Method for detecting fastening state of intelligent equipment installed on amusement equipment

Abstract

The invention discloses a method for detecting the fastening state of intelligent equipment installed on amusement equipment, and the method comprises the steps: collecting sensor data of the intelligent equipment, comparing the sensor data with reference data or historical data, and judging the fastening state of the intelligent equipment. According to the scheme, the fastening state of the intelligent equipment is judged by monitoring the sensor data of the intelligent equipment, and an alarm can be sent, so that the operation safety of the equipment is improved, and manual intervention and misjudgment are reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of amusement equipment monitoring, and in particular to a method for detecting the fastening state of an intelligent device installed on an amusement equipment. Background Art

[0002] With the increasing number of amusement venues and facilities, safety accidents caused by large amusement equipment occur from time to time. The risk factors leading to accidents include equipment factors such as design defects, fatigue fracture and damage, operation failure, safety protection failure, improper maintenance, illegal operation, unsafe behavior of tourists, and management factors such as improper emergency rescue measures and natural environmental factors. Therefore, the operation monitoring of amusement equipment is an issue that all amusement parks attach great importance to.

[0003] In some amusement parks, in order to monitor the status of amusement equipment in real time, smart devices are widely used in various types of amusement equipment to meet the needs of monitoring the operating status of equipment and providing real-time data. However, smart devices are installed in various locations on amusement equipment, and their own tightness is crucial to their normal operation and data accuracy. If the smart device is loose, it may cause data anomalies or even equipment failure, and the status of the amusement equipment cannot be accurately monitored. At the same time, the demand for providing a richer visitor experience on amusement equipment is also increasing. For example, additional image shooting and sound effect equipment are added to the amusement equipment, and these devices also need to be ensured to be tight and safe. Therefore, it is necessary to develop an effective method to detect the tightness of smart devices to ensure their stable installation and reliable operation. Summary of the invention

[0004] Based on the above technical problems, the present invention proposes a method for detecting the fastening status of a smart device installed on an amusement device. The specific technical solution is as follows: A method for detecting the fastening state of a smart device installed on an amusement device, comprising: installing the smart device on the amusement device, wherein the smart device has a built-in sensor or an external sensor, or both the built-in sensor and the external sensor; collecting sensor data built into the smart device, analyzing the collected sensor data, comparing the data with benchmark data or comparing the data with historical data, determining the fastening state of the smart device according to the data analysis result or the comparison result, and issuing a notification or an alarm according to the fastening state; The 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.

[0005] Specifically, the smart device installed on the amusement equipment includes one or more separate components, each of which has a built-in sensor that can independently determine its own fastening status in real time and issue an alarm.

[0006] Specifically, the sensor data collection built into the smart device includes timed collection and real-time collection. The timed collection includes: collecting sensor data at a specified time point, collecting at a specified time period, or collecting sensor data at a specified frequency.

[0007] Specifically, the sensor data analysis method includes but is not limited to pattern recognition, visual analysis, big data analysis, deep learning or other data feature analysis methods.

[0008] 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.

[0009] Specifically, the sensor data calibration further includes: when it is determined that the fastening state is abnormal and an alarm is issued, the time or position of the abnormal alarm is calculated according to the calibration starting time or starting position.

[0010] Specifically, the built-in sensors of the installed smart device include, but are not limited to, one or more of a gyroscope, an accelerometer, a barometer, a timer, a pressure gauge, a dynamometer, a magnetometer, a GPS or a Beidou navigation chip.

[0011] Specifically, the reference data is a specific threshold value set according to needs, or reference data calculated from the sensor.

[0012] Specifically, the comparison with the reference data to determine the fastening state of the smart 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.

[0013] Specifically, the analysis of sensor data to determine the fastening status of the smart device also includes: comparing data between different rounds of the amusement equipment; 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 smart device based on the calculation result.

[0014] Specifically, the comparison with historical data to determine the fastening state of the smart device is specifically: calculating the deviation between the sensor data and the historical data, and issuing an alarm message when the deviation value reaches a set threshold or threshold range.

[0015] Specifically, it also includes: calculating the change trend of sensor data, and issuing an alarm message when the change trend reaches a preset critical point.

[0016] Specifically, it also includes: storing and reporting the calculated deviation data.

[0017] 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.

[0018] Beneficial effects of the invention: The invention proposes an intelligent detection method for detecting the tightness of smart devices on amusement equipment. By installing a built-in sensor on the smart device, data is collected and compared with the benchmark data. When looseness is detected, the system automatically sends an alarm message, thereby realizing intelligent detection of the tightness of the smart device, which has the following advantages: (1) Timeliness: This solution can monitor the fastening status of smart devices in a timely manner, detect and warn of loose equipment in a timely manner, and improve the safety of the equipment.

[0019] (2) Intelligence: Through intelligent equipment and automation systems, intelligent detection and alarm of equipment tightening status are achieved, reducing manual intervention and misjudgment.

[0020] (3) Reliability: The accuracy and reliability of test results are improved through intelligent equipment and automated systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 It is a basic process architecture diagram of the intelligent detection method of the tightness of the intelligent device in an embodiment of the present invention; Figure 2 It is a schematic diagram of comparing the characteristic curve of the real-time data of the sensor and the sensor data in the tightened state 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] The present invention proposes a method for detecting the fastening state of an intelligent device installed on an amusement device to ensure the stable installation and safe operation of the intelligent device; the method comprises: installing the intelligent device on the amusement device, wherein the intelligent device has a built-in sensor or an external sensor, or both the built-in sensor and the external sensor are provided; collecting sensor data built in the intelligent device, analyzing the collected sensor data, comparing the data with the benchmark data or comparing the data with the historical data, judging the fastening state of the intelligent device according to the data analysis result or the comparison result, and issuing a notification or an alarm according to the fastening state; In this embodiment, the sensor data is analyzed, compared, and notification or alarm processing is performed in the smart device, or the data is transmitted to other computing devices for processing.

[0027] In this embodiment, the smart device installed on the amusement equipment includes one or more separate components, each of which has a built-in sensor that can independently determine its own tightening state in real time and issue an alarm.

[0028] Sensor data analysis methods include but are not limited to pattern recognition, visual analysis, big data analysis, deep learning or other data feature analysis methods.

[0029] In one embodiment, the intelligent detection of the tightness of the intelligent device on the amusement equipment also includes calibrating the sensor data collected in real time, and the calibration method includes but is not limited to calibrating the sensor data at a time point or a physical position point; when it is determined in real time that the tightness state is abnormal and an alarm is issued, the time or position of the abnormal alarm is calculated based on the calibrated starting time or starting position.

[0030] In one embodiment, depending on the operation of the amusement equipment, the built-in sensors include but are not limited to one or more of a gyroscope, an accelerometer, a barometer, a timer, a pressure gauge, a dynamometer, a magnetometer, a GPS and a Beidou navigation chip.

[0031] In this embodiment, the baseline data is a specific threshold value set according to needs, or is baseline data obtained by statistical calculation of the sensor; by comparing with the baseline data, judging the fastening state of the smart device is specifically as follows: calculating the deviation between the sensor data and the baseline data, and issuing an alarm message when the deviation value reaches a certain threshold.

[0032] Take the accelerometer as an example: When the equipment is in a state of high-speed rotation or large-amplitude swing, the normal range of acceleration acting on the seat in the x, y, and z directions is: 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.

[0033] In this embodiment, analyzing the sensor data and determining the fastening status of the smart device further includes: comparing the data between different rounds of the sensor; calculating the similarity or correlation coefficient between different rounds through a correlation coefficient algorithm, and determining whether there is any abnormality in the state of the smart device based on the calculation result.

[0034] By comparing with historical data, judging the fastening state of the smart device specifically includes: calculating the deviation between the sensor data and the historical data, and issuing an alarm message when the deviation value reaches a preset threshold. It also includes: calculating the change trend of the sensor data, and issuing an alarm message when the change trend reaches a preset critical point.

[0035] In this embodiment, the smart device stores and reports the calculated deviation data; and transmits the sensor data, deviation data and alarm information to an external mobile terminal, server or other receiving device via network communication.

[0036] In one embodiment, a smart detection method for detecting the fastening degree of a smart device on an amusement device is as follows: Figure 1 As shown, it includes the following parts: Installation of smart devices: Smart devices are installed at different locations on the amusement equipment, and the smart devices have built-in sensors. In this embodiment, the smart device can be composed of multiple separate parts, each of which has built-in sensors; smart devices are installed at different key locations on the amusement equipment, and each smart device has built-in sensors that can monitor its own vibration, displacement and other parameters in a timely manner.

[0037] Collection of benchmark data: After the equipment is installed, the sensor data of the smart device in a fastened state when the amusement equipment is running is first collected to obtain the benchmark data in the fastened state. In this embodiment, the data collection specifically includes: The collection of benchmark data should cover the full cycle operation status of the amusement equipment to ensure the accuracy and comprehensiveness of the benchmark curve; The collected data can be smoothed and filtered through data processing algorithms to remove noise; After the smart device is installed, start the amusement equipment and let it operate under normal operating conditions; During the operation of the equipment, the smart device starts to collect sensor data and records the sensor baseline data of the smart device in a tightened state; Through multiple experiments and data analysis, the accuracy and reliability of benchmark data are ensured.

[0038] Sensor data collection and comparison: When the amusement equipment is running, the smart device collects sensor data and compares it with the benchmark data in the fastened state. The comparison is performed using methods such as pattern recognition, visual analysis, big data analysis, deep learning or other data feature analysis. In this embodiment, the comparison method is specifically as follows: If the collected data has a high matching degree with the reference data of the tightening state, it is determined that the smart device is in the tightening state; If the collected data and the baseline data of the tightening state exceed the preset threshold, it is determined that the smart device is in a loose state.

[0039] In one 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:

[0040] in: : input array; : input array; : is a vector The mean and; : is a vector The mean and; : Pearson's correlation coefficient, between [-1, 1].

[0041] like Figure 2 The acceleration curve of an amusement equipment running for one round is shown. The top is the baseline curve of the equipment running, the middle 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 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 loose is significantly smaller than the Pearson correlation coefficient when the equipment is tightened.

[0042] In another embodiment, the intelligent detection device collects sensor data during each round of the amusement equipment and compares the similarity of the data from different rounds, and calculates the correlation coefficient between the data from different rounds through the Pearson correlation coefficient. Figure 3The acceleration curve of an amusement device running for one round 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 smart device is loose or has a risk of loosening.

[0043] In another embodiment, the intelligent detection device collects sensor data of the amusement device during each round of operation and compares it with the historical data of the device. Figure 4 The maximum value curve of the resultant acceleration force of each round of operation of an amusement equipment is shown. When the equipment is operating 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 equipment is loose or at risk of loosening.

[0044] In another embodiment, the intelligent detection device collects sensor data of the amusement equipment during each round of operation, 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 is loose or at risk of loosening.

[0045] Tightness determination and alarm mechanism: Based on the comparison results of sensor data, the tightness of the smart device is determined, and an alarm message is sent when it is loose or at risk of loosening: When it is determined that the smart device is in a loose state or at risk of loosening, the system automatically sends an alarm message to remind maintenance personnel to conduct inspections and maintenance through sound and light alarms, text message notifications, etc.

[0046] 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 fastening state of an intelligent device installed on an amusement device, characterized in that: include: Installing a smart device on the amusement equipment, wherein the smart device has a built-in sensor or an external sensor, or is provided with both a built-in sensor and an external sensor; collecting sensor data built into the smart device, analyzing the collected sensor data, comparing the data with benchmark data or historical data, determining the fastening state of the smart device according to the data analysis result or the comparison result, and issuing a notification or an alarm according to the fastening state; The 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.

2. A method for detecting the fastening state of an intelligent device installed on an amusement device according to claim 1, characterized in that: The intelligent device installed on the amusement equipment includes one or more separate components, each of which has a built-in sensor, which can independently determine its own fastening state in real time and issue an alarm.

3. A method for detecting the fastening state of an intelligent device installed on an amusement device according to claim 1, characterized in that: The sensor data collection built into the smart device includes timed collection and real-time collection. The timed collection includes: collecting sensor data at a specified time point, collecting at a specified time period, or collecting sensor data at a specified frequency.

4. A method for detecting the fastening state of a smart device installed on an amusement device according to claim 1, characterized in that: The sensor data analysis methods 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 fastening state of a smart device installed on 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 fastening state of a smart device installed on an amusement device according to claim 5, characterized in that: The sensor data calibration further includes: when it is determined that the fastening state is abnormal and an alarm is issued, the time or position of the abnormal alarm is calculated according to the calibration starting time or starting position.

7. A method for detecting the fastening state of a smart device installed on an amusement device according to claim 1, characterized in that: The sensors built into the installed smart device include, but are not limited to, one or more of a gyroscope, an accelerometer, a barometer, a timer, a pressure gauge, a dynamometer, a magnetometer, a GPS or a Beidou navigation chip.

8. A method for detecting the fastening state of a smart device installed on an amusement device according to claim 1, characterized in that: The reference data is a specific threshold value set according to needs, or reference data calculated from the sensor.

9. A method for detecting the fastening state of a smart device installed on an amusement device according to claim 8, characterized in that: The comparison with the reference data to determine the fastening state of the smart 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 fastening state of a smart device installed on an amusement device according to claim 8, characterized in that: The analysis of sensor data to determine the fastening status of the smart 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 smart device based on the calculation result.

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

12. A method for detecting the fastening state of a smart device installed on an amusement device according to claim 11, characterized in that: Also includes: Calculate the changing trend of sensor data and issue an alarm when the changing trend reaches a preset critical point.

13. A method for detecting the fastening state of a smart device installed on an amusement device according to claim 12, characterized in that: Also includes: The calculated deviation data is stored and reported.

14. A method for detecting the fastening state of a smart device installed on an amusement device according to claim 13, 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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