Logistics detection method and device, medium and electronic equipment

Abstract

The invention provides a logistics detection method and device, a medium and electronic equipment. The logistics detection method comprises the following steps: obtaining production logistics data; performing a simulation test on a digital twin model of the production logistics based on the production logistics data to obtain a duration ratio of an idle state, a running state and a waiting state of equipment in the digital twin model; obtaining logistics detection information based on the duration ratio, if the duration ratio of an idle state in the duration ratio is maximum, the logistics detection information is that bottleneck equipment for producing logistics is at the preceding stage of the target equipment, and if the duration ratio of an operation state in the duration ratio is maximum, the logistics detection information is that bottleneck equipment for producing logistics is at the preceding stage of the target equipment. If yes, the logistics detection information indicates that the bottleneck equipment is the target equipment, and otherwise, the logistics detection information indicates that the bottleneck equipment is at the rear stage of the target equipment. According to the logistics detection method, bottleneck equipment in production logistics can be quickly checked, so that the detection efficiency is improved.

Description

Technical Field

[0001] The present application belongs to the field of warehousing and logistics, and relates to a logistics detection method, and in particular to a logistics detection method, device, medium and electronic equipment. Background Art

[0002] The automated logistics system of a cigarette factory primarily handles the in-plant transportation and storage of raw tobacco leaves, rolling and packaging materials, semi-finished tobacco, finished tobacco, and finished cigarettes. The transportation capacity of the automated logistics system is directly related to the production rate of each production and processing workshop, and the bottleneck equipment in each automated logistics system directly affects the system's transportation capacity. Current automated logistics system detection methods for potential bottleneck equipment primarily rely on temporary on-site cameras to capture the real-time production process of key equipment, manually analyzing the equipment's operating status in the recorded footage, and finally analyzing and comparing upstream and downstream equipment to infer potential bottleneck equipment. Therefore, current logistics detection methods suffer from low detection efficiency. Summary of the Invention

[0003] The purpose of this application is to provide a logistics detection method, device, medium and electronic equipment to solve the current problem of low detection efficiency.

[0004] In the first aspect, the present application provides a logistics detection method, including: obtaining production logistics data; performing simulation testing on a digital twin model of the production logistics based on the production logistics data to obtain the time proportions of the idle state, running state and waiting state of the target device in the digital twin model; based on the time proportions, obtaining logistics detection information, if the time proportion of the idle state is the largest in the time proportions, then the logistics detection information is that the bottleneck device of the production logistics is at the front stage of the target device, if the time proportion of the running state is the largest in the time proportions, then the logistics detection information is that the bottleneck device is the target device, if the time proportion of the waiting state is the largest in the time proportions, then the logistics detection information is that the bottleneck device is at the back stage of the target device.

[0005] In the logistics detection method, by performing simulation tests on the digital twin model and obtaining logistics detection information based on the obtained time proportion value, bottleneck equipment in production logistics can be quickly identified, thereby improving detection efficiency.

[0006] In one embodiment of the present application, the operating state includes a first operating state and a second operating state. The first operating state is the operating state when the material of the production logistics is transported from the upstream equipment of the target equipment to the target equipment, and the second operating state is the operating state when the material is transported from the target equipment to the downstream equipment of the target equipment.

[0007] In one embodiment of the present application, the duration of the running state is expressed as:

[0008]

[0009] Among them, T R Indicates the duration of the operation state during the process of transporting the material to the target equipment, represents the stop time point of the target device after completing the i-th first operation state, Indicates the start time point of the target device at the beginning of the i-th first operating state, represents the stop time point of the target device after completing the i-th second operation state, represents the starting time point of the target device in the i-th second operating state, and n represents the number of times the target device experiences the operating state during the material transportation process.

[0010] In one embodiment of the present application, the duration of the idle state is expressed as:

[0011]

[0012] Among them, T F Indicates the duration of the idle state of the target equipment during the process of transporting the material. Indicates the stop time point of the target device after completing the second operation state for the i-1th time, represents the starting time point of the target device in the i-th first operating state, and n represents the number of times the target device experiences the operating state during the material transportation process.

[0013] In one embodiment of the present application, the duration of the waiting state is expressed as:

[0014]

[0015] Among them, T W Indicates the duration of the waiting state during the process of the target equipment delivering the material. represents the stop time point of the target device after completing the i-th first operation state, represents the starting time point of the target device in the i-th second operating state, and n represents the number of times the target device experiences the operating state during the material transportation process.

[0016] In one embodiment of the present application, the simulation test includes a flow test of the digital twin model.

[0017] In one embodiment of the present application, the production logistics data is production logistics data of cigarette manufacturing.

[0018] In the second aspect, the present application provides a logistics detection device, including: a data acquisition module for acquiring production logistics data; a simulation test module for performing simulation testing on the digital twin model of the production logistics based on the production logistics data to obtain the time proportions of the idle state, running state and waiting state of the equipment in the digital twin model; an information acquisition module for acquiring logistics detection information based on the time proportions. If the time proportion of the idle state is the largest in the time proportions, then the logistics detection information is that the bottleneck device of the production logistics is at the front stage of the target device. If the time proportion of the running state is the largest in the time proportions, then the logistics detection information is that the bottleneck device is the target device. If the time proportion of the waiting state is the largest in the time proportions, then the logistics detection information is that the bottleneck device is at the back stage of the target device.

[0019] In a third aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the logistics detection method described in any one of the first aspects.

[0020] In a fourth aspect, the present application provides an electronic device comprising: a memory storing a computer program; and a processor communicatively connected to the memory, for executing any of the logistics detection methods described in the first aspect when the computer program is called.

[0021] As described above, the logistics detection method, device, medium, and electronic equipment described in this application have the following effective and beneficial effects:

[0022] In the logistics detection method, by performing simulation tests on the digital twin model and obtaining logistics detection information based on the obtained time proportion value, bottleneck equipment in production logistics can be quickly identified, thereby improving detection efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 Shown is a schematic diagram of the hardware structure for running the logistics detection method in an embodiment of the present application.

[0024] Figure 2 Shown is a flow chart of the logistics detection method described in an embodiment of the present application.

[0025] Figure 3 Shown is a layout structure diagram of the digital twin model described in an embodiment of the present application.

[0026] Figure 4 Shown is a schematic diagram of the idle state described in an embodiment of the present application.

[0027] Figure 5Shown is a schematic diagram of the operating state described in an embodiment of the present application.

[0028] Figure 6 The diagram shows the waiting state described in the embodiment of this application.

[0029] Figure 7 Shown is a structural schematic diagram of the logistics detection device described in an embodiment of the present application.

[0030] Component number description

[0031] 10 Electronic devices

[0032] 110 Memory

[0033] 120 processors

[0034] 130 bus

[0035] 140 Access Device

[0036] 150 databases

[0037] 700 Logistics Detection Device

[0038] 710 Data Acquisition Module

[0039] 720 simulation test module

[0040] 730 Information Acquisition Module

[0041] Steps S11-S13 DETAILED DESCRIPTION

[0042] The following describes the embodiments of the present application through specific examples. Those skilled in the art can easily understand the other advantages and effects of the present application from the content disclosed in this specification. The present application can also be implemented or applied through other different specific embodiments. The details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present application. It should be noted that the following embodiments and features in the embodiments can be combined with each other unless they conflict.

[0043] It should be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present application. Therefore, the illustrations only show components related to the present application and are not drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component can be changed at will, and the component layout type may also be more complicated.

[0044] The technical solutions in the embodiments of the present application are described in detail below in conjunction with the drawings in the embodiments of the present application.

[0045] The logistics detection method provided in the embodiment of the present application can be run in a computing device. Figure 1 For example, Figure 1 The hardware structure block diagram of the computing device for running the logistics detection method is shown in FIG. The computing device 10 includes but is not limited to a memory 110 and a processor 120. The processor 120 is connected to the memory 110 via a bus 130, and a database 150 is used to store data.

[0046] The computing device 10 also includes an access device 140 that enables the computing device 10 to communicate via one or more networks 160. Examples of such networks include a public switched telephone network, a local area network, a wide area network, a personal area network, or a combination of communication networks such as the Internet. The access device 140 may include any type of network interface, whether wired or wireless, such as one or more network interface cards, such as an IEEE 802.11 wireless LAN radio interface, a World Wide Interoperability for Microwave Access interface, an Ethernet interface, a Universal Serial Bus interface, a cellular network interface, a Bluetooth interface, a near field communication interface, or the like.

[0047] In the embodiment of the present application, the above components of the computing device 10 and Figure 1 Other components not shown in the figure may also be connected to each other, for example, via a bus. Figure 1 The computing device structure block diagram shown is for illustrative purposes only and is not intended to limit the scope of the present application. Those skilled in the art may add or replace other components as needed.

[0048] Computing device 10 may be any type of stationary or mobile computing device, including a mobile computer or mobile computing device (e.g., a tablet computer, a personal digital assistant, a laptop computer, a notebook computer, a netbook computer, etc.), a mobile phone (e.g., a smartphone), a wearable computing device (e.g., a smartwatch, smart glasses, etc.), or other types of mobile devices, or a stationary computing device such as a desktop computer or PC. Computing device 10 may also be a mobile or stationary server.

[0049] like Figure 2 As shown, this embodiment provides a logistics detection method, including:

[0050] S11, obtaining production logistics data.

[0051] Alternatively, the production logistics data may refer to data related to production and logistics activities, specifically cigarette manufacturing production logistics data. The production logistics data may include inbound data, outbound data, and inventory data. Inbound data may refer to records and information regarding the entry of materials or products into a production or warehouse environment from outside sources; outbound data may refer to records and information regarding the exit of materials or products from a production or warehouse environment; and inventory data may refer to the quantity of materials or products available within the production or warehouse environment.

[0052] Optionally, the production logistics data may include production data and equipment data. The production data may include: material distribution plan, material feeding plan, operation tasks and inventory statistics. The production data may be derived from the production history data of a real warehouse management system. The equipment data may include: equipment performance, equipment size, equipment layout and flow path. The specific design of the material distribution plan, material feeding plan, operation tasks, inventory statistics, equipment performance, equipment size, equipment layout and flow path can be flexibly set according to actual conditions, and will not be repeated in this embodiment.

[0053] S12: Perform a simulation test on the digital twin model of the production logistics based on the production logistics data to obtain the time proportion values ​​of the idle state, running state and waiting state of the target equipment in the digital twin model.

[0054] Optionally, the implementation method of simulating the digital twin model of the production logistics based on the production logistics data includes: simulating the digital twin model based on a virtual warehouse management system, a virtual warehouse control system and the production logistics data to obtain the time proportion of the idle state, running state and waiting state of the target equipment in the digital twin model. The virtual warehouse management system can be a management system for simulating a real warehouse management system, the virtual warehouse management system can be used to simulate the generation and issuance of business data in a real warehouse management system, the virtual warehouse control system can be a control system for simulating a real warehouse control system, and the virtual warehouse control system can be used to simulate the generation and scheduling of tasks in a real warehouse control system. The simulation test can refer to the digital twin model simulating actual production logistics for simulation testing.

[0055] Optionally, the method for implementing a simulation test of the digital twin model based on a virtual warehouse management system, a virtual warehouse control system, and the production logistics data includes: processing the production logistics data through the virtual warehouse management system to obtain twin data of the production logistics data; and performing a simulation test on the digital twin model of production logistics based on the virtual warehouse management system, the virtual warehouse control system, and the twin data of the production logistics data to obtain the percentage of the idle state, running state, and waiting state of the target equipment in the digital twin model. The twin data of the production logistics data may refer to data of the production logistics data that, after data processing, enables the digital twin model to simulate the on-site electrical control system and mechanical equipment.

[0056] Optionally, the twin data of the production logistics data may be in a data table in the database as shown in Table 1 below:

[0057] Table 1

[0058]

[0059]

[0060] Optionally, the virtual warehouse management system may include: a packing list initialization unit, used to process the imported material planning data to obtain initialized packing list data; a packing list initialization unit, used to process the imported material feeding plan data to obtain initialized packing list data; a storage location initialization unit, used to generate new storage location data; an inventory initialization unit, used to process the imported daily inventory data to obtain initialized inventory data; a historical task extraction unit, used to extract and process the imported historical task data to obtain extracted historical tasks; a task query unit, used to query the tasks generated by the digital twin model during the simulation process to track the execution status of the tasks; a log output unit, used to output the operation log of the virtual warehouse management system; an initialization unit, used to generate the initialized packing list data, the initialized packing list data, the initialized inventory data, the new storage location data, and the initialized inventory data with one click. The material distribution plan data may be xlsx data associated with the material distribution plan, the material input plan data may be xlsx data associated with the material input plan, the daily balance data may be xlsx data associated with the inventory statistics, and the historical task data may be xlsx data associated with the running task.

[0061] Optionally, the virtual warehouse control system may include: a start unit for starting the scheduling service of the virtual warehouse control system; a stop unit for shutting down the scheduling service of the virtual warehouse control system; and a log output unit for outputting the operation log of the virtual warehouse control system.

[0062] Optionally, the implementation method of simulating and testing the digital twin model based on the virtual warehouse management system, the virtual warehouse control system and the twin data includes: the virtual warehouse control system receives the twin data sent by the virtual warehouse management system; the virtual warehouse control system sends the twin data and simulation tasks to the digital twin model; the digital twin model simulates according to the received twin data and the simulation tasks to generate operation data, and the operation data may include the duration of the idle state, running state and waiting state of the target device in the digital twin model.

[0063] Optionally, the operation data may include operation data of the equipment in the digital twin model, for example, including loading time, operation time, idle time, equipment utilization, and operation distance. The loading refers to the equipment being loaded when not in operation, and the idle refers to the equipment being unloaded when not in operation. The equipment utilization may refer to the ratio of the equipment operation time to the total operation time simulated by the digital twin model, and the operation distance may refer to the distance traveled by the mobile equipment. The operation data may also be used for production planning decision analysis, operation parameter setting decision analysis, bottleneck identification decision analysis, and optimization solution decision analysis. The production planning decision analysis refers to analyzing whether the current production plan is feasible. For example, the production logistics includes finished tobacco logistics, which includes the intermediate link connecting the two major production workshops of silk making and packaging. The production planning decision analysis can reasonably plan the batch quantity, material distribution sequence, total weight of each batch, and weight of each box for the packaging of finished tobacco, based on the packaging production plan, under the condition of limited storage space, to ensure a dynamic balance between material distribution and wire feeding. The operation parameter setting decision analysis refers to analyzing whether the current key operation parameters are feasible, such as the distribution flow and feeding flow of the finished tobacco logistics system. The operation parameter setting decision analysis can complete the evaluation and setting of key operation parameters efficiently, accurately and comprehensively without occupying production resources. The bottleneck identification decision analysis refers to analyzing the bottleneck equipment existing in the digital twin model based on the operation data. The bottleneck equipment may refer to equipment that cannot reduce the completion time of the production task even if the operation parameters are adjusted. The optimization solution decision analysis refers to the analysis of the parameters of the single equipment performance in the digital twin model, the regional material flow path optimization, and the equipment upgrade and transformation based on the operation data after identifying the bottleneck equipment.

[0064] Optionally, the digital twin model can also be combined with VR technology. Specifically, through VR equipment and STEAMVR software, users can freely travel through real scenes in the digital twin model, thereby providing a new teaching and training model for industrial logistics systems, namely the VR teaching and training model.

[0065] Optionally, the communication process between the virtual warehouse control system and the digital twin model includes: the digital twin model sends path information to the virtual warehouse control system; the virtual warehouse control system receives the path information and sends inventory information to the digital twin model; the digital twin model receives the inventory information and generates a cigarette box corresponding to the inventory information based on the inventory information; after the virtual warehouse control system receives the cigarette box completion information, the virtual warehouse control system sends a request to the digital twin model; the digital twin model completes the request and sends the request completion information to the virtual warehouse control system; after the virtual warehouse control system receives the request completion information, it sends a task to the digital twin model; the digital twin model completes the task, and after the digital twin model completes the task, the material in the production logistics arrives at the last device in the path. Requests can include: empty box packing requests, empty box outbound requests, full box outbound requests, complete box turnaround requests, empty box inbound requests, and full box inbound requests. Tasks can include: empty box packing tasks, empty box buffer tasks, full box turnaround tasks, empty box inbound tasks, and full box inbound tasks. During the interaction between the virtual warehouse control system and the digital twin model, multiple handshakes are performed to ensure data and status synchronization between the two parties.

[0066] Optionally, the path information may refer to the logistics flow path information in the digital twin model, and the logistics flow path may include: empty box flow-turning box feeding empty box return platform-straight shuttle car-chain machine channel-straight shuttle car-empty box buffer area, empty box flow-turning box feeding empty box return platform-straight shuttle car-chain machine channel-straight shuttle car-chain machine channel-inbound stacker-main storage area, empty box flow-main storage area-outbound stacker-chain machine channel-straight shuttle car-empty box buffer area, empty box flow-main storage area-outbound stacker-chain machine channel-straight shuttle car-chain machine channel- Paths include: inbound stacker-main storage area, empty box circulation-empty box buffer area-straight shuttle-depacker-distribution area, full box circulation-distribution area-chain conveyor channel-stacker-chain conveyor channel-inbound stacker-main storage area, full box circulation-main storage area-outbound stacker-stacker unloading platform-straight shuttle-turning feeding platform, other-circulation within the main storage area-storage area head chain-chain conveyor channel-storage area tail chain, other-circulation within the empty box buffer area-empty box buffer area head chain-chain conveyor channel-empty box buffer area tail chain, other-feeding circulation-turning feeding platform-ABB-turning feeding empty box return platform, etc. The inventory information may refer to the initialized inventory data described above. The smoke box completion information refers to information indicating that the digital twin model has completed generating the smoke box, the request completion information refers to information indicating that the digital twin model has completed generating the request, and the task completion information refers to information indicating that the digital twin model has completed generating the task. ABB refers to industrial robots.

[0067] In order to meet the requirements of specific equipment operation or production process, logistics flow needs to follow certain rules. These rules are set as the logistics flow rule information in the digital twin model. The logistics flow rules include: the rules for the feeding platform in the feeding area, which stipulates that only one group of full boxes can be placed in the first row of the six platforms to provide space for ABB boxes to avoid collisions; the rules for the longitudinal feeding vehicles in the feeding area, which stipulate that the longitudinal vehicle driving order can be 1#-3#-4#-2# to reduce the travel distance of the transverse feeding vehicles; the rules for the feeding lanes in the feeding area, which stipulate that only one lane can deliver full boxes at the same time to increase the efficiency of filling empty boxes; the rules for the main storage area's incoming stacker, which stipulates that two stations can receive goods at the same time and a single station can unload goods one by one. Receiving goods at the same time at both stations can reduce storage space waste; the rules for the main storage area's outgoing stacker, which stipulates that a single station can receive goods one by one and two stations can unload goods at the same time. Receiving goods at the same time at both stations can reduce storage space waste.

[0068] Optionally, the communication connection between the digital twin model and the virtual warehouse control system can adopt a communication tool of the TCP / IP protocol, and the communication tool includes functions such as disconnection reconnection, packet sticking processing, and data transmission encryption. Among them, the virtual warehouse control system can be used as a server TCPServer object, and the digital twin model can be used as a client TCPClient object. The communication protocol between the virtual warehouse control system and the digital twin model can be defined in JSON format, which may include: data packet ID, Time (data sending time), Type (business data type), Data (data content), Code (message code) and Msg (abnormal information). For example, the virtual warehouse control system and the digital twin model have an interface for the digital twin model to receive work tasks issued by the virtual warehouse control system. The definition of this interface can be shown in Table 2:

[0069] Table 2

[0070]

[0071] The WCS is the virtual warehouse control system described above. The Road list is the path list, and the PLT list is the inventory list. The Id, Type, Time, Data, Code, and Msg fields in the communication content can all be string types.

[0072] Alternatively, the digital twin model can refer to a model that digitally models physical production processes and supply chain networks to monitor, simulate, and optimize production and logistics activities in real time. It is a virtual representation of physical systems that integrates real-time data, sensor information, and simulation algorithms to provide highly visualized, real-time analysis, and predictive capabilities for production and logistics processes. Simulation testing refers to the process of simulating and emulating real-world production and logistics using the digital twin model.

[0073] Optionally, the operating state includes a first operating state and a second operating state, the first operating state being the operating state when the material of the production logistics is transported from the upstream equipment of the target equipment to the target equipment, and the second operating state being the operating state when the material is transported from the target equipment to the downstream equipment of the target equipment. The upstream equipment of the target equipment refers to the equipment located upstream of the target equipment in the production logistics. The downstream equipment of the target equipment refers to the equipment located downstream of the target equipment in the production logistics. The target equipment is the equipment that is the target of interest in the digital twin model, and can be any virtual device in the digital twin model. The virtual device is used to simulate or emulate an actual physical device. For example, if the target of interest in the digital twin model is to be virtual device 4, then the target device is virtual device 4.

[0074] Optionally, the idle state may refer to a state in which the target device is not processing any task or work, the running state may refer to a state in which the target device is executing a task or work, and the waiting state may refer to a state in which the target device is waiting for certain conditions or resources to be met in order to continue executing the task.

[0075] Optionally, the simulation test includes a flow test of the digital twin model. The flow may refer to the flow of materials in the digital twin model, and may be specifically set in the material flow of the simulation parameters in the digital twin model. Figure 3 , Figure 3 Schematic diagram of the layout structure of the digital twin model. The layout of the digital twin model can be flexibly set according to actual conditions, and will not be described in detail in this embodiment.

[0076] S13, based on the duration proportion value, obtain logistics detection information, if the duration proportion value of the idle state is the largest in the duration proportion value, then the logistics detection information is that the bottleneck device of the production logistics is at the front stage of the target device, if the duration proportion value of the running state is the largest in the duration proportion value, then the logistics detection information is that the bottleneck device is the target device, and the duration proportion value of the waiting state is the largest in the duration proportion value, otherwise the logistics detection information is that the bottleneck device is at the back stage of the target device.

[0077] Optionally, the bottleneck equipment refers to equipment that limits process throughput or efficiency in the production logistics.

[0078] Optionally, the duration of the running state is expressed as:

[0079]

[0080] Among them, TR Indicates the duration of the operation state during the process of transporting the material to the target equipment, represents the stop time point of the target device after completing the i-th first operation state, Indicates the start time point of the target device at the beginning of the i-th first operating state, represents the stop time point of the target device after completing the i-th second operation state, represents the starting time point of the target device in the i-th second operating state, and n represents the number of times the target device experiences the operating state during the material transportation process.

[0081] Optionally, the duration of the idle state is expressed as:

[0082]

[0083] Among them, T F Indicates the duration of the idle state of the target equipment during the process of transporting the material. Indicates the stop time point of the target device after completing the second operation state for the i-1th time, represents the starting time point of the target device in the i-th first operating state, and n represents the number of times the target device experiences the operating state during the material transportation process.

[0084] The duration of the waiting state is expressed as:

[0085]

[0086] Among them, T W Indicates the duration of the waiting state during the process of the target equipment delivering the material. represents the stop time point of the target device after completing the i-th first operation state, represents the starting time point of the target device in the i-th second operating state, and n represents the number of times the target device experiences the operating state during the material transportation process.

[0087] See also Figures 4 to 6 , which are schematic diagrams of the idle state, running state and waiting state respectively.

[0088] According to the above description, the logistics detection method described in this embodiment includes: obtaining production logistics data; performing simulation tests on the digital twin model of the production logistics based on the production logistics data to obtain the time proportions of the idle state, running state and waiting state of the equipment in the digital twin model; based on the time proportions, obtaining logistics detection information, if the time proportion of the idle state is the largest in the time proportions, then the logistics detection information is that the bottleneck equipment of the production logistics is at the front stage of the target equipment, if the time proportion of the running state is the largest in the time proportions, then the logistics detection information is that the bottleneck equipment is the target equipment, if the time proportion of the waiting state is the largest in the time proportions, then the logistics detection information is that the bottleneck equipment is at the back stage of the target equipment.

[0089] In the logistics detection method, by performing simulation tests on the digital twin model and obtaining logistics detection information based on the obtained time ratio, bottleneck equipment in production logistics can be quickly identified, thereby improving detection efficiency. In addition, since the material flow cannot be increased at will during normal factory production, the judgment of potential bottleneck equipment is based on operating data and equipment parameters under normal flow. However, due to factors such as "flow inclusion", "diversion" and "uneven material flow" in the material system, the current logistics detection method cannot accurately identify potential bottleneck equipment under large flow. By performing flow testing on the digital twin model through the logistics detection method, bottleneck equipment under large flow can be accurately identified. In addition, the logistics detection method can identify all bottleneck equipment in a short period of time by performing simulation tests on the digital twin model. And with the help of other functions of the digital twin model, such as "automatic report generation" and "1-60 times speed simulation" functions, the efficiency of data collection can be further improved.

[0090] The protection scope of the logistics detection method described in the embodiment of the present application is not limited to the execution order of the steps listed in this embodiment. All solutions implemented by adding, reducing or replacing steps in the existing technology based on the principles of the present application are included in the protection scope of the present application.

[0091] See also Figure 7 This embodiment provides a logistics detection device 700, including:

[0092] The data acquisition module 710 is used to acquire production logistics data.

[0093] The simulation test module 720 is used to perform simulation testing on the digital twin model of the production logistics based on the production logistics data to obtain the time proportion values ​​of the idle state, running state and waiting state of the equipment in the digital twin model.

[0094] The information acquisition module 730 is used to obtain logistics detection information based on the duration ratio. If the duration ratio of the idle state is the largest in the duration ratio, then the logistics detection information indicates that the bottleneck device of the production logistics is at the front stage of the target device. If the duration ratio of the running state is the largest in the duration ratio, then the logistics detection information indicates that the bottleneck device is the target device. If the duration ratio of the waiting state is the largest in the duration ratio, then the logistics detection information indicates that the bottleneck device is at the back stage of the target device.

[0095] In the logistics detection device 700 provided in this embodiment, the data acquisition module 710 and Figure 2 The simulation test module 720 corresponds to step S12, and the information acquisition module 730 corresponds to step S13.

[0096] In the several embodiments provided in this application, it should be understood that the disclosed devices or methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of modules / units is only a logical function division. There may be other division methods in actual implementation. For example, multiple modules or units can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or modules or units, which can be electrical, mechanical or other forms.

[0097] The modules / units described as separate components may or may not be physically separate, and the components displayed as modules / units may or may not be physical modules, that is, they may be located in one place or distributed across multiple network elements. Some or all of the modules / units may be selected according to actual needs to achieve the purpose of the embodiments of the present application. For example, the functional modules / units in the various embodiments of the present application may be integrated into a processing module, or each module / unit may exist physically separately, or two or more modules / units may be integrated into a single module / unit.

[0098] Those skilled in the art should further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the above description has generally described the composition and steps of each example according to function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0099] The embodiment of the present application also provides a computer-readable storage medium. Those skilled in the art will understand that all or part of the steps in the method for implementing the above embodiment can be completed by instructing the processor through a program, and the program can be stored in a computer-readable storage medium, and the storage medium is a non-transitory medium, such as a random access memory, a read-only memory, a flash memory, a hard disk, a solid-state drive, a magnetic tape, a floppy disk, an optical disc, and any combination thereof. The above storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or a data center that includes one or more available media. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a tape), an optical medium (for example, a digital video disc (DVD)), or a semiconductor medium (for example, a solid-state drive (SSD)), etc.

[0100] The embodiment of the present application may also provide a computer program product, the computer program product including one or more computer instructions. When the computer instructions are loaded and executed on a computing device, the process or function described in the embodiment of the present application is generated in whole or in part. 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 or data center to another website, computer or data center via a wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) method.

[0101] The embodiment of the present application also provides an electronic device, comprising: a memory storing a computer program; a processor communicating with the memory and executing the computer program when the computer program is called; Figure 2 The logistics detection method.

[0102] When the computer program product is executed by a computer, the computer executes the method described in the above method embodiment. The computer program product can be a software installation package. When the above method is needed, the computer program product can be downloaded and executed on the computer.

[0103] The descriptions of the processes or structures corresponding to the above figures have different emphases. For parts that are not described in detail in a certain process or structure, please refer to the relevant descriptions of other processes or structures.

[0104] The above embodiments are merely illustrative of the principles and effects of this application and are not intended to limit this application. Anyone skilled in the art may modify or alter the above embodiments without departing from the spirit and scope of this application. Therefore, all equivalent modifications or alterations made by one of ordinary skill in the art without departing from the spirit and technical concepts disclosed in this application shall be covered by the claims of this application.

Claims

1. A logistics detection method, characterized in that: include: Obtain production logistics data; Performing a simulation test on the digital twin model of the production logistics based on the production logistics data to obtain the time proportions of the target equipment in the digital twin model that are in an idle state, a running state, and a waiting state; Based on the duration proportion value, logistics detection information is obtained. If the duration proportion value of the idle state is the largest in the duration proportion value, then the logistics detection information indicates that the bottleneck device of the production logistics is at the front stage of the target device. If the duration proportion value of the running state is the largest in the duration proportion value, then the logistics detection information indicates that the bottleneck device is the target device. If the duration proportion value of the waiting state is the largest in the duration proportion value, then the logistics detection information indicates that the bottleneck device is at the back stage of the target device.

2. The detection method according to claim 1, wherein The operating state includes a first operating state and a second operating state. The first operating state is the operating state when the material of the production logistics is transported from the upstream equipment of the target equipment to the target equipment, and the second operating state is the operating state when the material is transported from the target equipment to the downstream equipment of the target equipment.

3. The detection method according to claim 2, characterized in that The duration of the running state is expressed as: Among them, T R Indicates the duration of the operation state during the process of transporting the material to the target equipment, represents the stop time point of the target device after completing the i-th first operation state, Indicates the start time point of the target device at the beginning of the i-th first operating state, represents the stop time point of the target device after completing the i-th second operation state, represents the starting time point of the target device in the i-th second operating state, and n represents the number of times the target device experiences the operating state during the material transportation process.

4. The detection method according to claim 3, characterized in that The duration of the idle state is expressed as: Among them, T F Indicates the duration of the idle state of the target equipment during the process of transporting the material. Indicates the stop time point of the target device after completing the second operation state for the i-1th time, represents the starting time point of the target device in the i-th first operating state, and n represents the number of times the target device experiences the operating state during the material transportation process.

5. The detection method according to claim 3, characterized in that The duration of the waiting state is expressed as: Among them, T W Indicates the duration of the waiting state during the process of the target equipment delivering the material. represents the stop time point of the target device after completing the i-th first operation state, represents the starting time point of the target device in the i-th second operating state, and n represents the number of times the target device experiences the operating state during the material transportation process.

6. The detection method according to claim 1, characterized in that The simulation test includes a flow test of the digital twin model.

7. The detection method according to claim 1, characterized in that The production logistics data is production logistics data of cigarette manufacturing.

8. A logistics detection device, characterized in that: include: Data acquisition module, used to obtain production logistics data; A simulation test module is used to perform a simulation test on the digital twin model of the production logistics based on the production logistics data to obtain the time proportion values ​​of the idle state, running state and waiting state of the target equipment in the digital twin model; An information acquisition module is used to obtain logistics detection information based on the duration ratio value. If the duration ratio value of the idle state is the largest in the duration ratio value, then the logistics detection information is that the bottleneck device of the production logistics is at the front stage of the target device. If the duration ratio value of the running state is the largest in the duration ratio value, then the logistics detection information is that the bottleneck device is the target device. If the duration ratio value of the waiting state is the largest in the duration ratio value, then the logistics detection information is that the bottleneck device is at the back stage of the target device.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the logistics detection method according to any one of claims 1 to 8 is implemented.

10. An electronic device, characterized in that: include: a memory storing a computer program; A processor is communicatively connected to the memory and executes the logistics detection method according to any one of claims 1 to 7 when calling the computer program.

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