High-altitude track state detection method, processing device and carrying system

Abstract

The invention discloses a high-altitude rail state detection method, a processing device and a carrying system. The method for detecting the state of the high-altitude orbit can be executed by the processing device. The high-altitude orbit detection method comprises the steps of repeatedly executing a collection step, then executing a data conversion step, counting and judging. In the collection step, the operation information of each carrying vehicle on each section of track is collected. In the data conversion step, the operation information is classified into track information. In the statistics step, whether the data of each piece of track information exceeds a default range or not is judged, and the number of all the data exceeding the default range is counted to generate statistical information of each track. In the judgment step, whether the proportion of the total number of the data contained in the statistical information and the total number of the data contained in the track information exceeds a preset warning proportion or not is judged; and if so, correspondingly generating track warning information.

Description

Technical Field

[0001] The present application relates to a method for detecting the state of a track, a processing device and a transport system, and in particular to a method for detecting the state of an overhead track, a processing device capable of executing the method for detecting the state of an overhead track, and a transport system comprising the processing device, an overhead track and multiple transport vehicles. Background Art

[0002] In common industrial plants, especially high-tech plants such as semiconductor plants, overhead transport systems are often used to transport cargo. These systems primarily utilize an overhead track installed near the ceiling, allowing the transporter to move cargo along the track above the plant. Because the overhead track is located near the ceiling, technicians in this field cannot readily inspect it. Furthermore, no existing system exists for real-time inspection of the track status. Consequently, technicians can only periodically inspect the track to ensure the condition of each section.

[0003] In practice, when inspecting the aerial track, those skilled in the art must stop at least some of the transport vehicles from moving, which directly affects the transport schedule of the transport vehicles. Summary of the Invention

[0004] The present application discloses a method for detecting the state of a high-altitude track, a processing device, and a handling system, which are mainly used to improve the problem in the prior art that it is difficult for those skilled in the art to grasp the state of the high-altitude track in real time.

[0005] One embodiment of the present application discloses a method for detecting the status of an aerial track, which can be executed by a processing device. The processing device is communicatively connected to a plurality of transport vehicles, each transport vehicle is used to carry an object to be transported, and the plurality of transport vehicles travel on the same track system. The track system is set in the air, and the track system includes a plurality of track sections. The aerial track detection method includes: first, within a preset collection time, repeatedly executing a collection step, and then executing a data conversion step, a statistical step and a judgment step; the collection step is: collecting operation information of each transport vehicle on each track section; the operation information includes at least one of at least one time measurement data and at least one non-time measurement data; the data conversion step is: classifying the plurality of operation information into a plurality of track information; the plurality of time measurement data included in each track information and / or multiple non-time measurement data, which are the time measurement data and / or non-time measurement data in the operation information corresponding to multiple transport vehicles passing through the same section of track; the statistical step is: judging whether each time measurement data and / or non-time measurement data contained in each track information exceeds a default range, and counting the number of all time measurement data and / or non-time measurement data exceeding the default range to generate statistical information corresponding to each track; the judgment step is: judging whether the ratio of each statistical information to the total number of time measurement data and / or non-time measurement data contained in each track information exceeds a default warning ratio. If it exceeds the default warning ratio, a track warning information is generated accordingly, and the track warning information includes track identification data of the corresponding track.

[0006] Optionally, in the collection step, during the process of each transport vehicle passing through any track, the processing device or a controller of the transport vehicle performs multiple sampling steps. Each time the processing device or controller performs the sampling step, the processing device or controller will obtain at least one sampling time measurement data and / or at least one sampling non-time measurement data of the transport vehicle; at least one time measurement data in each piece of operation information is at least one of the average value of multiple sampling time data, the maximum value of multiple sampling time data and the minimum value of multiple sampling time data; at least one non-time measurement data in each piece of operation information is at least one of the average value of multiple sampling non-time data, the maximum value of multiple sampling non-time data and the minimum value of multiple sampling non-time data.

[0007] Optionally, each piece of operation information includes at least one time measurement data and / or at least one non-time measurement data; in the statistical step, it is determined whether each piece of time measurement data contained in each piece of track information exceeds a default range; wherein, in the collection step, after multiple sampling steps, it also includes the following steps: at least one data cleaning step: removing at least one sampled time measurement data in each piece of operation information, so that each sampled time measurement data in each piece of operation information falls within a default time measurement range; wherein, in the collection step, after multiple sampling steps, it also includes the following steps: a screening step: removing at least one sampled non-time measurement data in each piece of operation information, so that each sampled non-time measurement data in each piece of operation information falls within a default screening range.

[0008] Optionally, each piece of operation information includes multiple time measurement data, one of which is the total time taken for the transport vehicle to pass through one of the tracks, and the remaining time measurement data are: during the process of the transport vehicle passing through one of the tracks, at least one of the command speed of the transport vehicle, the maximum speed of the transport vehicle's driving wheels, the minimum speed of the transport vehicle's driving wheels, the average speed of the transport vehicle's driving wheels, the maximum speed of the transport vehicle's driven wheels, the minimum speed of the transport vehicle's driven wheels, and the average speed of the transport vehicle's driven wheels.

[0009] Optionally, the non-time measurement data included in each piece of operation information is: the maximum torque of the driving shaft of the transport vehicle, the minimum torque of the driving shaft of the transport vehicle, the average torque of the driving shaft of the transport vehicle, the peak torque of the driving shaft of the transport vehicle, the maximum torque of the driven shaft of the transport vehicle, the minimum torque of the driven shaft of the transport vehicle, the average torque of the driven shaft of the transport vehicle, the peak torque of the driven shaft of the transport vehicle, a vibration value peak value sensed by a vibration sensor installed on the transport vehicle, a vibration root mean square, and a total vibration amount, and at least one of the following:

[0010] One embodiment of the present application discloses a processing device capable of executing the high-altitude orbit state detection method of the present application.

[0011] One embodiment of the present application discloses a transport system, which includes the processing device of the present application, a track system, and a plurality of transport vehicles.

[0012] Optionally, the transport system further includes a display device, which is electrically connected to the processing device. After the processing device executes the judgment step, the processing device can control the display device to display the track warning information.

[0013] One embodiment of the present application discloses a method for detecting the status of an aerial track, which can be executed by a processing device. The processing device is communicatively connected to a plurality of transport vehicles, each transport vehicle is used to carry an object to be transported, and the plurality of transport vehicles travel on the same track system. The track system is set in the air, and the track system includes a plurality of track sections. The aerial track detection method includes: first, within a default collection time, repeatedly executing a collection step, and then executing a data conversion step and a judgment step; the collection step is: collecting operation information of each transport vehicle on each track section; the operation information includes at least one of at least one time measurement data and / or at least one non-time measurement data; the data conversion step is: classifying the plurality of operation information into a plurality of track information; the plurality of track information included in each track information The time measurement data and / or multiple non-time measurement data are the time measurement data and / or non-time measurement data in the operation information corresponding to multiple transport vehicles passing through the same section of track; the statistical step is: using a statistical anomaly detection method, or using an outlier detection method in machine learning, to determine whether each time measurement data and / or non-time measurement data contained in each track information is an abnormal data, and to count the total number of abnormal data to generate statistical information corresponding to each track; the judgment step is: judging whether each statistical information exceeds a default warning ratio. If it exceeds the default warning ratio, a corresponding track warning information is generated, and the track warning information includes a track identification data of the corresponding track.

[0014] In summary, the high-altitude track status detection method, processing device, and handling system of the present application, through the design of collection steps, data conversion steps, statistical steps, and judgment steps, can allow technical personnel in this field to know the real-time status of any section of the high-altitude track in real time by viewing track warning information, especially any situation where any section of track needs repair or is damaged. The high-altitude track status detection method, processing device, and handling system of the present application can directly detect the track status while the handling vehicle is traveling on the track system, without stopping the handling vehicle.

[0015] To further understand the features and technical content of this application, please refer to the following detailed description and drawings of this application. However, such description and drawings are only used to illustrate this application and do not limit the scope of protection of this application. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 This is a block diagram of the transport system of the present application.

[0017] Figure 2 Schematic diagram of the transport vehicle and overhead track of the transport system of this application.

[0018] Figure 3FIG. 1 is a flow chart of an embodiment of a high-altitude orbit detection method of the present application.

[0019] Figure 4 and Figure 5 They are schematic diagrams of one of the operation interfaces displayed on a display device of the high-altitude orbit detection method of the present application.

[0020] Figure 6 This is a partial flow chart of the collection step of the high-altitude orbit detection method of the present application.

[0021] Figure 7 FIG. 4 is a flow chart of another embodiment of the high-altitude orbit detection method of the present application. DETAILED DESCRIPTION

[0022] In the following description, if it is indicated to refer to a specific figure or as shown in a specific figure, it is only used to emphasize that most of the relevant content described in the subsequent description appears in the specific figure, but it does not limit the subsequent description to only refer to the specific figure.

[0023] Please also refer to Figures 1 to 5 , Figure 1 This is a block diagram of the handling system of this application. Figure 2 This is a schematic diagram of the transport vehicle and overhead track of the transport system of this application. Figure 3 This is a flow chart of an embodiment of the high-altitude orbit detection method of the present application. Figure 4 and Figure 5 They are schematic diagrams of one of the operation interfaces displayed on a display device of the high-altitude orbit detection method of the present application.

[0024] The transport system 100 of the present application includes a processing device 1, a track system and multiple transport vehicles 2. The processing device 1 is, for example, a central computer (such as a central control computer, a server, etc. in a factory). The processing device 1 is communicatively connected to multiple transport vehicles 2, and the processing device 1 and each transport vehicle 2 can exchange information. In actual applications, the processing device 1 can, for example, control any transport vehicle 2 to move to a specific position along the track system; any transport vehicle 2 can also transmit information to the processing device 1 in real time, and the processing device 1 can use the information transmitted in real time by the transport vehicle 2 to obtain the current position of the transport vehicle 2 in the track system. The track system at least includes an overhead track and related components or sensors arranged on or around the overhead track to assist in positioning the transport vehicle 2. The overhead track refers to a track arranged adjacent to the ceiling of the factory building.

[0025] like Figure 1 and Figure 2As shown, each transport vehicle 2 includes, for example, a controller 21, a carrier body 22, at least two movable vehicles (a front vehicle 23A and a rear vehicle 23B), a drive module 24, driven wheels 25A, driving wheels 25B, upper guide wheels 26, a switching module 27, and lower guide wheels 28. Each movable vehicle is provided with the controller 21, the drive module 24, the driven wheels 25A, the driving wheels 25B, the upper guide wheels 26, the switching module 27, and the lower guide wheels 28.

[0026] The controller 21 can communicate with the processing device 1 and control the driving module 24 and the switching module 27 in a timely manner according to the information sent by the processing device 1, so that the transport vehicle 2 can move along the track system to a specific location in the factory. The controller 21 is, for example, an industrial computer.

[0027] The carrier body 22 is connected to all the mobile carts. The carrier body 22 is used to carry an object to be transported. Each mobile cart includes a body 23 rotatably connected to the carrier body 22. The two mobile carts are defined as a front cart 23A and a rear cart 23B. In practical applications, the carrier body 22 can be roughly shaped like a box, and the box structure can be used to hold the object to be transported. In one embodiment, the object to be transported can be, for example, a wafer cassette, but the invention is not limited thereto.

[0028] In practical applications, the two mobile vehicles may include identical or substantially identical electronic components and mechanical components, but are not limited thereto. The vehicle bodies 23 of the front vehicle 23A and the rear vehicle 23B may be respectively provided with a drive module 24, four upper guide wheels 26, a switching module 27, and four lower guide wheels 28, and the vehicle bodies 23 of the front vehicle 23A and the rear vehicle 23B may also be respectively provided with two driven wheels 25A and two driving wheels 25B. The controller 21 is electrically connected to the drive module 24, and the drive module 24 is connected to a drive shaft, which is connected to the two driving wheels 25B. For example, the drive module 24 includes a motor, which is connected to the drive shaft via a transmission component such as a belt or gears, and the two ends of the drive shaft are fixed to the two driving wheels 25B. It should be noted that in this embodiment, the power source for the transport vehicle 2 to travel on the elevated track primarily comes from the drive module 24 of the rear vehicle 23B (i.e., the transport vehicle 2 is rear-wheel drive as a whole). Therefore, the wheels provided on the rear vehicle 23B are defined as the driving wheels 25B, while the wheels provided on the front vehicle 23A are defined as the driven wheels 25A. However, if the power source for the transport vehicle 2 to travel on the elevated track is changed to the drive module 24 of the front vehicle 23A (i.e., the transport vehicle 2 is front-wheel drive as a whole), the wheels provided on the front vehicle 23A are defined as the driving wheels, and the wheels provided on the rear vehicle 23B are defined as the driven wheels. Of course, in special applications, the transport vehicle 2 can also be four-wheel drive, that is, during the process of the transport vehicle 2 moving along the elevated track, the drive modules 24 of the front vehicle 23A and the rear vehicle 23B will drive the wheels connected thereto to rotate. In addition, in the embodiment where both the front vehicle 23A and the rear vehicle 23B of the transport vehicle 2 are provided with the drive module 24 , the transport vehicle 2 can also switch between front-wheel drive, rear-wheel drive and four-wheel drive according to needs.

[0029] The controller 21 of the rear vehicle 23B controls the drive module 24, which rotates the two driving wheels 25B via the drive shaft, thereby moving the transport vehicle 2 on the overhead track. Simultaneously, the two driven wheels 25A of the front vehicle 23A move synchronously along the overhead track, and the driven shafts to which each driven wheel 25A is connected also rotate accordingly. A switching module 27 is mounted on the vehicle body 23, and the controller 21 controls the activation of the switching module 27 to move the multiple upper guide wheels 26 to an upper position away from the vehicle body 23 or a lower position close to the vehicle body 23.

[0030] Four lower guide wheels 28 are disposed below the vehicle body 23. In one embodiment, the switching module 27 may include, for example, a slide rail, a slider, a motor, a belt (or related transmission components such as gears), etc. Therefore, when the controller 21 activates the switching module 27, the motor drives the slider to move, thereby allowing the upper guide wheels 26 connected to the slider to move between an upper position and a lower position.

[0031] exist Figure 2In the figure, one of the transport vehicles 2 is passing through a fork in the track system and the transport vehicle 2 will continue to go straight. The multiple tracks included in the track system can be divided into lower tracks and upper tracks according to their installation positions. Figure 2 In the example shown, the lower track can be divided into a straight lower track 31 and a curved lower track 32 based on their appearance, and the upper track can be divided into a straight upper track 33 and a curved upper track 34. In front of the fork in the road, the two straight lower tracks 31 are arranged in parallel, and the driven wheels 25A and driving wheels 25B included in the front and rear carts 23A and 23B of the transport vehicle 2 travel on the two parallel straight lower tracks 31.

[0032] At the fork in the road, one of the straight lower rails 31 is connected to the turning lower rail 32, and the turning lower rail 32 and the other turning lower rail 32 are arranged side by side with a gap C between them; above the turning lower rail 32 connected to the straight lower rail 31, a turning upper rail 34 is arranged, and one end of the turning upper rail 34 can be connected to the straight upper rail 33.

[0033] exist Figure 2 In the state shown, one of the driven wheels 25A of the front vehicle 23A has been traveling on the lower curved track 32, and two of the upper guide wheels 26 of the front vehicle 23A are already located in an upper position away from the vehicle body 23, and the two upper guide wheels 26 have been in contact with the outer side of the straight upper track 33. In this case, if the front vehicle 23A and the rear vehicle 23B continue to move forward, the two upper guide wheels 26 of the front vehicle 23A will guide the front vehicle 23A to continue to move straight. Similarly, when the rear vehicle 23B passes through the fork in the road, the two upper guide wheels 26 of the rear vehicle 23B will also be guided by the straight upper track 33 and continue to move straight.

[0034] Relatively speaking, before the front vehicle 23A (and the rear vehicle 23B) passes through the fork in the road, if the upper guide wheels 26 of the front vehicle 23A (and the rear vehicle 23B) are both located in a lower position close to the vehicle body 23, then when the front vehicle 23A (and the rear vehicle 23B) passes through the fork in the road, two of the upper guide wheels 26 will abut against one side of the turning upper track 34, and the front vehicle 23A (and the rear vehicle 23B) will turn under the guidance of the turning upper track 34.

[0035] In practice, the positions of the straight upper rail 33 and the turning upper rail 34 can be adjusted so that when the upper guide wheels 26 of the movable vehicle (the front vehicle 23A and the rear vehicle 23B) abut against the straight upper rail 33 or the turning upper rail 34, the vehicle body 23 of the movable vehicle (the front vehicle 23A and the rear vehicle 23B) can tilt to one side, thereby allowing the movable vehicle to smoothly pass through the gap C between the two turning lower rails 32.

[0036] The processing device 1 is capable of executing the method for detecting the status of the overhead track of the present application, thereby determining the status of each track section of the track system using relevant information generated by multiple transport vehicles 2 traveling along the track system. In practical applications, the processing device 1 capable of executing the method for detecting the status of the overhead track of the present application can be implemented or sold separately (e.g., in the form of an industrial computer or software package), and the processing device 1 of the present application is not necessarily required to be sold together with the transport system of the present application.

[0037] Specifically, the high-altitude orbit detection method includes: first, within a default collection time, repeatedly executing a collection step S11, then performing a data conversion step S12, a statistical step S13, and a determination step S14. In actual applications, the default collection time can be designed based on actual needs, for example, daily, weekly, or biweekly.

[0038] The collecting step S11 is to collect operation information 12 of each transport vehicle 2 on each track section. The operation information 12 includes at least one of at least one time measurement data 2111 and at least one non-time measurement data 2112 .

[0039] Among them, the time measurement data 2111 is, for example, various time-related data, such as: the time when the transport vehicle 2 enters a specific section of track, the time when the transport vehicle 2 leaves a specific section of track, the total time consumed by the transport vehicle 2 through the specific section of track, the average speed of the transport vehicle 2 through the specific section of track, the maximum speed of the transport vehicle 2 during the process of passing through the specific section of track, the minimum speed of the transport vehicle 2 during the process of passing through the specific section of track, the average acceleration of the transport vehicle 2 during the process of passing through the specific section of track, the maximum acceleration of the transport vehicle 2 during the process of passing through the specific section of track, the minimum acceleration of the transport vehicle 2 during the process of passing through the specific section of track, the driving wheel of the transport vehicle 2 during the process of passing through the specific section of track The information includes the average speed of the driving wheels 25B of the transport vehicle 2, the maximum speed of the driving wheels 25B of the transport vehicle 2 during the process of the transport vehicle 2 passing through a specific track section, the minimum speed of the driving wheels 25B of the transport vehicle 2 during the process of the transport vehicle 2 passing through a specific track section, the average angular velocity of the driving wheels 25B of the transport vehicle 2 during the process of the transport vehicle 2 passing through a specific track section, the maximum angular velocity of the driving wheels 25B of the transport vehicle 2 during the process of the transport vehicle 2 passing through a specific track section, the minimum angular velocity of the driving wheels 25B of the transport vehicle 2 during the process of the transport vehicle 2 passing through a specific track section, and the command speed of the transport vehicle 2 (which is issued by the controller 21 of the transport vehicle 2 to the drive module 24 of the transport vehicle 2 to indicate the desired speed of the transport vehicle 2 on the track). In actual applications, the controller 21 of the transport vehicle 2 controls the drive module 24 of the transport vehicle 2 based on the real-time speed and real-time position of the transport vehicle 2 to accelerate or decelerate the transport vehicle 2 to a specific speed, which is the command speed. The above-mentioned speed or acceleration may refer to the speed or acceleration of the entire transport vehicle 2, or may refer to the speed or acceleration of specific components of the transport vehicle 2 (such as the driving wheel 25B, the driven wheel 25A, etc. of the transport vehicle 2).

[0040] It should be particularly emphasized that since the high-altitude track detection method of the present application is mainly to detect the status of a specific section of track, and the position where each transport vehicle 2 is in direct contact with the specific section of track is the driving wheel (and the driving shaft connected to the driving wheel) and the driven wheel (and the driven shaft connected to the driven wheel), therefore, in actual application, the above-mentioned time measurement data 2111 uses speed data related to the driving wheel, driving shaft, driven wheel and driven shaft, which will better present the status of the track than using speed data related to the transport vehicle 2 as a whole.

[0041] The non-time measurement data 2112 may include, for example, various non-time-related data, such as the maximum torque of the driving wheel 25B of the transporter 2, the minimum torque of the driving wheel 25B of the transporter 2, the average torque of the driving wheel 25B of the transporter 2, the maximum torque of the driven wheel 25A of the transporter 2, the minimum torque of the driven wheel 25A of the transporter 2, the average torque of the driven wheel 25A of the transporter 2, audio data collected by an audio collector installed on the transporter 2, vibration data collected by a vibration sensor installed on the transporter 2 (e.g., root mean square (RMS) of the vibration signal, peak value of the vibration signal, etc.), and measurement data collected by an inertial measurement unit (IMU) installed on the transporter 2. The root mean square (RMS) of the vibration signal is calculated by first calculating the square of multiple vibration data points, then averaging the squared data, and finally taking the square root of the average. The peak value of the vibration signal is calculated by subtracting the minimum value from the maximum value of the multiple vibration data points, and then dividing the result by two.

[0042] It should be emphasized that the time measurement data 2111 and non-time measurement data 2112 cited above are merely examples. In practice, they may be selected or modified based on actual needs. Specifically, before determining which data to use as the time measurement data 2111 and non-time measurement data 2112, those skilled in the art may first install various sensors on the transport vehicle 2, such as a speed sensor, an angular velocity sensor, a torque sensor, a vibration sensor, an audio collector, an inertial measurement unit, a three-axis accelerometer, etc. Then, after collecting a large amount of operation information 12 and data on track damage (or repair), those skilled in the art may analyze the data to determine which of the time measurement data 2111 and / or non-time measurement data 2112 are positively correlated with the track before damage, and accordingly determine which of the time measurement data 2111 and / or non-time measurement data 2112 to collect in the collection step S11.

[0043] The data conversion step S12 involves classifying the plurality of pieces of operation information 12 into a plurality of pieces of track information 11. The plurality of pieces of time measurement data 2111 and the plurality of pieces of non-time measurement data 2112 contained in each piece of track information 11 are the time measurement data 2111 and non-time measurement data 2112 generated by the operation information 12 corresponding to the passage of multiple transport vehicles 2 through the same track segment. In other words, the plurality of pieces of operation information 12 generated by the passage of multiple transport vehicles 2 through the same track segment are considered as one piece of track information 11.

[0044] The statistical step S13 determines whether each time measurement data 2111 and / or non-time measurement data 2112 included in each track information 11 exceeds a default range, and then counts all time measurement data 2111 and / or non-time measurement data 2112 that exceed the default range to generate statistical information 211 corresponding to each track. In practice, the default range can be set manually. For example, a person skilled in the art can analyze multiple historical time measurement data 2111 and / or non-time measurement data 2112 manually or through machine learning to determine the default range in the statistical step S13.

[0045] In one specific application, a person skilled in the art may, for example, collect the operation information 12 corresponding to the passage of multiple transport vehicles 2 through each section of track while ensuring that there are no abnormalities in each section of track and multiple transport vehicles 2, and use the track information 11 corresponding to each section of track to formulate a default range based on the 3 times standard deviation criterion (3σ criterion). For example, after 10 transport vehicles 2 pass through the same section of track, 5 pieces of operation information 12 are generated. Each piece of operation information 12 includes 1 piece of time measurement data 2111 (for example, the average speed of the driving wheel 25B) and 1 piece of non-time measurement data 2112 (for example, the average torque of the driving shaft). Then, one of the default ranges can be the average value of the 5 pieces of time measurement data 2111 ± 3 times the standard deviation, and the other default range can be the average value of the 5 pieces of non-time measurement data 2112 ± 3 times the standard deviation; that is, if the 5 pieces of time measurement data 2111 are 922, 925, 926, 921, and 924 respectively, then the default range can be 923.6 ± 5.57, that is: 918.03 ~ 929.17.

[0046] Of course, in different embodiments, the default range in the statistical step S13 can also be dynamically adjusted over time. For example, the default range can be established by using multiple pieces of operation information 12 in the past 30 days (for example only, not limited to this value) in which no track anomalies or transport vehicle 2 anomalies were found.

[0047] The determination step S14 is to determine whether the ratio of each statistical information 211 to the total amount of the time measurement data 2111 and / or the non-time measurement data 2112 included in each track information 11 exceeds a default warning ratio;

[0048] If the default warning ratio is exceeded, the output step S15 is executed: a track warning message 13 is generated accordingly, and the track warning message 13 includes track identification data of the corresponding track; if the default warning ratio is not exceeded, the process ends.

[0049] like Figure 4As shown, for example, it is assumed that the track system includes 16 sections of track, which are 10 horizontal tracks A1 and A2. [1] , A2, A3, A4, A5, A6, A7, A8, A9, A10; four curving tracks B1, B2, B3, B4 and two longitudinal tracks C1, C2. The transport vehicle 2 can follow a first path P1, sequentially passing through the transverse tracks A1, A2, A3, A4, A5, A6, A7, or it can follow a second path P2, sequentially passing through the transverse track A1, curving track B1, longitudinal track C1, curving track B2, transverse track A8, transverse track A9, transverse track A10, curving track B3, longitudinal track C2, curving track B4 and transverse track A7.

[0050] Assuming that six transport vehicles 2 each move along the first path P1, after collecting step S11, the processing device 1 will obtain 12 pieces of track information 11 corresponding to the seven sections of track. Each piece of track information 11 includes 10 pieces of operation information 12. More specifically, after collecting step S11, the processing device 1 will obtain six pieces of time measurement data 2111 (e.g., the total time spent by the transport vehicle 2 passing through the transverse track A1) and six pieces of non-time measurement data 2112 (e.g., the average torque of the drive wheel 25B of the transport vehicle 2 during the transport vehicle 2 passing through the transverse track A1).

[0051] Table 1 below shows 42 pieces of time measurement data 2111 (the total time taken for each of the six transport vehicles 2 to traverse the seven transverse track sections) obtained by the processing device 1 after executing the collection step S11. The six pieces of time measurement data 2111 included in each column of the table represent the time measurement data 2111 contained in a single piece of track information 11, while the non-time measurement data 2112 included in each row of the table represent the time measurement data 2111 contained in the seven pieces of operation information 12 corresponding to the movement of a single transport vehicle 2 along the first path P1. It should be noted that all values ​​shown in Table 1 below are for illustrative purposes only.

[0052] Table 1 (The values ​​in the table below represent the total time consumed, in seconds):

[0053]

[0054]

[0055] It should be noted that, in practical applications, any value in the table above can be the average value obtained after the same transport vehicle 2 passes through the same track multiple times. That is, the total time taken for transport vehicle N-1 to pass through transverse track A1 in the table above is 1.1 seconds. This 1.1 second can actually be the average value of all the total time taken after transport vehicle N-1 passes through transverse track A1 at least twice. Of course, the 1.1 second is not limited to the average value of all the total time taken after transport vehicle N-1 passes through transverse track A1 multiple times. In different embodiments, the 1.1 second can be calculated based on needs. For example, after transport vehicle N-1 passes through transverse track A1 100 times, a total of 100 total time taken are obtained. The processing device 1 can first remove the abnormal values ​​in the 100 total time taken and then take the average of the remaining values ​​to obtain the 1.1 second.

[0056] Continuing from the above, in the statistical step S13, it is assumed that the processing device 1 determines whether the six time measurement data 2111 included in each row of track information 11 exceed the default range based on the above table, and the default range is 0.8 to 1.3 seconds, and generates the corresponding statistical information 211 accordingly. Then, in the statistical step S13, the processing device 1 determines that the time measurement data 2111 corresponding to the track information 11 corresponding to the transverse track A1 (2 seconds and 0.7 seconds) for the transport vehicles N-3 and N-6 have exceeded the default range, while the time measurement data 2111 corresponding to the remaining transport vehicles N-1, N-2, N-4, and N-5 do not exceed the default range. Therefore, after the statistical step S13, the statistical information 211 corresponding to the transverse track A1 generated by the processing device 1 is 2 (i.e., two time measurement data 2111 exceed the default range). In contrast, the statistical information 211 corresponding to the transverse tracks A2 to A7 generated by the processing device 1 are 2, 5, 1, 2, 2, and 1, respectively.

[0057] In determination step S14, assuming the default warning ratio is 45%, processing device 1 determines that the ratio of the statistical information 211 corresponding to transverse track A1 to the total amount of time measurement data 2111 included in the track information 11 corresponding to transverse track A1 is 33.3%, i.e., 2 / 6 ≒ 33.3%. Because 33.3% is less than 45%, processing device 1 does not generate track warning information 13 corresponding to transverse track A1. Conversely, in determination step S14, processing device 1 calculates the ratios corresponding to transverse tracks A2-A7 as 33.3%, 83.3%, 16.4%, 33.3%, 33.3%, and 16.4%, respectively. After determination step S14, processing device 1 generates track warning information 13 corresponding to transverse track A3. Track warning information 13 includes track identification data for transverse track A3, such as LT-A3.

[0058] In practical applications, the transport system 100 may further include a display device A electrically connected to the processing device 1. After determination step S14, the processing device 1 may transmit the track warning information 13 to the display device A. When the display device A displays the track warning information 13 corresponding to the transverse track A3, the user may view warning text or graphics such as "Transverse track A3 may have an abnormality" on the operation interface D, prompting the user to inspect the transverse track A3. The operation interface D may also display, for example, the position of the transverse track A3 in a graphical manner. In one embodiment, the operation interface D may also display statistical information 211 corresponding to each track segment and the ratio of the statistical information 211 to the total amount of time measurement data 2111 and / or non-time measurement data 2112 included in each track information 11. In other words, in addition to viewing the warning text or graphics such as "Transverse track A3 may have an abnormality" on the operation interface D, the user may also view the number of abnormalities for each track segment that may have an abnormality. Furthermore, the operation interface D may also display the real-time status of the track and the transport vehicle 2, such as the message "The maximum torque of transport vehicle N-1 on transverse track A3 exceeds the upper limit" shown in the figure. The maximum torque described in this message is one of the non-time metric data 2112. The specific information displayed in the operation interface D can be designed and modified according to actual needs. The figure shows only one exemplary embodiment.

[0059] It is worth mentioning that in one of the variant embodiments, after the judgment step S14, a vehicle condition judgment step S15 of the transport vehicle 2 can also be included: based on all the track warning information 13 and the operation information 12 corresponding to each transport vehicle 2, the total number of each time measurement information 2111 or non-time measurement information 2112 in the operation information 12 that exceeds the default range of the corresponding track segment is counted, and the vehicle condition ratio of the total number of time measurement information 2111 contained in the operation information 12 to the aforementioned total number is calculated to determine whether the aforementioned vehicle condition ratio exceeds a default vehicle condition ratio; if it exceeds the aforementioned default vehicle condition ratio, a vehicle condition warning message is generated.

[0060] Specifically, in Table 1, after transport vehicle N-1 travels through transverse tracks A1-A7, the total time taken (1.8 seconds) exceeds the default range (0.8-1.3 seconds) for transverse track A3 only when it passes through transverse track A3. However, processing device 1 has already determined in determination step S14 that transverse track A3 may be damaged. Therefore, processing device 1 will not generate a vehicle condition warning message corresponding to transport vehicle N-1 after executing vehicle condition determination step S15. Conversely, in Table 1, after transport vehicles N-3 and N-6 travel through transverse tracks A1-A7, the vehicle condition ratios corresponding to transport vehicles N-3 and N-6 are 100% and 57%, respectively. Assuming the default vehicle condition ratio is 45%, processing device 1 will generate two vehicle condition warning messages corresponding to transport vehicles N-3 and N-6 after executing vehicle condition determination step S15. After processing device 1 generates vehicle condition warning information, it may transmit it to display device A. A user may, for example, view warning text or graphics such as "Transporter N-3 and N-6 may be experiencing an abnormality" on user interface D of display device A. Of course, in various embodiments, user interface D may also display all time measurement data 2111 and vehicle condition ratios contained in the operational information 12 for each transporter 2.

[0061] As described above, in one of the variations of the high-altitude track detection method of the present application, not only the status of each track section can be detected, but also the status of each transport vehicle can be detected. Therefore, it allows technical personnel in this field to simply, quickly and in real time understand the status of each track section and each transport vehicle in the transport system.

[0062] It is worth mentioning that common track anomalies include: track sagging, track shrinkage, and track skew. Common transport vehicle anomalies include: master (slave) wheel debonding, master (slave) shaft offset, master (slave) wheel detachment, guide wheel debonding, guide shaft offset connected to the guide wheel, and guide wheel detachment. In the prior art, no device or system can detect these common track anomalies and transport vehicle anomalies. The transport system and high-altitude track detection method of the present application can be used to detect these common track anomalies and transport vehicle anomalies.

[0063] As shown in Table 2 below, it shows 66 pieces of non-time measurement data 2112 (hereinafter indicated as torque) obtained by the processing device 1 after executing the collection step S11 (6 transport vehicles 2 respectively pass through 11 sections of track). The 6 pieces of non-time measurement data 2112 contained in each column of the table below are the time measurement data 2111 contained in a single piece of track information 11, and the non-time measurement data 2112 contained in each row of the table below are the time measurement data 2111 contained in a single transport vehicle 2 along the track. Figure 4After the second path P2 moves, the corresponding 11 pieces of operation information 12 contain the time measurement data 2111. It should be noted that all the values ​​shown in Table 2 below are only examples.

[0064] Table 2 (The values ​​in the table below represent the torque of the driving shaft of the transport vehicle 2, and the unit is N·m):

[0065]

[0066]

[0067] It should be noted that, in actual applications, any value in the above table may be the average value obtained after the same transport vehicle 2 passes through the same track multiple times. That is, the torque of the driving shaft corresponding to the transport vehicle N-1 passing through the transverse track A1 in the above table is 1.6 N·m. This 1.6 N·m can actually be the average value of the torque of all driving shafts obtained after the transport vehicle N-1 passes through the transverse track A1 at least twice.

[0068] Continuing from the above, in the statistical step S13, it is assumed that the processing device 1 determines whether the 6 time measurement data 2111 included in each row of track information 11 exceeds the default range according to the above table, and generates corresponding statistical information 211 accordingly, and the default range is 1.4 to 1.7 (N·m). Then, after the statistical step S13, the processing device 1 generates the corresponding lateral track A1, the curved track B1, the longitudinal track C1, the curved track B2, the lateral track A8, the lateral track A9, the lateral track B1, the lateral track B1, the lateral track B2, the lateral track A1, the lateral track B1 ... The statistical information 211 for the lateral track A9, transverse track A10, curved track B3, longitudinal track C2, curved track B4, and transverse track A7 is 2, 2, 2, 2, 2, 0, 1, 3, 3, 4, 2. The corresponding ratios calculated by the processing device 1 in determination step S14 are 33.3%, 33.3%, 33.3%, 33.3%, 33.3%, 0%, 16.7%, 50%, 50%, 66.7%, and 33.3%, respectively. Assuming the default warning ratio is 45%, the processing device 1 will generate three corresponding track warning messages 13 after determination step S14, indicating that there may be problems with the curved track B3, longitudinal track C2, and curved track B4.

[0069] In one of the above examples, in the counting step S13, the processing device 1 counts how many transport vehicles 2 have a total time spent passing through the same track that exceeds a default range, and makes a subsequent judgment in the judgment step S14 based on the counting result. In another example, in the counting step S13, the processing device 1 counts how many transport vehicles 2 have a torque on their driving shaft that exceeds a default range when passing through the same track, and makes a subsequent judgment in the judgment step S14 based on the counting result. However, in a different embodiment, in the counting step S13, the processing device 1 may count how many transport vehicles 2 have a total time spent passing through the same track and a torque on their driving shaft that exceeds the corresponding default range, and makes a subsequent judgment in the judgment step S14 based on the counting result.

[0070] In summary, the high-altitude track status detection method, processing device and handling system of the present application, through the design of collection steps, data conversion steps, statistical steps and judgment steps, can allow users to watch the track warning information through the display device, so as to quickly, in real time and simply know the status of each section of the track. Moreover, the high-altitude track status detection method of the present application can be executed during the normal handling operation of the transport vehicle without stopping the operation of the transport vehicle.

[0071] The prior art lacks any system or method for real-time monitoring of the condition of each track segment within a transport system's overhead track. Instead, technicians must regularly conduct manual inspections or deploy maintenance vehicles to monitor the condition of each track segment. However, these inspections require stopping the transport vehicle, consuming significant time and manpower to inspect the condition of each track segment. Consequently, if the track deteriorates before the scheduled inspection and the technician fails to promptly detect the anomaly, the transport vehicle may derail or experience severe vibrations while passing through the track segment. Derailment or severe vibrations can directly damage the transported goods, resulting in significant losses for the manufacturer. Furthermore, a derailed transport vehicle can potentially collide with other transport vehicles behind it.

[0072] like Figure 5As shown, in one of the practical applications, the processing device 1 can also control the display device A to display a data chart Z corresponding to one of the transport vehicles 2 passing through one of the tracks in the operation interface D. In the data chart Z, a trend line Z3, an upper limit value Z1 and a lower limit value Z2 are displayed. The trend line Z3 is drawn by multiple time measurement data 2111 generated by the transport vehicle 2 passing through the track, and the upper limit value Z1 and the lower limit value Z2 are the upper limit value and the lower limit value of the default range. With such a design, those skilled in the art can view the data chart Z by viewing the data chart Z. Figure 5 The data chart Z shown shows that when the transport vehicle 2 passed through the track 1 between 2023 / 07 / 07 and 2023 / 07 / 17, the maximum torque of the driving wheel 25B of the transport vehicle 2 did not exceed the upper limit Z1 and the lower limit Z2 of the default range. However, after 2023 / 07 / 13, the maximum torque of the driving shaft of the transport vehicle 2, although the trend line Z3 still fell between the upper limit Z1 and the lower limit Z2, the swing amplitude of the trend line Z3 became significantly larger. Moreover, when the transport vehicle 2 passed through the track 1 on 2023 / 07 / 17, the maximum torque of the driving wheel 25B of the transport vehicle 2 exceeded the upper limit Z1 of the default range. Thus, those skilled in the art can understand the above. Figure 4 From the data chart Z shown, we can know that the driving wheel 25B of the transport vehicle 2 may be damaged after 2023 / 07 / 17, and we can even know that the driving wheel 25B of the transport vehicle 2 has already shown signs of impending damage after 2023 / 07 / 13. Figure 5 The operation interface D shown may also display relevant detection result information Z4, and those skilled in the art can know which transport vehicle 2 may have experienced an abnormality at which time and on which road section by viewing the detection result information Z4.

[0073] It is worth noting that in actual applications, in the collection step S11, the processing device 1 or the controller 21 of the transport vehicle 2 may perform multiple sampling steps while each transport vehicle 2 passes through any track. Each time the processing device 1 or the controller 21 performs a sampling step, the processing device 1 obtains a sampled time measurement data 2111 and a sampled non-time measurement data 2112 of the transport vehicle 2. In actual applications, the sampling rate of the processing device 1 or the controller 21 may be, for example, 50 Hz, 100 Hz, 150 Hz, etc., depending on the needs. That is, the processing device 1 or the controller 21 may perform the sampling step 50, 100, or 150 times per second.

[0074] The at least one time measurement data item 2111 in each piece of operation information 12 can be, for example, at least one of the following: an average value of multiple pieces of sampled time data, a maximum value of multiple pieces of sampled time data, or a minimum value of multiple pieces of sampled time data. The at least one non-time measurement data item 2112 in each piece of operation information 12 can be, for example, at least one of the following: an average value of multiple pieces of sampled non-time data, a maximum value of multiple pieces of sampled non-time data, or a minimum value of multiple pieces of sampled non-time data.

[0075] For example, as shown in Table 3 below, assuming that the processing device 1 performs a sampling step once per second, and it takes 5 seconds for the transport vehicle N-1 to pass through the track N-1, then after the transport vehicle N-1 passes through the track N-1, the processing device 1 will collect 5 sampling time measurement data and 5 sampling non-time measurement data as shown in Table 3 below; similarly, assuming that it takes 6 seconds for the transport vehicle N-2 to pass through the track N-1, then after the transport vehicle N-2 passes through the track N-1, the processing device 1 will collect 6 sampling time measurement data and 6 sampling non-time measurement data as shown in Table 4 below.

[0076] Table 3:

[0077]

[0078] Table 4:

[0079]

[0080]

[0081] Based on the above description, after the transport vehicle N-1 passes through the track section N-1, the processing device 1 obtains 5 real-time driving wheel speeds and 5 real-time driving shaft torques. The processing device 1 will calculate that the average driving wheel speed of the transport vehicle N-1 passing through the track section N-1 is 923.4 mm / s, the maximum driving wheel speed is 926 mm / s, the minimum driving wheel speed is 921 mm / s, the average driving shaft torque is 1.66 N·m, the maximum driving shaft torque is 1.68 N·m, and the minimum driving shaft torque is 1.64 N·m. Similarly, after the transport vehicle N-2 passes through the section of track N-2, the processing device 1 will calculate that the average driving wheel speed of the transport vehicle N-2 passing through the section of track is 923.2 mm / s, the maximum driving wheel speed is 926 mm / s, the minimum driving wheel speed is 920 mm / s, the average driving shaft torque is 1.64 N·m, the maximum driving shaft torque is 1.66 N·m and the minimum driving shaft torque is 1.61 N·m.

[0082] Continuing from the above, after transport vehicles N-1 and N-2 respectively pass through track A, the track information 11 corresponding to track A obtained by the processing device 1 may include, for example, two average driving wheel speeds (923.4 mm / s and 923.2 mm / s). Assuming that the default range in the statistical step S13 is 920-925 mm / s, when the processing device 1 executes the statistical step S13, the processing device 1 will determine that the two average driving wheel speeds do not exceed the default range, and the processing device 1 will not generate statistical information 211, or the statistical information 211 generated by the processing device 1 may include 0. Of course, in this case, the processing device 1 uses the two average driving wheel speeds and the corresponding default range to perform judgment and statistics in the statistical step S13. In one variation of this, the processing device 1 may also determine whether the two maximum driving shaft torques (1.68 N·m and 1.66 N·m) exceed the corresponding default range in the statistical step S13, and count the total number of times that they exceed the default range. That is, in the counting step S13 , which default range corresponding to the time measurement data 2111 or the non-time measurement data 2112 to use can be selected according to actual needs.

[0083] As described above, by having the processing device or control execute multiple sampling steps while each transport vehicle passes through any track, the processing device can use relatively accurate data to perform relevant statistical operations when performing the statistical step, thereby significantly reducing the probability of misjudgment by the subsequent processing device.

[0084] See also Figure 6 , which shows a partial flow chart of the second embodiment of the high-altitude track status detection method of the present application. It should be noted that in the collection step S11 of this embodiment, the processing device (or the controller 21 disposed on the transport vehicle 2) performs multiple sampling steps as each transport vehicle 2 passes through any track. For a detailed description of the sampling steps, please refer to the previous description and will not be repeated here.

[0085] like Figure 6 As shown, one difference between this embodiment and the previous embodiment is that in the collecting step S11, after the multiple sampling steps, the following steps are further included:

[0086] A first data cleaning step SX1: removing at least one piece of sampling time measurement data from each piece of operation information 12 so that each piece of sampling time measurement data in each piece of operation information 12 falls within a first default time measurement range;

[0087] A second data cleaning step SX2 is performed to remove at least one piece of sampling time measurement data from each piece of operation information 12 remaining after the first data cleaning step SX1, so that each piece of sampling time measurement data in each piece of operation information 12 falls within a second default time measurement range.

[0088] A screening step SX3: removing at least one piece of sampled non-time measurement data from each piece of operating information 12 remaining after the second data cleaning step SX2, so that each piece of sampled non-time measurement data in each piece of operating information 12 falls within a default screening range;

[0089] A third data cleaning step SX4 is to remove at least one piece of sampling time measurement data from each piece of operation information 12 remaining after the screening step SX3, so that each piece of sampling time measurement data in each piece of operation information 12 falls within a third default time measurement range.

[0090] For example, in the first data cleaning step SX1, all sampling time measurement data of the same type (such as total time) contained in the operation information 12 can be used to calculate the median of the total time, and then the median ±1.5*(the fourth quartile range of the total time) is used to establish a first default time measurement range to remove the operation information 12 corresponding to the sampling time measurement data outside the first default time measurement range; wherein, the fourth quartile range (Interquartile Range, IQR) refers to first calculating the first quartile (Q1) and the third quartile (Q3) of all total time, and then subtracting the first quartile (Q1) from the third quartile (Q3) to calculate the IQR.

[0091] In the second data cleaning step SX2, the Z-score can be calculated using the sampling time measurement data (total time consumption) of the plurality of operation information 12 remaining after the first data cleaning step SX1, and the sampling time measurement data with a total time consumption greater than or equal to 3 times the Z-score is removed, so that each sampling time measurement data contained in the remaining operation information 12 is less than 3 times the Z-score. The mathematical formula is: the Z-score (total time consumption) of any sampling time measurement data < 3*(Z-score). Wherein, Z-score = (x-μ) / σ, x is the value of any sampling time measurement data in the operation data 12 remaining after the first data cleaning step SX1, μ and σ are respectively the average value and standard deviation of all the sampling time measurement data (total time consumption) in the operation information 12 remaining after the first data cleaning step SX1.

[0092] In the screening step SX3, the Z-score can be calculated using the sampled non-time measurement data (e.g., the average torque of the active shaft, the maximum torque of the active shaft, the minimum torque of the active shaft, the average torque of the driven shaft, the maximum torque of the driven shaft, the minimum torque of the driven shaft, etc.) of the operating information 12 remaining after the second data cleaning step SX2. The sampled non-time measurement data having a value greater than or equal to 1 times the Z-score is removed, so that each piece of sampled non-time measurement data in the remaining operating information 12 is less than 1 times the Z-score. In mathematical terms, this is expressed as: any piece of sampled non-time measurement data < 1 Z-score. Here, Z-score = (x - μ) / σ, where x is the value of any piece of sampled time measurement data in the operating information 12 remaining after the second data cleaning step SX2, and μ and σ are, respectively, the average and standard deviation of all the sampled non-time measurement data in the operating information 12 remaining after the second data cleaning step SX2.

[0093] In the third data cleaning step SX4, all the sampling time measurement data in the operation information 12 remaining after the screening step SX3 are subjected to the time consumption variation coefficient = s / (x - Calculation of s and (x - are the standard deviation and mean, respectively, of all the sampled time measurement data in the remaining operation information 12 after filtering step SX3. If the coefficient of variation of any sampled time measurement data in the remaining operation information 12 after filtering step SX3 is greater than 10, then that data item is not used in subsequent steps or processes. If the coefficient of variation of any sampled time measurement data in the remaining operation information 12 after filtering step SX3 is less than or equal to 10, then that data item can be retained and used in subsequent steps or processes.

[0094] In the above embodiment, the collection step S11 includes three data cleaning steps and one screening step SX3 as an example. However, in different embodiments, the number of data cleaning steps and screening step SX3 included in the collection step S11 can be designed according to actual needs.

[0095] For example, in one variation, in the collecting step S11, after the multiple sampling steps, the following steps are included:

[0096] A data cleaning step SY1: removing at least one piece of sampling time measurement data from each piece of operation information 12 so that each piece of sampling time measurement data in each piece of operation information 12 falls within a default time measurement range;

[0097] A screening step SY2 is performed to remove at least one non-time measurement data from each piece of operation information 12 remaining after the data cleaning step SY1 , so that each piece of non-time measurement data in each piece of operation information 12 falls within a default screening range.

[0098] For example, in the data cleaning step SY1, all sampling time measurement data of the same type (such as total time consumption) contained in the operation information 12 can be used to calculate the third quartile (Q3) and the first quartile (Q1) of the said sampling time measurement data, and then the third quartile + 1.5 times the interquartile range (IQR) and the first quartile - 1.5 times the interquartile range (IQR) are used to establish a default time measurement range to remove the operation information 12 outside the default time measurement range.

[0099] The screening step SY2 in this example can be performed in the same manner as the aforementioned screening step SX3, which will not be described in detail herein.

[0100] Similarly, in one of the variant embodiments, in the collecting step S11, after the multiple sampling steps, the following steps are included:

[0101] A first data cleaning step SZ1: removing at least one piece of sampling time measurement data from each piece of operation information 12 so that each piece of sampling time measurement data in each piece of operation information 12 falls within a default time measurement range;

[0102] A screening step SZ2: removing at least one piece of sampled non-time measurement data from each piece of operating information 12 remaining after the first data cleaning step SX1, so that each piece of sampled non-time measurement data in each piece of operating information 12 falls within a default screening range;

[0103] A second data cleaning step SZ3 is to remove at least one piece of sampling time measurement data from each piece of operation information 12 remaining after the screening step SZ2, so that each piece of sampling time measurement data in each piece of operation information 12 falls within a second default time measurement range.

[0104] In this example, the first data cleaning step SZ1 may be the same as the data cleaning step SY1 , and the second data cleaning step SZ3 may be the same as the third data cleaning step SX4 .

[0105] The screening step SZ2 can be: using all the sampled non-time measurement data of the same type contained in the operating information 12 (such as the average torque of the driving shaft), calculate the median of the sampled non-time measurement data, and then use the median ±1.5* (the fourth quartile range of the sampled non-time measurement data) to establish a default non-time measurement range to remove the operating information 12 outside the default non-time measurement range; wherein, the fourth quartile range (IQR) refers to first calculating the first quartile (Q1) and the third quartile (Q3) of the sampled non-time measurement data, and then subtracting the first quartile (Q1) from the third quartile (Q3) to calculate the IQR.

[0106] As described above, in other words, in the collection step S11 of this embodiment, when the transport vehicle 2 passes through a single section of track, multiple sampling time measurement data and multiple sampling non-time measurement data will be generated, and then the processing device 1 will perform at least one data cleaning step and at least one screening step SX3 to remove a portion of the multiple sampling time measurement data and the multiple sampling non-time measurement data. After the processing device 1 completes the data cleaning step and the screening step SX3, the average value, maximum value or minimum value of the remaining multiple sampling time measurement data will become the time measurement data 2111 generated corresponding to the transport vehicle 2 passing through the section of track, and the average value, maximum value, minimum value, peak value or square root of the remaining multiple sampling non-time measurement data will become the non-time measurement data 2112 generated corresponding to the transport vehicle 2 passing through the section of track.

[0107] More specifically, assuming that after the transport vehicle N-1 passes through the track N-1, 5 sampling time measurement data x1, x2, x3, x4, x5 and 5 non-time measurement data y1, y2, y3, y4, y5 are generated, where y5 is the maximum value, and after the processing device 1 performs the above-mentioned data cleaning step and screening step SZ2, 2 of the sampling time measurement data x1 and x2 and 1 of the non-time measurement data y1 are removed, then after the processing device 1 completes the collection step S11, the time measurement data X obtained by the processing device 1 is, for example, the average value of the remaining sampling time measurement data, that is: X = (x3 + x4 + x5) / 3; the non-time measurement data Y obtained by the processing device 1 is, for example, the maximum value of the remaining sampling non-time measurement data, that is, Y = y5.

[0108] In summary, the data cleaning and filtering steps of this embodiment enable the processing device to make more accurate statistics and judgments in the subsequent statistical and judgment steps, thereby effectively reducing the possibility of misjudgments by the processing device. More specifically, in this embodiment, during the collection step, when each transport vehicle passes through a single track section, the processing device (or the transport vehicle's controller) performs multiple sampling steps. Therefore, after each transport vehicle passes through a single track section, the processing device will obtain multiple sets of sampled time measurement data and sampled non-time measurement data. Due to the large amount of data, if the aforementioned data cleaning and filtering steps are not performed, the processing device may make erroneous judgments in the subsequent statistical and judgment steps. For example, if a transport vehicle passes through a certain track section and a traffic jam occurs in front of it, the transport vehicle is forced to reduce its speed. However, under normal circumstances, this track section would not be congested. In this scenario, in this embodiment, the multiple sets of sampled time data generated by the traffic jam when the transport vehicle passed through the track section may be eliminated during the data cleaning and filtering steps.

[0109] It should be emphasized that the examples given in the above-mentioned data cleaning steps or screening steps of using relevant statistical methods in statistics to remove sampling time measurement data or sampling non-time measurement data are only examples. In actual applications, technical personnel in this field can design corresponding data cleaning steps and data removal methods in the screening steps based on the actual conditions of the transport vehicle running on the track, as well as historical sampling time measurement data and historical sampling non-time measurement data.

[0110] See also Figure 7 , which shows a flow chart of a third embodiment of the high-altitude orbit detection method of the present application. This embodiment of the high-altitude orbit detection method includes: first, within a default collection time, repeatedly executing a collection step S21, then executing a data conversion step S22, a statistics step S23, and a determination step S24.

[0111] The collection step S21 is to collect operation information 12 of each transport vehicle 2 on each track segment. The operation information 12 includes at least one of time measurement data 2111 and non-time measurement data 2112. For detailed descriptions of the time measurement data 2111 and the non-time measurement data 2112, please refer to the description of the previous embodiment and will not be repeated here.

[0112] Data conversion step S22 classifies the plurality of operation information 12 into a plurality of track information 11. The plurality of time measurement data 2111 and the plurality of non-time measurement data 2112 contained in each track information 11 are the time measurement data 2111 and non-time measurement data 2112 generated in the operation information 12 corresponding to the passage of multiple transport vehicles 2 through the same track segment. Data conversion step S22 is identical to the aforementioned data conversion step S12 and is not further described here.

[0113] The statistical step S23 is: using a statistical anomaly detection method, or using an outlier detection method in machine learning, to determine whether each time measurement data 2111 or non-time measurement data 2112 contained in each track information 11 is an abnormal data, and counting the total number of abnormal data to generate statistical information 211 corresponding to each track.

[0114] The determination step S24 is: determining whether each statistical information 211 exceeds a default warning ratio;

[0115] If the default warning ratio is exceeded, step S25 is executed: a corresponding track warning message 13 is generated, which includes track identification data of the corresponding track. Conversely, if the default warning ratio is not exceeded, the process ends. Determination step S24 is the same as determination step S14 above and will not be repeated here.

[0116] The anomaly detection method is: 3σ method, Z-score method, box plot, or Grubbs' test. In practice, when the processing device 1 executes each process step included in the high-altitude orbit detection method, it can be regarded as the processing device 1 sequentially executing different programs. When the processing device 1 executes the statistical step S23, the program fragment executed by the processing device 1 may include relevant functions provided by relevant libraries such as NumPy, Pandas, or SciPy to compile programs corresponding to the 3σ method, Z-score method, box plot, or Grubbs' test.

[0117] The outlier detection method is: KNN (K-Nearest Neighbors), LOF (Local Outlier Factor), COF (Connectivity-Based Outlier Factor), SOS (Sum of Similarities), DBSCAN (Density-Based Spatial Clustering of Applications with NOise), iForest (Isolation Forest), PCA (Principal Component Analysis), or AutoEncoder. Similar to the above description, in actual applications, when the processing device 1 performs the statistical step S23, the program fragment executed by the processing device 1 may include a KNN, LOF, COF, SOS, DBSCAN, iForest, PCA, or AutoEncoder program written using the relevant functions provided by the scikit-learn library.

[0118] In summary, the high-altitude track status detection method of the present application, the processing device capable of executing the high-altitude track status detection method, and the handling system, through the design of collection steps, data conversion steps, statistical steps, and judgment steps, can judge the status of each section of the track in real time without stopping the normal handling operation of the transport vehicle, and generate track warning information in real time if there may be an abnormality in the track. Therefore, users can grasp the status of each section of the track in real time, quickly, and easily.

[0119] The above description is only a preferred embodiment of the present application and does not limit the patent scope of the present application. Therefore, all equivalent technical changes made using the description and drawings of the present application are included in the protection scope of the present application.

Claims

1. A method for detecting high-altitude orbit status, characterized in that: The method for detecting the state of an aerial track can be executed by a processing device, the processing device being communicatively connected to a plurality of transport vehicles, each of the transport vehicles being used to carry an object to be transported, the plurality of transport vehicles traveling on the same track system, the track system being disposed in the air and comprising a plurality of track sections, the method for detecting the aerial track comprising: first, within a predetermined collection time, repeatedly executing a collection step, then executing a data conversion step, a statistical step, and a determination step; The collecting step comprises: collecting operation information of each transport vehicle on each section of the track; the operation information comprises at least one of at least one time measurement data and at least one non-time measurement data; The data conversion step comprises: classifying the plurality of operation information into a plurality of track information; the plurality of time measurement data and / or the plurality of non-time measurement data contained in each track information are the time measurement data and / or the non-time measurement data in the operation information corresponding to the plurality of transport vehicles passing through the same section of the track; The counting step comprises: determining whether each of the time measurement data and / or the non-time measurement data included in each of the track information exceeds a default range, and counting the number of all the time measurement data and / or the non-time measurement data exceeding the default range to generate statistical information corresponding to each of the tracks; The judgment step is: judging whether the ratio of each of the statistical information to the total amount of the time measurement data and / or the non-time measurement data contained in each of the track information exceeds a default warning ratio; if it exceeds the default warning ratio, a corresponding track warning information is generated, and the track warning information includes a track identification data of the corresponding track.

2. The method for detecting the high altitude orbit state according to claim 1, characterized in that: In the collection step, during the process of each of the transport vehicles passing through any of the tracks, the processing device or a controller of the transport vehicle performs multiple sampling steps. Each time the processing device or the controller performs the sampling step, the processing device or the controller will obtain at least one sampling time measurement data and / or at least one sampling non-time measurement data of the transport vehicle; at least one of the time measurement data in each piece of the operation information is at least one of the average value of multiple pieces of the sampling time data, the maximum value of multiple pieces of the sampling time data, and the minimum value of multiple pieces of the sampling time data; at least one of the non-time measurement data in each piece of the operation information is at least one of the average value of multiple pieces of the sampling non-time data, the maximum value of multiple pieces of the sampling non-time data, and the minimum value of multiple pieces of the sampling non-time data.

3. The method for detecting the high altitude orbit state according to claim 2, characterized in that: Each piece of the running information includes at least one piece of the time measurement data and / or at least one piece of non-time measurement data; in the statistical step, it is determined whether each piece of the time measurement data included in each piece of the track information exceeds the default range; Wherein, in the collecting step, after the multiple sampling steps, the following steps are further included: At least one data cleaning step: removing at least one piece of sampling time measurement data from each piece of the operation information so that each piece of the sampling time measurement data in each piece of the operation information falls within a default time measurement range; Wherein, in the collecting step, after the multiple sampling steps, the following steps are further included: A screening step is performed to remove at least one piece of the sampled non-time measurement data from each piece of the operation information, so that each piece of the sampled non-time measurement data in each piece of the operation information falls within a default screening range.

4. The method for detecting high-altitude orbit status according to claim 2, characterized in that: Each piece of operation information includes multiple pieces of time measurement data, one of which is the total time taken for the transport vehicle to pass through one of the tracks, and the remaining time measurement data are: during the process of the transport vehicle passing through one of the tracks, at least one of the command speed of the transport vehicle, the maximum speed of the driving wheel of the transport vehicle, the minimum speed of the driving wheel of the transport vehicle, the average speed of the driving wheel of the transport vehicle, the maximum speed of the driven wheel of the transport vehicle, the minimum speed of the driven wheel of the transport vehicle, and the average speed of the driven wheel of the transport vehicle.

5. The method for detecting high altitude orbit status according to claim 2, characterized in that: The non-time measurement data contained in each piece of operation information is: the maximum torque of the driving shaft of the transport vehicle, the minimum torque of the driving shaft of the transport vehicle, the average torque of the driving shaft of the transport vehicle, the torque peak of the driving shaft of the transport vehicle, the maximum torque of the driven shaft of the transport vehicle, the minimum torque of the driven shaft of the transport vehicle, the average torque of the driven shaft of the transport vehicle, the torque peak of the driven shaft of the transport vehicle, and at least one of a vibration value peak value, a vibration root mean square, and a total vibration value sensed by a vibration sensor installed on the transport vehicle during the process of the transport vehicle passing through one of the tracks.

6. A processing device, characterized in that The processing device can execute the high-altitude orbit state detection method according to any one of claims 1 to 5.

7. A transport system, characterized in that: The transport system includes the processing device according to claim 6, the rail system, and a plurality of the transport vehicles.

8. The transport system according to claim 7, wherein: The transport system further includes a display device, which is electrically connected to the processing device. After the processing device performs the determination step, the processing device can control the display device to display the track warning information.

9. A method for detecting high altitude orbit status, characterized in that: The method for detecting the state of an aerial track can be executed by a processing device, the processing device being communicatively connected to a plurality of transport vehicles, each of the transport vehicles being used to carry an object to be transported, the plurality of transport vehicles traveling on the same track system, the track system being disposed in the air and comprising a plurality of track sections, the method for detecting the state of an aerial track comprising: first, within a predetermined collection time, repeatedly executing a collection step, then executing a data conversion step, and then executing a determination step; The collecting step comprises: collecting operation information of each transport vehicle on each section of the track; the operation information comprises at least one of at least one time measurement data and / or at least one non-time measurement data; The data conversion step comprises: classifying the plurality of operation information into a plurality of track information; the plurality of time measurement data and / or the plurality of non-time measurement data contained in each track information are the time measurement data and / or the non-time measurement data in the operation information corresponding to the plurality of transport vehicles passing through the same section of the track; The statistical step comprises: using a statistical anomaly detection method, or using an outlier detection method in machine learning, to determine whether each piece of the time measurement data and / or the non-time measurement data included in each piece of the track information is an abnormal data, and counting the total number of pieces of abnormal data to generate statistical information corresponding to each track; The judging step comprises judging whether each of the statistical information exceeds a default warning ratio. If the statistical information exceeds the default warning ratio, a track warning message is generated correspondingly. The track warning message includes track identification data of the corresponding track.

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