Method for detecting running state of amusement equipment

Abstract

The invention discloses a method for detecting the running state of amusement equipment, and the method comprises the steps: installing intelligent equipment for monitoring the state of the amusement equipment, and enabling the intelligent equipment to be internally provided with a sensor; the method comprises the following steps: acquiring sensor data, calibrating the data, analyzing the calibrated sensor data, or comparing the calibrated sensor data with reference data or historical data, judging the running state of the amusement equipment according to a data analysis result or a comparison result, and sending a notification or an alarm according to the running state. According to the scheme, the operation state of the amusement equipment is judged by monitoring the data of the sensor for monitoring the amusement equipment, and the alarm can be sent, so that the safety and the operation reliability of the amusement equipment are improved, and manual intervention and misjudgment are reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of amusement equipment safety management, and in particular to a method for detecting the operating state of an amusement equipment. Background Art

[0002] Ensuring the healthy operation of amusement equipment is the safety management responsibility of the amusement park. The amusement park will arrange special personnel to regularly inspect the equipment to eliminate safety hazards. The inspection usually includes two aspects: 1. Basic inspection when the equipment stops running; 2. Check whether the equipment is in normal condition during operation.

[0003] In the existing technical solutions, mainly through personnel training, operators are allowed to manually inspect the overall structure and parts of the amusement equipment. However, for amusement equipment, different types of amusement equipment have different installation methods and track construction, such as aerial, ground, water, etc. Therefore, the traditional manual inspection method is inefficient, difficult, and costly, and has certain risks and limitations, and cannot meet the current amusement park's demand for equipment inspection. Summary of the invention

[0004] The present invention proposes a method for detecting the operating status of an amusement device, adopts intelligent detection, sets multiple detection points, and compares or analyzes the data of multiple detection points to improve the detection accuracy, and can judge and warn of abnormal conditions. The specific technical scheme is as follows.

[0005] A method for detecting the operating status of an amusement device, comprising: installing an intelligent device for monitoring the status of the amusement device, collecting sensor data and calibrating the data, analyzing the calibrated sensor data, or comparing the calibrated sensor data with benchmark data or historical data, judging the operating status of the amusement device according to the data analysis result or the comparison result, and sending a notification or an alarm according to the operating status; The smart device has a built-in sensor or an external sensor, or both a built-in sensor and an external sensor; the calibration, analysis, comparison, notification or alarm processing of the sensor data is performed in the smart device, or the data is transmitted to other computing devices for processing; The intelligent device for monitoring the state of the amusement equipment is installed in one or more units at different locations; the intelligent device for monitoring the state of the amusement equipment also includes one or more external independent sensors.

[0006] Specifically, the smart device is composed of one or more separate components, and each separate component has a built-in sensor.

[0007] Specifically, the collecting of sensor data includes timed collection and real-time collection. The timed collection of sensor data includes: collecting sensor data at a specified time point, collecting sensor data during a specified time period, or collecting sensor data at a specified frequency.

[0008] Specifically, the analysis method of the sensor data includes but is not limited to pattern recognition, visual analysis, big data analysis, deep learning or other data feature analysis methods.

[0009] Specifically, the collected sensor data is calibrated, and the calibration method includes but is not limited to calibrating the sensor data at a time point or a physical position point.

[0010] Specifically, it also includes: when it is determined that the operating state is abnormal and an alarm is issued, the time or position of the abnormal alarm of the amusement equipment is calculated according to the calibrated starting time or starting position.

[0011] Specifically, the separately installed sensors are: one or more sensors installed at different locations on the amusement equipment or around the amusement equipment, and the sensors include but are not limited to a combination of one or more gyroscopes, accelerometers, barometers, timers, magnetometers, GPS or Beidou navigation chips.

[0012] Specifically, the reference data is a specific threshold value or a threshold value range set according to needs, or reference data calculated based on the sensor data.

[0013] Specifically, the comparison with the reference data to determine the operating state of the amusement device specifically includes: calculating the deviation between the sensor data and the reference data, and issuing an alarm message when the deviation value reaches a set threshold or a threshold range.

[0014] Specifically, the analyzing of sensor data to determine the operating status of the amusement device further includes: comparing data between different rounds of the amusement device; calculating similarities or correlation coefficients between different rounds by a correlation coefficient algorithm, and determining that there is no abnormality in the state of the amusement device based on the calculation result.

[0015] Specifically, the comparison with the historical data to determine the operating status of the amusement device is specifically: calculating the deviation between the data of the sensor and the historical data, and issuing an alarm message when the deviation value reaches a set threshold.

[0016] Specifically, it also includes: comparing sensor data collected by smart devices installed in different locations, and issuing an alarm message when the deviation value reaches a set threshold.

[0017] Specifically, it also includes: calculating the change trend of historical data, and issuing an alarm message when the change trend reaches a preset critical point.

[0018] Specifically, it also includes: storing and reporting the calculated deviation data.

[0019] Specifically, it also includes: transmitting the sensor data, deviation data and alarm information to an external mobile terminal, server or other receiving device through network communication.

[0020] Beneficial effects of the present invention: The present invention proposes a method for detecting the operating status of amusement equipment, which uses intelligent detection equipment installed around the amusement equipment or at various key positions to collect sensor data at various detection points, and compares the data of multiple detection points with benchmark data to improve detection accuracy. The data changes of multiple sensors are used and multi-point comparison is used to judge and warn of abnormal conditions. This not only reduces the cost of manpower investment and ensures data accuracy, but also can directly upload relevant data to a big data platform, timely display the operating status of the equipment, and can discover safety hazards in the equipment at the first time, realize all-round monitoring of amusement equipment, discover abnormal conditions in time and take corresponding measures, thereby improving the safety and operational reliability of amusement equipment. The multi-point detection and data comparison method effectively reduces the false alarm rate and ensures the accuracy and timeliness of abnormality detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 It is a basic process architecture diagram of the intelligent detection method for abnormal state of amusement equipment in an embodiment of the present invention; Figure 2 It is a schematic diagram of the comparison between real-time data and a dynamic reference curve in an embodiment of the present invention.

[0022] Figure 3 2 is a schematic diagram of data comparison in different rounds in an embodiment of the present invention.

[0023] Figure 4 3 is a schematic diagram of the deviation comparison between the embodiment of the present invention and the historical data.

[0024] Figure 5 Schematic diagram of analyzing the trend of historical data changes in an embodiment of the present invention. DETAILED DESCRIPTION

[0025] In order to have a clearer understanding of the technical features, purposes and effects of the present invention, specific embodiments of the present invention are now described with reference to the accompanying drawings.

[0026] In order to solve the problems of low efficiency, great difficulty and high investment cost of traditional manual detection methods of various amusement equipment in amusement parks, the present invention proposes a method for detecting the operating status of amusement equipment, such as Figure 1As shown, an intelligent device for monitoring the status of an amusement device is installed, sensor data is collected and calibrated, the calibrated sensor data is analyzed, or the calibrated sensor data is compared with baseline data or historical data, the operating status of the amusement device is judged according to the data analysis result or the comparison result, and a notification or an alarm is sent according to the operating status; in this embodiment, the intelligent device has a built-in sensor or an external sensor, or a built-in sensor and an external sensor at the same time; the calibration, analysis, comparison, sending of notifications or alarms of the sensor data are performed in the intelligent device, or the data is transmitted to other computing devices for processing; in this embodiment, one or more intelligent devices for monitoring the status of an amusement device are installed at different locations; the intelligent device for monitoring the status of an amusement device also includes one or more external independent sensors. In one embodiment, the intelligent device installed on the amusement device is composed of one or more separate components, each of which has a built-in sensor.

[0027] Analysis methods for sensor data include, but are not limited to, pattern recognition, visual analysis, big data analysis, deep learning, or other data feature analysis methods.

[0028] In this embodiment, collecting sensor data includes timed collection and real-time collection. The timed collection of sensor data includes: collecting sensor data at a specified time point, collecting sensor data during a specified time period, or collecting sensor data at a specified frequency.

[0029] In this embodiment, the collected sensor data is calibrated, and the calibration method includes but is not limited to calibrating the sensor data at a time point or a physical location point; it also includes: when it is determined that the operating status is abnormal and an alarm is issued, the time or location of the abnormal alarm of the amusement equipment is calculated based on the calibrated starting time or starting position.

[0030] In another embodiment, the sensors monitored by this solution are provided with separately installed sensors in addition to the built-in sensors in the smart device. The separately installed sensors can be installed at different locations on or around the amusement equipment according to the actual needs of each amusement park, and one or more sensors can be installed. The sensors used in actual situations include, but are not limited to, one or more combinations of gyroscopes, accelerometers, barometers, timers, magnetometers, GPS or Beidou navigation chips.

[0031] In this embodiment, the benchmark data is a specific corresponding threshold value set according to security requirements, or is benchmark data obtained by statistical calculation of the sensor data; the historical data is data recorded and stored by the sensor.

[0032] By comparing with the benchmark data, the operating status of the amusement equipment is judged specifically as follows: the deviation between the sensor data and the benchmark data is calculated, and an alarm message is issued when the deviation value reaches a preset threshold.

[0033] By comparing with the historical data, the operating status of the amusement equipment is judged specifically by calculating the deviation between the data of the sensor and the historical data, and when the deviation value reaches a certain threshold, an alarm message is issued.

[0034] In one embodiment, whether the device status is abnormal can be determined by calculating the change trend of sensor data, and an alarm message can be issued when the change trend reaches a preset critical point.

[0035] In this embodiment, the calculated deviation data is stored and reported.

[0036] According to actual needs, sensor data, deviation data and alarm information can also be transmitted to external mobile terminals, servers or other receiving devices through network communication to facilitate timely observation and processing.

[0037] In one embodiment, when comparing with the benchmark data, different thresholds are set according to needs or sensor types. Taking the accelerometer as an example: When the device is in a state of high-speed rotation or large-amplitude swing, the normal range and threshold of acceleration acting on the seat in the x, y, and z directions are set to: The maximum acceleration value in the x-direction shall not exceed 6g; The maximum acceleration value in the y direction does not exceed 4g; The maximum acceleration value in the z direction does not exceed 6g.

[0038] The intelligent detection method of abnormal status of amusement equipment compares or analyzes sensor data to judge and warn of abnormal status, which not only reduces the cost of manpower investment and ensures data accuracy, but also directly uploads relevant data to the big data platform to display the operating status of the equipment in a timely manner, and can discover safety hazards of the equipment at the first time. The specific process includes the following steps: 1. Collected sensor data Embodiment 1 In this embodiment, intelligent detection equipment is installed, and one or more sensors are built into the intelligent detection equipment. The intelligent detection equipment is installed at different key positions of the amusement equipment; the key positions include but are not limited to the support structure, moving parts, connection points, etc., to ensure comprehensive monitoring of the operation status of the equipment. Each intelligent detection device has multiple sensors built in, and the sensor types include but are not limited to acceleration sensors, temperature sensors, pressure sensors, vibration sensors, etc., to perceive the operation status of the equipment in multiple dimensions.

[0039] Embodiment 2 In this embodiment, the separately installed sensors can be installed at different locations on or around the amusement equipment according to the actual needs of each amusement park, and one or more sensors can be installed, and the installed sensors are connected to the intelligent detection device. The sensors used in actual situations include but are not limited to a combination of one or more gyroscopes, accelerometers, barometers, timers, magnetometers, GPS or Beidou navigation chips.

[0040] 2. Set the warning threshold: According to the detection data type of different sensors, combined with historical data and expert experience, set the corresponding warning threshold. The warning threshold setting should ensure that the normal state and abnormal state can be accurately distinguished. For dynamic data such as acceleration and vibration, warning thresholds in different frequency bands can be set to improve the sensitivity of abnormal detection. Static data such as temperature and pressure should be set with reasonable upper and lower limits to prevent false alarms.

[0041] 3. Sensor data analysis and comparison: When the amusement equipment is running, the intelligent detection equipment collects sensor data, analyzes and processes it on the intelligent detection equipment, and also transmits it to other computing devices through wireless or wired networks for processing and analysis. The collected data is compared with the benchmark data, and methods such as pattern recognition, visual analysis, big data analysis, deep learning or other data feature analysis are used to determine whether the current data exceeds the preset warning threshold. The time series characteristics of the sensor data are considered in the comparison process, and algorithms such as Dynamic Time Warping (DTW) are used to improve the accuracy of the comparison.

[0042] Embodiment 3 In this embodiment, the intelligent detection device collects sensor data during each round of the amusement equipment operation and compares it with the benchmark data for similarity, and the data between each round are also compared with each other. The correlation coefficient between the benchmark data and the real-time data is calculated using the Pearson correlation coefficient. The value of the correlation coefficient is between -1 and 1. The closer the correlation coefficient is to 1, the more similar the two are. The specific calculation formula is:

[0043] in: : input array; : input array; : is a vector The mean and; : is a vector The mean and; : Pearson's correlation coefficient, between [-1, 1].

[0044] like Figure 2The acceleration curve of an amusement equipment running for one round is shown. The top one is the baseline curve of the equipment, the middle one is the acceleration curve when the equipment is running abnormally, and the Pearson correlation coefficient between it and the baseline curve is 0.87. The bottom one is the acceleration curve when the amusement equipment is running normally, and the Pearson correlation coefficient between it and the baseline curve is 0.99. It can be seen that the Pearson correlation coefficient when the equipment is running abnormally is significantly smaller than the Pearson correlation coefficient when the equipment is running normally.

[0045] Embodiment 4 In this embodiment, the intelligent detection device collects sensor data during each round of operation of the amusement equipment and performs similarity comparison on the data of different rounds, and calculates the correlation coefficient between the data of different rounds through the Pearson correlation coefficient. Figure 3 The acceleration curve of one round of amusement equipment is shown in the figure. The acceleration curves of four rounds are shown in the figure. The Pearson correlation coefficient between the third round and the first round is 0.991, and the Pearson correlation coefficient between the third round and the second round is 0.997; the Pearson correlation coefficient between the fourth round and the first round is 0.819, and the Pearson correlation coefficient between the fourth round and the second round is 0.876. It can be seen that there is a significant difference between the data of the fourth round and the other rounds, so it can be determined that the equipment in this round is running abnormally.

[0046] Embodiment 5 In this embodiment, the intelligent detection device collects sensor data of the amusement equipment during each round of operation and compares it with the historical data of the equipment. Figure 4 The maximum value curve of the resultant acceleration force of each round of operation of an amusement device is displayed. When the device operates normally, the value fluctuates around 50, and the fluctuation range is small. The value of a certain round is 75, which is significantly larger than the values ​​of other rounds, indicating that the amusement device is operating abnormally in that round.

[0047] Embodiment 6 In this embodiment, the intelligent detection device collects sensor data during each round of operation of the amusement equipment, records the Pearson correlation coefficient between the sensor data and the benchmark data, and analyzes the change trend of the Pearson correlation coefficient of all rounds. Figure 5 The Pearson correlation coefficient curve of each round of acceleration of an amusement equipment and the baseline curve is shown. It can be seen that there is a sudden drop in the dotted part of the curve, indicating that the equipment operation is abnormal at this time.

[0048] 5. Multi-point detection confirms abnormality: When the system detects that the data of a certain intelligent detection device is abnormal, it further detects the data of other intelligent detection devices on the amusement equipment.

[0049] By analyzing the data from multiple intelligent detection devices, it is possible to confirm whether there are the same abnormal features in the data from multiple devices, thereby reducing the false alarm rate and improving the reliability of detection.

[0050] When it is confirmed that the amusement equipment is operating abnormally, the system will promptly send out an alarm signal and take corresponding measures according to the preset safety strategy, such as shutdown and maintenance.

[0051] The present invention has shown and described the basic principles and main features of the present invention and the advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A method for detecting the operating status of an amusement device, characterized in that: include: Install smart devices to monitor the status of amusement equipment, collect sensor data and calibrate the data, analyze the calibrated sensor data, or compare the calibrated sensor data with benchmark data or historical data, determine the operating status of the amusement equipment based on the data analysis results or comparison results, and send notifications or alarms based on the operating status; The smart device has a built-in sensor or an external sensor, or both a built-in sensor and an external sensor; the calibration, analysis, comparison, notification or alarm processing of the sensor data is performed in the smart device, or the data is transmitted to other computing devices for processing; The intelligent device for monitoring the state of the amusement equipment is installed in one or more units at different locations; the intelligent device for monitoring the state of the amusement equipment also includes one or more external independent sensors.

2. A method for detecting the operating status of an amusement device according to claim 1, characterized in that: The smart device is composed of one or more separate components, and each separate component has a built-in sensor.

3. A method for detecting the operating status of an amusement device according to claim 1, characterized in that: The collecting of sensor data includes timed collection and real-time collection. The timed collection of sensor data includes: collecting sensor data at a specified time point, collecting sensor data during a specified time period, or collecting sensor data at a specified frequency.

4. A method for detecting the operating status of an amusement device according to claim 1, characterized in that: The analysis methods of the sensor data include but are not limited to pattern recognition, visual analysis, big data analysis, deep learning or other data feature analysis methods.

5. A method for detecting the operating status of an amusement device according to claim 1, characterized in that: The collected sensor data is calibrated, and the calibration method includes but is not limited to calibrating the sensor data at a time point or a physical position point.

6. A method for detecting the operating status of an amusement device according to claim 5, characterized in that: Also includes: When it is determined that the operating state is abnormal and an alarm is issued, the time or position of the abnormal alarm of the amusement equipment is calculated according to the calibrated starting time or starting position.

7. A method for detecting the operating status of an amusement device according to claim 1, characterized in that: The separately installed sensors specifically include: one or more sensors installed at different locations on the amusement equipment or around the amusement equipment, and the sensors include but are not limited to a combination of one or more gyroscopes, accelerometers, barometers, timers, magnetometers, GPS or Beidou navigation chips.

8. A method for detecting the operating status of an amusement device according to claim 1, characterized in that: The reference data is a specific threshold value or a threshold value range set according to needs, or is reference data calculated based on the sensor data.

9. A method for detecting the operating status of an amusement device according to claim 8, characterized in that: The comparison with the reference data to determine the operating state of the amusement device specifically includes: calculating the deviation between the sensor data and the reference data, and issuing an alarm message when the deviation value reaches a set threshold or a threshold range.

10. A method for detecting the operating status of an amusement device according to claim 1, characterized in that: The analysis of sensor data to determine the operating status of the amusement device also includes: comparing data between different rounds of the amusement device; calculating the similarity or correlation coefficient between different rounds through a correlation coefficient algorithm, and determining that there is no abnormality in the state of the amusement device based on the calculation result.

11. A method for detecting the operating status of an amusement device according to claim 1, characterized in that: The comparison with the historical data to determine the operating status of the amusement device specifically includes: calculating the deviation between the data of the sensor and the historical data, and issuing an alarm message when the deviation value reaches a set threshold.

12. A method for detecting the operating status of an amusement device according to claim 1, characterized in that: Also includes: The sensor data collected by smart devices installed in different locations are compared, and an alarm message is issued when the deviation value reaches the set threshold.

13. A method for detecting the operating status of an amusement device according to claim 12, characterized in that: Also includes: Calculate the trend of historical data changes and issue an alarm when the trend reaches a preset critical point.

14. A method for detecting the operating status of an amusement device according to claim 13, characterized in that: Also includes: The calculated deviation data is stored and reported.

15. A method for detecting the operating status of an amusement device according to claim 14, characterized in that: Also includes: The sensor data, deviation data and alarm information are transmitted to external mobile terminals, servers or other receiving devices through network communication.

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