A method and system for synchronous manual and robot-assisted picking of fresh goods

Abstract

A method and system for synchronous manual and robot-assisted picking of fresh goods, relating to the field of logistics and distribution. The method is applied to an intelligent sorting system, and comprises: obtaining management information of a target warehouse, the management information including daily incoming quantity, storage quantity, daily outgoing quantity, and shelf life of various fresh goods; calculating a distribution index of the target warehouse based on the management information; generating a resource allocation planning table for the target warehouse based on the distribution index; and sending the resource allocation planning table to warehouse management personnel so that they can allocate the required picking resources for the target warehouse, including human resources and intelligent equipment resources. Implementing the technical solution provided by this application solves the current problems of low picking efficiency and high loss rate of fresh goods.

Description

Technical Field

[0001] The present application relates to the technical field of logistics and distribution, and specifically to a method and system for synchronous manual and robot-assisted picking of fresh goods. Background Art

[0002] In recent years, with the significant improvement in people's living standards, the demand for fresh food quality and variety has become increasingly diversified, accelerating the continued expansion of the fresh food delivery market. This has led to an increasingly urgent demand for instant fresh food delivery. In this context, fresh food warehouses have introduced intelligent picking robots to assist manual sorting to improve delivery efficiency.

[0003] However, fresh produce, due to its perishability, fragility, and short shelf life, places higher demands on delivery efficiency. Under traditional manual operations, the allocation of goods in and out of the warehouse is not scientific, and the routes for goods to storage shelves are not scientifically planned. This results in low utilization of intelligent picking robots and irrational work allocation for pickers, which reduces fresh produce picking efficiency and increases fresh produce waste. Summary of the Invention

[0004] In response to the current problems of low picking efficiency and high loss rate of fresh goods, the present application provides a method and system for synchronous manual and robot assisted picking of fresh goods.

[0005] In a first aspect, the present application provides a method for synchronous manual and robot-assisted picking of fresh produce, which is applied to an intelligent sorting system. The method comprises:

[0006] Obtaining management information of the target warehouse, including daily incoming quantity, storage quantity, daily outgoing quantity, and shelf life of various fresh goods;

[0007] Calculating the distribution index of the target warehouse based on the management information;

[0008] Based on the distribution index, generating a resource allocation planning table for the target warehouse;

[0009] The resource allocation planning table is sent to the warehouse management personnel so that the warehouse management personnel can allocate the required picking resources of the target warehouse, and the required picking resources include human resources and intelligent equipment resources.

[0010] Optionally, calculating the distribution index of the target warehouse based on the management information specifically includes:

[0011] Calculating the remaining shelf life of the plurality of fresh products in the target warehouse based on the management information;

[0012] Calculating the urgency coefficients of the plurality of fresh products according to the remaining shelf life;

[0013] The distribution index of the target warehouse is calculated using the distribution index calculation formula and the urgency coefficients of the various fresh products.

[0014] Optionally, the distribution index calculation formula is specifically as follows:

[0015]

[0016] Among them, P is the distribution index of the target warehouse, is the urgency coefficient of the i-th fresh product, is the daily outbound quantity of the i-th fresh product, is the current storage volume of the i-th fresh product, is the daily inventory requirement for the i-th fresh product, i is the category number of the fresh product, and there are n categories in total. is the adjustment coefficient.

[0017] Optionally, generating a resource allocation planning table for the target warehouse based on the distribution index specifically includes:

[0018] Calculating a delivery index for a plurality of fresh products;

[0019] Calculating a ratio of the delivery index of the plurality of fresh products to the delivery index of the target warehouse;

[0020] Determining resource allocation priorities for the multiple fresh products based on the ratio results;

[0021] Based on the resource allocation priority, the required picking resources are allocated to the target warehouse step by step to obtain the resource allocation planning table.

[0022] Optionally, the resource allocation planning table includes a warehousing resource plan, and the resource allocation planning table is obtained by allocating the required picking resources to the target warehouse step by step based on the resource allocation priority, and specifically further includes:

[0023] Allocate a storage area for the first fresh product in the target warehouse according to the delivery index of the first fresh product, the storage area including a core area, a buffer area, and a peripheral area, wherein the first fresh product is any one of a plurality of fresh products;

[0024] Obtaining the remaining inventory of the first fresh product after storage and the expected future inventory volume;

[0025] Calculating the pre-allocated capacity of the first fresh product based on the remaining inventory and the expected future inventory volume;

[0026] Based on the storage area and pre-allocated capacity of the first fresh product, storage resource planning is performed for the first fresh product and stored in the resource allocation planning table.

[0027] Optionally, the resource allocation planning table includes outbound resource planning, and the resource allocation planning table is obtained by allocating the required picking resources to the target warehouse step by step based on the resource allocation priority, and specifically further includes:

[0028] Acquire multiple outbound nodes of a second fresh product, where the second fresh product is any one of the multiple fresh products;

[0029] Calculating the contribution of the plurality of delivery nodes to the delivery of the second fresh product;

[0030] According to the contribution of the plurality of outbound nodes, inbound resources are planned for the plurality of outbound nodes of the second fresh product, and the planning is stored in the resource allocation planning table.

[0031] Optionally, the resource allocation planning table includes commodity storage resource planning, and the resource allocation planning table is obtained by allocating required picking resources to the target warehouse step by step based on the resource allocation priority, and specifically further includes:

[0032] Acquire multiple storage targets for a third fresh product, wherein the multiple storage targets include available resources, storage time, storage loss, and available storage paths, and the third fresh product is any one of the multiple fresh products;

[0033] Based on the multiple storage targets, an initial storage population is generated using a greedy strategy;

[0034] Using the NSGA-II algorithm, the optimal operation and storage path of the initial operation and storage population is calculated;

[0035] According to the optimal storage path, commodity storage resources are planned for the third fresh product and stored in the resource allocation planning table.

[0036] In a second aspect, the present application provides a system for synchronous manual and robot-assisted picking of fresh produce, wherein the system is an intelligent sorting system, and the intelligent sorting system includes an acquisition module, a processing module, and a sending module, wherein:

[0037] The acquisition module is used to acquire management information of the target warehouse, wherein the management information includes daily incoming quantity, storage quantity, daily outgoing quantity and shelf life of various fresh goods;

[0038] The processing module is configured to calculate a distribution index of the target warehouse based on the management information; and generate a resource allocation planning table for the target warehouse based on the distribution index, wherein the resource allocation planning table includes an inbound resource planning, an outbound resource planning, and a commodity transportation and storage resource planning;

[0039] The sending module is used to send the resource allocation planning table to the warehouse management personnel so that the warehouse management personnel can allocate the required picking resources of the target warehouse, and the required picking resources include human resources and intelligent equipment resources.

[0040] In a third aspect, the present application provides an electronic device comprising a processor, a memory, a user interface, and a network interface, wherein the memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes a method as described in any one of the first aspects.

[0041] In a fourth aspect, the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores instructions, and when the instructions are executed, the method as described in any one of the first aspects is executed.

[0042] In summary, one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0043] 1. This application obtains management information from the target warehouse (daily incoming inventory, storage volume, daily outgoing inventory, and shelf life of various fresh goods) and calculates a distribution index based on this information. This generates a resource allocation plan covering incoming inventory resource planning, outgoing inventory resource planning, and commodity transportation and storage resource planning. Ultimately, the plan is sent to warehouse management personnel to guide the allocation of human resources and intelligent equipment resources. In this process, with the help of intelligent distribution index calculation and resource allocation planning, efficient and dynamic allocation of warehouse picking resources is achieved, significantly improving the picking efficiency of fresh goods and reducing losses caused by manual operation errors and improper resource allocation. At the same time, it optimizes the warehouse management process, reduces labor and time costs, and provides reliable technical support for the rapid circulation and shelf life management of fresh goods.

[0044] 2. When generating the resource allocation planning table, this application further refines the resource planning methods of the three according to the corresponding operation modes of the warehousing, outbound and storage of fresh products, so as to make the dynamic allocation of storage and picking resources more reasonable; among them, for the warehousing of fresh products, the corresponding storage area is configured according to the distribution index of fresh products, and then the pre-allocated capacity is determined according to the remaining inventory and the expected future warehousing volume. Finally, the resource allocation of fresh products in the warehousing process is further refined in combination with the storage area, thereby reducing the loss of fresh products in the warehousing process and improving the warehousing efficiency; for the outbound delivery of fresh products, since there is a There are many uncertain factors, so this application plans the resource allocation of each outbound node according to the impact (contribution) of each outbound node of fresh goods on the fresh goods distribution index, so as to improve the response speed to emergency events during the outbound process; for the transportation and storage of fresh goods, in order to ensure the highest transportation and storage efficiency, multiple transportation and storage targets are set. The transportation and storage targets can be understood as feasible solutions for various indicators in the transportation and storage process. Then, a greedy strategy is used to generate multiple transportation and storage targets into an initial transportation and storage population. Finally, the optimal transportation and storage path is calculated in combination with the NSGA-II algorithm. At this time, the transportation and storage resources are allocated according to the optimal transportation and storage path, which can maximize the balance between efficiency and warehouse picking resources. In general, by refining the resource planning methods of warehouse, outbound and transportation and storage, while greatly improving the risk resistance of fresh food storage, it can also ensure that the distribution efficiency meets market demand. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 This is a flow chart of a method for synchronous manual and robot-assisted picking of fresh goods provided in an embodiment of the present application.

[0046] Figure 2 This is a structural diagram of a system for synchronous manual and robot-assisted picking of fresh goods provided in an embodiment of the present application.

[0047] Figure 3 This is a structural diagram of an electronic device provided in an embodiment of the present application.

[0048] Explanation of the reference numerals: 1. Acquisition module; 2. Processing module; 3. Sending module; 300. Electronic device; 301. Processor; 302. Communication bus; 303. User interface; 304. Network interface; 305. Memory. DETAILED DESCRIPTION

[0049] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0050] In the description of the embodiments of this application, words such as "for example" or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as "for example" or "for instance" in the embodiments of this application should not be construed as being preferred or advantageous over other embodiments or designs. Rather, the use of words such as "for example" or "for instance" is intended to present the relevant concepts in a concrete manner.

[0051] In the description of the embodiments of the present application, the term "multiple" means two or more. For example, multiple systems refer to two or more systems, and multiple screen terminals refer to two or more screen terminals. In addition, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as indicating or implying relative importance or implicitly indicating the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized.

[0052] In today's rapidly developing economy, people's living standards have significantly improved, which has greatly driven the booming fresh food delivery market. In recent years, as the scale of the fresh food delivery market continues to expand, consumers' demand for instant fresh food delivery has become increasingly urgent. Everyone hopes to receive fresh, intact fresh food within a short period of time after placing an order. To meet this demand, intelligent picking robots have emerged. By introducing intelligent picking robots to assist manual sorting of goods, they can improve sorting efficiency and thus increase the speed of fresh food delivery.

[0053] However, the inherent perishability, fragility, and short shelf life of fresh produce place extremely high demands on distribution efficiency. This has led to a lack of scientific and rational planning for the inbound and outbound distribution of goods under traditional manual operations. Arrangements are often made based solely on experience, without fully considering factors such as product characteristics, storage conditions, and order requirements. Furthermore, the routes used to transport goods to storage shelves are also poorly planned, resulting in significant waste of time and manpower during transportation, and even the potential for damage to goods due to inappropriate routing.

[0054] These issues directly lead to low utilization of intelligent picking robots, which spend most of their time idle or working inefficiently. Furthermore, the workload of pickers is not properly distributed, with some overburdened while others have relatively light workloads. This impacts work efficiency and, in turn, reduces the efficiency of picking fresh produce.

[0055] In order to solve the above problems, the present application provides a method for manually and robotically assisting in picking fresh goods, which is applied to an intelligent sorting system, such as Figure 1 As shown, the method includes steps S101 to S104, which are as follows:

[0056] S101. Acquire management information of a target warehouse, where the management information includes daily incoming quantity, storage quantity, daily outgoing quantity, and shelf life of various fresh products.

[0057] In the above steps, the intelligent sorting system generates daily inbound and outbound tasks based on the purchase orders and sales orders of the target warehouse, and then stores the inbound and outbound tasks in the management log, and updates the inventory and storage time of the goods in the target warehouse in real time. Before the warehouse starts operating every day, based on the inbound and outbound tasks of the day, the daily required inbound quantity, storage quantity, daily outbound quantity and shelf life of various fresh goods are retrieved.

[0058] S102. Calculate the distribution index of the target warehouse based on the management information.

[0059] In the above steps, the distribution index can be understood as a quantitative indicator of the overall status and complexity of the target warehouse's fresh food distribution. It comprehensively considers multiple factors such as daily incoming inventory, storage volume, daily outbound inventory, and shelf life. When the distribution index is high, it means that the warehouse's distribution tasks are heavy and it is necessary to increase the number of picking personnel and dispatch more intelligent robots and other equipment to improve distribution efficiency. When calculating the distribution index, first calculate the remaining shelf life of the fresh food based on the shelf life and storage time of the various fresh food stored in the target warehouse. Then, based on the remaining shelf life of the various fresh food products, calculate the corresponding urgency coefficient of each fresh food product. The specific calculation can be performed using the following formula:

[0060]

[0061] Among them, t is the remaining shelf life, and T is the urgency coefficient. It can be understood that the shorter the remaining shelf life, the greater the urgency coefficient.

[0062] Finally, the distribution index calculation formula and the urgency coefficients of various fresh products are used to calculate the overall distribution index of the target warehouse. The distribution index calculation formula is as follows:

[0063]

[0064] Among them, P is the distribution index of the target warehouse, is the urgency coefficient of the i-th fresh product, is the daily outbound quantity of the i-th fresh product, is the current storage volume of the i-th fresh product, is the daily inventory requirement for the i-th fresh product, i is the category number of the fresh product, and there are n categories in total. is the adjustment coefficient.

[0065] In the above formula, It can be understood as inventory turnover pressure. The less the current storage of fresh goods, the greater the turnover pressure. is the adjustment coefficient when the inventory of fresh goods is 0. It is an extremely small value, which is used to represent the extreme shortage of fresh goods. The supply-demand imbalance between incoming and outgoing fresh produce can be measured. The greater the difference between the daily incoming and outgoing quantities, the tighter the supply and demand of fresh produce. The total fresh produce delivery pressure is dynamically adjusted by multiplying the fresh produce turnover pressure and the supply-demand imbalance by the urgency coefficient. Finally, the overall distribution index of the target warehouse is determined by calculating the distribution index of all fresh produce types that require turnover on the same day.

[0066] S103. Generate a resource allocation planning table for the target warehouse based on the distribution index.

[0067] In the above steps, for the target warehouse, its configurable resources include human resources and intelligent equipment resources. Intelligent equipment resources include AGV intelligent handling robots, intelligent loading devices, intelligent unloading devices, and intelligent picking devices. In order to improve the effective utilization of configurable resources, this application first calculates the corresponding distribution index of various fresh products. The calculation formula is:

[0068]

[0069] in, is the distribution index of the i-th fresh product, is the urgency coefficient of the i-th fresh product, is the daily outbound quantity of the i-th fresh product, is the current storage volume of the i-th fresh product, is the daily inventory requirement for the i-th fresh product, i is the category number of the fresh product, and there are n categories in total. is the adjustment coefficient

[0070] Then, the ratio of the distribution index corresponding to each of the multiple fresh products and the distribution index of the target warehouse as a whole is calculated to determine the resource allocation priority corresponding to each of the multiple fresh products. It can be understood that if the ratio of the distribution index of a certain fresh product to the target warehouse is greater, it means that the pressure on the fresh product is greater, and the resource allocation priority is higher; at this time, according to the resource allocation priority corresponding to each type of fresh product, resources are allocated first to fresh products with high priority, so as to achieve efficient and dynamic allocation of warehouse picking resources, improve the effective utilization of resources, and reduce losses caused by manual operation errors and improper resource allocation.

[0071] Resource allocation for fresh produce includes planning for incoming inventory, commodity storage, and outgoing inventory. Here are some key aspects:

[0072] With respect to warehousing resource planning, in order to ensure that fresh goods have sufficient storage space and suitable storage conditions, this application configures the storage area of ​​fresh goods in the target warehouse according to the distribution index of fresh goods, wherein the storage area includes a core area, a cache area and an edge area. The core area is an emergency shipping area for fresh goods, the cache area is a short-term storage area for fresh goods, and the edge area is a long-term storage area for fresh goods. At this time, when the distribution index of fresh goods is high, it is configured in the core area, and when the distribution index of fresh goods is low, it is configured in the cache area or the edge area. Then, by obtaining the current remaining inventory of fresh goods in the target warehouse and the expected future inventory, the future pre-allocated capacity of fresh goods is predicted, thereby reducing the possible distribution pressure of fresh goods. Specifically, the following formula can be used:

[0073] Pre-allocated capacity = remaining inventory - expected future inventory × safety factor

[0074] Among them, the safety factor can be understood as the loss ratio of fresh goods during the warehousing process, which can be taken as 0.9 in actual situations.

[0075] Finally, based on the storage area and pre-allocated capacity of fresh goods, the warehousing resources for fresh goods are planned, and the planning results are stored in the resource allocation planning table. The allocated resources include the available smart devices corresponding to the storage area and the corresponding proportion of human resources.

[0076] Regarding outbound resource planning, since there are many uncertainties in the outbound delivery of fresh goods, for example, a sudden increase in sales orders may disrupt the original outbound delivery plan, thereby reducing the delivery efficiency of fresh goods. Based on this, this application divides the outbound delivery of fresh goods into multiple outbound nodes, which specifically include picking, handling, and loading steps, and then calculates the contribution of multiple outbound nodes to the outbound delivery of fresh goods, so as to clarify the impact of each outbound node on the overall outbound delivery efficiency. Specifically, the contribution of the outbound node to the delivery of goods is divided into efficiency contribution, priority contribution, and capacity contribution, and then the contribution values ​​of the three are calculated respectively, among which:

[0077]

[0078]

[0079]

[0080] Among them, the efficiency contribution value represents the response capability of the current outbound node relative to the most efficient outbound node. It represents the processing capacity of the current outbound node for the outbound delivery of high-priority fresh goods. The capacity contribution value represents whether the remaining processing capacity of the current outbound node can meet the remaining processing capacity of the outbound node with the highest processing capacity among other outbound nodes. Then, the efficiency contribution value, priority contribution value and capacity contribution value of each outbound node are multiplied by their respective weights and then added together to obtain the contribution of the outbound node to the outbound delivery of fresh goods.

[0081] Finally, based on the contribution of multiple outbound nodes, inbound resources are planned for multiple outbound nodes of fresh goods and stored in the resource allocation planning table. For example, for outbound nodes with lower contribution, more resources are allocated to improve their outbound processing efficiency for fresh goods.

[0082] Regarding commodity transportation and storage resource planning, it should be noted that the transportation and storage of fresh goods is generally completed automatically by AGV handling robots. At this time, multiple transportation and storage objectives need to be considered, namely, the highest effective utilization rate of available resources, the shortest transportation and storage time, the lowest transportation and storage loss, and the shortest transportation and storage path. Under multiple transportation and storage objectives, the commonly used algorithm is the NSGA-II algorithm, but this algorithm has some defects, such as: the solution quality is highly dependent on the initial population level, and it may fall into local optimality and converge prematurely. Therefore, in order to solve this problem, this application accelerates the population convergence speed by adopting a greedy strategy to improve the quality of the initial population. That is, based on multiple transportation and storage objectives, a greedy strategy is adopted to generate an initial transportation and storage population, and then the NSGA-II algorithm is used to calculate the optimal transportation and storage path of the initial transportation and storage population. Finally, based on the optimal transportation and storage path, commodity transportation and storage resource planning is performed for the third fresh product and stored in the resource allocation planning table. In this way, while meeting the optimal solution of multiple transportation and storage objectives, the required storage and picking resources are clearly allocated, thereby improving the transportation and storage efficiency of fresh goods.

[0083] S104: Send the resource allocation planning table to the warehouse management personnel so that the warehouse management personnel can allocate the required picking resources for the target warehouse. The required picking resources include human resources and intelligent equipment resources.

[0084] In the above steps, the resource allocation planning table contains the resource allocation status of various fresh products in three aspects: warehousing, transportation and outbound. This provides warehouse management personnel with reference opinions on warehouse picking resource planning, thereby optimizing the warehouse management process, reducing labor costs and time costs, and providing reliable technical support for the rapid circulation and shelf life management of fresh products.

[0085] Reference Figure 2 The present application also provides a system for manually and robotically assisting in picking fresh produce. The system is an intelligent sorting system. The intelligent sorting system includes an acquisition module 1, a processing module 2, and a sending module 3, wherein:

[0086] Acquisition module 1 is used to obtain management information of the target warehouse, including daily incoming quantity, storage quantity, daily outgoing quantity and shelf life of various fresh goods;

[0087] Processing module 2 is used to calculate the distribution index of the target warehouse based on the management information; based on the distribution index, generate a resource allocation planning table for the target warehouse, which includes the inbound resource planning, outbound resource planning, and commodity storage resource planning;

[0088] The sending module 3 is used to send the resource allocation planning table to the warehouse management personnel so that the warehouse management personnel can allocate the required picking resources of the target warehouse. The required picking resources include human resources and intelligent equipment resources.

[0089] In one possible implementation, calculating the distribution index of the target warehouse based on the management information specifically includes:

[0090] Calculate the remaining shelf life of various fresh products in the target warehouse based on management information;

[0091] Calculate the urgency coefficient of various fresh products based on their remaining shelf life;

[0092] The distribution index of the target warehouse is calculated using the distribution index calculation formula and the urgency coefficients of various fresh products.

[0093] In a possible implementation, the distribution index calculation formula is specifically:

[0094]

[0095] Among them, P is the distribution index of the target warehouse, is the urgency coefficient of the i-th fresh product, is the daily outbound quantity of the i-th fresh product, is the current storage volume of the i-th fresh product, is the daily inventory requirement for the i-th fresh product, i is the category number of the fresh product, and there are n categories in total. is the adjustment coefficient.

[0096] In a possible implementation, generating a resource allocation planning table for the target warehouse based on the distribution index specifically includes:

[0097] Calculating a delivery index for a plurality of fresh products;

[0098] Calculating a ratio of the delivery index of the plurality of fresh products to the delivery index of the target warehouse;

[0099] Determining resource allocation priorities for the multiple fresh products based on the ratio results;

[0100] Based on the resource allocation priority, the required picking resources are allocated to the target warehouse step by step to obtain the resource allocation planning table.

[0101] In a possible implementation, the resource allocation planning table includes an incoming resource plan, and the resource allocation planning table is obtained by allocating the required picking resources to the target warehouse in a step-by-step manner based on the resource allocation priority, and specifically further includes:

[0102] Allocate a storage area for the first fresh product in the target warehouse according to the delivery index of the first fresh product, the storage area including a core area, a buffer area, and a peripheral area, wherein the first fresh product is any one of a plurality of fresh products;

[0103] Obtaining the remaining inventory of the first fresh product after storage and the expected future inventory volume;

[0104] Calculating the pre-allocated capacity of the first fresh product based on the remaining inventory and the expected future inventory volume;

[0105] Based on the storage area and pre-allocated capacity of the first fresh product, storage resource planning is performed for the first fresh product and stored in the resource allocation planning table.

[0106] In a possible implementation, the resource allocation planning table includes outbound resource planning, and the resource allocation planning table is obtained by allocating required picking resources to the target warehouse in a step-by-step manner based on the resource allocation priority, and specifically further includes:

[0107] Acquire multiple outbound nodes of a second fresh product, where the second fresh product is any one of the multiple fresh products;

[0108] Calculating the contribution of the plurality of delivery nodes to the delivery of the second fresh product;

[0109] According to the contribution of the plurality of outbound nodes, inbound resources are planned for the plurality of outbound nodes of the second fresh product, and the planning is stored in the resource allocation planning table.

[0110] In a possible implementation, the resource allocation planning table includes commodity transportation and storage resource planning, and the resource allocation planning table is obtained by allocating required picking resources to the target warehouse in a step-by-step manner based on the resource allocation priority, and specifically further includes:

[0111] Acquire multiple storage targets for a third fresh product, wherein the multiple storage targets include available resources, storage time, storage loss, and available storage paths, and the third fresh product is any one of the multiple fresh products;

[0112] Based on the multiple storage targets, an initial storage population is generated using a greedy strategy;

[0113] Using the NSGA-II algorithm, the optimal operation and storage path of the initial operation and storage population is calculated;

[0114] According to the optimal storage path, commodity storage resources are planned for the third fresh product and stored in the resource allocation planning table.

[0115] It should be noted that the above embodiments provide devices that implement their functions using only the division of the above functional modules as examples. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the device and method embodiments provided in the above embodiments are based on the same concept. The specific implementation process is detailed in the method embodiment and will not be repeated here.

[0116] This application also discloses an electronic device. Figure 3 , Figure 3 The electronic device 300 may include: at least one processor 301 , at least one network interface 304 , a user interface 303 , a memory 305 , and at least one communication bus 302 .

[0117] The communication bus 302 is used to implement the connection and communication between these components.

[0118] The user interface 303 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 303 may also include a standard wired interface and a wireless interface.

[0119] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0120] The processor 301 may include one or more processing cores. Using various interfaces and circuits, the processor 301 connects to various components within the server. It executes instructions, programs, code sets, or instruction sets stored in the memory 305, as well as accesses data stored in the memory 305, to perform various server functions and process data. Optionally, the processor 301 may be implemented using at least one of the following hardware forms: a digital signal processing (DSP), a field-programmable gate array (FPGA), or a programmable logic array (PLA). The processor 301 may integrate one or a combination of a central processing unit (CPU), a graphics processing unit (GPU), and a modem. The CPU primarily processes the operating system, user interface, and application programs; the GPU is responsible for rendering and drawing content displayed on the display screen; and the modem handles wireless communications. It is understood that the modem may not be integrated into the processor 301 but implemented as a separate chip.

[0121] Among them, the memory 305 may include a random access memory (RAM) or a read-only memory (Read-Only Memory). Optionally, the memory 305 includes a non-transitory computer-readable storage medium. The memory 305 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 305 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 305 may also optionally be at least one storage device located away from the aforementioned processor 301. Refer to Figure 3 , the memory 305 as a computer storage medium may include an operating system, a network communication module, a user interface module, and an application program for a method for synchronous manual and robot assisted picking of fresh goods.

[0122] exist Figure 3In the electronic device 300 shown, the user interface 303 is mainly used to provide an input interface for the user and obtain the data input by the user; and the processor 301 can be used to call the application program stored in the memory 305 for a method of manual and robot synchronous assisted picking of fresh goods. When executed by one or more processors 301, the electronic device 300 executes one or more of the methods described in the above embodiments. It should be noted that for the aforementioned method embodiments, for the sake of simplicity of description, they are all expressed as a series of action combinations, but those skilled in the art should know that this application is not limited to the order of the actions described, because according to this application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for this application.

[0123] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0124] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are merely schematic, such as the division of units, which is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components 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 service interface, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0125] Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0126] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0127] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of this application, or the portion that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the various embodiments of the method of this application. The aforementioned memory includes various media that can store program code, such as USB flash drives, mobile hard drives, magnetic disks, or optical disks.

[0128] The foregoing is merely an exemplary embodiment of the present disclosure and is not intended to limit the scope of the present disclosure. In other words, any equivalent variations and modifications made in accordance with the teachings of the present disclosure are still within the scope of the present disclosure. Those skilled in the art will readily conceive of other embodiments of the present disclosure after considering the disclosure and the practical implications thereof.

[0129] This application is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary techniques in the art not described herein. The description and examples are to be considered as exemplary only, and the scope and spirit of the present disclosure are to be defined by the claims.

Claims

1. A method for synchronously assisting manual and robot-assisted picking of fresh produce, characterized in that: Applied to an intelligent sorting system, the method includes: Obtaining management information of the target warehouse, including daily incoming quantity, storage quantity, daily outgoing quantity, and shelf life of various fresh goods; Calculating the distribution index of the target warehouse based on the management information; Based on the distribution index, generating a resource allocation planning table for the target warehouse; The resource allocation planning table is sent to the warehouse management personnel so that the warehouse management personnel can allocate the required picking resources of the target warehouse. The required picking resources include human resources and intelligent equipment resources, wherein: Generating a resource allocation planning table for the target warehouse based on the distribution index specifically includes: Calculating a delivery index for a plurality of fresh products; Calculating a ratio of a delivery index of the plurality of fresh products to a delivery index of the target warehouse; Determining resource allocation priorities for the multiple fresh products based on the ratio results; Based on the resource allocation priorities, the required picking resources are allocated to the target warehouse in a step-by-step manner to obtain the resource allocation planning table, wherein the resource allocation planning table includes a commodity transportation and storage resource plan, wherein the required picking resources are allocated to the target warehouse in a step-by-step manner based on the resource allocation priorities to obtain the resource allocation planning table, specifically further includes: Acquire multiple storage targets for a third fresh product, wherein the multiple storage targets include available resources, storage time, storage loss, and available storage paths, and the third fresh product is any one of the multiple fresh products; Based on the multiple storage targets, an initial storage population is generated using a greedy strategy; Using the NSGA-II algorithm, the optimal operation and storage path of the initial operation and storage population is calculated; According to the optimal storage path, commodity storage resources are planned for the third fresh product and stored in the resource allocation planning table.

2. The method according to claim 1, characterized in that The calculating of the distribution index of the target warehouse according to the management information specifically includes: Calculating the remaining shelf life of the plurality of fresh products in the target warehouse based on the management information; Calculating the urgency coefficients of the plurality of fresh products according to the remaining shelf life; The distribution index of the target warehouse is calculated using the distribution index calculation formula and the urgency coefficients of the various fresh products.

3. The method according to claim 2, characterized in that The distribution index calculation formula is specifically as follows: ； Among them, P is the distribution index of the target warehouse, is the urgency coefficient of the i-th fresh product, is the daily outbound quantity of the i-th fresh product, is the current storage volume of the i-th fresh product, is the daily inventory requirement for the i-th fresh product, i is the category number of the fresh product, and there are n categories in total. is the adjustment coefficient.

4. The method according to claim 1, wherein The resource allocation planning table includes a warehousing resource plan. Based on the resource allocation priority, the required picking resources are allocated to the target warehouse step by step to obtain the resource allocation planning table, which specifically includes: Allocate a storage area for the first fresh product in the target warehouse according to the delivery index of the first fresh product, the storage area including a core area, a buffer area, and a peripheral area, wherein the first fresh product is any one of a plurality of fresh products; Obtaining the remaining inventory of the first fresh product after storage and the expected future inventory volume; Calculating the pre-allocated capacity of the first fresh product based on the remaining inventory and the expected future inventory volume; Based on the storage area and pre-allocated capacity of the first fresh product, storage resource planning is performed for the first fresh product and stored in the resource allocation planning table.

5. The method according to claim 1, wherein The resource allocation planning table includes outbound resource planning. Based on the resource allocation priority, the required picking resources are allocated to the target warehouse step by step to obtain the resource allocation planning table, which specifically includes: Acquire multiple outbound nodes of a second fresh product, where the second fresh product is any one of the multiple fresh products; Calculating the contribution of the plurality of delivery nodes to the delivery of the second fresh product; According to the contribution of the plurality of outbound nodes, inbound resources are planned for the plurality of outbound nodes of the second fresh product, and the planning is stored in the resource allocation planning table.

6. A system for synchronous manual and robot-assisted picking of fresh produce, characterized in that: The system is an intelligent sorting system, comprising an acquisition module (1), a processing module (2) and a sending module (3), wherein: The acquisition module (1) is used to acquire management information of the target warehouse, wherein the management information includes daily incoming quantity, storage quantity, daily outgoing quantity and shelf life of various fresh goods; The processing module (2) is used to calculate the distribution index of the target warehouse based on the management information; based on the distribution index, generate a resource allocation planning table for the target warehouse, the resource allocation planning table includes inbound resource planning, outbound resource planning and commodity storage resource planning, wherein: Generating a resource allocation planning table for the target warehouse based on the distribution index specifically includes: Calculating a delivery index for a plurality of fresh products; Calculating a ratio of a delivery index of the plurality of fresh products to a delivery index of the target warehouse; Determining resource allocation priorities for the multiple fresh products based on the ratio results; Based on the resource allocation priorities, the required picking resources are allocated to the target warehouse in a step-by-step manner to obtain the resource allocation planning table, wherein the resource allocation planning table includes a commodity transportation and storage resource plan, wherein the required picking resources are allocated to the target warehouse in a step-by-step manner based on the resource allocation priorities to obtain the resource allocation planning table, specifically further includes: Acquire multiple storage targets for a third fresh product, wherein the multiple storage targets include available resources, storage time, storage loss, and available storage paths, and the third fresh product is any one of the multiple fresh products; Based on the multiple storage targets, an initial storage population is generated using a greedy strategy; Using the NSGA-II algorithm, the optimal operation and storage path of the initial operation and storage population is calculated; Performing commodity storage resource planning for the third fresh product according to the optimal storage path, and storing the planning in the resource allocation planning table; The sending module (3) is used to send the resource allocation planning table to the warehouse management personnel so that the warehouse management personnel can allocate the required picking resources of the target warehouse, and the required picking resources include human resources and intelligent equipment resources.

7. An electronic device, characterized in that: The electronic device (300) comprises a processor (301), a memory (305), a user interface (303) and a network interface (304), wherein the memory (305) is used to store instructions, the user interface (303) and the network interface (304) are used to communicate with other devices, and the processor (301) is used to execute the instructions stored in the memory (305) so that the electronic device (300) executes the method according to any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that The computer-readable storage medium stores instructions, and when the instructions are executed, the method according to any one of claims 1 to 5 is performed.

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