Verification method of coal mine digital twinning system driven by digital scene and generative data

Abstract

The invention provides a coal mine digital twinning system verification method driven by a digital scene and generative data, and the method comprises the steps: constructing a digital scene corresponding to a target coal mine, and enabling the digital scene to be used for simulating the related information of the target coal mine in the production process; and verifying the digital twin system of the target coal mine based on the digital scene. By implementing the method disclosed by the invention, a visual, simple and effective digital verification environment can be provided for the development of the coal mine digital twinning system, the development and test cost is reduced, and the research and development period is shortened.

Description

Technical Field

[0001] The present disclosure relates to the technical field of coal mine production management, and in particular to a coal mine digital twin system verification method driven by digital scenes and generative data. Background Art

[0002] In the process of digital transformation of coal mines, coal mining enterprises need to use digital twin systems to conduct all-round monitoring and control of the human-machine environment of coal mine production. In the digital twin system, connection and interaction are key technologies to achieve dynamic operation of digital twins and efficient integration of virtual and real space. This technology conforms to the connection and interaction theoretical system of "perception-communication-mapping-linkage-fusion".

[0003] In related technologies, the development of the interactive and connected functions of the digital twin system is verified by connecting physical entities such as "human-machine-object-environment" and the real data perceived from them.

[0004] In this way, the verification process of the digital twin system is relatively complicated, resulting in higher R&D costs for the digital twin system. Summary of the invention

[0005] The present disclosure aims to solve one of the technical problems in the related art at least to some extent.

[0006] To this end, the purpose of the present invention is to propose a digital scene and generative data driven coal mine digital twin system verification method, which can provide an intuitive, simple and effective digital verification environment for the development of coal mine digital twin system, reduce its development and testing costs, and shorten its research and development cycle.

[0007] To achieve the above-mentioned purpose, the digital scene and generative data-driven coal mine digital twin system verification method proposed in the embodiment of the present disclosure includes:

[0008] Constructing a digital scene corresponding to the target coal mine, wherein the digital scene is used to simulate and emulate relevant information of the target coal mine during the production process;

[0009] The digital twin system of the target coal mine is verified based on digital scenarios.

[0010] The digital scene and generative data-driven coal mine digital twin system verification method provided by the present disclosure builds a digital scene corresponding to the target coal mine, wherein the digital scene is used to simulate and emulate the relevant information of the target coal mine in the production process; and the digital twin system of the target coal mine is verified based on the digital scene. Thus, it can provide an intuitive, simple and effective digital verification environment for the development of the coal mine digital twin system, reduce its development and testing costs, and shorten its research and development cycle.

[0011] Additional aspects and advantages of the present disclosure will be given in part in the following description and in part will be obvious from the following description or learned through practice of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] The above and / or additional aspects and advantages of the present disclosure will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0013] Figure 1 It is a flow chart of a method for verifying a coal mine digital twin system driven by digital scenes and generative data proposed in an embodiment of the present disclosure;

[0014] Figure 2 It is a flow chart of a method for verifying a coal mine digital twin system driven by digital scenes and generative data proposed in another embodiment of the present disclosure. DETAILED DESCRIPTION

[0015] Embodiments of the present disclosure are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present disclosure, and are not to be construed as limitations of the present disclosure. On the contrary, the embodiments of the present disclosure include all changes, modifications, and equivalents that fall within the spirit and connotation of the appended claims.

[0016] Figure 1 It is a flow chart of a method for verifying a coal mine digital twin system driven by digital scenes and generative data proposed in an embodiment of the present disclosure.

[0017] like Figure 1 As shown in the figure, the digital scenario and the generative data-driven coal mine digital twin system verification method include:

[0018] S101: Construct a digital scene corresponding to the target coal mine, wherein the digital scene is used to simulate and emulate relevant information of the target coal mine during the production process.

[0019] Among them, the target coal mine may refer to the coal mine whose digital twin system is to be verified.

[0020] Among them, the digital scene can refer to the use of digital technology to simulate and emulate the production process of the above-mentioned target coal mine, and to display various conditions, parameters and states of the coal mine production site in a digital form.

[0021] In the disclosed embodiment, by constructing a digital scene, the geological structure, mining working face, equipment layout, personnel distribution and other physical parts of the coal mine can be displayed in real time, and the data, indicators and various influencing factors in the production process can be presented in a digital way. Thus, a reliable verification environment can be provided for the subsequent verification of the digital twin system.

[0022] S102: Verify the digital twin system of the target coal mine based on the digital scenario.

[0023] The digital twin system refers to a system that uses digital technology to combine real-time data from the target coal mine site with a virtual simulation model to achieve real-time monitoring, prediction, and optimization management of the coal mine production process. Based on this digital twin system, coal mining companies can improve production efficiency, reduce production costs, and improve safety and sustainability.

[0024] That is to say, after constructing the digital scene corresponding to the target coal mine in the embodiment of the present disclosure, the digital twin system of the target coal mine can be verified directly based on the digital scene, thereby effectively reducing the verification cost and improving the verification efficiency.

[0025] In this embodiment, a digital scene corresponding to the target coal mine is constructed, wherein the digital scene is used to simulate and emulate the relevant information of the target coal mine in the production process; and the digital twin system of the target coal mine is verified based on the digital scene. Thus, an intuitive, simple and effective digital verification environment can be provided for the development of the coal mine digital twin system, reducing its development and testing costs and shortening its R&D cycle.

[0026] Figure 2 It is a flow chart of a method for verifying a coal mine digital twin system driven by digital scenes and generative data proposed in another embodiment of the present disclosure.

[0027] like Figure 2 As shown in the figure, the digital scenario and the generative data-driven coal mine digital twin system verification method include:

[0028] S201: Construct a digital model of a coal mine corresponding to the target coal mine.

[0029] Among them, the digital model of coal mines can reflect the real situation of the coal mine system by modeling and simulating data such as the coal mine geological structure, equipment layout, and working face conditions, and help researchers and managers better understand and manage the coal mine production process.

[0030] In the disclosed embodiment, the coal mine digital model may be, for example, one or more of a coal mine statistical model, a coal mine optimization model, a coal mine risk model, a coal mine simulation model, a coal mine three-dimensional model, and a coal mine mechanism model, without limitation.

[0031] Optionally, in some embodiments, the digital model of the coal mine includes at least: a three-dimensional model of the coal mine; and a mechanism model of the coal mine.

[0032] Among them, the 3D model of a coal mine is a digital presentation of the underground and surface structures of the entire coal mine. Through 3D modeling software or laser scanning technology, the geological structure, mining working face, tunnels, equipment layout and other physical parts of the coal mine can be accurately 3D modeled to form a real digital coal mine scene. This 3D model can be used for visualization, planning and design, safety assessment, training and education, etc.

[0033] In the disclosed embodiment, surface three-dimensional scanning data and underground three-dimensional scanning data of the target coal mine can be obtained based on the radar scanning equipment; then three-dimensional modeling is performed based on the surface three-dimensional scanning data and the underground three-dimensional scanning data to obtain a three-dimensional model of the coal mine.

[0034] Among them, coal mine mechanism models refer to mathematical models used to describe and predict coal mine system operation, output, safety, etc. These models are usually based on physical understanding or empirical knowledge of the internal working principles and external influencing factors of coal mines, and are designed to simulate various complex processes and interactions in coal mine operations.

[0035] Specifically, the coal mine mechanism model may cover the following aspects:

[0036] (1) Production prediction sub-model: predicts the output and output quality of coal mines based on factors such as ore deposit characteristics, mining technology, and equipment efficiency.

[0037] (2) Safety analysis sub-model: By considering the geological structure, working environment, equipment operating conditions, etc., it evaluates the possible safety risks and accident probabilities that may occur in coal mine operations.

[0038] (3) Equipment status and fault analysis sub-model: Analyzes the operating status of coal mine equipment, predicts possible failure modes and maintenance requirements, and optimizes the equipment operation and maintenance plan.

[0039] (4) Environmental impact analysis sub-model: Consider the possible impact of coal mining activities on the surrounding environment (such as water quality, soil, and air quality) and conduct environmental risk assessment and management.

[0040] (5) Optimization management sub-model: Improve coal mine production efficiency and cost-effectiveness through the rational allocation and optimization of resources (such as manpower, equipment, and energy).

[0041] S202: Collecting operating data of the physical scene of the target coal mine.

[0042] Among them, the working condition data of the target coal mine physical scene refers to various data related to the physical scene collected by sensors, monitoring equipment, etc. during the coal mine production process. These data can include information on geological structure, ground pressure, gas concentration, temperature, humidity, equipment operation status, etc., which are used to analyze and evaluate the production conditions of coal mines.

[0043] S203: Acquire the working status data of the target coal mine, wherein the working status data includes: equipment status data and environment status data.

[0044] The working status data may refer to data describing the real-time status of the target coal mine during the production process.

[0045] The equipment status data may refer to data describing the real-time status of the production equipment of the target coal mine.

[0046] The environmental status data may refer to data describing the real-time status of the production environment of the target coal mine.

[0047] In the embodiment of the present disclosure, when obtaining the working status data of the target coal mine, the real working status data of the target coal mine may be obtained, or a communication link may be pre-established between the execution subject of the embodiment of the present disclosure and the big data center, and then the working status data of the target coal mine may be obtained from the big data center, and there is no limitation on this.

[0048] Optionally, in some embodiments, when obtaining the working status data of the target coal mine, the historical status data of the target coal mine may be obtained, wherein the historical status data includes: historical equipment status data and historical environment status data; the initial generative adversarial network is trained according to the historical status data to obtain the target generative adversarial network; and the working status data is generated based on the target generative adversarial network. Thus, the initial generative adversarial network can be trained based on the historical status data of the target coal mine to obtain a target generative adversarial network that is compatible with the target coal mine, thereby ensuring the practicality and applicability of the working status data generated by the target generative adversarial network, and effectively reducing the cost of obtaining the working status data.

[0049] The historical status data may refer to the real data describing the working status of the target coal mine acquired at a historical moment. The historical equipment status data and the historical environment status data may refer to the historical data describing the real equipment status and the real environment status of the target coal mine, respectively.

[0050] Optionally, in some embodiments, the target generative adversarial network includes: a target generator and a target discriminator; wherein, when the initial generative adversarial network is trained according to the historical state data to obtain the target generative adversarial network, it can be: generating synthetic data based on the initial generator in the initial generative adversarial network; training the initial discriminator in the initial generative adversarial network based on the historical state data and the synthetic data to obtain the target discriminator; adjusting the parameters of the initial generator based on the discrimination result of the target discriminator for the synthetic data to obtain the target generator. Thus, the historical state data and the synthetic data can be combined to effectively improve the training effect of the initial generator and the initial discriminator, and ensure the reliability and practicality of the obtained target generative adversarial network.

[0051] In the disclosed embodiment, when the working status data of the target coal mine is obtained, reliable driving data can be provided for the subsequent construction of the digital scene.

[0052] S204: Construct a digital scene according to the digital model of the coal mine, the working condition data and the working status data, wherein the digital scene is driven based on the working status data.

[0053] That is to say, in the disclosed embodiment, a digital model of a coal mine corresponding to a target coal mine can be constructed; working condition data of the physical scene of the target coal mine can be collected; working status data of the target coal mine can be obtained, wherein the working status data includes: equipment status data and environmental status data; a digital scene can be constructed according to the digital model of the coal mine, working condition data and working status data, wherein the digital scene is driven based on the working status data. Thus, the digital presentation effect of the obtained digital scene for scenes related to the production process of the target coal mine can be effectively improved to ensure the practicality of the obtained digital scene for the verification process of the digital twin system.

[0054] S205: Verify the digital twin system of the target coal mine based on the digital scenario.

[0055] The description of S205 can be specifically referred to the above embodiment, which will not be repeated here.

[0056] In this embodiment, by constructing a digital model of a coal mine corresponding to the target coal mine; collecting the working condition data of the physical scene of the target coal mine; obtaining the working status data of the target coal mine, wherein the working status data includes: equipment status data and environmental status data; constructing a digital scene according to the digital model of the coal mine, the working condition data and the working status data, wherein the digital scene is driven based on the working status data. In this way, the digital presentation effect of the obtained digital scene for the scenes related to the production process of the target coal mine can be effectively improved to ensure the practicality of the obtained digital scene for the verification process of the digital twin system.

[0057] Based on the above embodiments, the present invention constructs a digital scene of coal mine production driven by generative data based on the three-dimensional model of the coal mine, the mechanism model and the working condition data of the physical scene, combined with the data generated by the generative adversarial network (GAN). This digital scene can be used as a multi-dimensional verification environment in the development process of the coal mine digital twin system, so that the functions and performance of the coal mine digital twin system can be verified intuitively and efficiently during the development process.

[0058] The present disclosure not only ensures the consistency between the digital scene and the physical scene, but also ensures that the data generated in the digital scene is consistent with the data perceived in the physical scene (or the error is very small). In this way, during the research and development and testing phase of the digital twin system, all functions of the basic system can be assisted in development and verification through the digital scene of the present invention.

[0059] Based on the digital scene and generative data-driven coal mine digital twin system verification method proposed in this disclosure, the long-term iterative development and testing of the coal mine digital twin system no longer rely on huge coal mine electromechanical equipment and real coal mine production environment. This verification method is simple, convenient and intuitive, which can not only shorten the research and development cycle of the coal mine digital twin system, but also avoid a lot of energy consumption and environmental pollution, and has important practical value.

[0060] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the specification and practicing the invention disclosed herein. The present disclosure is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary techniques in the art that are not disclosed in the present disclosure. The description and examples are to be considered exemplary only, and the true scope and spirit of the present disclosure are indicated by the following claims.

[0061] It should be understood that the present disclosure is not limited to the exact structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

[0062] It should be noted that, in the description of the present disclosure, the terms "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance. In addition, in the description of the present disclosure, unless otherwise specified, the meaning of "plurality" is two or more.

[0063] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code that includes one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present disclosure includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present disclosure belong.

[0064] It should be understood that the various parts of the present disclosure can be implemented in hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0065] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0066] In addition, each functional unit in each embodiment of the present disclosure may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0067] The storage medium mentioned above can be a read-only memory, a magnetic disk or an optical disk, etc.

[0068] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present disclosure. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0069] Although the embodiments of the present disclosure have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present disclosure. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present disclosure.

Claims

1. A digital scene and generative data driven coal mine digital twin system verification method, characterized in that: include: Constructing a digital scene corresponding to a target coal mine, wherein the digital scene is used to simulate and emulate relevant information of the target coal mine during the production process; The digital twin system of the target coal mine is verified based on the digital scene.

2. The method according to claim 1, characterized in that The construction of a digital scene corresponding to the target coal mine includes: Constructing a digital coal mine model corresponding to the target coal mine; Collecting working condition data of the target coal mine physical scene; Acquire the working status data of the target coal mine, wherein the working status data includes: equipment status data and environment status data; The digital scene is constructed according to the coal mine digital model, the working condition data and the working status data, wherein the digital scene is driven based on the working status data.

3. The method according to claim 2, characterized in that The obtaining of the working status data of the target coal mine comprises: Acquire historical status data of the target coal mine, wherein the historical status data includes: historical equipment status data and historical environment status data; Training an initial generative adversarial network according to the historical state data to obtain a target generative adversarial network; The working status data is generated based on the target generative adversarial network.

4. The method according to claim 2, characterized in that The coal mine digital model at least includes: 3D model of coal mine; Coal mine mechanism model.

5. The method according to claim 2, characterized in that The operating condition data includes at least one of the following: geological structure data; Ground pressure conditions; Gas concentration; temperature; humidity; Device operating status.

6. The method according to claim 4, characterized in that The three-dimensional model of the coal mine is constructed based on the following method: Acquire surface three-dimensional scanning data and underground three-dimensional scanning data of the target coal mine; Three-dimensional modeling is performed based on the surface three-dimensional scanning data and the underground three-dimensional scanning data to obtain the three-dimensional model of the coal mine.

7. The method according to claim 4, characterized in that The coal mine mechanism model includes at least one of the following: A production forecast sub-model for predicting coal mine output and / or the quality of the output coal; Safety analysis sub-model, used to analyze safety risks and accident probabilities in coal mine work; Equipment status and fault analysis sub-model, used to analyze the operating status of coal mine equipment and predict equipment failure probability and maintenance requirements; An environmental impact analysis sub-model, used to analyze the impact of coal mining activities on the surrounding environment of the target coal mine; The optimization management sub-model is used to optimize the management of resources in the target coal mine.

8. The method according to claim 3, characterized in that The target generation adversarial network comprises: Target generator and target discriminator; The step of training the initial generative adversarial network according to the historical state data to obtain a target generative adversarial network includes: Generate synthetic data based on the initial generator in the initial generative adversarial network; Training an initial discriminator in the initial generative adversarial network based on the historical state data and the synthetic data to obtain the target discriminator; The parameters of the initial generator are adjusted based on the discrimination result of the target discriminator on the synthetic data to obtain the target generator.

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