Stacker monitoring method, device and system

Abstract

The present disclosure provides a stacker monitoring method, device and system, and relates to the field of automatic control. By collecting and storing the operation data of the stacker, the multiple operation data belonging to the same task section are retrieved from the stored data and analyzed to find the abnormal values. Thus, the working state of the stacker is effectively monitored, and support is provided for the fault warning and fault handling of the stacker.

Description

Technical Field

[0001] This disclosure relates to the field of automatic control, and in particular to a stacker crane monitoring method, apparatus and system. Background Technology

[0002] Stacker cranes are key equipment in automated warehouse systems, crucial for ensuring stable warehouse operations. The crane's movement is achieved through a travel motor that drives the wheels along lower guide rails for horizontal movement, a lifting motor that uses a lifting chain to move the loading platform vertically, and single / double extension motors on the loading platform that extend and retract the forks. This three-dimensional motion allows for the retrieval or placement of goods from designated locations. A laser rangefinder measures the crane's horizontal movement; a barcode reader controls the lifting position of the loading platform; and an encoder is used for fork direction positioning. Summary of the Invention

[0003] This embodiment of the disclosure collects and stores the stacker crane's operational data, retrieves multiple operational data points belonging to the same task segment from the stored data, and analyzes them to identify anomalies. This effectively monitors the stacker crane's operational status, providing support for fault early warning and fault handling.

[0004] This disclosure provides a stacker crane monitoring method in some embodiments, including:

[0005] Collect operational data from the stacker crane;

[0006] Store the collected stacker crane operation data;

[0007] Retrieve and analyze multiple runtime data entries belonging to the same task segment from the stored data to identify outliers.

[0008] In some embodiments, retrieving and analyzing multiple pieces of runtime data belonging to the same task segment from stored data includes:

[0009] Retrieve multiple operational data entries belonging to the same task segment for the same parameter of the same component of the stacker crane from the stored data;

[0010] Comparative analysis or standard deviation analysis of multiple operational data points can be performed to identify outliers in multiple operational data points of the stacker crane.

[0011] In some embodiments, retrieving and analyzing multiple pieces of runtime data belonging to the same task segment from stored data includes:

[0012] Retrieve multiple first operation data belonging to the same task segment from the first parameter of the first component of the stacker crane from the stored data;

[0013] Comparative analysis or standard deviation analysis of multiple first operation data points is performed to identify abnormal undetermined values ​​in multiple first operation data points of the stacker crane;

[0014] Retrieve multiple second operation data entries belonging to the same task segment from the second parameter of the second component of the stacker crane associated with the first component, based on the stored data;

[0015] Comparative analysis or standard deviation analysis of multiple sets of secondary operation data is performed to identify outliers in multiple sets of secondary operation data of the stacker crane;

[0016] Based on the outliers of multiple second-run data, determine whether the outliers of multiple first-run data are outliers.

[0017] In some embodiments, determining whether the abnormal pending values ​​of multiple first running data are abnormal values ​​includes:

[0018] If the time difference between the occurrence time of multiple outliers in the second running data and the occurrence time of multiple undetermined outliers in the first running data is less than a preset time, then the undetermined outliers in the multiple first running data are determined to be outliers.

[0019] In some embodiments, the comparative analysis includes:

[0020] The operational data to be analyzed is compared with the standard operational data. If the difference is greater than a preset value, the operational data is determined to be an outlier.

[0021] The standard operating data is determined by statistical analysis of multiple operating data points occurring within a preset time period for the same parameter of the same component of the stacker crane, belonging to the same task segment.

[0022] In some embodiments, the standard deviation analysis includes: calculating the standard deviation of multiple operational data to be analyzed, and determining that operational data with a standard deviation greater than a preset value are outliers.

[0023] In some embodiments, the collected stacker crane operating data includes: the torque, current, and speed of the stacker crane's operating drive frequency converter, or at least one of the vibration, temperature, and noise of the stacker crane's motor.

[0024] In some embodiments, the method further includes: visualizing the collected stacker crane operation data and any outliers discovered during analysis.

[0025] In some embodiments, the method further includes: issuing an alert for outliers detected by the analysis.

[0026] Some embodiments of this disclosure provide a stacker crane monitoring device, including: a memory; and a processor coupled to the memory, the processor being configured to execute a stacker crane monitoring method based on instructions stored in the memory.

[0027] Some embodiments of this disclosure provide a computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the steps of a stacker crane monitoring method.

[0028] This disclosure provides some embodiments of a stacker crane monitoring system, including:

[0029] Sensors are used to collect operational data from the stacker crane;

[0030] The memory is used to store the collected operational data of the stacker crane;

[0031] The stacker crane monitoring device is configured to execute stacker crane monitoring methods. Attached Figure Description

[0032] The accompanying drawings used in the description of the embodiments or related technologies will be briefly introduced below. This disclosure can be more clearly understood from the following detailed description with reference to the accompanying drawings.

[0033] Obviously, the accompanying drawings described below are merely some embodiments of this disclosure. Those skilled in the art can obtain other drawings based on these drawings without any creative effort.

[0034] Figure 1 A flowchart illustrating a stacker crane monitoring method according to some embodiments of the present disclosure is shown.

[0035] Figure 2A The diagram shows the real-time current curves for each area of ​​the stacker crane. Figure 2B A schematic diagram of the standard current curves for each zone of the stacker crane is shown. Figure 2C A schematic diagram of the current deviation curves in different areas of the stacker crane is shown.

[0036] Figure 3 The diagram illustrates a stacker crane monitoring implementation according to some embodiments of this disclosure.

[0037] Figure 4 A schematic diagram of the structure of a stacker crane monitoring device according to some embodiments of the present disclosure is shown.

[0038] Figure 5 A schematic diagram of a stacker crane monitoring system according to some embodiments of the present disclosure is shown. Detailed Implementation

[0039] It should be noted that, unless otherwise specifically stated, the relative arrangement, numerical expressions, and values ​​of the components and steps set forth in these embodiments do not limit the scope of this disclosure.

[0040] Those skilled in the art will understand that the terms "first," "second," etc., in the embodiments of this disclosure are only used to distinguish different steps, devices, or modules, and do not represent any specific technical meaning, nor do they indicate a necessary logical order between them.

[0041] It should also be understood that in the embodiments disclosed herein, "a plurality of" may refer to two or more, and "at least one" may refer to one, two or more.

[0042] It should also be understood that any component, data or structure mentioned in the embodiments of this disclosure can generally be understood as one or more unless expressly defined or given to the contrary in the context.

[0043] Furthermore, the term "and / or" in this disclosure is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this disclosure generally indicates that the preceding and following related objects have an "or" relationship.

[0044] It should also be understood that the description of the various embodiments in this disclosure emphasizes the differences between the various embodiments, and the similarities or similarities can be referred to each other. For the sake of brevity, they will not be described in detail.

[0045] At the same time, it should be understood that, for ease of description, the dimensions of the various parts shown in the accompanying drawings are not drawn according to actual scale.

[0046] The following description of at least one exemplary embodiment is merely illustrative and is in no way intended to limit this disclosure or its application or use.

[0047] Techniques, methods, and equipment known to those skilled in the art may not be discussed in detail, but where appropriate, such techniques, methods, and equipment should be considered part of the specification.

[0048] It should be noted that similar labels and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be discussed further in subsequent figures.

[0049] Furthermore, to avoid obscuring this disclosure with unnecessary detail, only processing steps and / or apparatus structures closely related to at least the solutions according to this disclosure are shown in the accompanying drawings, while other details not closely related to this disclosure are omitted. It should also be noted that similar reference numerals and letters in the drawings indicate similar items, and therefore once an item is defined in one drawing, it need not be discussed again in subsequent drawings.

[0050] Stacker cranes are mainly composed of several parts, including the main structure, loading platform, horizontal running mechanism, lifting mechanism, fork mechanism, and guide wheel device.

[0051] 1. Main Structure: The main structure of the stacker crane consists of an upper crossbeam, uprights, a lower crossbeam, and a control cabinet support. The upper and lower crossbeams are box-shaped structures welded from steel plates and shaped steel, offering excellent cross-sectional performance. The running wheel axle holes on both sides of the lower crossbeam are machined in a single setup on a floor-type milling machine, ensuring the parallelism of the drive and driven wheel axes and thus improving the overall stability of the machine. The uprights are made of square steel tubing, with two flat steel guide rails welded to both sides. The guide rail surfaces are hardened for good wear resistance. The upper crossbeam is welded onto the uprights, and the uprights and lower crossbeams are positioned via flanges and connected with high-strength bolts. The entire main structure is characterized by its light weight, torsional resistance, bending resistance, high rigidity, and high strength.

[0052] 2. Cargo Platform: The cargo platform is a component that moves vertically up and down under the power of the lifting mechanism. It is an L-shaped structure welded together from a vertical frame and a horizontal frame. The vertical frame is used to install lifting guide wheels and some safety protection devices. The horizontal frame is made of seamless steel pipes, which fully meets the requirements for cargo loading.

[0053] 3. Horizontal running mechanism: The horizontal running mechanism consists of a power drive and a set of active and passive wheels, and is used for the operation of the entire equipment roadway.

[0054] 4. Lifting Mechanism: The lifting mechanism consists of a drive motor, lifting chain, and sliding assembly, and is used to lift the loading platform for vertical movement.

[0055] 5. Fork Mechanism: The fork extension mechanism is a power-driven mechanism consisting of upper, middle, and lower forks, used for storing and retrieving goods perpendicular to the aisle direction. The lower fork is fixed to the loading platform, and the three forks extend and retract linearly differentially via chain drive.

[0056] 6. Guide Roller System: The stacker crane employs three sets of guide roller systems: upper and lower horizontal guide rollers and lifting guide rollers. The upper and lower horizontal guide rollers are installed on the upper and lower crossbeams, respectively, to guide the stacker crane's horizontal movement along the aisle. The lifting guide rollers are installed on the loading platform and move up and down along the column guide rails, guiding the vertical movement of the loading platform. Simultaneously, the guide rollers support the load and transfer it to the main structure.

[0057] Figure 1 A flowchart illustrating a stacker crane monitoring method according to some embodiments of the present disclosure is shown.

[0058] like Figure 1 As shown, the stacker crane monitoring method of this embodiment includes steps 110-130, and may also include at least one of steps 140 or 150.

[0059] In step 110, the stacker crane's operating data is collected.

[0060] The collected stacker crane operating data includes: the torque, current, and speed of the stacker crane's operating drive frequency converter, or at least one of the following: vibration, temperature, and noise of the stacker crane's motor.

[0061] like Figure 3 As shown, a data acquisition server, such as the Kepware data acquisition server, is used to collect real-time operating data of the stacker crane system. The data acquisition server and the stacker crane control PLC (Programmable Logic Controller) are connected via industrial Ethernet communication. A DB (Data Block) is created in the PLC to collect key parameters of the frequency converters, such as torque, current, and speed of the travel drive frequency converter and the lifting drive frequency converter, as well as the stacker crane alarm status. The data acquisition server establishes a data connection channel to collect data to the upper-level system in real time. In addition to collecting the control parameters of the stacker crane control system PLC, additional sensors need to be installed to collect parameters that need to be monitored during operation. Key parameters include stacker crane motor vibration, temperature, and noise. Three types of sensors (vibration sensor, temperature sensor, and noise sensor) are installed on the stacker crane's travel motor. These sensors have built-in wireless communication modules, such as Wi-Fi modules, and upload data to a cloud platform via a wireless communication network, such as a 5G network. After data processing, the cloud platform transmits the data to the field PLC, which then creates a corresponding DB to store the parameters.

[0062] In step 120, the collected stacker crane operation data is stored.

[0063] like Figure 3 As shown, for example, the Thingworx platform is used to implement data storage functionality. After the Kepware data acquisition server collects data, the Thingworx service and subscription function are used to complete database data storage, facilitating historical data retrieval and feature value analysis. A database, such as MySQL, is used to establish the corresponding data storage space. The Thingworx service function periodically stores operational data that meets the storage conditions, including parameters such as fault count statistics, inverter alarm codes, inverter current values, inverter torque values, inverter speed values, motor temperature, motor vibration, and motor noise.

[0064] In step 130, multiple running data belonging to the same task segment are retrieved from the stored data and analyzed to identify outliers.

[0065] Data analysis includes data filtering and data analysis. Data filtering involves screening data to obtain highly comparable data. This involves selecting operational data from the same task segment of the stacker crane, specifically parameter data with the same target address. For example, the stacker crane's picking status is obtained through the PLC, and the same address with the same picking task is selected as the trigger point for the selected parameters. Selecting operational data from the same task segment ensures good comparability for each data point. Data analysis, for example, uses comparative analysis or standard deviation analysis to identify system outliers, providing data support for preventative maintenance and status early warning.

[0066] The standard deviation analysis includes: calculating the standard deviation of multiple operational data to be analyzed, and determining that operational data with a standard deviation greater than a preset value are outliers.

[0067] The comparative analysis includes: comparing the operational data to be analyzed with the operational standard data; if the difference is greater than a preset value, the operational data is determined to be an outlier. The operational standard data is determined by statistical analysis of multiple operational data points of the same parameter of the same component of the stacker crane that belong to the same task segment within a preset time period.

[0068] For example, selecting 10,000 recent sets of operational data, with 120 data points in each set, and using the average value as the operational standard data, the expression is:

[0069]

[0070] Then, the difference between the operational data to be analyzed and the standard operational data is calculated, expressed as:

[0071]

[0072] For example, Figure 2A The diagram shows the real-time current curves for each area of ​​the stacker crane. Figure 2B A schematic diagram of the standard current curves for each zone of the stacker crane is shown. Figure 2C A schematic diagram of the current deviation curves for different areas of the stacker crane is shown. Among them, Figure 2C The curve can be based on Figure 2A and Figure 2B The curve difference is determined.

[0073] In some embodiments, operational data analysis includes: retrieving multiple operational data points belonging to the same task segment for the same parameter of the same component of the stacker crane from stored data; and performing comparative analysis or standard deviation analysis on the multiple operational data points to identify outliers in the multiple operational data points of the stacker crane.

[0074] For example, multiple operating data points of the current of inverter A of the stacker crane in task segment A can be retrieved from the stored data. Comparative analysis or standard deviation analysis of these multiple operating data points can be performed to identify abnormal current values ​​in these multiple operating data points of the stacker crane. A similar method can also be used to identify abnormal torque and speed values ​​of the stacker crane's inverter.

[0075] For example, multiple vibration data points of the stacker crane's motor B in task segment B can be retrieved from stored data. Comparative analysis or standard deviation analysis of these multiple data points can then be performed to identify abnormal vibration values. A similar method can also be used to detect abnormal temperature and noise values ​​in the stacker crane's motor.

[0076] In some embodiments, operational data analysis includes: retrieving multiple first operational data points belonging to the same task segment for the first parameter of a first component of the stacker crane from stored data; performing comparative analysis or standard deviation analysis on the multiple first operational data points to identify outlier values ​​in the multiple first operational data points; retrieving multiple second operational data points belonging to the same task segment for the second parameter of a second component of the stacker crane associated with the first component from stored data; performing comparative analysis or standard deviation analysis on the multiple second operational data points to identify outliers in the multiple second operational data points; and determining whether the outlier values ​​in the multiple first operational data points are outliers based on the outliers in the multiple second operational data points. Thus, by jointly performing anomaly analysis on multiple related components, outliers can be identified more accurately.

[0077] Specifically, if the time difference between the occurrence times of multiple outliers in the second set of running data and the occurrence times of multiple undetermined outliers in the first set of running data is less than a preset time, it indicates that the occurrence times of the two are very close and have a strong correlation. Therefore, the undetermined outliers in the first set of running data are determined to be outliers. Thus, by performing correlation analysis on multiple outliers and joint analysis on multiple outliers with strong correlation, outliers can be identified more accurately.

[0078] For example, inverter C and its driven motor C are related components. Multiple first operating data points of the stacker crane's motor C temperature in task segment C are retrieved from stored data; comparative analysis or standard deviation analysis is performed on these multiple first operating data points to identify undetermined temperature anomalies in the stacker crane's first operating data. Multiple second operating data points of the stacker crane's inverter C current in task segment C are retrieved from stored data; comparative analysis or standard deviation analysis is performed on these multiple second operating data points to identify undetermined current values ​​in the stacker crane's second operating data. If the undetermined current values ​​in the multiple second operating data points occur substantially simultaneously with the undetermined temperature anomalies in the multiple first operating data points, then the undetermined temperature anomalies in the multiple first operating data points are determined to be temperature anomalies. Thus, by jointly analyzing multiple related components, anomalies can be identified more accurately.

[0079] In step 140, the collected stacker crane operating data and any anomalies discovered during analysis are visualized. This facilitates monitoring and timely handling of anomalies by staff.

[0080] like Figure 3 As shown, data monitoring, for example, relies on the QuicBI platform to achieve data visualization. The data monitoring system analyzes and visualizes operational data such as stacker crane current parameters, stacker crane torque parameters, stacker crane speed parameters, stacker crane vibration parameters, and stacker crane noise parameters.

[0081] In step 150, an alert is issued for the abnormal values ​​detected by the analysis. This allows for early warning of anomalies before a fault occurs, enabling timely handling of the anomalies.

[0082] like Figure 3 As shown, the data push function can be implemented through the Thingworx push service, which requires configuring the communication interface between the Thingworx platform and WeChat Work. When the data exceeds the warning threshold, an alarm is triggered to remind equipment maintenance personnel to carry out preventative maintenance work in advance.

[0083] This embodiment of the disclosure collects and stores the stacker crane's operational data, retrieves multiple operational data points belonging to the same task segment from the stored data, and analyzes them to identify anomalies. This effectively monitors the stacker crane's operational status, providing support for fault early warning and fault handling.

[0084] The real-time equipment monitoring function of this disclosure utilizes sensor technology, industrial Ethernet technology, wireless transmission technology, 5G technology, PLC control technology, Kepware data acquisition technology, the Thingworx platform, and the QuicBI platform to achieve real-time online acquisition, monitoring, and storage of key parameters of the stacker crane's operation control system. This allows for real-time monitoring of the stacker crane's operating status and timely understanding of its operational conditions.

[0085] In this embodiment of the equipment fault tracing, the monitoring system stores a large amount of equipment operation data in a database. At the same time, the operation data records information such as the time and task number of the equipment parameters. When an abnormality is detected in the monitoring status, maintenance personnel can find the operating status of the equipment at the time of the fault based on the time and task number, which facilitates the location of the fault.

[0086] The data cleaning and analysis method of this disclosure selects data from the corresponding task segment for collection, filters out incomparable data, and enhances data comparability. Data analysis obtains standard data by averaging a large amount of data, and identifies outliers through comparative analysis or analysis of variance.

[0087] The key parameter anomaly alarm function in this embodiment employs a periodic push function. When equipment parameters are abnormal, they can be promptly pushed to maintenance personnel, enabling them to detect the fault immediately, reducing secondary damage caused by operating faulty equipment, and shortening the fault detection time.

[0088] Figure 4 A schematic diagram of the structure of a stacker crane monitoring device according to some embodiments of the present disclosure is shown.

[0089] like Figure 4 As shown, the stacker crane monitoring device 400 of this embodiment includes: a memory 410 and a processor 420 coupled to the memory 410. The processor 420 is configured to execute the stacker crane monitoring method of each embodiment based on instructions stored in the memory 410.

[0090] The device 400 may also include an input / output interface 430, a network interface 440, a storage interface 450, etc. These interfaces 430, 440, 450, as well as the memory 410 and the processor 420, can be connected, for example, via a bus 460.

[0091] The memory 410 may include, for example, system memory, fixed non-volatile storage media, etc. The system memory stores, for example, the operating system, application programs, boot loader, and other programs.

[0092] The processor 420 can be implemented using a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gates, or transistors, or other discrete hardware components.

[0093] The input / output interface 430 provides a connection interface for input / output devices such as monitors, mice, keyboards, and touchscreens. The network interface 440 provides a connection interface for various networked devices. The storage interface 450 provides a connection interface for external storage devices such as SD cards and USB flash drives. The bus 460 can use any bus architecture from various bus structures. For example, bus architectures include, but are not limited to, Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MCA) bus, and Peripheral Component Interconnect (PCI) bus.

[0094] Figure 5 A schematic diagram of a stacker crane monitoring system according to some embodiments of the present disclosure is shown.

[0095] like Figure 5 As shown, the stacker crane monitoring system 500 of this embodiment includes:

[0096] Sensor 510 is used to collect operational data from the stacker crane. Different types of operational data can be collected using different sensors. Sensor 510 includes, but is not limited to, vibration sensors, temperature sensors, and noise sensors.

[0097] Memory 520 is used to store the collected operational data of the stacker crane. The memory may be, for example, a data storage database.

[0098] The stacker crane monitoring device 530 is configured to execute the stacker crane monitoring method.

[0099] In some embodiments (1), the stacker crane monitoring method includes:

[0100] Collect operational data from the stacker crane;

[0101] Store the collected stacker crane operation data;

[0102] Retrieve and analyze multiple runtime data entries belonging to the same task segment from the stored data to identify outliers.

[0103] In some embodiments (2), according to (1), retrieving and analyzing multiple pieces of runtime data belonging to the same task segment from the stored data includes:

[0104] Retrieve multiple operational data entries belonging to the same task segment for the same parameter of the same component of the stacker crane from the stored data;

[0105] Comparative analysis or standard deviation analysis of multiple operational data points can be performed to identify outliers in multiple operational data points of the stacker crane.

[0106] In some embodiments (3), according to any one of (1-2), retrieving and analyzing multiple pieces of runtime data belonging to the same task segment from the stored data includes:

[0107] Retrieve multiple first operation data belonging to the same task segment from the first parameter of the first component of the stacker crane from the stored data;

[0108] Comparative analysis or standard deviation analysis of multiple first operation data points is performed to identify abnormal undetermined values ​​in multiple first operation data points of the stacker crane;

[0109] Retrieve multiple second operation data entries belonging to the same task segment from the second parameter of the second component of the stacker crane associated with the first component, based on the stored data;

[0110] Comparative analysis or standard deviation analysis of multiple sets of secondary operation data is performed to identify outliers in multiple sets of secondary operation data of the stacker crane;

[0111] Based on the outliers of multiple second-run data, determine whether the outliers of multiple first-run data are outliers.

[0112] In some embodiments (4), according to (3), determining whether multiple pending abnormal values ​​of the first running data are abnormal values ​​includes:

[0113] If the time difference between the occurrence time of multiple outliers in the second running data and the occurrence time of multiple undetermined outliers in the first running data is less than a preset time, then the undetermined outliers in the multiple first running data are determined to be outliers.

[0114] In some embodiments (5), according to any one of (2-4), the comparative analysis includes:

[0115] The operational data to be analyzed is compared with the standard operational data. If the difference is greater than a preset value, the operational data is determined to be an outlier.

[0116] The standard operating data is determined by statistical analysis of multiple operating data points occurring within a preset time period for the same parameter of the same component of the stacker crane, belonging to the same task segment.

[0117] In some embodiments (6), according to any one of (2-4), the standard deviation analysis includes: calculating the standard deviation of multiple operational data to be analyzed, and determining that operational data with a standard deviation greater than a preset value are outliers.

[0118] In some embodiments (7), according to any one of (1-6), the collected operating data of the stacker crane includes: the torque, current, and speed of the operating drive frequency converter of the stacker crane, or at least one of the vibration, temperature, and noise of the motor of the stacker crane.

[0119] In some embodiments (8), according to any one of (1-7), the stacker crane monitoring method further includes: visualizing the collected stacker crane operation data and the abnormal values ​​found in the analysis.

[0120] In some embodiments (9), according to any one of (1-8), the stacker crane monitoring method further includes: issuing an early warning for abnormal values ​​detected by analysis.

[0121] Those skilled in the art will understand that embodiments of this disclosure can be provided as methods, systems, or computer program products. Therefore, this disclosure can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this disclosure can take the form of a computer program product embodied on one or more non-transitory computer-readable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer program code.

[0122] This disclosure is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a machine for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0123] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0124] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0125] The above description is only a preferred embodiment of this disclosure and is not intended to limit this disclosure. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this disclosure should be included within the protection scope of this disclosure.

Claims

1. A stacker crane monitoring method, characterized in that, include: Collect operational data from the stacker crane; Store the collected stacker crane operation data; Retrieve and analyze multiple runtime data entries belonging to the same task segment from the stored data to identify outliers, including: Retrieve multiple first operation data belonging to the same task segment from the first parameter of the first component of the stacker crane from the stored data; Comparative analysis or standard deviation analysis of multiple first operation data points is performed to identify abnormal undetermined values ​​in multiple first operation data points of the stacker crane; Retrieve multiple second operation data entries belonging to the same task segment from the second parameter of the second component of the stacker crane associated with the first component, based on the stored data; Comparative analysis or standard deviation analysis of multiple sets of second operation data is performed to identify outliers in multiple sets of second operation data of the stacker crane; Based on the outliers of multiple second-run data, determine whether the outliers of multiple first-run data are outliers.

2. The method according to claim 1, characterized in that, Determining whether multiple pending values ​​of the first running data are outliers includes: If the time difference between the occurrence time of multiple outliers in the second running data and the occurrence time of multiple undetermined outliers in the first running data is less than a preset time, then the undetermined outliers in the multiple first running data are determined to be outliers.

3. The method according to any one of claims 1-2, characterized in that, The comparative analysis includes: The operational data to be analyzed is compared with the standard operational data. If the difference is greater than a preset value, the operational data is determined to be an outlier. The standard operating data is determined by statistical analysis of multiple operating data points occurring within a preset time period for the same parameter of the same component of the stacker crane, belonging to the same task segment.

4. The method according to any one of claims 1-2, characterized in that, The standard deviation analysis includes: Calculate the standard deviation of multiple data points to be analyzed, and identify data points with a standard deviation greater than a preset value as outliers.

5. The method according to any one of claims 1-2, characterized in that, The collected stacker crane operating data includes: The torque, current, and speed of the frequency converter driving the stacker crane's operation, or at least one of the vibration, temperature, and noise of the stacker crane's motor.

6. The method according to any one of claims 1-2, characterized in that, Also includes: The collected stacker crane operation data and the outliers found during analysis are visualized.

7. The method according to any one of claims 1-2, characterized in that, Also includes: Issue warnings for outliers detected during analysis.

8. A stacker crane monitoring device, comprising: Memory; And a processor coupled to the memory, the processor being configured to execute the stacker crane monitoring method of any one of claims 1-7 based on instructions stored in the memory.

9. A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the stacker crane monitoring method according to any one of claims 1-7.

10. A stacker crane monitoring system, comprising: Sensors are used to collect operational data from the stacker crane; The memory is used to store the collected operational data of the stacker crane; A stacker crane monitoring device is configured to perform the stacker crane monitoring method according to any one of claims 1-7.

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