDFInfo
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Abstract
The application provides a processing method and device for fuel cell design data and electronic equipment, relates to the technical field of cell design, and solves the technical problem that an existing cell product design mode is too dependent on a database. The method comprises the following steps: performing scene division on the fuel cell scene data according to a preset standard to obtain a scene division result; analyzing a coupling physical field under each scene in the scene division result; determining an influence factor in the coupling physical field based on a preset rule; splitting each influencing factor into unit particles, wherein the unit particles are of minimum granularity; combining similar items of target unit particles with the inter-particle association degree larger than a preset association degree in all unit particles to obtain similar items of particles; analyzing a coupling relation of association among a plurality of particle similar items through machine learning, and generating a fused fuel cell relation according to the coupling relation; the design of the fuel cell is determined by using the fuel cell relation.
Description
Technical Field
The present disclosure relates to the field of battery design technologies, and in particular, to a method and an apparatus for processing fuel cell design data, and an electronic device.
Background
In the design of battery products, the design method has been continuously developed in the time. At present, the design method of the battery product can be divided into a third generation core technology, namely a first generation experimental trial-and-error method, a second generation simulation driven forward design method and a third generation full-automatic intelligent battery product design method. The core of the third generation method is that the database is combined with artificial intelligence to replace manpower to carry out optimization iteration, so that the time and the labor cost are shortened.
However, for the design of the existing battery products, the mathematical permutation and combination of the fixed modules and parameters in the big data is excessively dependent on the data volume, the data type and the data authenticity of the big database, and lacks practical interpretability, so that it is difficult to guide new materials and new structures. Therefore, existing battery products are designed in a manner that is too dependent on the database.
Disclosure of Invention
The invention aims to provide a processing method and device of fuel cell design data and electronic equipment, so as to solve the technical problem that the design mode of the existing battery product is too dependent on a database.
In a first aspect, embodiments of the present application provide a method for processing fuel cell design data, where the method includes:
acquiring fuel cell scene data, and performing scene division on the fuel cell scene data according to a preset standard to obtain a scene division result;
analyzing a coupling physical field under each scene in the scene division result;
determining an influence factor in the coupled physical field based on a preset rule;
splitting each influence factor into unit particles, wherein the unit particles are of minimum granularity;
combining the similar items of the target unit particles with the inter-particle association degree larger than the preset association degree in all the unit particles to obtain particle similar items;
analyzing a coupling relation of association among a plurality of particle similar items through machine learning, and generating a fused fuel cell relation according to the coupling relation;
and determining the design mode of the fuel cell by using the relation of the fuel cell.
In one possible implementation, after the merging of the similar items of the target unit particles with the actual association degree greater than the preset association degree in all the unit particles to obtain the similar items of the particles, the method further includes:
generating tree-like hierarchical structural factors according to the particle similar items;
and analyzing an artificial neural network (Artificial Neural Network, ANN for short) and/or a Random Forest (RF) tree corresponding to the structural factors in a data driven manner.
In one possible implementation, the determining the design manner of the fuel cell by using the relation of the fuel cell includes:
analyzing intrinsic parameters through target data based on the fuel cell relational expression to obtain a first sexual principle formula; the target data are obtained through the experimental process;
and determining the design mode of the fuel cell based on the first sexual principle formula.
In one possible implementation, the first sexual principle formula includes the following six dimensions associated with the fuel cell:
the fuel cell is structured, materials, processes, durability, performance, and cost.
In one possible implementation, before the merging of the similar items of the target unit particles with the inter-particle association degree greater than the preset association degree in all the unit particles, the method further includes:
the degree of association between a plurality of said unit particles is determined by a graphical neural network (Graph Neural Network, GNN) or an evolutionary algorithm.
In one possible implementation, before the merging of the similar items of the target unit particles with the inter-particle association degree greater than the preset association degree in all the unit particles, the method further includes:
determining the association degree among a plurality of unit particles through the trained neural network model; the trained neural network model is generated by training a unit particle sample with a correlation degree label.
In one possible implementation, the preset criteria includes at least one of a preset formula and preset parameters.
In a second aspect, there is provided a processing apparatus of fuel cell design data, comprising:
the division module is used for acquiring the fuel cell scene data, and carrying out scene division on the fuel cell scene data according to a preset standard to obtain a scene division result;
the analysis module is used for analyzing the coupling physical field under each scene in the scene division result;
the first determining module is used for determining an influence factor in the coupling physical field based on a preset rule;
the splitting module is used for splitting each influence factor into unit particles, wherein the unit particles are of minimum granularity;
the merging module is used for merging the similar items of the target unit particles with the inter-particle association degree larger than the preset association degree in all the unit particles to obtain particle similar items;
the generation module is used for analyzing the coupling relation among a plurality of particle similar items through machine learning and generating a fused fuel cell relation according to the coupling relation;
and the second determining module is used for determining the design mode of the fuel cell by using the fuel cell relational expression.
In a third aspect, an embodiment of the present application further provides an electronic device, including a memory, and a processor, where the memory stores a computer program that can be executed by the processor, and the processor executes the method according to the first aspect.
In a fourth aspect, embodiments of the present application further provide a computer-readable storage medium storing computer-executable instructions that, when invoked and executed by a processor, cause the processor to perform the method of the first aspect described above.
The embodiment of the application brings the following beneficial effects:
according to the processing method, the device and the electronic equipment for the fuel cell design data, the fuel cell scene data can be obtained, scene division is carried out on the fuel cell scene data according to the preset standard to obtain scene division results, the coupling physical fields in each scene in the scene division results are analyzed, influence factors in the coupling physical fields are determined based on the preset rules, each influence factor is split into unit particles, wherein the unit particles are of minimum granularity, target unit particles with the inter-particle association degree larger than the preset association degree in all the unit particles are combined to obtain particle similar items, the coupling relation among the particle similar items is analyzed through machine learning, the fused fuel cell relation is generated according to the coupling relation, the design mode of the fuel cell is determined by utilizing the fuel cell relation, and in the scheme, the characteristics of multi-field coupling of the fuel cell and the development direction of future artificial intelligence are combined, the fourth-generation fuel cell design method is provided in the embodiment of the application: by providing the fuel cell design method based on the first sexual principle, the fuel cell shape integrated design method based on the first sexual principle can be truly realized in the aspect of intelligent design, the restriction of a database can be eliminated on the basis of the advantages of the prior art, the design is independent of the database, the design is truly realized, and the fuel cell design method based on the first sexual principle has universality and interpretability without iteration.
In order to make the above objects, features and advantages of the present application more comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are needed in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flow chart of a method for processing fuel cell design data according to an embodiment of the present disclosure;
fig. 2 is another flow chart of a method for processing fuel cell design data according to an embodiment of the present disclosure;
fig. 3 is a schematic structural diagram of a processing device for fuel cell design data according to an embodiment of the present application;
fig. 4 shows a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
For the purposes of making the objects, technical solutions and advantages of the embodiments of the present application more apparent, the technical solutions of the present application will be clearly and completely described below with reference to the accompanying drawings, and it is apparent that the described embodiments are some, but not all, embodiments of the present application. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present disclosure.
The terms "comprising" and "having" and any variations thereof, as used in the embodiments of the present application, are intended to cover non-exclusive inclusion. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those listed but may optionally include other steps or elements not listed or inherent to such process, method, article, or apparatus.
In the design of battery products, the prior art theory can deal with hidden correlations between a large amount of data and ignore the characteristics of complex internal mechanisms, so that the research and development period is shortened, the cost is reduced, but the following problems exist:
from the theoretical framework, the mathematical permutation and combination of fixed modules and parameters in the existing big data depend on the data volume, the data type and the data authenticity of the big database excessively, and the actual interpretability is lacking, so that new materials and new structures are difficult to guide. From the aspect of the characteristics of the fuel cell, the complex multi-field coupling characteristics (electricity, electrochemistry, hydrodynamics, theoretical mechanics and complex working conditions) of the fuel cell cannot provide an interpretable mechanism for a complex physical field at present by simply relying on machine learning of big data, and the method has no universality, and after design preconditions are changed (such as materials, processing technology and the like), a design model is distorted. From the effect point of view, iteration is still needed, and real instantaneity cannot be achieved. From the input end, depending on the definition and the authenticity of the input end data, the human language needs to be translated into the machine language, and the man-machine interaction is poor.
Based on the above, the embodiment of the application provides a method and a device for processing fuel cell design data and electronic equipment, and by the method, the technical problem that the design mode of the existing battery product is too dependent on a database can be relieved.
Embodiments of the present invention are further described below with reference to the accompanying drawings.
Fig. 1 is a flow chart of a method for processing fuel cell design data according to an embodiment of the present application. As shown in fig. 1, the method includes:
step S110, obtaining fuel cell scene data, and performing scene division on the fuel cell scene data according to preset standards to obtain a scene division result.
Wherein the preset criteria includes at least one of a preset formula and preset parameters. For example, the fuel cell scene is divided according to classical standards such as national standards or large enterprise standards and classical formulas,
step S120, analyzing the coupled physical field in each scene in the scene division result.
In this step, the system may analyze the coupled physical fields in each scene, such as a multi-field coupling ambiguity function, and the specific analysis is shown in fig. 2, where the horizontal axis is a lifting sub-term, the vertical axis is performance, and for the relation of the multi-to-multi performance mapping, each factor has an influence, and the relation of the influence factors of the influence is processed in the following manner from step S130 to step S170.
Step S130, determining an influence factor in the coupling physical field based on a preset rule.
In this step, the system may condense the influence factors in the coupled physical field according to a preset rule.
Step S140, splitting each influencing factor into unit particles.
Wherein the unit particles are of minimum granularity. In this step, the system may split each influencing factor to unit particles, i.e. not resolvable, and further form finely divided granularities.
And step S150, merging the similar items of the target unit particles with the inter-particle association degree larger than the preset association degree in all the unit particles to obtain the similar items of the particles.
In practical application, the system can combine the influence factors with high association degree into the same category.
Step S160, analyzing the coupling relation among the plurality of particle similar items through machine learning, and generating a fused fuel cell relation according to the coupling relation.
And (3) mining the implicit coupling relation of the association among the influence factor items through machine learning, and providing a fused fuel cell relational expression.
In step S170, the design method of the fuel cell is determined using the fuel cell relation.
In the embodiment of the application, in combination with the characteristics of multi-field coupling of the fuel cell and the development direction of future artificial intelligence, the embodiment of the application provides a fourth generation fuel cell design method: by providing the fuel cell design method based on the first sexual principle, the fuel cell shape integrated design method based on the first sexual principle can be truly realized in the aspect of intelligent design, the restriction of a database can be eliminated on the basis of the advantages of the prior art, the design is independent of the database, the design is truly realized, and the fuel cell design method based on the first sexual principle has universality and interpretability without iteration.
The above steps are described in detail below.
In some embodiments, after the step S150, the method may further include the steps of:
step a), generating tree-like hierarchical structural factors according to the particle similar items;
and b), analyzing an artificial neural network (Artificial Neural Network, ANN for short) and/or a Random Forest (RF) tree corresponding to the structural factors in a data driven mode.
The particle similar items are summarized as tree-like hierarchical structural factors, and the structure (ANN, RF tree) is mined by utilizing data driving, so that the intelligent allocation and macroscopic control can be combined. Through tree-like hierarchical structure factors, the association relationship between data can be clearer and clearer.
In some embodiments, the step S170 may include the following steps:
step c), analyzing intrinsic parameters through target data based on a fuel cell relation to obtain a first sexual principle formula;
and d), determining the design mode of the fuel cell based on the first principle formula.
Wherein, the target data is obtained through the experimental process. In the embodiment of the application, the intrinsic parameters are mined by using experimental big data, as shown in fig. 2, and the intrinsic parameters are solved by machine learning, so that a first principle formula is completed, as shown in fig. 2, and the design mode of the fuel cell is determined based on the first principle formula.
The determined design mode of the fuel cell can be more accurate through analysis of the intrinsic parameters and generation of the first sexual principle formula.
In some embodiments, the first sexual principle formula includes the following six dimensions associated with the fuel cell: the structure, materials, processes, durability, performance, and cost of the fuel cell.
In practical application, six dimensions (namely six dimensions of structure, material, process, durability, performance and cost) of the whole fuel cell design are unified into an equation, so that a six-dimensional shape co-fusion design method of the fuel cell based on a first principle is formed, and breakthroughs of the fuel cell design mode in theory and practical design are realized.
In some embodiments, before the step S150, the method may further include the steps of:
step e) determining the degree of association between the plurality of elementary particles by means of a graphic neural network (Graph Neural Network, GNN) or an evolutionary algorithm.
In the embodiment of the application, the association degree among the plurality of unit particles can be determined by a GNN or evolution algorithm mode, and the association degree among the plurality of unit particles can be determined more accurately and efficiently by the GNN or evolution algorithm mode.
In some embodiments, before the step S150, the method may further include the steps of:
and f), determining the association degree among a plurality of unit particles through the trained neural network model.
The trained neural network model is generated by training a unit particle sample with a correlation degree label. In the embodiment of the application, the association degree among a plurality of unit particles can be determined through a model training generation mode. The neural network model generated through training the unit particle sample with the association degree label enables the association degree among a plurality of unit particles to be more accurate and efficient.
Fig. 3 provides a schematic structural diagram of a processing device for fuel cell design data. As shown in fig. 3, the processing device 300 of the fuel cell design data includes:
the division module 301 is configured to obtain fuel cell scene data, and perform scene division on the fuel cell scene data according to a preset standard to obtain a scene division result;
a first analysis module 302, configured to analyze a coupled physical field in each scene in the scene division result;
a first determining module 303, configured to determine an influence factor in the coupled physical field based on a preset rule;
a splitting module 304, configured to split each of the influencing factors into unit particles, where the unit particles are of a smallest granularity;
the merging module 305 is configured to merge similar items of target unit particles in which the inter-particle association degree is greater than a preset association degree in all the unit particles, so as to obtain similar items of particles;
a first generating module 306, configured to analyze, by machine learning, a coupling relation of association between a plurality of the particle like items, and generate a fused fuel cell relation according to the coupling relation;
a second determining module 307, configured to determine a design manner of the fuel cell by using the fuel cell relational expression.
In some embodiments, the apparatus further comprises:
the second generation module is used for generating tree-like hierarchical structural factors according to the particle similar items;
and the second analysis module is used for analyzing the ANN and/or the RF tree corresponding to the structural factors in a data-driven mode.
In some embodiments, the second determining module 307 is specifically configured to:
analyzing intrinsic parameters through target data based on the fuel cell relational expression to obtain a first sexual principle formula; the target data are obtained through the experimental process;
and determining the design mode of the fuel cell based on the first sexual principle formula.
In some embodiments, the first principles formula includes the following six dimensions associated with a fuel cell:
the fuel cell is structured, materials, processes, durability, performance, and cost.
In some embodiments, the apparatus further comprises:
and the third determining module is used for determining the association degree among a plurality of unit particles through the GNN or an evolution algorithm.
In some embodiments, the apparatus further comprises:
a fourth determining module, configured to determine a degree of association between a plurality of unit particles through the trained neural network model; the trained neural network model is generated by training a unit particle sample with a correlation degree label.
In some embodiments, the preset criteria includes at least one of a preset formula and preset parameters.
The processing device for fuel cell design data provided in the embodiment of the present application has the same technical characteristics as the processing method for fuel cell design data provided in the foregoing embodiment, so that the same technical problems can be solved, and the same technical effects can be achieved.
As shown in fig. 4, an electronic device 400 provided in the embodiment of the present application includes a processor 402 and a memory 401, where a computer program capable of running on the processor is stored in the memory, and the steps of the method provided in the foregoing embodiment are implemented when the processor executes the computer program.
Referring to fig. 4, the electronic device further includes: a bus 403 and a communication interface 404, the processor 402, the communication interface 404 and the memory 401 being connected by the bus 403; the processor 402 is used to execute executable modules, such as computer programs, stored in the memory 401.
The memory 401 may include a high-speed random access memory (Random Access Memory, abbreviated as RAM), and may further include a non-volatile memory (non-volatile memory), such as at least one magnetic disk memory. The communication connection between the system network element and at least one other network element is implemented via at least one communication interface 404 (which may be wired or wireless), and may use the internet, a wide area network, a local network, a metropolitan area network, etc.
Bus 403 may be an ISA bus, a PCI bus, an EISA bus, or the like. The buses may be classified as address buses, data buses, control buses, etc. For ease of illustration, only one bi-directional arrow is shown in FIG. 4, but not only one bus or type of bus.
The memory 401 is configured to store a program, and the processor 402 executes the program after receiving an execution instruction, and a method executed by an apparatus defined by a process disclosed in any embodiment of the present application may be applied to the processor 402 or implemented by the processor 402.
The processor 402 may be an integrated circuit chip with signal processing capabilities. In implementation, the steps of the methods described above may be performed by integrated logic circuitry in hardware or instructions in software in processor 402. The processor 402 may be a general-purpose processor, including a central processing unit (Central Processing Unit, CPU), a network processor (Network Processor, NP), etc.; but may also be a digital signal processor (Digital Signal Processing, DSP for short), application specific integrated circuit (Application Specific Integrated Circuit, ASIC for short), off-the-shelf programmable gate array (Field-Programmable Gate Array, FPGA for short), or other programmable logic device, discrete gate or transistor logic device, discrete hardware components. The disclosed methods, steps, and logic blocks in the embodiments of the present application may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of a method disclosed in connection with the embodiments of the present application may be embodied directly in hardware, in a decoded processor, or in a combination of hardware and software modules in a decoded processor. The software modules may be located in a random access memory, flash memory, read only memory, programmable read only memory, or electrically erasable programmable memory, registers, etc. as well known in the art. The storage medium is located in the memory 401 and the processor 402 reads the information in the memory 401 and in combination with its hardware performs the steps of the above method.
Corresponding to the above method for processing fuel cell design data, the embodiments of the present application further provide a computer-readable storage medium storing computer-executable instructions that, when invoked and executed by a processor, cause the processor to execute the steps of the above method for processing fuel cell design data.
The processing device of the fuel cell design data provided in the embodiment of the present application may be specific hardware on a device or software or firmware installed on the device. The device provided in the embodiments of the present application has the same implementation principle and technical effects as those of the foregoing method embodiments, and for a brief description, reference may be made to corresponding matters in the foregoing method embodiments where the device embodiment section is not mentioned. It will be clear to those skilled in the art that, for convenience and brevity, the specific operation of the system, apparatus and unit described above may refer to the corresponding process in the above method embodiment, which is not described in detail herein.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other manners. The above-described apparatus embodiments are merely illustrative, for example, the division of the units is merely a logical function division, and there may be other manners of division in actual implementation, and for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some communication interface, device or unit indirect coupling or communication connection, which may be in electrical, mechanical or other form.
As another example, the flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described as separate units may or may not be physically separate, and units shown 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 may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments provided in the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in 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 solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, or in a form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the processing method of fuel cell design data according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
It should be noted that: like reference numerals and letters in the following figures denote like items, and thus once an item is defined in one figure, no further definition or explanation of it is required in the following figures, and furthermore, the terms "first," "second," "third," etc. are used merely to distinguish one description from another and are not to be construed as indicating or implying relative importance.
Finally, it should be noted that: the foregoing examples are merely specific embodiments of the present application, and are not intended to limit the scope of the present application, but the present application is not limited thereto, and those skilled in the art will appreciate that while the foregoing examples are described in detail, the present application is not limited thereto. Any person skilled in the art may modify or easily conceive of the technical solution described in the foregoing embodiments, or make equivalent substitutions for some of the technical features within the technical scope of the disclosure of the present application; such modifications, changes or substitutions do not depart from the spirit of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present application. Are intended to be encompassed within the scope of this application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (10)
1. A method of processing fuel cell design data, the method comprising:
acquiring fuel cell scene data, and performing scene division on the fuel cell scene data according to a preset standard to obtain a scene division result;
analyzing a coupling physical field under each scene in the scene division result;
determining an influence factor in the coupled physical field based on a preset rule;
splitting each influence factor into unit particles, wherein the unit particles are of minimum granularity;
combining the similar items of the target unit particles with the inter-particle association degree larger than the preset association degree in all the unit particles to obtain particle similar items;
analyzing a coupling relation of association among a plurality of particle similar items through machine learning, and generating a fused fuel cell relation according to the coupling relation;
and determining the design mode of the fuel cell by using the relation of the fuel cell.
2. The method according to claim 1, wherein after the step of merging the same items of the target unit particles with the actual association degree larger than the preset association degree in all the unit particles to obtain the same items of the particles, the method further comprises:
generating tree-like hierarchical structural factors according to the particle similar items;
and analyzing the ANN and/or the RF tree corresponding to the structural factors in a data-driven mode.
3. The method of claim 1, wherein determining the design of the fuel cell using the fuel cell relationship comprises:
analyzing intrinsic parameters through target data based on the fuel cell relational expression to obtain a first sexual principle formula; the target data are obtained through the experimental process;
and determining the design mode of the fuel cell based on the first sexual principle formula.
4. A method according to claim 3, wherein the first sexual principle formula comprises the following six dimensions in relation to the fuel cell:
the fuel cell is structured, materials, processes, durability, performance, and cost.
5. The method according to claim 1, wherein before the step of merging the same items of the target unit particles with the inter-particle association degree greater than the preset association degree in all the unit particles to obtain the same items of the particles, the method further comprises:
the degree of association between a plurality of said unit particles is determined by GNN or an evolutionary algorithm.
6. The method according to claim 1, wherein before the step of merging the same items of the target unit particles with the inter-particle association degree greater than the preset association degree in all the unit particles to obtain the same items of the particles, the method further comprises:
determining the association degree among a plurality of unit particles through the trained neural network model; the trained neural network model is generated by training a unit particle sample with a correlation degree label.
7. The method of claim 1, wherein the predetermined criteria comprises at least one of a predetermined formula and a predetermined parameter.
8. A processing apparatus for fuel cell design data, comprising:
the division module is used for acquiring the fuel cell scene data, and carrying out scene division on the fuel cell scene data according to a preset standard to obtain a scene division result;
the analysis module is used for analyzing the coupling physical field under each scene in the scene division result;
the first determining module is used for determining an influence factor in the coupling physical field based on a preset rule;
the splitting module is used for splitting each influence factor into unit particles, wherein the unit particles are of minimum granularity;
the merging module is used for merging the similar items of the target unit particles with the inter-particle association degree larger than the preset association degree in all the unit particles to obtain particle similar items;
the generation module is used for analyzing the coupling relation among a plurality of particle similar items through machine learning and generating a fused fuel cell relation according to the coupling relation;
and the second determining module is used for determining the design mode of the fuel cell by using the fuel cell relational expression.
9. An electronic device comprising a memory, a processor, the memory having stored therein a computer program executable on the processor, characterized in that the processor, when executing the computer program, implements the steps of the method of any of the preceding claims 1 to 7.
10. A computer readable storage medium storing computer executable instructions which, when invoked and executed by a processor, cause the processor to perform the method of any one of claims 1 to 7.
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|---|---|---|---|---|
| CN105825060A (en) * | 2016-03-17 | 2016-08-03 | 西南交通大学 | Electromagnetic effect influence calculation method for transition layer of multilayer covering fiber enhanced intelligent material |
| CN114048525A (en) * | 2021-10-22 | 2022-02-15 | 电子科技大学 | Design system of energy storage battery system and preparation of energy storage |
| CN114398347A (en) * | 2021-12-15 | 2022-04-26 | 国家电投集团氢能科技发展有限公司 | Fuel cell data analysis system and electronic device based on data correlation |
| CN115270633A (en) * | 2022-08-03 | 2022-11-01 | 西安交通大学 | Prediction method, system, device and medium for three-dimensional physical field of fuel cell |
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Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105825060A (en) * | 2016-03-17 | 2016-08-03 | 西南交通大学 | Electromagnetic effect influence calculation method for transition layer of multilayer covering fiber enhanced intelligent material |
| CN114048525A (en) * | 2021-10-22 | 2022-02-15 | 电子科技大学 | Design system of energy storage battery system and preparation of energy storage |
| CN114398347A (en) * | 2021-12-15 | 2022-04-26 | 国家电投集团氢能科技发展有限公司 | Fuel cell data analysis system and electronic device based on data correlation |
| CN115270633A (en) * | 2022-08-03 | 2022-11-01 | 西安交通大学 | Prediction method, system, device and medium for three-dimensional physical field of fuel cell |
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