CN112115613A - Method, device, equipment and storage medium for collaborative construction of ore body implicit model - Google Patents
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Abstract
The invention discloses a collaborative construction method, a collaborative construction device, a collaborative construction equipment and a collaborative construction storage medium for an ore body implicit model. The method comprises the following steps: the method comprises the steps of determining a plurality of modeling data for ore body modeling of a target ore body, updating model data for implicit modeling of the target ore body, executing the modeling data by different program modules, responding to each modeling data, updating the model data for implicit modeling of the target ore body, displaying a visual model of the target ore body based on the model data after the target ore body is updated, improving the building efficiency of the visual model, and facilitating the real-time adjustment of the implicit modeling of the ore body, so that the efficiency of the ore body modeling can be improved, and the quality of the ore body modeling can be improved.
Description
Technical Field
The invention relates to the field of ore body modeling, in particular to a method, a device, equipment and a storage medium for cooperatively constructing an ore body implicit model.
Background
The method for establishing the model based on the implicit function is well applied to many fields, including model reconstruction of an object in computer graphics, three-dimensional curved surface reconstruction of point cloud data obtained by three-dimensional laser scanning of an original object in reverse engineering, three-dimensional reconstruction of an organ model in biomedical engineering and the like.
The implicit modeling method is a method of expressing a model by defining an implicit function on a three-dimensional model space. The core of the implicit modeling method is the construction and adjustment of the constraint rules, but the model interpolation and reconstruction process involved in modeling needs a lot of intensive computation and is a very time-consuming process, and the higher complexity and fineness requirements of the model are limited by the memory capacity of a computer, the processing performance of a CPU and the like. When the implicit modeling method is applied to the field of mining digitization, when three-dimensional modeling is carried out on an ore body, the model needs to be continuously updated and reconstructed according to production and exploration data with the advance of the production process of a mine, the covered business process comprises multiple stages of exploration, production and the like, personnel and result data which participate in different business works of geology, measurement and mining are involved, and the building efficiency of the model can be influenced by the cooperation degree, the exchange timeliness and the communication efficiency among all the participants.
Disclosure of Invention
In view of this, embodiments of the present invention provide a method, an apparatus, a device, and a storage medium for collaborative construction of an ore body implicit model, and aim to optimize the construction efficiency of the ore body implicit model.
The technical scheme of the embodiment of the invention is realized as follows:
the embodiment of the invention provides a collaborative construction method of an ore body implicit model, which comprises the following steps:
determining a plurality of modeling data for ore body modeling of a target ore body, wherein each of the modeling data comprises at least one of: the system comprises a data acquisition module, a data processing module and a data processing module, wherein the data acquisition module is used for acquiring data of ore body modeling;
updating the implicitly modeled model data for the target ore body in response to each of the modeled data;
displaying a visualization model of the target ore body based on the updated model data of the target ore body;
wherein the determining a plurality of modeling data for ore body modeling of a target ore body, the updating model data for implicit modeling of the target ore body are performed by different program modules, respectively.
The embodiment of the invention also provides a collaborative construction device of the ore body implicit model, which comprises the following steps:
a visualization module to determine a plurality of modeling data for ore body modeling of a target ore body, wherein each of the modeling data includes at least one of: the system comprises a data acquisition module, a data processing module and a data processing module, wherein the data acquisition module is used for acquiring data of ore body modeling;
a processing module for updating the implicitly modeled model data for the target ore body in response to each of the modeled data;
the visualization module is further configured to display a visualization model of the target ore body based on the updated model data of the target ore body.
The embodiment of the invention also provides ore body modeling equipment, which comprises: a processor and a memory for storing a computer program capable of running on the processor, wherein the processor, when running the computer program, is configured to perform the steps of the method according to an embodiment of the invention.
The embodiment of the present invention further provides a storage medium, where a computer program is stored on the storage medium, and when the computer program is executed by a processor, the steps of the method according to the embodiment of the present invention are implemented.
According to the technical scheme provided by the embodiment of the invention, the plurality of modeling data for determining the ore body modeling of the target ore body and the model data for updating the implicit modeling of the target ore body are respectively executed by different program modules, the model data for updating the implicit modeling of the target ore body are updated in response to each modeling data, and the visual model of the target ore body is displayed based on the updated model data of the target ore body, so that the construction efficiency of the visual model can be improved, the real-time adjustment of the implicit modeling of the ore body is facilitated, and therefore, the efficiency of the ore body modeling can be improved, and the quality of the ore body modeling can be improved.
Drawings
FIG. 1 is a schematic flow chart of a collaborative construction method of an ore body implicit model according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a framework structure of hidden modeling software for an ore body in an application example of the present invention;
FIGS. 3A to 3C are schematic views showing the results of ore body modeling in an application example of the present invention;
FIG. 4 is a schematic structural diagram of a collaborative construction apparatus for an implicit model of an ore body according to an embodiment of the present invention;
FIG. 5 is a schematic structural diagram of an ore body modeling apparatus according to an embodiment of the invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention.
In the related technology, the implicit modeling process of an ore body is usually based on the constraint of geological rules, a three-dimensional model of the geologic body is constructed by adopting a spatial interpolation algorithm, but the constraint of the geological rules is usually required to be continuously updated and reconstructed along with the continuous supplement of production and exploration data, for example, the business process of the ore body comprises multiple stages of exploration, production and the like, personnel and result data which participate in different business works of geology, measurement and mining are involved, and the cooperation degree, the communication timeliness and the communication efficiency among all the participants influence the construction efficiency of the model.
Based on the above, the invention provides a collaborative construction method of an ore body implicit model, which separates the constraint adding process from the calculation-intensive model updating process in the modeling process and can realize the rapid, efficient and accurate construction of the ore body model.
The embodiment of the invention provides a collaborative construction method of an ore body implicit model, which comprises the following steps of:
103, displaying a visual model of the target ore body based on the updated model data of the target ore body;
wherein the determining a plurality of modeling data for ore body modeling of a target ore body, the updating model data for implicit modeling of the target ore body are performed by different program modules, respectively.
The method comprises the steps of determining a plurality of modeling data for ore body modeling of a target ore body, updating model data for implicit modeling of the target ore body, executing the modeling data by different program modules, responding to each modeling data, updating the model data for implicit modeling of the target ore body, displaying a visual model of the target ore body based on the updated model data of the target ore body, improving the building efficiency of the visual model, and facilitating the real-time adjustment of the implicit modeling of the ore body, so that the efficiency of the ore body modeling can be improved, and the quality of the ore body modeling can be improved.
It can be understood that the visual model of the target ore body is repeatedly iterated based on the modeling data which is continuously generated, so that the quality requirement of ore body modeling is met, and the visual model corresponding to each modeling data can be displayed in near real time, so that a user can reasonably input information such as constraint rules and modeling parameters for generating the modeling data, and the quality of ore body modeling is improved.
In some embodiments, if the modeling data includes the modeling parameters and/or the constraint data, the updating the implicitly modeled model data for the target ore body includes:
solving an implicit function characterizing an ore body model based on the modeling parameters and/or the constraint data;
and performing implicit curved surface reconstruction on the current model data based on the implicit function to obtain the updated model data.
It will be appreciated that if there is currently updated modeling data including at least one of modeling parameters and constraint data, then an implicit function characterizing the ore body model is solved based on the modeling data and historical modeling data.
In some embodiments, the determining a plurality of modeling data for ore body modeling of the target ore body comprises:
for each of the modeling data, receiving externally input information for dynamically adjusting the ore body modeling of the target ore body, generating the modeling data based on the input information and updating the modeling data required for the current dynamic adjustment, wherein each of the modeling data is stored based on version information.
It will be appreciated that as the mining flow of an ore body progresses, the modeling data may be generated for different phases of the ore body. Further, the modeling data of the same stage may be different according to the adjustment of the constraint rule.
The modeling data are distinguished by version information, so that modeling can be reasonably controlled according to historical version modeling data, misoperation recovery and data conflict resolution are realized, and meanwhile, the model updating process at any stage is recorded, so that the modeling process has traceability.
In some embodiments, before generating the modeling data based on the input information and updating the modeling data required for the current dynamic adjustment, the method further comprises:
judging whether the modeling data is in a locked state, if so, shielding the input information; if not, updating the modeling data based on the input information; the locked state refers to a state in which the modeling data is being adjusted, that is, the modeling data has not been successfully generated, and is still in a state of receiving user input information.
Therefore, when the user adjusts the constraint conditions of different constraint rules, the corresponding modeling data can be set to be in a locked state, so that other users can be prevented from repeatedly editing the constraint rules.
In some embodiments, the input information includes at least one of: the method comprises the following steps of generating modeling data based on the input information and updating the modeling data to modeling data required by current dynamic adjustment, wherein the modeling data comprises first information of a constraint rule expressed by a geometric primitive, second information used for determining modeling parameters and third information used for representing collected data of the target ore body, and the first information comprises:
and generating the modeling data and updating the modeling data to the modeling data required by the current dynamic adjustment based on at least one of the first information, the second information and the third information.
Here, geometric primitives for user interactive addition, modification, and adjustment may be provided to represent various different types of constraint rules. The parameters of each constraint rule are recorded in the attributes of the corresponding geometric primitive, and a user can adjust the corresponding constraint rule by modifying the attributes of the geometric primitive. For example, for a multi-potential field combination constraint, to distinguish the constraints of different potential fields, the potential field represented by the constraint may be labeled in a multi-label manner and the label of the potential field may be recorded in the primitive attribute of the constraint.
Exemplary modeling parameters may include: the method comprises three types of function parameters, solving parameters and reconstruction parameters, wherein the function parameters mainly comprise parameters such as polynomial orders, kernel function types, kernel function coefficients and the like. The user may select different kernel function types (global or local types) for different implicit function fields. For the kernel function of local interpolation, proper variation function parameters such as Alpha value, base station, lump, variable range, drift and the like can be automatically determined according to interpolation data, so that a user can adjust the kernel function according to the actual modeling effect.
In some embodiments, said updating model data of said target ore body in response to each of said modeling data comprises:
determining that the modeling data required for the current dynamic adjustment is updated, performing the updating of the model data of the target ore body.
In this way, when new modeling data is successfully generated, the updating of the model data of the target ore body can be automatically performed, thereby improving the modeling efficiency.
In some embodiments, the type of constraint rule comprises at least one of: domain constraints, gradient constraints, tangential constraints, and anisotropic constraints.
It will be appreciated that for orientation class constraints, a convenient interactive rotation may be provided. For primitives of constraint lines and trend lines, the primitives can be represented by primitives of free curve class (such as Bezier curve) which have control points and are convenient to adjust. For anisotropic constraints, ellipsoid primitives can be used for representation, and parameters of the anisotropic constraints can be stored in the primitive attributes of the ellipsoids. The relative strength of anisotropy for the ellipsoid primitive can be represented by the three axial lengths of the ellipsoid. Longer axial length indicates greater strength of anisotropy. For the local anisotropy constraint, in order to simplify the setting of the anisotropy, only the relative strength, the minor axis and the minor axis of the anisotropy of the main axis of the ellipsoid can be considered, and an isotropic expression mode is adopted. The local anisotropy constraints are well suited for modeling cases where sparse profile data is continuous along a certain planar direction over large distances. Since the anisotropic distance of the points aligned with the strongest anisotropic direction will be less than their isotropic distance, so that their impact on the estimate is greater and the interpolation weights have a higher correlation in this direction.
The ore body implicit model collaborative construction method according to the embodiment of the present invention is specifically described below with reference to an application example:
in an application example, the ore body implicit model collaborative construction method is applied to ore body implicit modeling software shown in fig. 2, and the ore body implicit modeling software comprises three modules: the system comprises a data center, a computing center and a visualization platform. Multiple participants at different stages may be allowed to cooperatively model complex ore body models. The following describes each module:
data center
The data center is used for managing data of different business processes, and particularly, the data center adopts a shared database to manage data in a modeling process. For example, model data and model data at different stages are managed.
Here, the modeling data mainly includes: sample data, constraint data, interpolation data, and modeling parameters. The sampled data is of a wide variety of data types, including raw sampled data obtained in geological and production exploration, and manually interpreted contour information. The sampled data provides important information for constructing the constraint data. The constraint data is constructed based on various constraint rules. The interpolated data consists of constraints that can be used directly for interpolation. Implicit functions do not directly interpolate all constraint rules. Therefore, the constraint data needs to be converted into interpolation data in the interpolation.
The model data can be divided into implicit models and explicit models, and the corresponding mesh models are implicit meshes and explicit meshes. The implicit mesh is a mesh model which contains implicit information and is obtained by adopting an implicit surface reconstruction method. Because of the inclusion of implicit information, implicit meshes can be applied directly to the composition constraints without the need to compute signed distance fields, in contrast to explicit meshes, and reference can be made specifically to the contents of the multi-potential field composition constraint portion. The continuous implicit function is essentially a vector model, except that the vector model is not explicitly expressed and only appears as a discretized mesh model (grid model) when visualized. In a sense, the implicit grid belongs to a unified grid vector hybrid model and is a three-dimensional model represented by explicit and implicit hybrid representation. The implicit mesh can be converted into explicit meshes or block segment models of arbitrary precision (including face meshes and volume meshes) as needed, but the explicit meshes after conversion will no longer contain implicit information.
Illustratively, during modeling, modeling data is controlled by historical versions, and stakeholders of different business processes obtain three-dimensional models supporting their business needs by modifying, adding, and updating constraints and parameters. And issuing the dynamically updated ore body model to a data center, and butting with the mine cooperation platform, so that dynamic update of the service flow information integrated in the three-dimensional model is realized. According to the mode, the efficiency of complex ore body modeling is improved, but in the mode of multi-person collaborative modeling, each user can submit modeling data of different stages to the data center, and the risk that related data are modified and covered for multiple times exists. The application example reasonably controls the historical version of the data operation to realize the misoperation recovery and solve the data conflict, and simultaneously records the model updating process at any stage. When a user adjusts a constraint, the data center will lock the constraint to prevent other users from repeatedly editing the constraint.
Illustratively, a dmm-formatted file is defined to store constraint and solution information in implicit modeling software. The file stores all information used for expressing the implicit function of the ore body model, is essentially a meshless model file, does not store a three-dimensional mesh model occupying a large amount of storage space any more, and can save a large amount of storage space. And when the database is designed, determining an optimal data model, storage mode and access method according to the requirements of implicit modeling and data related to each stage.
Computing center
The computation center is used for running computation related to model updating, in particular, a spatial interpolation process (namely, implicit function solving shown in FIG. 2) for constructing an implicit function based on sampling data and a three-dimensional model reconstruction process (namely, implicit curved surface reconstruction shown in FIG. 2) for evaluating interpolation points and extracting an isosurface based on a spatial rule data field.
Illustratively, the solving of the implicit function and the reconstruction process of the implicit model are packaged into a solver. The whole computing process can be automatically run on a server side (or a client side background) in a multithreading mode. It will be appreciated that appropriate modeling parameters are automatically determined when specifying solution and reconstruction parameters, while allowing the user to make adjustments to the parameters.
It can be appreciated that the solver is largely divided into two separate parts according to computational requirements: implicit function solution and implicit surface reconstruction. Before the implicit function is solved, the solver can check parameter settings and constraint conditions, and automatically remove repeated constraints to avoid solving failure; ambiguous or anomalous constraints are alerted for processing by the user. After the implicit curved surface is reconstructed, the solver can carry out validity detection on the reconstructed model and automatically repair abnormal problems of self-intersection, singular edges or opening edges and the like of the model.
The modeling parameters related to calculation by the solver mainly comprise three types of function parameters, solving parameters and reconstruction parameters, and the function parameters mainly comprise parameters such as polynomial orders, kernel function types and kernel function coefficients. The user selects different kernel function types (global or local types) for different implicit function fields. For the kernel function of local interpolation, software needs to automatically determine proper Alpha value, base station, lump, variable range, drift and other variable function parameters according to interpolation data, so that a user can adjust the kernel function according to the actual modeling effect.
The solver automatically determines a corresponding implicit function solving method according to the interpolation data, for example, a local interpolation or global interpolation mode is determined according to the type of the kernel function, an isotropic interpolation or anisotropic interpolation mode is determined according to the type of the constraint, and the type of the radial basis function type interpolation method is determined according to the interpolation constraint.
In order to enhance the convenience of software interaction, the trigger condition of the operation of the calculation module is set, so that after the user adjusts the geological rule constraint or parameter (namely, the modeling data is determined to be updated), the software can automatically update the modeling result. In addition, the steps of the updating calculation are subdivided, and the influence of different updating operations of the user on the solving process is analyzed, so that the user can fully utilize the existing calculation result when performing the updating operation in the calculating process, and the calculation does not need to be performed again from the beginning.
Visualization platform
The visualization platform can provide a graphical operation interface, so that a user can add constraint rules interactively and visualize a three-dimensional modeling result.
It can be understood that the visualization platform can be configured with a geological rule constraint module which is mainly used for constructing constraint data required by implicit modeling interpolation. This module keeps the sampled data (including the conventional interpreted data) separate from the constraint data and cannot be used to adjust and add the sampled data, but can convert the sampled data into constraint data (e.g., borehole constraints and profile constraints constructed in an implicit modeling method based on borehole data or profile data). Constraints based on discretized sampling of the borehole data need to be associated with the original borehole data, facilitating different constraint effects to be obtained by means of dynamically adjusting sampling parameters.
Illustratively, because the result of the implicit modeling mainly depends on the added constraint, the modeling software can also provide the functions of checking the abnormal constraint and the repeated constraint, and meanwhile, the constraint data is subjected to color matching according to the relation between the domain value and the inside and the outside of the model, so that the abnormal constraint data is convenient to correct.
Illustratively, geometric primitives that facilitate user interaction addition, modification, and adjustment can be provided in the implicit modeling software to represent a variety of different types of constraint rules. The parameters of the constraint are recorded in the attributes of the corresponding geometric primitives, and the user adjusts the constraint by modifying the attributes of the geometric primitives. For example, for a multi-potential field combination constraint, to distinguish the constraints of different potential fields, the potential field represented by the constraint may be labeled in a multi-label manner and the label of the potential field may be recorded in the primitive attribute of the constraint.
Illustratively, the visualization platform may download the sampled data from the data center, and the user will add constraints of geological rules based on the sampled data and upload the constraints to the data center. And the calculation center automatically determines the calculation requirement of implicit function solution according to the updating of the data center constraint and returns the solution result to the data center. The visualization platform sends the calculation requirement of implicit curved surface reconstruction to the calculation center, and the calculation center returns the reconstructed model to the visualization platform for display and returns the reconstruction result to the data center. The solution process of the computation center is run automatically in an event-driven manner.
In this manner, multiple participants at different stages may be allowed to cooperatively model a complex ore body model. A shared data center is adopted to manage data flow related to a modeling process, including a sampling data processing flow before modeling and a model data application flow after modeling, so that personnel participating in each flow of geological modeling and mining business can realize cooperative work. When the model is updated, the corresponding business process can be correspondingly corrected. For example, when the sampled data of the data center is updated, modelers may react to new data features by adjusting geological rules; when the ore body model of the data center is updated, a designer can reflect the change of the model by adjusting the design result.
In an application example, based on original mine drilling data, three-dimensional ore body models (as shown in fig. 3A to 3C) of a plurality of real mines are established by adopting ore body implicit modeling software shown in fig. 2, and the results show that the ore body implicit modeling method provided by the application is reliable and can establish ore body models with better quality at a higher speed.
In order to implement the method according to the embodiment of the present invention, an embodiment of the present invention further provides a device for cooperatively building an ore body implicit model, where as shown in fig. 4, the device includes: a visualization module 401 and a processing module 402, wherein the visualization module 401 is configured to determine a plurality of modeling data for modeling an ore body of a target ore body, wherein each of the modeling data includes at least one of: the system comprises a data acquisition module, a data processing module and a data processing module, wherein the data acquisition module is used for acquiring data of ore body modeling; the processing module 402 is configured to update the implicitly modeled model data of the target ore body in response to each of the modeled data; the visualization module 401 is further configured to display a visualization model of the target ore body based on the updated model data of the target ore body.
In some embodiments, if the modeling data includes the modeling parameter and/or the constraint data, the processing module 402 is specifically configured to:
solving an implicit function characterizing an ore body model based on the modeling parameters and/or the constraint data;
and performing implicit curved surface reconstruction on the current model data based on the implicit function to obtain the updated model data.
In some embodiments, the visualization module 401 is specifically configured to:
for each of the modeling data, receiving externally input information for dynamically adjusting the ore body modeling of the target ore body, generating the modeling data based on the input information and updating the modeling data required for the current dynamic adjustment, wherein each of the modeling data is stored based on version information.
In some embodiments, before the visualization module 401 generates the modeling data based on the input information and updates the modeling data required for the current dynamic adjustment, it is further configured to:
judging whether the modeling data is in a locked state, if so, shielding the input information; if not, updating the modeling data based on the input information; wherein the locked state refers to a state in which the modeling data is being adjusted.
In some embodiments, the input information includes at least one of: the visualization module 401 generates modeling data based on the input information and updates the modeling data to the modeling data required for current dynamic adjustment, including:
and generating the modeling data and updating the modeling data to the modeling data required by the current dynamic adjustment based on at least one of the first information, the second information and the third information.
In some embodiments, the processing module 402 is specifically configured to:
determining that the modeling data required for the current dynamic adjustment is updated, performing the updating of the model data of the target ore body.
In some embodiments, the type of constraint rule comprises at least one of: domain constraints, gradient constraints, tangential constraints, and anisotropic constraints.
In practical application, the visualization module 401 and the processing module 402 can be implemented by a processor in the ore body implicit model cooperative construction device. Of course, the processor needs to run a computer program in memory to implement its functions.
It should be noted that: in the above embodiment, when the ore body implicit model collaborative construction apparatus is used to perform the ore body implicit model collaborative construction, only the division of the program modules is taken as an example, and in practical applications, the processing may be distributed to different program modules according to needs, that is, the internal structure of the apparatus is divided into different program modules to complete all or part of the processing described above. In addition, the embodiment of the ore body implicit model collaborative construction device and the ore body implicit model collaborative construction method provided by the above embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment and is not described herein again.
Based on the hardware implementation of the program module, in order to implement the method according to the embodiment of the present invention, an ore body modeling apparatus is also provided in the embodiment of the present invention. Fig. 5 shows only an exemplary structure of the apparatus and not the entire structure, and a part of or the entire structure shown in fig. 5 may be implemented as necessary.
As shown in FIG. 5, an ore body modeling apparatus 500 provided by an embodiment of the invention includes: at least one processor 501, memory 502, a user interface 503, and at least one network interface 504. The various components in the ore body modeling apparatus 500 are coupled together by a bus system 505. It will be appreciated that the bus system 505 is used to enable communications among the components of the connection. The bus system 505 includes a power bus, a control bus, and a status signal bus in addition to a data bus. For clarity of illustration, however, the various buses are labeled as bus system 505 in FIG. 5.
The user interface 503 may include a display, a keyboard, a mouse, a trackball, a click wheel, a key, a button, a touch pad, a touch screen, or the like, among others.
The memory 502 in embodiments of the present invention is used to store various types of data to support the operation of the ore body modeling apparatus. Examples of such data include: any computer program for operating on an ore body modeling apparatus.
The ore body implicit model collaborative construction method disclosed by the embodiment of the invention can be applied to the processor 501, or can be realized by the processor 501. The processor 501 may be an integrated circuit chip having signal processing capabilities. In the implementation process, the steps of the ore body implicit model collaborative construction method may be completed by an integrated logic circuit of hardware in the processor 501 or instructions in the form of software. The Processor 501 may be a general purpose Processor, a Digital Signal Processor (DSP), or other programmable logic device, discrete gate or transistor logic device, discrete hardware components, etc. Processor 501 may implement or perform the methods, steps, and logic blocks disclosed in embodiments of the present invention. A general purpose processor may be a microprocessor or any conventional processor or the like. The steps of the method disclosed by the embodiment of the invention can be directly implemented by a hardware decoding processor, or can be implemented by combining hardware and software modules in the decoding processor. The software module may be located in a storage medium located in the memory 502, and the processor 501 reads information in the memory 502, and completes the steps of the ore body implicit model collaborative construction method provided in the embodiment of the present invention in combination with hardware thereof.
In an exemplary embodiment, the ore body modeling apparatus may be implemented by one or more Application Specific Integrated Circuits (ASICs), DSPs, Programmable Logic Devices (PLDs), Complex Programmable Logic Devices (CPLDs), FPGAs, general purpose processors, controllers, Micro Controllers (MCUs), microprocessors (microprocessors), or other electronic components for performing the aforementioned methods.
It will be appreciated that the memory 502 can be either volatile memory or nonvolatile memory, and can include both volatile and nonvolatile memory. Among them, the nonvolatile Memory may be a Read Only Memory (ROM), a Programmable Read Only Memory (PROM), an Erasable Programmable Read-Only Memory (EPROM), an Electrically Erasable Programmable Read-Only Memory (EEPROM), a magnetic random access Memory (FRAM), a Flash Memory (Flash Memory), a magnetic surface Memory, an optical disk, or a Compact Disc Read-Only Memory (CD-ROM); the magnetic surface storage may be disk storage or tape storage. Volatile Memory can be Random Access Memory (RAM), which acts as external cache Memory. By way of illustration and not limitation, many forms of RAM are available, such as Static Random Access Memory (SRAM), Synchronous Static Random Access Memory (SSRAM), Dynamic Random Access Memory (DRAM), Synchronous Dynamic Random Access Memory (SDRAM), Double Data Rate Synchronous Dynamic Random Access Memory (DDRSDRAM), Enhanced Synchronous Dynamic Random Access Memory (ESDRAM), Enhanced Synchronous Dynamic Random Access Memory (Enhanced DRAM), Synchronous Dynamic Random Access Memory (SLDRAM), Direct Memory (DRmb Access), and Random Access Memory (DRAM). The described memory for embodiments of the present invention is intended to comprise, without being limited to, these and any other suitable types of memory.
In an exemplary embodiment, the embodiment of the present invention further provides a storage medium, i.e., a computer storage medium, which may be a computer-readable storage medium, for example, including a memory 502 storing a computer program, which is executable by a processor 501 of an ore body modeling apparatus to perform the steps described in the method of the embodiment of the present invention. The computer readable storage medium may be a ROM, PROM, EPROM, EEPROM, Flash Memory, magnetic surface Memory, optical disk, or CD-ROM, among others.
It should be noted that: "first," "second," and the like are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order.
In addition, the technical solutions described in the embodiments of the present invention may be arbitrarily combined without conflict.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and all the changes or substitutions should be covered within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. A collaborative construction method of an ore body implicit model is characterized by comprising the following steps:
determining a plurality of modeling data for ore body modeling of a target ore body, wherein each of the modeling data comprises at least one of: the system comprises a data acquisition module, a data processing module and a data processing module, wherein the data acquisition module is used for acquiring data of ore body modeling;
updating the implicitly modeled model data for the target ore body in response to each of the modeled data;
displaying a visualization model of the target ore body based on the updated model data of the target ore body;
wherein the determining a plurality of modeling data for ore body modeling of a target ore body, the updating model data for implicit modeling of the target ore body are performed by different program modules, respectively.
2. The method of claim 1, wherein said updating the implicitly modeled model data for the target ore body if the modeling data includes the modeling parameters and/or the constraint data comprises:
solving an implicit function characterizing an ore body model based on the modeling parameters and/or the constraint data;
and performing implicit curved surface reconstruction on the current model data based on the implicit function to obtain the updated model data.
3. The method of claim 1, wherein determining a plurality of modeling data for ore body modeling of a target ore body comprises:
for each of the modeling data, receiving externally input information for dynamically adjusting the ore body modeling of the target ore body, generating the modeling data based on the input information and updating the modeling data required for the current dynamic adjustment, wherein each of the modeling data is stored based on version information.
4. The method of claim 3, wherein prior to generating the modeling data based on the input information and updating the modeling data required for current dynamic adjustment, the method further comprises:
judging whether the modeling data is in a locked state, if so, shielding the input information; if not, updating the modeling data based on the input information; wherein the locked state refers to a state in which the modeling data is being adjusted.
5. The method of claim 3, wherein the input information comprises at least one of: the method comprises the following steps of generating modeling data based on the input information and updating the modeling data to modeling data required by current dynamic adjustment, wherein the modeling data comprises first information of a constraint rule expressed by a geometric primitive, second information used for determining modeling parameters and third information used for representing collected data of the target ore body, and the first information comprises:
and generating the modeling data and updating the modeling data to the modeling data required by the current dynamic adjustment based on at least one of the first information, the second information and the third information.
6. The method of claim 5, wherein said updating model data of said target ore body in response to each of said modeling data comprises:
determining that the modeling data required for the current dynamic adjustment is updated, performing the updating of the model data of the target ore body.
7. The method of claim 5, wherein the type of constraint rule comprises at least one of: domain constraints, gradient constraints, tangential constraints, and anisotropic constraints.
8. An ore body implicit model collaborative construction device is characterized by comprising:
a visualization module to determine a plurality of modeling data for ore body modeling of a target ore body, wherein each of the modeling data includes at least one of: the system comprises a data acquisition module, a data processing module and a data processing module, wherein the data acquisition module is used for acquiring data of ore body modeling;
a processing module for updating the implicitly modeled model data for the target ore body in response to each of the modeled data;
the visualization module is further configured to display a visualization model of the target ore body based on the updated model data of the target ore body.
9. An ore body modeling apparatus, comprising: a processor and a memory for storing a computer program capable of running on the processor, wherein,
the processor, when executing the computer program, is adapted to perform the steps of the method of any of claims 1 to 7.
10. A storage medium having a computer program stored thereon, the computer program, when executed by a processor, implementing the steps of the method of any one of claims 1 to 7.
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| CN109326002A (en) * | 2018-11-27 | 2019-02-12 | 中南大学 | Ore-body modeling method, apparatus, system and storage medium based on borehole data |
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| CN109326002A (en) * | 2018-11-27 | 2019-02-12 | 中南大学 | Ore-body modeling method, apparatus, system and storage medium based on borehole data |
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