A special equipment risk prediction and adaptive collection method and system for a cultural and travel scene

Abstract

The application discloses a kind of special equipment risk prediction and adaptive acquisition method and system for cultural tourism scene.The method is firstly aligned to multi-source perception data in space-time;Second, construct the neural symbolic knowledge graph that fuses safety specification logic;Then, based on the atlas in digital twin environment, risk evolution deduction is carried out by embodied agent, and risk trajectory is generated;According to the trajectory characteristics, dynamically generate and verify adaptive data acquisition strategy;Execute strategy and use new data to calibrate the deduction model online;Based on the calibration result, generate multi-granularity risk visualization view;Finally, when risk is over-limit, trigger the emergency linkage coordinated with BIM.The application realizes the closed loop of risk perception, prediction, decision and disposal, solves the problem of rigid data acquisition, uninterpretable model and lack of adaptive ability in the prior art, significantly improves the intelligent level and emergency response efficiency of cultural tourism special equipment safety monitoring.

Description

Technical Field

[0001] This invention belongs to the field of special equipment safety monitoring and intelligent operation and maintenance technology, and in particular relates to a method and system for risk prediction and adaptive data collection of special equipment for cultural and tourism scenarios. Background Technology

[0002] With the deep penetration of artificial intelligence and Internet of Things technologies into the field of public safety, sensor network-based special equipment condition monitoring systems have been gradually deployed in scenarios such as large amusement facilities and passenger ropeways. Current mainstream solutions typically employ fixed-frequency collection of multi-source data (such as vibration, temperature, and video), using shallow machine learning models (such as SVM and random forest) or end-to-end deep neural networks for fault classification or risk scoring. However, these methods face severe challenges in highly dynamic and open cultural tourism environments, and their technical limitations are mainly reflected in the following three aspects:

[0003] First, the data collection strategy is static and rigid, and cannot adapt to the evolving needs of risk.

[0004] Existing systems generally use a preset, uniform sampling frequency (e.g., 10Hz for vibration signals, 1fps for video), failing to consider the time-varying characteristics of equipment health status or to incorporate external disturbances (such as surges in passenger flow or extreme weather). This results in a large amount of redundant data transmission and wasted edge computing resources during low-risk periods; while in high-risk critical phases, insufficient sampling leads to the loss of key transient features, causing delayed early warnings. More seriously, this open-loop architecture lacks an online verification mechanism for the effectiveness of the data acquisition strategy, failing to ensure that the acquired data is sufficient to support high-confidence predictions.

[0005] Secondly, the risk prediction model lacks interpretability and physical consistency.

[0006] While mainstream deep learning models (such as LSTM and Transformer) can fit historical failure patterns, their "black box" nature makes the decision-making process untraceable, making it difficult to gain the trust of regulatory agencies and maintenance personnel. Furthermore, model training relies on a large number of labeled failure samples, while major accidents are rare in cultural and tourism scenarios, leading to a severe imbalance between positive and negative samples. More importantly, existing methods isolate heterogeneous data such as text inspection records, video footage, and sensor readings, severing the inherent connection between human experience, physical rules, and data-driven representations, making it impossible to build a risk knowledge system with causal reasoning capabilities. For example, when the model outputs "high risk," it cannot explain whether it stems from "loose bolts" or "track deformation," nor can it relate it to specific clauses in the "GB8408 Safety Specification for Large-scale Amusement Facilities."

[0007] Third, the system operates in an open-loop state and lacks adaptive evolution capabilities.

[0008] Current monitoring platforms mostly operate on a one-way "sensing-alarm" process. Predictive results are only used for post-event alarms and cannot drive the optimization of front-end sensing strategies, creating a vicious cycle where "the more dangerous the situation, the more refined the sensing needs, but the fixed strategies result in insufficient sensing." Furthermore, visualization interfaces typically only display raw data curves or simple threshold alarms, failing to integrate equipment topology, pedestrian flow distribution, and regulatory knowledge. This makes it difficult for managers to quickly pinpoint the root cause and develop response plans. Even when emergency shutdowns are triggered, they often rely on manual intervention to schedule broadcasts, lighting, and evacuation, resulting in high response delays and a lack of effective feedback of event data to model updates, leading to long-term system performance degradation.

[0009] For example, in a case involving the monitoring of a roller coaster at a large theme park, the traditional system, due to its fixed 1Hz sampling, failed to capture high-frequency impacts (>50Hz) caused by microcracks in the bearing, leading to a misclassification of the risk level as "medium." Furthermore, when bolt loosening actually occurred, although the visual model detected abnormal displacement, it incorrectly attributed it to "structural fatigue" because it failed to correlate it with the "recent maintenance" information in the inspection records, delaying accurate intervention. More importantly, the entire process did not trigger an upgrade to the data acquisition strategy, and subsequent similar potential hazards continued to be missed in the same manner.

[0010] Therefore, there is an urgent need for a new technological framework that can:

[0011] 1) Dynamically couple risk prediction and data collection to achieve "on-demand perception";

[0012] 2) Deeply integrate symbolic rules and neural representations to construct an interpretable and traceable risk knowledge graph;

[0013] 3) Establish a closed loop of "prediction-decision-execution-learning" to enable the system to continuously evolve in long-term operation.

[0014] How to achieve the above goals while ensuring real-time performance and resource constraints has become the core technical bottleneck for improving the inherent safety level of special equipment for cultural and tourism purposes, and it is also the key problem that this invention focuses on solving. Summary of the Invention

[0015] The purpose of this invention is to provide a method and system for risk prediction and adaptive data collection of special equipment in cultural and tourism scenarios, so as to solve the above-mentioned technical problems.

[0016] To address the aforementioned technical problems, the specific technical solution of this invention, a method and system for risk prediction and adaptive data acquisition of special equipment in cultural and tourism scenarios, is as follows:

[0017] A method for risk prediction and adaptive data collection of special equipment in cultural and tourism scenarios includes the following steps:

[0018] Step 1: Collect multi-source sensing data of special equipment in cultural and tourism scenarios and perform spatiotemporal alignment processing;

[0019] Step 2: Construct a neural symbolic knowledge graph;

[0020] Step 3: Based on the neural symbol knowledge graph, perform risk evolution simulation in the digital twin environment to generate a set of risk evolution trajectories for future time periods;

[0021] Step 4: Dynamically generate corresponding adaptive data acquisition strategies based on risk characteristics; and perform simulation verification.

[0022] Step 5: Execute the validated strategy and use the new data to calibrate the simulation model online to obtain the calibrated world model;

[0023] Step 6: Based on the calibrated world model and the set of risk evolution trajectories, generate a multi-granularity risk visualization view;

[0024] Step 7: When the risk exceeds the limit, trigger emergency linkage with BIM.

[0025] Furthermore, in step 1, the multi-source sensing data includes IoT sensor timing signals, video streams, manual inspection records, and pedestrian density data; the time synchronization adopts a hybrid clock synchronization mechanism; and the spatial coordinate normalization transforms all location information into a unified projected coordinate system.

[0026] Furthermore, step 2 includes extracting entities and relationships based on the original data packet, introducing security specification clauses to form specification constraints, and fusing entities, relationships, and specification constraints to generate a structured knowledge graph that integrates multimodal semantic features and symbolic logic; the node types of the neural symbolic knowledge graph include equipment components, defects, environmental states, and security specification clauses; the edges of the neural symbolic knowledge graph include relational edges representing semantic relationships between entities, and constraint edges representing compliance associations between defects and security specification clauses.

[0027] Furthermore, step 3 includes initializing an embodied risk world model in a digital twin environment, and conducting multi-step interactive deductions in the world model through multiple embodied agents. The embodied risk world model integrates device physical laws, security semantic rules, and a residual model driven by historical data. Each of the multiple embodied agents selects actions according to a neural symbolic policy network to explore different risk evolution paths in the world model. Each risk evolution trajectory is accompanied by a comprehensive risk confidence assessment value.

[0028] Furthermore, in step 4, the risk characteristics include high-risk component identification, risk escalation rate, predicted failure time, and human exposure intensity; the adaptive data acquisition strategy selects and modulates parameters from a predefined strategy template library based on the risk characteristics, and the strategy types include at least routine monitoring, enhanced observation, focused diagnosis, and emergency locking.

[0029] Furthermore, in step 5, the online calibration is a selective parameter update; only when the magnitude of the residual signal exceeds a preset threshold, the parameters of the corresponding subsystem model in the embodied risk world model are fine-tuned based on the sliding window average gradient.

[0030] Furthermore, in step 6, the multi-granularity risk visualization view includes at least: a scenic area global risk heat map generated based on equipment risk scores, a component-level risk status three-dimensional coloring map and key time-series index map for a single high-risk equipment, a multi-modal perception evidence fusion display map for a specific defect, and its associated safety specification clause information.

[0031] Furthermore, step 7 includes risk assessment results based on the calibrated world model. When the risk exceeds a preset threshold, an emergency plan is automatically matched, and emergency instructions are spatially linked with the building information model before being issued for execution, completing the emergency response. The emergency response is triggered based on a comprehensive risk index, which is calculated by weighting the basic risk score of the equipment, the intensity of human exposure, and the urgency of the predicted failure time. The emergency plan is three-dimensionally matched according to the equipment type, risk mode, and risk level. Emergency instructions are spatially linked and conflict detected in the building information model before being issued.

[0032] This invention also discloses a special equipment risk prediction and adaptive data acquisition system for cultural and tourism scenarios, used to implement the method described above. The system includes:

[0033] The multi-source sensing access module is used to access raw data from IoT sensors, video devices, manual inspection terminals, and people flow analysis modules.

[0034] The spatiotemporal alignment module is used to perform timestamp calibration and geographic coordinate normalization on the original data.

[0035] The neural symbolic knowledge graph construction module is used to build and update a knowledge graph that integrates multimodal semantics and security specification logic;

[0036] The embodied risk inference module is used to perform risk evolution inference in a digital twin environment based on the knowledge graph and generate risk trajectories;

[0037] An adaptive acquisition strategy generation module is used to dynamically generate a data acquisition strategy based on the risk trajectory; a digital twin verification module is used to verify the effectiveness and resource feasibility of the generated acquisition strategy in a simulation environment.

[0038] The edge acquisition control module is used to send the verified acquisition strategy to the edge nodes for execution;

[0039] A closed-loop feedback and self-calibration module is used to calibrate the world model parameters of the embodied risk inference module online using newly acquired data;

[0040] A multi-granularity visualization engine is used to generate a penetrating risk visualization view from the global situation to the micro-defects;

[0041] The emergency response module is used to trigger the execution of emergency plans in conjunction with the building information model when risks exceed limits.

[0042] The present invention provides a method and system for risk prediction and adaptive data acquisition of special equipment in cultural and tourism scenarios, which has the following advantages:

[0043] 1. Risk cognition mechanism of neurosymbolic fusion: This invention innovatively constructs a neurosymbolic knowledge graph that integrates multimodal embedding and safety norm logic. This mechanism unifies manual inspection experience, physical sensor signals, and mandatory clauses such as GB8408 into a reasonable graph structure. Compared to the shortcomings of pure data-driven models, which are susceptible to noise interference, or pure rule-based systems, which lack generalization ability, this mechanism retains the interpretability and compliance constraints of symbolic systems while possessing the ability of neural networks to perceive unknown failure modes, significantly improving the accuracy and reliability of risk identification.

[0044] 2. Dynamic extrapolation capability of the embodied risk world model: This invention proposes an embodied risk world model based on high-fidelity multibody dynamics simulation, which utilizes latent states... This model simulates the time-series evolution of equipment health degradation and employs a two-stage strategy of "simulation pre-training - real-world fine-tuning" to align with expert evaluation standards. Compared to static threshold alarms or black-box deep learning models, this model can not only output the current risk level... It can also predict future trends. This enables a leap from "post-event response" to "pre-event prediction," and is particularly suitable for early intervention in progressive faults such as loose bolts and track deformation.

[0045] 3. Multi-granularity visualization and integrated emergency response system: This invention deeply integrates risk causes, spatial distribution, and visitor flow patterns into the scenic area's 3D BIM model, generating differentiated views at the operation and maintenance, management, and supervision levels according to roles, and... Exceeding the threshold or In the event of a sudden change, the system automatically triggers a hard linkage between equipment shutdown, broadcast alerts, lighting guidance, and personnel dispatch. Compared to traditional isolated alarm systems that only provide "risk or no risk" information, this system achieves a seamless connection across the entire chain of "risk location—cause tracing—collaborative handling—review and optimization," significantly shortening emergency response time and effectively preventing mass casualty accidents.

[0046] 4. Long-term evolution capability with traceable self-calibration: This invention introduces an incremental self-calibration mechanism based on version number management, continuously utilizing manual correction of tags to update the knowledge graph during closed-loop operation. It fine-tunes with the world model decoder and supports one-click rollback of the entire operation. Compared to static systems whose performance degrades after one-time deployment, this mechanism enables the model to continuously evolve as equipment ages, the environment changes, and new failure modes emerge, ensuring that the system maintains high prediction accuracy and strategy effectiveness over a lifespan of more than three years, truly achieving the goal of intelligent operation and maintenance that "gets smarter with use". Attached Figure Description

[0047] Figure 1 This is a flowchart illustrating the principle of a special equipment risk prediction and adaptive data acquisition method according to an embodiment of the present invention.

[0048] Figure 2 This is a diagram illustrating the neural symbol knowledge graph construction and updating mechanism according to an embodiment of the present invention;

[0049] Figure 3 This is a structural diagram of the embodied risk world model according to an embodiment of the present invention;

[0050] Figure 4 This is a flowchart of the adaptive acquisition method according to an embodiment of the present invention;

[0051] Figure 5 This is a schematic diagram illustrating the emergency response mechanism and multi-granularity visualization view integrated with BIM according to an embodiment of the present invention; Detailed Implementation

[0052] To better understand the purpose, structure, and function of this invention, the following description, in conjunction with the accompanying drawings, provides a more detailed account of a special equipment risk prediction and adaptive data acquisition method and system for cultural and tourism scenarios.

[0053] like Figure 1As shown, the present invention discloses a neural symbol risk prediction and adaptive acquisition system for special equipment in cultural and tourism scenic areas. This system includes a multi-source sensing access module, a spatiotemporal alignment module, a neural symbol knowledge graph construction module, an embodied risk inference module, an adaptive acquisition strategy generation module, a digital twin verification module, an edge acquisition control module, a closed-loop feedback and self-calibration module, a multi-granularity visualization engine, and an emergency response module. The multi-source sensing access module receives raw data from IoT sensors, IP cameras, manual inspection terminals, and a crowd flow analysis module. The spatiotemporal alignment module performs timestamp calibration and geographic coordinate normalization on the multi-source data. The neural symbol knowledge graph construction module... The knowledge graph construction module integrates multimodal semantics and security specifications to generate an interpretable, structured knowledge graph. The embodied risk inference module infers risk evolution trajectories in a digital twin environment based on the knowledge graph. The adaptive acquisition strategy generation module dynamically formulates acquisition strategies according to risk levels. The digital twin verification module pre-verifies the effectiveness of the strategies in a simulation environment. The edge acquisition control module executes issued acquisition commands. The closed-loop feedback and self-calibration module updates model parameters online using new data. The multi-granularity visualization engine generates a penetrating dashboard from global situation to micro-defects. The emergency response module triggers BIM-linked shutdown, evacuation, and dispatch plans when risks exceed limits. This solves the problems in existing systems caused by time drift leading to event misordering, inconsistent spatial coordinates causing inaccurate positioning, inability to semantically fuse heterogeneous data, delayed risk response, and wasted acquisition resources.

[0054] The present invention provides a neural symbol risk prediction and adaptive acquisition method for special equipment in cultural and tourism scenic spots, comprising the following steps:

[0055] Step 1: The raw data from the multi-source sensing devices undergoes timestamp calibration and geographic coordinate normalization to generate spatiotemporally consistent raw data packets, which are then output to the downstream module. The multi-source sensing data includes timing signals from IoT sensors, video streams from IP cameras, manual inspection records, and heat maps of pedestrian density. Specifically, the multi-source sensing access module, working in conjunction with the spatiotemporal alignment module, receives timing signals from vibration, tilt, and temperature / humidity sensors deployed in the scenic area's cable car cabins, Ferris wheel hubs, roller coaster track nodes, and sightseeing elevator shafts. and video frame sequences from IP cameras installed at key viewpoints. It also receives defect records submitted by inspection personnel via mobile terminals. and heatmaps of crowd density generated based on Wi-Fi / Bluetooth probes or video analytics Subsequently, the spatiotemporal alignment module initiates the time synchronization subprocess, employing a hybrid clock synchronization mechanism of NTP coarse calibration + PTP fine calibration to synchronize the local timestamps of each device. Mapped to a globally coordinated timeline, this allows for time deviations between any two types of events. The time should be controlled within ±2ms, and the time after calibration should be recorded as... Simultaneously, for spatial location information from different sources—including GPS / WGS-84 coordinates, GCJ-02 coordinates from construction drawings, and local coordinate system coordinates from BIM models—the module calls the UTM projection conversion engine to uniformly convert it to the Cartesian coordinate system under the UTM region (such as Zone 50N) of the scenic area, and outputs a standardized spatial location vector. Among them, elevation Based on the scenic area's digital elevation model (DEM), bilinear interpolation is used to complete the model, ensuring centimeter-level positioning consistency of all equipment and structural components in three-dimensional space. Specifically, the time synchronization accuracy meets the requirements of special equipment safety monitoring for event causality (e.g., delayed braking response should not be misjudged as premature), and spatial normalization supports subsequent millimeter-level alignment with the BIM model. After completing spatiotemporal alignment, the module encapsulates each record as a structured event. Aggregates to form spatiotemporally consistent raw data packets. It performs integrity checks, removes abnormal data streams with a timestamp missing rate exceeding 5% or coordinates deviating from the scenic area boundary buffer (±500m), and finally transmits the valid data packets to the neural symbol knowledge graph construction module to provide a reliable input foundation for semantic fusion.

[0056] Step 2: Construct a neural symbolic knowledge graph that integrates multimodal semantic features and safety specification logic to generate a structured and interpretable device risk knowledge representation. The input is the raw data packet output by the spatiotemporal alignment module. The output is a neural symbolic knowledge graph. .

[0057] like Figure 2 As shown, the system utilizes a neural symbolic knowledge graph construction module, which mainly consists of an entity recognition unit, a relation extraction unit, a standard embedding unit, a graph fusion unit, and a consistency verification unit. These units operate collaboratively within the same knowledge construction chain, enabling the automatic transformation from raw perceived events to structured security knowledge.

[0058] More specifically, the execution details of the neural symbolic knowledge graph construction module include the following steps:

[0059] Step 2.1: The module first... Each event in Perform multi-granularity entity recognition to extract three core entities: equipment components, defect types, and environmental conditions.

[0060] Specifically, for The incident, from The sensor ID is parsed and mapped to a specific component in the BIM model (e.g., "Ferris wheel main shaft bearing #A3"); for The video frames are used to call a pre-trained special equipment-specific model to detect visible defects (such as "weld cracks" and "missing bolts"); for The manual records were used to extract structured elements from the defect descriptions using a joint model of domain dictionary matching and BERT-CRF; for The heat map then shows high-density areas. Associated with the nearest load-bearing structure (e.g., cableway platform railing). All identification results are uniformly represented in triplet form. The head entity For equipment component nodes, tail entity For defects or state nodes, relationships It indicates meanings such as "present", "located", and "loaded".

[0061] Step 2.2: After obtaining the initial set of triplets, the module introduces mandatory safety standards for special equipment in cultural and tourism industries (such as "GB8408-2018 Safety Standard for Large Amusement Facilities" and "TSGS1001-2023 Supervision and Inspection Regulations for Passenger Ropeways"), constructs a symbolic logic rule base, and embeds it into the graph structure.

[0062] This invention proposes a specification-defect satisfiability constraint function. This is used to determine whether a defect violates a specific clause. Let a defect entity be... With attribute vectors (e.g., crack length, tilt angle, rate of temperature rise), a certain safety clause Defined as a first-order logical predicate The strength of their correlation is defined as follows:

[0063]

[0064] In this formula, Indicates defects With safety terms The semantic association weight between them, with a value range of (0,1), the larger the value, the more likely the clause is to be violated; For defects A quantified attribute vector, such as for "bearing temperature rise". (Unit: ℃ / min); For safety terms The defined set of violation states, such as the clause "the bearing temperature rise rate shall not exceed 5°C / min" ; The Euclidean norm measures the distance between the current defective state and the violation boundary. These are learnable parameters that control the sensitivity of the weights to the proximity to the boundary. The Sigmoid function maps distances to probabilistic weights, ensuring that the "defect-regulation" edges in the knowledge graph are differentiable and interpretable. This function enables the knowledge graph to not only contain factual relationships but also embed the ability to judge regulatory compliance.

[0065] Step 2.3: The module integrates multimodal entities, relations, and normative constraints into a unified knowledge graph. , where the node set It includes four types of nodes: equipment components, defects, environmental conditions, and regulatory provisions, and edge sets. It includes semantic relation edges and normative constraint edges. To support subsequent neural inference, each node... Assigned a hybrid embedding vector ,in Generated by a graph neural network (GNN) encoder. This is a vector of symbolic attributes (such as component material and specification thresholds). Final map. It is passed to the embodied risk projection module as the initial knowledge base for the world model.

[0066] Step 3: Initialize the embodied intelligent agent based on the neural symbolic knowledge graph, conduct interactive risk evolution simulation in a high-fidelity digital twin environment, generate a set of future risk trajectories, and input the neural symbolic knowledge graph. The output is the risk evolution trajectory. .

[0067] like Figure 3 As shown, the system utilizes an embodied risk inference module, which mainly consists of a world model initialization unit, an embodied agent generation unit, a multi-step interactive inference unit, a risk trajectory aggregation unit, and a confidence assessment unit. These units operate collaboratively within the same simulation pipeline, enabling the transformation from static knowledge to dynamic risk prediction.

[0068] More specifically, the execution details of the embodied risk simulation module include the following steps:

[0069] Step 3.1: The module first uses a neural symbolic knowledge graph. As an initial state, construct a computable world model of the special equipment in the scenic area. .

[0070] Specifically, In the BIM model, equipment component nodes are mapped to physical entities in the digital twin (such as "cableway drive wheel" and "roller coaster brake caliper"), and their geometry, materials, and connection relationships are automatically imported from the BIM model; defect nodes (such as "bearing temperature rise" and "loose bolts") are transformed into initial damage state variables. Environmental state nodes (such as "instantaneous pedestrian density" and "wind speed") serve as external disturbance inputs; safety specification nodes are compiled into hard constraints or soft penalty terms in the world model. Therefore, It becomes a hybrid simulation environment that integrates physical laws (Newtonian mechanics, heat conduction), semantic rules ("stop if tilt > 5°"), and data-driven residuals (from the historical fault database).

[0071] Step 3.2: In the world model Based on this, the module generates a set of embodied intelligent agents. Each agent represents an explorer of a typical risk evolution path. The value of ranges from 1 to K, where K represents the total number of embodied agents deployed or simulated in the system; each It represents an embodied intelligent agent with the ability to perceive, make decisions, and act.

[0072] Each agent It possesses perception-decision-action capabilities: its perception module reads the sliding observation window corresponding to the current time t in real time. The state vector in The decision-making module employs the neural symbolic strategy network proposed in this invention. The network consists of parallel symbolic conditional branches and neural value branches, ensuring that action selection is both physically feasible and meets safety regulations. The action module applies virtual interventions (such as "simulating bolts continuing to loosen" or "increasing passenger eccentricity") to drive the system towards higher-risk areas. Number of agents. The default value is 32, which covers common failure mode combinations.

[0073] Step 3.3: The module performs multi-step interactive deduction to generate the future. Step (default) The risk evolution trajectory (corresponding to 30 minutes).

[0074] At every step All agents in parallel and Interact and update the world state to And record trajectory fragments:

[0075]

[0076] Among the actions Depend on Output, Reward Taking into account the rate of decline in structural safety margin, the degree of violation of regulations, and the risk of human exposure, during the simulation, if a certain trajectory triggers an emergency shutdown condition (such as stress > yield strength × 0.9), it will be terminated early and marked as a high-risk trajectory.

[0077] Step 3.4: After the simulation is completed, the module aggregates all trajectories to generate a set of risk evolution trajectories. And calculate the overall risk confidence level for each trajectory.

[0078] This invention defines a multidimensional risk confidence function. Used for quantizing trajectories Credibility:

[0079]

[0080] in Measure the similarity between the trajectory and historical failure cases. Assess whether it violates symbol rules (such as energy conservation and gauge logic). Reflecting the stability of numerical simulation, Configurable weights.

[0081] In this formula, Indicates the first The overall confidence level of the risk trajectory ranges from [0,1], with a higher value indicating a more reliable risk evolution path; Calculated via Dynamic Time Warping (DTW) The minimum distance between the trajectory and the historical fault database is obtained by normalization; The symbolic reasoning engine verifies whether there is a logical contradiction in the trajectory (such as "temperature rises but heat flow is negative"). If there is no contradiction, the value is 1; otherwise, it is reduced by the number of violations. Based on the calculation of the rate of change of the second derivative of the state variables within the simulation step, spurious risks caused by numerical oscillations are suppressed. The preset weights are (0.4, 0.4, 0.2) by default, emphasizing historical experience and consistency of sign over pure numerical stability. This function ensures the output... It not only includes possibilities, but also has explainability and engineering credibility.

[0082] Finally, after confidence filtering (retaining) (the trajectory) after It is passed to the adaptive acquisition strategy generation module to drive subsequent on-demand acquisition decisions.

[0083] Step 4: Based on the level and temporal characteristics of the risk evolution trajectory, dynamically generate differentiated edge detection strategies, and pre-validate their effectiveness and resource feasibility in a digital twin environment. The input is the set of risk evolution trajectories output by the embodied risk inference module. The output is a set of validated adaptive acquisition strategies. .

[0084] like Figure 4As shown, the system utilizes an adaptive acquisition strategy generation module and a digital twin verification module to work together. The adaptive acquisition strategy generation module includes a risk feature extraction unit, a strategy template matching unit, and a parameter optimization unit. The digital twin verification module includes a high-fidelity simulation engine, a resource consumption tracker, and a fault symptom capture evaluator. The two are linked through a strategy injection-feedback correction closed-loop link to ensure that the generated strategy can accurately respond to potential risks and can be reliably executed on actual edge nodes.

[0085] More specifically, the process includes the following steps:

[0086] Step 4.1: The module first... Each trajectory in Multidimensional risk features are decoupled to extract key indicators driving data acquisition decisions. Specifically, the system identifies equipment components that exceed 80% of the safety threshold for the first time in the trajectory, forming a set of high-risk components. Calculate the average slope of risk state variables (such as the reciprocal of remaining life or stress growth rate) over the most recent 5 simulation steps as the rate of risk escalation. If the trajectory terminates prematurely due to an emergency shutdown, the termination time will be recorded as the predicted failure time. Otherwise, set it as the endpoint of the simulation window; simultaneously, combining the spatial location of the component in the digital twin, overlay the corresponding area's pedestrian density heatmap and integrate it to obtain the pedestrian exposure intensity. These characteristics collectively form the context for strategy generation, ensuring that data collection actions are commensurate with the severity, urgency, and public safety impact of the risks.

[0087] Step 4.2: Based on the above characteristics, the module retrieves the optimal basic mode from the predefined acquisition strategy template library and fine-tunes its parameters. The strategy template library contains four typical modes: routine monitoring (low sampling rate, aggregated transmission), enhanced observation (medium sampling rate, original waveform), focused diagnosis (high sampling rate, multimodal synchronization), and emergency locking (ultra-high sampling rate, full-channel buffering). To achieve end-to-end mapping from risk semantics to acquisition commands, this invention proposes a risk-driven strategy generation function. Its expression is:

[0088]

[0089] In this formula, Indicates that for the first Candidate collection strategies for generating risk trajectories; The collection of high-risk components determines the target equipment for strategic action. The risk escalation rate (unit: 1 / min) reflects the urgency of the risk development; The predicted failure time (in minutes) is used to determine the data acquisition duration window; The intensity of human exposure (unit: person·min / m²) affects whether to enable high-cost operations such as video linkage; For the first Class strategy templates, covering sampling patterns and data formats; For the template index selected based on the urgency of the risk: If min or If so, select "Focus on Diagnosis" or "Emergency Lockout"; For example, when the set of execution parameters is adjusted based on the intensity of human exposure, Automatically activate camera to capture images; This function embeds optimized parameters (such as actual sampling rate, buffer duration, and trigger threshold) into a template to generate an executable strategy. This function is the core mechanism by which this invention achieves "on-demand, hierarchical, and collaborative" data collection.

[0090] Step 4.3: Generating the candidate policy set The system is injected with a digital twin verification module and pre-executed end-to-end in a simulation environment that reproduces the dynamics of real devices and the characteristics of edge networks. The system first performs each policy... Construct a corresponding digital twin environment, loading the physical model, sensor configuration, and communication link parameters of the target device; then simulate edge node execution. Throughout the entire process, the system monitors in real time the resulting bandwidth usage, local storage growth, and power consumption changes, and dynamically compares these with preset hardware resource limits. Based on this, the system analyzes whether the collected simulation data can be used... Previously, the system stably captured key fault precursors—for example, for vibration strategies, it verified whether it could detect sudden increases in the amplitude of bearing fault characteristic frequencies; for vision strategies, it checked whether the image processing pipeline could identify the propagation of surface cracks. If multiple strategies simultaneously target the same physical device (e.g., multiple trajectories warn of the same brake caliper), the system will also automatically detect whether there are sampling rate conflicts (e.g., concurrent 100Hz and 1000Hz commands) or situations where the total bandwidth exceeds the edge gateway's throughput capacity. Only when a strategy neither breaks resource constraints nor fails to reliably capture fault symptoms within the entire simulation window, and there are no internal scheduling conflicts, is it marked as valid.

[0091] Step 4.4: All validated strategies are aggregated into the final output. The system attaches metadata (including validity period, execution priority, target device ID list and required sensor type) and transmits it to the edge acquisition control module for execution. For strategies that fail verification, the system automatically triggers a feedback loop, returns to step 4.2, generates a suboptimal solution by reducing the sampling rate, shortening the buffer window, or canceling unnecessary modalities (such as disabling video linkage), and re-verifies until a feasible solution is obtained, thereby ensuring that all issued strategies have both technical effectiveness and engineering deployability.

[0092] Step 5: Deploy the validated adaptive acquisition strategy to the edge nodes for execution, and use the newly acquired data to drive the online self-calibration of the world model parameters, with the validated strategy set as input. The real-time perception stream outputs a calibrated embodied risk world model. .

[0093] More specifically, the process includes the following steps:

[0094] Step 5.1: Edge acquisition control module receives... Then, first, for each strategy Perform syntax parsing and security checks to extract the target device ID and sampling rate. The module configures the data format, cache duration, and linkage commands (such as "trigger camera #C7 capture"); subsequently, it sends configuration commands to the corresponding edge nodes (embedded gateways deployed in cable car stations, Ferris wheel bases, or roller coaster control cabinets) and initiates security execution monitoring—if a device response timeout, sensor offline, or sampling rate deviation exceeding ±5% is detected, it immediately downgrades to the default security policy. And report the alert; within the policy's validity period, edge nodes collect raw data as needed, and through lightweight compression (such as wavelet-based threshold coding) and priority queue mechanisms, compress critical data streams. Upload to the central platform.

[0095] Step 5.2: Closed-loop feedback and self-calibration module reception Next, anomaly filtering is performed to remove outliers caused by communication jitter or momentary sensor failures. Then, the cleaned data is compared with the digital twin world model currently used by the embodied risk inference module. Align and compare the predicted outputs to calculate the observation-prediction residual vector of the state variables:

[0096]

[0097] In this formula, Indicates at time The multidimensional state residual vector reflects the deviation between the world model prediction and the actual observation; This is the actual observed state vector, with dimensions consistent with the key monitoring indicators of the equipment (such as [temperature, vibration RMS, tilt angle]). For the embodied risk world model The predicted state is generated by simulation under the same initial conditions and environmental disturbances in time and space; the residual calculation is only performed within the time window covered by the strategy to ensure consistency with the benchmark; the residual vector is the core error signal that drives the model self-calibration, and its amplitude and direction directly indicate the correction direction of the model parameters.

[0098] Step 5.3: Based on residuals The module initiates an online parameter update mechanism to fine-tune the learnable components in the world model. Specifically, the system identifies residual-dominated physical subsystems (such as the heat conduction module or structural dynamics module) and employs the selective gradient backpropagation strategy proposed in this invention: only when... (default Only when the time is right will the parameter update of the corresponding sub-model be activated to avoid model oscillation caused by noise interference; the update process uses sliding window average gradient descent, with the most recent gradient as the reference. The average gradient is calculated for each valid residual sample, and the parameter change in a single step is limited to no more than 1% to ensure model stability; the updated world model is denoted as... This serves as the initial state for the next round of risk simulation, thus forming a positive reinforcement loop of "prediction bias → parameter correction → prediction optimization".

[0099] Step 5.4: After calibration is complete, the module will... The data is synchronized to the embodied risk projection module, and a log of this data acquisition-calibration event is recorded (including policy ID, amount of data acquired, mean residual, and parameter update amount) for long-term performance evaluation and iterative optimization of the policy template library. This closed-loop mechanism enables the system to continuously adapt to equipment aging, environmental changes, and new failure modes, significantly improving the long-term accuracy and robustness of risk prediction.

[0100] Step 6: Based on the calibrated world model and real-time risk trajectory, generate a penetration-style risk visualization dashboard that supports multi-level drilling. The input is the calibrated embodied risk world model. With risk evolution trajectory set The output is a multi-granularity visualization view set. The system utilizes a multi-granularity visualization engine, which mainly consists of a global thermal rendering unit, an equipment focusing unit, a defect detail unfolding unit, and an interactive navigation unit. These units work collaboratively based on a unified spatiotemporal reference, enabling seamless switching and data linkage across four levels of views: "scenic area—equipment—component—defect."

[0101] More specifically, the process includes the following steps:

[0102] Step 6.1: The engine first uses the scenic area's geographical boundaries as a base map and then integrates... Current risk score of all devices (Values ​​range [0,1], obtained by normalization of the remaining safety margin), generating a global risk heatmap. Specifically, for each device... Location A two-dimensional plane mapped to the UTM coordinate system, with its risk score As Gaussian kernel weights, a continuous thermal field is generated through kernel density estimation; thermal intensity is encoded using a red-yellow-green gradient, with the red region (… This indicates a high-risk equipment cluster, which automatically triggers map zooming and alarm markers; this view serves as the default homepage, allowing administrators to quickly grasp the overall security situation of the entire area.

[0103] Step 6.2: When a user clicks on a high-risk device (such as "Ferris Wheel #3"), the engine automatically drills down to the device's focused view, overlaying and displaying its digital twin 3D model and dynamic risk indicators. The system then... Extract the device's structure tree (including sub-components such as the main shaft, hub, and pod), and map the risk status of each sub-component to the corresponding geometric surface using color. Simultaneously, display key timing curves in the sidebar, including predicted failure times. Rate of risk escalation Current data collection strategy and historical residuals If there are multiple competing risk trajectories (such as "bearing overheating" and "hub crack"), their evolution path differences are presented in the form of a parallel coordinate graph to support users in judging the dominant failure mode.

[0104] Step 6.3: Further click on high-risk sub-components (such as "spindle bearing"), and the engine will expand the defect detail view, integrating multimodal perception evidence for interpretable presentation. The system calls the original acquired data. The system retrieves vibration waveforms, infrared thermographs, or video frames corresponding to the specified time period, and overlays AI detection results (such as crack outlines and temperature rise areas) onto the images; simultaneously, it correlates these with a neural symbolic knowledge graph. The engine displays the violated regulations in the form of text bubbles (e.g., "GB8408-2018 Clause 5.3.2: The temperature rise of rotating parts shall not exceed 40K"). In addition, the engine also replays the evolution simulation animation of the defect by the embodied intelligent agent during the deduction process, intuitively showing the dynamic process that "if no intervention is made, the crack will expand to the critical size in 15 minutes", enhancing the credibility of risk prediction.

[0105] Step 6.4: Throughout the visualization process, the interactive navigation unit maintains contextual consistency across the four levels of views. Users can use the time slider to rewind to any historical moment regarding risk status, and the system will synchronously update the heatmap, equipment model coloring, defect evidence, and projection trajectory; all views share the same spatiotemporal coordinate system and risk measurement standard, ensuring lossless information transfer from macro to micro levels; the final generated... It supports access via web and mobile devices, and can export PDF reports or push them to the emergency command dashboard as needed, providing a unified decision-making view for operation and maintenance, supervision and emergency response.

[0106] Step 7: Based on the calibrated risk world model and multi-granularity visualization results, trigger the hierarchical emergency response mechanism and realize the spatial collaborative execution of disposal instructions and the BIM system. The input is the calibrated embodied risk world model. With multi-granularity visualization view sets The output is the set of emergency response instructions that have been executed. .

[0107] like Figure 5 As shown, the system utilizes an emergency response module, which mainly includes a risk level assessment unit, a contingency plan matching engine, a BIM spatial collaboration unit, and an instruction issuance and execution unit. Each unit operates collaboratively within a unified spatiotemporal framework, achieving a closed-loop linkage from risk identification to physical response, and ensuring that all emergency operations have accurate spatial positioning and process traceability within the BIM digital foundation.

[0108] More specifically, the process includes the following steps:

[0109] Step 7.1: The module first... The system performs real-time assessments of the risk status of equipment to determine whether a preset emergency trigger threshold has been reached. Specifically, the system calculates the risk status of each high-risk device. Overall risk index:

[0110]

[0111] In this formula, Indicates the first The emergency linkage trigger index of each device ranges from [0,1]. Its basic risk score reflects the degree of deterioration in structural health; The intensity of human exposure (unit: person·min / m²) reflects the weight of public safety impact. This is an indicator function (it takes the value 1 if the condition is true, and 0 otherwise). This indicates that if the predicted failure time is less than 15 minutes, the emergency response level will be forcibly upgraded. Configurable weights and satisfying The decision-making logic emphasizes the risk itself as the primary factor, and population exposure and time urgency as secondary factors; when (default When this occurs, the system determines that an emergency response process needs to be initiated.

[0112] Step 7.2: Once an emergency condition is triggered, the contingency plan matching engine immediately retrieves a matching solution from the pre-set emergency plan database. The database is organized using a three-dimensional index based on equipment type (e.g., cable car, Ferris wheel, roller coaster), risk mode (e.g., mechanical failure, electrical short circuit, overload overturning), and risk level (Level I / II / III). The system uses the equipment ID, dominant risk trajectory type, and... The value is matched with the most specific contingency plan template and dynamic parameters (such as the coordinates of the affected area, the recommended number of evacuees, and the required rescue resources) are automatically filled in. The matching results include a series of standardized emergency actions, such as "shutting down and locking", "activating the sound and light alarm", "pushing AR navigation to the inspection terminal", and "notifying the nearest medical point to stand by".

[0113] Step 7.3: Successfully matched emergency commands are sent to the BIM spatial collaboration unit for spatial semantic binding and conflict pre-checking in the BIM model. The system maps each command to a specific component or spatial area in the BIM—for example, the "stop" command is bound to the PLC node of the equipment control cabinet, and the "evacuation guidance" command is associated with the wayfinding signage system of the platform exit passage. At the same time, it checks whether there are spatial or temporal conflicts between multiple commands (such as two adjacent devices simultaneously requesting to occupy the same maintenance passage). If a conflict exists, arbitration is carried out according to the priority of the contingency plan (Level I > Level II > Level III), and coordination waiting steps are inserted if necessary to ensure physical feasibility.

[0114] Step 7.4: The instruction set after BIM collaborative verification is encapsulated as... The commands are transmitted to the corresponding execution terminals, including device controllers, broadcast systems, mobile inspection apps, and the park's emergency command platform, via secure communication protocols. The execution status of all commands (success / failure / timeout) is transmitted back in real time. Dynamic icons are overlaid on the BIM model (e.g., flashing red indicates "stop order has taken effect"); at the same time, the system automatically records a complete emergency log, including trigger time, plan version, execution sequence, personnel response delay, etc., for post-event review and plan optimization, forming a complete emergency closed loop of "perception-decision-execution-feedback".

[0115] It is understood that the present invention has been described through some embodiments, and those skilled in the art will recognize that various changes or equivalent substitutions can be made to these features and embodiments without departing from the spirit and scope of the invention. Furthermore, under the teachings of the present invention, these features and embodiments can be modified to adapt to specific situations and materials without departing from the spirit and scope of the invention. Therefore, the present invention is not limited to the specific embodiments disclosed herein, and all embodiments falling within the scope of the claims of this application are within the protection scope of the present invention.

Claims

1. A method for risk prediction and adaptive data collection of special equipment in cultural and tourism scenarios, characterized in that, Includes the following steps: Step 1: Collect multi-source sensing data of special equipment in cultural and tourism scenarios and perform spatiotemporal alignment processing; Step 2: Construct a neural symbolic knowledge graph; Step 3: Based on the neural symbol knowledge graph, perform risk evolution simulation in the digital twin environment to generate a set of risk evolution trajectories for future time periods; Step 4: Dynamically generate corresponding adaptive data acquisition strategies based on risk characteristics; And perform simulation verification; Step 5: Execute the validated strategy and use the new data to calibrate the simulation model online to obtain the calibrated world model; Step 6: Based on the calibrated world model and the set of risk evolution trajectories, generate a multi-granularity risk visualization view; Step 7: When the risk exceeds the limit, trigger emergency linkage with BIM.

2. The method according to claim 1, characterized in that, In step 1, the multi-source sensing data includes IoT sensor timing signals, video streams, manual inspection records, and pedestrian density data; the time synchronization adopts a hybrid clock synchronization mechanism; and the spatial coordinate normalization transforms all location information into a unified projected coordinate system.

3. The method according to claim 1, characterized in that, Step 2 includes extracting entities and relationships based on the original data packet, introducing security specification clauses to form specification constraints, and integrating entities, relationships, and specification constraints to generate a structured knowledge graph that integrates multimodal semantic features and symbolic logic. The node types of the neural symbolic knowledge graph include equipment components, defects, environmental states, and security specification clauses. The edges of the neural symbolic knowledge graph include relational edges representing semantic relationships between entities, and constraint edges representing compliance associations between defects and security specification clauses.

4. The method according to claim 1, characterized in that, Step 3 includes initializing an embodied risk world model in a digital twin environment, and having multiple embodied agents perform multi-step interactive deductions in the world model. The embodied risk world model integrates device physical laws, security semantic rules, and a residual model driven by historical data. Each of the multiple embodied agents selects actions according to a neural symbolic policy network to explore different risk evolution paths in the world model. Each risk evolution trajectory is accompanied by a comprehensive risk confidence assessment value.

5. The method according to claim 1, characterized in that, In step 4, the risk characteristics include high-risk component identification, risk escalation rate, predicted failure time, and human exposure intensity; the adaptive data acquisition strategy selects and modulates parameters from a predefined strategy template library based on the risk characteristics, and the strategy types include at least routine monitoring, enhanced observation, focused diagnosis, and emergency locking.

6. The method according to claim 1, characterized in that, In step 5, the online calibration is a selective parameter update; the parameters of the corresponding subsystem model in the embodied risk world model are fine-tuned based on the sliding window average gradient only when the magnitude of the residual signal exceeds a preset threshold.

7. The method according to claim 1, characterized in that, In step 6, the multi-granularity risk visualization view includes at least: a scenic area global risk heat map generated based on equipment risk scores, a component-level risk status three-dimensional coloring map and key time-series index map for a single high-risk equipment, a multi-modal perception evidence fusion display map for a specific defect, and its associated safety specification clause information.

8. The method according to claim 1, characterized in that, Step 7 includes risk assessment results based on the calibrated world model. When the risk exceeds a preset threshold, an emergency plan is automatically matched, and emergency instructions are spatially linked with the building information model before being issued for execution, completing the emergency response. The emergency response is triggered based on a comprehensive risk index, which is calculated by weighting the basic risk score of the equipment, the intensity of human exposure, and the urgency of the predicted failure time. The emergency plan is matched in three dimensions according to the equipment type, risk mode, and risk level. Emergency instructions are issued after spatial binding and conflict detection are performed in the building information model.

9. A special equipment risk prediction and adaptive data acquisition system for cultural and tourism scenarios, characterized in that, The system for implementing the method of any one of claims 1-8 comprises: The multi-source sensing access module is used to access raw data from IoT sensors, video devices, manual inspection terminals, and people flow analysis modules. The spatiotemporal alignment module is used to perform timestamp calibration and geographic coordinate normalization on the original data; The neural symbolic knowledge graph construction module is used to build and update a knowledge graph that integrates multimodal semantics and security specification logic; The embodied risk inference module is used to perform risk evolution inference in a digital twin environment based on the knowledge graph and generate risk trajectories; An adaptive acquisition strategy generation module is used to dynamically generate a data acquisition strategy based on the risk trajectory; a digital twin verification module is used to verify the effectiveness and resource feasibility of the generated acquisition strategy in a simulation environment. The edge acquisition control module is used to distribute the verified acquisition strategy to the edge nodes for execution; the closed-loop feedback and self-calibration module is used to calibrate the world model parameters of the embodied risk inference module online using the newly acquired data. A multi-granularity visualization engine is used to generate a penetrating risk visualization view from the global situation to the micro-defects; The emergency response module is used to trigger the execution of emergency plans in conjunction with the building information model when risks exceed limits.

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