Stacker state prediction method and device

Abstract

The present disclosure provides a state prediction method and device of a stacker, and relates to the technical field of intelligent chemical fiber. The specific scheme is: obtaining historical working data and historical maintenance data of the stacker; obtaining a working task of the stacker in a preset time period; predicting the state of the stacker in the process of executing the working task based on the historical working data and the historical maintenance data; simulating the execution of the working task of the stacker in the preset time period based on the historical working data to obtain a simulation execution result of the stacker; and generating a state prediction result of the stacker based on the time when the stacker fails and the position where the stacker stays when it fails, in combination with the simulation execution result. According to the scheme of the present disclosure, the working state of the stacker can be predicted based on the historical working data and the historical maintenance data of the stacker, the accuracy of predicting the working state of the stacker is improved, which helps to improve the efficiency of silk spool out-of-warehouse and in-warehouse, and thus realizes automatic warehouse management.

Description

Technical Field

[0001] This disclosure relates to the field of intelligent chemical fiber technology, and in particular to a method and apparatus for predicting the state of a stacker crane. Background Technology

[0002] In the chemical fiber production industry, stacker cranes are widely used as warehousing equipment in automated storage and retrieval systems (AS / RS). As a crucial piece of logistics equipment in AS / RS, stacker cranes can store, retrieve, or transport yarn spindles between warehouses or storage locations. In AS / RS, the status of the stacker crane directly determines whether the system can successfully complete outbound or inbound operations. Therefore, accurately predicting the status of stacker cranes has become a pressing technical problem to be solved. Summary of the Invention

[0003] This disclosure provides a method and apparatus for predicting the state of a stacker crane.

[0004] According to a first aspect of this disclosure, a method for predicting the state of a stacker crane is provided, comprising:

[0005] Obtain historical operating and maintenance data of the stacker crane;

[0006] Get the stacker crane's work tasks within a preset time period. The work tasks are tasks pre-assigned to the stacker crane by the warehouse management system.

[0007] Predict the status of the stacker crane during the execution of work tasks based on historical work data and historical maintenance data. This status includes at least the time when the stacker crane malfunctions and the location where the stacker crane stops when it malfunctions.

[0008] The stacker crane is simulated to perform its work tasks within a preset time period based on historical work data, and the simulation results of the stacker crane are obtained.

[0009] Based on the time of the stacker crane failure and its location at the time of failure, the state prediction results of the stacker crane are generated by combining the simulation results.

[0010] According to a second aspect of this disclosure, a stacker crane status prediction device is provided, comprising:

[0011] The first acquisition module is used to acquire the historical working data and historical maintenance data of the stacker crane;

[0012] The second acquisition module is used to acquire the work tasks of the stacker crane within a preset time period. The work tasks are tasks pre-assigned to the stacker crane by the warehouse management system.

[0013] The first prediction module is used to predict the status of the stacker crane during the execution of work tasks based on historical work data and historical maintenance data. The status includes at least the time when the stacker crane malfunctions and the location where the stacker crane stops when it malfunctions.

[0014] The simulation module is used to simulate the stacker crane's work tasks within a preset time period based on historical work data, and to obtain the simulation execution results of the stacker crane.

[0015] The first generation module is used to generate a state prediction result for the stacker crane based on the time of the stacker crane failure and the location where the stacker crane stopped at the time of the failure, combined with the simulation execution results.

[0016] According to a third aspect of this disclosure, an electronic device is provided, comprising:

[0017] At least one processor; and

[0018] The memory is communicatively connected to the at least one processor; wherein,

[0019] The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform any of the methods described in the present disclosure.

[0020] According to a fourth aspect of this disclosure, a non-transitory computer-readable storage medium is provided storing computer instructions, wherein the computer instructions are used to cause the computer to perform any of the methods according to embodiments of this disclosure.

[0021] According to the technology disclosed herein, the working status of a stacker crane can be predicted based on its historical working data and historical maintenance data, which improves the accuracy of predicting the working status of the stacker crane, helps to improve the efficiency of warehousing and unloading of silk spindles, and thus realizes automated warehouse management.

[0022] It should be understood that the description in the Summary of the Invention is not intended to limit the key or essential features of the embodiments of this disclosure, nor is it intended to restrict the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description

[0023] The above and other features, advantages, and aspects of the embodiments of this disclosure will become more apparent from the accompanying drawings and the following detailed description. In the drawings, the same or similar reference numerals denote the same or similar elements, wherein:

[0024] Figure 1 This is a flowchart illustrating the state prediction method for a stacker crane according to an embodiment of the present disclosure;

[0025] Figure 2This is a schematic diagram illustrating the adjustment of the stacker crane's working tasks according to an embodiment of this disclosure;

[0026] Figure 3 This is a schematic diagram illustrating the determination of a first duration and a second duration based on a first faulty device, according to an embodiment of this disclosure.

[0027] Figure 4 This is a schematic diagram illustrating the determination of the first duration and the second duration based on the second faulty device according to an embodiment of this disclosure;

[0028] Figure 5 This is a schematic diagram of the structure of the stacker crane state prediction device according to an embodiment of the present disclosure;

[0029] Figure 6 This is a block diagram of an electronic device used to implement the stacker crane state prediction method of the embodiments of this disclosure. Detailed Implementation

[0030] The exemplary embodiments of this disclosure are described below with reference to the accompanying drawings, including various details of the embodiments to aid understanding, and should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this disclosure. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0031] The terms "first," "second," and "third," etc., used in the embodiments, claims, and accompanying drawings of this disclosure are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion, such as including a series of steps or units. A method, system, product, or apparatus is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to these processes, methods, products, or apparatuses.

[0032] Before introducing the technical solutions of the embodiments of this disclosure, the technical terms that may be used in this disclosure will be further explained:

[0033] Automated storage and retrieval systems (AS / RS), also known as high-bay warehouses, are warehouses that use multi-layered, dozens-layered, or even high-rise racking systems to store goods and employ corresponding material handling equipment for inbound and outbound operations. Their main components consist of racking, aisle-type stacker cranes, inbound / outbound workbenches, and automated transport and control systems.

[0034] In related technologies, storing finished silk spindles in an automated warehouse, retrieving finished silk spindles from the warehouse, and transferring silk spindles within the warehouse all require the use of stacker cranes. A stacker crane is a type of warehousing equipment used to store, retrieve, and move silk spindles between shelves in a warehouse or storage location.

[0035] In related technologies, finished silk spindles are stored in multiple automated warehouses, each with multiple stacker cranes operating. If a stacker crane malfunctions, it cannot perform its current task, thus affecting the efficiency of silk spindle outbound operations. Furthermore, in the aisles of the automated warehouse, the stacker cranes operate up and down along designated lines; if a stacker crane malfunctions, staff or the warehouse management system cannot accurately determine its operational status.

[0036] To at least partially address one or more of the aforementioned problems and other potential issues, this disclosure proposes a method and apparatus for predicting the status of a stacker crane. By predicting the working status of the stacker crane based on its historical operating and maintenance data, the accuracy of the predicted working status is improved, which helps to increase the efficiency of warehousing and outbound operations, thereby achieving automated warehouse management.

[0037] This disclosure provides a method for predicting the state of a stacker crane. Figure 1 This is a flowchart illustrating a stacker crane state prediction method according to an embodiment of the present disclosure. This method can be applied to a stacker crane state prediction device. The stacker crane state prediction device is located on an electronic device. This electronic device includes, but is not limited to, fixed devices and / or mobile devices. For example, fixed devices include, but are not limited to, servers, which can be cloud servers or ordinary servers. Mobile devices include, but are not limited to, mobile phones, tablets, etc. In some possible implementations, the stacker crane state prediction method can also be implemented by a processor calling computer-readable instructions stored in memory. Figure 1 As shown, the stacker crane's state prediction method includes:

[0038] S101: Obtain historical operating data and historical maintenance data of the stacker crane;

[0039] S102: Obtain the work tasks of the stacker crane within a preset time period. The work tasks are tasks pre-assigned to the stacker crane by the warehouse management system.

[0040] S103: Predict the status of the stacker crane during the execution of work tasks based on historical work data and historical maintenance data. The status includes at least the time when the stacker crane malfunctions and the location where the stacker crane stops when it malfunctions.

[0041] S104: Simulate the stacker crane's work tasks within a preset time period based on historical work data, and obtain the simulation execution results of the stacker crane;

[0042] S105: Based on the time of the stacker crane failure and the location where the stacker crane stopped when the failure occurred, the state prediction result of the stacker crane is generated by combining the simulation execution results.

[0043] In this embodiment of the disclosure, the historical work data refers to the work data of the stacker crane over a past period. This historical work data may include: the number of historical work tasks, the content of the historical work tasks, and the time of the historical work tasks. The above is merely an illustrative example and is not intended to limit all possible contents included in the historical work data; it is simply not an exhaustive list.

[0044] In this embodiment of the disclosure, the historical maintenance data refers to the maintenance data of the stacker crane over a past period. This historical maintenance data may include: maintenance time, number of maintenance operations, fault type, faulty components, maintenance personnel, and maintenance tools. The above is merely an illustrative example and is not intended to limit the scope of all possible contents included in historical maintenance data; it is simply not exhaustive.

[0045] In this embodiment, the stacker crane is an important handling equipment in an automated warehouse. The stacker crane can automatically, quickly, and accurately store and retrieve goods within the warehouse, improving warehouse utilization and operational efficiency. The main components of the stacker crane may include: forks for picking up, handling, and stacking goods in the warehouse or workshop; a platform for placing goods; a lifting mechanism for lifting goods; a traveling mechanism for moving the stacker crane within the warehouse; and a control system for controlling various operations and actions of the stacker crane. Here, the forks can be electrically or hydraulically driven and can extend, retract, and rotate as needed; the platform can be made of steel or aluminum alloy, possessing sufficient load-bearing capacity and stability; the lifting mechanism can be electrically or hydraulically driven, featuring high precision, stability, and reliability; the traveling mechanism can be wheeled or tracked, featuring high speed and stable operation; and the control system can use PLC or microcontroller control components, featuring high automation and ease of operation.

[0046] In this embodiment of the disclosure, the preset time can be 1 day, 3 days, 5 days, etc. Specifically, the preset time can be set and adjusted according to actual needs.

[0047] In this embodiment of the disclosure, the work task may include a spindle warehousing task, a spindle warehousing task, and a spindle location transfer task. The main types of yarn involved in the scheme of this embodiment may include one or more of the following: partially oriented yarns (POY), fully drawn yarns (FDY), and drawn textured yarns (DTY) (or low-elasticity yarn). For example, the specific types of yarn may include polyester partially oriented yarns, polyester fully drawn yarns, polyester drawn yarns, polyester low-elasticity yarn, and polyester staple fiber (PSF).

[0048] In this embodiment, the warehouse management system can issue inbound task management, outbound task, and silk spindle storage location transfer task. The Warehouse Management System (WMS) is a system specifically designed for warehouse management. It focuses on efficient task execution and process planning strategies, and, in conjunction with location management, barcode management, and automated warehouse equipment, can significantly improve operational efficiency and resource utilization.

[0049] In some embodiments, the state of the stacker crane may include: the stacker crane in a critical fault state, the stacker crane in a minor fault state, the stacker crane in a normal state, the stacker crane in a dormant state, the stacker crane in a powered-off state, and the stacker crane in a charging state. The above is merely an illustrative example and is not intended to limit all possible types of stacker crane states; it is simply not an exhaustive list.

[0050] In some embodiments, a serious malfunction of the stacker crane may include: the stacker crane failing to start, which may be caused by electrical faults, mechanical faults, or other reasons; abnormal noise or vibration in the stacker crane, which may be caused by damage to mechanical parts, bearing wear, or other problems; damage to components such as the stacker crane's cargo lifting mechanism, loading platform, or forks, which may cause the stacker crane to be unable to properly store or retrieve goods or to operate unstablely; failure of the stacker crane's control panel, display screen, or communication equipment, which may cause the stacker crane to be unable to be remotely controlled or its operating status monitored; and the stacker crane experiencing a safety device triggering during operation, which may be caused by overloading, overload, or other abnormal conditions.

[0051] In some embodiments, the stacker crane being in a minor fault state may include: a minor fault or error in a sensor of the stacker crane, which may affect the accuracy and safety of the stacker crane; minor wear or loosening of a mechanical component of the stacker crane, which may affect the operating efficiency of the stacker crane; or a minor error in the control program of the stacker crane, which may cause the stacker crane to operate less smoothly, thereby affecting the working efficiency of the stacker crane.

[0052] In some embodiments, the status of the stacker crane may further include: the time of the stacker crane malfunction and the location where the stacker crane was stopped when the malfunction occurred. The time of the malfunction helps maintenance personnel determine the cause of the malfunction and also helps the warehouse management system calculate the progress of the stacker crane in completing its work tasks. The location where the stacker crane was stopped when the malfunction occurred helps maintenance personnel determine whether the malfunction was caused by external factors and also helps the warehouse management system calculate the progress of the stacker crane in completing its work tasks.

[0053] In some embodiments, the warehouse management system can communicate with a stacker crane. When communicating with the stacker crane, the warehouse management system can send tasks to be executed and task data to the stacker crane; the stacker crane can send task completion status to the warehouse management system. This improves the intelligence from silk spindle production to silk spindle storage, helping to increase the efficiency of silk spindle production and silk spindle outbound / inbound processes.

[0054] In some embodiments, the simulation execution result is obtained by analyzing the stacker crane's historical operating data during a preset time period. This simulation execution result comprises data and analysis results obtained during the simulation process. The simulation execution result helps maintenance personnel better understand and control the stacker crane's operation, thereby facilitating maintenance and control. During the simulation process, different stacker crane operating parameters and conditions need to be set, and the simulation execution results for each simulation need to be recorded for comparison and analysis. Through the simulation execution results, information regarding the stacker crane's performance indicators, stability, and reliability can be obtained.

[0055] In some embodiments, the state prediction result may include: the probability that the stacker crane can complete the work task normally within a preset time, the probability that the stacker crane will malfunction within a preset time, the time when the stacker crane malfunctions, and the stopping position of the stacker crane when it malfunctions. The above is only an illustrative example and is not intended to limit all possible contents included in the state prediction result; it is simply not exhaustive.

[0056] The technical solution of this disclosure involves acquiring historical working data and historical maintenance data of a stacker crane; acquiring the working tasks of the stacker crane within a preset time period, where the working tasks are pre-assigned to the stacker crane by the warehouse management system; predicting the state of the stacker crane during the execution of the working tasks based on the historical working data and historical maintenance data, the state including at least the time of the stacker crane malfunction and the stopping position of the stacker crane at the time of the malfunction; simulating the execution of the working tasks by the stacker crane within the preset time period based on the historical working data to obtain the simulation execution result of the stacker crane; and generating the state prediction result of the stacker crane based on the time of the stacker crane malfunction and the stopping position of the stacker crane at the time of the malfunction, combined with the simulation execution result. In this way, the working state of the stacker crane can be predicted based on its historical working data and historical maintenance data, improving the accuracy of the predicted working state of the stacker crane, helping to improve the efficiency of silk spindle outbound and inbound operations, and thus realizing automated warehouse management.

[0057] Figure 2 The diagram illustrates the adjustment of the stacker crane's working tasks, such as... Figure 2 As shown, the stacker crane status prediction method further includes: predicting the first duration of the stacker crane in a fault state and the second duration required to repair the stacker crane based on historical work data and historical maintenance data; and adjusting the stacker crane's work tasks within a preset time based on the first duration and the second duration.

[0058] In some embodiments, the first duration of the stacker crane's fault state when it malfunctions may be the duration from when the stacker crane stops operating until maintenance personnel begin maintenance. The second duration required to maintain the stacker crane may be the duration predicted by maintenance personnel based on the cause of the stacker crane's fault; it may also be the historical maintenance duration based on the cause of the fault determined by maintenance personnel; or it may be the period from the start time of maintenance of the stacker crane to the end time of maintenance of the stacker crane.

[0059] In some embodiments, the time taken for maintenance personnel to repair various faults of the stacker crane can be recorded as a second time required for stacker crane maintenance. Specifically, stacker crane fault types are divided into two main categories: serious faults and minor faults. Historical maintenance data of the stacker crane is acquired; the historical maintenance data of the stacker crane is classified to obtain serious fault data and minor fault data; data analysis is performed on the serious fault data and minor fault data respectively to obtain a fault maintenance schedule. For example, the fault maintenance schedule may record serious faults: Serious fault type 1 is that the stacker crane cannot be started, with a maintenance time of 2 hours; serious fault type 2 is that the stacker crane has abnormal noise or vibration, with a maintenance time of 1.5 hours; serious fault type 3 is that the stacker crane's cargo lifting mechanism, loading platform, or forks are damaged, with a maintenance time of 5 hours; serious fault type 4 is that the stacker crane's control panel, display screen, or communication equipment malfunctions, with a maintenance time of 3 hours; serious fault type 5 is that the stacker crane's safety device is triggered during operation, with a maintenance time of 1 hour. The fault repair schedule can record minor faults: Minor fault type 1 is a minor fault or error in a sensor of the stacker crane, with a repair time of 1 hour; Minor fault type 2 is a minor wear or loosening in a mechanical part of the stacker crane, with a repair time of 0.5 hours.

[0060] In some embodiments, the warehouse management system issues outbound task 1 to stacker crane A, which is to transport POY spindles with batch number A011 from storage location 207 to the outbound port from 13:00 to 18:00 tomorrow; if based on the historical working data and historical maintenance data of stacker crane A, it is predicted that stacker crane A may experience a serious failure type 2 tomorrow afternoon, and the predicted maintenance time of stacker crane A is 1.5 hours; the warehouse management system reissues the task, that is, stacker crane B performs outbound task 1.

[0061] In this way, based on the historical working data and maintenance data of the stacker crane, it is possible to predict the first duration of the stacker crane being in a fault state and the second duration of the stacker crane being repaired. Adjusting the stacker crane's work tasks based on the first and second durations helps to improve the efficiency of the stacker crane in performing tasks and avoids affecting the efficiency of the silk spindle warehousing / outbound tasks due to temporary stacker crane failures.

[0062] Figure 3 A schematic diagram showing the determination of the first duration and the second duration based on the first faulty device is shown, as follows. Figure 3 As shown, the historical maintenance data includes: maintenance records and component lifespan of the first type of components included in the stacker crane; wherein, based on historical working data and historical maintenance data, predicting the first duration of the stacker crane in a fault state and the second duration required to repair the stacker crane includes: determining the first faulty component when the stacker crane malfunctions based on the maintenance records and component lifespan of the first type of components; and determining the first duration and the second duration based on the first faulty component when the stacker crane malfunctions.

[0063] In some embodiments, the first type of device may be a device that was replaced when the stacker crane malfunctioned, as recorded in historical maintenance data. There may be one or more of the first type of device.

[0064] In some embodiments, the maintenance record of the first type of device may include the name of the first type of device, the replacement time of the first type of device, the reason for the replacement of the first type of device, the model of the first type of device, and the size of the first type of device. The above is only an illustrative example and is not intended to limit all possible contents included in the maintenance record of the first type of device; it is simply not an exhaustive list.

[0065] In some embodiments, the maintenance records of the first type of device can be obtained from the stacker crane's maintenance log; the maintenance records of the first type of device can also be obtained by searching the stacker crane's display screen; the maintenance records of the first type of device can also be obtained from the data source of the control equipment. The above are merely illustrative examples and are not intended to limit all possible methods of obtaining the maintenance records of the first type of device; they are simply not exhaustive.

[0066] In some embodiments, the device lifespan may refer to the time during which it can be used normally under normal operating conditions and loads.

[0067] In some embodiments, the device lifespan is obtained by: obtaining the device installation time from the maintenance records of the first type of device; obtaining the device usage time from the device user manual; and obtaining the device lifespan based on the device installation time and the device usage time.

[0068] In some embodiments, the first faulty device is determined based on the maintenance records of the first type of device and the equipment life when the stacker crane is in a faulty state.

[0069] In this way, the first faulty component of the stacker crane can be identified based on the maintenance records and equipment life of the first type of components. Based on the first faulty component when the stacker crane fails, the first duration and the second duration can be determined. This helps to flexibly adjust the working time of the stacker crane based on the first duration and the second duration, thereby helping to improve the efficiency of the stacker crane in performing its work tasks.

[0070] Figure 4 A schematic diagram showing the determination of the first duration and the second duration based on the first faulty device is shown, as follows. Figure 4As shown, the historical operating data includes: the total runtime of the second type of components included in the stacker crane and the operating parameters of the second type of components when the stacker crane is working; wherein, based on the historical operating data and historical maintenance data, predicting the first duration of the stacker crane in a fault state and the second duration required to repair the stacker crane includes: determining the second faulty component in a fault state when the stacker crane malfunctions based on the total runtime and operating parameters of the second type of components; and determining the first duration and the second duration based on the second faulty component in a fault state when the stacker crane malfunctions.

[0071] In this embodiment of the disclosure, the total runtime of the second type of device can be obtained from the stacker crane's work log; the total runtime of the second type of device can also be obtained by searching the stacker crane's display screen; the total runtime of the second type of device can also be obtained from the data source of the control device. The above are merely illustrative examples and are not intended to limit all possible methods of obtaining the total runtime of the second type of device; they are simply not exhaustive.

[0072] In some embodiments, the operating parameters of the second type of device can be obtained from the stacker crane's work log; the operating parameters of the second type of device can also be obtained by searching the stacker crane's display screen; the operating parameters of the second type of device can also be obtained from the data source of the control device. The above are merely illustrative examples and are not intended to limit all possible methods of obtaining the operating parameters of the second type of device; they are simply not exhaustive.

[0073] In some embodiments, the operating parameters of the second type of device can be obtained from the programmable logic controller (PLC) through a Supervisory Control and Data Acquisition (SCADA) system. The SCADA system can display the status of the clicked device in a configured manner; for example, clicking on a stacker crane on the operating interface will directly display the stacker crane's operating parameters. The operating parameters of the second type of device can also be obtained from the stacker crane's operating parameters through software that supports the Object Linking and Embedding for Process Control (OPC) protocol.

[0074] In some embodiments, the operating parameters of the stacker crane refer to the operating status parameters of the devices when the stacker crane is running. For example, if the second type of device is a motor, the operating parameters include operating parameters such as current value, voltage value, rotor speed, and whether it is overloaded; if the second type of device is a pressure sensor, the operating parameters include pressure value; if the second type of device is a temperature sensor, the operating parameters include temperature value.

[0075] In some embodiments, the operating parameters of the stacker crane during operation may include: lifting capacity, which refers to the sum of the maximum material weight that the stacker crane is allowed to pick up and the weight of the forks; maximum lifting height, which refers to the vertical distance between the upper surface of the horizontal section of the forks and the ground when the goods are lifted to the highest position under the rated lifting capacity; maximum lifting speed, which refers to the maximum speed at which the goods are lifted under the rated lifting capacity; maximum slewing speed, which refers to the maximum speed that the slewing platform can reach when slewing under the rated lifting capacity; and fork deflection, which refers to the distance that the forks bend downwards when the stacker crane forks are raised to the maximum height under the rated lifting capacity.

[0076] In some embodiments, the second fault device is a second fault device determined when the stacker crane is in a fault state, based on the total running time and operating parameters of the second type of devices.

[0077] In this way, the second faulty device of the stacker crane can be determined based on the total running time of the second type of device and the operating parameters of the second type of device. Based on the second faulty device when the stacker crane fails, the first duration and the second duration can be determined. This helps to flexibly adjust the working time of the stacker crane based on the first duration and the second duration, thereby helping to improve the efficiency of the stacker crane in performing its work tasks.

[0078] In this embodiment of the disclosure, the stacker crane status prediction method further includes: generating a maintenance task based on a first duration and a second duration, wherein the maintenance task includes maintenance time, maintenance tools and maintenance personnel skill requirements; and sending the maintenance task to the maintenance management center so that the maintenance management center can arrange maintenance personnel based on the maintenance task.

[0079] In this embodiment of the disclosure, the maintenance task may include maintenance time, maintenance tools, and the skill requirements of maintenance personnel. The above is merely an illustrative example and is not intended to limit all possible contents included in the maintenance task; it is simply not an exhaustive list.

[0080] In this embodiment, the maintenance management center is an organization specifically responsible for equipment maintenance and management. Its responsibilities may include: developing equipment maintenance plans, including daily maintenance, periodic inspections, and troubleshooting; diagnosing and repairing equipment faults to ensure normal equipment operation; managing equipment safety, including conducting safety inspections and correcting safety issues; managing spare parts to ensure their sufficiency and effective use; recording equipment maintenance records and reports, including equipment fault conditions, maintenance processes, and results; training and managing maintenance personnel to improve their skills and work efficiency; improving and optimizing equipment to enhance its performance and efficiency; and assisting other departments in completing tasks, such as helping the production department improve production efficiency and the quality department improve product quality.

[0081] In this way, maintenance personnel and repair time can be promptly arranged for stacker cranes that are experiencing malfunctions based on maintenance tasks. This helps improve the efficiency of stacker crane malfunction repair, thereby improving the working efficiency of stacker cranes and the efficiency of silk spindle outbound and inbound operations.

[0082] In this embodiment of the disclosure, the stacker crane state prediction method further includes: determining a first target task and a second target task in the work task based on the time of the stacker crane failure and the location where the stacker crane stopped when the failure occurred; the first target task is a task in the work task that the stacker crane can complete within a preset time period, and the second target task is a task in the work task that the stacker crane cannot complete within the preset time period; and sending the second target task for the preset time period to other stacker cranes so that the other stacker cranes can execute the second target task.

[0083] Here, the preset time period is the time period during which the stacker crane performs the task of putting or taking out the silk spindle.

[0084] In some embodiments, the first target task is a task that the stacker crane can complete normally when the stacker crane is in a faulty state. For example, if stacker crane A is performing outbound tasks at storage locations 120 and 130, and the lifting mechanism of stacker crane A malfunctions while performing the outbound task at storage location 120, then the first target task is the outbound task at storage location 120.

[0085] In some embodiments, the second target task is a task that the stacker crane cannot complete normally when it is in a faulty state. For example, if stacker crane A is performing outbound tasks at storage locations 120 and 130, and the lifting mechanism of stacker crane A malfunctions while performing the outbound task at storage location 120, then the second target task becomes the outbound task at storage location 130. The warehouse management system sends the second target task to stacker crane B so that stacker crane B can perform the second target task.

[0086] In this way, when the stacker crane is in a faulty state, it can complete only the first objective task that can be completed normally, and send the second objective task that cannot be completed normally to other stacker cranes, which helps to improve the working efficiency of the stacker crane.

[0087] In this embodiment of the disclosure, the method of predicting the state of a stacker crane during the execution of a work task based on historical work data and historical maintenance data includes: inputting historical work data and historical maintenance data into a state prediction model; and obtaining the state of the stacker crane output by the state prediction model.

[0088] In some embodiments, the state of the stacker crane may include: the stacker crane is in a fault state, the stacker crane is in a normal state, the stacker crane is in a dormant state, the stacker crane is in a powered-off state, the stacker crane is in a charging state, and the stacker crane is in a maintenance state.

[0089] Thus, by inputting historical work data and historical maintenance data into the status prediction model, the status of the stacker crane output by the status prediction model can be obtained, which helps to predict the working status of the stacker crane in advance, thereby improving the efficiency of the inbound and outbound of the silk spindle.

[0090] In this embodiment of the disclosure, the state prediction model is trained in the following manner: acquiring historical working sample data and historical maintenance sample data of the stacker crane; inputting the historical working sample data and historical maintenance sample data into a preset model; acquiring the state prediction value of the stacker crane output by the preset model; constructing a loss function based on the true state value and the state prediction value of the stacker crane; and training the preset model based on the loss function to obtain the state prediction model.

[0091] In some embodiments, the historical working sample data and historical maintenance sample data of the stacker crane can be obtained from the data source of the control equipment; they can also be obtained from the stacker crane's working logs and maintenance logs. The above is merely an illustrative example and is not intended to limit all possible methods of obtaining the historical working sample data and historical maintenance sample data of the stacker crane; it is simply not an exhaustive list.

[0092] Thus, historical operating sample data and historical maintenance sample data of the stacker crane are acquired; this data is then input into a pre-set model; the predicted state values ​​of the stacker crane output by the pre-set model are obtained; a loss function is constructed based on the true and predicted state values ​​of the stacker crane; and the pre-set model is trained based on the loss function to obtain the state prediction model. This allows for the prediction of the stacker crane's state in advance based on the trained state prediction model, thereby improving the accuracy of predicting the stacker crane's operating state.

[0093] It should be understood that Figures 2 to 4 This is merely illustrative and not restrictive; the content in the diagram can be adapted or modified according to operational needs, and will not be elaborated further here. Those skilled in the art can use it as a basis... Figures 2 to 4 Even with various obvious changes and / or substitutions to the examples, the resulting technical solutions still fall within the scope of this disclosure.

[0094] This disclosure provides a stacker crane status prediction device, such as... Figure 5 As shown, the stacker crane's status prediction device may include:

[0095] The first acquisition module 510 is used to acquire historical working data and historical maintenance data of the stacker crane;

[0096] The second acquisition module 520 is used to acquire the work tasks of the stacker crane within a preset time period. The work tasks are tasks pre-assigned to the stacker crane by the warehouse management system.

[0097] The first prediction module 530 is used to predict the status of the stacker crane during the execution of work tasks based on historical work data and historical maintenance data. The status includes at least the time when the stacker crane malfunctions and the location where the stacker crane stops when it malfunctions.

[0098] The simulation module 540 is used to simulate the stacker crane's work tasks within a preset time period based on historical work data, and to obtain the simulation execution results of the stacker crane.

[0099] The first generation module 550 is used to generate a state prediction result of the stacker crane based on the time of the stacker crane failure and the position of the stacker crane when the failure occurred, combined with the simulation execution results.

[0100] In some embodiments, the stacker crane state prediction device further includes: a second prediction module ( Figure 5 (Not shown in the image), used to predict the first duration of the stacker crane's fault state and the second duration required to repair the stacker crane based on historical work data and historical maintenance data; adjustment module ( Figure 5 (Not shown in the image), used to adjust the stacker crane's work tasks within a preset time based on a first duration and a second duration.

[0101] In some embodiments, the historical maintenance data includes: maintenance records and device lifespan of a first type of component included in the stacker crane; wherein, the second prediction module ( Figure 5 (Not shown in the image), including: a first determining submodule, used to determine the first faulty device when the stacker crane fails based on the maintenance records and device life of the first type of device; and a second determining submodule, used to determine a first duration and a second duration based on the first faulty device when the stacker crane fails.

[0102] In some embodiments, the historical operating data includes: the total runtime of the second type of devices included in the stacker crane and the operating parameters of the second type of devices during stacker crane operation; wherein, the second prediction module ( Figure 5 (Not shown in the image), including: a third determining submodule, used to determine the second faulty device in a faulty state when the stacker crane malfunctions, based on the total running time and operating parameters of the second type of device; and a fourth determining submodule, used to determine the first duration and the second duration based on the second faulty device in a faulty state when the stacker crane malfunctions.

[0103] In some embodiments, the stacker crane state prediction device further includes: a second generation module ( Figure 5 (Not shown in the image), used to generate maintenance tasks based on a first duration and a second duration, the maintenance tasks including maintenance time, maintenance tools, and maintenance personnel skill requirements; the first sending module ( Figure 5 (Not shown in the image) is used to send maintenance tasks to the maintenance management center, so that the maintenance management center can arrange maintenance personnel based on the maintenance tasks.

[0104] In some embodiments, the stacker crane state prediction device further includes: a determination module ( Figure 5 (Not shown in the image), used to determine the first target task and the second target task in the work task based on the time of the stacker crane failure and the location where the stacker crane stopped at the time of the failure. The first target task is the task that the stacker crane can complete within a preset time period, and the second target task is the task that the stacker crane cannot complete within the preset time period; the second sending module ( Figure 5 (Not shown in the image), used to send a second target task for a preset time period to other stacker cranes so that the other stacker cranes can execute the second target task.

[0105] In some embodiments, the first prediction module 530 includes: an input submodule for inputting historical working data and historical maintenance data into the status prediction model; and an acquisition submodule for acquiring the status of the stacker crane output by the status prediction model.

[0106] In some embodiments, the state prediction model is trained by: acquiring historical working sample data and historical maintenance sample data of the stacker crane; inputting the historical working sample data and historical maintenance sample data into a preset model; acquiring the state prediction value of the stacker crane output by the preset model; constructing a loss function based on the true state value and the state prediction value of the stacker crane; and training the preset model based on the loss function to obtain the state prediction model.

[0107] Those skilled in the art should understand that the functions of each processing module in the stacker crane state prediction device of the present disclosure embodiments can be understood with reference to the relevant description of the stacker crane state prediction method described above. Each processing module in the stacker crane state prediction device of the present disclosure embodiments can be implemented by an analog circuit that implements the functions of the present disclosure embodiments, or by running software that executes the functions of the present disclosure embodiments on an electronic device.

[0108] The stacker crane status prediction device of this disclosure can predict the working status of the stacker crane based on the stacker crane's historical working data and historical maintenance data, thereby improving the accuracy of predicting the stacker crane's working status, thus improving the efficiency of silk spindle outbound and inbound operations, and realizing automated warehouse management.

[0109] According to embodiments of this disclosure, this disclosure also provides an electronic device and a readable storage medium.

[0110] Figure 6 This is a structural block diagram of an electronic device according to an embodiment of the present disclosure. Figure 6 As shown, the electronic device includes a memory 610 and a processor 620. The memory 610 stores a computer program that can run on the processor 620. There can be one or more memories 610 and processors 620. The memory 610 can store one or more computer programs, which, when executed by the electronic device, cause the electronic device to perform the methods provided in the above-described method embodiments. The electronic device may also include a communication interface 630 for communicating with external devices and performing data exchange and transmission.

[0111] If the memory 610, processor 620, and communication interface 630 are implemented independently, they can be interconnected via a bus to communicate with each other. This bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. This bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 6 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0112] Optionally, in a specific implementation, if the memory 610, processor 620, and communication interface 630 are integrated on a single chip, then the memory 610, processor 620, and communication interface 630 can communicate with each other through an internal interface.

[0113] It should be understood that the aforementioned processor can be a Central Processing Unit (CPU), or other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. General-purpose processors can be microprocessors or any conventional processor. It is worth noting that the processor can be a processor supporting Advanced Reduced Instruction Set Machines (ARM) architecture.

[0114] Further, optionally, the aforementioned memory may include read-only memory and random access memory, and may also include non-volatile random access memory. The memory may be volatile or non-volatile, or may include both. Non-volatile memory may include read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. Volatile memory may include random access memory (RAM), which serves as an external cache. Many forms of RAM are available by way of example, but not limitation. Examples include Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), Synchronous DRAM (SDRAM), Double Data Rate Synchronous DRAM (DDR SDRAM), Enhanced Synchronous DRAM (ESDRAM), Synchlink DRAM (SLDRAM), and Direct RAMBUS RAM (DR RAM).

[0115] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions. When the computer instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this disclosure are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another via wired (e.g., coaxial cable, fiber optic, Digital Subscriber Line, DSL) or wireless (e.g., infrared, Bluetooth, microwave, etc.) means. The computer-readable storage medium can be any available medium accessible to a computer, or a data storage device such as a server or data center that integrates one or more available media. The available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., Digital Versatile Discs (DVDs)), or semiconductor media (e.g., Solid State Disks (SSDs)). It is worth noting that the computer-readable storage media mentioned in this disclosure can be non-volatile storage media; in other words, it can be non-transient storage media.

[0116] Those skilled in the art will understand that all or part of the steps of the above embodiments can be implemented by hardware or by a program instructing related hardware. The program can be stored in a computer-readable storage medium, such as a read-only memory, a disk, or an optical disk.

[0117] In the description of the embodiments of this disclosure, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this disclosure. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of those different embodiments or examples.

[0118] In the description of the embodiments disclosed herein, unless otherwise stated, " / " means "or". For example, A / B can mean A or B. The "and / or" in this document 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, and B existing alone.

[0119] In the description of embodiments of this disclosure, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of embodiments of this disclosure, unless otherwise stated, "a plurality of" means two or more.

[0120] The above description is merely an exemplary 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.

[0121] In the description of this specification, it should be understood that the terms "center," "longitudinal," "transverse," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," "counterclockwise," "axial," "radial," and "circumferential" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing this disclosure and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this disclosure.

Claims

1. A method for predicting the state of a stacker crane, characterized in that, The method includes: Obtain historical operating and maintenance data of the stacker crane; The work tasks of the stacker crane within a preset time period are obtained, and the work tasks are tasks pre-assigned to the stacker crane by the warehouse management system. Based on the historical work data and the historical maintenance data, the state of the stacker crane during the execution of the work task is predicted, and the state includes at least the time when the stacker crane malfunctions and the stopping position when the stacker crane malfunctions; Based on the historical work data, the stacker crane is simulated to perform the work task within the preset time period, and the simulation execution result of the stacker crane is obtained; Based on the time of the stacker crane failure and the location where the stacker crane stopped when the failure occurred, the state prediction result of the stacker crane is generated in combination with the simulation execution result. The method further includes: Based on the time of the stacker crane failure and the location where the stacker crane stopped when it failed, a first target task and a second target task are determined in the work task. The first target task is a task in the work task that the stacker crane can complete within the preset time period, and the second target task is a task in the work task that the stacker crane cannot complete within the preset time period. The second target task for the preset time period is sent to other stacker cranes so that the other stacker cranes can execute the second target task.

2. The method according to claim 1, characterized in that, The method further includes: Based on the historical work data and the historical maintenance data, predict the first duration of the stacker crane in the fault state when a fault occurs and the second duration required to repair the stacker crane; The stacker crane's work tasks are adjusted based on the first duration and the second duration within the preset time period.

3. The method according to claim 2, characterized in that, The historical maintenance data includes: maintenance records and lifespan of the first type of components included in the stacker crane; The step of predicting the first duration of the stacker crane's fault state and the second duration required to repair the stacker crane based on the historical working data and the historical maintenance data includes: Based on the maintenance records and lifespan of the first type of device, the first faulty device when the stacker crane malfunctions is determined; The first duration and the second duration are determined based on the first faulty device when the stacker crane malfunctions.

4. The method according to claim 2, characterized in that, The historical operating data includes: the total runtime of the second type of devices included in the stacker crane and the operating parameters of the second type of devices when the stacker crane is working; The step of predicting the first duration of the stacker crane's fault state and the second duration required to repair the stacker crane based on the historical working data and the historical maintenance data includes: Based on the total runtime and operating parameters of the second type of device, the second faulty device in a faulty state when the stacker crane malfunctions is determined; The first duration and the second duration are determined based on the second faulty device that is in a faulty state when the stacker crane malfunctions.

5. The method according to claim 2, characterized in that, The method further includes: A maintenance task is generated based on the first duration and the second duration. The maintenance task includes maintenance time, maintenance tools, and maintenance personnel skill requirements. The maintenance task is sent to the maintenance management center, which then assigns maintenance personnel based on the maintenance task.

6. The method according to claim 1, characterized in that, The method of predicting the state of the stacker crane during the execution of the work task based on the historical work data and the historical maintenance data includes: Input the historical work data and the historical maintenance data into the status prediction model; Obtain the state of the stacker crane output by the state prediction model.

7. The method according to claim 6, characterized in that, in, The state prediction model is trained in the following way: Obtain historical operational sample data and historical maintenance sample data of the stacker crane; Input the historical work sample data and the historical maintenance sample data into the preset model; Obtain the state prediction value of the stacker crane output by the preset model; A loss function is constructed based on the true state value and the predicted state value of the stacker crane. The preset model is trained based on the loss function to obtain the state prediction model.

8. A stacker crane status prediction device, characterized in that, The device includes: The first acquisition module is used to acquire historical working data and historical maintenance data of the stacker crane; The second acquisition module is used to acquire the work tasks of the stacker crane within a preset time period, wherein the work tasks are tasks pre-assigned to the stacker crane by the warehouse management system. The first prediction module is used to predict the state of the stacker crane during the execution of the work task based on the historical work data and the historical maintenance data. The state includes at least the time when the stacker crane malfunctions and the stopping position when the stacker crane malfunctions. The simulation module is used to simulate the stacker crane performing the work task within the preset time period based on the historical work data, and to obtain the simulation execution result of the stacker crane. The first generation module is used to generate a state prediction result of the stacker crane based on the time when the stacker crane malfunctioned and the location where the stacker crane stopped when it malfunctioned, combined with the simulation execution result. The determination module is used to determine the first target task and the second target task in the work tasks based on the time of the stacker crane failure and the location where the stacker crane stopped when the failure occurred. The first target task is the task that the stacker crane can complete within a preset time period, and the second target task is the task that the stacker crane cannot complete within a preset time period. The second sending module is used to send the second target task within a preset time period to other stacker cranes so that the other stacker cranes can execute the second target task.

9. An electronic device, characterized in that, include: At least one processor; as well as A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium storing computer instructions, characterized in that, The computer instructions are used to cause the computer to perform the method according to any one of claims 1 to 7.

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