Cement-based composite material design method and device, electronic device and storage medium based on knowledge graph-guided machine learning

Abstract

The present application is applicable to the field of machine learning technology, and provides a cement-based composite material design method, device, electronic device and storage medium based on knowledge graph-guided machine learning, wherein the method includes: obtaining a knowledge graph of cement-based composite materials, the knowledge graph includes multiple nodes and directed edges connecting different nodes, and the nodes include design nodes and attribute nodes; searching the knowledge graph for attribute nodes corresponding to the design goals of cement-based composite materials as target attribute nodes; selecting the target design node from the candidate design nodes connected to the target attribute node; using the target design node as a variable to construct a cement-based composite material design model based on machine learning; and using the cement-based composite material design model to calculate the cement-based composite material design scheme.

Description

Technical Field

[0001] The present application belongs to the field of machine learning, and in particular relates to a cement-based composite material design method and device, electronic device, and computer-readable storage medium based on knowledge graph-guided machine learning. Background Art

[0002] Traditional methods for designing cement-based composites typically rely on labor-intensive laboratory experiments, resulting in inefficiencies in both time and cost. In recent years, machine learning methods have shown great efficiency in designing cement-based composites. However, machine learning-based cement-based composite design models often rely solely on experimental data for training, which can lead to violations of scientific principles and limited model reasoning capabilities, thus affecting the design results of cement-based composites. Summary of the Invention

[0003] The embodiments of the present application provide a cement-based composite material design method and device, electronic device and computer-readable storage medium based on knowledge graph-guided machine learning, which can solve the problem in the related art that the reasoning ability of the cement-based composite material design model based on machine learning is limited, affecting the design effect of the cement-based composite material.

[0004] In a first aspect, an embodiment of the present application provides a cement-based composite material design method based on knowledge graph-guided machine learning, the method comprising: obtaining a knowledge graph of cement-based composite materials, the knowledge graph comprising multiple nodes and directed edges connecting different nodes, the nodes comprising design nodes and attribute nodes; searching the knowledge graph for attribute nodes corresponding to the design targets of cement-based composite materials as target attribute nodes; selecting a target design node from candidate design nodes connected to the target attribute nodes; using the target design node as a variable to construct a cement-based composite material design model based on machine learning; and using the cement-based composite material design model to calculate a cement-based composite material design scheme.

[0005] In the second aspect, an embodiment of the present application provides a cement-based composite material design device based on knowledge graph-guided machine learning, the device including: an acquisition module for acquiring a knowledge graph of cement-based composite materials, the knowledge graph including multiple nodes and directed edges connecting different nodes, the nodes including design nodes and attribute nodes; a search module for searching the knowledge graph for attribute nodes corresponding to the design targets of cement-based composite materials as target attribute nodes; a selection module for selecting target design nodes from candidate design nodes connected to the target attribute nodes; a construction module for using the target design node as a variable to construct a cement-based composite material design model based on machine learning; and a calculation module for using the cement-based composite material design model to calculate a cement-based composite material design scheme.

[0006] In a third aspect, an embodiment of the present application provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable by the processor. When the processor executes the computer program, the cement-based composite material design method described in the first aspect above is implemented.

[0007] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the cement-based composite material design method described in the first aspect above.

[0008] In a fifth aspect, an embodiment of the present application provides a computer program product. When the computer program product is run on an electronic device, the electronic device executes the cement-based composite material design method described in the first aspect above.

[0009] Compared with the prior art, the embodiments of the present application have the following advantages: by obtaining a knowledge graph of cement-based composite materials, the knowledge graph includes multiple nodes and directed edges connecting different nodes, and the nodes include design nodes and attribute nodes; searching the knowledge graph for attribute nodes corresponding to the design target of the cement-based composite materials as target attribute nodes; selecting the target design node from the candidate design nodes connected to the target attribute node; using the target design node as a variable to construct a cement-based composite material design model based on machine learning; and using the cement-based composite material design model to calculate a cement-based composite material design solution. The knowledge graph is used to guide the construction of the cement-based composite material design model and assist in the selection of variables in the model, thereby improving the model's reasoning ability and, in turn, improving the design results of the cement-based composite material. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the embodiments or descriptions of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0011] Figure 1 This is a schematic structural diagram of an electronic device provided in one embodiment of the present application;

[0012] Figure 2 This is a flow chart of a cement-based composite material design method based on knowledge graph-guided machine learning provided in one embodiment of the present application;

[0013] Figure 3 is a partial schematic diagram of a knowledge graph of cement-based composite materials in a specific example of the present application;

[0014] Figure 4This is a structural schematic diagram of a cement-based composite material design device based on knowledge graph-guided machine learning provided in one embodiment of the present application. DETAILED DESCRIPTION

[0015] In the following description, specific details such as specific system structures and techniques are provided for purposes of illustration rather than limitation to facilitate a thorough understanding of the embodiments of the present application. However, it will be apparent to those skilled in the art that the present application may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid obscuring the description of the present application with unnecessary detail.

[0016] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, integers, steps, operations, elements and / or components, but does not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or collections thereof.

[0017] It will also be understood that the term "and / or" used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0018] As used in this specification and the appended claims, the term "if" can be interpreted as "when" or "upon" or "in response to determining" or "in response to detecting," depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" can be interpreted as meaning "upon determination" or "in response to determining" or "upon detection of [described condition or event]" or "in response to detecting [described condition or event]," depending on the context.

[0019] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0020] References to "one embodiment" or "some embodiments" in this specification mean that a particular feature, structure, or characteristic described in conjunction with that embodiment is included in one or more embodiments of the present application. Thus, phrases such as "in one embodiment," "in some embodiments," "in other embodiments," and "in other embodiments" appearing in various places in this specification do not necessarily refer to the same embodiment, but rather mean "one or more but not all embodiments," unless otherwise specifically emphasized. The terms "including," "comprising," "having," and variations thereof all mean "including but not limited to," unless otherwise specifically emphasized.

[0021] The cement-based composite material design method based on knowledge graph-guided machine learning provided in the embodiments of this application can be applied to electronic devices, including but not limited to servers, server clusters, mobile phones, tablet computers, laptops, desktop computers, personal digital assistants, wearable devices, and other electronic devices with computing functions. The embodiments of this application do not impose any restrictions on the specific type of electronic device.

[0022] Figure 1 FIG2 is a block diagram showing a partial structure of an electronic device provided by an embodiment of the present application. Figure 1 The electronic device includes: a processor 10, a memory 20, a bus 30, an input device 40, an output device 50, and a communication device 60. The processor 10 and the memory 20 are connected to each other via the bus 30, and the input device 40, the output device 50, and the communication device 60 are also connected to the bus 30. It will be understood by those skilled in the art that Figure 1 The structure of the electronic device shown in the figure does not constitute a limitation of the electronic device, and may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0023] The following combination Figure 1 A detailed introduction to the various components of electronic equipment:

[0024] The processor 10 is the control center of the electronic device, which can run the programs stored in the memory 20 to perform various functions and process data. The processor 10 can be a central processing unit (CPU), and the processor 10 can also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc. In some embodiments, the processor 10 may include an AI (Artificial Intelligence) processor, which is used to process computing operations related to machine learning.

[0025] The memory 20 is used to store an operating system, an application program, a boot loader (BootLoader), data, and other programs, such as the program code of a computer program. The memory 20 can also be used to temporarily store data required for and generated by executing the program. The memory 20 may include a high-speed random access memory, and may also include a non-volatile memory, such as a flash memory, a hard disk, a multimedia card, a card-type memory, etc. The memory 20 may include a storage unit provided inside the electronic device, such as the hard disk of the electronic device, and / or a removable external storage unit, such as a mobile hard disk, a USB flash drive, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, etc.

[0026] The input device 40 may include at least one of a keyboard, a mouse, a touch panel, a joystick, etc., and is used to collect user input operations to generate corresponding operation instructions.

[0027] The output device 50 is used to output information to be provided to the user. The output device 50 generally includes a display. Optionally, a liquid crystal display (LCD), an organic light-emitting diode (OLED), etc. can be used. In addition, the output device can further include a speaker.

[0028] The communication device 60 may include a modem, a network card, etc., and is used to establish a network connection with other electronic devices and communicate with each other.

[0029] The cement-based composite material design method based on knowledge graph-guided machine learning provided in the embodiment of the present application can be implemented as a computer software program. For example, the embodiment of the present application provides a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program contains program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication device 60, and / or installed from a detachable external storage unit. When the computer program is executed by the processor 10, the various functions defined in the cement-based composite material design method based on knowledge graph-guided machine learning provided in the embodiment of the present application are implemented.

[0030] Figure 2 A schematic flowchart of a cement-based composite material design method based on knowledge graph-guided machine learning provided in one embodiment of the present application is shown. As an example but not a limitation, the method can be applied to the above-mentioned electronic device.

[0031] S1: Construct a knowledge graph based on public knowledge of cement-based composites.

[0032] A knowledge graph is a semantic network used to describe the relationships between entities and is a form of storage and representation for structured knowledge. It transforms real-world information into a graphical knowledge base, using nodes to represent entities and edges to represent relationships between them. This reveals the dynamic development patterns of knowledge domains and provides practical and valuable references for disciplinary research.

[0033] Specifically, relevant information can be extracted from publicly available knowledge that conforms to objective laws, such as books, papers, and engineering reports, to construct a knowledge graph. The same approach can be applied to expanding the existing knowledge graph. The specific construction method is not limited here.

[0034] Cement-based composite materials may include paste, mortar, concrete, etc. For ease of description, concrete is used as an example below.

[0035] In an embodiment of the present application, the knowledge graph is stored and applied in the form of a directed graph, specifically including multiple nodes and directed edges connecting different nodes. The starting point of a directed edge is called its starting node, and the end point of the directed edge is called its ending node. Nodes include design nodes and attribute nodes. Design nodes are used to represent entities that need to be determined in the design process of cement-based composite materials, which may specifically include raw materials and design parameters. Raw materials refer to raw materials for preparing cement-based composite materials, such as cement, water, aggregates, admixtures, admixtures, etc.; design parameters refer to other parameters in the design of cement-based composite materials other than raw materials, such as the ratio of raw materials, production process, etc., which may specifically include mix ratio, curing method, ambient temperature and humidity, etc. Attribute nodes are used to represent the performance indicators of the prepared cement-based composite materials, such as rheology, work, mechanics, durability and other indicators.

[0036] For a design node of raw materials, multiple candidate materials for the node can be stored in a database, and each candidate material has multiple attributes. These attributes can also be added to the knowledge graph as intermediate nodes, thereby forming multiple levels in the knowledge graph. An example of a local knowledge graph of concrete constructed in this way is as follows: Figure 3 shown.

[0037] For example, for a cement node, the attributes of each candidate cement may include, but are not limited to: cement name, cement number, cement composition, cement manufacturer, cement strength, cement properties (such as frost resistance, impermeability, and chloride ion resistance), cement density, cement hydration heat, cement setting time (initial and final setting times), and cement particle size. For a water node, the attributes of each candidate water may include, but are not limited to: water name, water number, water composition, and water source (such as river, sea, or tap water). For an aggregate node, the attributes of each candidate aggregate may include, but are not limited to: aggregate name, aggregate number, aggregate composition, aggregate particle size, aggregate shape (which can be quantitatively characterized by sphericity and angularity), aggregate density, aggregate water absorption, aggregate compressive strength, and aggregate mud content. For an admixture node, the attributes of each candidate admixture may include, but are not limited to: admixture name, admixture number, admixture composition, admixture function, and admixture manufacturer. For the admixture node, the attributes of each candidate admixture may include but are not limited to: admixture name, admixture number, admixture composition, admixture function, admixture manufacturer, admixture particle size, and admixture micromorphology.

[0038] There is a large amount of experimental data in public knowledge, and each piece of experimental data generally includes the raw materials, design parameters and performance of cement-based composite materials. Since a single piece of experimental data is generally part of an experiment designed for a specific field / goal / scenario, the entities involved may not necessarily cover all the nodes in the knowledge graph. To this end, you can try to fill in the uncovered nodes by extracting information from the context of the experimental data and / or based on common sense. For nodes that cannot be filled, they can be recorded as unknown. The experimental data can be stored in the nodes involved in the knowledge graph, or it can be stored in a database independent of the knowledge graph.

[0039] S2: Assign values ​​to the directed edges in the knowledge graph.

[0040] In the knowledge graph provided in the embodiment of the present application, a directed edge from a design node to an attribute node means that the design node has an influence on the attribute node. The value of the edge represents the magnitude of the influence. To facilitate subsequent quantitative calculations, it is generally normalized to a preset interval, such as [-1, 1]. Positive values ​​represent positive correlations, and negative values ​​represent negative correlations. Directed edges between different design nodes usually represent inclusion relationships. For example, a certain raw material may include multiple components, and the influence of the raw material on the design node may be the influence of some of the components. To enrich the knowledge graph, these components can be added to the graph as intermediate nodes. In this case, the edge from the raw material to the component represents inclusion, and the edge from the component to the attribute node represents the existence of influence.

[0041] For directed edges between different design nodes, their values ​​can be the content of the ending node in the starting node; for directed edges from design nodes to attribute nodes, their values ​​represent the magnitude of the impact, and the value of the directed edge can be determined by relevant experimental data (i.e., experimental data involving the starting node and ending node of the directed edge).

[0042] For example, relevant experimental data in public knowledge can be used to calculate the correlation between the starting node and the ending node of a directed edge as the value of the directed edge. For example, the knowledge graph contains cement content, water-cement ratio, coarse aggregate content, sand content, and compressive strength. To calculate the size of the edge of cement content versus compressive strength, the seaborn library in Python can be used to draw a heat map of the correlation between database variables and obtain the correlation coefficient between the nodes "cement content" and "compressive strength" (for example, +0.6). The size of this edge is +0.6. The same method is used to calculate the edge "water-cement ratio-compressive strength" (-0.7), the edge "coarse aggregate content-compressive strength" (+0.3), and the edge "sand content-compressive strength" (+0.2).

[0043] Alternatively, the influence of the starting nodes of all directed edges connecting the same terminating node, as well as the influence of the starting nodes on the terminating nodes, can be determined based on publicly available knowledge. Specifically, relevant experimental data from publicly available knowledge can be used to train decision tree-based models, such as decision trees and random forests. The influence strength can then be determined based on the node hierarchy within the decision tree-based model. Specifically, the root node has the strongest influence, while the influence decreases with increasing distance from the root node. Leaf nodes at the same level can be considered to have the same influence.

[0044] When the influence of different starting nodes can be divided into multiple levels, directed edges can be assigned values ​​according to these levels. For example, three design nodes ABC have a positive influence on attribute node D, and A's influence is significantly stronger than B and C. In other words, the influence can be divided into two levels. Based on this, [0,1] can be divided into two intervals [0.5,1] and [0,0.5]. The midpoints of these intervals are used to assign values ​​to the edges, i.e., the edge size of AD is 0.75, and the edge sizes of BD and CD are 0.25.

[0045] When the influence of different starting nodes cannot be divided into multiple levels, assign a uniform value to all directed edges. That is, you can directly set the edge size to a fixed value, such as 0.5. If there is a concern about conflicts with the subinterval in the middle of the odd-numbered level, you can consider setting it to another value.

[0046] S3: Obtain the knowledge graph of cement-based composite materials.

[0047] The aforementioned constructed knowledge graph can be obtained. S1-S2 and S3-S7 can be executed by the same electronic device or by different electronic devices.

[0048] S4: Search the knowledge graph for the attribute node corresponding to the design goal of cement-based composite materials as the target attribute node.

[0049] S5: Select a target design node from candidate design nodes connected to the target attribute node.

[0050] The knowledge weight of a candidate design node can be determined based on the size of the edge between the candidate design node and the target attribute node. Specifically, if there is only one edge between the candidate design node and the target attribute node, that is, no intermediate nodes, the knowledge weight is the absolute value of that edge. If there are multiple edges between the candidate design node and the target attribute node, that is, there are intermediate nodes, the knowledge weight is the absolute value of the product of all edges between the candidate design node and the target attribute node. A specified number or proportion of candidate design nodes with the largest knowledge weights can then be selected as target design nodes.

[0051] Different candidate design nodes may be associated with each other. In the subsequent model training and use process, selecting two candidate design nodes that are too closely associated will cause one of the candidate design nodes to be wasted, thereby adversely affecting the effect of the model. To solve this problem, optionally, before selecting the target design node, the similarity between the two candidate design nodes can be calculated. Specifically, the nodes can be vectorized and then projected onto the hyperplane of the relationship, and the transH model can be trained by using the relationship between existing entities. The trained transH model can then be used to calculate the Euclidean distance between the projections of the two candidate design nodes on the hyperplane of the relationship. The shorter the distance, the higher the similarity. When the similarity is greater than the first threshold, a candidate design node is removed, and generally the candidate design node with a smaller knowledge weight is removed.

[0052] For example, the transH method can be used to calculate the similarity between two candidate design nodes. Similarity can be expressed as cosine similarity, which ranges from [-1 to 1], with larger values ​​indicating greater similarity. A cosine similarity of 1 indicates that the two vectors are identical, 0 indicates orthogonal (no similarity), and -1 indicates complete opposites. If the similarity between two nodes is greater than or equal to 0.8, the candidate design node with the lower knowledge weight is removed, and the candidate design node with the higher knowledge weight is retained.

[0053] S6: Using the target design nodes as variables, a cement-based composite design model is constructed based on machine learning.

[0054] The initial weights of the variables in the cementitious composite material design model can be determined based on the knowledge weights of the target design nodes. Specifically, the initial weights can be obtained by normalizing the knowledge weights of all target design nodes. This means that the knowledge weights of all target design nodes are geometrically scaled so that the sum of the knowledge weights of all target design nodes is 1.

[0055] The specific architecture and training process of the cement-based composite material design model are not limited here. The architecture can be random forest, LightGBM, XGBoost, CatBoost, etc. There can be more than one cement-based composite material design model. After training, the model performance indicators are calculated to select the cement-based composite material design model with the best performance indicators for subsequent calculations.

[0056] S7: Use the cementitious composite design model to calculate cementitious composite design options.

[0057] Substituting the design objectives and constraints of cement-based composite materials into the cement-based composite material design model, optimizing the calculation to obtain the values ​​of each variable, the design scheme of cement-based composite materials can be obtained.

[0058] Through the implementation of this embodiment, a knowledge graph of cement-based composite materials is obtained, the knowledge graph comprising multiple nodes and directed edges connecting different nodes, wherein the nodes include design nodes and attribute nodes; an attribute node corresponding to a design target of the cement-based composite material is searched in the knowledge graph as a target attribute node; a target design node is selected from candidate design nodes connected to the target attribute node; a cement-based composite material design model is constructed based on machine learning using the target design node as a variable; and a cement-based composite material design solution is calculated using the cement-based composite material design model. The knowledge graph is used to guide the construction of the cement-based composite material design model and assist in the selection of variables in the model, thereby improving the model's reasoning ability and, in turn, improving the design results of the cement-based composite material.

[0059] Figure 4 A structural schematic diagram of a cement-based composite material design device based on knowledge graph-guided machine learning provided in one embodiment of the present application is shown. The device includes an acquisition module 11, a search module 12, a selection module 13, a construction module 14 and a calculation module 15.

[0060] The acquisition module 11 is used to obtain a knowledge graph of cement-based composite materials. The knowledge graph includes multiple nodes and directed edges connecting different nodes. The nodes include design nodes and attribute nodes.

[0061] The search module 12 is used to search the knowledge graph for an attribute node corresponding to the design target of the cement-based composite material as a target attribute node.

[0062] The selection module 13 is configured to select a target design node from candidate design nodes connected to the target attribute node.

[0063] The construction module 14 is used to construct a cement-based composite material design model based on machine learning by taking the target design node as a variable.

[0064] The calculation module 15 is used to calculate the cement-based composite material design solution using the cement-based composite material design model.

[0065] It should be noted that the information interaction, execution process, etc. between the above-mentioned devices / modules / units are based on the same concept as the method embodiment of this application. Their specific functions and technical effects can be found in the method embodiment section and will not be repeated here.

[0066] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0067] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0068] An embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in the above-mentioned various method embodiments can be implemented.

[0069] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the process in the above-mentioned embodiment method by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, it can implement the steps of each of the above-mentioned method embodiments. The computer program includes computer program code, which can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium can at least include: any entity or device that can carry the computer program code to the camera / electronic device, recording medium, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium. For example, a USB flash drive, a mobile hard disk, a magnetic disk or an optical disk. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electric carrier signals and telecommunication signals.

[0070] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0071] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0072] In the embodiments provided in this application, it should be understood that the disclosed devices / network equipment and methods can be implemented in other ways. For example, the device / network equipment embodiments described above are merely illustrative. For example, the division of the modules or units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0073] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0074] The above-described embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present application, and should all be included in the scope of protection of the present application.

Claims

1. A cement-based composite material design method based on knowledge graph-guided machine learning, characterized in that: The method comprises: A knowledge graph of cement-based composite materials is obtained, wherein the knowledge graph includes a plurality of nodes and directed edges connecting different nodes, wherein the nodes include design nodes and attribute nodes; the design nodes are used to represent entities that need to be determined in the design process of cement-based composite materials, and the design nodes include raw materials and design parameters, wherein raw materials refer to raw materials for preparing cement-based composite materials, and the raw materials include: cement, water, aggregate, admixtures and admixtures; the design parameters refer to other parameters in the design of cement-based composite materials other than raw materials, including mix ratio, curing method and ambient temperature and humidity; the attribute nodes are used to represent performance indicators of the prepared cement-based composite materials, including rheological indicators, working indicators, mechanical indicators and durability indicators; a directed edge from a design node to an attribute node means that the design node has an influence on the attribute node, and the value of the edge indicates the magnitude of the influence; the value of the directed edge is determined by relevant experimental data; Searching the knowledge graph for an attribute node corresponding to a design goal of the cement-based composite material as a target attribute node; Selecting a target design node from candidate design nodes connected to the target attribute node; The target design node is used as a variable to construct a cement-based composite material design model based on machine learning; the target design node is used as a variable to construct a cement-based composite material design model based on machine learning, comprising: determining initial weights of variables in the cement-based composite material design model according to knowledge weights of the target design node; The cement-based composite material design model is used to calculate a cement-based composite material design solution.

2. The method according to claim 1, wherein The selecting a target design node from candidate design nodes connected to the target attribute node comprises: Determining a knowledge weight of the candidate design node according to a size of an edge between the candidate design node and the target attribute node; A specified number or a specified proportion of the candidate design nodes having the largest knowledge weights are selected as the target design nodes.

3. The method according to claim 2, wherein Before selecting the target design node from the candidate design nodes connected to the target attribute node, the method includes: Calculating the similarity between two candidate design nodes; When the similarity is greater than a first threshold, one of the candidate design nodes is removed.

4. The method according to claim 1, wherein The step of obtaining the knowledge graph of cement-based composite materials further includes: Constructing the knowledge graph based on public knowledge of cement-based composite materials; Assign values ​​to the directed edges in the knowledge graph.

5. The method according to claim 4, wherein The assigning of values ​​to the edges in the knowledge graph includes: The correlation between the starting node and the ending node of the directed edge is calculated using relevant experimental data in the public knowledge as the value of the directed edge.

6. The method according to claim 4, wherein The assigning of values ​​to the edges in the knowledge graph includes: Determine the starting nodes of all directed edges connected to the same terminating node and the strength of the influence of the starting nodes on the terminating node based on the public knowledge; When the influence of different starting nodes can be divided into multiple levels, assigning values ​​to the directed edges according to the levels; When the influence strengths of different starting nodes cannot be divided into multiple levels, the directed edges are uniformly assigned values.

7. The method according to claim 6, wherein The determining, based on the public knowledge, of the starting nodes of all the directed edges connected to the same terminating node and the strength of the influence of the starting nodes on the terminating node comprises: Training a decision tree-based model using relevant experimental data from the public knowledge; The strength of the influence is determined according to the hierarchy of the nodes in the decision tree-based model.

8. A cement-based composite material design device based on knowledge graph-guided machine learning, characterized in that: The device comprises: An acquisition module is used to obtain a knowledge graph of cement-based composite materials, wherein the knowledge graph includes multiple nodes and directed edges connecting different nodes, wherein the nodes include design nodes and attribute nodes; the design nodes are used to represent entities that need to be determined in the design process of cement-based composite materials, and the design nodes include raw materials and design parameters. Raw materials refer to raw materials for preparing cement-based composite materials, and the raw materials include: cement, water, aggregates, admixtures and admixtures; the design parameters refer to other parameters in the design of cement-based composite materials other than raw materials, including mix ratio, curing method and ambient temperature and humidity; the attribute nodes are used to represent performance indicators of the prepared cement-based composite materials, including rheological indicators, working indicators, mechanical indicators and durability indicators; a directed edge from a design node to an attribute node means that the design node has an influence on the attribute node, and the value of the edge indicates the magnitude of the influence; the value of the directed edge is determined by relevant experimental data; A search module, configured to search the knowledge graph for an attribute node corresponding to a design target of the cement-based composite material as a target attribute node; A selection module, configured to select a target design node from candidate design nodes connected to the target attribute node; A construction module is configured to construct a cement-based composite material design model based on machine learning using the target design node as a variable; the construction of the cement-based composite material design model based on machine learning using the target design node as a variable includes: determining initial weights of the variables in the cement-based composite material design model based on the knowledge weights of the target design node; A calculation module is used to calculate a cement-based composite material design solution using the cement-based composite material design model.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable by the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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