Method and system for dynamic motion simulation of a flight chain of a flight chain conveyor based on three-dimensional modeling

Abstract

The application discloses a dynamic motion simulation method and system of a scraper chain based on three-dimensional modeling, relates to the technical field of digital simulation of industrial equipment, and comprises the following steps: obtaining a three-dimensional model of a basic component of a scraper, reading vertex coordinates of an auxiliary motion trajectory model, and generating a motion trajectory curve; performing arc length parameterization processing on the motion trajectory curve, and generating a uniform motion control parameter; generating a chain instance according to a preset chain repetition number, binding the chain instance with the motion trajectory curve; receiving a chain motion state parameter, driving the chain instance to displace along the motion trajectory curve according to the uniform motion control parameter, and dynamically adjusting a motion simulation effect. Compared with a traditional implementation mode, the method effectively solves a performance bottleneck problem, reduces resource consumption, has certain guiding significance for equipment fault diagnosis and predictive maintenance, provides a practical mode for three-dimensional simulation of industrial equipment, and is a key technology for realizing digitalization and intelligentization transformation in the industrial field.

Description

Technical Field

[0001] This invention relates to the field of digital simulation technology for industrial equipment, specifically to a method and system for simulating the dynamic motion of scraper conveyor chains based on 3D modeling. Background Technology

[0002] Scraper conveyors are critical transportation equipment in fully mechanized coal mining faces. The smoothness and reliability of their chain operation directly affect equipment lifespan and production efficiency, making real-time 3D motion simulation extremely important. The simulation system can acquire and drive the 3D model in real time, accurately reflecting key kinematic and dynamic parameters such as the scraper conveyor's operating speed, the meshing state of the chain and sprockets, and the scraper's vibration. This allows operators and engineers to intuitively and dynamically observe the equipment's internal operating status. Furthermore, real-time 3D motion simulation is a core component in building a digital twin of the scraper conveyor. It deeply binds real-time data from the physical world with the virtual model, providing a visual foundation and analytical basis for data-driven equipment health assessments, predictive maintenance, remote expert diagnosis, and decision support, thus providing technical assurance for safe and efficient coal mine production.

[0003] Currently, 3D simulation technology for industrial equipment faces several pain points and limitations. Firstly, it relies heavily on static models with insufficient dynamic interaction. Traditional industrial models often remain static displays, lacking physical-level dynamic simulation of moving parts and failing to reflect the true operating state of the equipment. Secondly, it suffers from performance bottlenecks with large-scale moving objects. For example, a scraper conveyor chain contains hundreds to thousands of links, and traditional animation or skeletal systems based on a single mesh suffer from rendering stuttering due to the surge in vertex count. Thirdly, it suffers from poor real-time data-driven performance. Most simulation systems rely on preset animations or offline data, making it impossible to integrate with real-time IoT data, resulting in a disconnect between simulation and reality. Fourthly, it suffers from rigid path adaptation. Changes to the equipment model structure necessitate the re-creation of simulation effects, making path updates and adaptations costly. Summary of the Invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the technical problem solved by this invention is that existing 3D simulation methods for industrial equipment have insufficient dynamic interaction, are difficult to reflect the actual operating status of the equipment, suffer from rendering lag, and how to implement simulated 3D animation based on WebGL.

[0006] To address the aforementioned technical problems, this invention provides the following technical solution: a method for simulating the dynamic motion of a scraper conveyor chain based on 3D modeling, comprising: acquiring a 3D model of the basic components of the scraper conveyor and reading the vertex coordinates of the auxiliary motion trajectory model to generate a motion trajectory curve; performing arc length parameterization processing on the motion trajectory curve to generate uniform motion control parameters; generating chain instances according to a preset chain repetition number and binding the chain instances to the motion trajectory curve; receiving chain motion state parameters and driving the chain instances to displace along the motion trajectory curve according to the uniform motion control parameters, thereby dynamically adjusting the motion simulation effect.

[0007] As a preferred embodiment of the dynamic motion simulation method for scraper conveyor chains based on 3D modeling described in this invention, the following steps are taken: the 3D model of the basic components of the scraper conveyor is modified for simulation adaptation; the scraper chain segment components are used to replace the entire chain component model drawn from the physical equipment; based on the transmission gears and limiting devices of the scraper chain, the auxiliary motion trajectory model of the scraper chain is drawn in combination with the model scale; and a custom attribute of the number of component repetitions is added to the scraper chain segment components to form a chain model.

[0008] As a preferred embodiment of the dynamic motion simulation method for scraper conveyor chain based on three-dimensional modeling described in this invention, generating the motion trajectory curve includes: reading the properties of the geometry of the auxiliary motion trajectory model, obtaining the coordinates of discrete vertices, performing interpolation calculations based on the coordinates of adjacent control points, and generating a smooth three-dimensional spline curve with continuous first derivative at the control points.

[0009] As a preferred embodiment of the dynamic motion simulation method for scraper conveyor chains based on three-dimensional modeling described in this invention, the smooth three-dimensional spline curve includes a spline curve algorithm that relies on interpolation. The curve passes through control points, ensuring the continuity of the first derivative at the control points. The position of each curve segment is represented as follows: , in, For the position of each curve segment, These are interpolation parameters. These are adjacent control points.

[0010] As a preferred embodiment of the dynamic motion simulation method for scraper conveyor chains based on three-dimensional modeling described in this invention, the arc length parameterization processing of the motion trajectory curve includes: performing an integral operation on the motion trajectory curve to output the total arc length, establishing a mapping function between the curve parameters and the actual physical length; solving the inverse function of the mapping function based on the mapping function, and calculating the curve parameters corresponding to the arc length according to the time step.

[0011] As a preferred embodiment of the dynamic motion simulation method for scraper conveyor chains based on three-dimensional modeling described in this invention, generating a chain instance includes: creating an object based on instantiated mesh rendering technology; configuring the geometric data of individual chain segment components to be shared with the chain instance; binding the chain instance with a motion trajectory curve; and injecting the motion trajectory curve after arc length parameterization into the object as an animation driving path through an interface.

[0012] As a preferred embodiment of the dynamic motion simulation method for scraper conveyor chains based on three-dimensional modeling described in this invention, the method for driving chain instances to move along the motion trajectory curve includes: in each frame callback of the rendering pipeline, associating a normalized progress parameter with each chain instance; and by updating the progress parameter values ​​of each instance and executing the transformation matrix, enabling the chain instances to synchronously perform displacement transformations along the curve.

[0013] As a preferred embodiment of the dynamic motion simulation method for scraper conveyor chain based on three-dimensional modeling described in this invention, the displacement of the driving chain instance along the motion trajectory curve further includes dynamically adjusting the response frequency of the parameter mapping according to the numerical range of the chain motion state parameters, performing slow fine adjustment within the preset rated working condition range, and performing fast response adjustment when exceeding the preset range or in the dynamic transition phase.

[0014] As a preferred embodiment of the dynamic motion simulation method for scraper conveyor chains based on three-dimensional modeling described in this invention, the dynamic adjustment of the motion simulation effect includes: accessing the chain parameters provided by the Internet of Things (IoT) data collector in real time through a data interface, mapping them to simulation animation control parameters, and adjusting the motion simulation effect. The parameter adjustment speed depends on the sensitivity range, with slow and fine adjustment within the rated operating range, and rapid adjustment when the range is exceeded or during the dynamic transition phase.

[0015] Another objective of this invention is to provide a dynamic motion simulation system for scraper conveyor chains based on 3D modeling. This system can adapt and modify the 3D models of basic components for simulation, adding model parameters and components required for simulation animation, thus solving the problem of insufficient dynamic interaction in current 3D simulation technology for industrial equipment.

[0016] As a preferred embodiment of the scraper conveyor chain dynamic motion simulation system based on 3D modeling described in this invention, it includes: a static 3D modeling module, a simulation adaptation module, a simulation animation module, and an optimization and linkage module; the static 3D modeling module is used to perform 3D modeling of the basic components of the scraper conveyor to form a static 3D model of the scraper conveyor equipment; the simulation adaptation module is used to modify the static 3D model for simulation adaptation, adding model parameters and model components required for simulation animation, using chain segment components to replace the entire chain model, and drawing a chain auxiliary motion trajectory model; the simulation animation module is used to realize the simulation animation effect, read predefined chain auxiliary motion trajectory model information, generate a smooth 3D spline curve, bind the smooth 3D spline curve with the animation class, and return complete chain model data; the optimization and linkage module is used to render large-scale chain transmission through instantiation rendering of the animation class, and synchronize simulation parameters with real-time IoT data through a digital twin modeling system.

[0017] The beneficial effects of this invention are: The scraper conveyor chain dynamic motion simulation method based on 3D modeling provided by this invention is a new chain conveying animation method implemented based on ThreeJS front-end technology. It has certain universality for similar fluid flow and trajectory motion animations, shielding most of the impact of equipment model differences and changes on the animation. On the other hand, compared with traditional implementation methods, it effectively solves the performance bottleneck problem and reduces resource consumption. It is a 3D simulation practice based on a digital twin system. 3D dynamic simulation is no longer a simple case demonstration. The digital twin system simulation driven by the Internet of Things has achieved a qualitative change from demonstration simulation to monitoring simulation, realizing real-time state mapping and reflecting the physical equipment state on the 3D model in real time. It has certain guiding significance for equipment fault diagnosis and predictive maintenance. This invention has achieved better results in dynamic simulation, large-scale rendering performance, and equipment data synchronization. Attached Figure Description

[0018] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is an overall flowchart of a dynamic motion simulation method for a scraper conveyor chain based on three-dimensional modeling, provided in Embodiment 1 of the present invention.

[0020] Figure 2 This is an overall schematic diagram of a dynamic motion simulation method for a scraper conveyor chain based on three-dimensional modeling, provided in Embodiment 2 of the present invention. Detailed Implementation

[0021] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0022] Example 1, referring to Figure 1 As an embodiment of the present invention, a method for simulating the dynamic motion of a scraper conveyor chain based on three-dimensional modeling is provided, comprising: Specifically, 3D modeling software was first used to create a detailed 3D model of the scraper conveyor's basic components, constructing a static 3D model that includes core components such as the sprocket assembly, central groove, and motor reducer. During the modeling process, the structure of the physical drawings was analyzed, and the complex equipment was disassembled into parts and models. Geometric features were restored using techniques such as stretching, rotation, and Boolean operations. The relative positions of each component were defined according to the actual assembly constraints to ensure that the model was structurally highly consistent with the physical entity. To meet the needs of subsequent simulations, the static 3D model was adapted. Before exporting to the lightweight glTF format, key simulation parameters, such as the dynamic response threshold of the material, collision body level, and joint limits, were pre-embedded. Notably, the entire solid chain from the physical model was not directly used; instead, an auxiliary motion trajectory model matching the sprocket pitch circle and guide rail path was drawn. This trajectory model contains precise vertex coordinate data, which can eliminate the strong dependence on specific mesh topology, thereby enabling the programmatic definition of the chain motion path. This allows it to automatically adapt to scraper conveyor structures with different layouts, laying a data foundation for subsequent generalized dynamic simulation.

[0023] Furthermore, 3D modeling of the basic components of the scraper conveyor is performed, mainly including components such as the sprocket assembly, the central trough, and the motor reducer, forming a static 3D model of the scraper conveyor equipment. During the construction of the static 3D model, design drawings (including dimensional parameters and structural details), technical manuals, and physical photographs of each component (sprocket assembly, central trough, motor reducer, etc.) are collected. Component disassembly and structural analysis: The scraper conveyor is disassembled into basic components, and the constituent parts of each component are refined (e.g., the sprocket assembly includes sprockets, shafts, bearings, and end covers; the central trough includes the trough body, connecting lugs, and reinforcing ribs; the motor reducer includes the motor body, reducer housing, input / output shafts, gears, etc.), clarifying the components. Assembly relationships and key structural features between components; 3D modeling of individual components: Sprocket assembly: Draw a sprocket sketch based on the drawing (including tooth profile, hub, keyway), generate the sprocket body through "Extrude", "Revolve", and "Sweep", and model the shaft, bearing, end cap, and other parts in sequence, adding details such as chamfers and fillets; Central groove: Draw a groove cross-section sketch (U-shaped or rectangular), extrude to generate the groove body, model the connecting ears on both sides (including bolt holes) and bottom reinforcing ribs, ensuring that the groove length, width, and connection dimensions are consistent with the drawing; Motor reducer: Draw a sketch of the motor housing and reducer box, generate the shell structure through "Extrude" and "Cut", and model the input / output shaft ( (Including shaft shoulders and keyways), call standard gear parts (or model gears according to parameters), assemble the connecting flange of the motor and reducer; Component-level assembly: In the assembly environment, import the part models of each component, add assembly relationships according to design constraints (such as "coaxial" between the sprocket and shaft, "interference fit" between the bearing outer ring and end cover, and "coincident" between the connecting lugs of the central slot), complete the assembly of the sprocket assembly, central slot, motor and reducer, and check for interference between parts; Overall assembly and constraints: Assemble the assembled sprocket assembly, central slot, motor and reducer, etc., according to the actual layout of the scraper conveyor, and define the relative positions of the components through constraints such as "distance", "parallel", and "coincident". Position (e.g., the output shaft of the motor reducer and the input shaft of the sprocket set are "coaxial" through a coupling, and the central groove is "tangentially" matched with the teeth of the sprocket set); Model inspection and optimization: Use the software interference inspection function to verify whether there are structural conflicts in the overall structure and internal parts of the components, check the consistency of each dimension parameter with the drawings, simplify non-critical details (such as small chamfers, non-exposed bolt holes) to reduce the model volume, and retain the core structural features; Materials and rendering: Assign realistic materials to the model (e.g., use "45 steel" for the sprocket set and "Q345" for the central groove), add simple rendering (such as background and lighting) to enhance the static display effect, and finally save it in the universal format glTF.

[0024] By using 3D modeling software, based on the design drawings of scraper conveyor components, the system can disassemble components, model parts, and define assembly constraints. This solves the problem of accurate modeling of complex equipment structures and definition of assembly relationships, enabling the generation of high-precision static 3D models for visualization and simulation analysis. This improves design efficiency, reduces prototype costs, and promotes equipment maintenance and standardization.

[0025] It should be noted that the simulation adaptation of the basic component 3D model involves replacing the entire chain component model drawn from the physical equipment with a scraper chain segment component. Based on the transmission gears and limiting devices of the scraper chain, a chain auxiliary motion trajectory model is drawn in combination with the model scale. A custom attribute for the number of component repetitions is added to the scraper chain segment component to form the chain model. The required model parameters include: appearance and status parameters: color value (RGB), transparency, visibility flag, material dynamic parameters (reflectivity / roughness), texture switching index; interaction and trigger parameters: trigger threshold (such as pressure / temperature threshold), trigger source identifier, motion status flag (stationary / moving / faulty); size and position parameters: extension length range, diameter change, relative position offset (relative to the parent component); collision detection parameters: collision body size (collision box / sphere radius), collision level, collision response type (elastic / inelastic); constraint joint parameters: joint axis direction vector, joint limit (angle / stroke limit), connection stiffness, damping coefficient.

[0026] The motion trajectory curve is drawn based on the transmission gears and limiting devices of the scraper conveyor chain. A chain-assisted motion trajectory model required for simulation is drawn in conjunction with the model scale. The drawing process is as follows: 1. Establish a coordinate system: Define a fixed coordinate system, set the coordinates of key components (sprocket center, guide rail starting point), and use the rotation angle θ(t) of the drive wheel as the motion parameter.

[0027] 2. Stage-by-stage motion analysis: Analyze the trajectory according to the chain motion area (meshing segment, straight segment, transition segment): the trajectory of the meshing segment is the pitch circle of the sprocket; the trajectory of the straight segment is the parallel line connecting the centers of the sprockets; the trajectory of the transition segment is the connecting arc / involute.

[0028] 3. Derive the trajectory equation: Establish coordinate equations for key points (pin center / arbitrary point on the chain link). For example, coordinates of the pin center in the meshing section: ( As the center of the active wheel, (where is the pitch circle radius); the straight line segment trajectory is the straight line from the point of disengagement to the point of engagement of the two sprocket pitch circles.

[0029] 4. Calculate trajectory data: by time / turning angle step. The coordinates of key points at each time step are calculated based on the trajectory equation to obtain a discrete trajectory point set.

[0030] A custom attribute for the number of repetitions of components is added to the scraper chain segments, serving as input parameters for simulation animation to form a complete chain model. A glTF format file is exported using 3D modeling software. This format is designed for efficient transmission and loading of 3D content and contains preprocessed geometric data (vertex / normal / UV), PBR material data (metallicity / roughness), and scene hierarchy relationships, providing lightweight and efficient model data support for the WebGL rendering pipeline. Uniform motion control parameters refer to the mapping parameters obtained after parameterizing the arc length of the motion trajectory curve, used to ensure that the linear velocity of the chain instance remains constant as it moves along the curve. Through parametric design, motion trajectory modeling, and custom repetition attributes, the problems of high computational load and unrealistic motion in the simulation of the entire chain entity model are solved. This achieves lightweight, parametric dynamic simulation of the scraper chain, resulting in efficient and accurate operation of the virtual prototype, reducing simulation computational resource consumption and improving the realism of motion simulation.

[0031] It should be noted that after the static 3D model is prepared, the core simulation animation effects are implemented using ThreeJS. ThreeJS is a JavaScript library for creating and rendering interactive 3D graphics in a browser. The exported model file is loaded by the GLTFLoader loader, the scene is rendered, cameras and lights are added, and environment maps are used to optimize the model rendering effect. The bounding box size of the model is output, and the model position in the scene is adjusted based on the x, y, and z vector coordinates of the bounding box to make it centered in the scene, resulting in the basic model rendering. Predefined motion trajectory model data is read, and the attributes.position.array property of the auxiliary motion trajectory model geometry is used to iterate through the array and output the 3D coordinate data of the motion auxiliary lines. A Vector3 vector object is created, and a Vector3 vector array is output to generate a smooth 3D curve. The generation of the smooth 3D curve relies on the Catmull-Rom spline algorithm, i.e., the interpolation spline curve algorithm. The curve passes through control points to ensure the continuity of the first derivative at the control points. The position of each curve segment is represented as: , in, For the position of each curve segment, These are interpolation parameters. The two control points are adjacent, where 0.5 represents the tightness of the curve, with 0 being the most relaxed and 1 being the most tight.

[0032] Assume the coordinates of the four adjacent control points obtained by reading the auxiliary model are as follows: and When interpolation parameters When the value is 0.5, matrix operations yield a smoothed coordinate value. Through data quantification, it is shown that the system can transform discrete auxiliary line vertices into three-dimensional spline curves with continuous first derivatives. In actual simulation applications, when the scraper conveyor chain instance runs along the track to a corner or connection, its motion vector direction changes continuously and gradually, effectively avoiding visually jerky changes in the chain during movement, significantly improving the fidelity of the simulation, and ensuring that the simulation effect is highly consistent with the physical entity's operation.

[0033] The process involves reading predefined chain-assisted motion trajectory model information from the model, focusing on the model's vertex data. The `attributes.position.array` property of the auxiliary motion trajectory model geometry is read; this is a 32-bit floating-point array where every three consecutive elements represent a vertex coordinate. The array is traversed to form a 3D coordinate data set for the motion auxiliary lines. A Vector3 vector object is created. Custom model offsets, rotations, and scaling exist in the scene but do not affect the original geometry data. These factors must be considered when calculating the Vector3 vector array, and the vector data must be corrected accordingly. A smooth 3D spline curve is generated from the calculated Vector3 vector array, relying on the Catmull-Rom spline algorithm. It is an interpolation-type spline curve algorithm. Its core feature is that the curve strictly passes through all control points, while ensuring the continuity of the first derivative of the curve at the control points (smooth and without sharp corners). Each curve segment is determined only by four adjacent control points. It traverses the Vector3 vector array and performs polynomial calculations on the x, y, and z components respectively. Finally, it achieves a smooth curve through local polynomial interpolation, which is converted into motion trajectory data input parameters required for ThreeJS simulation animation. The basic model is loaded through ThreeJS and the trajectory geometry data is read. Combined with the Catmull-Rom spline algorithm, interpolation calculations are performed on discrete vertices to solve the problem of directly generating smooth and continuous motion trajectories from the model. This achieves accurate generation of high-fidelity motion curves, drives the components to move smoothly along the predetermined trajectory, and improves the realism and accuracy of the simulation animation.

[0034] It should also be noted that, for a single scraper component used in the chain simulation animation S, the geometric data is processed to eliminate the influence of scene scaling and displacement on the geometric data. The preset scraper component rendering loop count data is read from the model, and the model data is integrated with the input parameters required for the animation to create the InstancedFlow animation class. Its updateCurve method is called to bind the resulting smooth motion trajectory curve to the animation class. The return value is the complete chain model data. The core of binding the smooth motion trajectory curve to the animation class is to associate the time dimension with the curve parameter dimension through mathematical mapping, allowing the animated object to move along the curve over time.

[0035] Specifically, define the parametric equations for the smooth curve: using parametric equations Describe the trajectory, Curve parameters (usually) Establish time-parameter mapping: map animation time... ( , Start time, End time) mapped to curve parameters ,get Real-time position calculation: For animations, each frame is calculated based on the current time. calculate Substitute Once the position coordinates are obtained, the object's movement is driven.

[0036] The parametric equation for a smooth curve, taking a cubic Bézier curve as an example, is expressed as follows: , in, For the curve in parameters The coordinates of the point at that location, For curve parameters (normalized, range [0,1], corresponding to the curve start point to end point). These are the four control points of the Bézier curve.

[0037] The tangent vector of the curve is represented as: , in, For the curve in parameters The tangent vector at that point, For parameters The derivative, For curve parameters (normalized, range [0,1], corresponding to the curve start point to end point). These are the four control points of the Bézier curve.

[0038] The arc length is parameterized as follows: , in, For the curve from arrive The actual arc length at that point, The tangent vector, For the integration variable, and Distinguish between integration intervals .

[0039] The time-parameter mapping is represented as: , in, For time-parameter mapping, For time, The total arc length of the curve. It is the inverse function of the arc length function. The velocity is the constant velocity.

[0040] In actual simulation calculations, due to the parameters of the parametric curves in 3D modeling... The change is not equivalent to a proportional increase in the spatial arc length; the relationship is... The actuation causes the object to decelerate at curves and accelerate at straight sections. By parameterizing the arc length, we first perform numerical integration over the entire trajectory, assuming we obtain the total arc length of the curve. The preset speed of the scraper conveyor is 10 units / second, while the actual speed is 120 units / second. When the simulation reaches the 6th second (i.e....) When the chain instance reaches the geometric endpoint of the curve, the system uses an inverse function to solve for the precise parameters in the complex parameter space. By introducing an arc length function, i.e., an inverse function mapping, the influence of curve geometry on motion speed can be shielded, ensuring that when the chain instance moves along a complex trajectory, the physical displacement and real time always maintain a linear and constant proportional relationship. After specific data calculation, it is shown that the invention not only enhances the smoothness of simulation time, but also ensures that the device operation rhythm in the virtual simulation environment can be completely synchronized with the collected real-time data, providing accurate algorithm support for subsequent processing.

[0041] Furthermore, the animation class uses a chain of "time-arc length-parameter-position" to associate the smooth curve with time, ultimately outputting the position data that drives the object's movement.

[0042] The InstancedFlow animation class achieves high efficiency through instantiated rendering, GPU-accelerated computation, and a data-driven architecture. First, it replaces the independent mesh objects in the traditional implementation with InstancedMesh. All links are rendered via a single GPU call, and links share geometry and materials, avoiding redundant storage. Using typed arrays instead of object arrays further optimizes memory usage. Second, for matrix updates, the setMatrixAt method updates the matrices of all instances at once, reducing communication between the CPU and GPU. Matrix transformations are pre-calculated on the CPU and uploaded in batches, avoiding repeated calculations of each link's position frame by frame. An incremental progress update method is used, updating in groups frame by frame, updating only a portion of instances per frame. For complex chain physics (such as bending and collision), the InstancedFlow class uses compute shaders for parallel computation, leveraging the GPU's parallel cores. The InstancedFlow animation class employs Verlet integrals or target orientation algorithms to simulate the physical chain, directly updating the instanceMatrix with the physics calculation results. This synchronizes animation with physics and avoids CPU thread blocking. For intelligent rendering optimization, only links within the camera's field of view are rendered. Bounding box trees (BVH) accelerate frustum culling, reducing unnecessary calculations. Furthermore, the geometry precision is dynamically adjusted based on the distance between the link and the camera; lower-precision models are used for distant links to improve rendering efficiency. In terms of material and texture optimization, material complexity is simplified by using basic materials (such as MeshBasicMaterial) or customized simplified PBR materials to reduce shader computation. The instanceColor property sets an independent color for each link, while sharing the same texture atlas to avoid redundant loading. Through this combination of technologies, the InstancedFlow animation class significantly improves animation efficiency and reduces resource consumption.

[0043] The resulting smooth motion trajectory curve is bound to the animation class, and the return value is the complete chain model data. The `moveIndividualAlongCurve` method is called to control the movement of a single instance along a predefined curve path. The input parameters are the index of the target instance and the progress parameter along the curve. In this way, the chain model is assembled. Unlike the pre-set complete chain model, each chain instance is controlled individually by a transformation matrix. It is a high-level encapsulation based on InstancedMesh instantiated mesh, which can significantly reduce the number of GPU calls when implementing large-scale dynamic model object flow. Then, its `moveIndividualAlongCurve` method is called to control the movement of a single instance along a predefined curve path. The input parameters are the index of the target instance and the progress parameter along the curve. In this way, the chain model is assembled, and the scene renders a chain visual effect consistent with the static complete chain model.

[0044] It should be noted that dynamically adjusting the dynamic motion simulation effect includes: real-time access to the chain parameters provided by the IoT data collector via a data interface, mapping them to simulation animation control parameters, and adjusting the motion simulation effect. The parameter adjustment speed depends on the sensitivity range; slow and fine adjustments are made within the rated operating range, while rapid adjustments are made outside the range or during dynamic transition phases to achieve the dynamic motion animation effect of the chain. Based on the requestAnimationFrame animation rendering API provided by the browser, the chain parameters are updated in the callback after each frame is rendered, calling the moveAlongCurve method of the animation class instance. Each chain instance is associated with a progress parameter. This indicates the position on the curve, achieved by updating all instances. The chain's movement is synchronized by a parameter whose positive or negative value indicates the direction of movement: a positive value represents clockwise movement, and a negative value represents counterclockwise movement. Furthermore, the magnitude of the value determines the chain's speed; a larger value results in a faster speed, and a smaller value in a slower speed. A data interface is reserved to define the chain's motion state, allowing access to real-time chain parameter data from an IoT data collector. This dynamically adjusts the simulation effect of the scraper conveyor chain's movement. The chain parameters, including drive wheel speed, tension, load, and friction coefficient, are accessed in real-time through the data interface. The adjustment speed depends on the sensitivity range: slow, fine adjustments are made within the rated operating range (speed ±5%, tension ±10%, load ±15%) to ensure simulation accuracy; rapid adjustments are made outside this range or during dynamic transition phases (starting, braking, sudden load application) to simulate real-time response. This enhances the realism and real-time feedback capability of the chain motion simulation in the industrial monitoring system, supporting equipment condition diagnosis and predictive maintenance.

[0045] It should also be noted that, using a 3D configuration program, based on digital twin technology, abstract 3D model components are associated with physical equipment, and real-time chain parameter cards are added to display IoT measurement point parameter information in real time. The start and stop information fed back by the measurement points controls the start and stop of the animation. The chain speed measurement point information is mapped to the chain speed in the simulation animation. The larger the measurement point value, the faster the chain moves, and vice versa. Thus, the dynamic motion simulation effect of the scraper conveyor chain is completed.

[0046] The digital twin modeling system addresses the issue of synchronizing simulation and real-time data. Starting from the data acquisition layer, a wide variety of sensors are deployed at the bottom layer, including temperature sensors, pressure sensors, and vibration sensors. These sensors continuously collect key physical parameters such as temperature, pressure, and vibration frequency during equipment operation at millisecond or even microsecond frequencies. The high-frequency signal acquisition unit shields the differences between sensors in terms of communication protocols, data formats, and sampling frequencies, and performs parsing, filtering, and normalization on the raw data, transforming it into a unified data format to ensure data consistency and availability.

[0047] At the data storage level, standardized data requires a reliable storage solution. Time-series databases are indexed by time and have high-efficiency time-series data read and write performance, which can meet the storage needs of massive sensor data. When data is written to the time-series database, it carries a precise timestamp to record the specific time when the data was generated. At the same time, the database adopts a partitioned storage strategy, dividing the data according to the time range to improve data query efficiency.

[0048] At the level of digital twin modeling, the abstract modeling of physical equipment is a key step in digital twin creation. By conducting in-depth analysis of the structure, function, and operating logic of the physical equipment, a corresponding virtual model is constructed. During the modeling process, various monitoring parameters are clearly associated and used as input variables for the virtual model. Through a 3D configuration program, the virtual model is precisely bound to the modeling equipment. During the binding process, a mapping relationship is established between the virtual model and the parameter data in the underlying time series database, enabling the virtual model to access the real-time parameter data at the underlying level and achieving data interaction between the virtual and physical worlds.

[0049] In the real-time synchronization layer, the business program actively queries the latest sensor data from the time-series database at fixed time intervals using a timer polling method. After obtaining the latest data, the business program drives the setting and execution of simulation parameters and updates the state of the 3D simulation model. Thus, a bridge connecting the physical world and virtual space is built through digital twin technology, realizing the dynamic simulation effect of the scraper conveyor chain. Based on real-time data-driven, the InstancedFlow animation class combined with time-parameter mapping algorithm and instantiation rendering technology solves the performance bottleneck of real-time motion simulation of large-scale chain models, realizing dynamic motion synchronization based on IoT data. This achieves the effect of smooth display and real-time response to changes in equipment operating conditions in the web environment, providing a visual interface for equipment status monitoring and predictive maintenance.

[0050] Example 2, refer to Figure 2 As an embodiment of the present invention, a dynamic motion simulation system for scraper conveyor chains based on three-dimensional modeling is provided, including a static three-dimensional modeling module 100, a simulation adaptation module 200, a simulation animation module 300, and an optimization and linkage module 400.

[0051] The static 3D modeling module 100 is used to perform 3D modeling of the basic components of the scraper conveyor, forming a static 3D model of the scraper conveyor.

[0052] The simulation adaptation module 200 is used to perform simulation adaptation and transformation on the static 3D model, add model parameters and model components required for simulation animation, replace the whole chain model with chain segment components, and draw the chain-assisted motion trajectory model.

[0053] Among them, the simulation animation module 300 is used to realize simulation animation effects through ThreeJS, read the predefined chain-assisted motion trajectory model information, generate a smooth three-dimensional spline curve, bind the smooth curve to the animation class, and return the complete chain model data.

[0054] Among them, the optimization and linkage module 400 is used to render large-scale chain transmission through the InstancedFlow animation class using instantiated rendering, and to synchronize simulation parameters with real-time IoT data through the digital twin modeling system.

[0055] This embodiment also provides a computer device, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements the dynamic motion simulation method of scraper conveyor chain based on three-dimensional modeling as proposed in the above embodiment.

[0056] This embodiment also provides a computer-readable storage medium storing a computer program thereon. When the computer program is executed by a processor, it implements the dynamic motion simulation method for scraper conveyor chains based on three-dimensional modeling as proposed in the above embodiment.

[0057] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0058] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-including system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0059] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0060] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0061] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for simulating the dynamic motion of a scraper conveyor chain based on three-dimensional modeling, characterized in that, include: Obtain the 3D model of the basic components of the scraper conveyor, and read the vertex coordinates of the auxiliary motion trajectory model to generate the motion trajectory curve; The arc length parameterization process is performed on the motion trajectory curve to generate uniform motion control parameters; Generate chain instances based on a preset chain repetition count, and bind the chain instances to the motion trajectory curve; Receive chain motion state parameters, drive the chain instance to move along the motion trajectory curve according to uniform motion control parameters, and dynamically adjust the motion simulation effect; The 3D model of the basic components of the scraper conveyor is modified for simulation adaptation. The scraper chain segment components are used to replace the whole chain component model drawn from the physical equipment. Based on the transmission gears and limiting devices of the scraper chain, the auxiliary motion trajectory model of the scraper chain is drawn in combination with the model scale. Custom attributes such as the number of component repetitions are added to the scraper chain segment components to form the chain model. The modified model is exported as a glTF file containing vertex, normal, UV, PBR material data and scene hierarchy through 3D modeling software, providing lightweight and efficient model data support for the WebGL rendering pipeline. The arc length parameterization processing of the motion trajectory curve includes... The total arc length is output by performing an integral operation on the motion trajectory curve, and a mapping function between the curve parameters and the actual physical length is established. The inverse function of the mapping function is obtained by solving the mapping function, and the curve parameters corresponding to the arc length are calculated according to the time step. Define the parametric equations for a smooth curve: using parametric equations Describe the trajectory; establish a time-parameter mapping: map animation time... Mapping to curve parameters ,get ,in, , The start time, End time; Real-time position calculation: Each frame of the animation is calculated based on the current time. calculate Substitute Obtain the position coordinates and drive the object's movement; A cubic Bézier curve is used as the smoothing curve, and the parametric equation of the cubic Bézier curve is: ， in, For the curve in parameters The coordinates of the point at that location, For curve parameters, the normalized range is [0,1], corresponding to the curve's start and end points; These are the four control points for the cubic Bézier curve; The tangent vector of the curve is represented as: ， in, For the curve in parameters The tangent vector at that point, For parameters The derivative; The arc length is parameterized as follows: ， in, For the curve from arrive The actual arc length at that point, The tangent vector, For the integration variable, and Distinguish between integration intervals ; The time-parameter mapping is represented as: ， in, For time-parameter mapping, For time, The total arc length of the curve. It is the inverse function of the arc length function. The velocity is the speed of uniform motion; The displacement of the drive chain instance along the motion trajectory curve includes... In each frame callback of the rendering pipeline, a normalized progress parameter is associated with each chain instance; By updating the progress parameter values ​​of each instance and executing the transformation matrix, the chain instances synchronously perform displacement transformations along the curve. The response frequency of the parameter mapping is dynamically adjusted according to the numerical range of the chain motion state parameters, and slow fine adjustment is performed within the preset rated working condition range. Perform rapid response adjustments when the input exceeds the preset range or is in a dynamic transition phase.

2. The method for simulating the dynamic motion of a scraper conveyor chain based on three-dimensional modeling as described in claim 1, characterized in that: The generated motion trajectory curve includes, The properties of the geometry of the auxiliary motion trajectory model are read to obtain the coordinates of discrete vertices. Interpolation calculations are performed based on the coordinates of adjacent control points to generate a smooth three-dimensional spline curve with continuous first derivative at the control points.

3. The method for simulating the dynamic motion of a scraper conveyor chain based on three-dimensional modeling as described in claim 2, characterized in that: The smoothed three-dimensional spline curve includes, Using an interpolation-based spline curve algorithm, the curve passes through control points, ensuring the continuity of the first derivative at those control points. The position of each curve segment is represented as follows: ， in, For the position of each curve segment, These are interpolation parameters. These are adjacent control points.

4. The method for simulating the dynamic motion of a scraper conveyor chain based on three-dimensional modeling as described in claim 3, characterized in that: The generated chain instances include, Create objects based on instantiated mesh rendering technology, and configure the geometry data of individual chain segment components to be shared with the chain instance; The chain instance is bound to the motion trajectory curve, and the motion trajectory curve after arc length parameterization is injected into the object as the animation driving path through the interface.

5. The method for simulating the dynamic motion of a scraper conveyor chain based on three-dimensional modeling as described in claim 4, characterized in that: The dynamically adjusted motion simulation effect includes, The chain parameters provided by the IoT data collector are accessed in real time through the data interface and mapped to the simulation animation control parameters to adjust the motion simulation effect. The parameter adjustment speed depends on the sensitivity range. Within the rated operating range, the adjustment is slow and fine. When the range is exceeded or during the dynamic transition phase, the adjustment is rapid.

6. A dynamic motion simulation system for scraper conveyor chains based on three-dimensional modeling, employing the dynamic motion simulation method for scraper conveyor chains based on three-dimensional modeling as described in any one of claims 1 to 5, characterized in that: It includes a static 3D modeling module, a simulation adaptation module, a simulation animation module, and an optimization and linkage module; The static 3D modeling module is used to create 3D models of the basic components of the scraper conveyor, forming a static 3D model of the scraper conveyor equipment. The simulation adaptation module is used to perform simulation adaptation and modification on the static 3D model, add model parameters and model components required for simulation animation, use chain segment components to replace the whole chain model, and draw the chain auxiliary motion trajectory model. The simulation animation module is used to achieve simulation animation effects, read predefined chain-assisted motion trajectory model information, generate smooth three-dimensional spline curves, bind the smooth three-dimensional spline curves to the animation class, and return complete chain model data; The optimization and linkage module is used to render large-scale chain transmissions through instantiated rendering of animation classes, and to synchronize simulation parameters with real-time IoT data through a digital twin modeling system.

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