Logistics state prediction method and device, electronic equipment and storage medium

Abstract

The embodiment of the invention provides a logistics state prediction method and device, electronic equipment and a storage medium, and belongs to the technical field of logistics. The method comprises the following steps: acquiring a target expected income and a target expected profit of a to-be-predicted project in a preset demand input view; according to the target expected income and the target expected profit, constraint parameters of a plurality of preset quantity-cost-profit models are updated, and adjustable operation calculation items used for profit calculation in the quantity-cost-profit models are different; based on the first transaction logistics data, carrying out logistics state optimization prediction through each amount-cost-benefit model to obtain operation state prediction data in one-to-one correspondence with the amount-cost-benefit models; and displaying the operation state prediction data in the prediction view corresponding to the to-be-predicted project, thereby obtaining the operation state prediction data under the optimization of a plurality of different dimensions, and displaying the operation state prediction data in the prediction view, so that the logistics state prediction precision is higher.

Description

Technical Field

[0001] This application relates to the field of logistics technology, and in particular to a method and apparatus for predicting logistics status, electronic equipment, and storage medium. Background Technology

[0002] In the field of logistics technology, logistics status forecasting is often used to optimize logistics resource deployment and marketing strategies. Therefore, more accurate logistics status forecasting is crucial. However, with the increasing scale of transportation and the diversification of product demand, the business volume of specialized transportation projects with strong seasonality, regional characteristics, and specific needs is growing year by year, such as lychee projects, hairy crab projects, and flower and plant projects. These specialized transportation projects exhibit significant cyclical fluctuations in business volume (including the number of orders and total order weight), with strong peak characteristics. Therefore, existing logistics status forecasting methods are not suitable for these specialized transportation projects, resulting in insufficient accuracy in logistics status forecasting. Thus, providing a logistics status forecasting method to improve the accuracy of logistics status forecasting is a pressing technical problem that needs to be solved. Summary of the Invention

[0003] The main objective of this application is to provide a method, apparatus, electronic device, and storage medium for predicting logistics status, aiming to improve the accuracy of logistics status prediction.

[0004] To achieve the above objectives, a first aspect of this application proposes a method for predicting logistics status, the method comprising:

[0005] Obtain the expected operational targets of the project to be predicted from the preset demand input view, wherein the expected operational targets include expected revenue and expected profit;

[0006] Obtain the first transaction logistics data corresponding to each operational calculation item in the project to be predicted;

[0007] Based on the target expected revenue and the target expected profit, the constraint parameters of multiple preset cost-volume-profit models are updated respectively, wherein the adjustable operational calculation items used for profit calculation in each cost-volume-profit model are different;

[0008] Based on the first transaction logistics data, logistics status optimization and prediction are performed through each of the volume-cost-profit models to obtain operational status prediction data that corresponds one-to-one with the volume-cost-profit models.

[0009] The predicted operational status data is displayed in the prediction view corresponding to the project to be predicted.

[0010] In some embodiments, the plurality of cost-volume-profit (CVP) models include a revenue enhancement model, a profit enhancement model, a transportation structure change model, and a warehouse capacity utilization model; the step of optimizing and predicting logistics status based on the first transaction logistics data using each of the aforementioned CVP models to obtain operational status prediction data corresponding one-to-one with each of the CVP models includes:

[0011] The first operational status prediction data is obtained by using the revenue enhancement model to predict the profit of the first transaction logistics data and iteratively calculating the profit under different discounts based on the first transaction logistics data, so as to achieve the target expected revenue and maximize the profit.

[0012] The profit prediction data of the first transaction logistics data is obtained by using the profit enhancement model to predict the profit of the first transaction logistics data and iteratively calculating the profit under different discounts based on the first transaction logistics data, so as to obtain the second operation status prediction data when the target expected profit is achieved and the profit is increased by a preset percentage.

[0013] The transportation structure change model is used to predict the profit of the first transaction logistics data and to iteratively calculate the profit under the proportion of different logistics types based on the first transaction logistics data, so as to obtain the third operation state prediction data when the target expected profit is achieved and the profit is maximized.

[0014] By using the aforementioned model for utilizing remaining capacity for warehouse filling, profit prediction is performed on the first transaction logistics data, and profit is iteratively calculated based on the first transaction logistics data under different filling ratios, resulting in fourth operational state prediction data when the target expected profit is achieved and the profit is maximized.

[0015] In some embodiments, the first transaction logistics data includes transaction data and logistics data, and the profit prediction based on the first transaction logistics data includes:

[0016] Calculate the total logistics cost based on the logistics data and the preset logistics unit price data;

[0017] Discount strategy data and basic business volume data are extracted from the transaction data, respectively.

[0018] Obtain historical business volume data and estimated extended business volume of the project to be predicted, and obtain the predicted total business volume based on the historical business volume data, the basic business volume data and the estimated extended business volume.

[0019] Based on the discount strategy data, the predicted total business volume, and the preset transaction price data, the total revenue data is determined;

[0020] Based on the total revenue data and the total logistics cost data, the predicted profit data is obtained.

[0021] In some embodiments, obtaining the first transaction logistics data corresponding to each operational calculation item in the project to be predicted includes:

[0022] In response to a transaction information input request, extract the single-direction single-customer product business volume data, single-direction product weight data, and discount strategy data of the project to be predicted from the demand input view;

[0023] In response to a transportation information input request, the system extracts logistics transportation resource data, logistics non-transportation resource data, and logistics unit price data for the project to be predicted from a preset resource input view.

[0024] The first transaction logistics data is obtained based on the single-direction single-customer product business volume data, the single-direction product weight data, the discount strategy data, the logistics transportation resource data, the logistics non-transportation resource data, and the logistics unit price data.

[0025] In some embodiments, the operational status prediction data includes second transaction logistics data and cost and profit data corresponding to the second transaction logistics data; displaying the operational status prediction data in a preset prediction view includes:

[0026] Configure multiple first display areas corresponding to the cost-volume-profit model in the preset analysis view;

[0027] For each of the first display areas, the second transaction logistics data of the volume-cost-profit model corresponding to the first display area is displayed according to the preset first display rule, and the cost-profit data corresponding to the second transaction logistics data is displayed according to the second display rule.

[0028] In some embodiments, the method further includes:

[0029] In response to the scheme comparison request, scheme comparison data for each of the volume-cost-profit models is generated based on the first transaction logistics data and the second transaction logistics data for each of the volume-cost-profit models.

[0030] The cost and profit data corresponding to the first transaction logistics data and the cost and profit data corresponding to the second transaction logistics data are compared to generate a cost and profit comparison result.

[0031] In the preset comparison view, multiple second display areas are configured that correspond one-to-one with the cost-volume-profit model. In each of the second display areas, the constraint parameters of the corresponding cost-volume-profit model, the scheme comparison data, and the cost-profit comparison results are displayed according to the third display rule.

[0032] In some embodiments, the method further includes:

[0033] In the comparison view, in response to the selection request of each of the second display areas, the first transaction logistics data and the second transaction logistics data of the selected second display area are obtained;

[0034] The data items in the first transaction logistics data that correspond one-to-one with the preset multiple display items are respectively displayed in the third display area below each of the second display areas;

[0035] The data items in the second transaction logistics data that correspond one-to-one with the plurality of display items are respectively displayed in the fourth display area below each of the second display areas.

[0036] To achieve the above objectives, a second aspect of this application provides a device for predicting the status of logistics, the device comprising:

[0037] The first acquisition module is used to acquire the expected operational target data of the project to be predicted from the preset demand input view, wherein the expected operational target data includes expected revenue and expected profit;

[0038] The second acquisition module is used to acquire the first transaction logistics data corresponding to each operational calculation item in the project to be predicted.

[0039] The configuration module is used to update the constraint parameters of multiple preset cost-volume-profit models according to the target expected revenue and the target expected profit, wherein the adjustable operational calculation items used for profit calculation in each cost-volume-profit model are different.

[0040] The prediction module is used to perform logistics status optimization prediction based on the first transaction logistics data and through each of the volume-cost-profit models to obtain operational status prediction data that corresponds one-to-one with the volume-cost-profit models.

[0041] The display module displays the predicted operational status data in the prediction view corresponding to the project to be predicted.

[0042] To achieve the above objectives, a third aspect of this application provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the method described in the first aspect.

[0043] To achieve the above objectives, a fourth aspect of the present application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described in the first aspect.

[0044] The logistics status prediction method, apparatus, electronic device, and storage medium proposed in this application optimize and predict logistics status by setting multiple different cost-volume-profit (CVP) models and performing CVP prediction on each model based on the input operational target expectation data. This achieves optimization of the first transaction logistics data in multiple dimensions, thereby obtaining multiple operational status prediction data that satisfy the constraint parameters. Furthermore, by displaying multiple operational status prediction data in a prediction view, the relationship between the optimized and unoptimized logistics status of the project under different dimensions can be more intuitively understood, further ensuring higher accuracy in logistics status prediction. Therefore, compared with related technologies, the embodiments of this application can improve the accuracy of logistics status prediction. Attached Figure Description

[0045] Figure 1 This is a flowchart of the logistics status prediction method provided in the embodiments of this application;

[0046] Figure 2 This is a schematic diagram of the flow of the first transaction logistics data in the logistics status prediction method provided in the embodiments of this application;

[0047] Figure 3 This is a schematic diagram illustrating the principle of the objective function of each quantity-cost-profit model in the logistics status prediction method provided in the embodiments of this application;

[0048] Figure 4 This is a schematic diagram of the interface display of the demand input view of an embodiment of the logistics status prediction method provided in this application.

[0049] Figure 5 This is a schematic diagram of the interface display of the resource input view of an embodiment of the logistics status prediction method provided in this application.

[0050] Figure 6 This is a system block diagram corresponding to the logistics status prediction method provided in the embodiments of this application;

[0051] Figure 7 This is a hardware block diagram corresponding to the logistics status prediction method provided in the embodiments of this application. Detailed Implementation

[0052] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0053] It should be noted that although functional modules are divided in the device schematic diagram and a logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flowchart. The terms "first," "second," etc., in the specification, claims, and the aforementioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

[0054] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0055] The logistics status prediction method provided in this application relates to the field of logistics technology. This method can be applied to a terminal, a server, or software running on either a terminal or a server. In some embodiments, the terminal can be a smartphone, tablet, laptop, desktop computer, etc.; the server can be configured as an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms; the software can be an application implementing the logistics status prediction method, but is not limited to the above forms.

[0056] This application can be used in a wide variety of general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices. This application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific tasks or implement specific abstract data types. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0057] It should be noted that in all specific embodiments of this application, when processing data related to user identity or characteristics, such as user information, user behavior data, user historical data, and user location information, user permission or consent is obtained first. Furthermore, the collection, use, and processing of this data comply with relevant laws, regulations, and standards. In addition, when embodiments of this application require access to sensitive personal information of users, separate permission or consent from the user is obtained through pop-ups or redirection to confirmation pages. Only after obtaining the user's separate permission or consent is the necessary user-related data required for the proper functioning of these embodiments acquired.

[0058] Reference Figure 1 As shown, Figure 1 This is an optional flowchart of the logistics status prediction method provided in the embodiments of this application. Figure 1 The method may include, but is not limited to, steps S100 to S500.

[0059] Reference Figure 1 As shown, to achieve the above objective, a first aspect of this application proposes a method for predicting logistics status, the method comprising:

[0060] Step S100: Obtain the expected operational target data of the project to be predicted from the preset demand input view, wherein the expected operational target data includes expected revenue and expected profit;

[0061] Step S200: Obtain the first transaction logistics data corresponding to each operational calculation item in the project to be predicted;

[0062] Step S300: Update the constraint parameters of multiple preset cost-volume-profit models according to the target expected revenue and target expected profit. The adjustable operational calculation items used for profit calculation in each cost-volume-profit model are different.

[0063] Step S400: Based on the first transaction logistics data, optimize and predict the logistics status through each volume-cost-profit model to obtain the operational status prediction data that corresponds one-to-one with the volume-cost-profit model.

[0064] Step S500: Display the operational status forecast data in the forecast view corresponding to the project to be forecasted.

[0065] Therefore, by setting up multiple different cost-volume-profit (CVP) models and using each model to optimize and predict logistics status based on the input operational target expectation data, optimization of the first transaction logistics data is achieved in multiple dimensions, resulting in multiple operational status prediction data that satisfy the constraint parameters. Furthermore, by displaying these multiple operational status prediction data in a prediction view, the relationship between the optimized and unoptimized logistics status of the project under different dimensions can be more intuitively understood, further ensuring higher accuracy in logistics status prediction.

[0066] This application does not limit the type or specific content of the target expected profit in its embodiments. The target expected profit can be a specific profit value or a profit margin. In other embodiments, the target expected profit may also include both profit value and profit margin. The target expected profit can be gross profit or net profit, etc. Those skilled in the art can selectively set these parameters according to actual needs.

[0067] Operational calculation items are those used in profit calculation. For example, if the profit generated from logistics and transportation needs to be based on order weight, order quantity, transportation distance, and transportation vehicle, then order weight, order quantity, transportation distance, and type of transportation vehicle can all be used as items for profit calculation. Those skilled in the art can selectively set operational calculation items based on profit testing-related data in actual business scenarios.

[0068] The first transaction logistics data is a collection of data corresponding to each operational calculation item involved in profit calculation in the project to be predicted. The first transaction logistics data may include unit price data, such as cost per kilogram and transportation costs, etc. Alternatively, it may only include data other than unit price data; this application does not limit this, and those skilled in the art can selectively set it according to actual needs. In some embodiments, the first transaction logistics data also includes some predictive data, such as predicted ticket counts, etc. This application also does not limit the method of obtaining the first transaction logistics data. The first transaction logistics data can be obtained in real time by inputting into a preset user interface, or it can be obtained in real time by importing a file.

[0069] Reference Figure 3 As shown, the cost-volume-profit (CVP) model is a model that constructs an objective function based on output cost-profit analysis. Adjustable operational calculation terms are used to determine the changes in the objective function within the corresponding CVP model, thus allowing for different optimization directions in each CVP model. Constraint parameters determine the search range during the calculation process of the corresponding CVP model. Therefore, by setting different adjustable operational calculation terms in different CVP models, the prediction of logistics status under different dimensional requirements can be met.

[0070] This application does not limit the model structure of the cost-volume-profit (CVP) model. In some embodiments, the CVP model can be a network model constructed using ant colony optimization (ACO) and particle swarm optimization (PSO), or it can be a machine learning model. Those skilled in the art can selectively set these parameters based on the efficiency required for profit calculation using the CVP model. Taking a CVP model based on PSO as an example, some CVP models can automatically adjust product mix, customer discount values, and transportation selection based on the profit maximization objective and the basic constraint parameters of the CVP model (such as discounts not being more than 10% lower than the contract price) using PSO, ultimately obtaining a recommended product strategy for the profit maximization scenario (e.g., recommending time-sensitive products at 40% and reducing discounts by 5%). Another example is a CVP model that adjusts from profit maximization to a 5% profit increase, while simultaneously refining the discount strategy. With cost as a constraint parameter, it explores the inflection point of business volume improvement through optimal value combinations, achieving an increase in manageable business volume while ensuring profit.

[0071] The embodiments of this application do not limit the number of cost-volume-profit models, and those skilled in the art can set them according to the dimensions that need to be predicted in actual needs.

[0072] The operational status prediction data includes cost and profit data before optimization, second transaction logistics data after optimization of the first transaction logistics data, and cost and profit data corresponding to the second transaction logistics data. The cost and profit data before optimization can be calculated uniformly or separately through various cost-volume-profit models; this application does not impose any restrictions on this.

[0073] This application embodiment does not impose constraints on how the operational status prediction data is displayed in step S500; those skilled in the art can selectively set it according to actual needs. At this time, since different cost-volume-profit models provide second transaction logistics data and corresponding cost-profit data under different optimization directions and transportation strategies, the logistics status can be predicted from multiple dimensions, thereby improving the accuracy of logistics status prediction. This enhances user experience while ensuring that costs and profits meet preset expectations.

[0074] For example, refer to Figure 2As shown, multiple cost-volume-profit (CVP) models are all based on revenue and cost calculations to model CVP. Each CVP model uses different discount strategies and different resource allocations (transportation types) as optimization directions to determine the objective function, so that the adjustable operational calculation items in each CVP model are different. This allows for the output of transportation strategy schemes with different preferences, such as time-sensitive priority and profit-sensitive priority. When the target expected revenue and target expected profit are input in the demand view, the constraint parameters under different CVP models are configured based on the target expected revenue and target expected profit. The profit optimization calculation is performed on the first transaction logistics data composed of the input product composition, business volume composition, etc., through each CVP model to obtain the discount strategies and resource allocations under different combinations.

[0075] The target expected profit is the minimum profit that the project is expected to achieve, and the target expected revenue is the minimum revenue that the project is expected to achieve.

[0076] Understandably, multiple cost-volume-profit (CVP) models include revenue enhancement models, profit enhancement models, transportation structure change models, and warehouse capacity utilization models. Based on the first transaction logistics data, logistics status is optimized and predicted using each CVP model, resulting in operational status prediction data corresponding to each CVP model, including:

[0077] By using a revenue enhancement model to predict profits from the first transaction logistics data and iteratively calculating profits under different discounts based on the first transaction logistics data, the first operational status prediction data when the target expected revenue is achieved and the profit is maximized is obtained.

[0078] By using a profit enhancement model to predict profits from the first transaction logistics data and iteratively calculating profits under different discounts based on the first transaction logistics data, the second operational status prediction data is obtained when the target expected profit is achieved and the profit is increased by a preset percentage.

[0079] By using a transportation structure change model to predict profits from the first transaction logistics data and iteratively calculating profits under the proportion of different logistics types based on the first transaction logistics data, the third operational state prediction data when the target expected profit is achieved and the profit is maximized is obtained.

[0080] By using the model for utilizing the remaining capacity of the warehouse to predict profits from the first transaction logistics data and iteratively calculating profits under different filling ratios based on the first transaction logistics data, we obtain the fourth operational state prediction data when the target expected profit is achieved and the profit is maximized.

[0081] Each logistics type corresponds to a transportation method, for example, such as Figure 5As shown, transportation modes include general cargo flights, dedicated aircraft, trunk lines, and feeder lines. In some embodiments, trunk lines can be further divided into two types based on the dispatch and pickup patterns: ordinary trunk lines and cargo-filling trunk lines. Cargo-filling trunk lines refer to centralized transportation after orders accumulate to a predetermined quantity, while ordinary trunk lines are transportation methods that do not require waiting for centralized transportation. Iterative calculation of the proportion of different logistics types means calculating the profit for each configured transportation mode after adjusting its proportion. Iterative calculation of different cargo-filling proportions means calculating the profit for each combination of transportation modes when increasing the proportion of different cargo-filling trunk lines.

[0082] In the objective function of the revenue enhancement model, the cost-related parameters in the first transaction logistics data are fixed, and the discount-related operational calculation items in the first transaction logistics data are adjustable. The target expected revenue and the maximization of profit are the constraint parameters, thereby achieving positive adjustment through the cost-volume-profit model.

[0083] In the profit enhancement model, the cost-related parameters in the first transaction logistics data are fixed, while the discount-related operational calculation items in the first transaction logistics data are adjustable. The target expected profit and the preset profit enhancement ratio are constraint parameters, thereby achieving negative adjustment through the cost-volume-profit model.

[0084] In the transportation structure change model, the operational calculation items related to the product composition ratio and transportation ratio in the first transaction logistics data are adjustable items, so that the profit under the different logistics type ratios can be calculated. Among them, the target expected profit and profit maximization are constraint parameters, so that positive adjustment can be achieved.

[0085] In the model for utilizing remaining capacity in warehouses, a warehouse transportation method is added to calculate profits, and profits under different warehouse ratios are calculated. The target expected profit and profit maximization are the constraint parameters, which can achieve positive adjustment.

[0086] For example, if four cost-volume-profit models are set up, the logistics data of the first transaction can be optimized and predicted through the four cost-volume-profit models respectively, and four transportation strategies can be output. The four transportation strategies are displayed in the analysis view as Scheme 1-1, Scheme 1-2, Scheme 2 and Scheme 3 respectively. Based on the content displayed in the analysis view, the differences in transportation strategies of each scheme can be determined more intuitively.

[0087] Understandably, the first transaction logistics data includes both transaction data and logistics data. Profit forecasting based on the first transaction logistics data includes:

[0088] Calculate the total logistics cost based on logistics data and preset logistics unit price data;

[0089] Discount strategy data and basic business volume data are extracted from the transaction data.

[0090] Obtain historical business volume data and estimated extended business volume for the project to be predicted, and obtain the predicted total business volume based on the historical business volume data, basic business volume data and estimated extended business volume.

[0091] Based on the data of each discount strategy, the predicted total business volume, and the preset transaction price data, the total revenue data is determined;

[0092] Based on total revenue data and total logistics cost data, the projected profit data is obtained.

[0093] Transaction unit price data defines the transaction unit price for various products; logistics unit price data defines the logistics unit price for various transportation modes. Both logistics unit price data and transaction unit price data can be directly read from the database, or they can be pre-configured in the view and then saved, or they can be partially configured in the view and partially read from the database. Therefore, this application embodiment does not restrict the respective methods of obtaining logistics unit price data and transaction unit price data. For example, as shown... Figure 5 As shown, logistics unit price data can be extracted from the data item "Cost per kilogram_Base period value".

[0094] Transaction data defines the data related to logistics orders, for example, refer to Figure 4 As shown, the transaction data includes product volume structure data, product weight structure data, and product discount strategy data. The product volume structure data defines the percentage of product volume for a single customer and the percentage of product weight for a single customer and the percentage of product weight for a single customer and the discount strategy data define the discounts for different types of customers within a single customer and customer flow. For example... Figure 4 As shown, in the table corresponding to the product business volume structure data, the "Origin Business Area," "Destination Business Area," and "Distance Range" indicate the flow of logistics orders; "Customer Type" defines the customer's payment method; and "Product Information" is used to identify products, such as... Figure 4 As shown, the product information is configured with a product name. In some embodiments, the "product information" also includes a product code. In other embodiments, the product information may only include the product code. Therefore, this application does not limit the content included in the product information. Under "Single-direction, single-customer product business volume percentage," a minimum threshold business volume percentage (i.e., base period percentage) is configured. In other embodiments, "Single-direction, single-customer product business volume percentage" also defines a predicted percentage. For example... Figure 4As shown, in the table corresponding to the product weight structure data, the "Flow Direction Information" and "Product Information" are consistent with those in the table corresponding to the product business volume structure data for easy viewing. In other embodiments, they can be set differently, as long as it can be identified as a single flow to a single customer. The "Single Flow to Product Weight Ratio" field in the table corresponding to the product weight structure data is configured with a base period ratio to record the minimum threshold weight. In other embodiments, a predicted ratio is also recorded. For example... Figure 4 As shown, the product discount strategy data records the discount rates for different customers with different destinations. For example, for a monthly settlement customer whose originating business area is SZQ and whose destination business area is SX, when the order is an express order, if the total pre-discount charge for the express order is within the range [0, 100), the base period gross profit margin can reach 10%, and the base period discount rate can be configured to 30%. Figure 4 As shown, after configuration, you can click "Save Draft" or "Submit" to enter the transportation resource input interface for entering logistics data. In some embodiments, such as Figure 4 As shown, the expected data for operational targets also includes the predicted number of votes for a single flow direction. In this case, the base period percentage of the single-flow-single-customer product business volume percentage records the proportion of single-flow-single-customer products to the total number of votes for the corresponding flow direction.

[0095] Logistics data defines the cost data corresponding to logistics resources. Those skilled in the art can selectively set the cost data to participate in cost-volume-profit calculations according to actual needs. For example, such as... Figure 5 As shown, the logistics data includes existing resource costs, temporary extra flight costs, refrigerated truck overflow costs, and narrow-to-wide conversion costs. Existing resource costs define the weight percentage and unit cost for different transportation modes. Temporary extra flight costs define the cost and capacity of additional flights. Refrigerated truck overflow costs record the cost data for refrigerated and ambient temperature trucks used to calculate refrigerated truck overflow costs. Narrow-to-wide conversion costs record the cost data required to convert the aircraft type to a wider type.

[0096] The estimated stretching business volume represents the additional business volume that the project is expected to generate beyond the minimum business volume; it can be estimated based on historical business data. This application does not impose constraints on how the estimated stretching business volume is estimated.

[0097] In some embodiments, the total logistics cost data is calculated as the sum of logistics transportation costs for different products under different flow directions, non-transportation costs (such as collection and delivery costs), and fixed costs (such as network equipment, rental fees, etc.). Specifically, the logistics transportation cost for [flow direction + product] = [transportation mode] cost per kilogram * [flow direction + product] predicted number of tickets * first correlation coefficient; the logistics non-transportation cost for [flow direction + product] = [non-transportation mode] cost per kilogram * [flow direction + product] predicted number of tickets * second correlation coefficient. Furthermore, the predicted number of tickets for [flow direction + product + customer type] = [flow direction] predicted number of tickets * [flow direction + product + customer type] ticket percentage, to distribute the flow direction tickets to a single flow direction, single product, and single customer dimension. The [flow direction + product + customer type] ticket percentage corresponds to... Figure 5 The base period percentage in "Single-to-single-customer product business volume percentage" is used to calculate the pre-discount revenue and post-discount revenue for a single customer type for a single product from a single flow, as well as the logistics cost for a single product from a single flow. The first and second correlation coefficients are used to convert the number of tickets into corresponding weights, reflecting the relationship between business volume and weight.

[0098] For example, such as Figure 3 As shown, the cost-volume-profit (CVP) model is based on the formula Profit = Revenue - Cost. The core objective function of the CVP model is: MAX p in =income in -cost in ;p in Let be the projected net profit of the project to be tested, i, in month n. The profit data can then be determined through the following steps:

[0099] Step 1: Forecasting Revenue:

[0100] ① Forecast future total business volume: v i =v0+v f +v s .

[0101] Among them, v i This represents the total number of votes for the predicted project in month i, where v0 represents the base number of votes, and the value is determined by the logic of max(v d ,∑v c ), where v d This represents the average number of items for the project during the off-season months over the past three years, ∑v c This represents the minimum total number of votes in the contracts of the clients who have signed up for this forecast project (which can be based on...). Figure 4 (The proportion of "single-direction but customer product business volume" is determined in the middle);

[0102] v fThis represents the estimated number of stretch parts for the project in month i. It can be selected from the same transportation distance segment, the same product type, and the average number of stretch parts under the same product type (the average number of stretch parts can be obtained by averaging the number of tickets in non-Pinggu months such as January, June, September, November, and December when the stretch sampling month is January).

[0103] v s This indicates the random fluctuation in the number of items for the predicted project in the i-th month, which can be characterized by the monthly variance of the number of votes for the predicted project in the i-th month of the same period in the previous year.

[0104] ② Distribute the business volume to the business dimension of single-direction, single-product, single-customer, to obtain the number of votes v for single-direction, single-product, single-customer. item =v i ×pr item , among which, pr item For the base period percentage of a single flow, single product, and single customer, PR item It is a variable parameter.

[0105] ③Integrated estimated revenue: income in =∑p0×(1-zr0)×v sd +∑p m ×(1-zr m )×v m .

[0106] The first part of the formula represents the predicted revenue for individual orders, v. sd For non-monthly settlement customers, the number of tickets for a single product in a single flow direction is calculated. The core is to multiply the product's list price p0 under the flow direction by the targeted marketing discount zr0 for individual orders under the flow direction to get the price, and then multiply it by the estimated number of individual orders for the flow direction product. Finally, the price is summarized based on the product and flow direction.

[0107] The latter part of the formula represents the projected revenue for monthly settlement customers, v m This refers to the number of single-product shipments for monthly settlement customers within different discount ranges (m). The core idea is to extract the tiered contract unit price and discount ratio for each monthly settlement card number under the current flow and product based on the customer's shipment volume, multiply these by the business volume, and then sum them up. m Let zr be the product price within the discount range m. m The monthly settlement discount is for the discount interval m.

[0108] In some embodiments, monthly settlement discount zr m It is a variable parameter, but the targeted marketing discount zr0 remains unchanged (such as the discount for individual customers), and the contract discount is only used as an initialization input.

[0109] Step 2: Cost Forecasting: The total cost is divided into two parts based on flow direction and product: transportation costs and non-transportation costs; details are as follows:

[0110] Step 3: Regarding transportation costs: Based on the revenue stream and product type, map the corresponding transportation mode. For example, the express product from Beijing to Shenzhen corresponds to a freighter, while the standard express product from Beijing to Tianjin corresponds to land transportation. Then, based on the principle of volume and price, convert the revenue volume into weight and obtain the benchmark cost per kilogram to derive the transportation cost. The same logic applies to non-transportation costs.

[0111] Understandably, the first transaction logistics data corresponding to each operational calculation item in the project to be predicted is obtained, including:

[0112] In response to the transaction information input request, extract the single-direction single-customer product business volume data, single-direction product weight data, and discount strategy data of the project to be predicted from the demand input view;

[0113] In response to a transportation information input request, the system extracts logistics transportation resource data, logistics non-transportation resource data, and logistics unit price data for the project to be predicted from a preset resource input view.

[0114] Based on the single-direction, single-customer product business volume data, single-direction product weight data, discount strategy data, logistics transportation resource data, logistics non-transportation resource data, and logistics unit price data, the first transaction logistics data is obtained.

[0115] For example, such as Figure 4 As shown, in the demand input view, after the user fills in the tables corresponding to the single-direction, single-customer product volume data, the single-direction product weight data, and the discount strategy data, submitting the request triggers a transaction information input request. This allows the determination of the volume data, weight data, and discount strategies for different customer types in different flow directions. For example, as shown... Figure 4 As shown, the proportion of business volume (i.e., base period proportion) of monthly settlement customers with the product name "Express" shipped from SZQ to SX can be extracted from the demand input view as the business volume data for a single customer in a single flow direction. Figure 4 As shown, the weight of products shipped from SZQ to SX with the product name "Express" to the total weight of that flow (i.e., the base period percentage) can be extracted from the demand input view as the single-flow product weight data. For example... Figure 4 As shown, the base period discount rate of 30% can be extracted from the demand input view for monthly settlement customers from SZQ to SX when the pre-discount revenue range is [0,100) and the base period gross profit margin is 10%, as the discount strategy data.

[0116] For example, such as Figure 5 As shown, the logistics and transportation resource data in the resource input view includes the weight data of the main transportation mode, the cost data of temporary extra flights, and the overflow cost and the cost of converting narrow-to-wide refrigerated trucks.

[0117] By inputting transaction and logistics data based on different views, data from different stages can be entered by the responsible persons at the corresponding stages, decoupling data between different operational stages and making operations more convenient.

[0118] In some embodiments, such as Figure 5 As shown, each piece of data can be imported into a table and exported for offline editing, further improving the convenience of operation.

[0119] In some embodiments, such as Figure 5 As shown, the logistics unit price data includes the base period value of the cost per kilogram for each mode of transportation. In some embodiments, a calculated cost per kilogram can also be configured. This allows for cost estimation based on both the base period value and the calculated value.

[0120] Understandably, the operational status forecast data includes the second transaction logistics data and the corresponding cost and profit data; the operational status forecast data is displayed in a preset forecast view, including:

[0121] Configure multiple first display areas corresponding to the cost-volume-profit model in the preset analysis view;

[0122] For each first display area, the second transaction logistics data corresponding to the first display area of ​​the cost-volume-profit model is displayed according to the preset first display rules, and the cost and profit data corresponding to the second transaction logistics data are displayed according to the second display rules.

[0123] This application does not limit the first display rule or the second display rule; those skilled in the art can selectively set them according to actual needs. The first display rule and the second display rule define the data items to be displayed and the display format.

[0124] The first display area and the cost-volume-profit (CVP) model can be set in a one-to-one correspondence, or the CVP model can be categorized and the first display area can be set based on the category. In this embodiment, the correspondence between the first display area and the CVP model is not limited. By setting multiple first display areas, the operational status prediction data of the same CVP model can be displayed centrally, and the second transaction data and cost-profit data are displayed in different forms, which can more intuitively determine the merits of the output strategy.

[0125] The cost and profit data includes total revenue data, total logistics cost data, and profit data. For example, taking four cost-volume-profit (CVP) models, each occupying a separate first display area, each area displays the optimized discount strategies and transportation structures for each product in the corresponding CVP model's second transaction logistics data. The discount strategies include the business volume percentage and discount for each product; the transportation structure includes the weight percentage for each transportation mode. Taking Scheme 2 as an example, there are four product categories: "SF Express," "Express," "DS Express," and air freight, with corresponding business volume percentages of "1.3%," "0.2%," "98%," and "0.3%" respectively; the weight percentage for trunk lines is 97.52%. The cost and profit data for each CVP model displays the optimized discounted revenue, total cost, and profit.

[0126] In some embodiments, cost-volume-profit models with the same discount strategy and transportation structure can be displayed in the same first display area.

[0127] Understandably, the method also includes:

[0128] In response to the scheme comparison request, scheme comparison data for each volume-cost-profit model is generated based on the first transaction logistics data and the second transaction logistics data for each volume-cost-profit model.

[0129] The cost and profit data corresponding to the first transaction logistics data and the cost and profit data corresponding to the second transaction logistics data are compared to generate a cost and profit comparison result.

[0130] Configure multiple second display areas in the preset comparison view that correspond one-to-one with the cost-volume-profit model, and display the constraint parameters of the corresponding cost-volume-profit model, scheme comparison data, and cost-profit comparison results in each second display area according to the third display rules.

[0131] By setting up a second display area that corresponds one-to-one with the cost-volume-profit model, the comparison effect can be highlighted more effectively, and the ease of use can be improved.

[0132] The scheme comparison request is used for real-time tracking of data for the current month and comparative analysis of historical data.

[0133] The scheme comparison data is used to summarize the improvement points between the optimized second transaction logistics data and the original first transaction logistics data. The cost-profit comparison results are used to compare the first and second transaction logistics data from three dimensions: revenue, total cost, and profit. The constraint parameters are used to display the constraints and optimization directions of the corresponding cost-volume-profit model.

[0134] The third display rule defines the display format of constraint parameters, scheme comparison data, and cost-profit comparison results, such as displaying them as graphics or text, or a combination of graphics and text, and sets the display color, size, etc.

[0135] For example, in some embodiments, after clicking the scheme comparison button in the analysis view, a scheme comparison request is triggered, which will then jump to a new view (such as the comparison view) for scheme comparison. In the new view, each second display area displays the scheme comparison data, cost-profit comparison results, and constraint parameters from top to bottom.

[0136] In some embodiments, the method further includes:

[0137] In the comparison view, in response to the selection request of each second display area, the first transaction logistics data and the second transaction logistics data of the selected second display area are obtained;

[0138] The data items in the first transaction logistics data that correspond one-to-one with the preset multiple display items are displayed in the third display area below each of the second display areas;

[0139] The data items in the second transaction logistics data that correspond one-to-one with the multiple display items are displayed in the fourth display area below each display area.

[0140] Display items are used to characterize operational calculation items that affect profits. In some embodiments, display items include gross profit margin, business volume, pre-discount revenue per shipment, trunk line cost, general aviation cost, feeder line cost, charter flight cost, other costs, and gross profit. By displaying the details of the first and second transaction logistics data respectively, the merits of the optimized and non-optimized solutions can be more intuitively determined. This allows for dynamic adjustment of resource deployment (such as collection and delivery capacity, transportation vehicles, etc.) to ensure timely fulfillment of market demands or to dynamically adjust product discount strategies.

[0141] The third and fourth display areas allow for the centralized display of data items corresponding to the display items in the first and second transaction logistics data, enabling a more intuitive comparison of overall advantages and disadvantages and providing a better user experience when calculating profits.

[0142] Understandably, referring to Figure 6 As shown, a second aspect of this application provides a device for predicting the status of logistics, the device comprising:

[0143] The first acquisition module 100 is used to acquire the expected operational target data of the project to be predicted from the preset demand input view, wherein the expected operational target data includes expected revenue and expected profit.

[0144] The second acquisition module 200 is used to acquire the first transaction logistics data corresponding to each operational calculation item in the project to be predicted.

[0145] The configuration module 300 is used to update the constraint parameters of multiple preset cost-volume-profit models according to the target expected revenue and the target expected profit. The adjustable operational calculation items used for profit calculation in each cost-volume-profit model are different.

[0146] The prediction module 400 is used to perform logistics status optimization prediction based on the first transaction logistics data and through each volume-cost-profit model to obtain operational status prediction data that corresponds one-to-one with the volume-cost-profit model.

[0147] The display module 500 displays the operational status forecast data in the forecast view corresponding to the project to be predicted.

[0148] It is understood that a third aspect of the embodiments of this application provides an electronic device, which includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the method of the first aspect described above.

[0149] To achieve the above objectives, a fourth aspect of the present application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method of the first aspect described above.

[0150] The specific implementation of the logistics status prediction device is basically the same as the specific implementation of the logistics status prediction method described above, and will not be repeated here.

[0151] It is understood that embodiments of this application also provide an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the aforementioned method for predicting the logistics status. This electronic device can be any smart terminal, including tablet computers, in-vehicle computers, etc.

[0152] Please see Figure 7 , Figure 7 The hardware structure of an electronic device according to another embodiment is illustrated. The electronic device includes:

[0153] The processor 601 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application.

[0154] The memory 602 can be implemented as a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 602 can store the operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 602 and is called and executed by the processor 601 to execute the logistics status prediction method of the embodiments of this application.

[0155] The input / output interface 603 is used to implement information input and output;

[0156] The communication interface 604 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0157] Bus 605 transmits information between various components of the device (e.g., processor 601, memory 602, input / output interface 603, and communication interface 604);

[0158] The processor 601, memory 602, input / output interface 603, and communication interface 604 are connected to each other within the device via bus 605.

[0159] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for predicting logistics status.

[0160] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0161] The logistics status prediction method, apparatus, electronic device, and storage medium provided in this application embodiment display virtual delivery object information on the initiation page after receiving an order delivery request, allowing the target requesting object to perceive that the order is being processed. Simultaneously, real delivery object scheduling is performed on the pending order in the background, and the initiation page is refreshed imperceptibly after finding the globally optimal delivery object. This ensures that the target requesting object has no noticeable process changes during the entire virtual scheduling process. Compared with related technologies, this application embodiment can quickly inform the target requesting object that the order is being processed after it is initiated, and handle the actual scheduling operation in the background, thereby improving the user experience of the target requesting object, reducing the probability of cancellation, and thus increasing the platform's order completion rate.

[0162] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0163] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this application, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0164] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0165] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0166] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0167] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0168] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0169] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0170] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0171] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.

Claims

1. A method for predicting the status of logistics, characterized in that, The method includes: Obtain the expected operational targets of the project to be predicted from the preset demand input view, wherein the expected operational targets include expected revenue and expected profit; Obtain the first transaction logistics data corresponding to each operational calculation item in the project to be predicted; Based on the target expected revenue and the target expected profit, the constraint parameters of multiple preset cost-volume-profit models are updated respectively, wherein the adjustable operational calculation items used for profit calculation in each cost-volume-profit model are different; Based on the first transaction logistics data, logistics status optimization and prediction are performed through each of the volume-cost-profit models to obtain operational status prediction data that corresponds one-to-one with the volume-cost-profit models. The predicted operational status data is displayed in the prediction view corresponding to the project to be predicted.

2. The method for predicting logistics status according to claim 1, characterized in that, The operational status prediction data includes second transaction logistics data and cost and profit data corresponding to the second transaction logistics data; The step of displaying the predicted operational status data in a preset prediction view includes: Configure multiple first display areas corresponding to the cost-volume-profit model in the preset analysis view; For each of the first display areas, the second transaction logistics data of the volume-cost-profit model corresponding to the first display area is displayed according to the preset first display rule, and the cost-profit data corresponding to the second transaction logistics data is displayed according to the second display rule.

3. The method for predicting logistics status according to claim 2, characterized in that, The method further includes: In response to the scheme comparison request, scheme comparison data for each of the volume-cost-profit models is generated based on the first transaction logistics data and the second transaction logistics data for each of the volume-cost-profit models. The cost and profit data corresponding to the first transaction logistics data and the cost and profit data corresponding to the second transaction logistics data are compared to generate a cost and profit comparison result. In the preset comparison view, multiple second display areas are configured that correspond one-to-one with the cost-volume-profit model. In each of the second display areas, the constraint parameters of the corresponding cost-volume-profit model, the scheme comparison data, and the cost-profit comparison results are displayed according to the third display rule.

4. The method for predicting logistics status according to claim 3, characterized in that, The method further includes: In the comparison view, in response to the selection request of each of the second display areas, the first transaction logistics data and the second transaction logistics data of the selected second display area are obtained; The data items in the first transaction logistics data that correspond one-to-one with the preset multiple display items are respectively displayed in the third display area below each of the second display areas; The data items in the second transaction logistics data that correspond one-to-one with the plurality of display items are respectively displayed in the fourth display area below each of the second display areas.

5. The method for predicting logistics status according to claim 1, characterized in that, The step of obtaining the first transaction logistics data corresponding to each operational calculation item in the project to be predicted includes: In response to a transaction information input request, extract the single-direction single-customer product business volume data, single-direction product weight data, and discount strategy data of the project to be predicted from the demand input view; In response to a transportation information input request, the system extracts logistics transportation resource data, logistics non-transportation resource data, and logistics unit price data for the project to be predicted from a preset resource input view. The first transaction logistics data is obtained based on the single-direction single-customer product business volume data, the single-direction product weight data, the discount strategy data, the logistics transportation resource data, the logistics non-transportation resource data, and the logistics unit price data.

6. The method for predicting logistics status according to claim 1, characterized in that, The multiple cost-volume-profit (CVP) models include a revenue enhancement model, a profit enhancement model, a transportation structure change model, and a warehouse capacity utilization model; the step of optimizing and predicting logistics status based on the first transaction logistics data using each of the aforementioned CVP models to obtain operational status prediction data corresponding one-to-one with each of the CVP models includes: The first operational status prediction data is obtained by using the revenue enhancement model to predict the profit of the first transaction logistics data and iteratively calculating the profit under different discounts based on the first transaction logistics data, so as to achieve the target expected revenue and maximize the profit. The profit prediction data of the first transaction logistics data is obtained by using the profit enhancement model to predict the profit of the first transaction logistics data and iteratively calculating the profit under different discounts based on the first transaction logistics data, so as to obtain the second operation status prediction data when the target expected profit is achieved and the profit is increased by a preset percentage. The transportation structure change model is used to predict the profit of the first transaction logistics data and to iteratively calculate the profit under the proportion of different logistics types based on the first transaction logistics data, so as to obtain the third operation state prediction data when the target expected profit is achieved and the profit is maximized. By using the aforementioned model for utilizing remaining capacity for warehouse filling, profit prediction is performed on the first transaction logistics data, and profit is iteratively calculated based on the first transaction logistics data under different filling ratios, resulting in fourth operational state prediction data when the target expected profit is achieved and the profit is maximized.

7. The method for predicting logistics status according to claim 6, characterized in that, The first transaction logistics data includes transaction data and logistics data, and the profit prediction based on the first transaction logistics data includes: Calculate the total logistics cost based on the logistics data and the preset logistics unit price data; Discount strategy data and basic business volume data are extracted from the transaction data, respectively. Obtain historical business volume data and estimated extended business volume of the project to be predicted, and obtain the predicted total business volume based on the historical business volume data, the basic business volume data and the estimated extended business volume. Based on the discount strategy data, the predicted total business volume, and the preset transaction price data, the total revenue data is determined; Based on the total revenue data and the total logistics cost data, the predicted profit data is obtained.

8. A device for predicting the status of logistics, characterized in that the device comprises: The first acquisition module is used to acquire the expected operational target data of the project to be predicted from the preset demand input view, wherein the expected operational target data includes expected revenue and expected profit; The second acquisition module is used to acquire the first transaction logistics data corresponding to each operational calculation item in the project to be predicted. The configuration module is used to update the constraint parameters of multiple preset cost-volume-profit models according to the target expected revenue and the target expected profit, wherein the adjustable operational calculation items used for profit calculation in each cost-volume-profit model are different. The prediction module is used to perform logistics status optimization prediction based on the first transaction logistics data and through each of the volume-cost-profit models to obtain operational status prediction data that corresponds one-to-one with the volume-cost-profit models. The display module displays the predicted operational status data in the prediction view corresponding to the project to be predicted.

9. An electronic device, characterized in that, The electronic device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the logistics status prediction method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the method for predicting the logistics status as described in any one of claims 1 to 7.

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