Stacker state prediction method, electronic device, storage medium, and program

Abstract

To provide a stacker state prediction method, apparatus, electronic device, storage medium, and program relating to the field of chemical fiber manufacturing intelligent technology.SOLUTION: The method includes: obtaining historical work data and historical maintenance data of a stacker; obtaining a work task of the stacker within a preset time period; predicting a state of the stacker during execution of the work task based on the historical work data and the historical maintenance data; simulating the execution of the work task by the stacker within the preset time period based on the historical work 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 a stop position of the stacker when failing in combination with the simulation execution result.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The present disclosure relates to the field of intelligent technologies for chemical fiber production, and particularly to a method and apparatus for predicting the state of a stacker.

Background Art

[0002] In the field of chemical fiber production, stackers are widely applied as warehouse facilities in three-dimensional automated warehouses. As one of the important logistics facilities in a three-dimensional automated warehouse, a stacker can take in and out or transport winding packages between warehouses or warehouse locations.

Summary of the Invention

Problems to be Solved by the Invention

[0003] In a three-dimensional automated warehouse, whether the three-dimensional automated warehouse can successfully complete the outbound and inbound operations depends on the state of the stacker. Therefore, how to accurately predict the stacker state has become a technical problem that needs to be solved urgently.

Means for Solving the Problems

[0004] The present disclosure provides a method and apparatus for predicting the state of a stacker, an electronic device, a storage medium, and a program.

[0005] According to a first aspect of the present disclosure, a method for predicting the state of a stacker is provided, the method comprising: acquiring the historical operation data and historical repair data of the stacker; acquiring the operation tasks of the stacker in a preset time period, where the operation tasks are tasks pre-assigned by a warehouse management system to the stacker; predicting the state of the stacker in the process of the stacker executing the operation tasks based on the historical operation data and the historical repair data, where the state includes at least the time when the stacker fails and the staying position when the stacker fails; Based on the historical operation data, simulate the execution of the work tasks by the stacker in a preset time period to obtain the simulation execution result of the stacker, and generate a state prediction result of the stacker in combination with the simulation execution result based on the time when the stacker fails and the staying position when the stacker fails, including.

[0006] According to the second aspect of the present disclosure, a stacker state prediction device is provided, and the device includes: a first acquisition module for acquiring the historical operation data and historical repair data of the stacker; a second acquisition module for acquiring the work tasks of the stacker in a preset time period, where the work tasks are tasks pre-assigned to the stacker by the warehouse management system; a first prediction module for predicting the state of the stacker in the process of the stacker executing the work task based on the historical operation data and historical repair data, where the state includes at least the time when the stacker fails and the staying position when the stacker fails; a simulation module for simulating the execution of the work tasks by the stacker in a preset time period based on the historical operation data to obtain the simulation execution result of the stacker; a first generation module for generating a state prediction result of the stacker in combination with the simulation execution result based on the time when the stacker fails and the staying position when the stacker fails.

[0007] According to the third aspect of the present disclosure, an electronic device is provided, and the device includes: at least one processor; a memory communicatively connected to the at least one processor. The memory stores instructions executable by the at least one processor, and when the instructions are executed by the at least one processor, they cause any one of the methods in the embodiments of the present disclosure to be executed.

[0008] According to a fourth aspect of the present disclosure, there is provided a non-transitory computer-readable storage medium storing computer instructions for causing a computer to execute any one of the methods in the embodiments of the present disclosure.

[0009] According to a fifth aspect of the present disclosure, there is provided a program which, when executed by a processor, implements any one of the methods in the embodiments of the present disclosure.

[0010] According to the technology of the present disclosure, it is possible to predict the working state of a stacker based on the historical working data and historical repair data of the stacker, improve the prediction accuracy of the working state of the stacker, help improve the efficiency of winding package shipping and warehousing, and realize automatic warehouse management.

[0011] It should be understood that the content described herein is not intended to describe the key points or important features of the embodiments of the present disclosure, nor is it used to limit the scope of the present disclosure. For other features of the present disclosure, understanding is promoted through the following description.

Brief Description of the Drawings

[0012] The above and other features, advantages, and aspects of the embodiments of the present disclosure will become more apparent by referring to the following detailed description in connection with the drawings. In the drawings, the same or similar reference numerals represent the same or similar elements.

[0013]

Figure 1

Figure 2

Figure 3

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Figure 6

Embodiments for Carrying Out the Invention

[0014] Hereinafter, exemplary embodiments of the present disclosure will be described with reference to the drawings. Here, various details of the embodiments of the present disclosure are included for ease of understanding, and these should be considered merely exemplary. Therefore, those skilled in the art should recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, in the following description, descriptions of known functions and structures are omitted for clarity and brevity.

[0015] In the embodiments of the specification of the present disclosure, the claims, and the above-mentioned drawings, terms such as "first", "second", and "third" are not necessarily used to describe a specific order or priority, but are for distinguishing similar objects. Furthermore, the terms "include" and "have" and their variants are intended for non-exclusive inclusion, for example, the inclusion of a series of steps or units. A method, system, product, or device does not necessarily have to be limited to the explicitly listed steps or units, and may include other steps or units not explicitly listed or inherent to these processes, methods, products, or devices.

[0016] Before explaining the technical aspects of the embodiments of the present disclosure, first, technical terms that may be used in the present disclosure will be explained.

[0017] A stereoscopic warehouse, also known as a high-rise warehouse or a high-bay warehouse, is a warehouse that stores unit goods on shelves that are multi-layered and tens of floors high, and performs the inbound and outbound operations of goods using corresponding material handling equipment. The warehouse body is composed of shelves, aisle stacker cranes, inbound (outbound) workbenches, and an automatic loading (unloading) and operation control system.

[0018] In the related art, the storage of finished product winding packages in a stereoscopic warehouse, the outbound of finished product winding packages from the warehouse, and the transfer between locations of winding packages in the stereoscopic warehouse need to be carried out by stackers. A stacker is a warehouse facility for taking in and out and transporting winding packages between the shelves of a warehouse or location.

[0019] In the related art, multiple stereoscopic warehouses are used to store finished product winding packages, and multiple stackers perform operations in each stereoscopic warehouse. When a stacker fails, the current work task cannot be executed, which affects the efficiency of the outbound task of the winding package or the winding package outbound task. Also, in the aisle of the stereoscopic warehouse, the stacker operates up and down along the lane. When the stacker fails, the operator and the warehouse management system cannot accurately grasp the working state of the stacker.

[0020] The present disclosure proposes a method and device for predicting the state of a stacker to at least partially solve one or more of the above problems and other potential problems. Based on the historical work data and historical repair data of the stacker, the working state of the stacker is predicted, the prediction accuracy of the working state of the stacker is improved, which helps to improve the efficiency of the outbound and inbound of the winding package, and realize automatic warehouse management.

[0021] Embodiments of the present disclosure provide a method for predicting the state of a stacker. FIG. 1 is a flowchart of the method for predicting the state of a stacker according to an embodiment of the present disclosure. The method for predicting the state of the stacker can be applied to a state prediction device of the stacker. The state prediction device of the stacker is provided in an electronic device. The electronic device includes, but is not limited to, a fixed device and / or a mobile device. For example, the fixed device includes, but is not limited to, a server, and the server may be a cloud server or a normal server. For example, the mobile device includes, but is not limited to, a mobile phone, a tablet, etc. In some possible implementations, the method for predicting the state of the stacker can also be implemented by a processor calling computer-readable instructions stored in a memory. As shown in FIG. 1, the method for predicting the state of the stacker includes the following.

[0022] In S101, obtain the historical work data and historical repair data of the stacker.

[0023] In S102, obtain the work tasks of the stacker in a preset time period, and the work tasks are tasks previously assigned to the stacker by a warehouse management system.

[0024] In S103, based on the historical work data and historical repair data, predict the state of the stacker in the process of the stacker executing the work task, where the state includes at least the time when the stacker fails and the staying position when the stacker fails.

[0025] In S104, based on the historical work data, simulate the execution of the work task by the stacker in a preset time period to obtain the simulation execution result of the stacker.

[0026] In S105, based on the time when the stacker fails and the staying position when the stacker fails, generate the state prediction result of the stacker in combination with the simulation execution result.

[0027] In an embodiment of the present disclosure, the historical operation data is the operation data of the stacker over a certain period in the past. The historical operation data can include the number of historical operation tasks, the content of historical operation tasks, and the historical operation task time. The above is only an exemplary description and does not limit all possible contents included in the historical operation data, which are not enumerated here.

[0028] In an embodiment of the present disclosure, the historical repair data is the repair data of the stacker over a certain period in the past. The historical repair data can include the repair time, the number of repairs, the failure type, the failed parts, the repair personnel, and the repair tools. The above is only an exemplary description and does not limit all possible contents included in the historical repair data, which are not enumerated here.

[0029] In an embodiment of the present disclosure, the stacker is an important transportation facility in the three-dimensional warehouse. The stacker can automatically, quickly, and accurately load and unload goods in the three-dimensional warehouse, improving the utilization rate of the warehouse and the work efficiency. The main components of the stacker can include a fork for picking up, transporting, and stacking goods in the warehouse or work area, a platform for placing goods, a lifting mechanism for realizing the lifting operation of goods, a traveling mechanism for realizing the movement of the stacker in the warehouse, and a control system for controlling various operations and movements of the stacker. Here, the fork can adopt an electric or hydraulic drive mode and can be extended, retracted, and rotated as required. The platform can adopt a steel structure or an aluminum alloy structure with sufficient supporting capacity and stability. The lifting mechanism can adopt an electric or hydraulic drive mode and has the characteristics of high precision, good stability, and reliability. The traveling mechanism can adopt a wheel or crawler structure and has the characteristics of high moving speed and stable operation. The control system can adopt control devices such as PLCs or single-chip microcomputers and has the characteristics of high automation degree and simple operation.

[0030] In an embodiment of the present disclosure, the preset time period may be 1 day, 3 days, 5 days, etc. Specifically, the preset time period can be set and adjusted according to actual demand.

[0031] In an embodiment of the present disclosure, the work task may include a winding package warehousing task, a winding package warehousing task, and a location movement task of the winding package. The main types of filaments according to the embodiments of the present disclosure may include one or more of Partially Oriented Yarns (POY), Fully Drawn Yarns (FDY), Draw Textured Yarns (DTY) (or low-elastic filaments), etc. For example, specific examples of the types of yarns include, for example, Polyester Partially Oriented Yarns, Polyester Fully Drawn Yarns, Polyester Drawn Yarns, Polyester Staple Fiber, etc.

[0032] In an embodiment of the present disclosure, the warehouse management system can issue warehousing task management, warehousing tasks, and location movement tasks of winding packages in the warehouse. The Warehouse Management System (WMS) is a system specialized for warehouse management, with efficient task execution and process planning strategies at its core. Combined with location management, barcode management, and warehouse automation equipment, it can greatly improve work efficiency and resource utilization rate.

[0033] In some embodiments, the state of the stacker can include that the stacker is in a serious failure state, the stacker is in a minor failure state, the stacker is in a normal state, the stacker is in a standby state, the stacker is in a stopped state, and the stacker is in a charging state. The above is only an exemplary description and does not limit all possible types included in the state of the stacker, which are not enumerated here.

[0034] In some embodiments, the stacker being in a serious failure state includes the following. That is, when the stacker fails to power on, the failure may be caused by an electrical failure, a mechanical failure, or other reasons. When abnormal noise or vibration occurs in the stacker, the failure may be caused by damage to mechanical parts, wear of bearings, or other problems. When components such as the cargo lifting mechanism, platform, or fork of the stacker are damaged, the failure may cause the stacker to be unable to access the cargo normally or the operation of the stacker to become unstable. When the control panel, display, or communication device of the stacker fails, the failure may cause the stacker to be unable to remotely control or monitor the operating state of the stacker. When the safety device is activated during the operation of the stacker, the failure may be due to overload or other abnormalities of the stacker.

[0035] In some embodiments, the stacker being in a minor failure state includes the following. That is, when a minor failure or error occurs in the sensor of the stacker, the failure may affect the accuracy and safety of the stacker. When minor wear or looseness occurs in the mechanical parts of the stacker, the failure may affect the operating efficiency of the stacker. When a minor error occurs in the control program of the stacker, the failure may cause the operation of the stacker not to be smooth and may affect the working efficiency of the stacker.

[0036] In some embodiments, the state of the stacker can also include the time when the stacker fails and the dwelling position when the stacker fails. The time when the stacker fails is helpful for the repair personnel to determine the cause of the stacker failure and is also helpful for the warehouse management system to calculate the progress of the stacker's work tasks. The dwelling position when the stacker fails is helpful for the repair personnel to determine whether the cause of the stacker failure is due to an external cause and is also helpful for the warehouse management system to calculate the progress of the stacker's work tasks.

[0037] In some embodiments, the warehouse management system can communicate with the stacker. When the warehouse management system communicates with the stacker, the warehouse management system can send the tasks to be executed and task data to the stacker, and the stacker can send the completion status of the tasks to the warehouse management system. In this way, the intelligence from winding package production to storage can be enhanced, and it can contribute to improving the efficiency of winding package production and the warehousing / warehouse-out of winding packages.

[0038] In some embodiments, the simulation execution result is obtained by executing work tasks for the stacker in a preset time period based on the historical work data of the stacker. The simulation execution result is the data and analysis result obtained during the simulation. The simulation execution result is helpful for the repair personnel to better understand and grasp the operating status of the stacker, and repair and control can be performed. In the process of simulation, it is necessary to set different stacker operating parameters and stacker operating conditions, and it is necessary to record the simulation execution result each time the simulation is performed for comparison and analysis. Through the simulation execution result, information such as the performance index, stability, and reliability of the stacker can be obtained.

[0039] In some embodiments, the state prediction result may include the probability that the stacker can complete the work task normally within a preset time period, the probability that the stacker fails within the preset time period, the time when the stacker fails, and the staying position of the stacker when it fails. The above is only an exemplary description and does not limit all possible contents included in the state prediction result, which are not enumerated here.

[0040] The solution of the embodiment of the present disclosure is to obtain the historical work data and historical repair data of the stacker, obtain the work task of the stacker within a preset time period, which is the task assigned to the stacker by the warehouse management system in advance, predict the state of the stacker in the process of executing the work task based on the historical work data and historical repair data, including at least the time when the stacker fails and the staying position when the stacker fails, simulate the execution of the work task by the stacker within the preset time period based on the historical work data to obtain the simulation execution result of the stacker, and generate the state prediction result of the stacker in combination with the simulation execution result based on the time when the stacker fails and the staying position when the stacker fails. In this way, the work state of the stacker is predicted based on the historical work data and historical repair data of the stacker, the prediction accuracy of the work state of the stacker is improved, which is helpful to improve the efficiency of the winding package outbound and inbound, and realize the automatic warehouse management.

[0041] FIG. 2 is a schematic diagram showing the adjustment of the work task of the stacker according to the embodiment of the present disclosure. As shown in FIG. 2, the state prediction method of the stacker further includes predicting a first time period in a failure state when the stacker fails and a second time period required to repair the stacker based on the historical work data and historical repair data, and adjusting the work task of the stacker within a preset time period based on the first time period and the second time period.

[0042] In some embodiments, the first time period in the failure state when the stacker fails may be the time from when the stacker stops operating until the repair personnel starts the repair. The second time period required for repairing the stacker may be the time period predicted by the repair personnel based on the cause of the stacker failure, or may be the historical repair time period based on the cause of the failure determined by the repair personnel, or may be the time period from the start time of repairing the stacker to the end time of repairing the stacker.

[0043] In some embodiments, the repair personnel can record the time period for repairing various failures of the stacker and use it as the second time period required for repairing the stacker. Specifically, the failure types of the stacker can be classified into two types: major failures and minor failures. Obtain the historical repair data of the stacker, classify the historical repair data of the stacker to obtain major failure data and minor failure data, and analyze the data for the major failure data and minor failure data respectively to obtain a failure repair time table. Exemplarily, the failure repair time table can record major failures. Major failure type 1 is that the stacker cannot be started and the repair time is 2h. Major failure type 2 is that abnormal noise or vibration occurs in the stacker and the repair time is 1.5h. Major failure type 3 is that components such as the cargo elevator mechanism, platform or fork of the stacker are damaged and the repair time is 5h. Major failure type 4 is a failure of the control panel, display or communication device of the stacker and the repair time is 3h. Major failure type 5 is that the safety device is activated during the operation of the stacker and the repair time is 1h. The failure repair time table can record minor failures. Minor failure type 1 is that a minor failure or error occurs in a certain sensor of the stacker and the repair time is 1h. Minor failure type 2 is that minor wear or looseness occurs in a certain mechanical component of the stacker and the repair time is 0.5h.

[0044] In some embodiments, the warehouse management system issues the outbound task 1 to stacker A, that is, transports the POY wound package with lot number A011 from location 207 to the outbound port from 13:00 to 18:00 tomorrow. Based on the historical operation data and historical repair data of stacker A, if it is predicted that stacker A may have a major failure type 2 tomorrow afternoon and the repair time of stacker A is predicted to be 1.5 hours, the warehouse management system reissues the task for stacker B to execute the outbound task 1.

[0045] In this way, based on the historical operation data and historical repair data of the stacker, the first time duration when the stacker is in a failure state and the second time duration of the repair stacker can be predicted, and based on the first time duration and the second time duration, the operation task of the stacker can be adjusted, which helps to improve the efficiency of the tasks executed by the stacker and avoid affecting the efficiency of the winding package inbound / outbound tasks due to the temporary failure of the stacker.

[0046] Figure 3 is a schematic diagram showing the determination of the first time duration and the second time duration based on the first failed part. As shown in Figure 3, the historical repair data includes the repair records and part lifetimes of the first type of parts that the stacker is equipped with. Here, predicting the first time duration when the stacker is in a failure state and the second time duration required to repair the stacker based on the historical operation data and historical repair data includes determining the first failed part when the stacker fails based on the repair records and part lifetimes of the first type of parts, and determining the first time duration and the second time duration based on the first failed part when the stacker fails.

[0047] In some embodiments, the first type of parts may be the parts replaced when a failure occurs in the stacker recorded in the historical repair data. The first type of parts may be one or more.

[0048] In some embodiments, the repair record of the first type of component may include the name of the first type of component, the replacement time of the first type of component, the reason for replacing the first type of component, the part number of the first type of component, and the size of the first type of component. The above is only an exemplary description and does not limit all possible contents included in the repair record of the first type of component, which will not be enumerated here.

[0049] In some embodiments, the repair record of the first type of component can be obtained from the repair log of the stacker. The repair record of the first type of component can also be retrieved by searching on the display screen of the stacker. The repair record of the first type of component can also be obtained from the data source of the control device. The above is only an exemplary description and does not limit all possible acquisition methods related to the repair record of the first type of component, which will not be enumerated here.

[0050] In some embodiments, the component life can refer to the time that can be normally used under normal usage environment and load.

[0051] In some embodiments, the method for obtaining the component life includes obtaining the installation time of the component from the repair record of the first type of component, obtaining the usage time of the component from the component usage manual, and obtaining the component life based on the installation time and the usage time of the component.

[0052] In some embodiments, the first failed component is the first failed component when the stacker fails, which is determined based on the repair record of the first type of component and the device life when the stacker is in a failed state.

[0053] In this way, the first failed component of the stacker can be determined based on the repair record of the first type of component and the device life. Based on the first failed component when the stacker fails, the first time period and the second time period can be determined, which helps to flexibly adjust the working time of the stacker based on the first time period and the second time period, and helps to improve the execution efficiency of the working tasks of the stacker.

[0054] FIG. 4 is a schematic diagram showing the determination of the first time period and the second time period based on the second failed component. As shown in FIG. 4, the historical operation data includes the total operation time of the second type of component provided in the stacker and the operation parameters of the second type of component while the stacker is operating. Here, predicting the first time period in the failure state when the stacker fails and the second time period required to repair the stacker based on the historical operation data and the historical repair data includes determining the second failed component in the failure state when the stacker fails based on the total operation time of the second type of component and the operation parameters, and determining the first time period and the second time period based on the second failed component in the failure state when the stacker fails.

[0055] In an embodiment of the present disclosure, the total operation time of the second type of component can be obtained from the operation log of the stacker. The total operation time of the second type of component can also be obtained by searching on the display screen of the stacker. The total operation time of the second type of component can also be obtained from the data source of the control device. The above is only an exemplary description and does not limit all possible acquisition methods related to the total operation time of the second type of component, and will not be listed here.

[0056] In some embodiments, the operation parameters of the second type of component can be obtained from the operation log of the stacker. The operation parameters of the second type of component can also be obtained by searching on the display screen of the stacker. The operation parameters of the second type of component can also be obtained from the data source of the control device. The above is only an exemplary description and does not limit all possible acquisition methods related to the operation parameters of the second type of component, and will not be listed here.

[0057] In some embodiments, the operating parameters of the second type of component can be obtained from a Programmable Logic Controller (PLC) via a Supervisory Control And Data Acquisition (SCADA) system. The SCADA system can display the status of the clicked device in a grouped form, such as by clicking on the stacker on the operation interface, and the SCADA system can directly display the operating parameters of the stacker. The operating parameters of the second type of component can also be obtained from the operating parameters of the stacker by software that supports the Object Linking and Embedding for Process Control (OPC) protocol.

[0058] In some embodiments, the operating parameters during the operation of the stacker refer to the operating state parameters of the device during the operation of the stacker. Exemplarily, when the second type of component is a motor, the operating parameters include operating parameters such as current value, voltage value, rotor rotation speed, and presence or absence of overload. When the second type of component is a pressure sensor, the operating parameter includes the pressure value. When the second type of component is a temperature sensor, the operating parameter includes the temperature value.

[0059] In some embodiments, the operating parameters during the operation of the stacker can include load, maximum load height, maximum lifting speed, maximum turning speed, and fork deflection amount. The load refers to the sum of the maximum member weight of the fork lift and the fork weight that the stacker can tolerate. The maximum load height refers to the vertical distance between the upper surface of the fork horizontal stage and the ground when the cargo is lifted to the highest position at the rated load. The maximum lifting speed refers to the maximum speed at which the cargo rises at the rated load. The maximum turning speed refers to the maximum speed that can be achieved when the turning platform turns at the rated load. The fork deflection amount refers to the distance by which the tip of the fork bends downward when the stacker rises to the maximum height at the rated load.

[0060] In some embodiments, when the stacker is in a failure state, the second failed component is the second failed component determined when the stacker fails, based on the total operating time length and operating parameters of the second type of component.

[0061] In this way, the second failed component of the stacker can be determined based on the total operating time length of the second type of component and the operating parameters of the second type of component. Based on the second failed component when the stacker fails, the first time length and the second time length can be determined, which helps to flexibly adjust the working time of the stacker based on the first time length and the second time length, and helps to improve the execution efficiency of the working tasks of the stacker.

[0062] In an embodiment of the present disclosure, the method for predicting the state of the stacker further includes generating a repair task including repair time, repair tools, and the required skills of the repair personnel based on the first time length and the second time length, and sending the repair task to a repair management center for the repair management center to arrange repair personnel based on the repair task.

[0063] In an embodiment of the present disclosure, the repair task may include repair time, repair tools, and the required skills of the repair personnel. The above is only an exemplary description and does not limit all possible contents included in the repair task, which are not enumerated here.

[0064] In an embodiment of the present disclosure, the repair management center is an organization that solely undertakes the repair and management of equipment, and the content of its responsibilities can include the following. The repair management center formulates a repair plan including daily maintenance, regular inspection, troubleshooting, etc. of the equipment. The repair management center is responsible for diagnosing and repairing equipment failures to ensure the normal operation of the equipment. The repair management center is responsible for the daily safety management of the equipment including safety inspections, correction of safety problems, etc. The repair management center manages the spare parts of the equipment to ensure the sufficient and effective use of the spare parts. The repair management center is responsible for the repair records and reports of the equipment including the failure status, repair process, and repair results of the equipment. The repair management center is responsible for the training and management of repair personnel to improve their skill levels and work efficiency. The repair management center is responsible for the improvement and optimization of the equipment, improving the performance and efficiency of the equipment, and cooperating with other departments, for example, cooperating with the production department to improve production efficiency and cooperating with the quality department to improve product quality.

[0065] In this way, based on the inspection task, repair personnel and repair time can be timely arranged for the malfunctioning stacker. This helps to improve the efficiency of stacker failure repair, improve the working efficiency of the stacker, and improve the efficiency of winding package warehousing and outbound.

[0066] In an embodiment of the present disclosure, the method for predicting the state of the stacker is based on the time when the stacker fails and the residence position when the stacker fails, and determines, in the work task, a first target task that is a task in the work tasks that the stacker can complete in a preset time period, and a second target task that is a task in the work tasks that the stacker cannot complete in a preset time period. Further including sending the second target task in the preset time period to other stackers for the other stackers to execute the second target task.

[0067] Here, the preset time period is the time period when the designated stacker executes the task of winding package warehousing or the task of winding package outbound.

[0068] In some embodiments, the first target task is a task that the stacker can normally complete when the stacker is in a fault state. Exemplarily, stacker A is performing the outbound tasks for location 120 and location 130. If a fault occurs in the lift mechanism while stacker A is performing the outbound task for location 120, the first target task will be the outbound task for location 120.

[0069] In some embodiments, the second target task is a task that the stacker cannot normally complete when the stacker is in a fault state. Exemplarily, stacker A is performing the outbound tasks for warehouse location 120 and warehouse location 130. If a fault occurs in the lift mechanism while stacker A is performing the outbound task for warehouse location 120, the second target task will be the outbound task for location 130, and the warehouse management system will send the second target task to stacker B for stacker B to execute the second target task.

[0070] In this way, when the stacker is in a fault state, only the first target task that can be normally completed is completed, and the second target task that cannot be normally completed can be sent to other stackers, which helps to improve the working efficiency of the stacker.

[0071] In the embodiments of the present disclosure, predicting the state of the stacker in the process of the stacker executing the work task based on the historical work data and the historical repair data includes inputting the historical work data and the historical repair data into a state prediction model, and obtaining the state of the stacker output by the state prediction model.

[0072] In some embodiments, the state of the stacker includes the stacker being in a fault state, the stacker being in a normal state, the stacker being in a standby state, the stacker being in a stopped state, the stacker being in a charging state, and the stacker being in a repair state.

[0073] In this way, by inputting the historical operation data and the historical repair data into the state prediction model, the state of the stacker output by the state prediction model can be obtained, which helps to predict the working state of the stacker at an early stage and improve the efficiency of winding package outgoing and incoming storage.

[0074] In an embodiment of the present disclosure, the state prediction model obtains historical operation sample data and historical repair sample data of the stacker, inputs the historical operation sample data and the historical repair sample data into a preset model, obtains a state prediction value of the stacker output by the preset model, constructs a loss function based on the true state value of the stacker and the state prediction value of the stacker, and trains the preset model based on the loss function to obtain a state prediction model.

[0075] In some embodiments, the historical operation sample data and the historical repair sample data of the stacker can be obtained from the data source of the control device, and can also be obtained from the operation log and repair log of the stacker. The above is only an exemplary description, and does not limit all possible acquisition methods related to the historical operation sample data and the historical repair sample data of the stacker, and will not be enumerated here.

[0076] In this way, the historical operation sample data and the historical repair sample data of the stacker are obtained, the historical operation sample data and the historical repair sample data are input into a preset model, the state prediction value of the stacker output by the preset model is obtained, a loss function is constructed based on the true state value and the state prediction value of the stacker, and the preset model is trained based on the loss function to obtain a state prediction model. Based on the trained state prediction model, the state of the stacker can be predicted in advance, which helps to improve the prediction accuracy of the operating state of the stacker.

[0077] FIGS. 2 to 4 are merely illustrative and not restrictive. The content in the figures can be adjusted or changed as appropriate according to work needs and will not be repeated here. It should be understood that those skilled in the art can make various obvious changes and / or substitutions based on the examples in FIGS. 2 to 4, and the obtained solutions belong to the disclosure scope of the embodiments of the present disclosure.

[0078] As shown in FIG. 5, an embodiment of the present disclosure provides a state prediction device for a stacker, and the state prediction device for the stacker includes a first acquisition module 510 for acquiring the historical work data and historical repair data of the stacker, a second acquisition module 520 for acquiring the work tasks of the stacker in a preset time period, where the work tasks are the tasks pre-assigned by the warehouse management system to the stacker, and the second acquisition module 520, a first prediction module 530 for predicting the state of the stacker in the process of the stacker executing the work tasks based on the historical work data and the historical repair data, where the state includes at least the time when the stacker fails and the staying position when the stacker fails, and the first prediction module 530, a simulation module 540 for simulating the execution of the work tasks by the stacker in a preset time period based on the historical work data to obtain the simulation execution result of the stacker, a first generation module 550 for generating the state prediction result of the stacker in combination with the simulation execution result based on the time when the stacker fails and the staying position when the stacker fails.

[0079] In some embodiments, the state prediction device of the stacker further includes a second prediction module (not shown in FIG. 5) for predicting a first time period in a failure state when the stacker fails and a second time period required for repairing the stacker based on historical operation data and historical repair data, and an adjustment module (not shown in FIG. 5) for adjusting the operation task of the stacker in a preset time period based on the first time period and the second time period.

[0080] In some embodiments, the historical repair data includes the repair records and part lifetimes of the first type of parts included in the stacker. Here, the second prediction module (not shown in FIG. 5) includes a first determination sub-module for determining a first failed part when the stacker fails based on the repair records and part lifetimes of the first type of parts, and a second determination sub-module for determining the first time period and the second time period based on the first failed part when the stacker fails.

[0081] In some embodiments, the historical operation data includes the total operation time length of the second type of parts included in the stacker and the operation parameters of the second type of parts while the stacker is operating. Here, the second prediction module (not shown in FIG. 5) includes a third determination sub-module for determining a second failed part in a failure state when the stacker fails based on the total operation time length and operation parameters of the second type of parts, and a fourth determination sub-module for determining the first time period and the second time period based on the second failed part in a failure state when the stacker fails.

[0082] In some embodiments, the state prediction device of the stacker further includes a second generation module (not shown in FIG. 5) for generating a repair task including repair time, repair tools, and required skills of the repair personnel based on the first time period and the second time period, and a first transmission module (not shown in FIG. 5) for transmitting the repair task to the repair management center so that the repair management center arranges the repair personnel based on the repair task.

[0083] In some embodiments, the state prediction device of the stacker is a determination module (not shown in FIG. 5) for determining a first target task and a second target task in a work task based on the time when the stacker fails and the residence position when the stacker fails. The first target task is a task among the work tasks that the stacker can complete within a preset time period, and the second target task is a task among the work tasks that the stacker cannot complete within a preset time period. The determination module further includes a second transmission module (not shown in FIG. 5) for transmitting the second target task in a preset time period to another stacker to execute the second target task by the other stacker.

[0084] In some embodiments, the first prediction module 530 includes an input sub-module for inputting historical work data and historical repair data into a state prediction model, and an acquisition sub-module for acquiring the state of the stacker output by the state prediction model.

[0085] In some embodiments, the state prediction model acquires historical work sample data and historical repair sample data of the stacker, inputs the historical work sample data and historical repair sample data into a preset model, acquires the state prediction value of the stacker output by the preset model, constructs a loss function based on the true state value of the stacker and the state prediction value of the stacker, and trains the preset model based on the loss function to obtain the state prediction model.

[0086] The functions of each processing module in the stacker state prediction device according to the embodiments of the present disclosure can be understood by referring to the description of the foregoing stacker state prediction method. Each processing module in the stacker state prediction device according to the embodiments of the present disclosure can be realized by an analog circuit that realizes the functions according to the embodiments of the present disclosure, and it should be understood by those skilled in the art that it can also be realized by the operation of software on an electronic device that executes the functions according to the embodiments of the present disclosure.

[0087] The state prediction device of the stacker according to an embodiment of the present disclosure can predict the working state of the stacker based on the historical working data and historical repair data of the stacker, improve the prediction accuracy of the working state of the stacker, and thus improve the efficiency of winding package outbound and inbound, and realize automatic warehouse management.

[0088] According to an embodiment of the present disclosure, the present disclosure further provides an electronic device and a non-transitory computer-readable storage medium.

[0089] FIG. 6 is a structural block diagram of an electronic device according to an embodiment of the present disclosure. As shown in FIG. 6, the electronic device includes a memory 610 and a processor 620, and a computer program executable by the processor 620 is stored in the memory 610. The number of the memory 610 and the processor 620 can be one or more. The memory 610 can store one or more computer programs, and when the one or more computer programs are executed by the electronic device, the electronic device is caused to execute the method provided by the embodiment of the above method. The electronic device can further include the following. The communication interface 630 is used to communicate with an external device and perform data interaction and transmission.

[0090] When the memory 610, the processor 620, and the communication interface 630 are independently implemented, the memory 610, the processor 620, and the communication interface 630 are connected to each other via a bus and can communicate with each other. The bus can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus, etc. The bus can be classified into an address bus, a data bus, a control bus, etc. For ease of explanation, only a single thick line is shown in FIG. 6, but it does not represent only a single bus or a single type of bus.

[0091] Optionally, in a specific implementation form, when the memory 610, the processor 620, and the communication interface 630 are integrated on one chip, the memory 610, the processor 620, and the communication interface 630 can communicate with each other via an internal interface.

[0092] It should be understood that the above processor may be a Central Processing Unit (CPU), and may further be other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware assemblies, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc. In addition, the processor may be a processor that supports the Advanced RISC Machines (ARM) architecture.

[0093] Furthermore, optionally, the memory may include a read-only memory and a random access memory, and may further include a non-volatile random access memory. The memory may be either a volatile memory or a non-volatile memory, or may include both a volatile memory and a non-volatile memory. Here, the non-volatile memory can include a ROM (Read-Only Memory), PROM (Programmable ROM), EPROM (Erasable PROM), EEPROM (Electrically EPROM), or flash memory. The volatile memory can include a random access memory (Random Access Memory, RAM) that functions as an external cache. By way of example and not limitation, many forms of RAM are available. For example, static random access memory (Static RAM, SRAM), dynamic random access memory (Dynamic Random Access Memory, DRAM), synchronous DRAM (Synchronous DRAM, SDRAM), double data rate SDRAM (Double Data Rate SDRAM, DDR SDRAM), enhanced SDRAM (Enhanced SDRAM, ESDRAM), synchlink DRAM (Synchlink DRAM, SLDRAM), and direct RAMBUS RAM (Direct RAMBUS RAM, DR RAM).

[0094] In the above embodiments, it may be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented in software, the whole or part may be implemented in the form of 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, the processes or functions according to the embodiments of the present disclosure are generated in whole or in part. The computer may be a general-purpose computer, a dedicated computer, a computer network, or other programmable devices. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wire (such as coaxial cable, optical fiber, Digital Subscriber Line (DSL)) or wireless (such as infrared, Bluetooth®, microwave, etc.). The computer-readable storage medium may be any available medium accessible by a computer or a data storage device including one or more available media integrated with a server, data center, etc. The available medium may be a magnetic medium (such as a floppy (registered trademark) disk, hard disk, magnetic tape), an optical medium (such as a Digital Versatile Disc (DVD)), or a semiconductor medium (such as a Solid State Disk (SSD)), etc. It should be noted that the computer-readable storage medium mentioned in the present disclosure may be a non-volatile storage medium, that is, a non-transitory storage medium.

[0095] Those skilled in the art can understand that all or some of the steps for implementing the above embodiments may be implemented by hardware, or may be implemented by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, and the above storage medium may be a read-only memory, a magnetic disk, an optical disk, or the like.

[0096] In the description of the embodiments of the present disclosure, the descriptions of reference terms such as "one embodiment", "some embodiments", "example", "specific example", or "some examples" mean that the specific features, structures, materials, or features described in relation to the embodiment or example are included in at least one embodiment or example of the present disclosure. Moreover, the specific features, structures, materials, or features described can be combined in any one or more embodiments or examples in a suitable manner. Furthermore, those skilled in the art may combine different embodiments or examples described in the present disclosure and the features of different embodiments or examples as long as they do not conflict with each other.

[0097] In the description of the embodiments of the present disclosure, " / " represents the meaning of "or" unless otherwise specified. For example, A / B may represent either A or B. The "and / or" in the present disclosure only explains the relationship of related objects and indicates that there may be three types of relationships. For example, A and / or B can indicate the following. There are three situations where A exists alone, A and B exist simultaneously, and B exists alone.

[0098] In the description of the embodiments of the present disclosure, the terms "first" and "second" are used only for the purpose of description and should not be construed as indicating or implying relative importance, nor should they be construed as implying the number of technical features shown. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of such features. In the description of the embodiments of the present disclosure, "a plurality" means two or more unless otherwise specified.

[0099] The above are only exemplary embodiments of the present disclosure, and do not limit the present disclosure. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle scope of the present disclosure should all be included within the protection scope of the present disclosure.

[0100] In the description of this specification, terms such as "center", "longitudinal direction", "lateral direction", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", "axial direction", "radial direction", "circumferential direction", etc. indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, and are only intended to facilitate the description of the present disclosure and simplify the description, and do not indicate or imply that the referred device or element must have a specific orientation, be configured and operate in a specific orientation, and therefore should not be construed as a limitation of the present disclosure.

Claims

1. A method for predicting a state of a stacker, comprising the steps of: obtaining historical work data and historical repair data for the stacker; Obtaining a work task for a preset time period of the stacker, the work task being a task previously assigned to the stacker by a warehouse management system; Predicting a state of the stacker in the process of the stacker performing the work task based on the historical work data and the historical repair data, the state including at least a time when the stacker breaks down and a staying position of the stacker when the stacker breaks down; simulating the execution of the work task by the stacker during the preset time period based on the historical work data to obtain a simulation execution result of the stacker; and generating a state prediction result of the stacker based on a time when the stacker failed and a position where the stacker was in a stopped state when the stacker failed, in combination with the result of the execution of the simulation. A method for predicting the state of a stacker.

2. The stacker state prediction method, predicting a first length of time that the stacker will be in a fault condition when the stacker fails and a second length of time required to repair the stacker based on the historical work data and the historical repair data; and adjusting the work tasks of the stacker during the preset time period based on the first time period and the second time period. The stacker state prediction method according to claim 1 .

3. The historical repair data includes repair records and part lifespans of a first type of part included in the stacker; predicting a first length of time that the stacker will be in a fault condition when the stacker fails and a second length of time required to repair the stacker based on the historical work data and the historical repair data; determining a first faulty part when the stacker breaks down based on the repair records and the part lifespan of the first type of part; determining the first length of time and the second length of time based on the first failed component when the stacker fails. The stacker state prediction method according to claim 2.

4. The historical operation data includes a total operating time length of a second type of part equipped to the stacker and an operating parameter of the second type of part while the stacker is operating; predicting a first length of time that the stacker will be in a fault condition when the stacker fails and a second length of time required to repair the stacker based on the historical work data and the historical repair data; determining a second faulty part that is in a faulty state when the stacker fails based on a total operating time length of the second type of part and the operating parameters; determining the first length of time and the second length of time based on the second faulty component being in a faulty state when the stacker fails. The stacker state prediction method according to claim 2.

5. The method for predicting a state of a stacker includes the steps of: generating a repair task based on the first length of time and the second length of time, the repair task including a repair time, a repair tool, and a required skill of a repair person; and sending the repair task to a repair management center so that the repair management center can dispatch a repair person based on the repair task. The stacker state prediction method according to claim 2.

6. The method for predicting a state of a stacker includes the steps of: determining a first target task and a second target task in the work task based on the time when the stacker broke down and the position where the stacker was stopped when the stacker broke down, the first target task being a task in the work task that the stacker can complete within the preset time period, and the second target task being a task in the work task that the stacker cannot complete within the preset time period; and transmitting the second target task during the preset time period to the other stackers so as to execute the second target task by the other stackers. The stacker state prediction method according to claim 1 .

7. predicting a state of the stacker in the process of the stacker performing the work task based on the historical work data and the historical repair data; inputting the historical work data and the historical repair data into a condition prediction model; and acquiring a state of the stacker output by the state prediction model. The stacker state prediction method according to claim 1 .

8. The state prediction model is Obtaining historical work sample data and historical repair sample data for the stacker; inputting the historical work sample data and the historical repair sample data into a preset model; Obtain a stacker state prediction value output by the preset model; A loss function is constructed based on the true state value of the stacker and the predicted state value of the stacker; The preset model is trained based on the loss function to obtain the state prediction model. The stacker state prediction method according to claim 7.

9. A stacker state prediction device, comprising: a first acquisition module for acquiring historical work data and historical repair data of the stacker; A second acquisition module for acquiring a work task of the stacker in a preset time period, the work task being a task previously assigned to the stacker by a warehouse management system; a first prediction module for predicting a state of the stacker in the process of the stacker performing the work task based on the historical work data and the historical repair data, the state including at least a time when the stacker breaks down and a staying position when the stacker breaks down; a simulation module for simulating the execution of the work task by the stacker during the preset time period based on the historical work data to obtain a simulation execution result of the stacker; a first generation module for generating a state prediction result of the stacker in accordance with the time when the stacker broke down and the position where the stacker was parked when the stacker broke down, in combination with the result of the simulation execution. Stacker condition prediction device.

10. At least one processor; a memory in communication with the at least one processor; The memory stores instructions executable by the at least one processor, the instructions, when executed by the at least one processor, causing the at least one processor to perform a method according to any one of claims 1 to 8. Electronic devices.

11. A non-transitory computer readable storage medium for storing instructions that cause a computer to perform the method of any one of claims 1 to 8.

12. A program for implementing the method according to any one of claims 1 to 8 when executed by a processor in a computer.

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