Engineering material selection method and device based on machine learning, medium and equipment

Abstract

The invention discloses an engineering material selection method and device based on machine learning, a medium and equipment, and belongs to the technical field of machine learning. The method comprises the following steps that construction and application working condition parameters of a to-be-constructed project are obtained; inputting the construction and application working condition parameters of the construction project into an engineering material selection mathematical model obtained based on machine learning; calculating the engineering material selection mathematical model obtained based on machine learning to obtain an optional engineering material; wherein the engineering material selection mathematical model obtained based on machine learning is selected from one or more of a regression model and a neural network model. The device, the medium and the equipment can realize the engineering material selection method based on machine learning. According to the method, the device, the medium and the equipment, operation is easy, errors or mistakes generated by manual operation can be reduced, and therefore the safety of construction engineering in the construction and application process can be improved, and the working efficiency of engineering material selection can be improved.

Description

Technical Field

[0001] The present invention relates to the field of machine learning technology, and in particular to a method, device, medium and equipment for selecting engineering materials based on machine learning. Background Art

[0002] In underground engineering, the strength of materials is directly related to the ability to withstand mine pressure and is an important part of ensuring safety. Excessive or low material strength may lead to mine pressure accidents, threaten the lives of workers, and cause equipment damage or production interruption. Therefore, it is crucial to ensure that the material has appropriate strength to cope with geological pressure and prevent the occurrence of mine pressure accidents. In the prior art, before construction of underground engineering, it is necessary to select engineering materials according to the construction and application parameters of the underground engineering. However, due to the large number of construction and application parameters of underground engineering, the material composition, internal crystal structure and other parameters of engineering materials are also large. Therefore, if the selection of engineering materials for a specific project to be constructed is carried out each time, the workload is large, and, due to the complex calculation process, once one or several steps in the manual calculation are wrong, the final selected engineering materials will be deviated, which may require the reselection of materials for construction, or even cause liability accidents in the process of engineering application, and some may even cause major liability accidents. Therefore, it is necessary to simplify the method of selecting engineering materials for the project to be constructed. Summary of the invention

[0003] In view of this, the present invention provides a method, device, medium and equipment for selecting engineering materials based on machine learning, which can improve the safety of construction projects during construction and application, and improve the work efficiency of engineering material selection, so as to be more suitable for practical use.

[0004] In order to achieve the first objective above, the technical solution of the engineering material selection method based on machine learning provided by the present invention is as follows:

[0005] The engineering material selection method based on machine learning provided by the present invention comprises the following steps:

[0006] Obtain the construction and application parameters of the project to be constructed;

[0007] Inputting the construction and application condition parameters of the construction project into a mathematical model for selecting engineering materials based on machine learning;

[0008] The engineering material selection mathematical model obtained based on machine learning is calculated to obtain the available engineering materials;

[0009] Wherein, the engineering material selection mathematical model obtained based on machine learning is selected from one or more of regression models and neural network models.

[0010] The engineering material selection method based on machine learning provided by the present invention can also be further implemented by adopting the following technical measures.

[0011] Preferably, the construction and application condition parameters of the construction project are input into the specific steps of the engineering material selection mathematical model obtained based on machine learning. The engineering material selection mathematical model obtained based on machine learning is obtained based on the following steps:

[0012] Obtaining historical construction project and application condition parameters, and data parameters of engineering materials that have been selected and applied to corresponding historical construction projects as training samples;

[0013] Inputting the historical construction projects and application condition parameters corresponding to the training samples, and the data parameters of the engineering materials that have been selected and applied to the corresponding historical construction projects into the engineering material selection mathematical model obtained based on machine learning;

[0014] The data parameters of the mathematical model for engineering material selection based on machine learning are adjusted so that the data parameters of the mathematical model for engineering material selection based on machine learning are compatible with the historical construction projects and application condition parameters, and the data parameters of the engineering materials that have been selected and applied to the corresponding historical construction projects, so as to obtain the mathematical model for engineering material selection based on machine learning.

[0015] Preferably, the engineering material selection method based on machine learning further comprises the following steps:

[0016] The data parameters of the current construction project and the application conditions, and the engineering materials currently selected and applied to the corresponding historical construction projects are added to the training samples, and the data parameters of the engineering material selection mathematical model obtained based on machine learning are further adjusted.

[0017] Preferably, the engineering material selection method based on machine learning further comprises the following steps:

[0018] Get the data parameters of the current engineering materials;

[0019] Inputting the data parameters of the current engineering material into the engineering material selection mathematical model obtained based on machine learning;

[0020] The engineering material selection mathematical model obtained based on machine learning is calculated to obtain construction and application condition parameters suitable for the current engineering materials.

[0021] Preferably, the construction and application parameters of the project to be constructed include one or more of coal rock type, water content, and sulfur content.

[0022] Preferably, the characteristic values ​​of the selectable engineering materials include the material composition of the engineering materials, the crystal structure of the engineering materials, the engineering strength of the engineering materials, the formula ratio of the engineering materials, the particle size of the engineering materials, and one or more of the compressive strength and tensile strength of the engineering materials under corresponding application conditions.

[0023] Preferably, the crystal structure of the engineering material includes crack density and porosity.

[0024] In order to achieve the above second purpose, the technical solution of the engineering material selection device based on machine learning provided by the present invention is as follows:

[0025] The engineering material selection device based on machine learning provided by the present invention comprises:

[0026] Parameter acquisition module, used to obtain the construction and application parameters of the project to be constructed;

[0027] A mathematical model for selecting engineering materials based on machine learning, into which the construction and application condition parameters of the construction project are input;

[0028] A calculation module, used for calculating the engineering material selection mathematical model obtained based on machine learning to obtain the available engineering materials;

[0029] Wherein, the engineering material selection mathematical model obtained based on machine learning is selected from one or more of regression models and neural network models.

[0030] In order to achieve the third objective above, the technical solution of the computer-readable storage medium provided by the present invention is as follows:

[0031] The computer-readable storage medium provided by the present invention stores an engineering material selection program based on machine learning. After the engineering material selection program based on machine learning is executed by a processor, the steps of the method for engineering material selection based on machine learning provided by the present invention are implemented.

[0032] In order to achieve the fourth objective, the technical solution of the electronic device provided by the present invention is as follows:

[0033] The electronic device provided by the present invention includes a memory and a processor, wherein the memory stores an engineering material selection program based on machine learning. After the engineering material selection program based on machine learning is executed by the processor, the steps of the method for engineering material selection based on machine learning provided by the present invention are implemented.

[0034] The method, device, medium and equipment for selecting engineering materials based on machine learning provided by the present invention first train a mathematical model for selecting engineering materials based on machine learning by using historical construction projects and application conditions parameters, and data parameters of engineering materials that have been selected and applied to corresponding historical construction projects as training samples. Then, the construction and application conditions parameters of the project to be constructed are input into the mathematical model. The mathematical model can calculate and obtain the available engineering materials. The method is not only simple to operate, but also can reduce errors or mistakes caused by human calculations. Therefore, the safety of construction projects during construction and application can be improved, and the work efficiency of engineering material selection can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0036] Figure 1 A flowchart of the steps of the engineering material selection method based on machine learning provided by an embodiment of the present invention;

[0037] Figure 2 A flowchart of the steps of a training method for a mathematical model for selecting engineering materials based on machine learning, which is involved in the method for selecting engineering materials based on machine learning provided in an embodiment of the present invention;

[0038] Figure 3 A flowchart of the steps of the reverse operation of the engineering material selection method based on machine learning provided in an embodiment of the present invention;

[0039] Figure 4 A schematic diagram of the signal flow relationship between the functional modules in the engineering material selection device based on machine learning provided in an embodiment of the present invention;

[0040] Figure 5 A schematic diagram of the structure of an engineering material selection device based on machine learning in the hardware operating environment provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0041] In order to solve the problems existing in the prior art, the present invention provides a method, device, medium and equipment for selecting engineering materials based on machine learning, which can improve the safety of construction projects during construction and application, and improve the work efficiency of engineering material selection, thereby being more suitable for practical use.

[0042] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of the engineering material selection method, device, medium and equipment based on machine learning proposed by the present invention, its specific implementation method, structure, characteristics and effects in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "embodiment" does not necessarily refer to the same embodiment. In addition, the features, structures, or characteristics in one or more embodiments may be combined in any suitable form.

[0043] The term "and / or" in this article is merely a description of the association relationship of associated objects, indicating that three relationships may exist, for example, A and / or B. Specifically, it is understood that: A and B may be included at the same time, A may exist alone, or B may exist alone, and any of the above three situations may be met.

[0044] Engineering material selection method based on machine learning

[0045] See attached Figure 1 The method for selecting engineering materials based on machine learning provided by an embodiment of the present invention comprises the following steps:

[0046] Step S1: Obtain the construction and application parameters of the project to be constructed.

[0047] Specifically, the project to be constructed includes parameters during the construction process and parameters in the application state after the construction is completed. Therefore, in the process of selecting engineering materials, it is necessary to ensure the safety of the construction process and the project in the application state after the construction is completed. For example, taking the construction project under the mine as an example, during the construction process, it includes excavating the mine. At this time, it is necessary to ensure the personal safety of the construction workers during the excavation of the mine. Therefore, when excavating the mine, the strength of the engineering materials used should be adapted to the force generated by the mine during the construction process, so as to avoid the risk of construction workers being buried during the excavation of the mine. After the excavation of the mine, it is necessary to set up support facilities in the mine to ensure the personal safety of the miners during the application of the mine. Therefore, the support materials set up in the mine should be able to bear the load adapted to the mine, so as to reduce or even avoid the personal safety hazards of the miners during the underground operation. In addition, the construction and application safety issues in the mine caused by natural disasters such as earthquakes and mudslides should also be considered. Combining these situations, the construction and application working condition parameters of the project to be constructed can be obtained.

[0048] Step S2: Inputting the construction and application condition parameters of the construction project into the engineering material selection mathematical model obtained based on machine learning.

[0049] Specifically, since there are many parameters that affect the construction and application conditions of the construction project, some of which are not numerical parameters but descriptive parameters. Therefore, under the premise of such multiple factors and non-numerical descriptive parameters, it is difficult to use simple functions to express the correlation between the selection of engineering materials and the construction and application condition parameters of the project to be constructed with simple mathematical expressions. Therefore, it is necessary to use multi-factor analysis methods such as regression analysis and artificial neural networks to learn and train historical data to obtain a mathematical model for the selection of engineering materials based on machine learning. In some cases, there is more than one mathematical model for the selection of engineering materials based on machine learning. In this case, it is also necessary to obtain multiple mathematical models through multiple mathematical models for the selection of engineering materials based on machine learning, as well as the construction and application conditions of the current project to be constructed, and use the multiple mathematical models for training to obtain multiple mathematical models for the selection of engineering materials based on machine learning.

[0050] Step S3: The engineering material selection mathematical model obtained based on machine learning is calculated to obtain the available engineering materials.

[0051] Specifically, after the construction and application parameters of the project to be constructed have been obtained, they are input into the engineering material selection mathematical model obtained based on machine learning. The mathematical model can obtain the various parameters of the available engineering materials according to its inherent calculation method. Then, the mathematical model traverses the parameters of various engineering materials stored in it to obtain the available engineering materials. For example, when a certain engineering material is selected as steel, the construction and application parameters of the project to be constructed are input into the engineering material selection mathematical model obtained based on machine learning, and the yield strength, tensile strength and other data of the required steel can be calculated. Then, the mathematical model is traversed according to the yield strength, tensile strength and other data corresponding to the stored engineering materials, and then the model of steel suitable for the project to be constructed is obtained for selection. In some cases, there are multiple types of steel suitable for the project to be constructed. In this case, the mathematical model can also provide data such as budgets and sales suppliers so that construction personnel can select engineering materials more efficiently.

[0052] Among them, the mathematical model for engineering material selection obtained based on machine learning is selected from one or more of regression models and neural network models.

[0053] The method for selecting engineering materials based on machine learning provided in an embodiment of the present invention first trains a mathematical model for selecting engineering materials based on machine learning by using historical construction projects and application conditions parameters, and data parameters of engineering materials that have been selected and applied to corresponding historical construction projects as training samples. Then, the construction and application conditions parameters of the project to be constructed are input into the mathematical model. The mathematical model can calculate and obtain the available engineering materials. This method is not only simple to operate, but also can reduce errors or mistakes caused by human calculations. Therefore, it can improve the safety of construction projects during construction and application, and improve the work efficiency of engineering material selection.

[0054] Among them, step S2 inputs the construction and application condition parameters of the construction project into the specific steps of the engineering material selection mathematical model obtained based on machine learning. The engineering material selection mathematical model obtained based on machine learning is obtained based on the following steps:

[0055] Step S21: Acquire historical construction projects and application condition parameters, and data parameters of engineering materials that have been selected and applied to corresponding historical construction projects as training samples;

[0056] Step S22: inputting the historical construction projects and application condition parameters corresponding to the training samples, and the data parameters of the engineering materials that have been selected and applied to the corresponding historical construction projects into the engineering material selection mathematical model obtained based on machine learning;

[0057] Step S23: Adjust the data parameters of the mathematical model for engineering material selection obtained based on machine learning so that the data parameters of the mathematical model for engineering material selection obtained based on machine learning are consistent with the historical construction projects and application conditions parameters, and the data parameters of engineering materials that have been selected and applied to the corresponding historical construction projects, to obtain the mathematical model for engineering material selection obtained based on machine learning.

[0058] The engineering material selection method based on machine learning also includes the following steps:

[0059] Step S24: Add the current construction project and application condition parameters, and the data parameters of the engineering materials currently selected and applied to the corresponding historical construction projects to the training samples, and further adjust the data parameters of the engineering material selection mathematical model obtained based on machine learning.

[0060] In this case, since the training process of the mathematical model for engineering material selection based on machine learning is basically consistent with its application process, the engineering materials selected by applying the mathematical model for engineering material selection based on machine learning can be better matched with the current project to be constructed.

[0061] The engineering material selection method based on machine learning also includes the following steps:

[0062] Step S4: Obtain data parameters of current engineering materials;

[0063] Step S5: inputting the data parameters of the current engineering material into the engineering material selection mathematical model obtained based on machine learning;

[0064] Step S5: The engineering material selection mathematical model obtained based on machine learning is calculated to obtain construction and application working condition parameters suitable for the current engineering materials.

[0065] In this case, the engineering material selection method based on machine learning can also provide market guidance for engineering material suppliers, guiding them to find suitable projects to be constructed, thereby improving the transaction accuracy of engineering materials and saving transaction costs.

[0066] Among them, the construction and application condition parameters of the project to be constructed include one or more of coal rock type, water content, and sulfur content.

[0067] Among them, the characteristic values ​​of the engineering materials that can be selected include the material composition of the engineering materials, the crystal structure of the engineering materials, the engineering strength of the engineering materials, the formula ratio of the engineering materials, the particle size of the engineering materials, and one or more of the compressive strength and tensile strength of the engineering materials under corresponding application conditions.

[0068] Among them, the crystal structure of engineering materials includes crack density and porosity.

[0069] Engineering materials selection device based on machine learning

[0070] The engineering material selection device based on machine learning provided by the present invention comprises:

[0071] The parameter acquisition module is used to obtain the construction and application condition parameters of the project to be constructed.

[0072] Specifically, the project to be constructed includes parameters during the construction process and parameters in the application state after the construction is completed. Therefore, in the process of selecting engineering materials, it is necessary to ensure the safety of the construction process and the project in the application state after the construction is completed. For example, taking the construction project under the mine as an example, during the construction process, it includes excavating the mine. At this time, it is necessary to ensure the personal safety of the construction workers during the excavation of the mine. Therefore, when excavating the mine, the strength of the engineering materials used should be adapted to the force generated by the mine during the construction process, so as to avoid the risk of construction workers being buried during the excavation of the mine. After the excavation of the mine, it is necessary to set up support facilities in the mine to ensure the personal safety of the miners during the application of the mine. Therefore, the support materials set up in the mine should be able to bear the load adapted to the mine, so as to reduce or even avoid the personal safety hazards of the miners during the underground operation. In addition, the construction and application safety issues in the mine caused by natural disasters such as earthquakes and mudslides should also be considered. Combining these situations, the construction and application working condition parameters of the project to be constructed can be obtained.

[0073] The mathematical model for engineering material selection obtained based on machine learning is used to input the construction and application condition parameters of the construction project into it.

[0074] Specifically, since there are many parameters that affect the construction and application conditions of the construction project, some of which are not numerical parameters but descriptive parameters. Therefore, under the premise of such multiple factors and non-numerical descriptive parameters, it is difficult to use simple functions to express the correlation between the selection of engineering materials and the construction and application condition parameters of the project to be constructed with simple mathematical expressions. Therefore, it is necessary to use multi-factor analysis methods such as regression analysis and artificial neural networks to learn and train historical data to obtain a mathematical model for the selection of engineering materials based on machine learning. In some cases, there is more than one mathematical model for the selection of engineering materials based on machine learning. In this case, it is also necessary to obtain multiple mathematical models through multiple mathematical models for the selection of engineering materials based on machine learning, as well as the construction and application conditions of the current project to be constructed, and use the multiple mathematical models for training to obtain multiple mathematical models for the selection of engineering materials based on machine learning.

[0075] The operation module is used to operate the engineering material selection mathematical model obtained based on machine learning to obtain the available engineering materials.

[0076] Specifically, after the construction and application parameters of the project to be constructed have been obtained, they are input into the engineering material selection mathematical model obtained based on machine learning. The mathematical model can obtain the various parameters of the available engineering materials according to its inherent calculation method. Then, the mathematical model traverses the parameters of various engineering materials stored in it to obtain the available engineering materials. For example, when a certain engineering material is selected as steel, the construction and application parameters of the project to be constructed are input into the engineering material selection mathematical model obtained based on machine learning, and the yield strength, tensile strength and other data of the required steel can be calculated. Then, the mathematical model is traversed according to the yield strength, tensile strength and other data corresponding to the stored engineering materials, and then the model of steel suitable for the project to be constructed is obtained for selection. In some cases, there are multiple types of steel suitable for the project to be constructed. In this case, the mathematical model can also provide data such as budgets and sales suppliers so that construction personnel can select engineering materials more efficiently.

[0077] Among them, the mathematical model for engineering material selection obtained based on machine learning is selected from one or more of regression models and neural network models.

[0078] The engineering material selection device based on machine learning provided by the present invention first trains a mathematical model for engineering material selection based on machine learning by using historical construction projects and application condition parameters, and data parameters of engineering materials that have been selected and applied to corresponding historical construction projects as training samples. Then, the construction and application condition parameters of the project to be constructed are input into the mathematical model. The mathematical model can calculate and obtain the available engineering materials. Not only is the operation simple, but also the errors or mistakes caused by human calculations can be reduced. Therefore, the safety of construction projects during construction and application can be improved, and the work efficiency of engineering material selection can be improved.

[0079] Computer readable storage medium

[0080] The computer-readable storage medium provided by the present invention stores an engineering material selection program based on machine learning. After the engineering material selection program based on machine learning is executed by the processor, the steps of the method for engineering material selection based on machine learning provided by the present invention are implemented.

[0081] The computer-readable storage medium and device provided by the present invention first train a mathematical model for engineering material selection based on machine learning by using historical construction projects and application conditions parameters, and data parameters of engineering materials that have been selected and applied to corresponding historical construction projects as training samples. Then, the construction and application conditions parameters of the project to be constructed are input into the mathematical model. The mathematical model can calculate and obtain the available engineering materials. This method is not only simple to operate, but also can reduce errors or mistakes caused by human calculations. Therefore, it can improve the safety of construction projects during construction and application, and improve the work efficiency of engineering material selection.

[0082] Electronic devices

[0083] The electronic device provided by the present invention includes a memory and a processor. The memory stores an engineering material selection program based on machine learning. After the engineering material selection program based on machine learning is executed by the processor, the steps of the method for engineering material selection based on machine learning provided by the present invention are implemented.

[0084] The electronic device provided by the present invention first trains a mathematical model for engineering material selection based on machine learning by using historical construction projects and application condition parameters, and data parameters of engineering materials that have been selected and applied to corresponding historical construction projects as training samples. Then, the construction and application condition parameters of the project to be constructed are input into the mathematical model. The mathematical model can calculate the available engineering materials. Not only is the operation simple, but also the errors or mistakes caused by human calculations can be reduced. Therefore, the safety of construction projects during construction and application can be improved, and the work efficiency of engineering material selection can be improved.

[0085] Reference Figure 5 , Figure 5 A schematic diagram of the structure of an engineering material selection device based on machine learning in the hardware operating environment involved in an embodiment of the present invention.

[0086] like Figure 5As shown, the engineering material selection device based on machine learning may include: a processor 1001, such as a central processing unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. Among them, the communication bus 1002 is used to realize the connection and communication between these components. The user interface 1003 may include a display screen (Display), an input unit such as a keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface and a wireless interface. The network interface 1004 may optionally include a standard wired interface and a wireless interface (such as a wireless fidelity (WIreless-FIdelity, WI-FI) interface). The memory 1005 may be a high-speed random access memory (Random Access Memory, RAM) memory, or a stable non-volatile memory (Non-Volatile Memory, NVM), such as a disk memory. The memory 1005 may also be a storage device independent of the aforementioned processor 1001.

[0087] Those skilled in the art will understand that Figure 5 The structure shown in the does not constitute a limitation of the engineering material selection device based on machine learning, and may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0088] like Figure 5 As shown, the memory 1005 as a storage medium may include an operating system, a data storage module, a network communication module, a user interface module, and an engineering material selection program based on machine learning.

[0089] exist Figure 5 In the engineering material selection device based on machine learning shown, the network interface 1004 is mainly used for data communication with the network server; the user interface 1003 is mainly used for data interaction with the user; the processor 1001 and the memory 1005 in the engineering material selection device based on machine learning of the present invention can be set in the engineering material selection device based on machine learning, and the engineering material selection device based on machine learning calls the engineering material selection program based on machine learning stored in the memory 1005 through the processor 1001, and executes the engineering material selection method based on machine learning provided in the embodiment of the present invention.

[0090] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other 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 present invention.

[0091] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for selecting engineering materials based on machine learning, characterized in that: The following steps are involved: Obtain the construction and application parameters of the project to be constructed; Inputting the construction and application condition parameters of the construction project into a mathematical model for selecting engineering materials based on machine learning; The engineering material selection mathematical model obtained based on machine learning is calculated to obtain the available engineering materials; Wherein, the engineering material selection mathematical model obtained based on machine learning is selected from one or more of regression models and neural network models.

2. The method for selecting engineering materials based on machine learning according to claim 1, characterized in that: The construction and application condition parameters of the construction project are input into the specific steps of the engineering material selection mathematical model obtained based on machine learning. The engineering material selection mathematical model obtained based on machine learning is obtained based on the following steps: Obtaining historical construction project and application condition parameters, and data parameters of engineering materials that have been selected and applied to corresponding historical construction projects as training samples; Inputting the historical construction projects and application condition parameters corresponding to the training samples, and the data parameters of the engineering materials that have been selected and applied to the corresponding historical construction projects into the engineering material selection mathematical model obtained based on machine learning; The data parameters of the mathematical model for engineering material selection based on machine learning are adjusted so that the data parameters of the mathematical model for engineering material selection based on machine learning are compatible with the historical construction projects and application condition parameters, and the data parameters of the engineering materials that have been selected and applied to the corresponding historical construction projects, so as to obtain the mathematical model for engineering material selection based on machine learning.

3. The method for selecting engineering materials based on machine learning according to claim 2, characterized in that: The following steps are also included: The data parameters of the current construction project and the application conditions, and the engineering materials currently selected and applied to the corresponding historical construction projects are added to the training samples, and the data parameters of the engineering material selection mathematical model obtained based on machine learning are further adjusted.

4. The method for selecting engineering materials based on machine learning according to claim 1, characterized in that: The following steps are also included: Get the data parameters of the current engineering materials; Inputting the data parameters of the current engineering material into the engineering material selection mathematical model obtained based on machine learning; The engineering material selection mathematical model obtained based on machine learning is calculated to obtain construction and application condition parameters suitable for the current engineering materials.

5. The method for selecting engineering materials based on machine learning according to claim 1, characterized in that: The construction and application parameters of the project to be constructed include one or more of coal rock type, water content, and sulfur content.

6. The method for selecting engineering materials based on machine learning according to claim 1, characterized in that: The characteristic values ​​of the optional engineering materials include the material composition of the engineering materials, the crystal structure of the engineering materials, the engineering strength of the engineering materials, the formula ratio of the engineering materials, the particle size of the engineering materials, and one or more of the compressive strength and tensile strength of the engineering materials under corresponding application conditions.

7. The method for selecting engineering materials based on machine learning according to claim 6, characterized in that: The crystal structure of the engineering material includes crack density and porosity.

8. An engineering material selection device based on machine learning, characterized in that: include: The parameter acquisition module is used to obtain the construction and application parameters of the project to be constructed; A mathematical model for selecting engineering materials based on machine learning, into which the construction and application condition parameters of the construction project are input; A calculation module, used for calculating the engineering material selection mathematical model obtained based on machine learning to obtain the available engineering materials; Wherein, the engineering material selection mathematical model obtained based on machine learning is selected from one or more of regression models and neural network models.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores an engineering material selection program based on machine learning. After the engineering material selection program based on machine learning is executed by the processor, the steps of the method for engineering material selection based on machine learning described in any one of claims 1-7 are implemented.

10. An electronic device, characterized in that: It includes a memory and a processor, wherein the memory stores an engineering material selection program based on machine learning, and after the engineering material selection program based on machine learning is executed by the processor, the steps of the method for engineering material selection based on machine learning described in any one of claims 1-7 are implemented.

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