A safety risk assessment method for a large amusement facility

Abstract

The application discloses a kind of safety risk assessment methods of large-scale amusement facilities, it is related to safety risk assessment field, the method includes: in the Q key position of roller coaster Deployment monitoring sensor builds state monitoring network;According to the running scene data of roller coaster in preset time window carries out state risk assessment timeliness analysis, determines risk assessment time limit constraint;Based on Q key position and Q associated monitoring index set fusion constructs state monitoring scheme optimization space;With risk assessment time limit constraint as limiting condition, the optimization search of state monitoring scheme is carried out, obtains adaptive state monitoring scheme;In preset time window, according to adaptive state monitoring scheme, the state monitoring and running risk assessment of roller coaster are carried out.The application is through a kind of safety risk assessment methods of large-scale amusement facilities, improves the safety risk assessment effect of large-scale amusement facilities, solves the problem of inaccurate, poor effect of safety risk assessment.

Description

Technical Field

[0001] This invention relates to the field of safety risk assessment, and more specifically to a method for assessing the safety risks of large-scale amusement facilities. Background Technology

[0002] Safety assessments of amusement rides involve identifying and analyzing the risks of rides such as roller coasters using safety monitoring equipment and other means, and taking appropriate measures to reduce risks and improve safety.

[0003] However, existing methods for assessing safety risks of large-scale amusement rides fail to monitor operational status and control timeliness, resulting in inaccurate safety risk assessments.

[0004] Therefore, there is an urgent need for a method that can simultaneously ensure the timeliness and accuracy of risk assessment and optimize the safety risk assessment of large amusement facilities. Summary of the Invention

[0005] This application provides a safety risk assessment method for large-scale amusement rides, addressing the problem that existing technologies cannot simultaneously ensure both timeliness and accuracy in risk assessments, leading to inaccurate and untimely assessments. Traditional safety risk assessments for large-scale amusement rides do not consider monitoring the real-time operating status and component fluctuations of the equipment, resulting in assessments that are difficult to guarantee in terms of timeliness and accuracy. By using a dynamically optimized status monitoring network, the method adjusts the time constraints for risk assessment, achieving accuracy and reliability within these constraints, and enabling efficient and precise control of safety risks.

[0006] In view of the above problems, this application provides a safety risk assessment method for large-scale amusement facilities, including:

[0007] A status monitoring network is established by deploying monitoring sensors at Q key locations on the roller coaster, where the Q key locations include Q sets of associated monitoring indicators;

[0008] Based on the operational scenario data of the roller coaster within a preset time window, a timeliness analysis of the status risk assessment is conducted to determine the time limit constraint for risk assessment.

[0009] The optimization space for the status monitoring scheme is constructed by fusing Q key locations and Q related monitoring indicator sets.

[0010] With the risk assessment time limit as a constraint and the accuracy and timeliness of risk assessment as dual optimization objectives, the optimization search of the state monitoring scheme is carried out within the optimization space of the state monitoring scheme to obtain a suitable state monitoring scheme.

[0011] Within a preset time window, the status monitoring network is invoked according to the adapted status monitoring scheme to conduct status monitoring and operational risk assessment of the roller coaster.

[0012] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0013] This application proposes a safety risk assessment method for large-scale amusement rides. It involves constructing a status monitoring network, analyzing the timeliness of status risk assessment, integrating and optimizing status monitoring schemes, obtaining suitable status monitoring schemes, and finally conducting status monitoring and operational risk assessment on the obtained suitable monitoring schemes to obtain the final safety risk assessment result. The construction of the status monitoring network ensures the breadth and reliability of data sources. Through the integration and optimization of the status monitoring scheme space and the acquisition of suitable status monitoring schemes, predictions are made. Machine algorithms are used to widely acquire monitoring schemes, and calculations are performed to obtain the suitable monitoring scheme with the highest matching degree, ensuring the reliability of the risk assessment and improving its effectiveness. Compared to previous methods that did not perform real-time status monitoring and safety risk timeliness assessment analysis, this invention solves the problems of inaccurate and untimely safety risk assessments for large-scale amusement rides. It avoids the problems of poor timeliness and low accuracy in safety risk assessments, improves the effectiveness of safety risk assessments, and achieves accuracy and reliability of risk assessments under the constraint of risk assessment time limits, enabling efficient and precise control of safety risks. Attached Figure Description

[0014] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0015] Figure 1 This is a flowchart illustrating the safety risk assessment method for large-scale amusement facilities used in this application.

[0016] Figure 2 This is a flowchart illustrating the process of obtaining an adaptive status monitoring scheme in the safety risk assessment method for large-scale amusement facilities, as described in this application. Detailed Implementation

[0017] This application provides a safety risk assessment method for large-scale amusement facilities. By obtaining an appropriate status monitoring scheme through predictive analysis, it obtains status monitoring results and timely operational risk assessment results for roller coasters. This solves the problems of poor timeliness and low accuracy in safety risk assessment of large-scale amusement equipment, and improves the effectiveness of safety assessment.

[0018] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0019] It should be noted that the terms "comprising" and "having" are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or modules that are not explicitly listed or that are inherent to these processes, methods, products, or devices.

[0020] The present invention will now be described in detail with reference to the accompanying drawings.

[0021] Example 1, as Figure 1 As shown, this application provides a method for safety risk assessment of large-scale amusement facilities, including:

[0022] S10: Deploy monitoring sensors at Q key locations on the roller coaster to build a status monitoring network, where the Q key locations include a set of Q related monitoring indicators.

[0023] In this embodiment of the application, sensors are deployed at several key parts of the roller coaster, each part including multiple monitoring indicators. The key locations and possible monitoring indicators are combined to construct a status monitoring network for monitoring its operating status.

[0024] Step S10 in the method provided in this application embodiment includes:

[0025] Based on the historical operation records of similar roller coasters, Q high-frequency risk areas of the roller coaster were identified as Q critical locations. Among them, the critical locations include at least the track curves, track joints, wheels, bearings, traction system, braking system, pneumatic system, and electronic control system.

[0026] Based on the structural attribute information of Q key locations, determine Q sets of associated monitoring indicators, and based on the Q sets of associated monitoring indicators, determine Q sets of monitoring sensors to be deployed at the Q key locations to generate a status monitoring network.

[0027] Specifically, based on the historical operation records of similar roller coasters, Q high-frequency risk areas are identified as Q critical locations. The historical status records of similar roller coasters refer to those of the same model and service life, including the number of times they are used per day and the duration of use per day. Furthermore, designating the high-frequency risk areas as critical locations ensures the effectiveness of monitoring and allows for the rapid acquisition of effective monitoring indicators. These monitoring indicators may vary depending on the critical location.

[0028] For example, key monitoring indicators may include: track misalignment, track structural fatigue, weld cracks at track joints; wheel wear, vibration, and overheating; bearing wear, vibration, and overheating; traction system fracture risk and tensile fatigue; braking system braking failure, overheating, and pneumatic system leakage; pressure insufficiency; and electrical control system short circuits, overloads, and signal malfunctions.

[0029] Furthermore, Q sets of associated monitoring indicators are determined based on the structural attribute information of Q key locations. Different associated monitoring indicators are determined according to the different structural attributes of the key locations, and Q sets of associated monitoring indicators are determined based on the associated monitoring indicators corresponding to the key locations.

[0030] Based on Q sets of associated monitoring indicators, Q sets of monitoring sensors are deployed at Q key locations to generate a status monitoring network. The status monitoring network consists of the key locations, the corresponding sets of associated monitoring indicators for each key location, and the associated sensor sets. Data can then be collected from these key locations using the monitoring sensor sets.

[0031] For example, there are Q key locations, such as track curves, track joints, wheels, bearings, traction system, braking system, pneumatic system, and electronic control system; and Q sets of related monitoring indicators, such as track misalignment, track structural fatigue, weld cracks at track joints; wheel wear, vibration, and overheating; bearing wear, vibration, and overheating; traction system fracture risk and tensile fatigue; braking system braking failure and overheating; pneumatic system leakage and insufficient pressure; and electronic control system short circuits, overloads, and signal malfunctions.

[0032] In this embodiment, monitoring sensors are deployed at key parts of the roller coaster, and a status monitoring network is built based on these sensors to ensure the validity and reliability of the monitoring data, thereby providing solid data support for the system.

[0033] S20: Based on the operation scenario data of the roller coaster within the preset time window, conduct a timeliness analysis of the status risk assessment and determine the time limit constraint for risk assessment.

[0034] In this embodiment, based on the roller coaster's operational scenario data within a preset time window, such as passenger load, the state risk sensitivity coefficient output by the risk assessment expert system is used to conduct a state risk assessment timeliness analysis, thereby determining the risk assessment time limit constraint. Based on the risk assessment time limit constraint, risks can be avoided, ensuring that the equipment's risks remain within a controllable range within a certain time limit.

[0035] Step S20 in the method provided in this application embodiment includes:

[0036] Acquire the operation scenario data of the roller coaster within a preset time window. The operation scenario data includes passenger load, maintenance interval duration, remaining component life, regional wind speed sequence, and regional temperature sequence.

[0037] The regional temperature difference sequence is obtained by calculating the adjacent temperature difference based on the regional temperature sequence. The mean values ​​of the regional wind speed sequence and the regional temperature difference sequence are calculated separately to obtain the mean regional wind speed and the mean regional temperature difference.

[0038] Using a risk assessment expert system, a state risk sensitivity analysis is conducted based on passenger load, maintenance interval, remaining component life, average regional wind speed, and average regional temperature difference, and the state risk sensitivity coefficient is output.

[0039] Risk assessment time constraints are set based on the state risk sensitivity coefficient.

[0040] Specifically, the first step is to acquire operational data of the roller coaster within a preset time window. This preset time window is a defined time interval that can be manually set. Based on risk assessment time constraints, the duration of risk prediction can be set, such as 10 minutes. Within this preset time window, a monitoring sensor array is used to monitor key indicators at critical locations to acquire operational data. This operational data includes passenger load, maintenance interval duration, remaining component lifespan, regional wind speed sequence, and regional temperature sequence.

[0041] Examples include preset time windows such as the next 12 hours or the next 24 hours, risk assessment time constraints such as 10 minutes, passenger load such as 20 people, maintenance interval such as 7 days, and remaining component lifespan such as 5 years.

[0042] Furthermore, the regional temperature difference sequence is obtained by calculating the adjacent temperature differences based on the regional temperature sequence. The regional temperature sequence represents the sequence of temperatures monitored at key locations within the monitoring area. The regional temperature difference sequence is obtained through the adjacent temperature differences within the regional temperature sequence, i.e., the differences between temperatures in adjacent areas. For a regional temperature sequence containing N temperature values, N-1 adjacent temperature differences can be calculated, forming the regional temperature difference sequence. The average regional temperature difference is (the sum of the N-1 regional temperature differences) / (N-1). For example, if the regional temperature sequence is A, B, C, and D, the adjacent temperature differences are calculated as AB, BC, and CD. The regional wind speed sequence represents the sequence of wind speeds monitored at key locations within the monitoring area. The average regional wind speed and the average regional temperature difference are calculated by averaging the regional wind speed and regional temperature difference sequences, respectively.

[0043] For example, the regional temperature sequence is 20℃, 30℃, 25℃, and 40℃. The differences between 20℃ and 30℃, 30℃ and 25℃, and 25℃ and 40℃ are calculated respectively, resulting in a regional temperature difference sequence of 10℃, 5℃, and 15℃. The average regional temperature difference is (10+5+15) / 3 = 10℃.

[0044] Temperature variations can cause material expansion and contraction, exacerbating fatigue. Tracks expand under summer heat, increasing the risk of misalignment. Therefore, it is necessary to monitor regional temperature sequences during operation for risk assessment. Monitoring temperature differences during roller coaster operation is helpful for safety risk assessment.

[0045] Furthermore, using a risk assessment expert system, a state risk sensitivity analysis is conducted based on passenger load, maintenance interval, remaining component lifespan, average regional wind speed, and average regional temperature difference, outputting a state risk sensitivity coefficient. An expert system is a type of computer program system in the field of artificial intelligence that can simulate the decision-making ability of human experts in a specific domain. By combining data or knowledge bases with an inference engine, it enables domain expert experience-based logical reasoning and analysis.

[0046] The risk assessment expert system inputs data such as passenger load, maintenance interval, component remaining life, average regional wind speed, and average regional temperature difference into its knowledge base. An inference engine is then used to perform state risk sensitivity analysis to obtain a state risk sensitivity coefficient. Specifically, higher passenger load, longer maintenance interval, shorter component remaining life, higher average regional wind speed, and higher average regional temperature difference all indicate a higher risk probability and a higher state risk sensitivity coefficient; conversely, lower probability factors result in a lower sensitivity coefficient. A higher state risk sensitivity coefficient generally indicates a higher risk.

[0047] Furthermore, based on the state risk sensitivity coefficient, a risk assessment time limit constraint is set. Referring to the state risk sensitivity coefficient output by the risk assessment expert system, the risk assessment time limit constraint is set. The larger the state risk sensitivity coefficient, the higher the risk, and the shorter the risk assessment time limit; the smaller the state risk sensitivity coefficient, the lower the risk, and the longer the risk assessment time limit.

[0048] For example, the state risk sensitivity coefficient is 0.5 and the risk assessment time limit is set to 10 minutes; the state risk sensitivity coefficient is 0.7 and the risk assessment time limit is set to 5 minutes.

[0049] In step S20 of the method provided in this application embodiment, setting a risk assessment time limit constraint based on the state risk sensitivity coefficient includes:

[0050] Parametric fluctuation analysis was performed on the regional wind speed series and regional temperature difference series respectively to obtain the wind speed fluctuation coefficient and temperature difference fluctuation coefficient. The fluctuation coefficient is the ratio of the standard deviation to the mean of the parameter series.

[0051] The ratio of the wind speed fluctuation coefficient to the preset standard wind speed fluctuation coefficient is set as the wind speed compensation coefficient.

[0052] The ratio of the temperature difference fluctuation coefficient to the preset standard temperature difference fluctuation coefficient is set as the temperature difference compensation coefficient.

[0053] The environmental compensation coefficient is obtained by weighted fusion of wind speed compensation coefficient and temperature difference compensation coefficient, and the product of environmental compensation coefficient and constant K plus 1 is used as risk-sensitive compensation coefficient, where K is 0.1;

[0054] The state risk sensitivity coefficient is optimized and corrected based on the risk sensitivity compensation coefficient to obtain the optimized state risk sensitivity coefficient.

[0055] The ratio of the optimized state risk sensitivity coefficient to the preset standard state risk sensitivity coefficient is multiplied by the preset standard risk assessment duration to obtain the adaptive risk assessment duration, which serves as a constraint on the risk assessment time limit.

[0056] Specifically, parameter fluctuation analysis was performed on the regional wind speed series and the regional temperature difference series to obtain the wind speed fluctuation coefficient and the temperature difference fluctuation coefficient. The fluctuation coefficient is the ratio of the standard deviation of the parameters to the mean of the parameters in the series. The standard deviation of the parameters is the square root of the arithmetic mean of the squared deviations of the parameters from the mean.

[0057] The larger the volatility coefficient, the greater the dispersion of the data, and the higher the risk may be; conversely, the smaller the volatility coefficient, the lower the risk may be.

[0058] For example, the regional wind speed sequence is 1 m / s, 3 m / s, and 1.5 m / s, with a mean of 2 m / s. The standard deviation of the parameter is √[(1-2)²+(3-2)²+(1.5-2)² / (3-1)]=1.06, and the fluctuation coefficient is 1.06 / 2=0.53. A large fluctuation coefficient indicates a high degree of dispersion in the regional wind speed sequence, suggesting a potentially high risk. The regional temperature difference sequence is 10℃, 5℃, and 15℃, with a mean of 10℃. The standard deviation of the parameter is √[(10-10)²+(10-5)²+(10-15)² / (3-1)]=5, and the fluctuation coefficient is 5 / 10=0.5. A large fluctuation coefficient indicates a high degree of dispersion in the regional temperature difference sequence, suggesting a potentially high risk.

[0059] Furthermore, the ratio of the wind speed fluctuation coefficient to the preset standard wind speed fluctuation coefficient is set as the wind speed compensation coefficient. Similarly, the ratio of the temperature difference fluctuation coefficient to the preset standard temperature difference fluctuation coefficient is set as the temperature difference compensation coefficient. The wind speed compensation coefficient and the temperature difference compensation coefficient represent the degree of deviation between the regional wind speed sequence and the regional temperature difference sequence. The larger the wind speed compensation coefficient and the temperature difference compensation coefficient, the greater the deviation between the regional wind speed sequence and the regional temperature difference sequence, and the greater the degree of compensation and correction required.

[0060] The preset standard wind speed fluctuation coefficient and preset standard temperature difference fluctuation coefficient are preset values ​​that can be adaptively adjusted by those skilled in the art to ensure that the preset values ​​match the needs of safety risk assessment. The preset standard wind speed fluctuation value can be set as, for example, a custom safety standard value or the average of historical wind speed fluctuation coefficients within a historical time range.

[0061] For example, the preset standard wind speed fluctuation coefficient and the preset standard temperature difference fluctuation coefficient are set to 0.25.

[0062] The wind speed compensation coefficient is 0.53 / 0.25=2.12, and the temperature difference compensation coefficient is 0.5 / 0.25=2.

[0063] Furthermore, an environmental compensation coefficient is obtained by weighted fusion of the wind speed compensation coefficient and the temperature difference compensation coefficient. The product of the environmental compensation coefficient and a constant K is used as the risk-sensitive compensation coefficient, where K is 0.1. The wind speed compensation coefficient and the temperature difference compensation coefficient are then weighted and fused, such as by calculating a weighted average. The larger the environmental compensation coefficient, the larger the risk-sensitive compensation coefficient, and the greater the risk value of the monitored location.

[0064] For example, the wind speed compensation coefficient and the temperature difference compensation coefficient are calculated using a weighted average. The weights are typically determined by the correlation between the parameter and the state risk, or the degree of influence of the parameter on the state risk; the greater the influence, the greater the weight. For instance, since the wind speed compensation coefficient and the temperature difference compensation coefficient have the same influence on the risk sensitivity compensation coefficient, their weights are 50% and 50%, respectively. The environmental compensation coefficient is 2.12 × 50% + 2 × 50% = 2.06. The risk sensitivity compensation coefficient is 2.06 × K, where K = 0.1, meaning the risk sensitivity compensation coefficient is 2.06 × 0.1 = 0.206.

[0065] Furthermore, the state risk sensitivity coefficient is optimized and corrected based on the risk sensitivity compensation coefficient to obtain the optimized state risk sensitivity coefficient. Multiplying the risk sensitivity compensation coefficient by the state risk sensitivity coefficient yields the optimized state risk sensitivity coefficient, which can be used as the correction result. The larger the optimized state risk sensitivity coefficient, the greater the risk, and the more optimization is needed to ensure the safety of the amusement facility.

[0066] For example, the risk sensitivity compensation coefficient is 0.206, the state risk sensitivity coefficient is 0.5, and the optimized state risk sensitivity coefficient is 0.206 × 0.5 = 0.1.

[0067] Finally, the ratio of the optimized state risk sensitivity coefficient to the preset standard state risk sensitivity coefficient is multiplied by the preset standard risk assessment duration to obtain the adapted risk assessment duration, which serves as the risk assessment time constraint. The preset standard risk assessment duration is a pre-set risk assessment duration.

[0068] There is a corresponding relationship between the state risk sensitivity coefficient and the risk assessment duration; their ratio is a constant. To obtain the appropriate adaptive risk assessment duration, the ratio of the optimized state risk sensitivity coefficient to the preset standard state risk sensitivity coefficient is multiplied by the preset standard risk assessment duration. The calculation formula is: Adaptive Risk Assessment Duration = (Optimized State Risk Sensitivity Coefficient / Preset Standard State Risk Sensitivity Coefficient) × Preset Standard Risk Assessment Duration.

[0069] The obtained adaptive risk assessment duration will be used as the risk assessment time constraint. That is, the risk assessment must be completed within the adaptive risk assessment duration, thus ensuring timely risk assessment of high-risk facilities to guarantee their safety. Furthermore, the higher the risk of the facility, the shorter the risk assessment time constraint.

[0070] For example, the optimized state risk sensitivity coefficient is 0.1, the preset standard state risk sensitivity coefficient is 0.3, and the preset standard risk assessment time is 7 minutes, resulting in an adaptation risk assessment time of (0.1 / 0.3)×7=21 minutes.

[0071] In this embodiment, by collecting roller coaster operation scenario data within a preset time window, the mean values ​​of regional wind speed and temperature difference are calculated based on regional wind speed and temperature difference sequences. Using a risk assessment expert system, a state risk sensitivity coefficient is output. Parameter fluctuation analysis is performed on the regional wind speed and temperature difference sequences to obtain wind speed fluctuation coefficients and temperature difference fluctuation coefficients. The ratios of these coefficients to preset standard wind speed and temperature difference fluctuation coefficients are calculated and set as wind speed compensation coefficients and temperature difference compensation coefficients, respectively. A weighted fusion is performed to obtain an environmental compensation coefficient, which is then used as the risk sensitivity compensation coefficient. The state risk sensitivity coefficient is optimized and corrected based on the risk sensitivity compensation coefficient to obtain an optimized state risk sensitivity coefficient. The optimized state risk sensitivity coefficient is used to calculate the appropriate risk assessment duration, which serves as the risk assessment time constraint. By imposing time constraints on risk assessments, the timeliness of risk assessments is ensured. At the same time, through precise analysis and calculation, an appropriate risk assessment duration corresponding to the risk sensitivity coefficient of the optimized state is obtained, which guarantees the accuracy of risk assessments and reduces the problem of inaccurate safety risk assessments caused by outdated safety monitoring equipment.

[0072] S30: Based on the fusion of Q key locations and Q related monitoring indicator sets, construct the optimization space for the status monitoring scheme.

[0073] The state monitoring scheme optimization space refers to the set of all possible key locations and all possible combinations of monitoring indicators. Each time, M key locations are randomly selected from Q key locations, and N related monitoring indicators are randomly selected from Q related monitoring indicators, until all possible schemes are obtained. All schemes are then combined into a set, resulting in the state monitoring scheme optimization space containing the set of all possible schemes.

[0074] For example, the optimization space of the condition monitoring scheme can contain a set of associated monitoring indicators for key locations such as the track, track joints, and wheels. For the track, two monitoring indicators are selected: misalignment value and structural fatigue. For the track joints, one monitoring indicator is weld cracks. For the wheels, two monitoring indicators are selected: wear condition.

[0075] In this embodiment, by constructing an optimization space for state monitoring schemes that includes all schemes, data omissions are avoided, thereby ensuring the accuracy of security risk assessment.

[0076] S40: Using the risk assessment time limit as a constraint and the accuracy and timeliness of risk assessment as dual optimization objectives, perform optimization search for state monitoring schemes within the state monitoring scheme optimization space to obtain suitable state monitoring schemes.

[0077] Using the risk assessment timeframe as a constraint, while ensuring the accuracy and timeliness of the risk assessment, the obtained state monitoring schemes are optimized in the space matching to obtain the most suitable state monitoring scheme, which is then used as the final scheme for risk assessment.

[0078] like Figure 2 As shown, step S40 in the method provided in this application embodiment includes:

[0079] Within the optimization space of the state monitoring scheme, several key locations are randomly selected as preset monitoring locations, and several associated monitoring indicators are randomly set at each preset monitoring location to construct the first state monitoring scheme.

[0080] The pre-training evaluation duration prediction plugin is used to perform risk assessment duration analysis on the first state monitoring scheme and output the first predicted evaluation duration.

[0081] If the duration of the first prediction assessment exceeds the time limit for risk assessment, then the first state monitoring scheme shall be abandoned.

[0082] If the first prediction assessment duration is less than or equal to the risk assessment time limit constraint, then the assessment accuracy prediction plugin is used to predict the risk assessment accuracy based on the first state monitoring scheme, and the first prediction assessment accuracy is output.

[0083] The first scheme matching degree is calculated based on the first prediction and evaluation duration and the first prediction and evaluation accuracy. The scheme matching degree is negatively correlated with the prediction and evaluation duration and positively correlated with the prediction and evaluation accuracy.

[0084] Within the optimization space of the state monitoring scheme, continue to randomly select state monitoring schemes and calculate the scheme matching degree until the preset number of convergences is reached. Output the state monitoring scheme corresponding to the maximum scheme matching degree and set it as the adaptive state monitoring scheme.

[0085] Specifically, several key locations are randomly selected as preset monitoring locations within the optimization space of the state monitoring scheme, and several related monitoring indicators are randomly set at each preset monitoring location as the first state monitoring scheme. Data is acquired using a random selection method to pre-train the evaluation duration prediction plugin.

[0086] For example, four key locations are randomly selected for monitoring: bearings, traction system, braking system, and pneumatic system. The monitoring indicators are as follows: for bearings, two indicators are wear and vibration; for traction systems, two indicators are fracture risk and tensile fatigue; for braking systems, one indicator is whether the brakes have failed; and for pneumatic systems, two indicators are whether the pressure is insufficient and whether the electronic control system is short-circuited or overloaded.

[0087] Furthermore, a pre-trained evaluation duration prediction plugin is used to perform risk assessment time limit analysis on the first state monitoring scheme and output the first predicted evaluation duration. A randomly selected first state monitoring scheme is input into the evaluation duration prediction plugin, and the first predicted evaluation duration is output. For example, if four key locations are input as pre-monitoring locations and their corresponding associated monitoring indicators, the evaluation duration prediction plugin will predict the corresponding evaluation durations, such as 10 minutes, 15 minutes, and 9 minutes.

[0088] Furthermore, if the duration of the first prediction assessment exceeds the time limit constraint of the risk assessment, it indicates that the duration of the first prediction assessment exceeds the time limit constraint of the risk assessment, and the result is inaccurate. If used, it will cause safety problems, and the first state monitoring scheme should be abandoned.

[0089] For example, the first prediction assessment time is 15 minutes, but the risk assessment time limit is 10 minutes. Since 15 minutes > 10 minutes, the first state monitoring scheme should be discarded.

[0090] Furthermore, if the first prediction assessment duration is less than or equal to the risk assessment time limit constraint, the assessment accuracy prediction plugin is used to predict the accuracy of the risk assessment based on the first state monitoring scheme, and outputs the first prediction assessment accuracy. The accuracy of the first prediction assessment determines whether the first state monitoring scheme should be adopted; the higher the accuracy, the more suitable the first state monitoring scheme. The assessment accuracy prediction plugin and the assessment duration prediction plugin are built on the same principle. The assessment accuracy prediction plugin outputs the assessment accuracy, and the percentage of historically identical accurately predicted events can be statistically analyzed and set as sample supervision data. For example, if the first prediction assessment duration is 5 minutes, but the risk assessment time limit constraint is 10 minutes, since 5 minutes < 10 minutes, further risk assessment accuracy prediction should be performed. Through the prediction of the assessment accuracy prediction plugin, the first prediction assessment accuracy is found to be 80%.

[0091] Furthermore, the matching degree of the first scheme is calculated based on the first prediction and evaluation duration and the first prediction and evaluation accuracy. First, the weights of the first prediction and evaluation duration and the first prediction and evaluation accuracy are calculated, and then the matching degree of the first scheme is calculated using these weights. For example, the weights of the first prediction and evaluation duration and the first prediction and evaluation accuracy are 0.5 and 0.5, respectively.

[0092] Furthermore, within the optimization space of the state monitoring scheme, random selection of state monitoring schemes continues, and scheme matching degree is calculated until the preset number of convergences is reached, and the output is stable. This yields the evaluation duration prediction plugin and the evaluation accuracy prediction plugin, which can set the state monitoring scheme corresponding to the output maximum scheme matching degree as the adaptive state monitoring scheme.

[0093] For example, four key locations are randomly selected and the matching degree of the scheme is calculated. After reaching the preset number of convergences, the four selected key locations are input as pre-monitoring locations and corresponding associated monitoring indicators to obtain the adapted state monitoring scheme.

[0094] Step S40 of the method provided in this application embodiment, which involves pre-training and evaluating the duration prediction plugin, includes:

[0095] Based on the historical status monitoring records of roller coasters, a sample status monitoring scheme set was collected, and the historical evaluation time under different sample status monitoring schemes was set as the sample evaluation time, resulting in a sample evaluation time set. The historical evaluation time is the sum of data collection time, data preprocessing time and risk assessment time.

[0096] Using a set of sample state monitoring schemes as input and a set of sample evaluation durations as supervision, a BP neural network is trained until convergence to generate an evaluation duration prediction plugin.

[0097] Specifically, based on the historical status monitoring records of the roller coaster, a sample status monitoring scheme set is collected. The historical evaluation duration under different sample status monitoring schemes is set as the sample evaluation duration, resulting in a sample evaluation duration set. The historical evaluation duration is the sum of data acquisition duration, data preprocessing duration, and risk assessment duration. For example, historical evaluation durations of roller coasters under different loads can be collected as samples, such as historical evaluation durations for holidays (high load) and weekdays (low load).

[0098] For example, the data acquisition time is 5 minutes, the data preprocessing time is 5 minutes, the risk assessment time is 7 minutes, and the historical assessment time is 17 minutes.

[0099] Furthermore, using the sample state monitoring scheme set as input and the sample evaluation duration set as supervision, a BP neural network is trained until convergence to generate an evaluation duration prediction plugin.

[0100] For example, the sample state monitoring scheme set is used as input, the sample evaluation duration set is used as supervision, the Adam optimizer is used (initial learning rate 1e-1), the mean squared error function (MSE) is selected, the parameters are updated through forward propagation and back propagation, and the performance is evaluated with the validation set after each training round to avoid overfitting. When the MSE loss of the training set decreases by less than 1e-6 for 5 consecutive rounds and the MSE loss of the validation set stabilizes below 0.01, the model is considered to have converged, training is stopped and the final network parameters are saved, resulting in the evaluation duration prediction plugin.

[0101] Step S40 of the method provided in this application embodiment, which calculates the first scheme matching degree based on the first prediction evaluation time and the first prediction evaluation accuracy, includes:

[0102] The difference between the preset standard risk assessment time and the adapted risk assessment time is taken as the assessment time difference, and the ratio of the assessment time difference to the preset standard assessment time difference is set as the efficiency weight compensation coefficient.

[0103] After compensating the initial evaluation efficiency weight with an efficiency weight compensation coefficient, the adaptive evaluation efficiency weight is obtained. The initial evaluation efficiency weight is 0.5, and the adaptive evaluation efficiency weight is greater than or equal to 0.25 and less than or equal to 0.75.

[0104] The adaptation evaluation accuracy weight is obtained by subtracting the adaptation evaluation efficiency weight from 1.

[0105] After dimensionless processing of the first prediction evaluation duration and the first prediction evaluation accuracy, the matching degree of the first scheme is obtained based on the matching evaluation efficiency weight and the matching evaluation accuracy weight.

[0106] Specifically, the difference between the preset standard risk assessment time and the adapted risk assessment time is taken as the assessment time difference, and the ratio of the assessment time difference to the preset standard assessment time difference is set as the efficiency weight compensation coefficient. The preset standard assessment time is a preset value that can be adaptively adjusted by those skilled in the art to ensure that the preset value matches the safety risk assessment requirements. The preset standard assessment time difference is the preset standard assessment time minus the risk assessment time. The efficiency weight compensation coefficient is positively correlated with the assessment time difference and negatively correlated with the predicted standard assessment time difference. The larger the efficiency weight compensation coefficient, the larger the assessment time difference, the shorter the risk assessment time limit, and in this case, the greater the weight of assessment efficiency should be.

[0107] For example, the preset standard risk assessment time is 7 minutes, and the actual risk assessment time is 4 minutes. The difference in assessment time is 7 - 4 = 3 minutes. If the preset standard assessment time difference is 4 minutes, the efficiency weight compensation coefficient is 3 / 4 = 0.75.

[0108] Furthermore, after compensating the initial evaluation efficiency weight with an efficiency weight compensation coefficient, an adapted evaluation efficiency weight is obtained. The initial evaluation efficiency weight is 0.5, and the adapted evaluation efficiency weight is greater than or equal to 0.25 and less than or equal to 0.75. The adapted evaluation efficiency weight is the product of the initial evaluation efficiency weight and the efficiency weight compensation coefficient. The larger the efficiency weight compensation coefficient, the greater the difference in evaluation time, and thus the shorter the time limit; therefore, the weight proportion of evaluation efficiency should be larger.

[0109] For example, the efficiency weight for adaptation evaluation is 0.5 × 0.75 = 0.375.

[0110] Furthermore, the fitting evaluation efficiency weight is obtained by subtracting the fitting evaluation efficiency weight from 1. The greater the difference in evaluation time and the shorter the time limit, the smaller the weight of the evaluation accuracy.

[0111] The accuracy weight of the adaptation assessment is 1-0.375=0.625.

[0112] Furthermore, the first prediction evaluation duration and the first prediction evaluation accuracy are dimensionlessly processed. Dimensionless processing is a data preprocessing technique designed to eliminate the influence of dimensions between different features, making the data comparable. Normalization scales the values ​​of a column of numerical features in the training set to between 0 and 1. Dimensionless processing is performed using software tools such as SPSSAU with the Min-Max normalization method. Enter the data processing module and select "Generate Variables." Then select the two variables to be normalized, or multiple indicators can be selected in batches. Next, select the normalization method and perform dimensionless processing using the formula: (X-Min) / (Max-Min). This method compresses the data to the range of 0 and 1, where the minimum value corresponds to 0 and the maximum value corresponds to 1. After confirming and executing the processing, the normalization operation is complete, generating new variables.

[0113] After dimensionless processing, the matching degree of the first scheme is obtained based on the efficiency weight and accuracy weight of the adaptation evaluation. The matching degree of the first scheme = (accuracy weight of adaptation evaluation × normalized accuracy) + (efficiency weight of adaptation evaluation × (1 - normalized time)). The larger the matching degree of the first scheme, the more accurate the prediction, and the more accurate the corresponding state monitoring scheme.

[0114] For example, if the accuracy of the first prediction assessment is 80% and the duration of the first prediction assessment is 5 minutes, inputting 80% and 5 minutes into SPSSAU yields results such as 0.8 and 0.17. The matching degree of the first scheme is (0.625×0.8)+(0.375×(1-0.17))=0.81. At this point, the matching degree of the first scheme is the highest, and its corresponding state monitoring scheme is taken as the adapted state monitoring scheme.

[0115] In this embodiment, a first state monitoring scheme is constructed. By training an evaluation duration prediction plugin and an evaluation accuracy prediction plugin, a first predicted evaluation duration and a first predicted evaluation accuracy are output. A first scheme matching degree is calculated based on the first predicted evaluation duration and the first predicted evaluation accuracy. After obtaining the first scheme matching degree, state monitoring schemes are randomly selected within the state monitoring scheme optimization space, and scheme matching degrees are calculated again to obtain an adapted state monitoring scheme. Dimensionless processing is used during prediction, and the obtained data is normalized, improving data comparability and analytical accuracy. Simultaneously, by comparing the obtained first scheme matching degrees, an accurate adapted state monitoring scheme is obtained, improving the accuracy of security risk assessment.

[0116] S50: Within a preset time window, the system invokes the status monitoring network to monitor the roller coaster's status and assess operational risks according to the adapted status monitoring scheme. Risk assessment is performed based on the monitored data, for example, through machine learning using neural networks. A neural network is an algorithm that simulates the structure of a human brain's neuronal network, enabling complex pattern recognition and prediction through multi-layered neuronal connections. In risk assessment, neural networks can be used to identify potential risk factors and predict risk events.

[0117] Ultimately, by obtaining further condition monitoring and operational risk assessment, the accuracy and reliability of monitoring can be improved, thereby enhancing the effectiveness of safety risk assessment.

[0118] In summary, the embodiments of this application, through the specific implementation methods described above, achieve the following technical effects:

[0119] Compared to existing technologies, this application first deploys monitoring sensors at Q key locations on the roller coaster to establish a status monitoring network, ensuring the validity and reliability of the status monitoring data. Secondly, it performs a timeliness analysis of status risk assessment based on the roller coaster's operational scenario data within a preset time window, determining the time constraint for risk assessment. Under the premise of ensuring safety, it limits the assessment duration, mitigating safety issues caused by excessively long risk assessment times and ensuring the safety of risk assessment. Furthermore, it constructs a status monitoring scheme optimization space based on the fusion of the Q key locations and Q related monitoring indicator sets. By combining schemes through this optimization space, the accuracy of monitoring is improved.

[0120] Finally, this application randomly selects several key locations as preset monitoring locations within the state monitoring scheme optimization space, and randomly sets several associated monitoring indicators at each preset monitoring location to construct a first state monitoring scheme. By training a BP neural network, an evaluation duration prediction plugin is generated. This plugin is used to perform risk assessment time limit analysis on the first state monitoring scheme, outputting the first predicted evaluation duration. The first predicted evaluation duration is used to predict the accuracy of risk assessment, obtaining the first predicted evaluation accuracy rate. The matching degree of the first scheme is calculated based on the first predicted evaluation duration and the first predicted evaluation accuracy rate. After obtaining the first scheme matching degree, the state monitoring scheme is randomly selected again within the state monitoring scheme optimization space, and the matching degree is calculated to obtain an adapted state monitoring scheme. Dimensionless processing is applied to the first predicted evaluation duration and the first predicted evaluation accuracy rate, and the obtained data is normalized, improving the comparability and analytical accuracy of the data. Interference from different directions and units in the first predicted evaluation accuracy rate and the first predicted evaluation duration is eliminated. Dimensionless normalization processing avoids interference with safety risk assessment and improves the accuracy of risk assessment. Meanwhile, the obtained adaptive status monitoring scheme is subjected to final status monitoring and operational risk assessment, and the monitoring process is given final feedback to ensure the accuracy and reliability of the safety risk assessment and solve the problems of untimely and inaccurate safety risk assessment.

[0121] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. Additionally, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0122] The above are merely preferred embodiments of this application and are not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

[0123] This specification and accompanying drawings are merely illustrative examples of this application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Therefore, if such modifications and variations fall within the scope of this application and its equivalents, this application intends to include such modifications and variations.

Claims

1. A method for safety risk assessment of large-scale amusement facilities, characterized in that, The methods include: S1: Deploy monitoring sensors at Q key locations on the roller coaster to build a status monitoring network, where the Q key locations include Q sets of associated monitoring indicators; S2: Based on the roller coaster's operational scenario data within a preset time window, conduct a timeliness analysis of the status risk assessment to determine the time constraints for risk assessment, including: Acquire the operation scenario data of the roller coaster within a preset time window. The operation scenario data includes passenger load, maintenance interval duration, remaining component life, regional wind speed sequence, and regional temperature sequence. The regional temperature difference sequence is obtained by calculating the adjacent temperature difference based on the regional temperature sequence. The mean values ​​of the regional wind speed sequence and the regional temperature difference sequence are calculated separately to obtain the mean regional wind speed and the mean regional temperature difference. Using a risk assessment expert system, a state risk sensitivity analysis is conducted based on passenger load, maintenance interval, remaining component life, average regional wind speed, and average regional temperature difference, and the state risk sensitivity coefficient is output. Risk assessment time constraints are set based on state risk sensitivity coefficients, including: Parametric fluctuation analysis was performed on the regional wind speed series and the regional temperature difference series to obtain the wind speed fluctuation coefficient and the temperature difference fluctuation coefficient. The fluctuation coefficient is the ratio of the standard deviation of the parameter to the mean of the parameter in the parameter series. The ratio of the wind speed fluctuation coefficient to the preset standard wind speed fluctuation coefficient is set as the wind speed compensation coefficient. The ratio of the temperature difference fluctuation coefficient to the preset standard temperature difference fluctuation coefficient is set as the temperature difference compensation coefficient. The environmental compensation coefficient is obtained by weighted fusion of wind speed compensation coefficient and temperature difference compensation coefficient, and the product of environmental compensation coefficient and constant K plus 1 is used as risk-sensitive compensation coefficient, where K is 0.1; The state risk sensitivity coefficient is optimized and corrected based on the risk sensitivity compensation coefficient to obtain the optimized state risk sensitivity coefficient. The ratio of the optimized state risk sensitivity coefficient to the preset standard state risk sensitivity coefficient is multiplied by the preset standard risk assessment duration to obtain the adaptive risk assessment duration, which serves as a risk assessment time constraint. S3: Construct the optimization space for the status monitoring scheme based on the fusion of Q key locations and Q related monitoring indicator sets; S4: Using the risk assessment timeframe as a constraint and the accuracy and timeliness of the risk assessment as dual optimization objectives, an optimization search for state monitoring solutions is conducted within the state monitoring solution optimization space to obtain suitable state monitoring solutions, including: Within the optimization space of the state monitoring scheme, several key locations are randomly selected as preset monitoring locations, and several associated monitoring indicators are randomly set at each preset monitoring location to construct the first state monitoring scheme. The pre-training evaluation duration prediction plugin is used to perform risk assessment duration analysis on the first state monitoring scheme and output the first predicted evaluation duration. If the duration of the first prediction assessment exceeds the time limit for risk assessment, then the first state monitoring scheme shall be abandoned. If the first prediction assessment duration is less than or equal to the risk assessment time limit constraint, then the assessment accuracy prediction plugin is used to predict the risk assessment accuracy based on the first state monitoring scheme, and the first prediction assessment accuracy is output. The first scheme matching degree is calculated based on the first prediction and evaluation duration and the first prediction and evaluation accuracy. The scheme matching degree is negatively correlated with the prediction and evaluation duration and positively correlated with the prediction and evaluation accuracy. Within the optimization space of the state monitoring scheme, continue to randomly select state monitoring schemes and calculate the scheme matching degree until the preset number of convergences is reached. Output the state monitoring scheme corresponding to the maximum scheme matching degree and set it as the adaptive state monitoring scheme. S5: Within a preset time window, the status monitoring network is invoked to monitor the status of the roller coaster and assess its operational risks according to the adapted status monitoring scheme.

2. The safety risk assessment method for large-scale amusement facilities according to claim 1, characterized in that, Establishing a condition monitoring network includes: Based on the historical operation records of similar roller coasters, Q high-frequency risk areas of the roller coaster were identified as Q critical locations. Among them, the critical locations include at least the track curves, track joints, wheels, bearings, traction system, braking system, pneumatic system, and electronic control system. Based on the structural attribute information of Q key locations, determine Q sets of associated monitoring indicators, and based on the Q sets of associated monitoring indicators, determine Q sets of monitoring sensors to be deployed at the Q key locations to generate a status monitoring network.

3. The safety risk assessment method for large-scale amusement facilities according to claim 1, characterized in that, The pre-training evaluation duration prediction plugin includes: Based on the historical status monitoring records of roller coasters, a sample status monitoring scheme set was collected, and the historical evaluation time under different sample status monitoring schemes was set as the sample evaluation time, resulting in a sample evaluation time set. The historical evaluation time is the sum of data collection time, data preprocessing time and risk assessment time. Using a set of sample state monitoring schemes as input and a set of sample evaluation durations as supervision, a BP neural network is trained until convergence to generate an evaluation duration prediction plugin.

4. The safety risk assessment method for large-scale amusement facilities according to claim 1, characterized in that, The matching degree of the first scheme is calculated based on the first prediction and evaluation duration and the first prediction and evaluation accuracy, including: The difference between the preset standard risk assessment time and the adapted risk assessment time is taken as the assessment time difference, and the ratio of the assessment time difference to the preset standard assessment time difference is set as the efficiency weight compensation coefficient. After compensating the initial evaluation efficiency weight with an efficiency weight compensation coefficient, the adaptive evaluation efficiency weight is obtained. The initial evaluation efficiency weight is 0.5, and the adaptive evaluation efficiency weight is greater than or equal to 0.25 and less than or equal to 0.

75. The adaptation evaluation accuracy weight is obtained by subtracting the adaptation evaluation efficiency weight from 1. After dimensionless processing of the first prediction evaluation duration and the first prediction evaluation accuracy, the matching degree of the first scheme is obtained based on the matching evaluation efficiency weight and the matching evaluation accuracy weight.

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