Large-scale amusement facility operation monitoring method, device, electronic equipment and storage medium

Abstract

The present invention proposes a method, device, electronic device, and storage medium for monitoring the operation of large-scale amusement facilities, including: constructing a three-dimensional model of the facility; establishing a basic failure model for the amusement facility; identifying key risk areas based on the model and the three-dimensional model; generating a multi-dimensional health assessment framework; testing key risk areas and monitoring operating parameters and structural health status; conducting automatic inspections of the amusement facility and collecting video images; deeply fusing non-destructive testing data, operating parameters, structural health data, and video images to obtain monitoring data; analyzing the monitoring data to automatically identify, diagnose, and predict faults; assessing the remaining service life of key risk areas of the amusement facility and conducting trend analysis in combination with historical data; quantitatively evaluating the health status of the facility, generating dynamic risk warnings and repair recommendations, and formulating a maintenance plan. The solution of the present invention significantly improves the safety and maintenance efficiency of the equipment.

Description

Technical Field

[0001] The present invention relates to the field of monitoring technology, and in particular to a method, device, electronic equipment and storage medium for monitoring the operation of a large-scale amusement facility. Background Art

[0002] With the rapid development of the economy and technology, the amusement industry is booming. In recent years, with the increasing number of amusement parks, both indoor and outdoor, aquatic and land-based, the incidence of safety accidents has been increasing year by year. This phenomenon has attracted widespread public attention. Failure or improper operation of amusement equipment can lead to serious accidents and injuries, making operation monitoring systems for amusement facilities particularly important. However, existing operation monitoring systems are incomplete and inefficient. Summary of the Invention

[0003] Based on the above problems, the present invention proposes a method, device, electronic equipment and storage medium for monitoring the operation of large-scale amusement facilities. By constructing a dynamic health management indicator system, combining multiple advanced technologies for data collection, fusion and intelligent analysis, and using deep learning and big data analysis technologies for fault diagnosis, prediction and remaining life assessment, it realizes comprehensive monitoring and quantitative evaluation of the operating status of large-scale amusement facilities, significantly improving the safe operation level and maintenance efficiency of the equipment.

[0004] In view of this, one aspect of the present invention provides a method for monitoring the operation of a large-scale amusement facility, comprising:

[0005] Acquiring three-dimensional facility data of a target amusement facility, and constructing a three-dimensional facility model based on the three-dimensional facility data;

[0006] Acquiring historical operating data, historical operating environment data, historical maintenance data, and product attribute data of the target amusement facility;

[0007] Establishing a basic failure model of the target amusement facility based on the historical working data, the historical working environment data, the historical maintenance data, and the product attribute data;

[0008] Identifying key risk locations of the target amusement facility based on the basic failure model and the facility three-dimensional model;

[0009] Develop dynamic risk characteristic indicators for the basic failure model to form a multi-dimensional health assessment framework;

[0010] Use non-destructive testing technology to test the key risk areas and obtain non-destructive testing data;

[0011] Deploy a variety of smart sensors at the key risk areas to monitor operating parameters and structural health data in real time;

[0012] Control intelligent inspection robots or drones to conduct automatic inspections and tests, and obtain video images captured by the intelligent inspection robots or drones;

[0013] Deeply fusing the nondestructive testing data, the operating parameters, the structural health data, and the video image to obtain monitoring data;

[0014] Based on deep learning models and expert systems, intelligent analysis of integrated monitoring data is performed to achieve automatic identification, diagnosis, and prediction of faults;

[0015] Based on the load, structural characteristics, material properties and monitoring data of the target amusement facility, a life assessment method based on mechanics, probability statistics and information technology is used to assess the remaining service life of the key risk parts, and trend analysis is conducted in combination with historical data;

[0016] Using a multi-index comprehensive evaluation method, combined with the health assessment framework, quantitatively evaluate the overall health status of the target amusement facility and generate dynamic risk warnings and maintenance recommendations;

[0017] Based on the dynamic risk warnings and maintenance recommendations, a maintenance and repair plan is formulated, and dynamic management is implemented to ensure the safe operation and performance optimization of the target amusement facilities.

[0018] Optionally, the step of establishing a basic failure model of the target amusement facility based on the historical working data, the historical working environment data, the historical maintenance data, and the product attribute data includes:

[0019] Classifying and arranging the historical working data according to parameters such as operating time, load, speed, and fault frequency, and establishing a first correlation relationship matrix among the operating time, load, speed, and fault frequency;

[0020] Normalizing the historical working environment data according to environmental factors such as temperature, humidity, wind speed, and rain and snow weather, and analyzing to obtain a first interactive influence relationship between different environmental factors;

[0021] Performing statistical analysis on the fault type, frequency, and location of the historical maintenance data, and establishing a first correspondence between the occurrence of the fault and the operating status and / or environmental conditions;

[0022] Structuring the product attribute data and constructing a mapping relationship between material performance, structural characteristics and service life to establish a material-structure-performance association database;

[0023] Use fault tree analysis to identify the root causes of various failures, apply failure mode and effects analysis (FMEA) to determine the severity, frequency, and detection difficulty of failure modes, establish a fault-symptom correlation matrix, quantify the characteristics of different failure modes, and output failure mode analysis results;

[0024] A life distribution model for key components was established based on the Weibull distribution. The Cox proportional hazard model was used to analyze the impact of environmental factors on failure, and a failure prediction model based on time series was established.

[0025] Establish fatigue damage accumulation model, corrosion degradation evolution model, and component wear degradation model to construct failure mechanism model;

[0026] The model is cross-validated using historical data, and model parameters are continuously updated and optimized through actual operation data to establish a model accuracy evaluation index system.

[0027] Optionally, the step of identifying key risk locations of the target amusement facility based on the basic failure model and the facility three-dimensional model includes:

[0028] Integrate the failure modes in the basic failure model with the structural features in the 3D facility model, and identify risk areas by comparing the characteristics of the failure modes with the structural components in the 3D facility model.

[0029] According to the risk characteristic indicators defined in the basic failure model, corresponding risk assessment indicators are set for each risk area in the 3D model of the facility;

[0030] Conduct risk analysis on key components in the three-dimensional model of the facility based on the risk assessment indicators, and utilize computer simulation and finite element analysis techniques to evaluate the stress, deformation, and fatigue of each component within each risk area under different operating conditions and environmental factors to obtain risk analysis data;

[0031] Based on the risk analysis data, identify key risk areas that have a risk of failure under specific operating conditions;

[0032] Compare the identified key risk areas with historical failure data to verify the accuracy of the identification results. Based on the feedback information, adjust the basic failure model or risk assessment indicators to improve the accuracy of identification;

[0033] Generate a report containing key risk areas and their risk assessment results to provide a basis for subsequent maintenance decisions and risk management.

[0034] Optionally, the step of developing dynamic risk characteristic indicators for the basic failure model to form a multi-dimensional health assessment framework includes:

[0035] Conduct in-depth analysis of the established basic failure models to identify different failure modes and their corresponding characteristics;

[0036] For each failure mode, a corresponding risk characteristic indicator system is constructed;

[0037] Establish a coupling relationship model between the indicators of the risk characteristic indicator system, calculate the contribution weight of each indicator to the failure state, analyze the interaction mechanism between the indicators, and obtain the indicator correlation analysis results;

[0038] Establish benchmark values ​​for each indicator based on reference indicator data, set dynamic fluctuation ranges based on environmental and operating conditions to determine dynamic thresholds, and establish a multi-level early warning threshold system;

[0039] Based on the risk characteristic indicator system, the indicator correlation analysis results and the multi-level warning threshold system, a multi-dimensional health assessment framework is constructed.

[0040] Optionally, the step of performing intelligent analysis on the fused monitoring data based on the deep learning model and the expert system to achieve automatic fault identification, diagnosis, and prediction includes:

[0041] Design and train the first deep learning model and select a suitable network architecture to process the fused monitoring data;

[0042] Develop expert systems to embed domain knowledge and empirical rules into the system to enhance the accuracy of fault identification and diagnosis;

[0043] Fusing the expert system with the first deep learning model to form an intelligent recognition system;

[0044] The intelligent recognition system is used to analyze the integrated monitoring data to realize automatic identification and prediction of faults, analyze the causes and impacts of faults, and identify potential fault risks.

[0045] Optionally, the step of assessing the remaining service life of the key risk parts based on the load, structural characteristics, material properties and monitoring data of the target amusement facility using a life assessment method based on mechanics, probability statistics and information technology, and performing trend analysis in combination with historical data includes:

[0046] Collect load data of target amusement facilities, including dynamic load and static load during operation;

[0047] Collect structural characteristics data and record the geometry, connection methods and stress conditions of key risk areas;

[0048] Collect material property data, including strength, fatigue limit, toughness and corrosion resistance;

[0049] Collect real-time monitoring data to obtain strain, temperature, and vibration data of key risk areas;

[0050] Establishing a corresponding mechanical model based on the load data, the structural characteristic data, the material property data, and the monitoring data to simulate the stress conditions of key risk parts under different loads;

[0051] Finite element analysis is used to calculate the mechanical model and evaluate the stress distribution and deformation of components under actual operating conditions;

[0052] Select a life assessment model based on mechanics, probability statistics and information technology, input the collected loads, material properties and monitoring data into the selected life assessment model, and calculate the remaining service life of key risk parts.

[0053] Optionally, the step of using a multi-index comprehensive evaluation method in combination with the health assessment framework to quantitatively evaluate the overall health status of the target amusement facility and generate dynamic risk warnings and maintenance recommendations includes:

[0054] The evaluation index system is constructed by adopting a multi-index comprehensive evaluation method, including: constructing an index hierarchy based on the health assessment framework; dividing the indicators into several dimensions such as structural safety, operating status, and performance parameters; establishing logical correlations between indicators; and setting evaluation standards and calculation methods for each indicator.

[0055] Determine the weight of each indicator in the evaluation index system, including: constructing a judgment matrix using the hierarchical analysis method; calculating the relative weight of each level of indicators; conducting consistency checks and corrections; and establishing a dynamic weight adjustment mechanism;

[0056] Constructing a fuzzy evaluation model, including: establishing the membership function of each indicator; constructing a fuzzy relationship matrix; determining fuzzy operation rules; calculating comprehensive evaluation results;

[0057] Evaluate health status, including: real-time calculation of the health of each indicator; use of fuzzy evaluation models to perform multi-level fuzzy comprehensive operations; determine the health status level of the entire machine; and generate a health status assessment report.

[0058] Construct a risk warning mechanism, including: setting multi-level warning thresholds; establishing a warning rule library; building a warning level determination model; and realizing real-time warning information push.

[0059] Generate maintenance recommendations, including: establishing a mapping relationship between health status and maintenance strategy; designing a maintenance plan generator based on case reasoning; building a maintenance priority ranking model; and forming standardized maintenance recommendation outputs.

[0060] Another aspect of the present invention provides a large-scale amusement facility operation monitoring device for executing a large-scale amusement facility operation monitoring method, comprising: a data acquisition module and a control processing module; wherein,

[0061] The data acquisition module is configured to:

[0062] Acquiring three-dimensional facility data of a target amusement facility, and constructing a three-dimensional facility model based on the three-dimensional facility data;

[0063] Acquiring historical operating data, historical operating environment data, historical maintenance data, and product attribute data of the target amusement facility;

[0064] The control processing module is configured to:

[0065] Establishing a basic failure model of the target amusement facility based on the historical working data, the historical working environment data, the historical maintenance data, and the product attribute data;

[0066] Identifying key risk locations of the target amusement facility based on the basic failure model and the facility three-dimensional model;

[0067] Develop dynamic risk characteristic indicators for the basic failure model to form a multi-dimensional health assessment framework;

[0068] Use non-destructive testing technology to test the key risk areas and obtain non-destructive testing data;

[0069] Deploy a variety of smart sensors at the key risk areas to monitor operating parameters and structural health data in real time;

[0070] Control intelligent inspection robots or drones to conduct automatic inspections and tests, and obtain video images captured by the intelligent inspection robots or drones;

[0071] Deeply fusing the nondestructive testing data, the operating parameters, the structural health data, and the video image to obtain monitoring data;

[0072] Based on deep learning models and expert systems, intelligent analysis of integrated monitoring data is performed to achieve automatic identification, diagnosis, and prediction of faults;

[0073] Based on the load, structural characteristics, material properties and monitoring data of the target amusement facility, a life assessment method based on mechanics, probability statistics and information technology is used to assess the remaining service life of the key risk parts, and trend analysis is conducted in combination with historical data;

[0074] Using a multi-index comprehensive evaluation method, combined with the health assessment framework, quantitatively evaluate the overall health status of the target amusement facility and generate dynamic risk warnings and maintenance recommendations;

[0075] Based on the dynamic risk warnings and maintenance recommendations, a maintenance and repair plan is formulated, and dynamic management is implemented to ensure the safe operation and performance optimization of the target amusement facilities.

[0076] Another aspect of the present invention provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, steps of a method for monitoring the operation of a large-scale amusement facility are implemented.

[0077] Another aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the steps of a method for monitoring the operation of a large-scale amusement facility are implemented.

[0078] By adopting the technical solution of the present invention, the operation monitoring method of large-scale amusement facilities includes: obtaining the three-dimensional facility data of the target amusement facility, and constructing a three-dimensional facility model based on the three-dimensional facility data; obtaining the historical working data, historical working environment data, historical maintenance data and product attribute data of the target amusement facility; establishing a basic failure model of the target amusement facility based on the historical working data, the historical working environment data, the historical maintenance data and the product attribute data; identifying the key risk parts of the target amusement facility based on the basic failure model and the three-dimensional facility model; developing dynamic risk characteristic indicators for the basic failure model to form a multi-dimensional health assessment framework; using non-destructive testing technology to detect the key risk parts and obtain non-destructive testing data; arranging a variety of intelligent sensors at the key risk parts to monitor the operating parameters and structural health status data in real time; and controlling intelligent inspection robots or drones to perform automatic inspections. and detection, and obtain video images collected by intelligent inspection robots or drones; deeply fuse the non-destructive testing data, the operating parameters, the structural health status data and the video images to obtain monitoring data; based on the deep learning model and expert system, intelligently analyze the fused monitoring data to realize automatic identification, diagnosis and prediction of faults; according to the load, structural characteristics, material properties and monitoring data of the target amusement facility, adopt a life assessment method based on mechanics, probability statistics and information technology to assess the remaining service life of the key risk parts, and perform trend analysis in combination with historical data; adopt a multi-indicator comprehensive evaluation method, combined with the health assessment framework, to quantitatively evaluate the health status of the whole machine of the target amusement facility, and generate dynamic risk warnings and maintenance suggestions; according to the dynamic risk warnings and maintenance suggestions, formulate maintenance and repair plans, and implement dynamic management to ensure the safe operation and performance optimization of the target amusement facility. By building a dynamic health management indicator system, combining multiple advanced technologies for data collection, fusion and intelligent analysis, and using technologies such as deep learning and big data analysis for fault diagnosis, prediction and remaining life assessment, we have achieved comprehensive monitoring and quantitative evaluation of the operating status of large-scale amusement facilities, significantly improving the safe operation level and maintenance efficiency of the equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0079] Figure 1 This is a flow chart of a method for monitoring the operation of a large-scale amusement facility provided by one embodiment of the present invention;

[0080] Figure 2 The figure is a schematic block diagram of a large-scale amusement facility operation monitoring device provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0081] In order to more clearly understand the above-mentioned objects, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that, in the absence of conflict, the embodiments of the present application and the features therein can be combined with each other.

[0082] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the scope of protection of the present invention is not limited to the specific embodiments disclosed below.

[0083] The terms "first," "second," and the like in the specification and claims of this application and the accompanying drawings are used to distinguish between different objects, not to describe a particular order. Furthermore, the terms "including," "having," and any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus comprising a series of steps or elements is not limited to the listed steps or elements but may optionally include steps or elements not listed, or may optionally include other steps or elements inherent to the process, method, product, or apparatus.

[0084] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute an independent or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0085] Refer to the following Figures 1 to 2 A method, device, electronic device and storage medium for monitoring the operation of a large-scale amusement facility according to some embodiments of the present invention will be described.

[0086] like Figure 1 As shown, one embodiment of the present invention provides a method for monitoring the operation of a large-scale amusement facility, comprising:

[0087] Acquiring three-dimensional facility data of a target amusement facility, and constructing a three-dimensional facility model based on the three-dimensional facility data;

[0088] In this step, 3D data of the target amusement facility is acquired using technologies such as 3D laser scanning, photogrammetry, or RGB-D cameras. These technologies can accurately capture the facility's geometry and surface features. The acquired 3D data is preprocessed, including noise removal, data cleaning, and data registration. This ensures data accuracy and consistency for subsequent modeling. The processed data is imported into professional 3D modeling software (such as Blender, Maya, or 3ds Max) to construct a 3D model of the facility (details and textures can be added as needed to enhance the model's realism). The constructed 3D model is optimized, including simplifying the number of polygons and adjusting the model's topology to improve rendering efficiency and real-time interactive performance. The constructed 3D model is compared with the actual facility to verify its accuracy and completeness. Adjustments are made as necessary to ensure the model truly reflects the facility's physical characteristics. The final 3D model is stored in an appropriate format for subsequent use, such as virtual reality (VR) presentations, simulation testing, or maintenance management. Through high-precision 3D data acquisition and processing, the model is ensured to accurately reflect the actual structure and characteristics of the target amusement facility. The constructed 3D model provides intuitive visual effects, facilitating design review, customer communication and marketing promotion. The 3D model provides important data support for subsequent risk assessment, maintenance management and facility optimization, helping decision makers make more scientific judgments.

[0089] Obtain historical operating data, historical operating environment data, historical maintenance data, and product attribute data (including material, structure, effective use data, etc.) of the target amusement facility;

[0090] In this step, historical operating data acquisition is performed: The ride's control system and monitoring equipment automatically record operational data, including operating time, load conditions, speed, and frequency. This data can be stored and managed in real time using a data acquisition system (such as a SCADA system). Historical operating environment data acquisition is performed: Environmental monitoring sensors can be used to collect environmental data related to the ride's operation, such as temperature, humidity, wind speed, and precipitation. These sensors can regularly record data and upload it to a central database. Historical maintenance data acquisition is performed: Maintenance records for the ride are organized and archived, including maintenance dates, details, materials used, maintenance personnel, fault descriptions, and handling results. This information is typically stored in a maintenance management system. Product attribute data acquisition is performed: Product attribute data for the ride is collected, including material properties (such as strength and corrosion resistance), structural design parameters (such as size and shape), and effective usage data (such as design life and frequency of use). This data can be obtained from product manuals, design documents, and information provided by the manufacturer. Data integration and storage: The acquired data is integrated into a comprehensive database, which is stored and managed using a database management system (such as an SQL database) for subsequent analysis and query. Data validation and cleaning: Validate and clean the collected data to ensure data accuracy and consistency, remove duplicate data and outliers, and improve data quality.

[0091] Establishing a basic failure model of the target amusement facility based on the historical working data, the historical working environment data, the historical maintenance data, and the product attribute data;

[0092] Identifying key risk locations of the target amusement facility based on the basic failure model and the facility three-dimensional model;

[0093] Develop dynamic risk characteristic indicators for the basic failure model (different failure modes in the basic failure model correspond to different indicators, including but not limited to: structural thickness, crack size, deformation, vibration acceleration, acoustic emission signal, voltage, current, arc signal, control function, etc.), forming a multi-dimensional health assessment framework;

[0094] Use non-destructive testing technologies (such as ultrasonic phased array technology, pulsed eddy current technology, infrared imaging technology, and drone visual inspection technology) to inspect the key risk areas and obtain non-destructive testing data;

[0095] In this step, based on the previous risk assessment and health monitoring results, the key risk areas that need to be inspected are determined; appropriate non-destructive testing technologies are selected, such as: ultrasonic phased array technology is suitable for detecting internal defects (such as cracks and pores). This technology can provide high-resolution imaging results by transmitting and receiving ultrasonic signals; pulsed eddy current technology is mainly used to detect surface and near-surface defects of conductive materials and is suitable for the inspection of metal structures; infrared imaging technology can identify temperature anomalies caused by defects by detecting the temperature distribution on the surface of an object and is suitable for the inspection of electrical equipment and mechanical components; drone visual inspection technology uses the camera on the drone for high-altitude shooting and is suitable for surface defect detection of large structures and can quickly cover large areas. Based on the selected inspection technology, prepare the corresponding inspection equipment and sensors to ensure that the equipment is in good condition; conduct on-site inspections at key risk areas, collect data according to the equipment operating procedures, and ensure that all relevant parameters such as environmental conditions and equipment settings are recorded during the inspection process; organize the collected non-destructive inspection data to ensure data integrity and accuracy; apply the corresponding signal processing algorithms to analyze the inspection data and extract useful information such as defect location, size, and type; compare the inspection results with historical data to verify the accuracy and reliability of the inspection; generate an inspection report that records the inspection process, results, and recommendations in detail to provide a basis for subsequent maintenance decisions. Through this step, non-destructive inspections can be effectively performed on key risk areas and accurate non-destructive inspection data can be obtained, thereby supporting the safety management of the facility.

[0096] Deploy a variety of smart sensors (such as strain sensors, acceleration sensors, temperature sensors, etc.) at the key risk areas to monitor operating parameters and structural health data in real time (and use edge computing technology to achieve real-time data processing);

[0097] In this step, appropriate sensor types (e.g., strain sensors: used to monitor structural strain changes and assess material stress; acceleration sensors: used to monitor vibration and dynamic response and assess structural dynamic characteristics; temperature sensors: used to monitor temperature changes and assess environmental impacts on the structure) are selected based on monitoring requirements and the characteristics of the key risk areas (e.g., structure, shape, and material). A sensor layout plan is designed based on the structural characteristics and risk assessment results to ensure coverage of all key risk areas. Sensors are installed in designated locations, ensuring they are securely fixed and functioning properly. The sensors should have real-time data collection capabilities and regularly record operating parameters and structural health data. The collected data should be transmitted to a data processing center via a wireless or wired network. Edge computing devices should be deployed near the sensors to reduce data transmission latency and bandwidth consumption. Real-time data processing, including data filtering, feature extraction, and preliminary analysis, should be performed using edge computing technology. This allows for rapid data response and timely detection of anomalies. A real-time monitoring platform should be established to centrally display the data and analysis results from each sensor, facilitating monitoring and decision-making by management personnel. This step effectively deploys smart sensors at key risk areas, enabling real-time monitoring and data processing of operating parameters and structural health.

[0098] Control intelligent inspection robots or drones to conduct automatic inspections and tests, and obtain video images captured by the intelligent inspection robots or drones;

[0099] In this step, based on the requirements of the inspection task, select drones or intelligent inspection robots with high-altitude operation capabilities. These devices should be equipped with high-definition cameras and stable flight control systems to ensure the quality of image acquisition in high-altitude environments; configure the necessary sensors on the drone, such as high-definition cameras, infrared sensors, etc., to facilitate multi-dimensional data acquisition; use geographic information systems (GIS) or dedicated software to plan the drone's inspection route to ensure that all high-altitude areas that need to be inspected are covered; set parameters such as inspection height, speed, and image acquisition frequency according to specific needs; use the drone's automatic flight control system to write an inspection program to ensure that the drone can automatically fly along the predetermined route and collect images; during the inspection process, monitor the drone's status and image acquisition in real time through the ground control station to ensure the smooth progress of the mission; the drone automatically obtains video images of high-altitude areas during the inspection process to ensure that the image quality meets the needs of subsequent analysis; the collected video images are transmitted to the ground control station in real time or stored in the drone's storage device for subsequent analysis and processing. Through this step, intelligent inspection robots or drones can be effectively used to automatically inspect and detect difficult-to-reach high-altitude areas, obtain high-quality video images, and provide important support for subsequent maintenance and safety management.

[0100] Deeply fusing the nondestructive testing data, the operating parameters, the structural health data, and the video image to obtain monitoring data;

[0101] In this step, various non-destructive testing technologies (such as ultrasound, X-ray, infrared thermal imaging, etc.) are used to obtain internal defect information (non-destructive testing data) of the structure; the operating parameters of the equipment, including temperature, pressure, vibration and other parameters, are monitored in real time; the structural health status data is collected by sensors (such as strain gauges, accelerometers, etc.); real-time video images of high altitude or difficult-to-reach areas are obtained through drones or intelligent inspection robots; the collected data are cleaned to remove noise and outliers to ensure data accuracy; data from different sources are standardized to make them comparable and facilitate subsequent fusion; Extract key features from the data, which can be achieved through machine learning or deep learning algorithms, such as using convolutional neural networks (CNNs) to extract features from video images; using deep learning models (such as multimodal neural networks) to fuse different types of data (by designing a suitable network architecture, the features of non-destructive testing data, operating parameters, structural health data, and video images are comprehensively analyzed; weighted averaging, feature splicing, or more complex fusion strategies (such as attention mechanisms) can be used to achieve deep fusion of data); and generating comprehensive monitoring data from the fused data, which can be used to assess the health of the structure, predict potential failures, and provide support for decision-making. Through this step, deep fusion of non-destructive testing data, operating parameters, structural health data, and video images can be achieved, ultimately obtaining comprehensive monitoring data to provide strong support for the safety management of the structure.

[0102] Based on deep learning models and expert systems, intelligent analysis of integrated monitoring data is performed to achieve automatic fault identification, diagnosis, and prediction (improving the accuracy of fault prediction and response speed);

[0103] Based on the load, structural characteristics, material properties and monitoring data of the target amusement facility, a life assessment method based on mechanics, probability statistics and information technology is used to assess the remaining service life of the key risk parts, and trend analysis is conducted in combination with historical data;

[0104] Using a multi-index comprehensive evaluation method (such as the analytic hierarchy process, fuzzy comprehensive evaluation method, etc.), combined with the health assessment framework, to quantitatively evaluate the overall health status of the target amusement facility, and generate dynamic risk warnings and maintenance recommendations;

[0105] Based on the dynamic risk warnings and maintenance recommendations, a maintenance and repair plan is formulated, and dynamic management is implemented to ensure the safe operation and performance optimization of the target amusement facilities.

[0106] The embodiments of the present invention, by constructing a dynamic health management indicator system, combining multiple advanced technologies for data collection, fusion and intelligent analysis, and utilizing deep learning and big data analysis technologies for fault diagnosis, prediction and remaining life assessment, achieve comprehensive monitoring and quantitative evaluation of the operating status of large-scale amusement facilities, significantly improving the safe operation level and maintenance efficiency of the equipment.

[0107] In some possible implementations of the present invention, the step of establishing a basic failure model of the target amusement facility based on the historical operating data, the historical operating environment data, the historical maintenance data, and the product attribute data includes:

[0108] Classifying and arranging the historical working data according to parameters such as operating time, load, speed, and fault frequency, and establishing a first correlation relationship matrix among the operating time, load, speed, and fault frequency;

[0109] Normalizing the historical working environment data according to environmental factors such as temperature, humidity, wind speed, and rain and snow weather, and analyzing to obtain a first interactive influence relationship between different environmental factors;

[0110] Performing statistical analysis on the historical maintenance data (including maintenance frequency, maintenance type, and fault description, etc.) in terms of fault type, frequency, and location, and establishing a first correspondence between the occurrence of the fault and the operating status and / or environmental conditions;

[0111] Structuring the product attribute data (including material properties, structural parameters, and design life, etc.), and constructing a mapping relationship between material performance, structural characteristics, and service life to establish a material-structure-performance correlation database;

[0112] Use the Fault Tree Analysis (FTA) method to identify the root causes of various failures, and the Failure Mode and Effects Analysis (FMEA) method to determine the severity, frequency, and detection difficulty of failure modes. Establish a fault-symptom correlation matrix, quantify the characteristics of different failure modes, and output failure mode analysis results.

[0113] In this step, a fault tree analysis is performed based on the historical maintenance data that has been processed as described above to identify the key factor chain that leads to the failure; the structured product attribute data is used to evaluate the impact of the failure on the product system; the frequency of occurrence of the failure mode is quantified in combination with the historical working data; and through environmental data analysis, the role of environmental factors in promoting each failure mode is evaluated.

[0114] A life distribution model for key components was established based on the Weibull distribution. The Cox proportional hazard model was used to analyze the impact of environmental factors on failure, and a failure prediction model based on time series was established.

[0115] In this step, the failure mode analysis results are used as input parameters for the Weibull distribution model; the normalized results of the environmental data are used to optimize the covariates of the Cox proportional hazard model; and the feature space of the failure prediction model is constructed based on the time series characteristics of the historical data.

[0116] Establish fatigue damage accumulation model, corrosion degradation evolution model, and component wear degradation model to construct failure mechanism model;

[0117] In this step, the fatigue damage accumulation model needs to integrate the prediction results of the failure probability model, the corrosion degradation evolution model needs to consider the probability distribution characteristics of environmental factors, and the wear degradation model needs to be combined with the qualitative conclusions of the failure mode analysis.

[0118] The model is cross-validated using historical data, and model parameters are continuously updated and optimized through actual operation data to establish a model accuracy evaluation index system.

[0119] In this step, the data preprocessing method is reversely optimized through cross-validation results; the weight coefficients of the failure modes are dynamically updated based on actual operating data; and the model accuracy assessment results are used to adjust the parameters of the probability model and mechanism model.

[0120] In this embodiment, by establishing a basic failure model, potential failure modes can be identified in advance, thereby improving the accuracy of failure prediction and reducing unexpected downtime; based on the analysis results of the model, a more scientific maintenance plan can be formulated, maintenance costs can be reduced, and equipment utilization efficiency can be improved; through the identification and analysis of failure modes, preventive measures can be taken in advance to improve the safety of amusement facilities and ensure the safety of tourists.

[0121] In some possible implementations of the present invention, the step of identifying key risk locations of the target amusement facility based on the basic failure model and the facility three-dimensional model includes:

[0122] Integrate the failure modes in the basic failure model with the structural features in the 3D facility model. By comparing the characteristics of the failure modes (such as stress concentrations, fatigue points, etc.) with the structural components in the 3D facility model, identify risk areas.

[0123] This step specifically includes: dividing the three-dimensional model of the facility into functional blocks such as the drive system area, the load-bearing structure area, and the control system area according to their functions; meshing each functional block and establishing a local coordinate system; building a database of connection relationships between the functional blocks; establishing a corresponding relationship between various failure modes in the basic failure model and the functional blocks; calculating the failure probability of each component in each functional block; analyzing the propagation path and impact range of the failure, and determining the risk areas where risks exist.

[0124] Based on the risk characteristic indicators defined in the basic failure model (such as structural thickness, crack size, deformation, etc.), corresponding risk assessment indicators are set for each risk area in the 3D facility model (these indicators will be used to quantify the risk level of each component);

[0125] Conduct risk analysis on key components in the three-dimensional model of the facility based on the risk assessment indicators. Utilize computer simulation and finite element analysis (FEA) technology to evaluate the stress, deformation, and fatigue of each component within each risk area under different operating conditions and environmental factors to obtain risk analysis data.

[0126] In this step, finite element analysis is performed based on the three-dimensional model to obtain the static stress distribution; dynamic load simulation is performed to obtain the stress time history under critical working conditions; a stress concentration factor distribution diagram is established; a "failure probability-failure consequence" risk matrix is ​​established; the risk index of each area is calculated by combining the stress distribution and failure probability; and the risk index is normalized to form risk analysis data.

[0127] Based on the risk analysis data, identify key risk areas that are at risk of failure under specific operating conditions (these areas are usually areas of stress concentration, fatigue damage, or significant environmental impact);

[0128] This step specifically includes: setting risk level thresholds and preliminarily screening high-risk areas; analyzing the accessibility and detectability of high-risk areas; and determining the final list of key risk areas.

[0129] Compare the identified key risk areas with historical failure data to verify the accuracy of the identification results. Based on the feedback information, adjust the basic failure model or risk assessment indicators to improve the accuracy of identification;

[0130] Generate a report containing key risk areas and their risk assessment results to provide a basis for subsequent maintenance decisions and risk management.

[0131] In this embodiment, zoning information provides a spatial location reference for the failure model, functional block division influences failure mode classification, and connectivity data is used for failure propagation analysis. Failure modes are located based on functional blocks, local failure probabilities are calculated using a mesh model, and the failure impact range is analyzed through connectivity. Zoning information guides the development of finite element models, failure modes influence the selection of load conditions, and stress distribution assists in failure probability assessment. A multi-dimensional risk assessment system is established by integrating failure probability and stress distribution results, taking into account the importance weights of functional blocks. Locations are screened based on risk levels and validated with actual maintenance experience to form a dynamically updated risk location database. By combining basic failure models with a three-dimensional facility model, critical risk locations within an amusement facility can be more accurately identified, improving the effectiveness of risk management. Identified critical risk locations can be targeted for monitoring and maintenance, reducing the probability of failure and enhancing the safety of the facility. Based on the risk assessment results, targeted maintenance and overhaul plans can be developed, resources can be allocated rationally, and maintenance efficiency can be improved. This systematic risk identification process provides managers with a scientific basis for decision-making, helping them make more informed choices in operation and maintenance.

[0132] In some possible implementations of the present invention, the step of developing dynamic risk characteristic indicators for the basic failure model to form a multi-dimensional health assessment framework includes:

[0133] Conduct in-depth analysis of established basic failure models to identify different failure modes and their corresponding characteristics (these failure modes may include material fatigue, structural damage, control system failure, etc.);

[0134] In this step, the physical characteristics of each failure mode are analyzed, a correspondence matrix between failure modes and measurable parameters is established, and the dominant and secondary characteristic indicators of each failure mode are determined.

[0135] For each failure mode, a corresponding risk characteristic indicator system is constructed;

[0136] In this step, these indicators should cover multiple dimensions, including but not limited to: structural indicators (such as structural thickness, crack size, deformation), dynamic response indicators (such as vibration acceleration and acoustic emission signals), electrical characteristic indicators (such as voltage, current, arc signals), and control function indicators (such as control system response time and functional integrity). Quantitative standards should be established for structural indicators; measurement specifications should be developed for dynamic response indicators (such as vibration and acoustic emission); monitoring requirements should be determined for electrical characteristic indicators; and performance evaluation standards should be established for control function indicators.

[0137] Establish a coupling relationship model between the indicators of the risk characteristic indicator system, calculate the contribution weight of each indicator to the failure state, analyze the interaction mechanism between the indicators, and obtain the indicator correlation analysis results;

[0138] Establish benchmark values ​​for each indicator based on reference indicator data, set dynamic fluctuation ranges based on environmental and operating conditions to determine dynamic thresholds, and establish a multi-level early warning threshold system;

[0139] Based on the risk characteristic indicator system, the indicator correlation analysis results and the multi-level warning threshold system, a multi-dimensional health assessment framework is constructed (the framework should be able to comprehensively consider the impact of different indicators to form an assessment of the overall health status of the amusement facility).

[0140] In this step, based on the risk characteristic indicator system, the indicator correlation analysis results and the multi-level warning threshold system, a multi-level indicator evaluation structure is designed, a health calculation model based on fuzzy comprehensive evaluation is established, and a visual health status display interface is developed to construct a health assessment framework.

[0141] In this embodiment, through multi-dimensional dynamic risk characteristic indicators, the health status of amusement facilities can be comprehensively monitored and potential risks can be discovered in a timely manner; the introduction of dynamic monitoring indicators can enhance the early warning capability of faults and reduce the occurrence of unexpected shutdowns and safety accidents; based on the analysis results of the health assessment framework, more scientific maintenance strategies can be formulated to improve maintenance efficiency and resource utilization; by timely identifying and handling potential risks, the safety of amusement facilities can be improved and the safety of tourists can be guaranteed.

[0142] In some possible implementations of the present invention, the steps of performing intelligent analysis on the fused monitoring data based on the deep learning model and the expert system to achieve automatic fault identification, diagnosis, and prediction include:

[0143] Design and train the first deep learning model and select a suitable network architecture (such as convolutional neural network (CNN) or long short-term memory (LSTM)) to process the fused monitoring data;

[0144] In this step, the first deep learning model is constructed. This involves designing a multi-layer temporal convolutional neural network to extract local features, constructing a long short-term memory (LSTM) network to capture long-term dependencies, designing an attention mechanism to highlight key temporal features, and establishing a multi-task learning framework for fault classification and prediction. During training, supervised learning is performed using labeled data to optimize model parameters and improve the model's ability to identify fault features.

[0145] Develop expert systems to embed domain knowledge and empirical rules into the system to enhance the accuracy of fault identification and diagnosis;

[0146] In this step, the expert system knowledge base construction steps include: establishing a fault symptom-cause association rule base; building an equipment maintenance experience knowledge graph; designing diagnosis rules based on case reasoning; and establishing a fault evolution path model.

[0147] Fusing the expert system with the first deep learning model to form an intelligent recognition system (using deep learning for data-driven analysis and the expert system for rule-based reasoning);

[0148] This step specifically includes: designing a result fusion strategy for deep learning models and expert systems; establishing a confidence-based multi-model voting mechanism; building a dynamic weight adaptive adjustment mechanism; and generating the final fault diagnosis and prediction results.

[0149] The intelligent recognition system is used to analyze the integrated monitoring data to realize automatic identification and prediction of faults, analyze the causes and impacts of faults, and identify potential fault risks.

[0150] In this embodiment, deep learning results serve as the input of the expert system, and expert rules guide feature selection and model design. The complementary advantages of the two improve diagnostic accuracy; deep learning and expert knowledge are comprehensively utilized to dynamically adjust the weights of different models to achieve optimized decision-making on multi-source information; the system can monitor and automatically identify equipment failures in real time, reduce manual intervention, and improve work efficiency; combining the advantages of deep learning and expert systems, it can more accurately diagnose the cause of failures and reduce misdiagnosis rates; through analysis of historical and real-time data, the system can predict potential failures in advance, improve the accuracy of fault prediction and response speed, thereby reducing downtime and maintenance costs.

[0151] In some possible implementations of the present invention, the steps of assessing the remaining service life of the key risk parts based on the load, structural characteristics, material properties and monitoring data of the target amusement facility using a life assessment method based on mechanics, probability statistics and information technology, and performing trend analysis in combination with historical data include:

[0152] Collect load data of target amusement facilities, including dynamic load and static load during operation;

[0153] Collect structural characteristics data and record the geometry, connection methods and stress conditions of key risk areas;

[0154] Collect material property data, including strength, fatigue limit, toughness and corrosion resistance;

[0155] Collect real-time monitoring data to obtain strain, temperature, and vibration data of key risk areas;

[0156] Establishing a corresponding mechanical model based on the load data, the structural characteristic data, the material property data, and the monitoring data to simulate the stress conditions of key risk parts under different loads;

[0157] Finite element analysis is used to calculate the mechanical model and evaluate the stress distribution and deformation of components under actual operating conditions;

[0158] Select a life assessment model based on mechanics, probability statistics and information technology, input the collected loads, material properties and monitoring data into the selected life assessment model, and calculate the remaining service life of key risk parts.

[0159] Common methods used in this step include fatigue life assessment (based on the SN curve and Miner's law to evaluate the fatigue life of materials under cyclic loads), probabilistic life model: using probability statistics such as Weibull distribution to evaluate the failure probability and remaining service life of components, etc.

[0160] The solution of this embodiment, combined with mechanical analysis and probability statistics, can accurately assess the remaining service life of key risk parts and help managers make scientific decisions.

[0161] In some possible implementations of the present invention, the steps of using a multi-index comprehensive evaluation method in combination with the health assessment framework to quantitatively evaluate the overall health status of the target amusement facility and generate dynamic risk warnings and maintenance recommendations include:

[0162] The evaluation index system is constructed by adopting a multi-index comprehensive evaluation method, including: constructing an index hierarchy based on the health assessment framework; dividing the indicators into several dimensions such as structural safety, operating status, and performance parameters; establishing logical correlations between indicators; and setting evaluation standards and calculation methods for each indicator.

[0163] Determine the weight of each indicator in the evaluation index system, including: constructing a judgment matrix using the hierarchical analysis method; calculating the relative weight of each level of indicators; conducting consistency checks and corrections; and establishing a dynamic weight adjustment mechanism;

[0164] Constructing a fuzzy evaluation model, including: establishing the membership function of each indicator; constructing a fuzzy relationship matrix; determining fuzzy operation rules; calculating comprehensive evaluation results;

[0165] Evaluate health status, including: real-time calculation of the health of each indicator; use of fuzzy evaluation models to perform multi-level fuzzy comprehensive operations; determine the health status level of the entire machine; and generate a health status assessment report.

[0166] Construct a risk warning mechanism, including: setting multi-level warning thresholds; establishing a warning rule library; building a warning level determination model; and realizing real-time warning information push.

[0167] Generate maintenance recommendations, including: establishing a mapping relationship between health status and maintenance strategy; designing a maintenance plan generator based on case reasoning; building a maintenance priority ranking model; and forming standardized maintenance recommendation outputs.

[0168] In this embodiment, the core indicators in the indicator system inheritance framework are used to expand the evaluation dimensions and levels, maintaining the consistency of the indicator system; weights are assigned based on the importance of indicators, and the weights reflect the strength of the correlation between indicators and are dynamically adjusted to adapt to state changes; weights participate in the fuzzy operation process, and the fuzzy results affect the weight adjustment, achieving scientific evaluation; multi-level evaluation results are integrated to generate a quantitative health index to support state classification judgment; based on the health status, early warning is triggered, and the early warning level corresponds to the health level, achieving real-time early warning; referring to the health assessment to determine the maintenance strategy, combining the early warning information to formulate a maintenance plan, and optimize the allocation of maintenance resources. The solution of this embodiment, through a multi-indicator comprehensive evaluation method, can comprehensively and accurately evaluate the health status of the entire amusement facility; the dynamic risk early warning mechanism can timely identify potential risks, reduce the probability of accidents, and ensure the safe operation of the amusement facility; the maintenance suggestions generated based on quantitative evaluation and risk analysis can help managers formulate scientific and reasonable maintenance plans and improve the efficiency and safety of the facility.

[0169] See Figure 2 Another embodiment of the present invention provides a large-scale amusement facility operation monitoring device for executing a large-scale amusement facility operation monitoring method, comprising: a data acquisition module and a control processing module; wherein,

[0170] The data acquisition module is configured to:

[0171] Acquiring three-dimensional facility data of a target amusement facility, and constructing a three-dimensional facility model based on the three-dimensional facility data;

[0172] Acquiring historical operating data, historical operating environment data, historical maintenance data, and product attribute data of the target amusement facility;

[0173] The control processing module is configured to:

[0174] Establishing a basic failure model of the target amusement facility based on the historical working data, the historical working environment data, the historical maintenance data, and the product attribute data;

[0175] Identifying key risk locations of the target amusement facility based on the basic failure model and the facility three-dimensional model;

[0176] Develop dynamic risk characteristic indicators for the basic failure model to form a multi-dimensional health assessment framework;

[0177] Use non-destructive testing technology to test the key risk areas and obtain non-destructive testing data;

[0178] Deploy a variety of smart sensors at the key risk areas to monitor operating parameters and structural health data in real time;

[0179] Control intelligent inspection robots or drones to conduct automatic inspections and tests, and obtain video images captured by the intelligent inspection robots or drones;

[0180] Deeply fusing the nondestructive testing data, the operating parameters, the structural health data, and the video image to obtain monitoring data;

[0181] Based on deep learning models and expert systems, intelligent analysis of integrated monitoring data is performed to achieve automatic identification, diagnosis, and prediction of faults;

[0182] Based on the load, structural characteristics, material properties and monitoring data of the target amusement facility, a life assessment method based on mechanics, probability statistics and information technology is used to assess the remaining service life of the key risk parts, and trend analysis is conducted in combination with historical data;

[0183] Using a multi-index comprehensive evaluation method, combined with the health assessment framework, quantitatively evaluate the overall health status of the target amusement facility and generate dynamic risk warnings and maintenance recommendations;

[0184] Based on the dynamic risk warnings and maintenance recommendations, a maintenance and repair plan is formulated, and dynamic management is implemented to ensure the safe operation and performance optimization of the target amusement facilities.

[0185] It should be known that Figure 2 The block diagram of the large-scale amusement ride operation monitoring device shown is for illustrative purposes only, and the number of modules shown does not limit the scope of protection of the present invention. The large-scale amusement ride operation monitoring device provided in this embodiment can be used to implement the various embodiments of the corresponding large-scale amusement ride operation monitoring method. For specific implementation procedures, please refer to the description of the respective method embodiments and will not be repeated here.

[0186] Another embodiment of the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, steps of a method for monitoring the operation of a large-scale amusement facility are implemented.

[0187] Another embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the computer program implements the steps of a method for monitoring the operation of a large-scale amusement facility.

[0188] It should be noted that for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that this application is not limited by the order of the actions described, because according to this application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by this application.

[0189] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0190] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the above-mentioned units is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0191] The units described above as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0192] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0193] If the above-mentioned integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a memory and includes a number of instructions for enabling a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the above-mentioned methods of each embodiment of the present application. The aforementioned memory includes: various media that can store program codes, such as a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk.

[0194] Those skilled in the art will appreciate that all or part of the steps in the various methods of the above embodiments can be completed by instructing related hardware through a program. The program can be stored in a computer-readable memory, and the memory can include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0195] The above is a detailed introduction to the embodiments of the present application. Specific examples are used herein to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method and core idea of ​​the present application. At the same time, for those skilled in the art, according to the idea of ​​the present application, there may be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

[0196] Although the present invention is disclosed above, it is not limited thereto. Any person skilled in the art may readily conceive of variations or substitutions, and may make various modifications and alterations without departing from the spirit and scope of the present invention. Combinations of the above-described functions and implementation steps, including software and hardware implementations, are all within the scope of protection of the present invention.

Claims

1. A method for monitoring the operation of a large-scale amusement facility, characterized in that: include: Acquiring three-dimensional facility data of a target amusement facility, and constructing a three-dimensional facility model based on the three-dimensional facility data; Acquiring historical operating data, historical operating environment data, historical maintenance data, and product attribute data of the target amusement facility; Establishing a basic failure model of the target amusement facility based on the historical working data, the historical working environment data, the historical maintenance data, and the product attribute data; Identifying key risk locations of the target amusement facility based on the basic failure model and the facility three-dimensional model; Develop dynamic risk characteristic indicators for the basic failure model to form a multi-dimensional health assessment framework, including: conducting in-depth analysis of the established basic failure model to identify different failure modes and their corresponding characteristics; constructing a corresponding risk characteristic indicator system for each failure mode; establishing a coupling relationship model between indicators of the risk characteristic indicator system, calculating the contribution weight of each indicator to the failure state, analyzing the interaction mechanism between indicators, and obtaining indicator correlation analysis results; establishing a baseline value for each indicator based on reference indicator data, setting a dynamic fluctuation range according to the influence of the environment and working conditions to determine the dynamic threshold, and establishing a multi-level early warning threshold system; constructing a multi-dimensional health assessment framework based on the risk characteristic indicator system, the indicator correlation analysis results, and the multi-level early warning threshold system; Use non-destructive testing technology to test the key risk areas and obtain non-destructive testing data; Deploy a variety of smart sensors at the key risk areas to monitor operating parameters and structural health data in real time; Control intelligent inspection robots or drones to conduct automatic inspections and tests, and obtain video images captured by the intelligent inspection robots or drones; Deeply fusing the nondestructive testing data, the operating parameters, the structural health data, and the video image to obtain monitoring data; Based on deep learning models and expert systems, intelligent analysis of integrated monitoring data is performed to achieve automatic identification, diagnosis, and prediction of faults; Based on the load, structural characteristics, material properties and monitoring data of the target amusement facility, a life assessment method based on mechanics, probability statistics and information technology is used to assess the remaining service life of the key risk parts, and trend analysis is conducted in combination with historical data; Using a multi-index comprehensive evaluation method, combined with the health assessment framework, quantitatively evaluate the overall health status of the target amusement facility and generate dynamic risk warnings and maintenance recommendations; Based on the dynamic risk warnings and maintenance recommendations, a maintenance and repair plan is formulated, and dynamic management is implemented to ensure the safe operation and performance optimization of the target amusement facilities.

2. The large-scale amusement facility operation monitoring method according to claim 1, characterized in that: The step of establishing a basic failure model of the target amusement facility based on the historical working data, the historical working environment data, the historical maintenance data, and the product attribute data comprises: Classifying and arranging the historical working data according to parameters such as operating time, load, speed, and fault frequency, and establishing a first correlation relationship matrix among the operating time, load, speed, and fault frequency; Normalizing the historical working environment data according to environmental factors such as temperature, humidity, wind speed, and rain and snow weather, and analyzing to obtain a first interactive influence relationship between different environmental factors; Performing statistical analysis on the fault type, frequency, and location of the historical maintenance data, and establishing a first correspondence between the occurrence of the fault and the operating status and / or environmental conditions; Structuring the product attribute data and constructing a mapping relationship between material performance, structural characteristics and service life to establish a material-structure-performance association database; Use fault tree analysis to identify the root causes of various failures, apply failure mode and effects analysis (FMEA) to determine the severity, frequency, and detection difficulty of failure modes, establish a fault-symptom correlation matrix, quantify the characteristics of different failure modes, and output failure mode analysis results; A life distribution model for key components was established based on the Weibull distribution. The Cox proportional hazard model was used to analyze the impact of environmental factors on failure, and a failure prediction model based on time series was established. Establish fatigue damage accumulation model, corrosion degradation evolution model, and component wear degradation model to construct failure mechanism model; The model is cross-validated using historical data, and model parameters are continuously updated and optimized through actual operation data to establish a model accuracy evaluation index system.

3. The large-scale amusement facility operation monitoring method according to claim 2, characterized in that: The step of identifying key risk locations of the target amusement facility based on the basic failure model and the facility three-dimensional model includes: Integrate the failure modes in the basic failure model with the structural features in the 3D facility model, and identify risk areas by comparing the characteristics of the failure modes with the structural components in the 3D facility model. According to the risk characteristic indicators defined in the basic failure model, corresponding risk assessment indicators are set for each risk area in the 3D model of the facility; Conduct risk analysis on key components in the three-dimensional model of the facility based on the risk assessment indicators, and utilize computer simulation and finite element analysis techniques to evaluate the stress, deformation, and fatigue of each component within each risk area under different operating conditions and environmental factors to obtain risk analysis data; Based on the risk analysis data, identify key risk areas that have a risk of failure under specific operating conditions; Compare the identified key risk areas with historical failure data to verify the accuracy of the identification results. Based on the feedback information, adjust the basic failure model or risk assessment indicators to improve the accuracy of identification; Generate a report containing key risk areas and their risk assessment results to provide a basis for subsequent maintenance decisions and risk management.

4. The large-scale amusement facility operation monitoring method according to claim 3, characterized in that: The steps of intelligently analyzing the integrated monitoring data based on the deep learning model and expert system to achieve automatic fault identification, diagnosis, and prediction include: Design and train the first deep learning model and select a suitable network architecture to process the fused monitoring data; Develop expert systems to embed domain knowledge and empirical rules into the system to enhance the accuracy of fault identification and diagnosis; Fusing the expert system with the first deep learning model to form an intelligent recognition system; The intelligent recognition system is used to analyze the integrated monitoring data to realize automatic identification and prediction of faults, analyze the causes and impacts of faults, and identify potential fault risks.

5. The large-scale amusement facility operation monitoring method according to claim 4, characterized in that: The steps of evaluating the remaining service life of the key risk parts based on the load, structural characteristics, material properties and monitoring data of the target amusement facility using a life assessment method based on mechanics, probability statistics and information technology, and performing trend analysis in combination with historical data include: Collect load data of target amusement facilities, including dynamic load and static load during operation; Collect structural characteristics data and record the geometry, connection methods and stress conditions of key risk areas; Collect material property data, including strength, fatigue limit, toughness and corrosion resistance; Collect real-time monitoring data to obtain strain, temperature, and vibration data of key risk areas; Establishing a corresponding mechanical model based on the load data, the structural characteristic data, the material property data, and the monitoring data to simulate the stress conditions of key risk parts under different loads; Finite element analysis is used to calculate the mechanical model and evaluate the stress distribution and deformation of components under actual operating conditions; Select a life assessment model based on mechanics, probability statistics and information technology, input the collected loads, material properties and monitoring data into the selected life assessment model, and calculate the remaining service life of key risk parts.

6. The large-scale amusement facility operation monitoring method according to claim 5, characterized in that: The steps of using the multi-index comprehensive evaluation method in combination with the health assessment framework to quantitatively evaluate the overall health status of the target amusement facility and generate dynamic risk warnings and maintenance recommendations include: The evaluation index system is constructed by adopting a multi-index comprehensive evaluation method, including: constructing an index hierarchy based on the health assessment framework; dividing the indicators into several dimensions such as structural safety, operating status, and performance parameters; establishing logical correlations between indicators; and setting evaluation standards and calculation methods for each indicator. Determine the weight of each indicator in the evaluation index system, including: constructing a judgment matrix using the hierarchical analysis method; calculating the relative weight of each level of indicators; conducting consistency checks and corrections; and establishing a dynamic weight adjustment mechanism; Constructing a fuzzy evaluation model, including: establishing the membership function of each indicator; constructing a fuzzy relationship matrix; determining fuzzy operation rules; calculating comprehensive evaluation results; Evaluate health status, including: real-time calculation of the health of each indicator; use of fuzzy evaluation models to perform multi-level fuzzy comprehensive operations; determine the health status level of the entire machine; and generate a health status assessment report. Build a risk warning mechanism, including: setting multi-level warning thresholds; establishing a warning rule library; building a warning level determination model; and implementing real-time warning information push; Generate maintenance recommendations, including: establishing a mapping relationship between health status and maintenance strategy; designing a maintenance plan generator based on case reasoning; building a maintenance priority ranking model; and forming standardized maintenance recommendation outputs.

7. A large-scale amusement facility operation monitoring device, used to execute the large-scale amusement facility operation monitoring method according to any one of claims 1 to 6, characterized in that: include: Data acquisition module and control processing module; wherein, The data acquisition module is configured to: Acquiring three-dimensional facility data of a target amusement facility, and constructing a three-dimensional facility model based on the three-dimensional facility data; Acquiring historical operating data, historical operating environment data, historical maintenance data, and product attribute data of the target amusement facility; The control processing module is configured to: Establishing a basic failure model of the target amusement facility based on the historical working data, the historical working environment data, the historical maintenance data, and the product attribute data; Identifying key risk locations of the target amusement facility based on the basic failure model and the facility three-dimensional model; Develop dynamic risk characteristic indicators for the basic failure model to form a multi-dimensional health assessment framework; Use non-destructive testing technology to test the key risk areas and obtain non-destructive testing data; Deploy a variety of smart sensors at the key risk areas to monitor operating parameters and structural health data in real time; Control intelligent inspection robots or drones to conduct automatic inspections and tests, and obtain video images captured by the intelligent inspection robots or drones; Deeply fusing the nondestructive testing data, the operating parameters, the structural health data, and the video image to obtain monitoring data; Based on deep learning models and expert systems, intelligent analysis of integrated monitoring data is performed to achieve automatic identification, diagnosis, and prediction of faults; Based on the load, structural characteristics, material properties and monitoring data of the target amusement facility, a life assessment method based on mechanics, probability statistics and information technology is used to assess the remaining service life of the key risk parts, and trend analysis is conducted in combination with historical data; Using a multi-index comprehensive evaluation method, combined with the health assessment framework, quantitatively evaluate the overall health status of the target amusement facility and generate dynamic risk warnings and maintenance recommendations; Based on the dynamic risk warnings and maintenance recommendations, a maintenance and repair plan is formulated, and dynamic management is implemented to ensure the safe operation and performance optimization of the target amusement facilities.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the large-scale amusement facility operation monitoring method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the large-scale amusement facility operation monitoring method according to any one of claims 1 to 6 are implemented.

Images