Method, System and Product for Constructing Metallogenic Prediction Model Based on Knowledge Graph

Abstract

The present invention relates to the technical field of mineral resource prediction, and discloses a method, a system and a product for constructing a metallogenic prediction model based on a knowledge graph. The method includes: constructing a knowledge graph of mineral resources, where the knowledge graph contains knowledge data of elements, minerals, rocks, ore deposits, metallogenic processes and geological phenomena; performing knowledge extraction on the knowledge graph based on the data types of the Earth system, the metallogenic system, the exploration system and the prediction and evaluation system to obtain first knowledge data; performing knowledge mining on the first knowledge data and / or the knowledge graph based on a preset knowledge mining method to obtain second knowledge data; and constructing a mineral resource prediction model based on the first knowledge data and the second knowledge data. The present invention can take into account the influence of factors such as the material structure of the lithosphere, the crust-mantle evolution, the evolution and cycle of ore-forming materials, etc. on mineralization, realizes the comprehensive utilization of mineral resource prediction elements, improves the accuracy of prediction results, and meets the intelligent requirements of mineral resource prediction.

Description

Technical Field

[0001] The present invention relates to the technical field of mineral resource prediction, and particularly to a method, system and product for constructing a metallogenic prediction model based on a knowledge graph. Background Art

[0002] Mineral resources refer to the aggregates of minerals or useful elements that are naturally occurring within the earth's crust or on the earth's surface and have the value of development and utilization after geological mineralization. Mineral resources are an important foundation and valuable window for promoting the development of human society and exploring the operation mode of the earth system, and accurate prediction of them is of great significance.

[0003] Currently, the relevant research on mineral resource prediction mainly estimates the metallogenic probability of the target area by using geological, geophysical, geochemical, remote sensing data, etc. based on expert experience and established typical ore deposit models. In this process, such research often only focuses on the important indicators directly related to mineralization.

[0004] However, the metallogenic system is not an isolated system, but a coupled product of the physical, chemical, biological and other interactions of the earth's multiple spheres, which are interconnected, mutually influential and mutually restrictive. The existing mineral resource prediction models pay less attention to the long-distance dynamic correlation and mutual restriction of related factors such as the material structure of the lithosphere, crust-mantle evolution, and the evolution and cycle of ore-forming materials, and cannot comprehensively utilize mineral resource elements for effective prediction, resulting in the established metallogenic models and prediction models being mostly exclusive and special, lacking universality and generality. Summary of the Invention

[0005] In view of this, the present invention provides a method, system and product for constructing a metallogenic prediction model based on a knowledge graph to solve the problems that the construction of the existing mineral resource prediction model lacks the comprehensive utilization of mineral resource elements, affects the accuracy of the prediction results, and is difficult to meet the intelligent requirements of mineral resource prediction.

[0006] In a first aspect, the present invention provides a method for constructing a metallogenic prediction model based on a knowledge graph, the method comprising:

[0007] Constructing a knowledge graph of mineral resources, the knowledge graph containing knowledge data of elements, minerals, rocks, ore deposits, metallogenic processes and geological phenomena;

[0008] Performing knowledge extraction on the knowledge graph based on the data types of the earth system, metallogenic system, exploration system and prediction and evaluation system to obtain first knowledge data;

[0009] Performing knowledge mining on the first knowledge data and / or the knowledge graph based on a preset knowledge mining method to obtain second knowledge data;

[0010] Constructing a mineral resource prediction model based on the first knowledge data and the second knowledge data.

[0011] Taking into account the influence of factors such as the material structure of the lithosphere, the evolution of the crust and mantle, and the evolution and cycle of ore-forming materials on mineralization, the present invention obtains corresponding knowledge data based on the data types of the Earth system, the ore-forming system, the exploration system, and the prediction and evaluation system, and further mines new knowledge data from this data to construct a mineral resource prediction model, realizing the comprehensive utilization of mineral resource elements, significantly improving the accuracy of prediction results, and meeting the intelligent requirements of mineral resource prediction.

[0012] In an alternative embodiment, constructing a knowledge graph of mineral resources includes:

[0013] Collecting relevant data of mineral resources including the Earth system, the ore-forming system, the exploration system, and the prediction and evaluation system;

[0014] Performing entity and relationship annotation on the relevant data to obtain a large amount of knowledge data, where the knowledge data is triple data including entities and entity relationships. Among them, the entity represents an element related to the mineralization of mineral resources, and the entity relationship represents the semantic relationship between different entities;

[0015] Extracting training data and test data from the large amount of knowledge data, and constructing a data generation model based on the training data, where the data generation model is used to generate triple data including entities and entity relationships;

[0016] Inputting the test data into the data generation model for prediction to correspondingly obtain a large amount of generated triple data;

[0017] Storing all the triple data to obtain the knowledge graph of mineral resources.

[0018] By collecting relevant data of mineral resources and annotating them to obtain standard triple knowledge data, and predicting the annotated knowledge data through a constructed machine learning model to generate a large amount of triple data, the present invention can ensure the standardization and richness of the data set, and to a certain extent improve the accuracy of the prediction results of the mineral resource prediction model.

[0019] In an alternative embodiment, the second knowledge data includes first mined knowledge. Based on a preset knowledge mining method, knowledge mining is performed on the first knowledge data and / or the knowledge graph to obtain the second knowledge data, including:

[0020] In response to two target entities optionally selected by the user from the first knowledge data and / or the knowledge graph, performing knowledge search on the two target entities based on a preset entity relationship type query method to correspondingly obtain search knowledge;

[0021] Integrating the search knowledge to obtain the first mined knowledge.

[0022] When the present invention obtains a target entity given by a user, it quickly finds the same or most similar knowledge data from a relevant knowledge graph through knowledge retrieval technology, further mines the entity relationships of strong associations and indirect associations existing between entities, and identifies the long-range associations and implicit connection entity relationships between entities, providing data indication and reference content for the subsequent construction of a full-factor mineral resource prediction model.

[0023] In an alternative embodiment, the preset entity relationship types include direct relationships and indirect relationships, where the indirect relationships include short-range relationships and long-range relationships; knowledge search is performed on two target entities based on the preset entity relationship type query method, and the corresponding search knowledge is obtained, including:

[0024] Obtain the minimum number of hops and the number of paths required for two target entities to be associated;

[0025] Determine whether the minimum number of hops is less than a preset hop threshold;

[0026] If the minimum number of hops is less than the preset hop threshold, it is determined that there is a direct relationship between the two target entities, and the corresponding search knowledge is obtained based on the two target entities and the direct relationship;

[0027] If the minimum number of hops is not less than the preset hop threshold, it is determined that there is an indirect relationship between the two target entities, and it is determined whether the number of paths is less than a preset path threshold;

[0028] If the number of paths is less than the preset path threshold, it is determined that there is a short-range relationship between the two target entities, and the corresponding search knowledge is obtained based on the two target entities and the short-range relationship;

[0029] If the number of paths is not less than the preset path threshold, it is determined that there is a long-range relationship between the two target entities, and the corresponding search knowledge is obtained based on the two target entities and the long-range relationship.

[0030] The present invention quantitatively determines the association relationship existing between entities through the minimum number of hops and the number of paths between entities, can mine richer entity relationships, and provides research data for the subsequent construction of a full-factor mineral resource prediction model.

[0031] In an alternative embodiment, the second knowledge data includes second mining knowledge, and second knowledge data is obtained by performing knowledge mining on the first knowledge data and / or the knowledge graph based on a preset knowledge mining method, including:

[0032] Construct a data set based on the first knowledge data and / or the knowledge graph, and the data set includes: a training data set and a test data set;

[0033] Train the training data set through a preset knowledge graph embedding model to obtain a knowledge inference model;

[0034] Input the test data set into the knowledge inference model for inference to obtain inference knowledge;

[0035] Integrate the inference knowledge to obtain the second mining knowledge.

[0036] Based on the existing knowledge data, the present invention uses a knowledge graph embedding model to perform knowledge inference on it, and can mine new possible entity relationships, that is, inference knowledge, providing research data for the subsequent construction of a full-factor mineral resource prediction model.

[0037] In an alternative embodiment, the second knowledge data includes the third mining knowledge. Based on a preset knowledge mining method, knowledge mining is performed on the first knowledge data and / or the knowledge graph to obtain the second knowledge data, including:

[0038] Obtain all paths in which the mineral resources are associated with any tail entity in the first knowledge data and / or the knowledge graph;

[0039] Statistically count the occurrence frequencies of different entity relationships in each path, calculate the relationship probability between the mineral resources and the tail entity, and determine the strength index of the corresponding path based on the occurrence frequency and the relationship probability;

[0040] Integrate the strength indices of all paths to obtain the third mining knowledge.

[0041] The present invention takes into account that different elements, minerals, and rocks have different probabilities of influencing mineralization of mineral resources. Based on the occurrence frequency and relationship probability of the association paths between entities, the strength index of the corresponding path is determined, and the strength of the association relationship between the corresponding entities is characterized by the strength index. It can realize knowledge mining of calculating new implicit knowledge based on graph analysis and assigning different weights to it, providing research data for the subsequent construction of a full-factor mineral resource prediction model.

[0042] In a second aspect, the present invention provides a mineralization prediction model construction system based on a knowledge graph. The system includes:

[0043] A data construction module for constructing a knowledge graph of mineral resources, where the knowledge graph contains knowledge data of elements, minerals, rocks, ore deposits, mineralization processes, and geological phenomena;

[0044] A knowledge extraction module for extracting knowledge from the knowledge graph based on the data types of the earth system, mineralization system, exploration system, and prediction and evaluation system to obtain the first knowledge data;

[0045] A knowledge mining module for performing knowledge mining on the first knowledge data and / or the knowledge graph based on a preset knowledge mining method to obtain the second knowledge data;

[0046] A model construction module for constructing a mineral resource prediction model based on first knowledge data and second knowledge data.

[0047] The ore-forming prediction model construction system based on a knowledge graph of the present invention takes into account the influence of factors such as the material structure of the lithosphere, the evolution of the crust and mantle, and the evolution and circulation of ore-forming materials on ore formation. It obtains corresponding knowledge data based on earth system data types, ore-forming system data types, exploration system data types, and prediction and evaluation system data types, and further mines new knowledge data from this data to construct a mineral resource prediction model, realizing the comprehensive utilization of mineral resource elements, helping to improve the accuracy of prediction results, and meeting the intelligent requirements of mineral resource prediction.

[0048] In a third aspect, the present invention provides a computer device, including: a memory and a processor, which are communicatively connected to each other. The memory stores computer instructions, and the processor executes the computer instructions to execute a method for constructing an ore-forming prediction model based on a knowledge graph according to the first aspect or any corresponding embodiment thereof.

[0049] In a fourth aspect, the present invention provides a computer-readable storage medium, on which computer instructions are stored, and the computer instructions are used to cause a computer to execute a method for constructing an ore-forming prediction model based on a knowledge graph according to the first aspect or any corresponding embodiment thereof.

[0050] In a fifth aspect, the present invention provides a computer program product, including computer instructions, and the computer instructions are used to cause a computer to execute a method for constructing an ore-forming prediction model based on a knowledge graph according to the first aspect or any corresponding embodiment thereof. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for use in the description of the specific embodiments or the prior art. Obviously, the following drawings are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0052] Figure 1 is a flowchart of a method for constructing an ore-forming prediction model based on a knowledge graph according to an embodiment of the present invention;

[0053] Figure 2 is a flowchart of another method for constructing an ore-forming prediction model based on a knowledge graph according to an embodiment of the present invention;

[0054] Figure 3 is a schematic diagram of the result of a knowledge graph query;

[0055] Figure 4 It is a schematic diagram of the result of knowledge reasoning;

[0056] Figure 5 It is a schematic diagram of the calculation based on graph analysis;

[0057] Figure 6 It is a schematic diagram of the structure for constructing the whole - element prediction model of porphyry copper deposit;

[0058] Figure 7 It is a structural block diagram of the system for constructing a metallogenic prediction model based on a knowledge graph according to an embodiment of the present invention;

[0059] Figure 8 It is a schematic diagram of the structure of a computer device according to an embodiment of the present invention. Detailed implementation manners

[0060] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0061] Mineral resources are an important foundation and valuable window for promoting the development of human society and exploring the operation mode of the Earth system. However, current mineral resource prediction research mainly relies on expert experience and established typical ore - deposit models, and uses geological, geophysical, geochemical, remote - sensing data, etc. to estimate the metallogenic probability of the target area. In this process, such research only focuses on important indicators directly related to mineralization. Since the metallogenic system is a coupled product of the physical, chemical, biological, etc. processes of each subsystem of the Earth, with a large number of entity elements and corresponding entity relationships involved, the aforementioned mineral resource prediction models pay less attention to the long - distance dynamic association and mutual restriction of related factors such as the material structure of the lithosphere, crust - mantle evolution, and the evolution and circulation of ore - forming materials. As a result, the established metallogenic models and prediction models are mostly exclusive and specific, lacking universality and generality.

[0062] In addition, with the explosive growth of geoscience big data and the rapid development of data science, opportunities have been brought for the transformation of the paradigm of earth science, and large-scale knowledge graph technology has been widely applied in various industries. Knowledge reasoning and knowledge discovery based on knowledge graphs, as well as data-driven artificial intelligence technologies represented by machine learning, are reactivating and enhancing the ability of earth science to solve new problems. However, the previous applications of mineral resource knowledge graphs have remained at simple knowledge query and summary, and the constructed knowledge graphs are not rich, with the problem of one-sided elements. Therefore, an intelligent construction system solution for a full-element prediction index system of mineral resources based on knowledge graph technology is expected.

[0063] Therefore, the present invention proposes a method, system and product for constructing a metallogenic prediction model based on a knowledge graph, using the knowledge graph to break through the research of complex metallogenic systems, constructing the dynamic association and mutual restriction between the metallogenic system and all metallogenic-related factors, and realizing the expansion of "earth system - metallogenic system - exploration system - prediction and evaluation system" through knowledge graph data-driven technology, from "static correlation analysis of key elements" to "full-element cross-scale dynamic knowledge reasoning and discovery". By constructing a knowledge graph expressing complex earth systems related to metallogeny, associating mechanisms such as deep geological processes - magmatic evolution - element migration and enrichment - ore mineral accumulation, and using technologies such as machine learning to construct an inference engine, the present invention can reveal hidden factors and long-range factors related to porphyry metallogeny in the earth system, can realize the comprehensive utilization of mineral resource elements, significantly improve the accuracy of prediction results, and greatly meet the intelligent requirements of mineral resource prediction.

[0064] An embodiment of the present invention provides an embodiment of a method for constructing a metallogenic prediction model based on a knowledge graph. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than here.

[0065] In this embodiment, a method for constructing a metallogenic prediction model based on a knowledge graph is provided. Figure 1 It is a schematic flowchart of the method for constructing a metallogenic prediction model based on the knowledge graph according to the embodiment of the present invention, as Figure 1 shown, and this process includes the following steps:

[0066] Step S101, construct a knowledge graph of mineral resources, and the knowledge graph contains knowledge data of elements, minerals, rocks, ore deposits, metallogenic processes and geological phenomena.

[0067] It should be noted that the specific type of mineral resources is not limited here and can be adaptively adjusted according to actual needs. For example, the mineral resources are porphyry copper deposits, which are only for illustrative purposes and are not limited thereto.

[0068] In a specific embodiment, the knowledge graph includes 12 types of knowledge data, including elements, minerals, rocks, hydrothermal alteration, ore deposits, structures, geophysics, geochemistry, geochronology, deep geological processes, exploration and evaluation data, and prediction models and methods. The specific content of each type of knowledge data can be determined based on the adaptability of actual project needs. For example, the inclusion of iron elements is only for illustrative purposes.

[0069] Step S102, extracting knowledge from the knowledge graph based on the data types of the earth system, mineralization system, exploration system and prediction and evaluation system to obtain first knowledge data.

[0070] It should be noted that the Earth system describes the connection between things from the macroscopic to the microscopic. It emphasizes starting from the whole. For example, the atmosphere, hydrosphere, lithosphere and biosphere are regarded as an organically connected Earth system. The global changes of various time scales occurring in this system are the result of the interaction between the various spheres of the Earth system and the interaction between humans and the environment (life and non-life). In-depth research on it will fundamentally understand the formation and evolution of the Earth and its future development trend, so as to better serve the rational use of resources and the improvement of the global environment for mankind, and strive to achieve the harmonious development of man and nature. The metallogenic system refers to the whole of all geological elements and metallogenic dynamic processes that control the formation and preservation of ore deposits in a certain time-space domain, as well as the formed ore deposit series and anomaly series. It is a natural system with metallogenic functions. The purpose of metallogenic system research is to understand the laws of metallogenesis and better guide mineral exploration. The exploration system is a series of work related to mineral exploration. It is guided by geological metallogenic theory, based on existing geological, mineral, geophysical, geochemical, remote sensing and other data and information, and uses mineral exploration and GIS technology to predict, evaluate and select target areas for mineral resources, and then implement drilling, pit exploration and other projects to discover mineral deposits and find out their quantity, quality and development and utilization conditions, so as to meet all geological exploration work required by actual construction. The prediction and evaluation system is a related evaluation system of the above system, such as the evaluation of geological exploration work.

[0071] In this embodiment, based on expert knowledge, knowledge can be extracted from the data types of four aspects: earth system, mineralization system, exploration system, and prediction and evaluation system to obtain corresponding knowledge data.

[0072] Step S103: perform knowledge mining on the first knowledge data and / or knowledge graph based on a preset knowledge mining method to obtain second knowledge data.

[0073] It should be noted that the specific content of the preset knowledge mining method in this embodiment will not be elaborated in detail here, and it is adaptively selected based on the actual project requirements and the conventional operation means in this field. For example, the preset knowledge mining methods include knowledge graph search methods, knowledge reasoning methods, knowledge completion methods, knowledge graph analysis methods, etc., which are only for illustrative purposes.

[0074] Step S104, construct a mineral resource prediction model based on the first knowledge data and the second knowledge data.

[0075] It should be noted that in this embodiment, after the mineral resource prediction model is constructed, the target mineral resources can be predicted for mineralization based on the actual project requirements.

[0076] In summary, the method for constructing a mineralization prediction model based on a knowledge graph according to the embodiment of the present invention takes into account the influence of factors such as the material structure of the lithosphere, crust-mantle evolution, and the evolution and cycle of ore-forming materials on mineralization. It obtains corresponding knowledge data based on the earth system data type, ore-forming system data type, exploration system data type, and prediction and evaluation system data type, and further mines new knowledge data from this data to construct a mineral resource prediction model, realizing the comprehensive utilization of mineral resource elements, significantly improving the accuracy of prediction results, and meeting the intelligent requirements of mineral resource prediction.

[0077] In this embodiment, a method for constructing a mineralization prediction model based on a knowledge graph is provided. Figure 2 It is a schematic flow chart of another method for constructing a mineralization prediction model based on a knowledge graph according to the embodiment of the present invention, as Figure 2 shown, and this process includes the following steps:

[0078] Step S201, construct a knowledge graph of mineral resources, and the knowledge graph contains knowledge data of elements, minerals, rocks, ore deposits, mineralization processes, and geological phenomena.

[0079] Specifically, the above step S201 includes:

[0080] Step S2011, collect relevant data of mineral resources including the earth system, ore-forming system, exploration system, and prediction and evaluation system.

[0081] In this embodiment, the specific content and collection means of mineral resource data are not limited here and are determined based on the actual project requirements. For example, for the mineral resources of porphyry copper deposits, they can be collected and sorted from publicly available research literature or geological survey data related to porphyry copper deposits, which is only for illustrative purposes.

[0082] It should be noted that the types of mineral resource data include various types such as text, images, and tables, and they need to be converted into a unified knowledge data format, that is, triple data.

[0083] In step S2012, relevant data are subjected to entity and relationship annotation to obtain a large amount of knowledge data. The knowledge data are triple data containing entities and entity relationships. Among them, the entities represent elements related to the mineralization of mineral resources, and the entity relationships represent the semantic relationships between different entities.

[0084] It should be noted that entities are one of the basic components of a knowledge graph, which represent objects or concepts in the real world. Entities can be specific people, places, organizations, events, or abstract concepts, attributes, or categories. Each entity has a unique identifier for accurate identification and reference in the knowledge graph. In the knowledge graph, entities are usually represented as nodes and are connected to other entities by edges, and these edges represent the relationships between entities. A triple is a representation method for describing the directed relationship between entities, also known as a relationship triple, which is generally represented as: <head entity, relationship, tail entity>.

[0085] In step S2013, training data and test data are extracted from a large amount of knowledge data, and a data generation model is constructed based on the training data. The data generation model is used to generate triple data containing entities and entity relationships.

[0086] In this embodiment, the triple data are predicted by the constructed machine learning model (i.e., the trained data generation model) to achieve the expansion of the sample data. Specifically, the specific type of the machine learning model is not limited here and is adaptively adjusted according to actual needs. For example, a convolutional neural network model is only used as an exemplary illustration.

[0087] In step S2014, the test data are input into the data generation model for prediction, and a large amount of generated triple data are correspondingly obtained.

[0088] In step S2015, all the triple data are stored to obtain the knowledge graph of mineral resources.

[0089] In a specific embodiment, the knowledge graph of mineral resources with porphyry copper deposits as the core can be composed of millions of triple data. Among them, each triple data contains two entities and one relationship; different entity types and relationship types are defined according to expert knowledge and theoretical models, and each entity type can contain hundreds to thousands of entities.

[0090] In the embodiment of the present invention, by collecting relevant data of mineral resources and annotating them to obtain standard triple knowledge data, and predicting the annotated knowledge data through the constructed machine learning model to generate a large amount of triple data, the standardization and richness of the data set can be guaranteed, and the accuracy of the prediction results of the mineral resource prediction model can be improved to a certain extent.

[0091] Step S202: Based on the data types of the Earth system, ore-forming system, exploration system, and prediction and evaluation system, knowledge extraction is performed on the knowledge graph to obtain the first knowledge data. For details, please refer to Figure 1 Step S102 of the embodiment shown in this document, which will not be elaborated here.

[0092] Step S203: Based on a preset knowledge mining method, knowledge mining is performed on the first knowledge data and / or the knowledge graph to obtain the second knowledge data.

[0093] In this embodiment, the second knowledge data includes the first mining knowledge, the second mining knowledge, and the third mining knowledge. The above-mentioned knowledge data are all obtained through corresponding knowledge graph technologies. Specifically, the three knowledge mining methods based on the knowledge graph in this embodiment include: (1) Long-range and implicit association based on knowledge graph search; (2) Knowledge reasoning and knowledge completion based on the knowledge graph embedding model; (3) Implicit knowledge discovery based on graph analysis and calculation, which is only for illustrative purposes.

[0094] Specifically, when the second knowledge data is the first mining knowledge, the above step S203 includes:

[0095] Step A1: In response to two target entities optionally selected by the user from the first knowledge data and / or the knowledge graph, the two target entities are searched for knowledge based on a preset entity relationship type query method, and the corresponding search knowledge is obtained.

[0096] It should be noted that the method for the user to select the target entity in this embodiment is not limited here and can be adjusted adaptively according to actual needs. For example, a corresponding query interface is developed based on the currently stored knowledge graph data to implement various query methods such as indirect, fuzzy, and composite queries for different entities, which is only for illustrative purposes.

[0097] In this embodiment, the preset entity relationship types include direct relationships and indirect relationships. Among them, indirect relationships include short-range relationships and long-range relationships. It should be noted that if only one hop (i.e., the number of hops) is required to associate between two entities, it is considered that there may be a direct relationship between them; if the minimum number of hops required to associate between two entities is greater than the set threshold, it is considered that there may be an indirect relationship between them. In addition, two entities may be linked through other different entities, that is, there are multiple association paths. Then, as the number of hops between the two entities increases, their relationship changes from direct to indirect, and the association path changes from short-range to long-range; at the same time, as the number of paths increases, the relationship between the two entities will become more and more complex from simple.

[0098] Specifically, in the above step A1, searching for knowledge for the two target entities based on the preset entity relationship type query method and obtaining the corresponding search knowledge includes:

[0099] Step A11: Obtain the minimum number of hops and the number of paths required for two target entities to be associated.

[0100] Step A12: Determine whether the minimum number of hops is less than a preset hop threshold.

[0101] In this embodiment, the specific value of the preset hop threshold is not limited here and is adaptively selected based on actual needs. For example, the preset hop threshold is 2, which is only for illustrative purposes.

[0102] Step A13: If the minimum number of hops is less than the preset hop threshold, determine that the two target entities have a direct relationship, and obtain the corresponding search knowledge based on the two target entities and the direct relationship.

[0103] It should be noted that the search knowledge in this embodiment is also binary data containing different entities and their corresponding entity relationships.

[0104] Step A14: If the minimum number of hops is not less than the preset hop threshold, determine that the two target entities have an indirect relationship, and determine whether the number of paths is less than a preset path threshold.

[0105] In this embodiment, when the two target entities have an indirect relationship, there may be multiple associated paths from the head target entity to the tail target entity. Therefore, it is necessary to further divide the entity relationship based on the number of associated paths. It should be noted that the specific value of the preset path threshold is not limited here and is adaptively selected based on actual needs. For example, the preset path threshold is 3, which is only for illustrative purposes.

[0106] Step A15: If the number of paths is less than the preset path threshold, determine that the two target entities have a short-range relationship, and obtain the corresponding search knowledge based on the two target entities and the short-range relationship.

[0107] Step A16: If the number of paths is not less than the preset path threshold, determine that the two target entities have a long-range relationship, and obtain the corresponding search knowledge based on the two target entities and the long-range relationship.

[0108] In the embodiment of the present invention, the association relationship between entities is quantitatively judged by the minimum number of hops and the number of paths between entities, which can mine richer entity relationships and provide research data for the construction of the subsequent all-element mineral resource prediction model.

[0109] Step A2: Integrate the search knowledge to obtain the first mining knowledge.

[0110] In this embodiment, all the search knowledge obtained through the knowledge search technology is sorted according to the attributes of the entities to obtain the corresponding first mining knowledge. Specifically, when obtaining the target entity given by the user in this embodiment, the knowledge retrieval technology is used to quickly find the same or most similar knowledge data from the relevant knowledge graph, further mine the entity relationships with strong associations and indirect associations existing between entities, and identify the entity relationships with long-range associations and implicit connections between entities, which can provide data indication and reference content for the subsequent construction of a mineral resource prediction model with all elements.

[0111] Specifically, when the second knowledge data is the second mining knowledge, step S203 described above includes:

[0112] Step B1, constructing a data set based on the first knowledge data and / or the knowledge graph, where the data set includes: a training data set and a test data set.

[0113] Step B2, training the training data set through a preset knowledge graph embedding model to obtain a knowledge inference model correspondingly.

[0114] In this embodiment, the specific content of the preset knowledge graph embedding model is not limited here and is adaptively adjusted according to actual needs. For example, the preset knowledge graph embedding model is a translation distance model, such as a series of models like TransE, TransH, TransR, TransD, etc.; or the preset knowledge graph embedding model is a semantic matching model, such as the DistMult model, which is only for illustrative purposes.

[0115] Step B3, inputting the test data set into the knowledge inference model for inference to obtain inference knowledge.

[0116] Step B4, integrating the inference knowledge to obtain the second mining knowledge.

[0117] The embodiment of the present invention is based on the existing knowledge data and uses the knowledge graph embedding model to perform knowledge inference on it, which can mine new possible entity relationships, that is, inference knowledge, and provide research data for the subsequent construction of a mineral resource prediction model with all elements.

[0118] It should be noted that traditional mineral resource prediction models based on knowledge graphs often perform artificial qualitative probabilities through prediction elements. For example, based on subjective evaluation, the probabilities of different elements are set as values such as 0, 1, or 0.5; or the results driven by local regional data are used as the probabilities of the elements in the prediction model to participate in calculating weights. This type of method has certain subjectivity and cannot well reflect the actual influence of different elements on mineralization. Therefore, when the second knowledge data is the third mining knowledge in this embodiment, step S203 designed above includes:

[0119] Step C1: Obtain all the paths where the mineral resources are associated with any tail entity in the first knowledge data and / or the knowledge graph.

[0120] Step C2: Count the occurrence frequencies of different entity relationships in each path, calculate the relationship probability between the mineral resources and the tail entity, and determine the strength index of the corresponding path based on the occurrence frequencies and the relationship probability.

[0121] Step C3: Integrate the strength indices of all the paths to obtain the third mined knowledge.

[0122] In the embodiment of the present invention, considering that different elements, minerals, and rocks have different probabilities of influencing the mineralization of mineral resources, the strength index of the corresponding path is determined based on the occurrence frequencies and the relationship probability of the association paths between entities. The strength index is used to characterize the strength of the association relationship between the corresponding entities, and it is possible to realize knowledge mining that calculates new implicit knowledge based on graph analysis and assigns different weights to it, providing research data for the subsequent construction of a mineralization prediction model based on the knowledge graph with all elements.

[0123] Step S204: Construct a mineral resource prediction model based on the first knowledge data and the second knowledge data. For details, please refer to Figure 1 Step S104 of the embodiment shown, which will not be elaborated here.

[0124] In a specific embodiment, taking porphyry copper deposit as an example of mineral resources, through knowledge retrieval, knowledge analysis based on graph calculation, data statistics, and association analysis, and knowledge reasoning based on machine learning, etc., corresponding knowledge mining is carried out to discover various relationships such as indirect, fuzzy, and composite relationships (discovering weakly associated and indirectly associated entities, identifying long-range associations and implicit dependencies between entities), and the construction of all elements of porphyry copper deposit prediction is realized through both knowledge-driven and data-driven aspects. Specifically, the detailed construction process of the intelligent construction system for the all-element prediction index system based on the mineral resource knowledge graph includes:

[0125] Step D1: Knowledge data mining of long-range associations and implicit associations based on knowledge graph search.

[0126] In this embodiment, a corresponding knowledge query method is developed based on the currently stored knowledge graph of porphyry copper deposit to realize various knowledge queries such as indirect, fuzzy, and composite queries. Specifically, the following two query methods are as follows:

[0127] 1. A human-computer interactive query interface based on query methods and parameter selection.

[0128] In this embodiment, six query methods for indirect, fuzzy, and composite relationships between entities are set up, including: (1) N paths between entity A and entity B with L - R hops; (2) N acyclic paths between entity A and entity B with L - R hops; (3) the shortest path between entity A and entity B with L - R hops; (4) the longest path between entity A and entity B with L - R hops; (5) N paths between entity A and entity B passing through entity C with L - R hops; (6) the maximum - weight path between entity A and entity B with L - R hops. Among them, the parameter L represents the minimum number of hops required for two entities to be associated, the parameter R represents the maximum number of hops required for two entities to be associated, and the parameter N represents the number of paths for which two entities are associated.

[0129] Specifically, the user queries the corresponding results by inputting the restrictions on the number of hops and the number of paths required for the association of two entities. Among them, the user can set the minimum number of hops or the maximum number of hops for the association of two entities, as well as the number of associated paths. Specifically, the number of hops is from small to large, and the number of paths is from few to many, which can gradually realize the mining and analysis of relationship links from short - range to long - range, from direct to indirect, and from simple to complex. Based on the above rules, strongly associated and indirectly associated entities are discovered, as well as mining results such as identifying long - range associations and implicit connections between entities, providing indications and references for constructing prediction elements of porphyry copper deposits.

[0130] 2. Provide an interface for users to query the knowledge graph by writing query languages.

[0131] In this embodiment, the user writes a corresponding query statement (such as an SQL statement) using the query language according to the information and conditions to be queried; then sends the query statement to the publicly available knowledge - graph interface to obtain the query result. Specifically, through the query language, flexible query of the knowledge graph and acquisition of the required information can be realized, and effective management and utilization of the knowledge - graph data can be achieved; at the same time, the provided standardized knowledge - graph interface enables different systems and applications to conveniently interact and integrate with the knowledge graph.

[0132] In a specific embodiment, Figure 3 is a schematic diagram of the result of the knowledge - graph query. The entity - relationship result of two entities, that is, elements, to be analyzed is selected through the query interface. That is, the user sets entity 1, the copper element, and entity 2, the sulfur element, and the path between them passing through entity 3, the sulfide. Among them, the hop - number interval (L - R) is set to 0 - 3, and the number of paths (N) is set to 10. Specifically, by Figure 3It can be seen that the behaviors of copper and sulfur elements in magma are closely related to magnetite and hematite. This result can provide a research direction and indication for exploring the formation mechanism of porphyry copper deposits. Through further expert analysis and atlas mining, different oxygen fugacity conditions can cause the mutual transformation between magnetite and hematite, which provides a new long-range correlation discovery between oxygen fugacity and porphyry copper deposits, that is, magnetite can be used as an indicator mineral for porphyry copper deposits as a prediction factor.

[0133] Step D2, knowledge data mining for knowledge reasoning and knowledge completion based on the knowledge graph embedding model.

[0134] In this embodiment, knowledge completion or link prediction is realized based on the Knowledge Graph Embedding (KGE) model, that is, it infers whether a certain entity has a specific relationship with another given entity, so as to achieve further knowledge discovery. The specific process includes:

[0135] 1. Model selection.

[0136] To represent the entities and relationships in the knowledge graph in an Embedding manner, this embodiment selects the bilinear ComplEx model for the subsequent knowledge reasoning process. Specifically, this model has great advantages in solving entity relationships of 1-N, N-1, and N-N. It can solve symmetric and asymmetric relationships, and its implementation ability is better than that of neural network models, the RESCAL and DisMult of bilinear models, etc. In addition, in terms of the embedding expression ability of the model, it can extend the expression of entities and relationships from the simple tensor space domain to the complex complex space domain and define a scoring function.

[0137] 2. Dataset preprocessing.

[0138] In this embodiment, the original dataset (i.e., the first knowledge data and the knowledge graph) is divided into a training set, a validation set, and a test set, and then corresponding data preprocessing is performed. An index is assigned to each different entity name and each different relationship name. It should be noted that the specific method of data preprocessing is not limited here and is adjusted adaptively according to actual needs. For example, data integration processing includes data information extraction and fusion, missing value and outlier processing, research area time slicing processing, spatial mapping and rasterization processing, etc., which are only for illustrative purposes.

[0139] 3. Initialize entity and relationship embeddings.

[0140] In this embodiment, the embeddings of entities and entity relationships are randomly initialized according to a Gaussian distribution. In practical applications, if the number of samples in the dataset is small, negative training examples can be generated to enrich the number of samples. Specifically, negative training examples are generated by randomly breaking positive training examples or by random sampling. Among them, in order to reduce the generation of false negative training examples, this embodiment also sets different probabilities of replacing the head and tail entities. That is, if the relationship is 1-N, more opportunities are given to replace the head entity, and if the relationship is N-1, more opportunities are given to replace the tail entity. In this way, the generation quality of negative training examples is guaranteed.

[0141] 4. Model optimization and evaluation.

[0142] In this embodiment, the stochastic gradient descent algorithm is used to optimize the model. This algorithm has great advantages in reducing computing resources. Finally, the optimal solution is obtained by adjusting the learning rate and continuously evaluating the model.

[0143] 5. Model application and inference.

[0144] In this embodiment, the KGE model is used to infer the long-range relationships in the porphyry copper deposit knowledge graph. First, the training results are loaded, and the triple to be predicted is input. The scoring function is used to score the link of the tail entity. Among them, the higher the score, the more suitable it is. Subsequently, the link scoring results are sorted by size, and the prediction results are explained by experts from top to bottom until the predicted relationship is determined by experts to be almost non-explainable, so as to set a threshold to ensure the rationality of the newly discovered long-range relationships.

[0145] In a specific embodiment, the relationship probability between the ocean ridge subduction entity and the slab melting entity predicted by the knowledge graph embedding model KGE is relatively large (i.e., the predicted link relationship score is 0.93), indicating that there may be a direct causal relationship between them. Among them, this relationship does not exist in the existing knowledge graph and is obtained through prediction and inference calculation by the KGE model. Figure 4 It is a schematic diagram of the result of knowledge inference. From Figure 4 it can be seen that there is no direct or indirect relationship between the two circled entities, ocean ridge subduction and slab melting. The probability predicted by the KGE model is relatively large, and there is a certain invisible association between the two entities. Therefore, the two entities are associated by a curved arrow.

[0146] Step D3, knowledge data mining of implicit knowledge calculated based on graph analysis.

[0147] Based on the existing porphyry copper deposit knowledge graph, this embodiment realizes the calculation of the weights of prediction factors applicable to porphyry copper deposit prediction with universality and interpretability through the dual drive of knowledge and data. The specific calculation process includes:

[0148] 1. Retrieve all the association paths between "porphyry copper deposits PCDs" and a specific entity (such as "Quartz") in the knowledge graph.

[0149] 2. Calculate the probability that there is a relationship between "porphyry copper deposits PCDs" and the specific entity based on the frequency of occurrence of the relationships between different entities in each path. Specifically, this probability can be predicted by the aforementioned knowledge graph embedding model KGE.

[0150] 3. Calculate the joint probability of all paths as the strength index of the association path, which is used to characterize the strength of the relationship between "porphyry copper deposits PCDs" and the specific entity. Among them, the strength of these relationships can be used as the calculation weight assigned when this element participates in the prediction in a mineral resource prediction model (such as a fuzzy evidence weight model, an artificial intelligence prospecting prediction model, etc.). Figure 5 It is a schematic diagram of the calculation based on graph analysis.

[0151] Step D4: Construction of a porphyry copper deposit all-factor prediction model based on the knowledge graph.

[0152] Figure 6 It is a schematic diagram of the structure of the construction of the porphyry copper deposit all-factor prediction model. It can be seen from Figure 6 that in this embodiment, the construction of the porphyry copper deposit all-factor prediction model is carried out from two aspects: knowledge-driven and data-driven. Among them, knowledge-driven is based on expert knowledge and theoretical models. By constructing a porphyry copper deposit knowledge system, a porphyry copper deposit metallogenic prediction element framework model including four systems of "earth system - metallogenic system - exploration system - prediction and evaluation system" is further summarized and refined, such as plate tectonic elements, rock combination characteristic elements, indicator element combination elements, indicator mineral combination elements, etc. Specifically, the corresponding plate tectonic elements, such as the dip angle, subduction rate, and thickness of subducted sediments, can be extracted based on Gplate (a software for plate tectonic simulation and earth evolution research); the rock combination characteristic elements such as the oxygen fugacity and water content of magma can be obtained based on rock and mineral chemical data; the indicator mineral combination elements such as rock types and ore-controlling structures can be obtained based on geological mapping; the indicator mineral combination elements such as the extraction of indicator minerals and alteration zoning based on remote sensing; the indicator element combination elements such as the chemical element anomaly combination based on geochemical mapping; the indicator mineral combination elements such as the identification of deep buried rock masses based on geophysics.

[0153] In this embodiment, data-driven is based on the constructed porphyry copper deposit knowledge graph and the prediction all-factor framework. Through knowledge discovery techniques such as the aforementioned spectral graph search, graph analysis, and knowledge reasoning on the porphyry copper deposit knowledge graph, direct, indirect, long-range, and implicit factor indicators within the scope of the factor framework are mined, and the relationship strength index of each factor indicator is calculated. After aiming to reflect the output mechanism and prospecting signs of porphyry copper deposits to the greatest extent, the scientific data and engineering exploration data corresponding to the all-factor model are mined and processed to obtain corresponding new knowledge data. Finally, through the integrated processing of all-factor data, including data information extraction and fusion, missing value and outlier processing, research area time slicing processing, spatial mapping and rasterization processing, etc., an artificial intelligence model is used to complete the accurate artificial intelligence prediction of porphyry copper deposits in typical metallogenic domains.

[0154] In the embodiment of the present invention, the porphyry copper deposit is regarded as a product in the process of earth evolution. By considering its interaction with the multi-layer system inside the earth, the "earth system" is incorporated into the mineral prediction research, and the complex connection between it and the metallogenic system, exploration system, and prediction and evaluation system is constructed, which will strengthen the research understanding of the long-distance dynamic association and mutual restriction of various metallogenic-related factors; at the same time, the cutting-edge technologies in machine learning and deep learning are used to construct a data mining algorithm to mine the hidden factors related to porphyry copper deposits from high-dimensional data, providing new technical solutions and references for the analysis and mining of knowledge graphs in various fields. The construction of the all-factor prediction model of porphyry copper deposits based on the knowledge graph in this embodiment can reflect the output mechanism and prospecting signs of porphyry copper deposits to the greatest extent, and is used as a guide for the processing of engineering exploration data, and can achieve the purpose of accurate prediction.

[0155] In summary, the method for constructing a metallogenic prediction model based on a knowledge graph in the embodiment of the present invention can consider the influence of factors such as the material structure of the lithosphere, crust-mantle evolution, and the evolution and cycle of ore-forming materials on mineralization, realizes the comprehensive utilization of mineral resource elements, improves the accuracy of prediction results, and meets the intelligent requirements of mineral resource prediction.

[0156] In this embodiment, a system for constructing a metallogenic prediction model based on a knowledge graph is also provided. This system is used to implement the above embodiments and preferred implementation manners, and those that have been described will not be repeated. As the term "module" used below, it can be a combination of software and / or hardware that can achieve a predetermined function. Although the systems described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware is also possible and contemplated.

[0157] The present invention provides a system for constructing a metallogenic prediction model based on a knowledge graph, as Figure 7 shown, the system includes:

[0158] The data construction module 701 is used to construct a knowledge graph of mineral resources, and the knowledge graph contains knowledge data of elements, minerals, rocks, ore deposits, mineralization processes, and geological phenomena.

[0159] The knowledge extraction module 702 is used to extract knowledge from the knowledge graph based on the data types of the earth system, ore-forming system, exploration system, and prediction and evaluation system to obtain first knowledge data.

[0160] The knowledge mining module 703 is used to perform knowledge mining on the first knowledge data and / or the knowledge graph based on a preset knowledge mining method to obtain second knowledge data.

[0161] The model construction module 704 is used to construct a mineral resource prediction model based on the first knowledge data and the second knowledge data.

[0162] In some alternative embodiments, the data construction module 701 includes: a first acquisition sub-module, a second acquisition sub-module, a third acquisition sub-module, a fourth acquisition sub-module, and a fifth acquisition sub-module; wherein, the first acquisition sub-module is used to collect relevant data of mineral resources including the earth system, ore-forming system, exploration system, and prediction and evaluation system; the second acquisition sub-module is used to perform entity and relationship annotation on the relevant data to obtain a large amount of knowledge data, and the knowledge data is triple data including entities and entity relationships, wherein the entity represents an element related to mineral resource formation, and the entity relationship represents the semantic relationship between different entities; the third acquisition sub-module is used to extract training data and test data from the large amount of knowledge data and construct a data generation model based on the training data, wherein the data generation model is used to generate triple data including entities and entity relationships; the fourth acquisition sub-module is used to input the test data into the data generation model for prediction to correspondingly obtain a large amount of generated triple data; the fifth acquisition sub-module is used to store all the triple data to obtain the knowledge graph of mineral resources.

[0163] In some alternative embodiments, the knowledge mining module 703 includes: a first search sub-module and a second search sub-module; wherein, the first search sub-module is used to respond to two target entities optionally selected by the user from the first knowledge data and / or the knowledge graph, perform knowledge search on the two target entities based on a preset entity relationship type query method, and correspondingly obtain search knowledge; the second search sub-module is used to integrate the search knowledge to obtain first mining knowledge.

[0164] In some alternative embodiments, the first search sub-module includes: a first search unit, a second search unit, a third search unit, a fourth search unit, a fifth search unit, and a sixth search unit; wherein, the first search unit is configured to obtain the minimum number of hops and the number of paths required for two target entities to be associated; the second search unit is configured to determine whether the minimum number of hops is less than a preset hop threshold; the third search unit is configured to, if the minimum number of hops is less than the preset hop threshold, determine that the two target entities have a direct relationship, and obtain corresponding search knowledge based on the two target entities and the direct relationship; the fourth search unit is configured to, if the minimum number of hops is not less than the preset hop threshold, determine that the two target entities have an indirect relationship, and determine whether the number of paths is less than a preset path threshold; the fifth search unit is configured to, if the number of paths is less than the preset path threshold, determine that the two target entities have a short-range relationship, and obtain corresponding search knowledge based on the two target entities and the short-range relationship; the sixth search unit is configured to, if the number of paths is not less than the preset path threshold, determine that the two target entities have a long-range relationship, and obtain corresponding search knowledge based on the two target entities and the long-range relationship.

[0165] In some alternative embodiments, the knowledge mining module 703 includes: a first reasoning sub-module, a second reasoning sub-module, a third reasoning sub-module, and a fourth reasoning sub-module; wherein, the first reasoning sub-module is configured to construct a data set based on the first knowledge data and / or the knowledge graph, and the data set includes: a training data set and a test data set; the second reasoning sub-module is configured to train the training data set through a preset knowledge graph embedding model to obtain a knowledge reasoning model correspondingly; the third reasoning sub-module is configured to input the test data set into the knowledge reasoning model for reasoning to obtain reasoning knowledge; the fourth reasoning sub-module is configured to integrate the reasoning knowledge to obtain second mined knowledge.

[0166] In some alternative embodiments, the knowledge mining module 703 includes: a first calculation sub-module, a second calculation sub-module, and a third calculation sub-module; wherein, the first calculation sub-module is configured to obtain all paths in which the mineral resources are associated with any tail entity in the first knowledge data and / or the knowledge graph; the second calculation sub-module is configured to count the occurrence frequency of different entity relationships in each path, calculate the relationship probability between the mineral resources and the tail entity, and determine the strength index of the corresponding path based on the occurrence frequency and the relationship probability; the third calculation sub-module is configured to integrate the strength indexes of all paths to obtain third mined knowledge.

[0167] The further function descriptions of the above-mentioned respective modules are the same as those in the corresponding foregoing embodiments, and will not be elaborated herein.

[0168] The system for constructing a metallogenic prediction model based on a knowledge graph in this embodiment is presented in the form of functional units. Here, the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and a memory that execute one or more software or fixed programs, and / or other devices that can provide the above functions.

[0169] The system for constructing a metallogenic prediction model based on a knowledge graph according to an embodiment of the present invention takes into account the influence of factors such as the material structure of the lithosphere, crust-mantle evolution, and the evolution and cycle of ore-forming materials on mineralization. Based on the data types of the Earth system, the metallogenic system, the exploration system, and the prediction and evaluation system, the corresponding knowledge data is obtained, and further new knowledge data is mined from this data to construct a mineral resource prediction model, realizing the comprehensive utilization of mineral resource elements, helping to improve the accuracy of prediction results, and meeting the intelligent requirements of mineral resource prediction.

[0170] An embodiment of the present invention also provides a computer device. Please refer to Figure 8 , Figure 8 is a schematic structural diagram of the above computer device provided by an alternative embodiment of the present invention. As shown in Figure 8 , the computer device includes: one or more processors 10, a memory 20, and an interface for connecting each component, including a high-speed interface and a low-speed interface. Each component communicates with each other using different buses and can be installed on a common main board or installed in other ways as needed. The processor can process instructions executed within the computer device, including instructions stored in the memory or on the memory to display graphical information of the GUI on an external input / output device (such as a display device coupled to the interface). In some alternative embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple computer devices can be connected, and each device provides some necessary operations (such as a server array, a set of blade servers, or a multi-processor system). Figure 8 In

[0171] , a single processor 10 is taken as an example.

[0172] The memory 20 stores instructions executable by at least one processor 10, so that at least one processor 10 executes the method shown in the above embodiment.

[0173] The memory 20 may include a program storage area and a data storage area. Among them, the program storage area can store an operating system and application programs required for at least one function; the data storage area can store data created according to the use of the computer device and the like. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transitory memory, such as at least one magnetic disk storage device, a flash memory device, or other non-transitory solid-state storage devices. In some alternative embodiments, the memory 20 may optionally include a memory remotely disposed relative to the processor 10, and these remote memories can be connected to the computer device through a network. Examples of the above-mentioned network include but are not limited to the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0174] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk, or a solid-state drive; the memory 20 may further include a combination of the above types of memories.

[0175] The computer device further includes a communication interface 30 for the main control chip to communicate with other devices or a communication network.

[0176] The embodiment of the present invention also provides a computer-readable storage medium. The method according to the embodiment of the present invention can be implemented in hardware, firmware, or be implemented as computer code that can be recorded on a storage medium, or be implemented as computer code originally stored in a remote storage medium or a non-transitory machine-readable storage medium and downloaded through a network and will be stored in a local storage medium, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only memory, a random access memory, a flash memory, a hard disk, or a solid-state drive, etc.; further, the storage medium may further include a combination of the above types of memories. It can be understood that a computer, a processor, a microprocessor main control chip, or programmable hardware includes a storage component that can store or receive software or computer code, and when the software or computer code is accessed and executed by the computer, the processor, or the hardware, the method shown in the above embodiment is implemented.

[0177] A part of the present invention can be applied as a computer program product, such as computer program instructions, which, when executed by a computer, can invoke or provide the methods and / or technical solutions according to the present invention through the operations of the computer. Those skilled in the art should understand that the forms of existence of computer program instructions in a computer-readable medium include but are not limited to source files, executable files, installation package files, etc. Correspondingly, the ways for computer program instructions to be executed by a computer include but are not limited to: the computer directly executes the instructions, or the computer compiles the instructions and then executes the corresponding compiled program, or the computer reads and executes the instructions, or the computer reads and installs the instructions and then executes the corresponding installed program. Herein, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible to the computer.

[0178] Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A method for constructing a mineralization prediction model based on a knowledge graph, characterized in that: The method comprises: Constructing a knowledge graph of mineral resources, the knowledge graph comprising knowledge data of elements, minerals, rocks, ore deposits, mineralization and geological phenomena; Extracting knowledge from the knowledge graph based on the data types of the earth system, the mineralization system, the exploration system, and the prediction and evaluation system to obtain first knowledge data; Performing knowledge mining on the first knowledge data and / or the knowledge graph based on a preset knowledge mining method to obtain second knowledge data; Constructing a mineral resource prediction model based on the first knowledge data and the second knowledge data; The second knowledge data includes first mined knowledge, and the first knowledge data and / or the knowledge graph are mined based on a preset knowledge mining method to obtain the second knowledge data, including: In response to two target entities selected by the user from the first knowledge data and / or the knowledge graph, the two target entities are searched for knowledge based on a preset entity relationship type query method, and corresponding search knowledge is obtained; wherein the preset entity relationship type includes a direct relationship and an indirect relationship, wherein the indirect relationship includes a short-range relationship and a long-range relationship; the two target entities are searched for knowledge based on a preset entity relationship type query method, and corresponding search knowledge is obtained, including: Get the minimum number of hops and paths required for the two target entities to be associated; Determine whether the minimum hop count is less than a preset hop count threshold; If the minimum hop count is less than a preset hop count threshold, determining that the two target entities have a direct relationship, and obtaining corresponding search knowledge based on the two target entities and the direct relationship; If the minimum hop count is not less than a preset hop count threshold, determining that the two target entities have an indirect relationship, and determining whether the number of paths is less than a preset path threshold; If the number of paths is less than a preset path threshold, determining that there is a short-range relationship between the two target entities, and obtaining corresponding search knowledge based on the two target entities and the short-range relationship; If the number of paths is not less than a preset path threshold, determining that there is a long-range relationship between the two target entities, and obtaining corresponding search knowledge based on the two target entities and the long-range relationship; The search knowledge is integrated to obtain first mined knowledge.

2. A method for constructing a mineralization prediction model based on a knowledge graph, characterized in that: The method comprises: Constructing a knowledge graph of mineral resources, the knowledge graph comprising knowledge data of elements, minerals, rocks, ore deposits, mineralization and geological phenomena; Extracting knowledge from the knowledge graph based on the data types of the earth system, the mineralization system, the exploration system, and the prediction and evaluation system to obtain first knowledge data; Performing knowledge mining on the first knowledge data and / or the knowledge graph based on a preset knowledge mining method to obtain second knowledge data; Constructing a mineral resource prediction model based on the first knowledge data and the second knowledge data; The second knowledge data includes second mined knowledge, and the first knowledge data and / or the knowledge graph are mined based on a preset knowledge mining method to obtain the second knowledge data, including: Constructing a data set based on the first knowledge data and / or the knowledge graph, the data set comprising: a training data set and a test data set; The training data set is trained through a preset knowledge graph embedding model to obtain a corresponding knowledge reasoning model; Inputting the test data set into the knowledge reasoning model for reasoning to obtain reasoning knowledge; The inference knowledge is integrated to obtain second mined knowledge.

3. A method for constructing a mineralization prediction model based on a knowledge graph, characterized in that: The method comprises: Constructing a knowledge graph of mineral resources, the knowledge graph comprising knowledge data of elements, minerals, rocks, ore deposits, mineralization and geological phenomena; Extracting knowledge from the knowledge graph based on the data types of the earth system, the mineralization system, the exploration system, and the prediction and evaluation system to obtain first knowledge data; Performing knowledge mining on the first knowledge data and / or the knowledge graph based on a preset knowledge mining method to obtain second knowledge data; Constructing a mineral resource prediction model based on the first knowledge data and the second knowledge data; The second knowledge data includes third mined knowledge, and the first knowledge data and / or the knowledge graph are mined based on a preset knowledge mining method to obtain the second knowledge data, including: Obtain all paths where mineral resources are associated with any tail entity in the first knowledge data and / or the knowledge graph; Counting the occurrence frequencies of different entity relationships in each path, calculating the relationship probability between the mineral resources and the tail entity, and determining the intensity index of the corresponding path based on the occurrence frequencies and the relationship probability; The strength indexes of all paths are integrated to obtain the third mining knowledge.

4. The method for constructing a mineralization prediction model based on a knowledge graph according to any one of claims 1 to 3, characterized in that: The construction of the knowledge graph of mineral resources includes: Collect relevant data on mineral resources including earth system, mineralization system, exploration system and prediction and evaluation system; Perform entity and relationship annotation on the relevant data to obtain a large amount of knowledge data, wherein the knowledge data is triple data including entities and entity relationships, wherein the entities represent elements related to the mineralization of the mineral resources, and the entity relationships represent semantic relationships between different entities; Extracting training data and test data from a large amount of knowledge data, and constructing a data generation model based on the training data, wherein the data generation model is used to generate triple data including entities and entity relationships; The test data is input into the data generation model for prediction, and a large amount of generated triple data is obtained accordingly; All triplet data are stored to obtain a knowledge graph of the mineral resources.

5. A system for building a mineralization prediction model based on knowledge graph, characterized in that: The system is used to execute the method for constructing a mineralization prediction model based on a knowledge graph according to any one of claims 1 to 4, and the system comprises: A data construction module, used to construct a knowledge graph of mineral resources, wherein the knowledge graph contains knowledge data of elements, minerals, rocks, ore deposits, mineralization and geological phenomena; A knowledge extraction module, used to extract knowledge from the knowledge graph based on the data types of the earth system, the mineralization system, the exploration system and the prediction and evaluation system to obtain first knowledge data; A knowledge mining module, configured to perform knowledge mining on the first knowledge data and / or the knowledge graph based on a preset knowledge mining method to obtain second knowledge data; A model building module is used to build a mineral resource prediction model based on the first knowledge data and the second knowledge data.

6. A computer device, characterized in that: The device includes: a memory and a processor, the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the method for constructing a mineralization prediction model based on a knowledge graph as described in any one of claims 1 to 4 by executing the computer instructions.

7. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the method for constructing a mineralization prediction model based on a knowledge graph as described in any one of claims 1 to 4.

8. A computer program product, characterized in that It includes computer instructions, which are used to enable a computer to execute the method for constructing a mineralization prediction model based on a knowledge graph as described in any one of claims 1 to 4.

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