Order allocation method and device and electronic equipment

Abstract

The embodiment of the invention provides an order allocation method and device and electronic equipment, and relates to the technical field of warehouse management, and the method comprises the steps: determining risk values of a plurality of workstations and uncompleted order data, the risk values being used for representing that the workstations have man-machine cooperation risks, and the uncompleted order data being used for representing that the workstations have man-machine cooperation risks; the uncompleted order data is used for representing the order processing efficiency of the plurality of carrying devices based on the current order distribution condition; determining an order allocation decision index based on the risk value and the uncompleted order data; determining a plurality of comprehensive scores of the plurality of to-be-allocated orders based on the order allocation decision-making indexes; and according to the plurality of comprehensive scores, allocating a first order to be allocated in the plurality of orders to be allocated to a first workstation corresponding to the first order to be allocated. The first to-be-allocated order is selected in real time according to the order allocation decision-making index to be allocated preferentially, efficient order allocation is achieved, the working efficiency is improved, and the probability of man-machine cooperation risks is reduced.

Description

Technical Field

[0001] The embodiments of the present disclosure relate to the technical field of warehouse management, and in particular to an order allocation method, device, and electronic device. Background Art

[0002] During actual operation, the intelligent warehousing system continuously monitors its own operating efficiency. If actual operating efficiency fails to meet preset standards, the system will signal to engineers that parameter adjustments are required. At this point, engineers will need to draw on their accumulated expertise to manually adjust the order allocation decision parameters of the algorithm model to optimize efficiency.

[0003] However, this technology relies heavily on engineers' personal experience to manually adjust algorithm parameters. When operational efficiency falls short of expectations, the system lacks the ability to automatically adapt to dynamic environmental changes and automatically trigger parameter optimization mechanisms based on these real-time changes. This leads to response delays, lowering overall work efficiency and creating risks in human-machine collaboration. Summary of the Invention

[0004] The present disclosure provides an order allocation method, device, and electronic device. The present disclosure provides the following technical solutions:

[0005] A first aspect of an embodiment of the present disclosure provides an order allocation method, the method comprising: determining risk values ​​and uncompleted order data of multiple workstations, the risk value being used to indicate the risk of human-machine collaboration at the workstation, and the uncompleted order data being used to indicate the order processing efficiency of multiple handling equipment based on the current order allocation situation; determining order allocation decision indicators based on the risk value and the uncompleted order data; determining multiple comprehensive scores of multiple orders to be allocated based on the order allocation decision indicators; and allocating a first order to be allocated among the multiple orders to be allocated to a first workstation corresponding to the first order to be allocated based on the multiple comprehensive scores.

[0006] In some embodiments, risk values ​​and unfinished order data of multiple workstations are determined, including: determining the total number of all workstations and the risk number of workstations where human-machine collaboration risks occur; taking the ratio between the risk number and the total number as the risk value; and determining the unfinished order data when multiple handling devices perform order processing tasks based on the current order allocation situation.

[0007] In some embodiments, order allocation decision indicators are determined based on the risk value and the uncompleted order data, including: when the risk value is greater than a preset risk threshold and the first uncompleted order data in the uncompleted order data deviates from the target range of the first uncompleted order data, based on the deviation status of the first uncompleted order data, the order allocation decision indicators of the first uncompleted order data are determined.

[0008] In some embodiments, when the risk value is greater than a preset risk threshold and the first uncompleted order data in the uncompleted order data deviates from the target range of the first uncompleted order data, the order allocation decision indicator of the first uncompleted order data is adjusted based on the deviation status of the first uncompleted order data, including: when the risk value is greater than the preset risk threshold, determining the first target range corresponding to the first uncompleted order data; determining whether the first uncompleted order data is within the first target range; if the first uncompleted order data is not within the first target range, determining the deviation status of the first uncompleted order data and the order allocation decision indicator corresponding to the first uncompleted order data, the deviation status indicating the degree and direction of the first uncompleted order data deviating from the first target range; determining the adjustment direction of the order allocation decision indicator based on the deviation status; and adjusting the order allocation decision indicator corresponding to the first uncompleted order data according to a preset step size based on the adjustment direction.

[0009] In some embodiments, based on the order allocation decision indicators, multiple comprehensive scores of multiple orders to be allocated are determined, including: determining the order data of each order to be allocated; based on the order data and the order allocation decision indicators corresponding to each unfinished order data in the unfinished order data, combined with the order allocation decision algorithm, determining the comprehensive score between each order to be allocated in the multiple orders to be allocated and the workstation corresponding to each order to be allocated, the comprehensive score is used to represent the degree of matching between each order to be allocated and the workstation corresponding to each order to be allocated.

[0010] In some embodiments, based on multiple comprehensive scores, the first to-be-allocated order among multiple to-be-allocated orders is allocated to the first workstation corresponding to the first to-be-allocated order, including: sorting each comprehensive score among the multiple comprehensive scores in order of score size; taking the comprehensive score whose sorting position is at the target position as the first comprehensive score; determining the to-be-allocated order corresponding to the first comprehensive score as the first to-be-allocated order, and determining the workstation corresponding to the first to-be-allocated order as the first workstation, so as to allocate the first to-be-allocated order to the first workstation.

[0011] In some embodiments, based on multiple comprehensive scores, a first to-be-assigned order among multiple to-be-assigned orders is assigned to a first workstation corresponding to the first to-be-assigned order. Thereafter, the method includes: determining unfinished order data of multiple handling devices based on executing order processing tasks when the first to-be-assigned order is assigned to the first workstation; when second unfinished order data in the unfinished order data deviates from the second target range of the second unfinished order data, adjusting the adjusted order allocation decision indicator corresponding to the second unfinished order data based on the deviation state of the second unfinished order data until the second unfinished order data is within the second target range.

[0012] In some embodiments, risk values ​​of multiple workstations and unfinished order data are determined. Before that, the method includes: determining multiple initial comprehensive scores of multiple initial allocated orders based on the initial order allocation decision indicators of each unfinished order data in the unfinished order data; and allocating the first initial allocated order among the multiple initial allocated orders to the initial workstation corresponding to the first initial allocated order according to the multiple initial comprehensive scores to obtain the current order allocation situation.

[0013] A second aspect of the present disclosure provides an order allocation device, the device comprising:

[0014] A first determining unit is configured to determine risk values ​​of multiple workstations and uncompleted order data, wherein the risk value indicates a risk of human-machine collaboration at the workstation, and the uncompleted order data indicates an efficiency of multiple handling devices in processing orders based on current order allocation;

[0015] A second determining unit is used to determine an order allocation decision indicator based on the risk value and the uncompleted order data;

[0016] A scoring unit, configured to determine a plurality of comprehensive scores of a plurality of orders to be allocated based on order allocation decision indicators;

[0017] The allocating unit is configured to allocate a first order to be allocated from among the multiple orders to be allocated to a first workstation corresponding to the first order to be allocated according to the multiple comprehensive scores.

[0018] A third aspect of an embodiment of the present disclosure provides an electronic device, comprising: a processor and a memory, the memory being used to store computer-executable instructions; and the processor being used to read instructions from the memory and execute the instructions to implement the method in any one of the implementation modes of the aforementioned first aspect.

[0019] A fourth aspect of an embodiment of the present disclosure provides a computer-readable storage medium, in which computer instructions are stored, and the computer instructions are configured to enable the computer to execute the method in any implementation manner of the aforementioned first aspect.

[0020] A fifth aspect of an embodiment of the present disclosure provides a computer program product, which includes a computer program stored on a computer-readable storage medium, and the computer program includes program instructions. When the program instructions are executed by a computer, the computer executes the method in any implementation manner of the first aspect above.

[0021] The order allocation method, device, and electronic device provided by the embodiments of the present disclosure can determine the risk values ​​and uncompleted order data of multiple workstations, the risk value is used to indicate the risk of human-machine collaboration at the workstation, and the uncompleted order data is used to indicate the efficiency of multiple handling equipment in processing orders based on the current order allocation situation; based on the risk value and the uncompleted order data, determine the order allocation decision index; based on the order allocation decision index, determine multiple comprehensive scores of multiple orders to be allocated; based on the multiple comprehensive scores, allocate the first order to be allocated among the multiple orders to be allocated to the first workstation corresponding to the first order to be allocated. The present disclosure adaptively determines the order allocation decision index in real time based on the risk value and the uncompleted order data, so as to select the appropriate first order to be allocated according to the order allocation decision index for priority allocation, thereby achieving efficient order allocation, improving work efficiency, and reducing the probability of human-machine collaboration risks. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 A flowchart of an order allocation method proposed in an embodiment of the present disclosure;

[0023] Figure 2 A flowchart of an order allocation method proposed in an embodiment of the present disclosure;

[0024] Figure 3 A flowchart of an order allocation method proposed in an embodiment of the present disclosure;

[0025] Figure 4 A schematic diagram of an order distribution system proposed in an embodiment of the present disclosure;

[0026] Figure 5 This is a structural diagram of an order distribution device 600 proposed in an embodiment of the present disclosure;

[0027] Figure 6 is a structural diagram of an electronic device 900 for implementing the above-mentioned order allocation method according to an exemplary embodiment. DETAILED DESCRIPTION

[0028] The following description sets forth many specific details to facilitate a full understanding of the present disclosure. However, the present disclosure can be implemented in many other ways than those described herein, and those skilled in the art can make similar generalizations without violating the scope of the present disclosure. Therefore, the present disclosure is not limited to the specific implementations disclosed below.

[0029] The terms used in one or more embodiments of the present disclosure are for the purpose of describing specific embodiments only and are not intended to limit one or more embodiments of the present disclosure. The singular forms "a", "the", and "the" used in one or more embodiments of the present disclosure and the appended claims are also intended to include plural forms, unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used in one or more embodiments of the present disclosure refers to and includes any or all possible combinations of one or more associated listed items.

[0030] It should be understood that although the terms first, second, etc. may be used to describe various information in one or more embodiments of the present disclosure, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from each other. For example, without departing from the scope of one or more embodiments of the present disclosure, the first may also be referred to as the second, and similarly, the second may also be referred to as the first. Depending on the context, the word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining".

[0031] The following describes the solutions of the embodiments of the present disclosure with reference to examples.

[0032] During actual operation, the intelligent warehousing system continuously monitors its own operating efficiency. If actual operating efficiency fails to meet preset standards, the system will signal to engineers that parameter adjustments are required. At this point, engineers will need to draw on their accumulated experience to manually adjust the order allocation decision parameters of the algorithm model to optimize work efficiency.

[0033] However, this technology relies heavily on engineers' personal experience to manually adjust algorithm parameters. When operational efficiency falls short of expectations, the system lacks the ability to automatically adapt to dynamic environmental changes and automatically trigger parameter optimization mechanisms based on these real-time changes. This leads to response delays, lowering overall work efficiency and creating risks in human-machine collaboration.

[0034] To address the above issues, the embodiments of the present disclosure propose an order allocation method. This method determines the risk values ​​and uncompleted order data of multiple workstations. The risk values ​​are used to indicate the risk of human-machine collaboration at the workstations, and the uncompleted order data are used to indicate the order processing efficiency of multiple handling equipment based on the current order allocation situation. Based on the risk values ​​and uncompleted order data, an order allocation decision indicator is determined. Based on the order allocation decision indicator, multiple comprehensive scores of multiple orders to be allocated are determined. Based on the multiple comprehensive scores, the first order to be allocated among the multiple orders to be allocated is allocated to the first workstation corresponding to the first order to be allocated. This method dynamically adjusts the order allocation strategy in real time based on equipment risk and handling efficiency, significantly improving operational efficiency and effectively reducing the possibility of human-machine collaboration risks.

[0035] Figure 1 A flowchart of an order allocation method proposed in an embodiment of the present disclosure is shown in FIG. Figure 1 As shown, the method includes the following steps:

[0036] Step 101: Determine risk values ​​and uncompleted order data of multiple workstations.

[0037] In some embodiments, a workstation refers to equipment used to select goods from storage areas such as warehouse shelves during logistics operations, such as a manual picking operation table.

[0038] The risk value is a quantitative indicator used to indicate the risk of human-robot collaboration at a workstation. It measures the degree of risk associated with human-robot collaboration during operations. Human-robot collaboration risk refers to the potential for safety incidents and reduced efficiency due to improper operation or equipment failure when humans and equipment work together. For example, the risk to humans and robots is present.

[0039] In some embodiments, the present disclosure may determine the total number of all workstations and the risk number of workstations that present human-machine collaboration risks; and use the ratio between the risk number and the total number as the risk value.

[0040] Specifically, the present disclosure can count the total number of all workstations in the warehouse; at the same time, record the number of devices that have human-machine collaboration risks within a certain period of time (that is, the risk number in the present disclosure), and then calculate the ratio of the risk data to the total number, and use the ratio as the risk value.

[0041] For example, if there are 10 workstations in a warehouse, and two of them have experienced human-robot collaboration risks in the past week, then the total number is 10, and the risk number is 2. Then, calculate the ratio of the risk number to the total number: 2 / 10 = 0.2. A higher risk value indicates a greater likelihood that a workstation will experience human-robot collaboration risks.

[0042] Handling equipment refers to intelligent robots, such as automated guided vehicles (AGVs), autonomous mobile robots (AMRs), or other automated handling equipment. These robots can autonomously or semi-autonomously perform handling tasks such as transporting goods, picking items, and stacking.

[0043] Unfinished order data is used to indicate the order processing efficiency of multiple handling equipment based on the current order allocation, reflecting the system's responsiveness to dynamic tasks and the rationality of resource scheduling. For example, it can be unfinished order data that reflects the effectiveness of the caching strategy and the convenience of equipment pickup, or it can be unfinished order data that reflects the complexity of the handling path and the rationality of equipment scheduling.

[0044] Prior to determining the risk values ​​of the multiple workstations and the uncompleted order data, the present disclosure further includes: determining multiple initial comprehensive scores for the multiple initially allocated orders based on an initial order allocation decision indicator for each uncompleted order in the uncompleted order data; and allocating a first initially allocated order from the multiple initially allocated orders to the initial workstation corresponding to the first initially allocated order based on the multiple initial comprehensive scores, thereby obtaining a current order allocation status. The initial order allocation decision indicator is pre-set based on expert experience.

[0045] Step 102: Determine order allocation decision indicators based on the risk value and uncompleted order data.

[0046] In some embodiments, the order allocation decision metric is a quantitative parameter used to guide order allocation decisions, prioritizing the selection of the most appropriate orders based on the metric. The metric comprehensively considers the safety of workstations and the efficiency of handling equipment, aiming to find a balance between ensuring the safety of human-machine collaboration and improving order processing efficiency.

[0047] Step 103: Determine a plurality of comprehensive scores of the plurality of orders to be allocated based on the order allocation decision indicator.

[0048] In some embodiments, the orders to be allocated refer to orders that have not yet been allocated to specific workstations. These orders are reasonably allocated according to order allocation decision indicators.

[0049] The comprehensive score is a quantitative assessment of the compatibility between each pending order and its corresponding workstation, combined with the order allocation decision indicators. In other words, the comprehensive score reflects the degree of match between each pending order and its corresponding workstation. A higher comprehensive score indicates a closer match between the pending order and its corresponding workstation, leading to higher work efficiency.

[0050] Step 104 : Allocate a first order to be allocated among the multiple orders to be allocated to a first workstation corresponding to the first order to be allocated based on the multiple comprehensive scores.

[0051] In some embodiments, the present disclosure can sort each of a plurality of comprehensive scores in order of score size; take the comprehensive score at the target position in the sorting position as the first comprehensive score; determine the to-be-allocated order corresponding to the first comprehensive score as the first to-be-allocated order, and determine the workstation corresponding to the first to-be-allocated order as the first workstation, so as to assign the first to-be-allocated order to the first workstation.

[0052] The target position can be set according to actual needs. It can be the first position after sorting from high to low or the last position after sorting from low to high (i.e., the highest score). The comprehensive score corresponding to this position is the first comprehensive score.

[0053] For example, suppose there are three pending orders. Three comprehensive scores are generated (i.e., the comprehensive score between each pending order and its corresponding workstation). These three scores are sorted from high to low. The score that comes first is the first comprehensive score. The pending order corresponding to the first comprehensive score is found and designated as the first pending order.

[0054] Because each pending order has corresponding order data, which includes the workstation corresponding to each pending order and the order allocation data for each pending order, the present disclosure can directly map the order data corresponding to the first pending order to the corresponding workstation, using the workstation corresponding to the first pending order as the first workstation. This allows the first pending order with the highest matching degree to be assigned to the first workstation, thereby improving overall operational efficiency and reducing the risks of human-machine collaboration.

[0055] The present disclosure can recalculate the comprehensive score and optimize the order allocation plan after each adjustment, continuously monitor the changes in the first unfinished order data, and ensure that the work efficiency and risk control are balanced. That is, based on multiple comprehensive scores, a first to-be-allocated order among multiple to-be-allocated orders is allocated to the first workstation corresponding to the first to-be-allocated order. Thereafter, the present disclosure can determine the unfinished order data of the multiple handling devices that perform the order processing task based on the allocation of the first to-be-allocated order to the first workstation; when a second unfinished order data among the unfinished order data deviates from the second target range of the second unfinished order data, based on the deviation state of the second unfinished order data, the adjusted order allocation decision indicator corresponding to the second unfinished order data is adjusted until the second unfinished order data is within the second target range.

[0056] Specifically, the second uncompleted order data refers to uncompleted order data of the same type as the first uncompleted order data, which is generated after the first pending order is assigned to the first workstation and multiple handling devices re-execute order processing tasks based on the current order allocation situation (at this time, the order allocation situation has changed due to the addition of the pending order). For example, if the first uncompleted order data is the ratio of dispatched uncompleted orders that hit the cached bins, then the second uncompleted order data is also the ratio of dispatched uncompleted orders that hit the cached bins.

[0057] The second target range is a preset reasonable value range of the second uncompleted order data. Since the second uncompleted order data and the first uncompleted order data belong to the same type, the second target range is the same as the first target range.

[0058] In summary, the above-mentioned embodiments of the present disclosure can determine the risk values ​​and unfinished order data of multiple workstations, the risk value is used to indicate the risk of human-machine collaboration at the workstation, and the unfinished order data is used to indicate the efficiency of multiple handling equipment in processing orders based on the current order allocation situation; based on the risk value and the unfinished order data, determine the order allocation decision index; based on the order allocation decision index, determine multiple comprehensive scores of multiple orders to be allocated; based on the multiple comprehensive scores, allocate the first order to be allocated among the multiple orders to be allocated to the first workstation corresponding to the first order to be allocated. The present disclosure adaptively determines the order allocation decision index in real time based on the risk value and the unfinished order data, so as to efficiently allocate orders according to the order allocation decision index, improve work efficiency, and reduce the probability of human-machine collaboration risks.

[0059] Figure 2 A flowchart of an order allocation method proposed in an embodiment of the present disclosure is shown in FIG. Figure 2 As shown, the method further includes the following steps:

[0060] Step 201: When the risk value is greater than a preset risk threshold and the first uncompleted order data in the uncompleted order data deviates from the target range of the first uncompleted order data, an order allocation decision indicator for the first uncompleted order data is determined based on the deviation state of the first uncompleted order data.

[0061] In some embodiments, when the risk value is greater than a preset risk threshold, the first target range corresponding to the first uncompleted order data is determined; whether the first uncompleted order data is within the first target range is determined; if the first uncompleted order data is not within the first target range, the deviation status of the first uncompleted order data and the order allocation decision indicator corresponding to the first uncompleted order data are determined; based on the deviation status, the adjustment direction of the order allocation decision indicator is determined; based on the adjustment direction, the order allocation decision indicator corresponding to the first uncompleted order data is adjusted according to the preset step size.

[0062] In some embodiments, a preset risk threshold is a reference value pre-determined by expert experience and serves as a criterion for determining whether a workstation's risk level warrants intervention. When the risk value exceeds the preset risk threshold, it indicates that the human-machine collaboration risk at the current workstation is at a high level, necessitating adjustments to the order allocation strategy.

[0063] The first unfinished order data refers to a certain unfinished order data generated when multiple handling devices perform order processing tasks based on the current order allocation situation (ie, the state after the first initial allocation order has been allocated to the initial workstation).

[0064] The first target range is a preset reasonable value range of the first uncompleted order data, and is used to measure whether the order processing efficiency of the current handling equipment is at a normal level.

[0065] The deviation status indicates the degree and direction of the deviation of the first uncompleted order data from the first target range, that is, greater than the upper limit of the target range or less than the lower limit of the target range.

[0066] The preset step size is the pre-set range of change each time the order allocation decision indicator is adjusted, which is used to control the precision and stability of the adjustment.

[0067] Among them, after determining the adjustment direction of the order allocation decision indicator, the present disclosure can further determine the data type (forward data or reverse data) of the first unfinished order data, and adjust the order allocation decision indicator corresponding to the first unfinished order data according to the data type and adjustment direction and the preset step size.

[0068] Specifically, assume that in a smart warehouse, the preset risk threshold is 0.7 (indicating a critical point where intervention is required due to a high risk level). The risk values ​​of multiple workstations have risen to 0.8 due to the recent frequent human-machine collaboration risks, exceeding the preset risk threshold. At this time, the handling equipment's order processing efficiency is low, and the first uncompleted order data deviates from the first target range. If it is determined that the deviation state is lower than the lower limit of the first target range, it is further determined whether the first uncompleted order data is positive data or negative data. If the first uncompleted order data is positive data (i.e., the larger the first uncompleted order data, the better), then the adjustment direction of its corresponding order allocation decision indicator is determined to be increased, and the order allocation decision indicator is increased according to the preset step size; conversely, if the first uncompleted order data is negative data (i.e., the smaller the first uncompleted order data, the better), then the corresponding order allocation decision indicator can be reduced according to the preset step size, or the corresponding order allocation decision indicator can be not adjusted.

[0069] Similarly, assume that in a smart warehouse, the preset risk threshold is 0.7 (indicating a critical point where intervention is required due to a high risk level). The risk values ​​of multiple workstations have risen to 0.8 due to the recent frequent human-machine collaboration risks, exceeding the preset risk threshold. At this time, the handling equipment's order processing efficiency is low, and the first uncompleted order data deviates from the first target range. If the deviation is determined to be higher than the upper limit of the first target range, it is further determined whether the first uncompleted order data is positive or negative. If the first uncompleted order data is positive (i.e., the larger the first uncompleted order data, the better), then the corresponding order allocation decision indicator can be reduced by a preset step size, or the corresponding order allocation decision indicator can be left unchanged. Conversely, if the first uncompleted order data is negative (i.e., the smaller the first uncompleted order data, the better), then the corresponding order allocation decision indicator is determined to be adjusted upward, and the order allocation decision indicator is increased by a preset step size.

[0070] In summary, the above-mentioned embodiments of the present disclosure, by constructing a dual monitoring mechanism of risk value and unfinished order data, when the workstation risk value exceeds the preset risk threshold and the handling efficiency data is abnormal, the present disclosure will accurately locate the abnormal data, and adjust the order allocation decision indicators in a targeted manner according to the deviation direction and degree, so as to respond to operational risks and efficiency fluctuations in a timely manner, so that the order allocation strategy can promote the order processing efficiency to converge to the target range while ensuring operational safety, and ultimately achieve a dynamic balance between controllable logistics operation risks and optimized efficiency.

[0071] Figure 3 A flowchart of an order allocation method proposed in an embodiment of the present disclosure is shown in FIG. Figure 4 As shown, the method further includes the following steps:

[0072] Step 301: Determine the order data of each order to be allocated.

[0073] In the embodiment of the present disclosure, since each order sent carries corresponding order data, the order data in the present disclosure includes the workstation corresponding to each order to be assigned and the order assignment data of each order to be assigned.

[0074] The order allocation data for each pending order refers to data that reflects the matching degree between the pending order and the workstation and the picking efficiency when the pending order is assigned to the corresponding workstation. For example, it can be the proportion of pending orders that hit the container in the cache when the pending order is assigned to the corresponding workstation, or the proportion of pending orders that hit the container across workstations when the pending order is assigned to the corresponding workstation.

[0075] Step 302: Based on the order data and the order allocation decision indicators corresponding to each uncompleted order data in the uncompleted order data, combined with the order allocation decision algorithm, determine the comprehensive score between each of the multiple orders to be allocated and the workstation corresponding to each order to be allocated.

[0076] In the present disclosure, the comprehensive score is used to indicate the degree of matching between each order to be assigned and the workstation corresponding to each order to be assigned.

[0077] In some embodiments, each unfinished order data in the unfinished order data of the present disclosure has its corresponding order allocation decision indicator. The present disclosure can use the order allocation decision indicator corresponding to each unfinished order data and each unfinished order data, combined with the order allocation decision algorithm, to calculate the comprehensive score between each order to be allocated and its corresponding workstation.

[0078] The unfinished order data in the present disclosure are unfinished order data generated by multiple handling devices in different scenarios based on the current order allocation situation. For example, the unfinished order data in the present disclosure may include the proportion of dispatched unfinished orders hitting cargo boxes in the cache position (forward data) and the average cross-station times of dispatched unfinished orders hitting cargo boxes (reverse data). The order allocation decision indicator corresponding to the proportion of dispatched unfinished orders hitting cargo boxes in the cache position is the weight parameter of the proportion of orders hitting cargo boxes in the cache position, and the order allocation decision indicator corresponding to the average cross-station times of dispatched unfinished orders hitting cargo boxes is the cross-workstation weight parameter of orders hitting cargo boxes.

[0079] The order allocation decision algorithm in this disclosure is shown in Formula 1:

[0080] y=a1x1+a2x2+......+b formula 1

[0081] Among them, y is the comprehensive score between each order and multiple picking workstations, a1 and a2 are the order allocation decision indicators in the order allocation decision algorithm, x1 and x2 are the evaluation indicators in the order allocation decision algorithm (i.e., the order data of the orders to be allocated), and b is a constant.

[0082] It is understood that the order allocation decision algorithm in this disclosure can be configured with multiple evaluation indicators, with the specific number depending on actual needs. If the first uncompleted order data is x1, this disclosure only needs to adjust the order allocation decision indicator a1 corresponding to x1. Based on this adjusted order allocation decision indicator and the order allocation decision indicators of other uncompleted order data, a comprehensive score for each pending order is calculated.

[0083] In an optional embodiment of the present disclosure, it is assumed that the unfinished order data in the present disclosure is the ratio of dispatched unfinished orders hitting cargo boxes in the cache position and the average cross-station times of dispatched unfinished orders hitting cargo boxes.

[0084] a1 in Formula 1 is the order allocation decision indicator corresponding to the proportion of dispatched but uncompleted orders hitting the cargo box in the cache position, that is, the weight parameter of the proportion of order-hitting cargo boxes in the cache position. a2 in Formula 1 is the order allocation decision indicator corresponding to the average cross-station number of dispatched but uncompleted orders hitting the cargo box, that is, the cross-workstation weight parameter of order-hitting cargo boxes.

[0085] The present disclosure can judge the proportion of the currently dispatched but uncompleted orders hitting the cargo box in the cache position and the average cross-station number of the dispatched but uncompleted orders hitting the cargo box to determine whether they are within the corresponding target range.

[0086] If the current judgment shows that the proportion of dispatched but uncompleted orders hitting the cargo box in the cache position is not within the preset target range, but is lower than the lower limit of the preset target range, it is determined that the weight parameter of the proportion of orders hitting the cargo box in the cache position (a1 in Formula 1) needs to be increased, that is, the weight parameter of the proportion of orders hitting the cargo box in the cache position is adjusted from the original weight parameter of 10 to 11 based on the preset step size (for example, 1).

[0087] At this time, based on the above-adjusted weight parameters of the proportion of order-hitting cargo boxes in the cache position, combined with Formula 1, the comprehensive scores of the currently existing orders to be assigned are calculated respectively, and the order with the highest comprehensive score is selected based on the calculated comprehensive score and assigned to its corresponding workstation first.

[0088] Specifically, assume there are two orders to be assigned: the first order and the second order. The order data for the first order is the ratio of the first order's bins in the cache and the ratio of the first order's bins across workstations. The order data for the second order is the ratio of the second order's bins in the cache and the ratio of the second order's bins across workstations.

[0089] Assume that the proportion of the first order hitting the cargo box in the cache position is 0.85, that is, x1 is 0.85; the proportion of the first order hitting the cargo box across workstations is 0.2, that is, x2 is 0.2; the proportion of the second order hitting the cargo box in the cache position is 0.2, that is, x1 is 0.2; the proportion of the second order hitting the cargo box across workstations is 0.9, that is, x2 is 0.9.

[0090] Before adjusting the order allocation decision indicator, a1 is 10, a2 is 10, and b is 1:

[0091] The comprehensive score of the first order and its corresponding workstation is y1 = 10 * 0.85 + 10 * 0.2 + 1 = 11.5;

[0092] The comprehensive score of the second order and its corresponding workstation is y2=10*0.2+10*0.9+1=12.

[0093] At this time, y2 is greater than y1. If the order allocation decision indicator is not adjusted, the second order will be sent to its corresponding workstation first, resulting in a problem in which the proportion of dispatched but unfinished orders hitting the cargo box in the cache position becomes lower and lower, affecting production efficiency.

[0094] After adjusting the above order allocation decision indicators, a1 is 11, a2 is 10, and b is 1:

[0095] The comprehensive score of the first order and its corresponding workstation is y1 = 11 * 0.85 + 10 * 0.2 + 1 = 12.35;

[0096] The comprehensive score of the second order and its corresponding workstation is y2=11*0.2+10*0.9+1=12.2.

[0097] At this time, if y1 is greater than y2, the first order will be sent to its corresponding workstation first to increase the proportion of current unfinished orders hitting the cache bit, thereby improving production efficiency.

[0098] It should be noted that the above embodiment is an example for cargo boxes, and the order allocation method disclosed in the present invention is also applicable to shelves. The specific adjustment method of the order allocation decision indicator can refer to the above-mentioned cargo box adjustment example and will not be repeated here.

[0099] Specifically, in the shelf scenario, the unfinished order data may include the proportion of dispatched but unfinished orders hitting the shelf in the cache area (forward data, the corresponding order allocation decision indicator is the shelf cache area proportion weight parameter), and the average moving distance of dispatched but unfinished orders hitting the shelf (reverse data, the corresponding order allocation decision indicator is the shelf moving distance weight parameter). Combined with the above-mentioned order allocation decision algorithm formula 1, the proportion of each order to be assigned that hits the shelf cache area and the moving distance of each order to be assigned that hits the shelf are substituted into formula 1 as the evaluation indicators x1 and x2 in the order allocation decision algorithm, and the comprehensive score between each order to be assigned and the corresponding workstation can be calculated to measure the degree of matching between the two.

[0100] For example, the proportion of order A hitting the shelf cache area is 80%, and the moving distance of hitting the shelf is 0.33. When a1=0.6, a2=-0.4, and b=1, its comprehensive score is 1.348. Based on this, the comprehensive scores of multiple orders to be assigned that currently exist can be calculated, thereby obtaining the comprehensive score of each order to be assigned among all orders to be assigned, and selecting the order to be assigned with the highest comprehensive score according to the comprehensive score of each order to be assigned and assigning it to its corresponding workstation first.

[0101] In summary, the above-described embodiments of the present disclosure, by fully utilizing uncompleted order data and its corresponding order allocation decision indicators, combined with advanced order allocation decision algorithms, can accurately calculate a comprehensive score for each pending order. This scoring system not only effectively reflects the degree of match between pending orders and their corresponding workstations, but also greatly improves the rationality and efficiency of order allocation, ensuring optimal resource allocation and significantly enhancing overall operational efficiency.

[0102] based on Figure 1 、 Figure 2 as well as Figure 3 The embodiment shown, as Figure 4 As shown, the present disclosure provides an order distribution system. The order distribution method of the present disclosure can be applied to the order distribution system, which can include a data twin system 310 and an intelligent warehousing system 320. The data twin system and the intelligent warehousing system can be designed independently or integrated together. The data twin system can receive and analyze data in real time and quickly generate optimization suggestions, thereby achieving real-time control of the production process.

[0103] In some embodiments, the data twin system 310 includes a parameter adaptation module 311, which is used to determine the risk values ​​and unfinished order data of multiple workstations. The risk value is used to indicate the risk of human-machine collaboration at the workstation, and the unfinished order data is used to indicate the order processing efficiency of multiple handling equipment based on the current order allocation situation; based on the risk value and the unfinished order data, the order allocation decision indicator is determined, and the determined order allocation decision indicator is sent to the intelligent warehousing system.

[0104] In some embodiments, the intelligent warehousing system 320 includes a decision algorithm module 321, which is used to determine multiple comprehensive scores of multiple orders to be allocated based on the order allocation decision indicators sent by the data twin system; and according to the multiple comprehensive scores, allocate the first order to be allocated among the multiple orders to be allocated to the first workstation corresponding to the first order to be allocated.

[0105] Figure 5 This is a structural diagram of an order distribution device 500 proposed in an embodiment of the present disclosure, as shown in FIG. Figure 5 As shown, the device includes:

[0106] A first determining unit 510 is configured to determine risk values ​​and uncompleted order data for multiple workstations, wherein the risk value indicates a risk of human-machine collaboration at the workstation, and the uncompleted order data indicates an efficiency of multiple handling devices in processing orders based on current order allocation.

[0107] A second determining unit 520 is configured to determine an order allocation decision indicator based on the risk value and the uncompleted order data;

[0108] A scoring unit 530 is configured to determine a plurality of comprehensive scores of the plurality of orders to be allocated based on the order allocation decision indicator;

[0109] The allocating unit 540 is configured to allocate a first order to be allocated from the plurality of orders to be allocated to a first workstation corresponding to the first order to be allocated according to the plurality of comprehensive scores.

[0110] In some embodiments, the first determination unit 510 is used to: determine the total number of all workstations and the risk number of workstations where human-machine collaboration risks occur; use the ratio between the risk number and the total number as the risk value; and determine the unfinished order data when multiple handling equipment performs order processing tasks based on the current order allocation situation.

[0111] In some embodiments, the second determination unit 520 is used to: when the risk value is greater than a preset risk threshold and the first uncompleted order data in the uncompleted order data deviates from the target range of the first uncompleted order data, determine the order allocation decision indicator of the first uncompleted order data based on the deviation state of the first uncompleted order data.

[0112] In some embodiments, the second determination unit 520 is used to: when the risk value is greater than a preset risk threshold, determine the first target range corresponding to the first uncompleted order data; determine whether the first uncompleted order data is within the first target range; if the first uncompleted order data is not within the first target range, determine the deviation status of the first uncompleted order data and the order allocation decision indicator corresponding to the first uncompleted order data, the deviation status indicating the degree and direction of the first uncompleted order data deviating from the first target range; based on the deviation status, determine the adjustment direction of the order allocation decision indicator; based on the adjustment direction, adjust the order allocation decision indicator corresponding to the first uncompleted order data according to a preset step size.

[0113] In some embodiments, the scoring unit 530 is used to: determine the order data of each order to be assigned; based on the order data and the order allocation decision indicators corresponding to each unfinished order data in the unfinished order data, combined with the order allocation decision algorithm, determine the comprehensive score between each order to be assigned in the multiple orders to be assigned and the workstation corresponding to each order to be assigned, and the comprehensive score is used to represent the degree of matching between each order to be assigned and the workstation corresponding to each order to be assigned.

[0114] In some embodiments, the allocation unit 540 is used to: sort each comprehensive score among multiple comprehensive scores in order of score size; take the comprehensive score at the target position in the sorting position as the first comprehensive score; determine the to-be-allocated order corresponding to the first comprehensive score as the first to-be-allocated order, and determine the workstation corresponding to the first to-be-allocated order as the first workstation, so as to allocate the first to-be-allocated order to the first workstation.

[0115] In some embodiments, the second determination unit 520 is used to: assign a first to-be-assigned order among multiple to-be-assigned orders to a first workstation corresponding to the first to-be-assigned order based on multiple comprehensive scores, and then determine the unfinished order data of multiple handling devices based on performing order processing tasks when the first to-be-assigned order is assigned to the first workstation; when second unfinished order data in the unfinished order data deviates from the second target range of the second unfinished order data, based on the deviation status of the second unfinished order data, adjust the adjusted order allocation decision indicator corresponding to the second unfinished order data until the second unfinished order data is within the second target range.

[0116] In some embodiments, the allocation unit 540 is used to: before determining the risk values ​​of multiple workstations and the unfinished order data, determine multiple initial comprehensive scores for the multiple initial allocated orders based on the initial order allocation decision indicators of each unfinished order data in the unfinished order data; and according to the multiple initial comprehensive scores, allocate the first initial allocated order among the multiple initial allocated orders to the initial workstation corresponding to the first initial allocated order to obtain the current order allocation situation.

[0117] Figure 6 Schematic diagram of an electronic device provided in an embodiment of the present disclosure. In some embodiments, the electronic device includes one or more processors and a memory. The memory is configured to store one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement the method in the above embodiment. Figure 6 As shown, the electronic device 600 includes a processor 601 and a memory 602. Exemplarily, the electronic device 600 may further include a communication interface 603 and a communication bus 604.

[0118] The processor 601, the memory 602 and the communication interface 603 communicate with each other via a communication bus 604. The communication interface 603 is used to communicate with other devices such as a client or other server network elements.

[0119] In some embodiments, the processor 601 is used to execute the program 605, specifically, the relevant steps in the above method embodiments. For example, the program 605 may include program code, which may include computer executable instructions.

[0120] For example, the processor 601 may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present disclosure. The electronic device 600 may include one or more processors of the same type, such as one or more CPUs, or different types of processors, such as one or more CPUs and one or more ASICs.

[0121] In some embodiments, the memory 602 is used to store the program 605. The memory 602 may include a high-speed RAM memory, and may also include a non-volatile memory (NVM), such as at least one disk memory.

[0122] The program 605 can be specifically called by the processor 601 to enable the electronic device 600 to execute the method shown in any of the above embodiments.

[0123] An embodiment of the present disclosure provides a computer-readable storage medium storing at least one executable instruction. When the executable instruction is executed on an electronic device 600, the electronic device 600 executes the method in the above embodiment.

[0124] For example, the computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a compact disc (CD-ROM), a magnetic tape, a floppy disk, an optical data storage device, and the like.

[0125] The beneficial effects that can be achieved by the order distribution device, electronic device, and computer-readable storage medium provided by the embodiments of the present disclosure can be referred to the beneficial effects of the corresponding methods provided above, and will not be repeated here.

[0126] The embodiments of the present disclosure further provide a computer program product, including a computer program, which executes the method described in the above embodiments of the present disclosure when a processor is used.

[0127] It should be noted that the terms "first," "second," and the like in the specification and claims of the present disclosure and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or precedence. It should be understood that the numbers used in this manner are interchangeable where appropriate so that the embodiments of the present disclosure described herein can be implemented in an order other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. Instead, they are merely examples of apparatus and methods consistent with certain aspects of the present disclosure as detailed in the appended claims.

[0128] Throughout this specification, reference to terms such as "one embodiment," "some embodiments," "illustrative embodiments," "examples," "specific examples," or "some examples" means that a specific feature, structure, material, or characteristic described in conjunction with an embodiment or example is included in at least one embodiment or example of the present disclosure. In this specification, the illustrative use of the above terms does not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0129] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code that includes one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present disclosure includes additional implementations in which functions may be performed out of the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present disclosure belong.

[0130] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processing module, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (non-exhaustive list) of computer-readable media include the following: an electrical connection having one or more wires (control method), a portable computer disk cartridge (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and programmable read-only memory (EPROM or flash memory), fiber optic devices, and a portable compact disc read-only memory (CDROM). Furthermore, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or otherwise processing it in a suitable manner if necessary, and then storing it in a computer memory.

[0131] It should be understood that the various parts of the embodiments of the present disclosure can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used to implement: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application-specific integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0132] Those skilled in the art will understand that all or part of the steps in the method of the above embodiment can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiment.

[0133] In addition, the functional units in the various embodiments of the present disclosure may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into a module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium. The above-mentioned storage medium may be a read-only memory, a magnetic disk, an optical disk, etc.

[0134] Although the embodiments of the present disclosure have been shown and described above, it is understood that the above embodiments are exemplary and are not to be construed as limitations on the present disclosure. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present disclosure.

Claims

1. An order allocation method, characterized in that: The method comprises: Determining risk values ​​and uncompleted order data for multiple workstations, wherein the risk value is used to indicate the risk of human-machine collaboration at the workstation, and the uncompleted order data is used to indicate the efficiency of multiple handling devices in processing orders based on the current order allocation; Determining an order allocation decision indicator based on the risk value and the uncompleted order data; Determining a plurality of comprehensive scores for a plurality of orders to be allocated based on the order allocation decision indicator; A first order to be allocated among the multiple orders to be allocated is allocated to a first workstation corresponding to the first order to be allocated according to the multiple comprehensive scores.

2. The method according to claim 1, characterized in that Determining the risk values ​​and uncompleted order data of multiple workstations includes: Determine the total number of all workstations and the risk number of workstations where human-robot collaboration risks occur; The ratio between the risk quantity and the total quantity is used as the risk value; Uncompleted order data when the plurality of transport devices perform order processing tasks based on current order allocation conditions is determined.

3. The method according to claim 1, characterized in that The determining of an order allocation decision indicator based on the risk value and the uncompleted order data includes: When the risk value is greater than the preset risk threshold and the first unfinished order data in the unfinished order data deviates from the target range of the first unfinished order data, the order allocation decision indicator of the first unfinished order data is determined based on the deviation status of the first unfinished order data.

4. The method according to claim 3, characterized in that When the risk value is greater than a preset risk threshold and first uncompleted order data in the uncompleted order data deviates from a target range of the first uncompleted order data, adjusting an order allocation decision indicator of the first uncompleted order data based on the deviation state of the first uncompleted order data includes: When the risk value is greater than the preset risk threshold, determining a first target range corresponding to the first uncompleted order data; determining whether the first uncompleted order data is within the first target range; If the first uncompleted order data is not within the first target range, determining a deviation status of the first uncompleted order data and an order allocation decision indicator corresponding to the first uncompleted order data, wherein the deviation status indicates the degree and direction of the deviation of the first uncompleted order data from the first target range; Based on the deviation state, determining an adjustment direction of the order allocation decision indicator; Based on the adjustment direction, the order allocation decision indicator corresponding to the first uncompleted order data is adjusted according to a preset step size.

5. The method according to claim 1, wherein Determining a plurality of comprehensive scores of a plurality of orders to be allocated based on the order allocation decision indicator includes: Determine the order data for each order to be assigned; Based on the order data and the order allocation decision indicators corresponding to each unfinished order data in the unfinished order data, combined with the order allocation decision algorithm, a comprehensive score between each of the multiple orders to be allocated and the workstation corresponding to each order to be allocated is determined, and the comprehensive score is used to represent the degree of matching between each order to be allocated and the workstation corresponding to each order to be allocated.

6. The method according to claim 1, wherein The allocating, according to the multiple comprehensive scores, a first order to be allocated among the multiple orders to be allocated to a first workstation corresponding to the first order to be allocated, comprises: Sorting each of the multiple comprehensive scores in order of score size; The comprehensive score of the ranking position at the target position is taken as the first comprehensive score; The to-be-allocated order corresponding to the first comprehensive score is determined as the first to-be-allocated order, and the workstation corresponding to the first to-be-allocated order is determined as the first workstation, so as to allocate the first to-be-allocated order to the first workstation.

7. The method according to claim 1, characterized in that After allocating a first order to be allocated from the plurality of orders to be allocated to a first workstation corresponding to the first order to be allocated based on the plurality of comprehensive scores, the method includes: determining uncompleted order data of the plurality of transport devices executing order processing tasks when allocating the first to-be-allocated order to the first workstation; When the second uncompleted order data in the uncompleted order data deviates from the second target range of the second uncompleted order data, based on the deviation status of the second uncompleted order data, the adjusted order allocation decision indicator corresponding to the second uncompleted order data is adjusted until the second uncompleted order data is within the second target range.

8. The method according to claim 1, characterized in that Before determining the risk values ​​and uncompleted order data of the plurality of workstations, the method includes: determining a plurality of initial comprehensive scores for a plurality of initially allocated orders based on an initial order allocation decision indicator for each of the uncompleted order data; According to the multiple initial comprehensive scores, a first initial allocated order among the multiple initial allocated orders is allocated to an initial workstation corresponding to the first initial allocated order to obtain a current order allocation situation.

9. An order distribution device, characterized in that: The device comprises: a first determining unit, configured to determine risk values ​​of a plurality of workstations and uncompleted order data, wherein the risk values ​​are used to indicate a risk of human-machine collaboration occurring at the workstations, and the uncompleted order data are used to indicate an efficiency of processing orders by the plurality of handling devices based on a current order allocation; a second determining unit, configured to determine an order allocation decision indicator based on the risk value and the uncompleted order data; A scoring unit, configured to determine a plurality of comprehensive scores of a plurality of orders to be allocated based on the order allocation decision indicator; An allocating unit is configured to allocate a first order to be allocated among the multiple orders to be allocated to a first workstation corresponding to the first order to be allocated based on the multiple comprehensive scores.

10. An electronic device comprising: processor and memory, wherein The memory is used to store computer-executable instructions; The processor is configured to read the instruction from the memory and execute the instruction to implement the method according to any one of claims 1 to 8.

11. A computer-readable storage medium, wherein: The storage medium stores computer program instructions, and when a computer reads the instructions, the method according to any one of claims 1 to 8 is executed.

12. A computer program product comprising a computer program, wherein When the computer program is executed by a processor, the method according to any one of claims 1 to 8 is implemented.

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