2202 results about "Collision detection" patented technology

The invention provides an industrial robot motion planning method based on a diffusion model. The method comprises the following steps: firstly, pre-training a point cloud encoder to compress and embed obstacle point cloud under task conditions into a potential space, and constructing a forward kinematics robot state coding algorithm and a path data collision detection module; then, task information is coded based on the pre-trained point cloud encoder, noise adding and noise reduction are carried out on task data based on a Transform diffusion model, and modeling is carried out on data probability distribution of a physical relationship between robot connecting rods; and finally, based on the trained neural network model, combining a small amount of motion feedback, generating an image from random Gaussian noise, and further improving the generation speed and the success rate. Finally, the neural network model can output an optimal path under a specific task condition.

The invention relates to the technical field of intelligent building data processing, and provides a BIM-based bridge collision detection method, which is used for solving the problem of collision early warning lag caused by space-time dislocation of a static model and real-time monitoring data in bridge construction. A comprehensive point cloud data set with a unified space-time reference is generated through multi-source data dynamic fusion and a quantum optimization algorithm, a self-evolutionary dynamic BIM model is constructed, and digital mapping of millimeter-level precision of a construction scene is achieved. A collision risk prediction model is constructed based on causal reasoning and multi-physics field coupling, a multi-dimensional relationship among construction parameters, environment variables and structural response is associated, and a linkage effect caused by mechanical motion is predicted. According to the method, the time dimension limitation of a traditional static model is broken through, full-process closed-loop control of perception-analysis-decision-verification is formed, the real-time performance of collision early warning and the controllability of construction safety are remarkably improved, and the method is suitable for high-precision construction scenes such as complex bridge rotation and cantilever casting.

The invention provides a construction site collision risk assessment method based on a digital twinborn scene, and relates to the technical field of building simulation and collision detection. According to the method, after collision risk evaluation factors are screened out by constructing a construction site digital twinborn scene, correlation analysis is carried out by using a weighted Heraonian average operator, and a determined weight is obtained; constructing a membership function of each collision risk evaluation factor, performing fuzzy reasoning, establishing a construction site collision risk evaluation mechanism, and outputting an evaluation result; dynamically generating and updating the working space of the digital twinborn scene according to the evaluation result, reconstructing the digital twinborn scene, and executing voxelization collision detection to obtain an accurate collision detection result; and according to an accurate collision detection result, in combination with the generated dynamic working space, calculating a collision time-space amount to obtain a final risk value. The method is high in collision detection speed and accurate, is applied to a dynamic complex environment of a construction site, and can achieve objective and accurate evaluation of space-time collision risks.

The invention discloses a two-arm robot cooperative motion planning method and system for multi-machine cooperative co-fusion, and the method comprises the steps: constructing a machine-machine-environment co-fusion collision information estimation model: carrying out the discretization of the whole reachable space of a two-arm robot, and obtaining the environment information through a depth camera; a gap estimation neural network model is adopted to obtain interaction information of the double-arm robot and the environment, meanwhile, interaction information of left and right mechanical arms is obtained, and synchronous collision detection between the robots and between the robot and the environment is achieved; on the basis of the machine-machine-environment co-fusion collision information estimation model, performing two-arm robot collaborative path planning to obtain an estimated feasible path; and based on the geometric collision checker, carrying out double-arm robot motion path safety check and path repair, and ensuring absolute safety of a planned path. The problems that an existing two-arm robot motion planning method is low in efficiency, and multi-robot collaborative operation is difficult to effectively achieve are solved.

The invention discloses an intelligent construction site safety monitoring method and system fused with a multi-modal large model, and relates to the technical field of building construction.The method comprises the steps that based on a Linux system platform, an Nginx server is used as a static website server to be combined with a camera to request video stream data, a client side and the Nginx server are connected, configuration is conducted according to a configuration file, and the Nginx server is connected with the client side; and sending response data according to an HTTP request of the client, and carrying out video online monitoring and image recognition real-time processing on the construction area. According to the invention, through detailed scene division and targeted safety guide formulation and in combination with a visual language model-based few-sample scene identification method, different scenes can be accurately identified, and it is ensured that monitoring meets actual requirements; through cooperation of the personnel article wearing detection module and the article attribute detection module, deep and comprehensive monitoring of personnel protection equipment and behaviors is realized; depth estimation and three-dimensional collision detection ensure full coverage of a monitoring scene.

The invention provides a vehicle surrounding obstacle detection method and system based on a multi-modal sensor, and relates to the technical field of intelligent driving, and the method comprises the steps: obtaining ultrasonic radar point clouds and millimeter wave radar point clouds at two sides of a vehicle, and image data at the rear side of the vehicle; the radar point clouds are clustered, and the spatial position, the movement speed and the reflection section characteristics of an obstacle are extracted; performing target detection on the image data, and outputting to obtain a bounding box and texture features of an obstacle; generating preliminary multi-modal data, inputting the multi-modal data into a collision detection model, respectively entering a radar branch network and a visual branch network to extract point cloud spatial-temporal features and image semantic features, and performing weighted fusion on the point cloud spatial-temporal features and the image semantic features through a Transform mechanism to obtain a collision detection result; outputting the three-dimensional position, the movement track, the type label and the collision risk probability of the obstacle; and when the collision risk probability exceeds a preset threshold value, triggering a corresponding early warning or control instruction.

The invention relates to a mechanical arm safe obstacle avoidance control method and system based on a high-order control obstacle function, aiming at a multi-degree-of-freedom mechanical arm system with an obstacle in a task trajectory space, the high-order control obstacle function suitable for a high-relative-order system is used, and the method comprises the following steps: establishing a dynamic model of a multi-degree-of-freedom mechanical arm; designing a stable tracking controller based on the task trajectory in combination with a backstepping control method; an obstacle collision detection model is established, and safety constraints are provided for a control system through a high-order control obstacle function; and in combination with the control constraint and the optimal control problem, solving the optimal control input through quadratic programming. According to the method, the optimal task trajectory tracking can be completed on the premise that the tail end of the mechanical arm meets the safety constraint, the calculation complexity is effectively reduced, and the real-time performance is guaranteed.

The invention relates to the technical field of radar detection, in particular to an intelligent collision detection method and system, and the method comprises the following steps: setting an initial emission angle of a radar, monitoring a reflection signal during the movement of an object, recording the return time, calculating a reflection distance according to the light velocity and the time, adjusting the emission angle, optimizing the coverage range, and obtaining angle correction data. According to the method, the position of the object can be more accurately determined by recording the return time of the reflection signal in real time and calculating the reflection distance by using the light velocity and the time difference, and potential collision risks can be more accurately predicted and identified by iteratively optimizing the scanning path, enhancing the adaptability to the dynamic environment and updating the target trajectory information. Intelligent judgment of risk levels and adjustment of emergency response measures are carried out, the response efficiency and accuracy of an early warning system are improved, the function of a collision detection system is enhanced, traffic safety is remarkably improved, and potential accident risks are reduced.

The invention discloses a BIM model structure design review method and system based on a large model, and relates to the technical field of building information modeling, and the method comprises the steps: carrying out BIM model data preprocessing based on an AI large model; adopting an AI large model built-in rule engine to perform structural design specification review on the BIM model, and automatically generating a review report based on a natural language processing technology; bIM model collision detection is executed based on a deep learning algorithm, potential structure conflicts are identified, error points are displayed in combination with three-dimensional visualization, and adjustment suggestions are provided according to historical data and an optimization algorithm. According to the method, the data quality and consistency are improved, key information in the BIM model is clearer and more concise, the accuracy of the review process is improved, potential problems in the structural design process can be found and processed in time, the overall intelligent level of the BIM model is improved, and the building design process is more accurate, intelligent and efficient.

The invention relates to the technical field of mechanical arm obstacle avoidance path planning, in particular to a multi-degree-of-freedom mechanical arm obstacle avoidance path planning method based on three-dimensional reconstruction, which comprises the following steps: step 1, three-dimensional environment perception and dynamic modeling; preferentially offsetting and expanding the near-obstacle nodes towards the concave area or the hole center to generate a candidate node set; and 4, three-dimensional grid collision verification and safe path correction are conducted, specifically, the working space of the mechanical arm is divided into three-dimensional voxel grids, and collision detection is achieved by judging whether path nodes fall into obstacle object elements or not. According to the method, the laser radar and the depth camera are adopted to synchronously collect data through hardware triggering, statistical filtering denoising and three-dimensional grid modeling are combined, geometrical characteristics of static obstacles and motion parameters of dynamic obstacles are restored, and the collision risk caused by environmental perception errors of the mechanical arm is effectively avoided.

The invention provides an agent path planning method based on improved RRT and related equipment, and the method comprises the steps: 1, obtaining a current position and a target position of an agent, and constructing a search tree; 2, in the expansion process, sampling is carried out based on a dynamic weight target bias sampling strategy; extending a distance of a dynamic step length from the extension starting point to the extension direction based on a dynamic step length adjustment strategy to obtain a new node, and performing kinematics constraint detection and collision detection on the new node; 3, obtaining an initial path through path backtracking; step 4, when a dynamic obstacle is detected, re-planning a path section conflicting with the dynamic obstacle in the initial path based on an incremental re-planning strategy to obtain a final path for indicating the intelligent agent to move; otherwise, taking the initial path as a final path. According to the invention, the accuracy of path planning can be improved.

The invention discloses an unmanned vehicle intelligent obstacle avoidance method and system based on a multi-modal sensor, and relates to the field of automatic driving and intelligent traffic. According to the technical key points of the invention, data of a laser radar and a depth camera are aligned through a space-time registration module; the laser radar point cloud is preprocessed, obstacle point cloud is segmented, fusion clustering is carried out in combination with three-dimensional semantic seed points output by a depth camera, and an obstacle cluster with a semantic tag is generated; and calculating a three-dimensional directed bounding box (OBB) of an obstacle, dynamically adjusting a safety distance according to the speed of the vehicle, expanding the OBB of the vehicle, and carrying out collision detection by adopting a separation axis theorem. When a collision risk is detected, the system sequentially triggers a first-level early warning instruction and a second-level emergency braking instruction, and safe and efficient unmanned vehicle dynamic obstacle avoidance is achieved. According to the method, the advantages of multiple sensors are fully utilized, the obstacle detection precision and the response speed are effectively improved, and reliable technical support is provided for application of the unmanned driving technology in a complex road environment.

Systems and methods are described for obtaining accurate responses from large language models (LLMs) and chatbots, including for question and answering, exposition, and summarization. These systems and methods accomplish these objectives via use of noun phrase avoiding processes such as a noun phrase collision detection process, a query splitting process, and a topical splitting process as well as by use of formatted facts, formatted fact model correction interfaces (FF MCIs), bounded-scope deterministic (BSD) neural networks, processes and methods, and intelligent storage and retrieval (ISAR) systems and methods. These systems and methods avoid and bypass noun phrase collisions and correct for errors caused by noun phrase collisions so that hallucinations are eliminated from LLM responses.

The invention discloses a BIM-based indoor decoration simulation display method and system, and relates to the technical field of indoor decoration, and the system automatically calculates an optimal furniture placement position through an algorithm by using modeling information in a BIM technology. The algorithm not only considers the distance between the furniture and collision detection, but also comprehensively considers the actual demand of the indoor space, so as to ensure the reasonable utilization and functionality of the indoor space. The intelligent layout algorithm can avoid a crowded or disordered layout condition, and ensures that the indoor space is full of vitality and not crowded by optimizing the placement position and the space utilization rate of furniture. The overall attractiveness and comfort of the space can be improved, and residents can live and work more pleasantly. The system simulates reflection, refraction and diffuse reflection processes of light on the surface of an indoor decoration object by using a light tracing technology. The system carries out detailed analysis on illumination conditions of different areas.

The invention discloses a multi-fingered dexterous hand grabbing posture generation method and system based on CVAE and a Ball Query algorithm, and belongs to the technical field of robot grabbing control. The method comprises the following steps: (1) a data sampling step; (2) a data preprocessing step: carrying out standardization processing on the collected data, generating enhanced point cloud data and constructing a training data set; (3) a model training step: through a multi-scale feature extraction module, combining global semantics and local geometric features extracted by a Ball Query algorithm, generating a grabbing attitude by using a conditional variation auto-encoder, and optimizing model parameters through reconstruction loss and KL divergence; and (4) real-time deployment: integrating the trained model to a physical platform, screening candidate grabbing postures based on parallel collision detection, and realizing real-time grabbing control in combination with inverse kinematics verification. The problem that a traditional method is insufficient in generalization ability in complex object grabbing is effectively solved, and self-adaptive grabbing of unknown objects is achieved while grabbing stability is guaranteed.

The invention discloses an anti-collision monitoring system based on a depth estimation and instance segmentation fused three-dimensional model, particularly relates to the field of intelligent driving security and protection, is used for solving the problem of target recognition and anti-collision in an environment, realizes efficient feature extraction of global semantics and local textures through a multi-branch network structure, and ensures the accuracy and comprehensiveness of target segmentation. In combination with a cross-frame identity association and conflict detection mechanism, the consistency of target identities is effectively maintained, and the mismatching problem caused by shielding or similar appearances is reduced; multi-scale space-time shielding mode analysis and boundary motion consistency verification are adopted, a fusion algorithm is utilized to comprehensively evaluate shielding complexity and segmentation robustness, a high-risk shielding area is identified, secondary difference correction is executed, and space positioning information of a target is remarkably optimized; and finally, integrating the optimized segmentation and depth data into a global coordinate system, constructing a high-precision and coherent three-dimensional semantic scene, and providing stable and high-quality input data for collision detection and trajectory prediction.

The invention discloses a mixed collision detection method in a virtual operation simulation system. The method comprises the following steps: 1) constructing a surgical instrument and soft tissue model; 2) dividing a virtual space into grids with the same size, indexing an object model, accelerating the construction and updating of a spatial information hash table by utilizing GPU multithreading, mapping the grids occupied by all primitive bounding boxes into the hash table, and determining objects in the same space according to the spatial hash table; 3) before object intersection solving, space reduction is carried out, and hash table data is optimized; 4) constructing a hierarchical bounding box for an object which may collide, and performing intersection detection; if it is detected that a certain node or child nodes of the node intersect, the next step is executed, and otherwise, detection is pushed out; and 5) accurately judging whether the basic triangular units contained in the nodes in the step 4) are intersected or not, and completing collision detection. According to the method, the problem of missed detection and wrong detection in the existing discrete collision detection algorithm is effectively solved, and the detection algorithm has excellent accuracy and calculation efficiency.

The invention discloses a logistics full-link risk control method and device based on an AI large model and a storage medium, and relates to the technical field of intelligent logistics, and the method comprises the steps: obtaining a logistics business risk control requirement inputted by a user through an interactive interface; analyzing logistics business risk control requirements, extracting logistics risk factors, and generating a structured risk control rule based on an industry knowledge base and business data in combination with a machine learning algorithm and a large model; identifying and solving rule conflicts of the structured risk control rule by using a collision detection algorithm; dynamically adjusting a rule threshold based on business volatility to obtain an optimized risk control rule; and pushing the risk control rule to a service supervision system based on the message queue or the API interface, and judging whether to trigger interception, alarm or transportation path correction operation based on a service node of the service system. The logistics risk control efficiency is improved, and the requirements of modern logistics for accuracy and real-time performance are met.

A three-dimensional encasement reinforcement learning method based on multi-modal sensing distillation comprises the steps that a high-fidelity physical simulation environment is constructed, strategy optimization is achieved through a teacher-student double-stage framework, a teacher network fuses vision, article attributes and internal physical state privilege information, and a high-stability encasement strategy is trained through hierarchical action masks and domain randomization; a student network only depends on visual input, teacher action distribution and value functions are aligned through KL divergence and L2 loss, and knowledge distillation is achieved; after deployment, a motion mask is generated in combination with lightweight collision detection. A failure scene can be played back to a simulation environment through a playback mechanism, and a student network is continuously optimized by using a teacher strategy. According to the method, the space utilization rate is remarkably improved in simulation and reality, the turnover rate is reduced, complex physical constraints can be adapted without expensive sensors, and an efficient and robust solution is provided for industrial automation.

The invention provides a middle and primary school multi-person interactive teaching method and system based on a VR technology, and relates to the technical field of virtual reality, and the method comprises the steps: generating a model through deep learning based on pre-obtained teaching aid model data, determining subcomponents, combining a teaching aid generation engine, and constructing a virtual teaching aid; a color image and a depth image are obtained through a structured light depth camera, two-dimensional pixel coordinates are obtained, depth information is combined, a three-dimensional skeleton curve of limb movement of a student is generated, an optical motion capture result is obtained, an inertial motion capture result is obtained through inertial drift calibration based on the three-dimensional skeleton curve, and a limb movement three-dimensional track of the student is reconstructed; generating a force feedback signal by solving a kinetic equation through a collision detection algorithm based on the three-dimensional trajectory of the limb movement of the trainee; and rendering the virtualization model into a virtual teaching scene, synchronizing the limb movement three-dimensional track and the force feedback signal in real time, and generating a multi-person collaborative interaction process of the virtual teaching aid.

The invention provides a machine grabbing learning method fusing shape features, contact modeling and physical constraints, and the machine grabbing learning method is a robot grabbing posture learning method capable of differentiating. According to the method, a three-dimensional point cloud serves as input, a deep neural network based on sparse voxel convolution is adopted to extract multi-scale shape features, and potential grabbing points and attitude parameters thereof are predicted. The differential contact modeling is realized by constructing a local area of direction perception and extracting geometric features reflecting the contact relationship between the mechanical claw and the object. Furthermore, the collision probability of the grabbing posture is estimated based on an implicit neural network, and a differentiable collision detection module is constructed. Meanwhile, physical constraints such as friction closing, surface alignment and geometric symmetry are introduced to serve as regular terms to jointly optimize grabbing scores, collision risks and stability; according to the method, end-to-end training is supported, efficient, robust and deployable grabbing posture estimation is achieved, and the method is suitable for various automatic grabbing tasks such as industrial robots and service robots.

The invention belongs to the field of data analysis and processing, and discloses a virtuality and reality combined automobile automatic driving test method. Comprising the following steps: connecting a sensor suite and an automatic driving system of an automobile with a virtual twin center by using a CAN bus or an Ethernet interface, operating the automatic driving system, and generating a performance report; the method comprises the following steps: initializing raindrop attributes, setting the transparency of raindrops based on real environment data, introducing a complex wind field model and a surface ponding dynamic evolution mechanism, carrying out collision detection on all raindrop pairs by using a space segmentation algorithm, and simulating dynamic behaviors of the raindrops in the air based on a discrete element method and a smoothed particle fluid dynamics method. Visual rendering is carried out on the water spray effect; under the influence of raindrop physical characteristic modeling, output of laser radar point cloud scattering modeling, camera image fuzzy modeling, millimeter wave radar signal attenuation and scattering modeling and ultrasonic sensor interference modeling is iteratively updated, and automatic driving testing of an automobile under virtual-real combination is achieved.

The invention belongs to the technical field of robot obstacle avoidance, and particularly relates to a double-arm collision detection and obstacle avoidance path planning method based on an improved artificial potential field method.The method comprises the steps that a three-dimensional model of double mechanical arms is obtained; the working space and environment obstacle information of the double mechanical arms is initialized; respectively calculating the repulsive force distance between each joint sphere and each connecting rod cylinder of the double mechanical arms and the obstacle; a mechanical arm performing path planning is defined as a main arm, the other mechanical arm is defined as a slave arm, positions of joints and connecting rod centers of the slave arm are calculated according to joint angles of the slave arm, a repulsive force field is set, meanwhile, the repulsive force field is set at a center point of an obstacle, and a gravitational field is set at a target point of the main arm; generating a track by using an improved artificial potential field method and updating the tail end position of the current main arm; and whether the tail end of the mechanical arm reaches the target point or not is judged, and if not, execution is repeated till the mechanical arm reaches the target point. According to the method, the calculation amount in a complex environment is reduced, and the planning efficiency and the safety of robot operation are improved.

The invention discloses a mobile robot path planning method and system based on a circular trajectory control obstacle function, and aims to realize trajectory smooth control in an obstacle avoidance process. Firstly, an envelope circle is used for modeling an obstacle, an actual boundary is approached in a continuous and simple geometric form, and the calculation complexity of collision detection is reduced. Secondly, designing a control obstacle function based on a circular trajectory, constructing a derivable constraint in combination with a geometrical relationship between a tangent line and an obstacle boundary, and achieving trajectory smoothing, reducing speed fluctuation and prolonging the service life of an execution mechanism while guaranteeing a safe distance. For disturbance caused by factors such as ground unevenness, historical data are learned by using a Gaussian process, the disturbance trend is predicted in real time, and the motion stability is improved. And finally, through nonlinear optimization solution, generating control input with safety, continuity and disturbance adaptive capability. According to the method, geometric modeling, safety constraint, disturbance prediction and a dynamic adjustment mechanism are fused, and the safety and adaptability of robot path planning are remarkably enhanced.

The invention discloses a shaping robot, a shaping system and a grain processing method thereof. An integrated moving and transferring device is used as a chassis for bearing all modules; the shaping industrial robot is used for driving a cutter on the mechanical arm to conduct shaping machining. The medicine scrap sucking and cleaning device is used for cleaning scraps generated by processing; the tool changing magazine is used for placing and managing tools and comprises a quick-changing tool rest, and a detection sensor used for detecting whether the tools exist or not is arranged on the quick-changing tool rest; the control system comprises a coordinate system management module, a tool path planning module, a robot kinematics module, a collision detection module, a motion simulation module and a robot program generation module. Automatic machining positioning, automatic machining path planning and automatic cutting tool switching and management can be achieved; the automation degree of grain cutting machining is effectively improved; in the shaping process, temperature and contact force detection sensors are used for detection, and early warning protection in the machining process is achieved.

The invention provides a cylinder processing cutter path adaptive optimization generation method, which belongs to the technical field of cylinder processing, and adopts fractal dimension analysis to identify surface geometric complexity and determine a minimum repetitive unit, and applies a four-color theorem to carry out non-intersection area division on the surface, so as to improve the processing precision of the cylinder processing cutter path. Multi-axis linkage machining parameters are initialized, global path planning and cutter axis vector optimization and collision detection are performed based on an improved Di jkstra algorithm, path adaptive adjustment is performed according to local curvature change of a minimum fractal matrix, a game model is established, and coevolution of a double-layer game model is realized by adopting a cross optimization rule. And finally, an optimized tool path NC code containing the position coordinates, the attitude angle and the feeding speed is generated, and the technical problem that the machining efficiency and the surface quality are difficult to consider in tool path planning under the complex geometrical characteristics of the cylinder surface is solved.

The invention relates to the field of mobile robot motion planning, and discloses a quadruped robot motion planning method and system based on height self-adaption. The method comprises the following steps: constructing 3D and 2D local obstacle maps according to coordinate transformation, and calculating a grid vertical distance through ray casting to generate a 2.5 D local obstacle map; determining an initial control sampling space by combining speed, acceleration and height constraints; discretizing a sampling space and simulation time, deducing a control value sequence and recursively calculating a local trajectory; then calculating a shaft alignment bounding box in a directed bounding box of the robot, screening grid units in the shaft alignment bounding box, comparing the height of a machine body with the allowable height of grids, and rejecting collision trajectories to obtain a feasible trajectory set; and finally selecting an optimal track by the evaluation function, extracting a control value and sending the control value to hardware for execution. According to the method, the 2.5 D local obstacle map containing height information is constructed, the four-dimensional control sampling space is designed, and the robot height self-adaptive adjustment is realized in combination with the collision detection and evaluation function.

The invention discloses a collision identification method and system for a window cleaning robot, and relates to the technical field of robot environment perception, and the method comprises the steps: carrying out the time difference compensation and space synchronization based on a glass material type and collision detection parameters through a distributed perception collaborative analysis method, and generating a three-dimensional space vibration information matrix; enhancing the three-dimensional space vibration information matrix through a dynamic wavelet packet decomposition algorithm and a sliding window analysis method according to the type of a glass material; according to the enhanced three-dimensional space vibration information matrix, collision coordinates are calculated through a time difference positioning algorithm, the characteristic frequency of the vibration waveform is analyzed, and collision characteristic information is obtained; based on the collision feature information, combining safety parameters of glass material types, adopting a hybrid decision algorithm to analyze the collision feature information, and generating and executing a robot obstacle avoidance instruction; according to the method, the high-resolution three-dimensional vibration field is constructed through space-time collaborative modeling, and the collision coordinate calculation precision and the anti-interference capability are improved.

The invention belongs to the technical field of pipeline arrangement optimization, and particularly relates to a pipeline optimization design auxiliary platform based on a BIM (Building Information Modeling) technology, which comprises a preprocessing module, a BIM modeling module, a collision detection module, a simulation module, a collaborative design module and a feedback module, comprising design drawings, landforms and existing pipe network distribution parameters. The BIM modeling module utilizes BIM software, the pipeline optimization design auxiliary platform based on the BIM technology can fully combine specifications or design requirements, input requirements for pipeline space arrangement, on-way resistance, construction cost and the like in advance, automatically provide various optimization schemes for comparison and selection, input according to parameters of the space arrangement, the on-way resistance and the construction cost, reduce the resistance of a pipeline system, and improve the pipeline optimization design efficiency. And the material cost in construction is reduced, so that the construction cost and the construction period are comprehensively controlled, and energy waste caused in the operation process of the pipeline system is prevented.

The invention relates to a biological soft tissue virtual simulation method and system and a medium, and the method comprises the following steps: constructing a three-dimensional soft tissue model based on medical image data, dispersing the three-dimensional soft tissue model into a particle system, and binding initial constraint parameters; detecting collision between the particles and the surgical instrument based on a symbolic distance field technology, if collision is not detected, outputting the positions of the particles, generating a simulation result, if collision is detected, applying repulsive force to the particles to correct a deformation path, judging whether stress is balanced or the maximum number of iterations is reached, if yes, returning to perform collision detection, and if not, returning to perform collision detection. The particle speed is updated in real time, the particle motion trend is predicted through an implicit time integration algorithm, position constraint, volume constraint, shape constraint and physical constraint are solved in a coupling mode through a Gaussian-Seidel iteration method, constraint rigidity is dynamically adjusted to be matched with real organization characteristics, and the particle position is updated. Compared with the prior art, the method has the advantage that deformation accords with a real physical change rule.