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

Abstract

The present invention relates to the field of intelligent technology for chemical fiber production, and in particular to a method, apparatus, electronic device, storage medium and program for predicting the state of a stacker. [Solution] A specific solution includes acquiring historical work data and historical repair data of the stacker, acquiring work tasks of the stacker in a preset time period, predicting the state of the stacker in the process of the stacker performing the work tasks based on the historical work data and historical repair data, simulating the performance of the work tasks by the stacker in the preset time period based on the historical work data to obtain a simulation execution result of the stacker, and generating a stacker state prediction result in combination with the simulation execution result based on the time when the stacker broke down and the position where the stacker was stuck when it broke down.

Description

[Technical field]

[0001] The present disclosure relates to the intelligent technology field of chemical fiber production, and in particular to a method and device for predicting the state of a stacker. [Background technology]

[0002] In the chemical fiber production field, stackers are widely applied in automated warehouses as warehouse equipment. As one of the important logistics equipment in automated warehouses, stackers can load, unload or transport yarn packages between warehouses or warehouse locations. Summary of the Invention [Problem to be solved by the invention]

[0003] In an automated warehouse, the status of the stacker determines whether the warehouse can normally complete its outgoing and incoming operations, so how to accurately predict the status of the stacker is a technical problem that needs to be solved as soon as possible. [Means for solving the problem]

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

[0005] According to a first aspect of the present disclosure, there is provided a method for predicting a state of a stacker, the method comprising: 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 the 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 when the stacker breaks down; simulating the execution of the work tasks by the stacker during a preset time period based on the historical work data to obtain a simulation execution result of the stacker; and generating a stacker state prediction result based on the time when the stacker failed and the position where the stacker was in a stopped state when the stacker failed, in combination with the simulation execution result.

[0006] According to a second aspect of the present disclosure, there is provided a stacker state prediction device, the 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 the 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 work tasks by the stacker during a preset time period based on the historical work data to obtain a simulation execution result of the stacker; and a first generation module for generating a stacker state prediction result in combination with the simulation execution result based on the time when the stacker failed and the position where the stacker was parked when the stacker failed.

[0007] According to a third aspect of the present disclosure, there is provided an electronic device, the device comprising: 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, which, when executed by the at least one processor, cause the implementation of any one of the methods in the embodiments of the present disclosure.

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

[0010] According to the technology disclosed herein, the working status of a stacker can be predicted based on the stacker's historical work data and historical repair data, thereby improving the accuracy of predicting the working status of the stacker and helping to improve the efficiency of receiving and shipping wound yarn packages, thereby enabling automated warehouse management to be realized.

[0011] It should be understood that the contents described herein are not intended to describe key points or important features of the embodiments of the present disclosure, nor are they used to limit the scope of the present disclosure, other features of the present disclosure are facilitated by the following specification. [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 from the following detailed description taken in conjunction with the drawings in which the same or similar reference numerals represent the same or similar elements.

[0013] [Figure 1] 1 is a flowchart of a stacker state prediction method according to an embodiment of the present disclosure. [Diagram 2] FIG. 13 is a schematic diagram illustrating the coordination of work tasks of a stacker according to an embodiment of the present disclosure. [Diagram 3]FIG. 13 is a schematic diagram illustrating determining a first length of time and a second length of time based on a first faulty component according to an embodiment of the present disclosure. [Figure 4] FIG. 13 is a schematic diagram of determining a first length of time and a second length of time based on a second faulty component according to an embodiment of the present disclosure. [Diagram 5] 1 is a schematic diagram showing a configuration of a stacker state prediction device according to an embodiment of the present disclosure. [Figure 6] FIG. 2 is a block diagram of an electronic device for implementing a stacker state prediction method according to an embodiment of the present disclosure. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0014] Hereinafter, exemplary embodiments of the present disclosure will be described with reference to the drawings. Various details of the embodiments of the present disclosure are included herein for ease of understanding, and they should be considered as 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 simplicity.

[0015] The terms "first," "second," and "third," etc. in the embodiments and claims of the specification of this disclosure and in the drawings described above are intended to distinguish between similar objects without necessarily being used to describe a particular order or priority. Furthermore, the terms "comprise" and "have" and variations thereof are intended to be non-exclusive inclusions, e.g., inclusion of a series of steps or units. Methods, systems, products, or devices need not be limited to explicitly recited steps or units, but may include other steps or units not explicitly recited or inherent to these processes, methods, products, or devices.

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

[0017] Multi-level warehouses, also known as high-rise warehouses or high-bay warehouses, are warehouses that store unit cargo on shelves that are several dozen or even several tens of stories high, and use corresponding material handling equipment to carry out cargo loading and unloading operations.The warehouse itself is made up of shelves, aisle stacker cranes, loading and unloading work tables, and automatic loading and unloading and operation control systems.

[0018] In the related art, storage of finished wound yarn packages in a multi-story warehouse, removal of finished wound yarn packages from the warehouse, and transportation of wound yarn packages between locations within the multi-story warehouse must be performed by a stacker. A stacker is a warehouse facility for transporting wound yarn packages in and out between shelves in a warehouse or locations.

[0019] In the related art, multiple multi-level warehouses store finished wound yarn packages, and multiple stackers work in each warehouse. If a stacker breaks down, the current work task cannot be performed, which affects the yarn package delivery task or the efficiency of the yarn package delivery task. In addition, in the aisles of the multi-level warehouse, stackers run up and down along lanes, and when a stacker breaks down, workers and the warehouse management system cannot accurately grasp the working status of the stacker.

[0020] In order to at least partially solve one or more of the above problems and other potential problems, the present disclosure proposes a method and apparatus for predicting the state of a stacker, which predicts the working state of a stacker based on the historical work data and historical repair data of the stacker, improves the prediction accuracy of the working state of the stacker, helps to improve the efficiency of sending and receiving wound yarn packages, and realizes automatic warehouse management.

[0021] An embodiment of the present disclosure provides a method for predicting a state of a stacker, and FIG. 1 is a flowchart of the method for predicting a state of a stacker according to an embodiment of the present disclosure, and the method for predicting a state of a stacker can be applied to a state prediction device of a stacker. The state prediction device of a 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 a state of a stacker can also be implemented by a processor calling a computer-readable instruction stored in a memory. As shown in FIG. 1, the method for predicting a state of a stacker includes the following:

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

[0023] In S102, the work task for the stacker in a preset time period is obtained, and the work task is a task that is assigned to the stacker in advance by the warehouse management system.

[0024] In S103, based on the historical work data and the historical repair data, predicting a state of the stacker in the process of the stacker performing the work task, the state including at least a time when the stacker breaks down and a staying position when the stacker breaks down.

[0025] In S104, the execution of the work tasks by the stacker during a preset time period is simulated based on the historical work data to obtain a simulation execution result of the stacker.

[0026] In S105, a stacker state prediction result is generated based on the time when the stacker broke down and the position where the stacker was parked when the stacker broke down, in combination with the simulation execution result.

[0027] In an embodiment of the present disclosure, the historical work data is the work data of the stacker for a certain period of time in the past. The historical work data can include the number of historical work tasks, the contents of historical work tasks, and the time of historical work tasks. The above is merely an exemplary description, and does not limit all possible contents included in the historical work data, and will not be listed here.

[0028] In an embodiment of the present disclosure, the historical repair data is the repair data of the stacker for a certain period of time in the past. The historical repair data may include repair time, repair count, failure type, failure part, repair person, and repair tool. The above is merely an exemplary description, and does not limit all possible contents included in the historical repair data, and will not be listed here.

[0029] In the embodiment of the present disclosure, the stacker is an important conveying equipment in a multi-level warehouse, and the stacker can automatically, quickly and accurately load and unload cargo in the multi-level warehouse, thereby improving the utilization rate and work efficiency of the warehouse. The main components of the stacker include forks for picking up, transporting and stacking cargo in a warehouse or workshop, a loading platform for placing the cargo, a lifting mechanism for lifting and lowering the cargo, a traveling mechanism for moving the stacker in the warehouse, and a control system for controlling various operations and movements of the stacker. Here, the forks can adopt an electric or hydraulic drive system, and can extend and rotate as needed. The stage can adopt a steel structure or an aluminum alloy structure with sufficient supporting capacity and stability, and the lifting mechanism can adopt an electric or hydraulic drive system, which is characterized by high accuracy, good stability and reliability. The traveling mechanism can adopt a wheel or track structure, which is characterized by high moving speed and stable operation. The control system can adopt a control device such as a PLC or a single chip, which is characterized by a high degree of automation and easy operation.

[0030] In the 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 tasks may include a winding package input task, a winding package output task, and a winding package location transfer task. The main types of filaments according to the embodiment of the present disclosure may include one or more of partially oriented yarns (POY), fully drawn yarns (FDY), and drawn textured yarns (DTY) (or low elasticity filaments). For example, specific examples of types of yarns include polyester partially oriented yarns, polyester fully drawn yarns, polyester draft yarns, polyester draft yarns, and polyester staple fibers.

[0032] In the embodiment of the present disclosure, the warehouse management system can issue incoming task management, outgoing task, and warehouse location movement task of the wound yarn package. The warehouse management system (WMS) is a system specialized for warehouse management, and takes efficient task execution and process planning strategy as its core, and can greatly improve work efficiency and resource utilization rate by combining location management, barcode management, and warehouse automation equipment.

[0033] In some embodiments, the stacker status may include: stacker is in a major fault state, stacker is in a minor fault state, stacker is in a normal state, stacker is in a pause state, stacker is in a stopped state, stacker is in a charging state, etc. The above is merely an exemplary description and is not limiting and will not be enumerated here for all possible types included in the stacker status.

[0034] In some examples, the stacker being in a critical fault state includes the following: if the stacker fails to power up, the fault may be caused by an electrical fault, a mechanical fault, or other cause; if the stacker produces abnormal noise or vibration, the fault may be caused by mechanical part damage, bearing wear, or other issues; if components of the stacker, such as the cargo lifting mechanism, platform, or forks, are damaged, the fault may cause the stacker to be unable to access the cargo normally or the operation of the stacker to become unstable; if the control panel, display, or communication device of the stacker fails, the fault may cause the stacker to be unable to remotely control or monitor the operating status of the stacker; if a safety device is activated during the operation of the stacker, the fault may be caused by an overload or other abnormality of the stacker.

[0035] In some embodiments, the stacker being in a minor fault state includes the following: if a sensor of the stacker has a minor fault or error, the fault may affect the accuracy and safety of the stacker; if a mechanical part of the stacker has minor wear or loosening, the fault may affect the operation efficiency of the stacker; if a control program of the stacker has a minor error, the fault may cause the stacker to not operate smoothly and affect the work efficiency of the stacker.

[0036] In some embodiments, the status of the stacker may also include the time when the stacker failed and the location where the stacker was when it failed. The time when the stacker failed helps the repair personnel determine the cause of the stacker failure and also helps the warehouse management system calculate the progress of the work task of the stacker. The location where the stacker was when it failed helps the repair personnel determine whether the cause of the stacker failure was due to an external cause and also helps the warehouse management system calculate the progress of the work task of the stacker.

[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 tasks to be performed and task data to the stacker, and the stacker can send task completion statuses to the warehouse management system. In this way, intelligence from yarn package production to storage can be improved, and the efficiency of yarn package production and yarn package shipping / receiving can be improved.

[0038] In some embodiments, the simulation results are obtained by performing work tasks on the stacker during a preset time period based on the stacker's historical work data. The simulation results are data and analysis results obtained during the simulation. The simulation results can help repair personnel better understand and grasp the working status of the stacker, so as to perform repair and control. During the simulation process, different stacker working parameters and stacker working conditions need to be set, and the simulation results need to be recorded every time the simulation is performed for comparison and analysis. The simulation results can obtain information such as the performance index, stability, reliability, etc. of the stacker.

[0039] In some embodiments, the state prediction result may include the probability that the stacker can successfully complete the work task in a preset time period, the probability that the stacker will break down in a preset time period, the time when the stacker breaks down, and the location where the stacker is when it breaks down. The above is merely an exemplary description, and does not limit all possible contents included in the state prediction result, and will not be listed here.

[0040] The solution of the embodiment of the present disclosure includes obtaining the historical work data and historical repair data of the stacker, obtaining the work tasks of the stacker in a preset time period, which are tasks that are assigned to the stacker in advance by the warehouse management system, predicting the state of the stacker in the process of the stacker performing the work tasks based on the historical work data and the historical repair data, which includes at least the time when the stacker breaks down and the position where the stacker is stuck when the stacker breaks down, simulating the execution of the work tasks by the stacker in the preset time period based on the historical work data to obtain a simulation execution result of the stacker, and generating a stacker state prediction result in combination with the simulation execution result based on the time when the stacker breaks down and the position where the stacker is stuck when the stacker breaks down. In this way, the work state of the stacker is predicted based on the historical work data and historical repair data of the stacker, which improves the prediction accuracy of the work state of the stacker and helps to improve the efficiency of the delivery and receipt of wound yarn packages, thereby realizing automatic warehouse management.

[0041] FIG. 2 is a schematic diagram showing the adjustment of work tasks of a stacker according to an embodiment of the present disclosure. As shown in FIG. 2, the stacker state prediction method further includes: predicting a first time length for which the stacker is in a faulty state when the stacker breaks down and a second time length required to repair the stacker based on historical work data and historical repair data; and adjusting the work tasks of the stacker in a preset time period based on the first time length and the second time length.

[0042] In some embodiments, the first length of time that the stacker is in a fault state when the stacker fails may be the time from when the stacker stops operating to when a repair technician begins repairing the stacker. The second length of time required to repair the stacker may be a length of time that a repair technician predicts based on a cause of the stacker failure, may be a historical repair length based on a cause of the failure determined by a repair technician, or may be a length of time from when repair of the stacker begins to when repair of the stacker ends.

[0043] In some embodiments, the length of time it takes for the repairman to repair various faults of the stacker may be recorded as a second length of time required to repair the stacker. Specifically, the fault types of the stacker may be classified into two types: major faults and minor faults. The stacker's historical repair data is obtained, the stacker's historical repair data is classified to obtain major fault data and minor fault data, and the major fault data and the minor fault data are analyzed to obtain a fault repair timetable. Exemplarily, the fault repair timetable may record major faults. Major fault type 1 is that the stacker cannot start, and the repair time is 2 hours. Major fault type 2 is that the stacker generates abnormal noise or vibration, and the repair time is 1.5 hours. Major fault type 3 is that the stacker's cargo lifting mechanism, loading platform, fork, or other parts are damaged, and the repair time is 5 hours. Major fault type 4 is that the stacker's control panel, display, or communication device is broken, and the repair time is 3 hours. Serious fault type 5 is a safety device activated while the stacker is in operation, and the repair time is 1 hour. The fault repair timetable can record minor faults. Minor fault type 1 is a minor fault or error in a sensor of the stacker, and the repair time is 1 hour. Minor fault type 2 is a minor wear or looseness in a mechanical part of the stacker, and the repair time is 0.5 hours.

[0044] In some embodiments, the warehouse management system issues outbound task 1 to stacker A, i.e., to transport the POY wound yarn package with lot number A011 from location 207 to the outbound port tomorrow between 13:00 and 18:00. If, based on the historical work data and historical repair data of stacker A, it is predicted that stacker A may experience a serious breakdown type 2 tomorrow afternoon and the repair time of stacker A is predicted to be 1.5 hours, the warehouse management system reissues a task to stacker B to perform outbound task 1.

[0045] In this way, based on the historical work data and historical repair data of the stacker, a first time length for which the stacker is in a faulty state and a second time length for which the stacker is to be repaired can be predicted, and the work tasks of the stacker can be adjusted based on the first time length and the second time length, which helps to improve the efficiency of the tasks performed by the stacker and avoid the efficiency of the winding package input / output tasks being affected due to a temporary failure of the stacker.

[0046] 3 is a schematic diagram showing determining the first time length and the second time length based on a first faulty part, and as shown in FIG. 3, the historical repair data includes repair records and part lifespans of a first type of part included in the stacker. Here, predicting the first time length for which the stacker is in a faulty state when the stacker breaks down and the second time length required to repair the stacker based on the historical work data and the historical repair data includes: determining the first faulty part when the stacker breaks down based on the repair records and part lifespans of the first type of part; and determining the first time length and the second time length based on the first faulty part when the stacker breaks down.

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

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

[0049] In some embodiments, the repair records of the first type of parts can be obtained from a repair log of a stacker. The repair records of the first type of parts can also be obtained by searching a display screen of a stacker. The repair records of the first type of parts can also be obtained from a data source of a control device. The above are merely exemplary descriptions, and all possible methods for obtaining the repair records of the first type of parts are not limited and will not be listed herein.

[0050] In some embodiments, the part life may refer to the time during which the part can be normally used under normal operating conditions and loads.

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

[0052] In some embodiments, the first faulty part is a first faulty part when the stacker fails that is determined based on the repair history and device life of the first type of part when the stacker is in a faulty state.

[0053] In this way, the first faulty part of the stacker can be determined based on the repair record and device life of the first type of part, and the first and second time lengths are determined based on the first faulty part when the stacker breaks down, which helps to flexibly adjust the working time of the stacker based on the first and second time lengths, and helps to improve the execution efficiency of the stacker's work tasks.

[0054] 4 is a schematic diagram showing determining the first length of time and the second length of time based on the second faulty part, and as shown in FIG. 4, the historical work data includes a total operating time length of the second type of parts included in the stacker and operating parameters of the second type of parts while the stacker is working. Here, predicting the first length of time that the stacker is in a faulty state when it breaks down and the second length of time required to repair the stacker based on the historical work data and the historical repair data includes: determining the second faulty part that is in a faulty state when the stacker breaks down based on the total operating time length and operating parameters of the second type of parts; and determining the first length of time and the second length of time based on the second faulty part that is in a faulty state when the stacker breaks down.

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

[0056] In some embodiments, the operating parameters of the second type of parts can be obtained from the operation log of the stacker. The operating parameters of the second type of parts can also be obtained by searching on the display screen of the stacker. The operating parameters of the second type of parts can also be obtained from a data source of a control device. The above is merely an exemplary description, and all possible methods for obtaining the operating parameters of the second type of parts are not limited and will not be listed here.

[0057] In some embodiments, the operating parameters of the second type of parts can be obtained from a 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 group format, such as clicking a 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 parts can also be obtained by software supporting 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 operating state parameters of the device during the operation of the stacker. Illustratively, when the second type of component is a motor, the operating parameters include operating parameters such as a current value, a voltage value, a rotor speed, and whether or not there is an overload, when the second type of component is a pressure sensor, the operating parameters include a pressure value, when the second type of component is a temperature sensor, the operating parameters include a temperature value.

[0059] In some embodiments, the operating parameters of the stacker during operation may include load, maximum load height, maximum lift speed, maximum rotation speed, and fork deflection, where the load refers to the sum of the maximum member weight of the fork loading permitted by the stacker and the fork weight, the maximum load height refers to the vertical distance between the top surface of the fork level and the ground when the cargo is raised to the highest position at the rated load, the maximum lift speed refers to the maximum speed at which the cargo rises at the rated load, the maximum rotation speed refers to the maximum speed that the rotating platform can achieve when rotating at the rated load, and the fork deflection refers to the distance that the tip of the fork bends downward when the stacker is raised to the maximum height at the rated load.

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

[0061] In this way, the second faulty part of the stacker can be determined based on the total operating time length of the second type of parts and the operating parameters of the second type of parts, and the first time length and the second time length can be determined based on the second faulty part when the stacker breaks down, 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 stacker's work tasks.

[0062] In an embodiment of the present disclosure, the stacker status prediction method further includes generating a repair task, including a repair time, a repair tool, and a required skill of a repair person, 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 dispatch a repair person 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 person. The above is merely an exemplary description and is not intended to limit or enumerate all possible contents that may be included in a repair task.

[0064] In the embodiment of the present disclosure, the repair management center is an organization that is solely responsible for the repair and management of equipment, and its duties include the following: The repair management center formulates a repair plan, including daily maintenance, regular inspection, fault rectification, etc. of the equipment; The repair management center is responsible for diagnosing and repairing the equipment faults, and ensuring the normal operation of the equipment; The repair management center is responsible for daily safety management of the equipment, including safety inspection of the equipment, correcting safety problems, etc.; The repair management center manages the equipment's equipment and ensures the sufficient and effective use of the equipment; The repair management center is responsible for recording and reporting the equipment repairs, including the equipment fault conditions, repair process, and repair results; The repair management center is responsible for training and managing the repair personnel to improve the skill level and work efficiency of the repair personnel; The repair management center is responsible for improving and optimizing the equipment, improving the performance and efficiency of the equipment, and cooperating with other departments, such as cooperating with the production department to improve production efficiency and cooperating with the quality department to improve product quality.

[0065] In this way, a repair person and repair time can be arranged for the faulty stacker in a timely manner based on the inspection task, which helps to improve the efficiency of stacker fault repair, improves the work efficiency of the stacker, and improves the efficiency of yarn package delivery and storage.

[0066] In an embodiment of the present disclosure, the method for predicting the state of a stacker further includes: determining a first target task, which is a task among the work tasks that the stacker can complete in a preset time period, and a second target task, which is a task among the work tasks that the stacker cannot complete in a preset time period, based on the time when the stacker breaks down and the staying position when the stacker breaks down; and transmitting the second target task in the preset time period to other stackers, so as to execute the second target task by the other stackers.

[0067] Here, the preset time period is a time period during which the designated stacker executes a task of storing a wound yarn package or a task of discharging a wound yarn package.

[0068] In some embodiments, the first target task is a task that the stacker can normally complete when the stacker is in a fault condition. Illustratively, if Stacker A is performing outbound tasks for location 120 and location 130, and a lift mechanism failure occurs while Stacker A is performing the outbound task for location 120, the first target task is 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. Illustratively, stacker A is performing outbound tasks for warehouse location 120 and warehouse location 130, and when stacker A is performing the outbound task for warehouse location 120 and a lift mechanism failure occurs, the second target task becomes the outbound task for location 130, and the warehouse management system sends the second target task to stacker B to have stacker B perform the second target task.

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

[0071] In an embodiment of the present disclosure, predicting a state of the stacker in the process of the stacker performing a 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 a state of the stacker output by the state prediction model.

[0072] In some embodiments, the stacker state includes: stacker is in a fault state, stacker is in a normal state, stacker is in an idle state, stacker is in a stopped state, stacker is in a charging state, and stacker is in a repair state.

[0073] In this way, by inputting historical work data and historical repair data into the condition prediction model, the stacker condition output by the condition prediction model is obtained, which helps to predict the stacker's working condition early, thereby improving the efficiency of winding package shipment and storage.

[0074] In an embodiment of the present disclosure, the state prediction model can be obtained by training by obtaining historical work sample data and historical repair sample data of the stacker, inputting the historical work sample data and the historical repair sample data into a preset model, obtaining a stacker state prediction value output by the preset model, constructing a loss function based on the stacker state true value and the stacker state prediction value, and training the preset model based on the loss function to obtain a state prediction model.

[0075] In some embodiments, the stacker's historical work sample data and historical repair sample data can be obtained from a data source of the control device, and can also be obtained from a stacker's work log and repair log. The above is merely an exemplary description, and all possible methods of obtaining the stacker's historical work sample data and historical repair sample data are not limited and will not be listed here.

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

[0077] It should be understood that Figures 2 to 4 are merely illustrative and not restrictive, and the contents in the figures can be appropriately adjusted or changed according to work needs, and will not be repeated here. Those skilled in the art can make various obvious changes and / or substitutions based on the examples of Figures 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 stacker state prediction device, and the stacker state prediction device includes: a first acquisition module 510 for acquiring historical operation data and historical repair data of the stacker; A second acquisition module 520 for acquiring a work task in a preset time period of the stacker, the work task being a task previously assigned to the stacker by the warehouse management system; A first prediction module 530 for predicting a state of the stacker in the process of the stacker performing a 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 540 for simulating the execution of work tasks by the stacker in a preset time period based on the historical work data to obtain a simulation execution result of the stacker; and a first generation module 550 for generating a stacker state prediction result in combination with the simulation execution result based on the time when the stacker broke down and the position where the stacker was parked when the stacker broke down.

[0079] In some embodiments, the stacker state prediction device further includes a second prediction module (not shown in FIG. 5) for predicting a first length of time that the stacker will be in a faulty state when it breaks down and a second length of time required to repair the stacker based on the historical work data and the historical repair data, and an adjustment module (not shown in FIG. 5) for adjusting work tasks of the stacker in a preset time period based on the first length of time and the second length of time.

[0080] In some embodiments, the historical repair data includes repair records and part lifespans of a first type of part equipped in the stacker, wherein the second prediction module (not shown in FIG. 5 ) includes a first determination submodule for determining a first faulty part when the stacker breaks down based on the repair records and part lifespans of the first type of part, and a second determination submodule for determining a first time length and a second time length based on the first faulty part when the stacker breaks down.

[0081] In some embodiments, the historical operation data includes a total operating time length of a second type of part equipped in the stacker and operating parameters of the second type of part while the stacker is operating, wherein the second prediction module (not shown in FIG. 5 ) includes a third determination sub-module for determining a second faulty part that is in a faulty state when the stacker breaks down based on the total operating time length and the operating parameters of the second type of part, and a fourth determination sub-module for determining the first time length and the second time length based on the second faulty part that is in a faulty state when the stacker breaks down.

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

[0083] In some embodiments, the stacker state prediction device further includes a determination module (not shown in FIG. 5) for 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 parked when it broke down, where the first target task is a task among the work tasks that the stacker can complete in a preset time period, and the second target task is a task among the work tasks that the stacker cannot complete in a preset time period, and a second transmission module (not shown in FIG. 5) for transmitting the second target task in the preset time period to other stackers so that the second target task is executed by the other stackers.

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

[0085] In some embodiments, the state prediction model is obtained by training by obtaining historical work sample data and historical repair sample data of a stacker, inputting the historical work sample data and the historical repair sample data into a preset model, obtaining a stacker state prediction value output by the preset model, constructing a loss function based on the stacker state true value and the stacker state prediction value, and training the preset model based on the loss function to obtain a state prediction model.

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

[0087] The stacker status prediction device of the embodiment of the present disclosure can predict the working status of a stacker based on the stacker's historical work data and historical repair data, thereby improving the prediction accuracy of the stacker's working status, thereby improving the efficiency of receiving and shipping wound yarn packages, and realizing automated 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 the memory 610 stores a computer program executable by the processor 620. The number of the memory 610 and the processor 620 may be one or more. The memory 610 may store one or more computer programs, which, when executed by the electronic device, cause the electronic device to perform the method provided by the above method embodiment. The electronic device may further include: The communication interface 630 is used for communicating with an external device and performing data interaction and transmission.

[0090] When the memory 610, the processor 620, and the communication interface 630 are implemented independently, 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 Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The bus can be classified into an address bus, a data bus, a control bus, and the like. For ease of explanation, only one thick line is shown in FIG. 6, but this does not indicate only one bus or only one type of bus.

[0091] Optionally, in a specific implementation, 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 processor may be a Central Processing Unit (CPU) or may be other general purpose processors, Digital Signal Processing (DSP), Application Specific Integrated Circuits (ASIC), Field Programmable Gate Arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware assemblies, etc. The general purpose processor may be a microprocessor or any conventional processor, etc. Additionally, the processor may be a processor supporting the Advanced RISC Machines (ARM) architecture.

[0093] Additionally, optionally, the memory may include read-only memory and random access memory, and may further include non-volatile random access memory. The memory may be either volatile or non-volatile memory, or may include both volatile and non-volatile memory. Here, non-volatile memory may include Read-Only Memory (ROM), Programmable ROM (PROM), Erasable PROM (EPROM), Electrically EPROM (EEPROM), or flash memory. Volatile memory may include Random Access Memory (RAM), which acts 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 (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (Enhanced SDRAM, ESDRAM), synchlink DRAM (Synchlink DRAM, SLDRAM), and direct RAMBUS RAM (DR RAM).

[0094] The above-mentioned embodiments may be implemented in whole or in part in software, hardware, firmware, or any combination thereof. When implemented in software, the whole or in part may be implemented in the form of a computer program product. A computer program product includes one or more computer instructions. When the computer instructions are loaded and executed on a computer, a process or function according to the embodiments of the present disclosure is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another, for example, the computer instructions may be transmitted from one website site, computer, server, or data center to another website site, computer, server, or data center via wire (e.g., coaxial cable, optical fiber, Digital Subscriber Line (DSL)) or wireless (e.g., infrared, Bluetooth, microwave, etc.). The computer-readable storage medium may be any available medium that can be accessed by a computer, or a data storage device including a server, a data center, etc. integrated with one or more available media. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a magnetic tape), an optical medium (e.g., a Digital Versatile Disc (DVD)), or a semiconductor medium (e.g., a Solid State Disk (SSD)). Note that the computer-readable storage medium referred to in this disclosure may be a non-volatile storage medium, in other words, a non-transitory storage medium.

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

[0096] In the description of the embodiments of the present disclosure, the reference words "one embodiment", "several embodiments", "examples", "specific examples", or "several examples" mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present disclosure. And, the specific features, structures, materials, or characteristics described can be combined in any suitable manner in any one or more embodiments or examples. Furthermore, a person skilled in the art may combine different embodiments or examples described in the present disclosure and features of different embodiments or examples to the extent that they are not mutually inconsistent.

[0097] In the description of the embodiments of the present disclosure, unless otherwise specified, " / " means "or", for example, A / B can mean either A or B. "And / or" in the present disclosure is merely for describing the related relationship of related objects, and indicates that three kinds of relationships may exist, for example, A and / or B can indicate the following three situations: 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 for descriptive purposes only and should not be construed as indicating or implying a relative importance, nor should they be construed as implying a number of technical features shown. Thus, 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, "plurality" means two or more, unless otherwise specified.

[0099] The above are merely illustrative examples of the present disclosure, and are not intended to limit the present disclosure. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present disclosure should be included within the scope of protection of the present disclosure.

[0100] In the description herein, the orientations or positional relationships indicated by terms such as "center," "longitudinal," "lateral," "length," "width," "thickness," "up," "down," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," "counterclockwise," "axial," "radial," "circumferential," and the like, are based on the orientations or positional relationships shown in the drawings and are intended merely to facilitate and simplify the description of the present disclosure, and are not intended to indicate or suggest that the devices or elements referred to have a particular orientation, be configured, or operate in a particular orientation, and therefore are not to be construed as limitations of the present disclosure.

Claims

1. A method for predicting a state of a stacker in an electronic device having a processor and a memory, the method being implemented by the processor invoking computer-readable instructions stored in the memory, comprising: Acquiring historical work data and historical repair data of a stacker, the historical work data including a number of historical work tasks, a content of a historical work task, and a time of a historical work task, and the historical repair data including a repair time, a number of repairs, a type of failure, a failed part, a repair person, and a repair tool; 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; inputting the historical work data and the historical repair data into a state prediction model to predict 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, and obtaining a state of the stacker output by the state prediction model, wherein the state includes at least a time when the stacker broke down and a staying position when the stacker broke down, and 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; training the preset model based on the loss function to obtain the state prediction model; and 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; generating a prediction result of the state of the stacker based on the time when the stacker broke down and the location where the stacker was staying when the stacker broke down, in combination with the results of the execution of the simulation, the prediction result including the probability that the stacker will be able to successfully complete the work task in a preset time period, the probability that the stacker will break down in a preset time period, the time when the stacker broke down, and the location where the stacker was staying when the stacker broke down; 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 length of time and the second length of time. 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. 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 6. Electronic devices.

8. 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 6.

9. A program for implementing the method according to any one of claims 1 to 6 when executed by a processor in a computer.

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