Tobacco distribution method applied to tobacco unmanned aerial vehicle front cabin and computer equipment

Abstract

The embodiment of the invention relates to the field of tobacco distribution applied to a tobacco unmanned aerial vehicle front cabin, in particular to a tobacco distribution method applied to the tobacco unmanned aerial vehicle front cabin and computer equipment. A specific embodiment of the method comprises the following steps: in response to received tobacco distribution request information corresponding to a target tobacco unmanned aerial vehicle front cabin, performing structured processing on the tobacco distribution request information and current tobacco storage information of the target tobacco unmanned aerial vehicle front cabin to obtain structured tobacco information; determining tobacco distribution limitation information corresponding to the tobacco distribution request information; inputting the pre-warehouse structured information, the pre-warehouse tobacco structured information and the pre-warehouse tobacco associated information into a pre-warehouse tobacco distribution model to obtain pre-warehouse tobacco distribution information; and controlling the to-be-distributed tobacco unmanned aerial vehicle to carry out tobacco distribution according to the pre-warehouse tobacco distribution information. According to the embodiment, the accuracy of the tobacco distribution information of the front bin can be improved, the utilization rate of the front bin is improved, and waste of tobacco distribution resources is reduced.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of tobacco delivery applied to tobacco drone forward warehouses, and specifically to tobacco delivery methods and computer equipment applied to tobacco drone forward warehouses. Background Art

[0002] The transportation of tobacco products is one of the important links. At present, transportation personnel are often required to deliver tobacco products to the corresponding delivery points (for example, sales points) in advance within a fixed time period. For the distribution of tobacco products, the usual method is to generate tobacco distribution information through a tobacco distribution model, transport tobacco to large warehouses, and then distribute it by distribution vehicles in various districts. However, when the above method is used for tobacco distribution, the following technical problems often exist: the accuracy of the generated tobacco scheduling and distribution information is low, tobacco distribution resources are wasted and distribution time is increased, and it is difficult to effectively ensure the timeliness of tobacco product distribution. Summary of the invention

[0003] The content of this application is used to introduce concepts in a brief form, which will be described in detail in the detailed implementation section below. The content of this application is not intended to identify the key features or essential features of the technical solution claimed for protection, nor is it intended to limit the scope of the technical solution claimed for protection.

[0004] Some embodiments of the present application propose a tobacco delivery method, a computer device, and a computer-readable storage medium applied to a tobacco drone forward warehouse to solve one or more of the technical problems mentioned in the above background technology section.

[0005] In a first aspect, some embodiments of the present application provide a tobacco delivery method applied to a tobacco drone forward warehouse, the method comprising: in response to receiving tobacco delivery request information corresponding to a target tobacco drone forward warehouse, structuring the above tobacco delivery request information and the current tobacco storage information of the above target tobacco drone forward warehouse to obtain structured tobacco information, wherein the above structured tobacco information includes: forward warehouse structured information, forward warehouse tobacco structured information, and forward warehouse tobacco-related information; determining tobacco delivery restriction information corresponding to the above tobacco delivery request information, wherein the above tobacco delivery restriction information is at least one restriction condition corresponding to a preset forward warehouse tobacco delivery model; inputting the above forward warehouse structured information, the above forward warehouse tobacco structured information, and the above forward warehouse tobacco-related information into the above forward warehouse tobacco delivery model to obtain the forward warehouse tobacco delivery information under the above tobacco delivery restriction information; determining at least one corresponding tobacco drone to be delivered according to the above forward warehouse tobacco delivery information; controlling the associated tobacco handling equipment to load tobacco on the above at least one tobacco drone to be delivered; in response to determining that tobacco loading is completed, controlling at least one tobacco drone to be delivered to deliver tobacco.

[0006] In a second aspect, the present application also provides a computer device, comprising a processor, a memory, and a computer program stored in the memory and executable by the processor, wherein when the computer program is executed by the processor, it implements the method described in any implementation of the first aspect.

[0007] In a third aspect, the present application further provides a computer-readable storage medium, on which a computer program is stored, wherein when the computer program is executed by a processor, the method described in any implementation of the first aspect is implemented.

[0008] The above-mentioned embodiments of the present application have the following beneficial effects: through the tobacco delivery method applied to the tobacco drone forward warehouse of some embodiments of the present application, the accuracy of the tobacco delivery information of the forward warehouse can be improved, the utilization rate of the forward warehouse can be improved, and the waste of tobacco delivery resources can be reduced. First, in response to receiving the tobacco delivery request information corresponding to the target tobacco drone forward warehouse, the above-mentioned tobacco delivery request information and the current tobacco storage information of the target tobacco drone forward warehouse are structured to obtain structured tobacco information, wherein the above-mentioned structured tobacco information includes: forward warehouse structured information, forward warehouse tobacco structured information, and forward warehouse tobacco association information. Secondly, the tobacco delivery restriction information corresponding to the above-mentioned tobacco delivery request information is determined. Among them, the above-mentioned tobacco delivery restriction information is at least one restriction condition corresponding to the preset forward warehouse tobacco delivery model. Therefore, considering the specific restriction information, the association relationship between the forward warehouse tobacco delivery model and the tobacco delivery request information is improved, which is convenient for the subsequent generation of the forward warehouse tobacco delivery information with higher accuracy under the corresponding tobacco delivery request information. Next, the above-mentioned forward warehouse structured information, the above-mentioned forward warehouse tobacco structured information, and the above-mentioned forward warehouse tobacco associated information are input into the above-mentioned forward warehouse tobacco distribution model to obtain the forward warehouse tobacco distribution information under the above-mentioned tobacco distribution restriction information. Thus, the forward warehouse tobacco distribution information under the corresponding tobacco distribution request information is generated, and the accuracy of the forward warehouse tobacco distribution information under the tobacco distribution request information and the utilization rate of the forward warehouse are improved. Then, according to the above-mentioned forward warehouse tobacco distribution information, at least one corresponding tobacco drone to be distributed is determined; the associated tobacco handling equipment is controlled to load tobacco on the above-mentioned at least one tobacco drone to be distributed. Finally, in response to determining that tobacco loading is completed, at least one tobacco drone to be distributed is controlled to distribute tobacco. Thus, the timeliness of the distribution of tobacco products can be effectively guaranteed. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] The above and other features, advantages and aspects of the embodiments of the present application will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. Throughout the accompanying drawings, the same or similar reference numerals represent the same or similar elements. It should be understood that the drawings are schematic and that components and elements are not necessarily drawn to scale.

[0010] Figure 1 It is a flowchart of some embodiments of the tobacco distribution method applied to the tobacco drone forward warehouse according to the present application; Figure 2 is a schematic diagram of a computer device suitable for implementing some embodiments of the present application; Figure 3 It is a structural schematic diagram of a tobacco drone (new type of drone delivery terminal) to be delivered in the tobacco delivery method applied to a tobacco drone forward warehouse in the present application; Figure 4It is a front structural schematic diagram of a tobacco drone (new type of drone delivery terminal) to be delivered in the tobacco delivery method applied to the tobacco drone forward warehouse of the present application. DETAILED DESCRIPTION

[0011] Embodiments of the present application will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present application are shown in the accompanying drawings, it should be understood that the present application can be implemented in various forms and should not be construed as being limited to the embodiments set forth herein. On the contrary, these embodiments are provided to provide a more thorough and complete understanding of the present application. It should be understood that the drawings and embodiments of the present application are only for exemplary purposes and are not intended to limit the scope of protection of the present application.

[0012] It should also be noted that, for ease of description, only the parts related to the invention are shown in the drawings. In the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other.

[0013] It should be noted that the concepts such as "first" and "second" mentioned in this application are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0014] It should be noted that the modifications of "one" and "plurality" mentioned in the present application are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, it should be understood as "one or more".

[0015] The names of the messages or information exchanged between multiple devices in the embodiments of the present application are only used for illustrative purposes and are not used to limit the scope of these messages or information.

[0016] The present application will be described in detail below with reference to the accompanying drawings and in combination with embodiments.

[0017] Figure 1 The process 100 of some embodiments of the tobacco delivery method applied to the tobacco drone forward warehouse according to the present application is shown. The tobacco delivery method applied to the tobacco drone forward warehouse includes the following steps: Step 101, in response to receiving tobacco delivery request information corresponding to a target tobacco drone forward warehouse, the tobacco delivery request information and the current tobacco storage information of the target tobacco drone forward warehouse are structured to obtain structured tobacco information.

[0018] In some embodiments, the execution subject (e.g., computing device) of the tobacco delivery method applied to the tobacco drone forward warehouse can respond to receiving the tobacco delivery request information corresponding to the target tobacco drone forward warehouse, and perform structured processing on the above tobacco delivery request information and the current tobacco storage information of the above target tobacco drone forward warehouse to obtain structured tobacco information. Among them, the above structured tobacco information includes: forward warehouse structured information, forward warehouse tobacco structured information, and forward warehouse tobacco association information. The target tobacco drone forward warehouse may refer to a tobacco forward warehouse loaded with a drone. The target tobacco drone forward warehouse can deliver tobacco by drone or by vehicle. The tobacco delivery request information may be request information for tobacco delivery. The above tobacco delivery request information may be tobacco demand information, forward warehouse tobacco information, and tobacco restriction information related to the target tobacco drone forward warehouse sent by the front-end tobacco demand user end. The tobacco demand information may indicate which varieties of tobacco and quantity are required. The forward warehouse tobacco information may refer to the information of the forward warehouse storing the forward warehouse tobacco and the information of the forward warehouse tobacco. The tobacco restriction information may refer to information restricting the tobacco delivery time. Among them, the above-mentioned forward warehouse tobacco association information can be association information between forward warehouse tobacco information, and the above-mentioned forward warehouse tobacco information can be corresponding forward warehouse information and forward warehouse tobacco information. The above-mentioned forward warehouse structured information can be structured information related to the forward warehouse that meets the input data requirements of the forward warehouse tobacco distribution model. The above-mentioned forward warehouse structured information is structured information stored in the form of a data table, including: forward warehouse name, forward warehouse upper limit information, forward warehouse status information and marked forward warehouse information. The above-mentioned forward warehouse upper limit information can be information including the upper limit of the forward warehouse storage warehouse tobacco and the forward warehouse volume. The above-mentioned forward warehouse status information can be restriction information related to the forward warehouse in the above-mentioned tobacco distribution request information. For example, the above-mentioned forward warehouse status field can include: deletion of information that needs to be shipped out in the tobacco shipping scene. The above-mentioned marked forward warehouse information can be information of the forward warehouse with special requirements in the forward warehouse information and the tobacco distribution request information. For example, the above-mentioned marked forward warehouse information can include: automated forward warehouse information and semi-automated forward warehouse information. The above-mentioned forward warehouse tobacco structured information may be structured information related to the forward warehouse tobacco that meets the input data requirements of the forward warehouse tobacco distribution model. The above-mentioned forward warehouse tobacco structured information may be structured information that includes the forward warehouse tobacco name and the forward warehouse tobacco storage information and is stored in the form of a data table. The above-mentioned forward warehouse tobacco-associated structured information may be structured information that represents the restriction information related to the forward warehouse information and tobacco information in the tobacco distribution request information in the form of a data table. The above-mentioned tobacco storage information may include the information of the forward warehouse storing the forward warehouse tobacco and the information of the forward warehouse tobacco. The forward warehouse may be composed of multiple sub-warehouses established in the same area.

[0019] For example, the execution entity may convert the tobacco delivery request information and the tobacco storage information into the table structured data required by the forward warehouse tobacco delivery model to obtain structured tobacco information.

[0020] Step 102: Determine tobacco delivery restriction information corresponding to the tobacco delivery request information.

[0021] In some embodiments, the execution subject may determine the tobacco delivery restriction information corresponding to the tobacco delivery request information. The tobacco delivery restriction information is at least one restriction condition corresponding to the preset forward warehouse tobacco delivery model. The forward warehouse tobacco delivery model includes: a forward warehouse tobacco restriction condition group. The forward warehouse tobacco delivery model includes: a forward warehouse tobacco calculation function group. The forward warehouse tobacco delivery model may be a model for performing forward warehouse tobacco scheduling planning on the input tobacco delivery request information and forward warehouse information to generate forward warehouse tobacco scheduling information between multiple warehouses. For example, the forward warehouse tobacco delivery model may include: a reinforcement learning model of delivery cost minimization objective function, delivery distance minimization objective function, delivery time restriction, and forward warehouse storage capacity restriction. The state space of the forward warehouse tobacco delivery model may include: the encoded tobacco delivery request information and forward warehouse information. The action space of the forward warehouse tobacco delivery model may include: the number of forward warehouse tobaccos that need to be delivered after encoding and the delivery path. The incentive function of the forward warehouse tobacco delivery model may be: the weighted sum of the delivery cost minimization objective function and the delivery distance minimization objective function, and the incentive function obtained by subtracting the penalty value. The above-mentioned penalty value may be the value that needs to be subtracted when the action performed by the drone violates the delivery time limit and the forward warehouse storage capacity limit conditions.

[0022] For example, first, the tobacco delivery request information and the model restriction condition set included in the preset model restriction condition pool can be matched to obtain a model restriction matching result set. The preset model restriction condition pool can be a condition pool that stores all restriction conditions corresponding to multiple preset scenarios. Then, at least one model restriction condition corresponding to at least one model restriction matching result representing a successful match in the model restriction matching result set is determined as tobacco delivery restriction information.

[0023] In an actual application scenario, the execution subject may determine each forward warehouse tobacco restriction condition corresponding to the tobacco delivery request information in the forward warehouse tobacco restriction condition group as tobacco delivery restriction information. The tobacco delivery restriction information includes: a basic forward warehouse tobacco restriction condition group, a tobacco delivery restriction condition group, and a forward warehouse tobacco adjustment restriction condition group. The forward warehouse tobacco restriction condition in the forward warehouse tobacco restriction condition group may be a full amount of restriction conditions related to the forward warehouse information and the forward warehouse tobacco information included in the forward warehouse tobacco delivery model under multiple scenarios. The basic forward warehouse tobacco restriction condition in the basic forward warehouse tobacco restriction condition group may be restriction information related to the forward warehouse information and the forward warehouse tobacco information that is common under multiple scenario information. The basic forward warehouse tobacco restriction condition group may include: forward warehouse storage restriction information, forward warehouse capacity upper limit restriction information, forward warehouse capacity lower limit restriction information, forward warehouse tobacco association restriction information, and forward warehouse use restriction information. The forward warehouse storage restriction information may represent restriction information that a category of forward warehouse tobacco can only be stored in one warehouse. The above-mentioned forward warehouse storage restriction information can be expressed as: the cumulative sum of the forward warehouse tobacco storage variables within the value range of the forward warehouse tobacco information set and the forward warehouse information is 1. Among them, the above-mentioned forward warehouse tobacco information set can be the information of all forward warehouse tobacco that can be stored in the forward warehouse cluster. The above-mentioned forward warehouse tobacco storage variable can be a variable that represents whether the forward warehouse tobacco is stored in the forward warehouse. If the forward warehouse tobacco is stored in the warehouse, the forward warehouse tobacco storage variable is 1. Otherwise, the forward warehouse tobacco storage variable takes the value of 0. The above-mentioned forward warehouse capacity upper limit restriction information can represent that after the forward warehouse tobacco is distributed, the amount of forward warehouse tobacco stored in the adjusted forward warehouse cannot exceed the upper limit of the forward warehouse capacity in each month. The above-mentioned forward warehouse capacity upper limit restriction information can represent that after the forward warehouse tobacco is distributed, the amount of forward warehouse tobacco stored in the adjusted forward warehouse cannot be lower than the lower limit of the forward warehouse capacity in each month. The above-mentioned forward warehouse tobacco association restriction information can represent whether any two warehouse tobaccos are stored in the same forward warehouse. The above-mentioned forward warehouse usage restriction information can represent that for any warehouse, when warehouse items are stored in the warehouse, the warehouse usage variable is 1, that is, when any warehouse tobacco is stored in the warehouse, the warehouse begins to be used. The forward warehouse tobacco adjustment restriction condition in the above-mentioned forward warehouse tobacco adjustment restriction condition group can be a restriction condition that represents the change in the proportion of the storage quantity of warehouse tobacco before and after tobacco scheduling and distribution. The above-mentioned forward warehouse tobacco adjustment restriction condition group may include: target forward warehouse tobacco proportion restriction information. The above-mentioned target forward warehouse tobacco proportion restriction information can be restriction information that represents that considering the type of tobacco racks in the warehouse, the proportion of target forward warehouse tobacco in the warehouse after forward warehouse tobacco scheduling is greater than or equal to the proportion before forward warehouse tobacco scheduling. The above-mentioned target forward warehouse tobacco can be the tobacco with the largest amount of stored tobacco.The tobacco delivery restriction condition group may represent the restriction conditions of delivery time and delivery route.

[0024] Step 103, input the forward warehouse structured information, the forward warehouse tobacco structured information, and the forward warehouse tobacco associated information into the forward warehouse tobacco distribution model to obtain the forward warehouse tobacco distribution information under the tobacco distribution restriction information.

[0025] In some embodiments, the execution entity may input the forward warehouse structured information, the forward warehouse tobacco structured information, and the forward warehouse tobacco associated information into the forward warehouse tobacco distribution model to obtain the forward warehouse tobacco distribution information under the tobacco distribution restriction information. Among them, the forward warehouse tobacco distribution information may be information for adjusting and distributing forward warehouse tobacco between multiple warehouses. For example, the forward warehouse tobacco distribution information may be: dispatching "001" tobacco in warehouse A and warehouse B to forward warehouse C, and distributing it by drone, and the distribution time is XX / XX / XX.

[0026] In an actual application scenario, the execution entity may input the forward warehouse structured information, the forward warehouse tobacco structured information, and the forward warehouse tobacco associated information into the forward warehouse tobacco distribution model through the following steps: The first step is to determine each forward warehouse tobacco calculation function corresponding to the tobacco delivery request information in the forward warehouse tobacco calculation function group as the target forward warehouse tobacco calculation function group. Among them, the forward warehouse tobacco calculation function in the forward warehouse tobacco calculation function group can be a full optimization function related to the forward warehouse information and the forward warehouse tobacco information included in the forward warehouse tobacco distribution model in multiple scenarios. The target forward warehouse tobacco calculation function group can include: a forward warehouse usage quantity minimization objective function and a forward warehouse tobacco correlation maximization objective function. The forward warehouse usage quantity minimization objective function can be an objective function for reducing the usage quantity of the forward warehouse by optimizing the utilization rate of the forward warehouse storage energy and production capacity. The forward warehouse usage quantity minimization objective function can be expressed as: the cumulative sum of the forward warehouse usage variables within the value range of the warehouse information set is minimized to obtain the forward warehouse usage quantity minimization objective function. The forward warehouse tobacco correlation maximization objective function can be an objective function for combining forward warehouse tobacco by the degree of association of the forward warehouse tobacco to reduce the rate of drone delivery order splitting. Among them, the forward warehouse tobacco correlation maximization objective function can be expressed as: maximizing the sum of the product of the forward warehouse tobacco correlation and the forward warehouse tobacco correlation variable within the value range of the forward warehouse tobacco information set to obtain the forward warehouse tobacco correlation maximization objective function. The forward warehouse tobacco correlation can be the correlation value of any two warehouse tobaccos. The forward warehouse tobacco correlation can be the correlation value obtained by statistically analyzing the proportion of the number of warehouse tobaccos included in each historical delivery information in the historical delivery information set.

[0027] In the second step, based on the above-mentioned basic forward warehouse tobacco restriction condition group, tobacco delivery restriction condition group and forward warehouse tobacco adjustment restriction condition group, the above-mentioned forward warehouse structured information, the above-mentioned forward warehouse tobacco structured information and the above-mentioned forward warehouse tobacco associated information are input into the above-mentioned target forward warehouse tobacco calculation function group to obtain the forward warehouse tobacco delivery information.

[0028] Step 104: Determine at least one corresponding tobacco drone to be delivered based on the tobacco delivery information of the forward warehouse.

[0029] In some embodiments, the execution subject may determine at least one tobacco drone to be delivered based on the tobacco delivery information of the forward warehouse. For example, the at least one tobacco drone to be delivered may be determined based on the tobacco quantity, tobacco volume, and weight included in the tobacco delivery information of the forward warehouse. For example, the maximum delivery volume and weight of each tobacco drone may be determined, and thus, the determination may be made based on the tobacco quantity, tobacco volume, and tobacco weight.

[0030] Step 105: Control the associated tobacco handling equipment to load tobacco onto the at least one tobacco delivery drone.

[0031] In some embodiments, the execution subject may control the associated tobacco handling equipment to load tobacco onto the at least one tobacco delivery drone. The tobacco handling equipment may be a tobacco handling mechanical arm that can grab tobacco and put it into the cabin of the drone.

[0032] Step 106: In response to determining that tobacco loading is complete, controlling at least one tobacco delivery drone to deliver the tobacco.

[0033] In some embodiments, the execution subject may control at least one tobacco drone to be delivered to deliver tobacco in response to determining that tobacco loading is complete. For example, at least one tobacco drone to be delivered may be controlled to deliver tobacco according to the delivery route included in the tobacco delivery information of the forward warehouse. Figure 3 The example shows a schematic diagram of the structure of a tobacco delivery drone (a new type of drone delivery terminal), including a drone and a drone cabin. Figure 3 The size of the drone and the size of the drone cabin are only schematic renderings and are not actual size drawings.

[0034] For example Figure 4 The example shows a front structural diagram of a tobacco delivery drone (a new type of drone delivery terminal), including a drone and a drone cabin, and illustrates the various sub-bins (ABCDEFGHIJK and other bins) included in the drone cabin, each of which is used to fixedly store tobacco products.

[0035] Furthermore, in response to receiving a forward warehouse resource conversion prediction request for the above-mentioned target tobacco drone forward warehouse, the forward warehouse resource conversion efficiency of the above-mentioned target tobacco drone forward warehouse at the target time is predicted through a pre-trained forward warehouse resource conversion efficiency prediction model.

[0036] In some embodiments, the execution subject may predict the forward warehouse resource conversion efficiency of the target tobacco drone forward warehouse at the target time in response to receiving a forward warehouse resource conversion prediction request for the target tobacco drone forward warehouse, through a pre-trained forward warehouse resource conversion efficiency prediction model. The forward warehouse resource conversion prediction request may refer to a detection and prediction request for the resource utilization rate of the target tobacco drone forward warehouse. The resources of the target tobacco drone forward warehouse may include storage resources and distribution resources. Storage resources may represent the storage resources of each warehouse. Distribution resources may represent the distribution resources of distribution drones and distribution vehicles. The forward warehouse resource conversion efficiency prediction model may be a pre-trained neural network model with the resource usage information of the tobacco drone forward warehouse as input and the forward warehouse resource conversion efficiency prediction result as output. The forward warehouse resource conversion efficiency prediction result may represent the resource utilization rate of the target tobacco drone forward warehouse.

[0037] Furthermore, based on the generated forward warehouse resource conversion efficiency prediction results, the drone delivery resources and tobacco storage resources of the target tobacco drone forward warehouse are adjusted.

[0038] In some embodiments, the above-mentioned execution subject can adjust the drone delivery resources and tobacco storage resources of the target tobacco drone forward warehouse according to the generated forward warehouse resource conversion efficiency prediction result. For example, if the forward warehouse resource conversion efficiency prediction result is less than 85%, the excess delivery resources and warehouse resources can be dispatched to other forward warehouses.

[0039] It should be noted that the forward warehouse resource conversion efficiency prediction model can be trained through the following steps: The first step is to obtain the historical forward warehouse resource conversion information sequence set of each tobacco drone forward warehouse in the target area. Among them, one tobacco drone forward warehouse corresponds to a historical forward warehouse resource conversion information sequence. The historical forward warehouse resource conversion information sequence is usually a set of data sorted by time. The historical forward warehouse resource conversion information can be used to characterize the data of resource conversion after the relevant resources are allocated to the forward warehouse. Among them, the historical forward warehouse resource conversion information may include resource conversion efficiency, resource allocation location and time. Among them, resource conversion efficiency can be an indicator data characterizing the resource conversion situation. The resource allocation location can characterize the location information of the allocated resources. Time can characterize the time information obtained by data collection.

[0040] The second step is to select a historical forward warehouse resource conversion information sequence from the above historical forward warehouse resource conversion information sequence set as the target historical forward warehouse resource conversion information sequence. The target historical forward warehouse resource conversion information includes: forward warehouse resource conversion efficiency.

[0041] The third step is to perform mask processing on the forward warehouse resource conversion efficiency included in any number of target historical forward warehouse resource conversion information in the above-mentioned target historical forward warehouse resource conversion information sequence to obtain the first forward warehouse resource conversion information sample. Among them, the arbitrary number is less than the number of target historical forward warehouse resource conversion information included in the target historical forward warehouse resource conversion information sequence. For example, the execution subject can randomly select some target historical forward warehouse resource conversion information from the target historical forward warehouse resource conversion information sequence, so as to perform mask processing on the forward warehouse resource conversion efficiency in the selected target historical forward warehouse resource conversion information. Among them, the method of mask processing is not limited, such as blocking or marking. Then, the target historical forward warehouse resource conversion information sequence after mask processing can be intercepted to obtain a historical forward warehouse resource conversion information subsequence group. Each historical forward warehouse resource conversion information subsequence obtained by interception contains at least one masked data. Afterwards, the masked historical forward warehouse resource conversion information subsequence group after mask processing can be determined as the first forward warehouse resource conversion information sample.

[0042] The third step can be implemented by the following steps: Step 1: intercept the above-mentioned target historical forward warehouse resource conversion information sequence through a sliding window method to obtain a historical forward warehouse resource conversion information subsequence group.

[0043] Step 2: For each historical forward warehouse resource conversion information subsequence in the above-mentioned historical forward warehouse resource conversion information subsequence group, a preset number of historical forward warehouse resource conversion information are selected from the historical forward warehouse resource conversion information subsequence, and through mask marking, the forward warehouse resource conversion efficiency included in each selected historical forward warehouse resource conversion information is masked to obtain each masked historical forward warehouse resource conversion information.

[0044] Step 3: Determine the masked historical forward warehouse resource conversion information subsequence group after mask processing as the first forward warehouse resource conversion information sample.

[0045] The fourth step is to pre-train the initial forward warehouse resource conversion efficiency prediction model through the above-mentioned first forward warehouse resource conversion information sample to obtain a pre-trained initial forward warehouse resource conversion efficiency prediction model. The initial forward warehouse resource conversion efficiency prediction model includes: an information input layer, an encoder network, and a prediction output layer. Among them, the information input layer is used to vectorize the input first forward warehouse resource conversion information sample; the encoder network is used to learn the forward warehouse resource conversion efficiency in the vector data of the above-mentioned first forward warehouse resource conversion information sample, the relationship between the forward warehouse resource configuration position and the information collection time, and the relationship between the forward warehouse resource conversion efficiency corresponding to different times under the same forward warehouse resource configuration position, wherein each encoder in the encoder network includes a multi-head self-attention mechanism network layer and a feedforward neural network layer; the prediction output layer is used to output the forward warehouse resource conversion efficiency prediction information of the above-mentioned first forward warehouse resource conversion information sample at the mask position. The initial forward warehouse resource conversion efficiency prediction model can be used to predict the resource conversion efficiency of mask processing. For example, the execution subject can input the first forward warehouse resource conversion information sample into the initial forward warehouse resource conversion efficiency prediction model, thereby outputting the resource conversion efficiency prediction value at the mask position. Then, according to the resource conversion efficiency prediction value and the actual value of the resource conversion efficiency in the corresponding target historical forward warehouse resource conversion information sequence, the model parameters of the initial forward warehouse resource conversion efficiency prediction model are adjusted. The information input layer can be used to vectorize the input first forward warehouse resource conversion information sample. That is, the resource conversion efficiency, resource configuration as position and time in the first forward warehouse resource conversion information sample are vectorized. And these vector data can be output to the encoder network. The encoder network can be used to learn the relationship between the vector data output by the information input layer. The prediction output layer can be used to output the resource conversion efficiency prediction value of the first forward warehouse resource conversion information sample at the mask position. The encoder network can use a multi-layer Transformer encoder. Among them, each encoder includes a multi-head self-attention mechanism network layer (Multi-head Self-Attention, MSA) and a feedforward neural network layer (Feedforward NeuralNetwork, FNN). The multi-layer stacking of encoder networks allows the model to learn different levels of abstract representation, from the bottom-level efficiency level to the higher-level embedding representation. In addition, the prediction output layer can include a fully connected layer. The activation function (ReLU) in the fully connected layer can be used to constrain the output result to be positive. That is, to ensure that the prediction result is positive.

[0046] The fourth step can be implemented by the following steps: Step 1: Input the first forward warehouse resource conversion information sample into the initial forward warehouse resource conversion efficiency prediction model to obtain the forward warehouse resource conversion efficiency prediction information at the mask position.

[0047] Step 2: Adjust the network parameters of the initial forward warehouse resource conversion efficiency prediction model based on the forward warehouse resource conversion efficiency prediction information and the actual forward warehouse resource conversion efficiency information corresponding to the target historical forward warehouse resource conversion information sequence.

[0048] The fifth step is to perform mask processing on the forward warehouse resource conversion efficiency included in at least two target historical forward warehouse resource conversion information located at the end of the above sequence in the above target historical forward warehouse resource conversion information sequence, and obtain the second forward warehouse resource conversion information sample. For example, in order to improve the learning effect of the model, the execution subject can also re-intercept the data in the target historical forward warehouse resource conversion information sequence to obtain a historical forward warehouse resource conversion information subsequence group. For each historical forward warehouse resource conversion information subsequence, the resource conversion efficiency in several consecutive data located at the end of the historical forward warehouse resource conversion information subsequence can be masked, such as the first five data from the end of the sequence. Afterwards, each historical forward warehouse resource conversion information subsequence after mask processing is used as sample data to obtain the second forward warehouse resource conversion information sample.

[0049] The sixth step is to fine-tune the pre-trained initial forward warehouse resource conversion efficiency prediction model based on the second forward warehouse resource conversion information sample to obtain a fine-tuned forward warehouse resource conversion efficiency prediction model. For example, the second forward warehouse resource conversion information sample can be input into the pre-trained initial forward warehouse resource conversion efficiency prediction model, and the resource conversion efficiency prediction value in the second forward warehouse resource conversion information sample can be output. Then, according to the resource conversion efficiency prediction value and the corresponding actual value of resource conversion efficiency, the model parameters of the pre-trained initial forward warehouse resource conversion efficiency prediction model are fine-tuned until the fine-tuning training is completed.

[0050] In this way, the resource utilization rate of the tobacco forward warehouse can be predicted, which makes it easier to adjust and optimize the resources of the tobacco forward warehouse to avoid idleness and waste of resources.

[0051] Figure 2 A schematic block diagram of the structure of a computer device provided in an embodiment of the present disclosure. The computer device may be a terminal.

[0052] like Figure 2 As shown, the computer device includes a processor, a memory, and a network interface connected via a system bus, wherein the memory may include a non-volatile storage medium and an internal memory.

[0053] The non-volatile storage medium can store an operating system and a computer program. The computer program includes program instructions, and when the program instructions are executed, the processor can execute any tobacco delivery method applied to the tobacco drone forward warehouse.

[0054] The processor is used to provide computing and control capabilities and support the operation of the entire computer equipment.

[0055] The internal memory provides an environment for the operation of the computer program in the non-volatile storage medium. When the computer program is executed by the processor, the processor can execute any tobacco delivery method applied to the tobacco drone forward warehouse.

[0056] The network interface is used for network communication, such as sending assigned tasks, etc. Those skilled in the art will appreciate that Figure 2 The structure shown in the figure is only a block diagram of a part of the structure related to the scheme of the present disclosure, and does not constitute a limitation on the computer device to which the scheme of the present disclosure is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0057] It should be understood that the processor may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among them, the general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0058] In one embodiment, the processor is used to run a computer program stored in a memory to implement the following steps: in response to receiving at least one tobacco change data sent by an external tobacco data monitoring terminal, the tobacco information of the associated target tobacco storage area is updated by the following steps: in response to receiving tobacco delivery request information corresponding to the target tobacco drone forward warehouse, the tobacco delivery request information and the current tobacco storage information of the target tobacco drone forward warehouse are structured to obtain structured tobacco information, wherein the structured tobacco information includes: forward warehouse structured information, forward warehouse tobacco structured information, and forward warehouse tobacco association information; determining the tobacco delivery request information; and The tobacco delivery restriction information corresponding to the request information, wherein the above-mentioned tobacco delivery restriction information is at least one restriction condition corresponding to the preset forward warehouse tobacco delivery model; the above-mentioned forward warehouse structured information, the above-mentioned forward warehouse tobacco structured information, and the above-mentioned forward warehouse tobacco-related information are input into the above-mentioned forward warehouse tobacco delivery model to obtain the forward warehouse tobacco delivery information under the above-mentioned tobacco delivery restriction information; according to the above-mentioned forward warehouse tobacco delivery information, at least one corresponding tobacco drone to be delivered is determined; the associated tobacco handling equipment is controlled to load tobacco on the above-mentioned at least one tobacco drone to be delivered; in response to determining that the tobacco loading is completed, at least one tobacco drone to be delivered is controlled to deliver tobacco.

[0059] An embodiment of the present disclosure also provides a computer-readable storage medium, on which a computer program is stored. The computer program includes program instructions. The method implemented when the program instructions are executed can refer to the various embodiments of the tobacco delivery method applied to the tobacco drone forward warehouse of the present disclosure.

[0060] The computer-readable storage medium may be an internal storage unit of the computer device in the aforementioned embodiment, such as a hard disk or memory of the computer device. The computer-readable storage medium may also be an external storage device of the computer device, such as a plug-in hard disk, a smart memory card (SmartMediaCard, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc., provided on the computer device.

[0061] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or system including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or system. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or system including the element.

[0062] The serial numbers of the embodiments of the present disclosure are only for description and do not represent the advantages and disadvantages of the embodiments. The above description is only a specific implementation mode of the present disclosure, but the protection scope of the present disclosure is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in the present disclosure, and these modifications or replacements should be included in the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure shall be based on the protection scope of the claims.

Claims

1. A tobacco distribution method applied to a tobacco drone forward warehouse, comprising: In response to receiving tobacco delivery request information corresponding to a target tobacco drone forward warehouse, the tobacco delivery request information and the current tobacco storage information of the target tobacco drone forward warehouse are structured to obtain structured tobacco information, wherein the structured tobacco information includes: forward warehouse structured information, forward warehouse tobacco structured information, and forward warehouse tobacco associated information; Determining tobacco delivery restriction information corresponding to the tobacco delivery request information, wherein the tobacco delivery restriction information is at least one restriction condition corresponding to a preset forward warehouse tobacco delivery model; Inputting the forward warehouse structured information, the forward warehouse tobacco structured information, and the forward warehouse tobacco associated information into the forward warehouse tobacco distribution model to obtain the forward warehouse tobacco distribution information under the tobacco distribution restriction information; Determine at least one corresponding tobacco drone to be delivered according to the tobacco delivery information of the forward warehouse; Controlling the associated tobacco handling equipment to load tobacco onto the at least one tobacco delivery drone; In response to determining that tobacco loading is complete, at least one tobacco delivery drone is controlled to deliver the tobacco.

2. The tobacco distribution method applied to the tobacco drone forward warehouse according to claim 1 is characterized in that: The forward warehouse tobacco distribution model includes: a forward warehouse tobacco restriction condition group; and the tobacco distribution restriction information corresponding to the tobacco distribution request information is determined, including: Each forward warehouse tobacco restriction condition in the forward warehouse tobacco restriction condition group corresponding to the tobacco delivery request information is determined as tobacco delivery restriction information.

3. The tobacco distribution method applied to the tobacco drone forward warehouse according to claim 2 is characterized in that: The tobacco distribution restriction information includes: a basic forward warehouse tobacco restriction condition group, a tobacco distribution restriction condition group and a forward warehouse tobacco adjustment restriction condition group; the forward warehouse tobacco distribution model includes: a forward warehouse tobacco calculation function group; and the forward warehouse structured information, the forward warehouse tobacco structured information, and the forward warehouse tobacco associated information are input into the forward warehouse tobacco distribution model to obtain the forward warehouse tobacco distribution information under the tobacco distribution restriction information, including: Determine each forward warehouse tobacco calculation function corresponding to the tobacco delivery request information in the forward warehouse tobacco calculation function group as a target forward warehouse tobacco calculation function group; According to the basic forward warehouse tobacco restriction condition group, the tobacco delivery restriction condition group and the forward warehouse tobacco adjustment restriction condition group, the forward warehouse structured information, the forward warehouse tobacco structured information and the forward warehouse tobacco associated information are input into the target forward warehouse tobacco calculation function group to obtain the forward warehouse tobacco delivery information.

4. The tobacco distribution method applied to the tobacco drone forward warehouse according to claim 1 is characterized in that: The method further comprises: In response to receiving a forward warehouse resource conversion prediction request for the target tobacco drone forward warehouse, predicting the forward warehouse resource conversion efficiency of the target tobacco drone forward warehouse at a target time through a pre-trained forward warehouse resource conversion efficiency prediction model; According to the generated forward warehouse resource conversion efficiency prediction results, the drone delivery resources and tobacco storage resources of the target tobacco drone forward warehouse are adjusted.

5. A computer device, wherein: The computer device comprises a processor, a memory, and a computer program stored in the memory and executable by the processor, wherein when the computer program is executed by the processor, the steps of the method according to any one of claims 1 to 4 are implemented.

6. A computer-readable storage medium, wherein: The computer-readable storage medium stores a computer program, wherein when the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 4 are implemented.

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