Safety detection method and system for child amusement, product and medium

Abstract

The invention discloses a safety detection method and system for child amusement, a product and a medium, and relates to the field of alarm devices, and the method comprises the steps: collecting the three-dimensional body surface contour and weight data of a child, and building a digital human body model; acquiring three-dimensional geometric data and operation parameters of the constraint device to establish a constraint interface model; placing the digital human body model in a standard riding posture, and calculating a gap distance with the constraint interface model; setting a safety activity boundary according to the gap distance and human engineering standards; calculating inertia force and impact force according to the operation parameters; performing simulation analysis based on the safety activity boundary, the inertia force and the impact force to obtain displacement data and stress data; the displacement exceeds the safety activity boundary to trigger a first-stage alarm, and the stress exceeds a preset threshold to trigger a second-stage alarm. By implementing the method, the accuracy of recreation facility safety risk assessment can be improved.

Description

Technical Field

[0001] This application relates to the field of alarm devices, and more particularly to a safety detection method, system, product, and medium for children's play areas. Background Technology

[0002] As children's amusement facilities develop towards larger scale, higher speed, and greater diversity, the dynamic loads such as inertial forces and impact forces generated during operation are constantly increasing, placing higher demands on safety testing technology. At the same time, the restraint devices of amusement facilities are also exhibiting characteristics of increasingly complex structures and diverse functions.

[0003] In related technologies, strain sensors and displacement sensors are used to conduct safety inspections of amusement rides. This method involves placing strain sensors at key locations of the restraint devices to collect deformation data, using displacement sensors to monitor relative displacement, and combining this with preset safety thresholds to perform real-time monitoring and safety assessments of the amusement ride's operational status.

[0004] However, the local measurement data collected by the sensor deviates from the actual movement state of the child, and the preset threshold is difficult to adapt to the safety needs of children of different body sizes, resulting in low accuracy of safety risk assessment. Summary of the Invention

[0005] This application provides a safety testing method, system, product, and medium for children's playgrounds, which improves the accuracy of safety risk assessment for playground facilities.

[0006] Firstly, this application provides a safety detection method for children's amusement facilities, applied to safety detection equipment for children's amusement facilities. The method includes: collecting three-dimensional body contour data and weight data of a child; establishing a digital human body model of the child based on the three-dimensional body contour data and weight data; acquiring three-dimensional geometric data of the safety restraint devices of the amusement facility and the operating parameters of the amusement facility; establishing a restraint interface model based on the three-dimensional geometric data; placing the child's digital human body model in a standard riding posture; calculating the gap distance between the child's digital human body model and the restraint interface model; determining the safe activity boundary of the child's digital human body model based on the gap distance and the minimum safe distance set by ergonomic standards; calculating the inertial force and impact force acting on the child's digital human body model based on the operating parameters; performing simulation analysis based on the safe activity boundary, inertial force, and impact force to obtain displacement data and force data of the child's digital human body model; triggering a first-level alarm signal when the displacement data exceeds the safe activity boundary, and triggering a second-level alarm signal when the force data exceeds a preset threshold.

[0007] In the above embodiments, a digital human body model and a constraint interface model of the child are established, enabling an accurate representation of the dynamic contact relationship between the constraint device and the child. Simulation analysis is performed based on the safe activity boundary to calculate the inertial and impact forces acting on the child, and displacement and force data are acquired. A two-level alarm mechanism is set up based on the displacement and force data to provide timely warnings before danger occurs. This improves the accuracy of safety risk assessment, overcomes the problem of deviation between local sensor measurement data and actual movement states, and achieves accurate assessment of the personalized safety needs of children of different body types.

[0008] In conjunction with some embodiments of the first aspect, in some embodiments, acquiring the three-dimensional geometric data of the safety restraint device of the amusement facility and the operating parameters of the amusement facility, and establishing a restraint interface model based on the three-dimensional geometric data, specifically includes: acquiring geometric contour point cloud data of the safety restraint device using a 3D scanner; performing meshing processing on the geometric contour point cloud data to obtain a three-dimensional mesh model of the restraint device; acquiring the velocity, acceleration, and angular velocity parameters of the amusement facility during operation; extracting the restraint surfaces that come into contact with the child's body based on the three-dimensional mesh model, and establishing a restraint interface model; and calculating the position and attitude change data of the restraint interface model during operation based on the velocity, acceleration, and angular velocity parameters.

[0009] In the above embodiments, point cloud data of the constraint device was acquired using 3D scanning and then meshed to establish an accurate 3D mesh model. Constraint surfaces were extracted based on the motion parameters of the amusement ride, and a dynamic constraint interface model was established. By calculating the position and attitude changes of the constraint interface during operation, a complete description of the motion characteristics of the constraint device was achieved. This yielded a high-precision geometric model and motion parameters of the constraint system, providing a reliable data foundation for subsequent safety analysis.

[0010] In conjunction with some embodiments of the first aspect, in some embodiments, determining the safe activity boundary of the child digital human body model based on the gap distance and the minimum safe distance set according to ergonomic standards specifically includes: placing the child digital human body model in the seating position of the constraint interface model, identifying multiple contact positions between the child digital human body model and the constraint interface model; determining the minimum safe distance required for each contact position according to ergonomic standards, comparing the gap distance with the minimum safe distance, and calculating the movable space at each contact position; generating a continuous activity area based on the movable space, and constructing a safe activity boundary based on the boundary contour of the activity area.

[0011] In the above embodiments, multiple contact points between the digital human body model and the constraint interface are identified, and the minimum safe distance is determined according to ergonomic standards. The movable space is calculated by comparing the gap distance and the safe distance, and a continuous safety boundary is constructed based on the movable area. This enables a quantitative assessment of the interaction between the constraint device and the human body and establishes an movable boundary model that meets ergonomic requirements. This ensures the scientific validity and rationality of the safe movable boundary, providing accurate criteria for dynamic safety monitoring.

[0012] In conjunction with some embodiments of the first aspect, in some embodiments, the calculation of the inertial force and impact force acting on the child digital human model based on operating parameters specifically includes: determining the centripetal acceleration and tangential acceleration during the operation of the amusement facility using operating parameters, and calculating the centrifugal inertial force acting on the center of gravity of the child digital human model based on the centripetal acceleration and weight data; analyzing the tangential inertial force acting on each joint of the child digital human model using angular velocity parameters and weight data, and obtaining the impact force acting on the child digital human model when the amusement facility starts and brakes through the rate of change of operating parameters and weight data.

[0013] In the above embodiments, centripetal and tangential accelerations are calculated using operating parameters to obtain the centrifugal inertial force acting on the center of gravity of the child's digital human body model. The tangential inertial force of each joint is analyzed in conjunction with angular velocity parameters, and the impact force during the start-up and braking phases is calculated using the rate of change of operating parameters. This achieves accurate calculation of various dynamic loads during the operation of the amusement facility and establishes a complete mechanical action model. Accurate inertial and impact force data are thus obtained, providing a quantitative basis for assessing the force state of children.

[0014] In conjunction with some embodiments of the first aspect, in some embodiments, the simulation analysis based on safety activity boundaries, inertial forces, and impact forces to obtain displacement and force data of the child digital human model specifically includes: performing dynamic simulation analysis on the child digital human model, applying centrifugal inertial forces to the center of gravity of the child digital human model, applying tangential inertial forces to each joint of the child digital human model, applying impact forces to the child digital human model during the start-up and braking phases of the amusement facility, and obtaining simulation analysis results, wherein the inertial forces include centrifugal inertial forces and tangential inertial forces; obtaining displacement data under safety activity boundary constraints, and obtaining force data based on the simulation analysis results.

[0015] In the above embodiments, dynamic simulation was performed on the digital human body model, applying centrifugal and tangential inertial forces to the center of gravity and joint positions, respectively, and applying impact forces during the starting and braking phases. Displacement and force data were obtained through simulation analysis, achieving a complete simulation of the child's motion state. Analysis was conducted under safe activity boundary constraints, ensuring the validity of the simulation results. This established a high-precision dynamic analysis model, enabling accurate prediction of the child's force and displacement responses.

[0016] In conjunction with some embodiments of the first aspect, in some embodiments, after the steps of triggering a first-level alarm signal when displacement data exceeds the safe activity boundary and triggering a second-level alarm signal when force data exceeds a preset threshold, the method further includes: acquiring the child's movement trajectory corresponding to the displacement data; determining the safety risk distribution based on the correspondence between the movement trajectory and the force data; dividing the safe activity boundary into multiple risk level areas based on the safety risk distribution; setting different preset thresholds for different risk level areas; and adjusting the triggering sequence of the first-level alarm signal and the second-level alarm signal according to the risk level area where the child is located in real time.

[0017] In the above embodiments, the child's movement trajectory is acquired, and the distribution of safety risks is determined based on the correspondence between the trajectory and force data. Multiple risk-level zones are divided based on the risk distribution, and preset thresholds are set for different zones to dynamically adjust the alarm triggering sequence. This achieves refined zoning management of safety risks and establishes an adaptive early warning mechanism. This improves the sensitivity and reliability of safety monitoring and realizes intelligent and personalized risk warnings.

[0018] In conjunction with some embodiments of the first aspect, in some embodiments, after the steps of triggering a first-level alarm signal when displacement data exceeds the safe activity boundary and triggering a second-level alarm signal when force data exceeds a preset threshold, the method further includes: when the first-level alarm signal is triggered, recording the riding position and activity area of ​​the child's digital human body model on the amusement facility, and marking the riding position and activity area as key protection areas; when the second-level alarm signal is triggered, recording the movement patterns that cause the movement to exceed the preset threshold, and adding the movement patterns to a list of prohibited actions; and formulating riding safety guidelines based on the key protection areas and the list of prohibited actions.

[0019] In the above embodiments, the riding position and activity area when the first-level alarm is triggered are recorded and marked as key protection areas; the movement patterns that cause exceeding limits when the second-level alarm is triggered are recorded, forming a list of prohibited actions. Riding safety guidelines are formulated based on the key protection areas and the list of prohibited actions, achieving systematic management of high-risk states. This constructs a complete safety prevention system, forms data-driven safety standards, provides a scientific basis for the safe operation of amusement facilities, and transforms safety management from passive response to proactive prevention.

[0020] Secondly, embodiments of this application provide a safety detection device for children's playgrounds, the safety detection device for children's playgrounds including: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code including computer instructions, and the one or more processors call the computer instructions to cause the safety detection device for children's playgrounds to perform the method as described in the first aspect and any possible implementation thereof.

[0021] Thirdly, embodiments of this application provide a computer program product containing instructions that, when the computer program product is run on a safety detection device for children's play, cause the safety detection device for children's play to perform the method described in the first aspect and any possible implementation thereof.

[0022] Fourthly, embodiments of this application provide a computer-readable storage medium including instructions that, when executed on a child amusement safety detection device, cause the child amusement safety detection device to perform the method described in the first aspect and any possible implementation thereof.

[0023] Understandably, the safety detection device for children's play areas provided in the second aspect, the computer program product provided in the third aspect, and the computer storage medium provided in the fourth aspect are all used to execute the methods provided in the embodiments of this application. Therefore, the beneficial effects they can achieve can be referred to the beneficial effects in the corresponding methods, and will not be repeated here.

[0024] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages: 1. This application achieves a precise representation of the dynamic contact relationship between the restraint device and the child by establishing a digital human body model and a constraint interface model for the child. Simulation analysis is performed based on the safe activity boundary to calculate the inertial and impact forces acting on the child, and displacement and force data are obtained. A two-level alarm mechanism is set up based on the displacement and force data to provide timely warnings before danger occurs. This improves the accuracy of safety risk assessment, overcomes the problem of deviation between local sensor measurement data and actual movement state, and enables precise assessment of the personalized safety needs of children of different body types.

[0025] 2. This application identifies multiple contact points between the digital human body model and the constraint interface, and determines the minimum safe distance according to ergonomic standards. By comparing the gap distance with the safe distance, the movable space is calculated, and a continuous safety boundary is constructed based on the movable area. This enables a quantitative assessment of the interaction between the constraint device and the human body, and establishes an movable boundary model that meets ergonomic requirements. This ensures the scientific validity and rationality of the safe movable boundary, providing accurate criteria for dynamic safety monitoring.

[0026] 3. This application utilizes dynamic simulation of a digital human body model, applying centrifugal and tangential inertial forces to the center of gravity and joint positions, respectively, and applying impact forces during the starting and braking phases. Displacement and force data are obtained through simulation analysis, achieving a complete simulation of the child's movement state. Analysis is conducted under safe activity boundary constraints, ensuring the validity of the simulation results. This establishes a high-precision dynamic analysis model, enabling accurate prediction of the child's force and displacement responses. Attached Figure Description

[0027] Figure 1 This is a flowchart illustrating a safety testing method for children's playgrounds in an embodiment of this application; Figure 2 This is another flowchart illustrating the safety testing method for children's playgrounds in this application embodiment; Figure 3 This is a schematic diagram of the physical device structure of a safety testing equipment for children's play areas, as described in this application. Detailed Implementation

[0028] The terminology used in the following embodiments of this application is for the purpose of describing particular embodiments only and is not intended to be limiting of this application. As used in the specification of this application, the singular expressions “a,” “an,” “the,” “the,” and “this” are intended to include the plural expressions as well, unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used in this application refers to any or all possible combinations including one or more of the listed items.

[0029] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as implying or suggesting relative importance or implicitly indicating the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature, and in the description of the embodiments of this application, unless otherwise stated, "multiple" means two or more.

[0030] To facilitate understanding, the application scenarios of the embodiments of this application are described below.

[0031] A high-speed rotating amusement ride in a large amusement park can reach speeds of 60 km / h, generating centrifugal acceleration of 3-5G during operation. The restraint system employs a double-shoulder fixed structure, including adjustable shoulder support plates, waist restraint belts, and foot fixation devices. During high-speed operation, the restraint system is subjected to significant inertial and impact forces, resulting in varying degrees of deformation at different connection points. Due to the significant differences in rider body shapes, the gap between the restraint system and the rider's body fluctuates considerably. Especially during 360-degree flips or rapid changes in direction, the relative displacement between the restraint system and the rider changes rapidly, generating substantial impact loads. This complex dynamic motion presents a significant challenge to safety monitoring, requiring real-time assessment of the stress state and deformation of the restraint system, as well as the rider's displacement within the restraint system, to ensure the safety of the ride.

[0032] Existing technologies employ a scheme of placing strain and displacement sensors at key locations within the restraint device. For example, four strain sensors are installed at the connection between the shoulder support plate and the base to monitor the deformation of the support structure; two displacement sensors are installed at the tension adjustment mechanism of the waist restraint belt to detect the relative displacement of the restraint belt. The system is set with a fixed safety threshold: an alarm is triggered when the strain value exceeds 2000 με or the displacement exceeds 50 mm. However, because the sensors can only collect data from local measuring points, they cannot reflect the overall stress distribution of the restraint device. For example, when a visitor is large, even if the data from local measuring points is within the threshold range, other parts of the restraint device may already be under excessive stress. Furthermore, the preset single threshold cannot be dynamically adjusted according to different body shape characteristics, resulting in overly strict requirements for some visitors and overly lenient requirements for others, reducing the accuracy of safety assessments.

[0033] The proposed solution first establishes a digital human body model library containing standard models with different combinations of height and weight. Precise geometric models of the restraint devices are obtained using 3D scanning technology, and a complete simulation model of the restraint system, including support structures, connectors, and buffer devices, is established. Based on the finite element analysis method, the interaction process between the restraint devices and the digital human body models under different working conditions is simulated. The system can calculate the stress distribution, deformation cloud map, and dynamic gap changes between the restraint devices and the human body models at various points. By setting multi-level safety boundaries, when local stress concentration or gap anomalies are detected, the system automatically adjusts the alarm threshold according to the current body shape and movement state. For example, for lighter individuals, the system reduces the maximum allowable acceleration value; for taller individuals, it tightens the displacement limit on the head position. This intelligent assessment method based on simulation analysis significantly improves the accuracy and reliability of safety detection.

[0034] To facilitate understanding, the method provided in this implementation will be described in detail below, using the above scenario as an example. Please refer to [link / reference]. Figure 1 This is a flowchart illustrating a safety testing method for children's play equipment in an embodiment of this application.

[0035] S101. Collect the child's three-dimensional body contour data and weight data, and establish a digital human body model of the child based on the three-dimensional body contour data and weight data.

[0036] The three-dimensional body contour data represents the geometric features of a child's external body surface, including key dimensions such as height, shoulder width, chest circumference, and waist circumference. Weight data refers to the child's mass characteristics, used to determine the inertial parameters of the digital human body model. The digital human body model represents a virtual human body model constructed in a computer environment, used to simulate the child's movement and stress during play.

[0037] This step is performed before the safety assessment of the amusement facility begins, and is used to build a basic analytical model. Specifically, firstly, a full-body scan of the child is performed using a 3D scanning device to obtain complete point cloud data of the body surface contour, while also recording weight information. Then, the point cloud data is reconstructed into a mesh to generate a closed body surface model. Next, based on human anatomical features, the model is divided into main parts such as the head, torso, and limbs, and each part is assigned corresponding mass distribution and joint mobility characteristics. Finally, the inertia tensor of each part is calculated based on the weight data, completing the construction of a digital human body model with biomechanical properties.

[0038] In some embodiments, the construction of a digital human body model can be achieved in several ways: Optionally, structured light 3D scanning is used to acquire body surface data, NURBS surface fitting is used to reconstruct the body surface model, internal structural parameters are configured based on an anatomical database, and a motion model is constructed using a multi-rigid-body dynamics method. First, a structured light scanner is used to scan the entire body with a precision of 0.1 mm to acquire a high-density point cloud. Then, a smooth body surface model is reconstructed using a surface fitting algorithm, and internal structures such as bones and joints are added according to anatomical features. Finally, the mass and inertia parameters of each part are configured to complete a movable digital human body model.

[0039] Optionally, based on a parametric human body model template, geometric deformation is driven by key dimensions, and biomechanical parameters are configured using a statistical human body database. First, a template model similar to the target body type is selected. Then, a scaling factor is calculated based on measured height, weight, and other data. Next, the template is geometrically deformed to conform to the target dimensions. Finally, the biomechanical parameters for the corresponding body type are retrieved from the human body database and configured accordingly.

[0040] It is understandable that other methods can be used to construct digital human models, such as medical image reconstruction or deep learning methods, which are not limited here.

[0041] S102. Obtain the three-dimensional geometric data of the safety restraint device of the amusement facility and the operating parameters of the amusement facility, and establish a restraint interface model based on the three-dimensional geometric data.

[0042] The three-dimensional geometric data represents the shape, size, and spatial relationship of the safety restraint device, including the geometric features of components such as supporting structures, connectors, and buffer devices. Operating parameters refer to the dynamic characteristics of the amusement ride, such as speed, acceleration, and trajectory. The restraint interface model represents the geometric and mechanical characteristics of the key areas where the restraint device contacts the child's body.

[0043] This step is performed after the digital human body model is completed, and is used to establish the analysis model for the amusement ride. Specifically, first, 3D data of the restraint device is collected, including the shape, relative position, and assembly relationship of each component. Then, the operating parameters of the amusement ride are obtained, including maximum speed, acceleration, and motion laws. Next, a 3D solid model of the restraint device is built, and key contact interfaces are labeled. Finally, based on material properties and structural features, the corresponding mechanical parameters are configured for the model.

[0044] In some embodiments, the constraint interface model can be constructed in several ways: Optionally, a 3D laser scanner can be used to acquire point cloud data of the constraint device, key interfaces can be extracted through feature recognition, and a parametric CAD model can be established. First, the constraint device is scanned from all directions to obtain complete geometric data. Then, the solid models of each component are reconstructed through surface fitting, and the parts are assembled according to the assembly relationship. Finally, the key interfaces that come into contact with the human body are identified, and a constraint model with adjustable parameters is established.

[0045] Optionally, a precise 3D model is created based on engineering drawings, and a computational model is formed through finite element mesh generation, configuring material parameters and boundary conditions. First, 3D models of each part are constructed according to the design drawings, and then a virtual assembly is completed according to the assembly relationships. Next, mesh generation and element type selection are performed, and material properties are configured. Finally, contact interfaces and constraints are defined to complete a computable mechanical model.

[0046] It is understandable that other methods can be used to construct the constraint interface model, such as reverse engineering modeling or parametric template method, etc., which are not limited here.

[0047] S103. Place the child digital human body model in a standard riding posture and calculate the gap distance between the child digital human body model and the constraint interface model.

[0048] The standard sitting posture refers to the child's proper sitting position and angles on the playground equipment, including the spatial relationship of the torso and limbs and joint angle parameters. The gap distance refers to the shortest distance between the surface of the digital human body model and the constraint interface model, used to represent the restraint effect of the restraint device on the child. The spatial relationship between the digital human body model and the constraint interface model represents the relative position and orientation of the two models in the three-dimensional coordinate system. The standard posture parameters are used to represent the position and angle requirements of various parts of the body in the standard sitting state.

[0049] This step is performed after the digital human body model and constraint interface model are completed, and is used to evaluate the basic compatibility of the constraint device. Specifically, firstly, the specific parameters of the standard riding posture are determined according to the design specifications of the amusement facility, including the torso tilt angle and limb bending angle. Then, the digital human body model is adjusted to the standard posture and placed in the designed position of the constraint device. Next, a sampling point grid is established in the key contact area, and the shortest distance from the sampling point to the constraint interface is calculated. Finally, based on the distance data of all sampling points, a gap distribution cloud map is generated, and the gap values ​​of key parts are determined.

[0050] In some embodiments, the gap distance can be calculated in several ways: Optionally, the nearest point projection method can be used. First, uniformly distributed sampling points are generated on the surface of the human body model, with a density of one point per square centimeter. Then, each sampling point is projected onto the constraint interface, and the Euclidean distance between the projected point and the original point is calculated. Next, the distance values ​​of all sampling points are statistically analyzed to identify areas where the gap is too large or too small. Finally, based on the safety requirements of different parts, the gap is evaluated to determine whether it meets the design specifications.

[0051] Optionally, distance field calculation can be performed based on spatial mesh generation. First, the spatial region containing the constraint interface is divided into a regular mesh, with a mesh size that can be set to 1 mm. Then, the signed distance from each mesh node to the constraint interface is calculated to construct a three-dimensional distance field. Next, trilinear interpolation is used to calculate the precise distance values ​​of points on the human body model surface in the distance field. Finally, distance threshold analysis is used to determine the gap conditions at different locations.

[0052] It is understandable that other methods can be used to calculate the gap distance, such as fast distance calculation based on bounding box hierarchical decomposition or ray casting, etc., which are not limited here.

[0053] S104. Determine the safe activity boundaries of the child digital human body model based on the gap distance and the minimum safe distance set by ergonomic standards.

[0054] Among these, the minimum safe distance represents the minimum required interval between the restraint device and the human body, referring to a safety threshold determined based on ergonomic research. The safe activity boundary refers to the spatial range within which children are allowed to move during play, used to represent the displacement restriction area. Ergonomic standards represent engineering design specifications developed considering human physiological characteristics and movement properties. Degrees of freedom of movement are used to represent the range of angles that each joint can move.

[0055] This step is performed after the gap distance calculation is completed and is used to determine the safe range for children's activities. Specifically, firstly, minimum safe distance standards are determined based on the ergonomic requirements of different body parts, such as no less than 120mm for the head area and no less than 80mm for the torso area. Then, the calculated actual gap distances are compared with the safety standards to delineate safe and dangerous zones. Next, an activity boundary envelope is constructed based on the safe zones, which defines the spatial range in which children can safely move. Finally, the activity boundary is dynamically adjusted according to different body shape characteristics to ensure a safety margin.

[0056] In some embodiments, the determination of the safe activity boundary can be achieved in several ways: Optionally, the activity boundary can be constructed based on a convex hull algorithm. First, a large number of uniformly distributed spatial sampling points are generated within the safe distance range. Then, sampling points whose distance from the constraint interface is less than the safe threshold are removed. Next, a closed boundary surface is constructed based on the remaining sampling points using a three-dimensional convex hull algorithm. Finally, the boundary surface is smoothed to obtain a continuous activity boundary model.

[0057] Optionally, the level set method can be used to construct the activity boundary. First, a distance field function is established with the constraint interface as the zero isosurface. Then, the target isosurface is determined according to the safety distance requirements. Next, the evolution process of the isosurface is obtained by solving the level set equation. Finally, the boundary is locally adjusted based on human activity characteristics to form the final safe activity boundary.

[0058] It is understandable that other methods can be used to determine the boundaries of safe activities, such as parametric modeling based on geometric features or deep learning-assisted construction, which are not limited here.

[0059] S105. The inertial force and impact force acting on the child's digital human body model are calculated based on the operating parameters.

[0060] Inertial force refers to the virtual force acting on a child due to the movement of the amusement ride, including centrifugal force and Coriolis force. Impact force refers to the instantaneous force generated when the speed of the amusement ride changes abruptly. Operating parameters are used to represent the motion characteristics of the amusement ride, including dynamic parameters such as speed, acceleration, and angular velocity. The force calculation model is used to represent the calculation method for the magnitude, direction, and point of application of the force.

[0061] This step, performed after determining the safety activity boundaries, is used to assess the impact of dynamic loads. Specifically, first, a motion analysis model is established based on the amusement ride's operating parameters, including calculation methods for various acceleration components. Then, the digital human model is divided into multiple point mass systems, and the inertial force acting on each point mass is calculated. Next, considering the speed variation characteristics of the amusement ride, the impact loads generated during acceleration and deceleration are analyzed. Finally, all forces are summarized to obtain the complete dynamic load distribution.

[0062] In some embodiments, forces can be calculated in several ways: optionally, forces can be calculated based on multi-rigid-body dynamics. First, the human body model is simplified into a system formed by connected rigid body elements. Then, the equations of motion for each rigid body are established, including the mass matrix and inertia tensor. Next, various accelerations are calculated based on the motion parameters of the amusement ride, and the inertial forces acting on each rigid body are solved. Finally, considering the constraints between the rigid bodies, the overall force distribution is calculated.

[0063] Optionally, the finite element method (FEM) is used to calculate the forces. First, the human body model is meshed, and a nodal mass matrix is ​​established. Then, an acceleration field is constructed based on the motion parameters of the amusement ride. Next, the inertial force and internal force of each node are calculated using an explicit dynamics solver. Finally, the nodal forces are integrated to obtain the resultant force and torque of each part.

[0064] It is understandable that other methods can be used to calculate the force, such as the simplified particle system method or the statistical model based on measured data, etc., which are not limited here.

[0065] S106. Based on the safety activity boundary, inertial force and impact force, simulation analysis is performed to obtain the displacement data and force data of the child's digital human body model.

[0066] Simulation analysis refers to simulating and calculating the dynamic process in a virtual environment to predict the system's motion response. Displacement data refers to the spatial positional changes of various parts of the digital human body model. Force data represents the resultant force and stress distribution acting on key parts of the human body model. Dynamic response is used to represent the changes in the system's motion state under the action of external forces. Constraints refer to the boundary conditions and contact conditions that restrict the model's motion.

[0067] This step, performed after force calculations are completed, is used to evaluate the dynamic response characteristics of children during play. Specifically, a complete simulation model including safe activity boundaries is first established, and boundary conditions and material parameters are set. Then, the calculated inertial and impact forces are used as load inputs to establish a set of nonlinear dynamic equations. Next, an explicit integration algorithm is used to solve the equations to obtain the model's response process in the time domain. Finally, the calculation results are post-processed to extract the displacement history and internal force distribution of each key point.

[0068] In some embodiments, simulation analysis can be implemented in several ways: optionally, simulation can be based on explicit dynamic methods. First, a nonlinear dynamic model considering large deformation and contact is constructed. Then, the equations of motion are discretized in the time domain using the central difference method, with a time step of 0.1 ms. Next, the acceleration field is solved at each time step, and the velocity and displacement are updated. Finally, the internal forces are calculated using the strain-stress relationship to obtain the complete dynamic response.

[0069] Optionally, an implicit dynamics method can be used for simulation. First, a system of nonlinear equilibrium equations including inertia terms is established. Then, the time is discretized using the Newmark integral scheme, and the nonlinear equations are solved at each time step. Next, an iterative process is used to ensure the convergence of the equilibrium equations. Finally, the displacement and stress field distributions at each time step are output.

[0070] It is understandable that other methods can be used to implement the simulation analysis process, such as simplified quasi-static analysis or modal superposition method, etc., which are not limited here.

[0071] S107. When the displacement data exceeds the safe activity boundary, a first-level alarm signal is triggered; when the force data exceeds the preset threshold, a second-level alarm signal is triggered.

[0072] The first-level alarm signal is a safety warning triggered by excessive displacement, indicating a potential collision risk. The second-level alarm signal is a safety warning triggered by excessive force, indicating a potential damage risk. Preset thresholds represent force and stress limits determined based on safety standards. Alarm levels indicate the severity of the safety risk. Triggering conditions are the criteria used to activate the alarm signal.

[0073] This step, executed after simulation analysis, is used to implement tiered safety early warning. Specifically, firstly, the displacement data obtained from the simulation is monitored in real time. When the displacement of any part exceeds the safe activity boundary, the system automatically triggers the first-level alarm. Then, the stress data is analyzed, and the calculated stress values ​​are compared with the bearing capacity of various parts of the human body. Next, when the stress on certain parts is found to exceed the safety threshold, the system triggers the second-level alarm. Finally, the urgency level of the alarm is determined based on the degree of exceeding the limit.

[0074] In some embodiments, alarm triggering can be implemented in several ways: Optionally, an alarm can be triggered based on a multi-threshold judgment method. First, multi-level warning thresholds for displacement and force are set, such as two levels for displacement: yellow and red warnings. Then, the ratio of displacement and force to the thresholds is calculated in real time. Next, the alarm level is determined based on the ratio, and different audible and visual signals are used to provide alerts. Finally, detailed information about the alarm event is recorded for subsequent analysis.

[0075] Optionally, a fuzzy logic method can be used to implement the alarm. First, a fuzzy rule base containing displacement and force values ​​is established. Then, the monitoring data is converted into a fuzzy set, and inference calculations are performed based on the rules. Next, the alarm level and urgency are determined based on the inference results. Finally, the specific alarm command is obtained through defuzzification.

[0076] It is understandable that other methods can be used to trigger the alarm process, such as machine learning-based anomaly detection or statistical process control, which are not limited here.

[0077] The following provides a more detailed description of the process of the method provided in this implementation. Please refer to [link / reference]. Figure 2 This is another flowchart illustrating the safety testing method for children's play equipment in this application.

[0078] S201. Collect three-dimensional body contour data and weight data of children, and establish a digital human body model of children based on the three-dimensional body contour data and weight data.

[0079] Three-dimensional body contour data represents the geometric shape features of the human body's outer surface, including three-dimensional coordinate lattices and surface feature information. Weight data represents the human body's mass characteristics and is used to determine the mechanical parameters of the digital model. A digital human body model is a virtual human representation with biomechanical properties constructed in a computer environment.

[0080] First, a full-body scan of the human body is performed using a structured light 3D scanner. The scanner emits structured light stripe patterns, which are then captured by a CCD camera to obtain surface point cloud data. During the scan, the user maintains a standard standing posture, and the scan resolution is set to 0.5 mm, acquiring approximately 500,000 spatial point coordinates in a single scan. Simultaneously, the user's weight is measured using an electronic scale with an accuracy of 0.1 kg. The acquired point cloud data is then registered and fused to eliminate occlusion and noise, forming a complete surface point cloud model. Next, surface reconstruction is performed, using the NURBS method to fit the surface features and establish a continuous and smooth surface model. Based on human anatomical features, the model is divided into anatomical regions such as the head, torso, and limbs, and assigned corresponding mass distributions. Finally, based on the weight data, the inertial parameters of each part are calculated to complete a digital human body model with motion characteristics.

[0081] S202. Use a 3D scanner to acquire the geometric contour point cloud data of the safety restraint device.

[0082] Geometric contour point cloud data represents a discrete set of points representing the surface shape of a constraint device, with each point containing three-dimensional coordinate information. A 3D scanner is an optical measurement device that acquires geometric data of an object's surface.

[0083] A handheld laser 3D scanner was used to scan and measure the constraint device. The scanner emits laser lines, and a binocular camera captures the deformation patterns of these lines, calculating the 3D coordinates of surface points. An optimal measurement distance of 50cm was maintained during the scanning process, with a scanning resolution of 0.1mm. First, the support structure of the constraint device was scanned to obtain point cloud data of the frame and connectors. Then, flexible components such as the padding and restraint straps were scanned individually. The scanner displayed the acquired point cloud data in real time during operation, ensuring comprehensive and complete data acquisition of the constraint device. Finally, a complete point cloud dataset of the constraint device was obtained, containing approximately 1 million spatial point coordinates.

[0084] S203. The geometric contour point cloud data is processed into a grid to obtain a three-dimensional mesh model of the constraint device.

[0085] Meshing refers to the data reconstruction process of converting discrete point clouds into continuous curved surfaces. A 3D mesh model is a curved surface model composed of triangular or quadrilateral mesh cells.

[0086] The collected point cloud data was meshed. First, data preprocessing was performed, including denoising, downsampling, and normal vector estimation. Noise removal employed statistical outlier filtering, analyzing the neighborhood distribution of each point to remove outliers. Then, an octree method was used to spatially partition the point cloud, extracting feature points within each grid to simplify the data. Next, the normal vector of each point was calculated, and covariance analysis was used to determine the local planar orientation. Based on the processed point cloud, surface reconstruction was performed, using the Poisson reconstruction algorithm to generate an initial triangular mesh. The mesh was then optimized, including boundary preservation, feature preservation, and mesh quality improvement. Finally, a three-dimensional mesh model with a good topological structure was obtained, with approximately 200,000 triangular elements, accurately representing the geometric characteristics of the constraint device.

[0087] S204. Obtain the speed, acceleration, and angular velocity parameters of the amusement facility during operation, extract the constraint surfaces where the safety restraint devices come into contact with the child's body based on the three-dimensional mesh model, and establish a constraint interface model.

[0088] Velocity parameters represent the linear velocity components of the amusement ride's motion. Acceleration parameters represent the rate of change of velocity, including tangential and normal acceleration. Angular velocity parameters represent the rate of change of rotational angle. Constraint surfaces represent the surface areas where the restraint device directly contacts the human body. The constraint interface model is a geometric and mechanical model describing the interaction between the restraint device and the human body.

[0089] First, motion sensors were used to measure the motion parameters of the amusement ride. The sensors were installed on the rotating axes and key moving parts of the equipment, with a sampling frequency of 100Hz. The linear velocity components in three directions were measured, with a maximum speed of 60 km / h; tangential and normal accelerations, with a maximum acceleration of 3g; and angular velocities around each axis, with a maximum angular velocity of 120° / s. Then, constraint interfaces were extracted from the 3D mesh model, including the shoulder support surface, waist restraint surface, and leg fixing surface. The extraction process was based on curvature analysis and feature recognition to identify surface areas that might come into contact with the human body. Parametric modeling was performed on the extracted constraint surfaces to establish a constraint interface model incorporating geometric features and material properties. Finally, the motion parameters were associated with the constraint interface model to construct a complete dynamic constraint model.

[0090] S205. Calculate the position and attitude change data of the constrained interface model during operation based on the velocity, acceleration and angular velocity parameters.

[0091] Position change data represents the translation of the constraint interface in space. Attitude change data represents the rotation angle of the constraint interface. The operation process represents the complete motion cycle of the amusement ride.

[0092] The motion trajectory of the constrained interface is calculated based on motion parameters using rigid body kinematics. First, a global coordinate system and a local coordinate system are established, with the initial position of the constrained interface serving as the origin of the local coordinate system. For translational motion, the displacement is obtained by integrating velocity and acceleration over time. The velocity integral uses the trapezoidal rule, with a time step of 0.01 s. For rotational motion, the angular velocity is converted to a quaternion representation, and the attitude matrix is ​​calculated. Then, by combining translation and rotation, the position and attitude matrices of the constrained interface at each moment are obtained. Finally, a complete motion dataset is generated, containing the spatial position and rotational angle data of the constrained interface throughout the entire runtime.

[0093] S206. Place the child digital human body model in a standard riding posture and calculate the gap distance between the child digital human body model and the constraint interface model.

[0094] Standard seating posture refers to the seating position and posture angle that conforms to design specifications. Gap distance refers to the shortest distance between the surface of the digital human body model and the constraint interface.

[0095] The digital human body model was placed in the designed position of the restraint device, and the posture angles of the torso and limbs were adjusted to conform to standard riding specifications. The torso was kept vertical, the angle between the torso and thighs was set to 90°, and the knee flexion angle was set to 100°. Then, uniformly distributed sampling points were generated on the surface of the human body model, with a sampling density of 4 points per square centimeter. For each sampling point, the shortest distance to the restraint interface was calculated. The distance calculation used the nearest point projection method: first, the projection point of the sampling point on the restraint interface was determined, and then the Euclidean distance between the two points was calculated. The distance data of all sampling points were summarized to generate a gap distribution cloud map. Special attention was paid to the gap values ​​of key areas such as the head, torso, and limbs for subsequent safety assessments.

[0096] S207. Place the child digital human body model in the riding position of the constraint interface model, and identify multiple contact points between the child digital human body model and the constraint interface model.

[0097] The seating position represents the standard operating position of the restraint device design. The contact position represents the local area where the human body interacts with the restraint interface. Contact recognition represents the process of determining potential contact points between two models.

[0098] First, the digital human body model is imported into the design space of the constraint system, and the reference positioning points are determined based on the structural characteristics of the constraint device. Contact positions are identified using a feature matching method. By calculating the distance field between the surfaces of two models, potential contact areas are identified when the distance is less than a set threshold (e.g., 10mm). Contact identification focuses on key areas such as the shoulder support area, chest constraint area, waist limiting area, and thigh fixation area. A local coordinate system is established for each contact area, recording the normal and tangential directions of the contact surface. Then, geometric feature analysis is performed on the contact areas, including parameters such as contact area, contact angle, and curvature distribution. Finally, a complete contact position database is established, containing information such as position coordinates, contact features, and relative directions.

[0099] S208. Determine the minimum safe distance required for each contact position according to ergonomic standards, compare the gap distance with the minimum safe distance, and calculate the movable space at each contact position.

[0100] Minimum safety distance indicates the minimum clearance required to ensure safe use. Allowable space indicates the spatial range permitted for movement under safety constraints. Ergonomic standards are engineering design specifications based on human characteristics.

[0101] Based on ergonomic data, safety distance requirements were determined, with a minimum safety distance of 120mm for the head region, 100mm for the neck region, 80mm for the torso region, and 60mm for the limb regions. A spatial mapping method was used to calculate the movable space. First, a 3D mesh with a mesh size of 5mm was created at each contact point. The distance from each mesh point to the constraint interface was calculated, and points with distances greater than the minimum safety distance were marked as movable points. For each contact point, the spatial distribution of movable points was statistically analyzed to form a local movable space. Considering limitations on the range of motion of human joints, such as neck rotation not exceeding 45° to the left and right and flexion / extension not exceeding 30°, the effective movable space data for each contact point was finally obtained.

[0102] S209. Generate a continuous active region based on the active space, and construct a safe active boundary according to the boundary contour of the active region.

[0103] The activity area represents a combined range of multiple movable spaces. The boundary contour represents the outer envelope of the activity area. The safe activity boundary is the spatial boundary surface that defines the range of human movement.

[0104] The movable spaces at each contact point are merged, and a continuous active region is generated using spatial interpolation. First, a three-dimensional spatial mesh is established, mapping the discrete movable space data onto the mesh. Radial basis functions are used for spatial interpolation to ensure a smooth transition of the active region. Then, the boundary features of the active region are extracted, and an isosurface extraction algorithm is used to obtain the boundary contour. The boundary contour is smoothed to remove local abrupt changes and sharp corners. A closed surface model is constructed based on the processed boundary contour; this surface represents the safe active boundary. Finally, the boundary model is optimized to ensure it meets the requirements of geometric continuity and topological integrity.

[0105] S210. Determine the centripetal acceleration and tangential acceleration during the operation of the amusement facility using operating parameters, and calculate the centrifugal inertial force acting on the center of gravity of the child's digital human body model based on the centripetal acceleration and body weight data.

[0106] Centripetal acceleration represents the acceleration component pointing towards the center of the circle in circular motion. Tangential acceleration represents the acceleration component in the direction of velocity. Centrifugal inertial force is the inertial force acting on an object caused by centripetal acceleration. The center of gravity represents the equivalent point of application of the object's mass distribution.

[0107] Acceleration components are calculated based on motion parameters: centripetal acceleration *an* = *v² / r*, where *v* is the linear velocity and *r* is the radius of rotation; tangential acceleration *at* = *dv / dt*, calculated using the derivative of velocity with respect to time. For complex trajectories, the motion is decomposed into tangential and normal components for separate calculation. Taking a rotating amusement ride as an example, when the rotation speed is 30 rpm and the radius of rotation is 3 m, the centripetal acceleration is approximately 2.96 m / s². Centrifugal inertial force *F* = *ma*, where *m* is the body weight and *a* is the centripetal acceleration. The body weight is substituted into the calculation of the centrifugal inertial force, with the direction of action opposite to the centripetal acceleration. For multi-axis rotation, vector synthesis is required to obtain the resultant force. The magnitude of the centrifugal inertial force is calculated separately for children of different weight classes, establishing a force-mass relationship database.

[0108] S211. The tangential inertial force acting on each joint of the child's digital human body model is analyzed using angular velocity parameters and weight data. The impact force acting on the child's digital human body model during the start-up and braking of the amusement facility is obtained through the rate of change of operating parameters and weight data.

[0109] Tangential inertial force represents the inertial force generated by tangential acceleration. Impact force represents the instantaneous force caused by a sudden change in velocity. Joint position refers to the kinematic connection point of the human skeletal structure.

[0110] First, calculate the tangential velocity of each joint based on the angular velocity parameters: vt = ωr, where ω is the angular velocity and r is the distance to the axis of rotation. Calculate the tangential acceleration: at = dω / dt × r, distributing body weight data to each joint according to the proportion of human mass distribution. Calculate the tangential inertial force: Ft = mat, where m is the mass corresponding to the joint. Analyze the start-up and braking phases of the amusement ride; the rate of change of acceleration da / dt represents the rate of change of acceleration over time. The impact force is calculated using the impulse-momentum theorem: FΔt = mΔv, where Δt is the time of velocity change and Δv is the magnitude of the velocity change. Determine Δt using the rate of change of acceleration, and then calculate the magnitude of the impact force at each stage.

[0111] S212. Perform dynamic simulation analysis on the digital human body model of children. Apply centrifugal inertial force to the center of gravity of the digital human body model of children and apply tangential inertial force to each joint of the digital human body model of children. Apply impact force to the digital human body model of children during the start-up and braking phases of the amusement facility, and obtain the simulation analysis results. The inertial force includes centrifugal inertial force and tangential inertial force.

[0112] Dynamic simulation analysis represents the process of numerically calculating the motion state of an object. The simulation analysis results include mechanical response parameters such as displacement, velocity, acceleration, and stress.

[0113] An explicit dynamics method was used for simulation analysis, establishing a set of equations of motion that included mass, stiffness, and damping. A centrifugal inertial force was applied at the center of gravity, with its magnitude and direction varying over time. Tangential inertial forces were applied at each joint, considering the joint degrees of freedom. Impact forces were applied during the start-up and braking phases, with a half-sine waveform time history. The central difference method was used to solve the equations of motion, with a time step of 0.001 s, covering the entire operation. Displacement and stress time history curves at key locations were extracted, and the timing and location of maximum response values ​​were analyzed. Comparative analysis of calculation results for different weight classes was performed to assess the sensitivity of the mechanical response. Finally, a complete simulation dataset was output, including information on spatial location, velocity, acceleration, and stress distribution.

[0114] S213. Obtain displacement data under the constraints of safe activity boundary, and obtain force data based on simulation analysis results.

[0115] Displacement data represents the change in position of each node over time. Force data represents the distribution of internal forces and stresses in various parts of the structure. The simulation analysis results include the complete mechanical response of the model under dynamic loads.

[0116] Data is extracted in real time during the dynamic simulation. Displacement data is obtained by tracking the spatial coordinates of model nodes. At each time step, the position coordinates (x, y, z) of key parts (head, torso, limbs) are recorded, and the displacement is obtained by comparing them with the initial position. Collision detection is performed between the displacement data and the safe activity boundary, and the minimum distance is calculated. Force data includes nodal forces and element stresses. Nodal forces are obtained by calculating the resultant force of internal and external forces, and element stresses are calculated using the strain-stress relationship. Special attention is paid to stress concentration areas in key parts of the human body, such as vulnerable areas like the neck and waist. A complete data recording system is established, including mechanical parameters such as time, position, displacement, velocity, acceleration, and stress, with a sampling frequency of 100Hz to ensure data continuity and integrity.

[0117] S214. When the displacement data exceeds the safe activity boundary, a first-level alarm signal is triggered; when the force data exceeds the preset threshold, a second-level alarm signal is triggered.

[0118] The first-level alarm signal indicates a warning of displacement exceeding limits. The second-level alarm signal indicates a warning of force exceeding limits. The preset thresholds are mechanical limit values ​​set based on safety standards.

[0119] A real-time monitoring system is established to perform boundary checks on displacement data. When the displacement of any node exceeds the safe operating boundary, the system records the location and amount of the exceedance, triggering a first-level alarm signal. The first-level alarm uses a yellow warning light and an audible alert. Simultaneously, stress data is monitored, and the measured stress values ​​are compared with the tolerance limits of different parts of the human body. Preset thresholds are set according to different body parts: head impact force not exceeding 100N, neck bending moment not exceeding 50N·m, and torso compressive force not exceeding 1000N. When the force on any part exceeds the corresponding threshold, a second-level alarm signal is triggered. The second-level alarm uses a red warning light and a rapid alarm sound. The system automatically records alarm events, including trigger time, trigger location, and exceedance value. The alarm signal is transmitted to operators and the safety monitoring center through the control system for timely response.

[0120] In some embodiments, for a preset threshold: In practical implementation, the preset thresholds can be dynamically adjusted based on children's weight characteristics. According to human biomechanical characteristics, children of different weights exhibit significant differences in their tolerance to external impacts. A weight-to-tolerance mathematical model is established, and a piecewise linear mapping method is used to determine the threshold parameters. First, based on children's growth and development data and research findings in sports biomechanics, the weight range is divided into multiple intervals, establishing discrete threshold correspondences. Then, a spline interpolation algorithm is used to achieve continuous threshold changes, ensuring that children of different weights receive appropriate warning thresholds. During real-time monitoring, the system dynamically calculates the mechanical limits of various body parts based on the child's current actual weight. The head impact force threshold, neck bending moment threshold, and trunk compression force threshold all increase with increasing weight, and their trends conform to the biomechanical characteristics of human tissue. This adaptive threshold mechanism based on weight characteristics overcomes the limitations of fixed thresholds, which are difficult to adapt to children of different body types, improving the targeting and accuracy of safety warnings and providing effective technical support for personalized safety management of children's playground facilities.

[0121] In some embodiments, after step S214, the following steps may also be included: Obtain the child's movement trajectory corresponding to the displacement data, and determine the distribution of safety risks based on the correspondence between the movement trajectory and the force data.

[0122] Based on the distribution of safety risks, the boundaries of safety activities are divided into multiple risk level zones; different preset thresholds are set for different risk level zones, and the triggering sequence of the first-level alarm signal and the second-level alarm signal is adjusted according to the risk level zone where the child is located in real time.

[0123] Specifically, the displacement data is first reconstructed into a time series representation of the child's movement trajectory, and cubic spline interpolation is used to achieve a continuous and smooth representation of the trajectory. A one-to-one correspondence between movement trajectory points and force data is established through spatial mapping, and the comprehensive mechanical parameters of each trajectory point are calculated, including the magnitude of the resultant force, principal stress distribution, and strain energy density. The trajectory is then divided into grid cells, and the peak and mean values ​​of the mechanical parameters are statistically analyzed within each grid cell. A multi-parameter weighted method is used to calculate the regional risk index. Based on the spatial distribution characteristics of the risk index, high-risk areas and key influencing factors are identified, and a complete safety risk distribution map is established. Based on the risk distribution map, the boundaries of safe activities are divided into zones, and cluster analysis is used to classify the risk index into high, medium, and low-risk areas. High-risk areas are mainly distributed in locations with large movement amplitudes and drastic acceleration changes, with a preset threshold set at 80% of the standard value; medium-risk areas correspond to the normal movement range, with a preset threshold of 90% of the standard value; low-risk areas serve as a buffer transition zone, maintaining the original threshold standard. A real-time positioning system is established to track children's locations. When a child enters a high-risk area, an alarm signal is triggered in advance. The first-level alarm is activated when the displacement reaches 90% of the threshold, and the second-level alarm is triggered when the force reaches 85% of the threshold. The original triggering logic is maintained in medium-risk areas, and the triggering conditions are appropriately relaxed in low-risk areas to improve the system's fault tolerance. Through dynamic threshold management, precise and hierarchical risk warning and control are achieved.

[0124] When the first-level alarm signal is triggered, the child's digital human model's riding position and activity area on the amusement facility are recorded, and the riding position and activity area are marked as key protection areas.

[0125] When the second-level alarm signal is triggered, the movement pattern that causes the movement to exceed the preset threshold is recorded and the movement pattern is added to the list of prohibited actions.

[0126] Based on the list of key protected areas and prohibited actions, develop safety guidelines for riding.

[0127] Specifically, the system records the state parameters triggered when the first-level alarm is activated, including the spatial coordinates of the human model relative to the restraint device, body posture angles, and motion characteristics. A spatial partitioning method is used to cluster the position data, identifying key areas with high frequency of displacement exceeding limits. These areas are mapped onto the structural design drawing of the restraint device and marked as key protection areas. A detailed feature description is established for each key protection area, including spatial range, critical state, and triggering conditions, forming a protection area database. The working conditions triggering the second-level alarm are decomposed into motion characteristic parameters that lead to force exceeding limits. The basic components of the motion mode are analyzed, including motion speed, acceleration, posture angle, and joint rotation. A correlation model between motion characteristics and force response is established to identify key motion sequences that cause exceeding limits. These dangerous actions are standardized and described to form a structured list of prohibited actions, each containing specific limitation parameters and judgment criteria. Safety guidance documents are compiled based on the key protection areas and the list of prohibited actions. First, specific usage requirements are formulated for each protection area, clarifying the correct usage methods and precautions for the limit devices. Then, the prohibited actions are transformed into clear behavioral norms, detailing the potential hazards of improper actions. Based on comprehensive analysis of data patterns, we propose protective measures, including adjusting seating posture, controlling the range of motion, and responding to emergencies. We have developed a tiered and categorized safety guidance system, presenting safety requirements through a combination of text and graphics to ensure users can accurately understand and implement safety regulations.

[0128] The following description, from a hardware processing perspective, describes the safety detection device for children's play areas in the embodiments of this invention. Please refer to [link / reference needed]. Figure 3 This is a schematic diagram of the physical device structure of a safety detection device for children's playgrounds in the embodiments of this application.

[0129] It should be noted that, Figure 3 The structure of the safety detection device for children's play areas shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of the present invention.

[0130] like Figure 3As shown, the safety detection device for children's play areas includes a Central Processing Unit (CPU) 301, which can perform various appropriate actions and processes based on programs stored in Read-Only Memory (ROM) 302 or programs loaded from storage section 308 into Random Access Memory (RAM) 303, such as performing the methods described in the above embodiments. The RAM 303 also stores various programs and data required for system operation. The CPU 301, ROM 302, and RAM 303 are interconnected via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0131] The following components are connected to I / O interface 305: input section 306 including audio input devices, push-button switches, etc.; output section 307 including a liquid crystal display (LCD) and audio output devices, indicator lights, etc.; storage section 308 including a hard disk, etc.; and communication section 309 including a network interface card such as a LAN (Local Area Network) card, modem, etc. Communication section 309 performs communication processing via a network such as the Internet. Drive 310 is also connected to I / O interface 305 as needed. Removable media 311, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., are installed on drive 310 as needed so that computer programs read from them can be installed into storage section 308 as needed.

[0132] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing computer programs for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 309, and / or installed from removable medium 311. When the computer program is executed by central processing unit (CPU) 301, it performs the various functions defined in the present invention.

[0133] It should be noted that specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disc read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0134] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. Each block in a flowchart or block diagram may represent a module, program segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those shown in the drawings.

[0135] Specifically, the safety detection device for children's playgrounds in this embodiment includes a processor and a memory. The memory stores a computer program, and when the computer program is executed by the processor, it implements the safety detection method for children's playgrounds provided in the above embodiment.

[0136] In another aspect, the present invention also provides a computer-readable storage medium, which may be included in the child amusement safety detection device described in the above embodiments; or it may exist independently and not assembled into the child amusement safety detection device. The storage medium carries one or more computer programs, which, when executed by a processor of the child amusement safety detection device, cause the child amusement safety detection device to implement the child amusement safety detection method provided in the above embodiments.

[0137] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit it. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

[0138] As used in the above embodiments, depending on the context, the term "when..." can be interpreted as meaning "if...", "after...", "in response to determining...", or "in response to detecting...". Similarly, depending on the context, the phrase "when determining..." or "if (the stated condition or event) is interpreted as meaning "if determining...", "in response to determining...", "when (the stated condition or event) is detected", or "in response to detecting (the stated condition or event)".

[0139] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. This program can be stored in a computer-readable storage medium, and when executed, it can include the processes described in the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM or random access memory (RAM), magnetic disks, or optical disks.

Claims

1. A safety testing method for children's playground equipment, characterized in that, The method, applied to safety testing equipment for children's playgrounds, includes: Collect three-dimensional body contour data and weight data of children, and establish a digital human body model of children based on the three-dimensional body contour data and weight data; Acquire the three-dimensional geometric data of the safety restraint device of the amusement facility and the operating parameters of the amusement facility, and establish a restraint interface model based on the three-dimensional geometric data; Place the child digital human body model in a standard sitting posture and calculate the gap distance between the child digital human body model and the constraint interface model; The safe activity boundaries of the child digital human body model are determined based on the gap distance and the minimum safe distance set by ergonomic standards. The inertial force and impact force acting on the child digital human body model are calculated based on the operating parameters. Based on the safety activity boundary, the inertial force, and the impact force, simulation analysis is performed to obtain the displacement data and force data of the child digital human body model; When the displacement data exceeds the safe activity boundary, a first-level alarm signal is triggered; when the force data exceeds a preset threshold, a second-level alarm signal is triggered.

2. The method according to claim 1, characterized in that, The process of acquiring the three-dimensional geometric data of the safety restraint device of the amusement ride and the operating parameters of the amusement ride, and establishing a constraint interface model based on the three-dimensional geometric data, specifically includes: The geometric contour point cloud data of the safety restraint device was acquired using a 3D scanner. The geometric contour point cloud data is meshed to obtain a three-dimensional mesh model of the constraint device; The speed, acceleration, and angular velocity parameters of the amusement facility during operation are obtained, and the constraint surfaces that come into contact with the child's body are extracted based on the three-dimensional mesh model to establish the constraint interface model. The position and attitude change data of the constraint interface model during operation are calculated based on the velocity, acceleration and angular velocity parameters.

3. The method according to claim 1, characterized in that, The determination of the safe activity boundaries of the child digital human body model based on the gap distance and the minimum safe distance set according to ergonomic standards specifically includes: The child digital human body model is placed in the seating position of the constraint interface model, and multiple contact points between the child digital human body model and the constraint interface model are identified. The minimum safe distance required for each of the contact positions is determined according to ergonomic standards, the gap distance is compared with the minimum safe distance, and the movable space of each of the contact positions is calculated. A continuous activity area is generated based on the available space, and the safe activity boundary is constructed according to the boundary contour of the activity area.

4. The method according to claim 2, characterized in that, The inertial force and impact force acting on the child digital human model, calculated based on the operating parameters, specifically include: The centripetal acceleration and tangential acceleration during the operation of the amusement facility are determined using the operating parameters, and the centrifugal inertial force acting on the center of gravity of the child digital human body model is calculated based on the centripetal acceleration and the weight data. The tangential inertial force acting on each joint of the child digital human model is analyzed using the angular velocity parameters and the weight data. The impact force acting on the child digital human model when the amusement facility starts and brakes is obtained through the rate of change of the operating parameters and the weight data.

5. The method according to claim 1, characterized in that, The simulation analysis based on the safety activity boundary, the inertial force, and the impact force yields the displacement and force data of the child's digital human body model, specifically including: A dynamic simulation analysis was performed on the child digital human model. Centrifugal inertial force was applied to the center of gravity of the child digital human model, and tangential inertial force was applied to each joint of the child digital human model. The impact force was applied to the child digital human model during the start-up and braking phases of the amusement facility. The simulation analysis results were obtained. The inertial force includes the centrifugal inertial force and the tangential inertial force. The displacement data is obtained under the constraints of the safe activity boundary, and the force data is obtained based on the simulation analysis results.

6. The method according to any one of claims 1 to 4, characterized in that, After the steps of triggering a first-level alarm signal when the displacement data exceeds the safe activity boundary and triggering a second-level alarm signal when the force data exceeds a preset threshold, the method further includes: Obtain the child's movement trajectory corresponding to the displacement data, and determine the distribution of safety risks based on the correspondence between the movement trajectory and the force data; Based on the safety risk distribution, the safety activity boundary is divided into multiple risk level areas; different preset thresholds are set for different risk level areas, and the triggering sequence of the first-level alarm signal and the second-level alarm signal is adjusted according to the risk level area where the child is located in real time.

7. The method according to claim 6, characterized in that, After the steps of triggering a first-level alarm signal when the displacement data exceeds the safe activity boundary and triggering a second-level alarm signal when the force data exceeds a preset threshold, the method further includes: When the first-level alarm signal is triggered, the child digital human model's riding position and activity area on the amusement facility are recorded, and the riding position and activity area are marked as key protection areas. When the second-level alarm signal is triggered, the movement mode that causes the movement mode to exceed the preset threshold is recorded and the movement mode is added to the prohibited action list. Based on the key protection areas and the list of prohibited actions, develop safety guidelines for riding.

8. A safety testing device for children's playgrounds, characterized in that, The safety detection device for children's play areas includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code including computer instructions, and the one or more processors call the computer instructions to cause the safety detection device for children's play areas to perform the method as described in any one of claims 1-7.

9. A computer-readable storage medium comprising instructions, characterized in that, When the instructions are executed on a safety detection device for children's play areas, the safety detection device for children's play areas performs the method as described in any one of claims 1-7.

10. A computer program product, characterized in that, When the computer program product is run on a safety detection device for children's play areas, the safety detection device for children's play areas performs the method as described in any one of claims 1-7.

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