Transportation capacity scheduling method and device, electronic equipment and storage medium

Abstract

Embodiments of the present disclosure provide a method and device for scheduling transportation capacity, an electronic device and a storage medium. The method for scheduling transportation capacity comprises: obtaining a first quantity of a specific transportation capacity type required in each region and a second quantity of the specific transportation capacity type that has been assigned; determining a first region with insufficient transportation capacity and a second region without insufficient transportation capacity based on the first quantity and the second quantity; and assigning a target order for the specific transportation capacity type in the second region, the target order being an order that enables the specific transportation capacity type to pass through the first region within a first preset time period. In the embodiments of the present disclosure, on the one hand, the assigned completed transportation capacity is further adjusted, and the accuracy of the transportation capacity scheduling can be further improved through the post-adjustment; on the other hand, the transportation capacity of the specific transportation capacity type is adjusted, and the accuracy of the transportation capacity scheduling can be improved through classified scheduling according to the different division of labor of the transportation capacity.

Description

Technical Field

[0001] This disclosure relates to the field of Internet technology, and in particular to a method, apparatus, electronic device and storage medium for scheduling transportation capacity. Background Technology

[0002] With the rapid development of internet technology, users are increasingly inclined to use the various convenient services provided by the internet, such as ordering food delivery using food delivery apps and hailing rides using ride-hailing apps. A large number of users means a large demand for transportation capacity, making accurate capacity scheduling crucial.

[0003] In existing technologies, capacity scheduling is typically performed through advance forecasting. One approach is to predict the total number of freight orders in the target area and then allocate capacity accordingly. Another approach is to predict the total capacity required by the target area and then allocate capacity based on that total capacity. However, the accuracy of these forecasting methods is relatively low, leading to lower accuracy in capacity scheduling. Summary of the Invention

[0004] In view of the above problems, this disclosure provides a capacity scheduling method, apparatus, electronic device and storage medium to improve the accuracy of capacity scheduling.

[0005] According to a first aspect of the embodiments of this disclosure, a capacity scheduling method is provided, comprising:

[0006] Obtain a first quantity of capacity of a specific capacity type required for each region and a second quantity of capacity of the specific capacity type already assigned;

[0007] Based on the first quantity and the second quantity, a first region with insufficient capacity and a second region without insufficient capacity are determined;

[0008] Target waybills are assigned to the specific capacity type in the second region, wherein the target waybill is a waybill that enables the specific capacity type to pass through the first region within a first preset time period.

[0009] Optionally, obtaining the first quantity of a specific capacity type required for any region includes: obtaining the number of waybills of a specific waybill type in the current region within a second preset time period; matching the specific waybill type with the specific capacity type; obtaining the historical delivery efficiency of the capacity of the specific capacity type; and calculating the first quantity of a specific capacity type required for the current region based on the number of waybills and the historical delivery efficiency.

[0010] Optionally, allocating target waybills to the specific capacity type in the second region includes: selecting candidate waybills and candidate capacity; repeating the following steps until all candidate waybills or candidate capacity are allocated: determining the effect score of each remaining candidate waybill after being allocated to each candidate capacity; determining whether the candidate capacity with the highest effect score corresponding to the current candidate waybill meets the acceptance criteria; if it does, allocating the current candidate waybill to the candidate capacity with the highest effect score, and deleting the current candidate waybill and the candidate capacity with the highest effect score.

[0011] Optionally, selecting candidate waybills includes: selecting waybills from those carried by the specific capacity type that pass through the first region and still pass through the first region after removal as candidate waybills; and / or selecting waybills from those carried by capacity of other capacity types besides the specific capacity type that pass through the first region as candidate waybills; and / or selecting waybills from those carried by capacity of the specific capacity type that do not pass through the first region as candidate waybills.

[0012] Optionally, selecting candidate transport capacity includes: selecting transport capacity that has not passed through the first region within the first preset time period from the transport capacity of the specific transport capacity type in the second region as the candidate transport capacity; or, selecting transport capacity that has not passed through the first region within the first preset time period from the transport capacity of the specific transport capacity type in the second region, and using the selected transport capacity and the original transport capacity of the candidate waybill as the candidate transport capacity.

[0013] Optionally, determining the effect score after assigning any candidate waybill to any candidate capacity includes: obtaining the scores and weights of each preset indicator after assigning the current candidate waybill to the current candidate capacity, as well as the number of first regions that the current candidate capacity can pass through within the first preset time period and the capacity shortage parameter; performing a weighted calculation on the scores and weights of each preset indicator, calculating the product of the number of first regions and the capacity shortage parameter, and adding the weighted calculation result to the product to obtain the effect score after assigning the current candidate waybill to the current candidate capacity.

[0014] Optionally, determining whether the candidate transport capacity with the highest effect score corresponding to the current candidate waybill meets the acceptance conditions includes: determining whether the candidate transport capacity with the highest effect score can pass through the first area within the first preset time period; obtaining a first matching score between the current candidate waybill and the original transport capacity of the current candidate waybill, and a second matching score between the current candidate waybill and the candidate transport capacity with the highest effect score; if the candidate transport capacity with the highest effect score can pass through the first area within the first preset time period, and the first score and the second score meet the preset conditions, then determining that the candidate transport capacity with the highest effect score meets the acceptance conditions.

[0015] According to a second aspect of the embodiments of the present disclosure, a capacity scheduling device is provided, characterized in that it includes:

[0016] The acquisition module is used to acquire a first quantity of capacity of a specific capacity type required by each region and a second quantity of capacity of the specific capacity type that has been assigned.

[0017] The determining module is used to determine a first area with insufficient capacity and a second area without insufficient capacity based on the first quantity and the second quantity;

[0018] The allocation module is used to allocate target waybills to the specific capacity type in the second region, wherein the target waybill is a waybill that enables the specific capacity type to pass through the first region within a first preset time period.

[0019] Optionally, the acquisition module includes: a volume acquisition unit, used to acquire the volume of a specific waybill type in the current region within a second preset time period in the future; the specific waybill type is matched with the specific transportation capacity type; an efficiency acquisition unit, used to acquire the historical delivery efficiency of the transportation capacity of the specific transportation capacity type; and a quantity calculation unit, used to calculate a first quantity of the transportation capacity of the specific transportation capacity type required in the current region based on the volume of waybills and the historical delivery efficiency.

[0020] Optionally, the allocation module includes: a selection unit for selecting candidate waybills and candidate transport capacity; and a waybill allocation unit for repeatedly executing the following steps until all candidate waybills or candidate transport capacity are allocated: determining the effect score of each remaining candidate waybill after being allocated to each candidate transport capacity; determining whether the candidate transport capacity with the highest effect score corresponding to the current candidate waybill meets the acceptance condition; if it does, allocating the current candidate waybill to the candidate transport capacity with the highest effect score, and deleting the current candidate waybill and the candidate transport capacity with the highest effect score.

[0021] Optionally, the selection unit includes: a first selection subunit, configured to select, from the waybills carried by the capacity of the specific capacity type, waybills that pass through the first region and whose capacity still passes through the first region after removal as candidate waybills; and / or, a second selection subunit, configured to select, from the waybills carried by the capacity of other capacity types besides the specific capacity type, waybills that pass through the first region as candidate waybills; and / or, a third selection subunit, configured to select, from the waybills carried by the capacity of the specific capacity type, waybills that do not pass through the first region as candidate waybills.

[0022] Optionally, the selection unit includes: a fourth selection subunit, configured to select, from the capacity of the specific capacity type in the second region, the capacity that has not passed through the first region within the first preset time period as the candidate capacity; or, a fifth selection subunit, configured to select, from the capacity of the specific capacity type in the second region, the capacity that has not passed through the first region within the first preset time period, and use the selected capacity and the original capacity of the candidate waybill as the candidate capacity.

[0023] Optionally, the waybill allocation unit includes: a parameter acquisition subunit, used to acquire the scores and weights of each preset indicator after allocating the current candidate waybill to the current candidate capacity, as well as the number of first regions that the current candidate capacity can pass through within the first preset time period and the capacity shortage parameter; and a score calculation subunit, used to perform a weighted calculation on the scores and weights of each preset indicator, calculate the product of the number of first regions and the capacity shortage parameter, and add the weighted calculation result to the product to obtain the effect score after allocating the current candidate waybill to the current candidate capacity.

[0024] Optionally, the waybill allocation unit includes: a judgment subunit, used to judge whether the candidate transport capacity with the highest effect score can pass through the first area within the first preset time period; a score acquisition subunit, used to acquire a first matching score between the current candidate waybill and the original transport capacity of the current candidate waybill, and a second matching score between the current candidate waybill and the candidate transport capacity with the highest effect score; and a condition determination subunit, used to determine that the candidate transport capacity with the highest effect score meets the acceptance condition if the candidate transport capacity with the highest effect score can pass through the first area within the first preset time period, and the first score and the second score satisfy a preset condition.

[0025] According to a third aspect of the embodiments of the present disclosure, an electronic device is provided, comprising: one or more processors; and one or more computer-readable storage media having instructions stored thereon; wherein, when the instructions are executed by the one or more processors, the processors cause the processors to perform the capacity scheduling method as described in any of the preceding claims.

[0026] According to a fourth aspect of the embodiments of the present disclosure, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, causes the processor to perform the capacity scheduling method as described in any of the preceding claims.

[0027] In this embodiment, on the one hand, the assigned capacity is further adjusted in a post-processing manner. For capacity in the second region that is not in a capacity-deficient area, the waybills that can pass through the capacity-deficient first region are allocated to the capacity within a first preset time period, so as to guide the capacity to the capacity-deficient first region and meet the capacity demand of the capacity-deficient first region. The accuracy of capacity scheduling can be further improved by the post-processing method. On the other hand, the capacity of a specific type is adjusted and guided to the capacity-deficient first region of that type. This allows for classified scheduling according to the different divisions of the capacity, thereby improving the accuracy of capacity scheduling. Attached Figure Description

[0028] To more clearly illustrate the technical solutions of the embodiments of this disclosure, the drawings used in the description of the embodiments of this disclosure will be briefly introduced below. Obviously, the drawings described below are only some drawings of the embodiments of this disclosure. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0029] Figure 1 This is a flowchart of an overall scheduling process according to an embodiment of the present disclosure.

[0030] Figure 2 This is a flowchart illustrating the steps of a capacity scheduling method according to an embodiment of this disclosure.

[0031] Figure 3 This is a flowchart of a process for allocating target waybills according to an embodiment of this disclosure.

[0032] Figure 4 This is a structural block diagram of a transportation capacity scheduling device according to an embodiment of the present disclosure.

[0033] Figure 5 This is a schematic diagram of the structure of an electronic device according to an embodiment of the present disclosure. Detailed Implementation

[0034] The technical solutions in the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this disclosure, and not all of the embodiments of this disclosure. Based on the embodiments of this disclosure, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this disclosure.

[0035] The capacity scheduling scheme in the embodiments of this disclosure can be applied to any applicable delivery business, such as food delivery, logistics delivery, etc. A waybill refers to a waybill that needs to be delivered. Capacity refers to the capacity used to deliver waybills.

[0036] In a illustrative sense, delivery capacity may include, but is not limited to: food delivery riders, delivery drones, delivery unmanned vehicles, couriers, etc.

[0037] Reference Figure 1 The diagram shows a flowchart of an overall scheduling process according to an embodiment of the present disclosure.

[0038] like Figure 1 As shown, the overall scheduling process may include: target assignment based on capacity and waybill; subsequent capacity adjustment; and subsequent consolidation.

[0039] In the process of target assignment based on capacity and waybills, capacity can be assigned to a certain area using a parallel assignment algorithm so that the capacity can take over waybills within that area. The specific process of target assignment will not be described in detail in this embodiment.

[0040] The process of adjusting the subsequent transport capacity is the transport capacity scheduling process in the embodiments of this disclosure, which will be described in detail in the following embodiments.

[0041] In the post-contracting process, waybills can be merged into waybill packages based on preset merging conditions, thereby facilitating delivery by transportation capacity. The specific process of post-contracting will not be described in detail in this embodiment.

[0042] Reference Figure 2 The diagram illustrates a flowchart of a capacity scheduling method according to an embodiment of the present disclosure.

[0043] like Figure 2 As shown, the capacity scheduling method may include the following steps:

[0044] Step 201: Obtain the first quantity of capacity of a specific capacity type required for each region and the second quantity of capacity of the specific capacity type already assigned.

[0045] The delivery area is pre-divided into multiple regions.

[0046] For illustrative purposes, the delivery area may include, but is not limited to: a city, a district within a city, a street within a district, etc.

[0047] As an illustration, the GeoHash algorithm can be used to divide regions, with each region corresponding to a GeoHash. The GeoHash algorithm converts two-dimensional latitude and longitude coordinates into strings, with each string representing a rectangular region. In other words, all points within this rectangular region share the same GeoHash string.

[0048] The transportation capacity is pre-classified into multiple types. In this embodiment, the specific transportation capacity type can be any type.

[0049] For illustrative purposes, delivery types may include, but are not limited to: dedicated delivery, flash delivery, errand running, crowdsourcing, fun running, and so on.

[0050] In the parallel assignment algorithm, the number of the specific capacity type already assigned to each region can be obtained, and this number is used as a second quantity. The specific capacity type already assigned to a region may include, but is not limited to: capacity of the specific capacity type that has no orders in that region, capacity of the specific capacity type that can pass through that region within a preset time period, etc.

[0051] In one alternative implementation, the process of obtaining a first quantity of capacity of a specific capacity type required for any region may include the following steps a1 to a3:

[0052] Step a1: Obtain the volume of a specific waybill type in the current region within the next second preset time period.

[0053] Choose any of these regions as the current region.

[0054] Optionally, the number of waybills in the current region within a second preset time period can be obtained.

[0055] In practical applications, a first order volume prediction model can be trained in advance using the first sample data. The first order volume prediction model is used to predict the order volume of a certain region within a second preset time period in the future.

[0056] The first sample data may include historical order volume characteristics of the sample area and the actual order volume of the sample area within a second preset time period. During model training, the historical order volume characteristics of the sample area are used as input to the first order volume prediction model to be trained, resulting in the output of the first order volume prediction model: the predicted order volume of the sample area within the second preset time period. Based on the actual and predicted order volumes of the sample area within the second preset time period, a model loss function can be calculated. If the model loss function does not meet preset conditions, the model parameters are adjusted and training continues; if the model loss function meets preset conditions, training is considered complete, and the trained model is used as the first order volume prediction model.

[0057] The process of obtaining the current region's order volume within a second preset time period may include: using the historical order volume characteristics of the current region as input to the first order volume prediction model trained above, and obtaining the order volume of the current region within a second preset time period output by the first order volume prediction model.

[0058] Indicatively, historical order volume characteristics may include, but are not limited to: order volume within at least one second preset duration prior to the current time point, order volume within a second preset duration that includes the same time point as the current time point in the previous day, order volume in the previous N days (N is a positive integer greater than 1), etc.

[0059] For illustrative purposes only, the second preset duration can be any applicable duration, and this embodiment does not limit the specific value of the second preset duration. For example, the second preset duration can be 15 minutes, 20 minutes, 30 minutes, etc.

[0060] Optionally, the volume of a specific waybill type in the current region within a second preset time period can be obtained.

[0061] Among them, specific waybill types are matched with specific capacity types.

[0062] In practical applications, matching relationships between specific waybill types and specific capacity types can be pre-set based on principles such as ensuring that capacity services can meet the needs of waybills and avoid wasting capacity services. For example, the Flash Delivery capacity type can be matched with waybill types with high timeliness requirements, and the Dedicated Delivery capacity type can be matched with the waybill types of franchised merchants, and so on.

[0063] In practical applications, a second order volume prediction model can be trained in advance using second sample data. The second order volume prediction model is used to predict the order volume of a specific order type in a certain region within a second preset time period in the future.

[0064] The second sample data can include historical order volume characteristics of a specific order type in the sample region and the actual order volume of that specific order type in the sample region within a second preset time period. During model training, the historical order volume characteristics of a specific order type in the sample region are used as input to the second order volume prediction model to be trained, resulting in the output of the second order volume prediction model: the predicted order volume of the specific order type in the sample region within the second preset time period. Based on the actual and predicted order volumes of the specific order type in the sample region within the second preset time period, a model loss function can be calculated. If the model loss function does not meet the preset conditions, the model parameters are adjusted and training continues; if the model loss function meets the preset conditions, training is considered complete, and the trained model is used as the second order volume prediction model.

[0065] The process of obtaining the volume of a specific waybill type in the current region within a second preset time period may include: using the historical volume characteristics of a specific waybill type in the current region as input to the second volume prediction model trained above, and obtaining the volume of a specific waybill type in the current region within a second preset time period output by the second volume prediction model.

[0066] Indicatively, the volume characteristics of a specific waybill type in history may include, but are not limited to: the volume of a specific waybill type within at least one second preset duration before the current time point, the volume of a specific waybill type within one second preset duration that includes the same time point as the current time point in the previous day, the volume of a specific waybill type in the previous N days (N is a positive integer greater than 1), and so on.

[0067] In illustrative terms, the first and second single-item prediction models mentioned above can include, but are not limited to: statistical models, linear models, machine learning models, and combinations of statistical, linear, and machine learning models, etc. For example, the XGBoost (eXtreme Gradient Boosting) model could be used.

[0068] Step a2: Obtain the historical delivery efficiency of the specific capacity type.

[0069] Historical delivery efficiency of a specific type of transportation capacity can be obtained from historical delivery data of that specific type of transportation capacity.

[0070] Indicatively, the historical delivery efficiency of the specific capacity type can be the average delivery efficiency of the previous day, the average delivery efficiency of the previous N days (N is a positive integer greater than 1), and so on.

[0071] Step a3: Calculate the first quantity of capacity of a specific capacity type required for the current region based on the order volume and the historical delivery efficiency.

[0072] The order volume here can be the order volume of the current region in the second preset time period in the future, or it can be the order volume of a specific order type in the current region in the second preset time period in the future.

[0073] First, calculate the ratio of the second preset duration to the unit time of the historical delivery efficiency. Then, calculate the product of the historical delivery efficiency and the ratio. Finally, calculate the quotient of the waybill volume divided by the product, and use this quotient as the first quantity of the specific capacity type required for the current region.

[0074] For example, if the unit time for the historical delivery efficiency is 1 hour and the preset duration is 15 minutes, then the ratio is 0.25.

[0075] Step 202: Based on the first quantity and the second quantity, determine the first region with insufficient capacity and the second region without insufficient capacity.

[0076] For any given region, if the first quantity of a specific type of capacity required by that region is greater than the second quantity of that specific type of capacity already assigned to that region, then that region can be determined as a first region with insufficient capacity; if the first quantity of a specific type of capacity required by that region is less than or equal to the second quantity of that specific type of capacity already assigned to that region, then that region can be determined as a second region without insufficient capacity.

[0077] Step 203: Assign target waybills to the specific capacity type in the second region. The target waybill is a waybill that enables the specific capacity type to pass through the first region within a first preset time period.

[0078] In one optional implementation, the process of allocating target waybills to the specific capacity type within the second region may include: selecting candidate waybills and candidate capacity; repeating the following steps until all candidate waybills and / or candidate capacity are allocated: determining the effect score of each remaining candidate waybill after allocation to each candidate capacity; determining whether the candidate capacity with the highest effect score corresponding to the current candidate waybill meets the acceptance criteria; if so, allocating the current candidate waybill to the candidate capacity with the highest effect score and deleting both the current candidate waybill and the candidate capacity with the highest effect score. (Specifically combined with...) Figure 3 Please provide an explanation.

[0079] Reference Figure 3 The diagram shows a flowchart of a process for allocating target waybills according to an embodiment of the present disclosure.

[0080] like Figure 3 As shown, the process of allocating target waybills may include the following steps:

[0081] Step 301: Select candidate waybills.

[0082] In one optional implementation, the process of selecting candidate waybills may include: selecting waybills from those carried by the specific capacity type that pass through the first region and whose capacity still passes through the first region after removal as candidate waybills; and / or selecting waybills from those carried by capacity of other capacity types besides the specific capacity type that pass through the first region as candidate waybills; and / or selecting waybills from those carried by capacity of the specific capacity type that do not pass through the first region as candidate waybills.

[0083] In illustrative terms, for waybills carried by the specific capacity type, selecting waybills that pass through the first region and whose capacity still passes through the first region after removal can involve selecting waybills with longer routes, waybills with more complex routes, or waybills of a specific type that matches the specific capacity type.

[0084] In illustrative terms, when selecting waybills from those carried by capacity types other than the specified capacity type, the waybills that pass through the first region can be selected from those with longer routes, more complex routes, or a specific waybill type that matches the specified capacity type.

[0085] In illustrative terms, when selecting waybills from those carried by the specific capacity type that have not passed through the first region, this can involve selecting waybills with lower matching scores to the specific capacity type, or waybills of a specific waybill type that match the specific capacity type. This selection method primarily aims to allocate waybills to the specific capacity type so that it can be assigned target waybills.

[0086] In this embodiment, a waybill that passes through the first region can be a waybill whose pick-up point (i.e., the origin of the waybill) and / or delivery point (i.e., the destination of the waybill) is the first region. A waybill that does not pass through the first region can be a waybill whose pick-up point and delivery point are not in the first region.

[0087] As an illustration, one or more waybills can be selected as candidate waybills from the waybills carried by the transport capacity. The smaller the number of candidate waybills selected from the waybills carried by the transport capacity, the simpler the scheduling process.

[0088] Step 302: Select candidate transport capacity.

[0089] In one alternative implementation, the process of selecting candidate transport capacity may include: selecting transport capacity that has not passed through the first region within the first preset time period from the transport capacity of the specific transport capacity type in the second region as the candidate transport capacity.

[0090] Whether a certain type of transport capacity can pass through the first area within the first preset time period can be calculated based on the navigation path planning (such as route length, driving speed, etc.) of that specific type of transport capacity. The specific calculation process will not be discussed in detail in this embodiment.

[0091] In another optional implementation, the process of selecting candidate transport capacity may include: selecting transport capacity that has not passed through the first region within the first preset time period from the transport capacity of the specific transport capacity type in the second region, and using the selected transport capacity and the original transport capacity of the candidate waybill as the candidate transport capacity.

[0092] The purpose of using the original capacity of the candidate waybill as the candidate capacity is to ensure that if the candidate waybill cannot be allocated to other specific capacity types, the candidate waybill can be returned to its original capacity.

[0093] For steps 301 and 302, the two steps can be executed in any order or in parallel, and this embodiment does not impose any restrictions on this.

[0094] Step 303: Determine the effect score of each remaining candidate waybill after it is allocated to each candidate capacity.

[0095] Step 301 above allows for the selection of multiple candidate waybills to obtain a candidate waybill set, and step 302 above allows for the selection of multiple candidate transport capacities to obtain a candidate transport capacity set. Initially, the remaining candidate waybills and the remaining candidate transport capacities are the entire set of candidate waybills and the entire set of candidate transport capacities selected above.

[0096] In one optional implementation, the process of determining the effect score after assigning any candidate waybill to any candidate capacity may include: obtaining the scores and weights of each preset indicator after assigning the current candidate waybill to the current candidate capacity, as well as the number of first regions that the current candidate capacity can pass through within the first preset time period and the capacity shortage parameter; performing a weighted calculation on the scores and weights of each preset indicator, calculating the product of the number of first regions and the capacity shortage parameter, and adding the weighted calculation result to the product to obtain the effect score after assigning the current candidate waybill to the current candidate capacity.

[0097] For illustrative purposes, the aforementioned preset indicators may include, but are not limited to, time, distance, delivery time, etc. The scores for each indicator can be calculated using any applicable assignment evaluation algorithm, and the weights for each indicator can be set based on practical experience; these will not be discussed in detail in this embodiment.

[0098] Schematic, the aforementioned parameter for the degree of capacity shortage in the first region can be the ratio of the first quantity of a specific type of capacity required by the first region to the second quantity of a specific type of capacity already assigned; or it can be the difference between the first quantity of a specific type of capacity required by the first region and the second quantity of a specific type of capacity already assigned, and so on.

[0099] Step 304, let i = 1.

[0100] Step 305: Determine the candidate transport capacity with the highest effect score corresponding to the i-th candidate waybill.

[0101] Step 306: Determine whether the candidate capacity with the highest performance score meets the acceptance criteria. If not, proceed to step 307; if yes, proceed to step 308.

[0102] In one optional implementation, the process of determining whether the candidate transport capacity with the highest effect score corresponding to the current candidate waybill meets the acceptance conditions may include: determining whether the candidate transport capacity with the highest effect score can pass through the first area within the first preset time period; obtaining a first matching score between the current candidate waybill and the original transport capacity of the current candidate waybill, and a second matching score between the current candidate waybill and the candidate transport capacity with the highest effect score; if the candidate transport capacity with the highest effect score can pass through the first area within the first preset time period, and the first score and the second score meet the preset conditions, then determining that the candidate transport capacity with the highest effect score meets the acceptance conditions.

[0103] In this embodiment, it is necessary to guide the capacity of a specific type to the first area with insufficient capacity, while meeting the requirements of timeliness and experience.

[0104] If the candidate transport with the highest effect score is assigned to the current candidate transport order, and the candidate transport with the highest effect score can pass through the first area with insufficient transport capacity within the first preset time period, then it can be considered that the candidate transport with the highest effect score has been successfully guided to the first area with insufficient transport capacity.

[0105] If the first matching score between the current candidate waybill and its original capacity and the second matching score between the current candidate waybill and the candidate capacity with the highest performance score meet the preset conditions, then the timeliness and experience requirements can be considered met.

[0106] As an example, the matching score can be calculated based on parameters such as time score, distance score, delivery time score, last-mile delivery timeout, and total time spent after allocating the waybill to capacity. For instance, these parameters can be weighted and the result can be used as the matching score, and so on.

[0107] For example, the first matching score and the second matching score can satisfy the preset conditions, such as the difference or ratio between the second matching score and the first matching score being less than a preset threshold.

[0108] Step 307: Let i = i + 1, and return to step 305.

[0109] Step 308: Assign the i-th candidate waybill to the candidate capacity with the highest effect score, and delete the i-th candidate waybill and the candidate capacity with the highest effect score.

[0110] Step 309: Determine whether the candidate waybills or candidate capacity have been fully allocated. If yes, end the process; otherwise, return to step 303.

[0111] In this embodiment, on the one hand, the assigned capacity is further adjusted in a post-processing manner. For capacity in the second region that is not in a capacity-deficient area, the waybills that can pass through the capacity-deficient first region are allocated to the capacity within a first preset time period, so as to guide the capacity to the capacity-deficient first region and meet the capacity demand of the capacity-deficient first region. The accuracy of capacity scheduling can be further improved by the post-processing method. On the other hand, the capacity of a specific type is adjusted and guided to the capacity-deficient first region of that type. This allows for classified scheduling according to the different divisions of the capacity, thereby improving the accuracy of capacity scheduling.

[0112] Reference Figure 4 The diagram shows a structural block diagram of a capacity scheduling device according to an embodiment of the present disclosure.

[0113] like Figure 4 As shown, the transportation capacity scheduling device may include the following modules:

[0114] The acquisition module 401 is used to acquire a first quantity of the specific capacity type required by each region and a second quantity of the specific capacity type that has been assigned.

[0115] The determining module 402 is used to determine a first area with insufficient capacity and a second area without insufficient capacity based on the first quantity and the second quantity;

[0116] The allocation module 403 is used to allocate target waybills to the specific capacity type of the capacity in the second region, wherein the target waybill is a waybill that enables the capacity of the specific capacity type to pass through the first region within a first preset time period.

[0117] Optionally, the acquisition module 401 includes: a shipment acquisition unit, used to acquire the shipment volume of a specific shipment type in the current area within a second preset time period in the future; the specific shipment type is matched with the specific transportation capacity type; an efficiency acquisition unit, used to acquire the historical delivery efficiency of the transportation capacity of the specific transportation capacity type; and a quantity calculation unit, used to calculate a first quantity of the transportation capacity of the specific transportation capacity type required in the current area based on the shipment volume and the historical delivery efficiency.

[0118] Optionally, the allocation module 403 includes: a selection unit for selecting candidate waybills and candidate transport capacity; and a waybill allocation unit for repeatedly executing the following steps until all candidate waybills or candidate transport capacity are allocated: determining the effect score of each remaining candidate waybill after being allocated to each candidate transport capacity; determining whether the candidate transport capacity with the highest effect score corresponding to the current candidate waybill meets the acceptance condition; if it does, allocating the current candidate waybill to the candidate transport capacity with the highest effect score, and deleting the current candidate waybill and the candidate transport capacity with the highest effect score.

[0119] Optionally, the selection unit includes: a first selection subunit, configured to select, from the waybills carried by the capacity of the specific capacity type, waybills that pass through the first region and whose capacity still passes through the first region after removal as candidate waybills; and / or, a second selection subunit, configured to select, from the waybills carried by the capacity of other capacity types besides the specific capacity type, waybills that pass through the first region as candidate waybills; and / or, a third selection subunit, configured to select, from the waybills carried by the capacity of the specific capacity type, waybills that do not pass through the first region as candidate waybills.

[0120] Optionally, the selection unit includes: a fourth selection subunit, configured to select, from the capacity of the specific capacity type in the second region, the capacity that has not passed through the first region within the first preset time period as the candidate capacity; or, a fifth selection subunit, configured to select, from the capacity of the specific capacity type in the second region, the capacity that has not passed through the first region within the first preset time period, and use the selected capacity and the original capacity of the candidate waybill as the candidate capacity.

[0121] Optionally, the waybill allocation unit includes: a parameter acquisition subunit, used to acquire the scores and weights of each preset indicator after allocating the current candidate waybill to the current candidate capacity, as well as the number of first regions that the current candidate capacity can pass through within the first preset time period and the capacity shortage parameter; and a score calculation subunit, used to perform a weighted calculation on the scores and weights of each preset indicator, calculate the product of the number of first regions and the capacity shortage parameter, and add the weighted calculation result to the product to obtain the effect score after allocating the current candidate waybill to the current candidate capacity.

[0122] Optionally, the waybill allocation unit includes: a judgment subunit, used to judge whether the candidate transport capacity with the highest effect score can pass through the first area within the first preset time period; a score acquisition subunit, used to acquire a first matching score between the current candidate waybill and the original transport capacity of the current candidate waybill, and a second matching score between the current candidate waybill and the candidate transport capacity with the highest effect score; and a condition determination subunit, used to determine that the candidate transport capacity with the highest effect score meets the acceptance condition if the candidate transport capacity with the highest effect score can pass through the first area within the first preset time period, and the first score and the second score satisfy a preset condition.

[0123] In this embodiment, on the one hand, the assigned capacity is further adjusted in a post-processing manner. For capacity in the second region that is not in a capacity-deficient area, the waybills that can pass through the capacity-deficient first region are allocated to the capacity within a first preset time period, so as to guide the capacity to the capacity-deficient first region and meet the capacity demand of the capacity-deficient first region. The accuracy of capacity scheduling can be further improved by the post-processing method. On the other hand, the capacity of a specific type is adjusted and guided to the capacity-deficient first region of that type. This allows for classified scheduling according to the different divisions of the capacity, thereby improving the accuracy of capacity scheduling.

[0124] As the device embodiment is basically similar to the method embodiment, the description is relatively simple, and relevant parts can be found in the description of the method embodiment.

[0125] In embodiments of this disclosure, an electronic device is also provided. This electronic device may include one or more processors and one or more computer-readable storage media storing instructions thereon, such as an application program. When the instructions are executed by the one or more processors, the processors cause the processors to perform the capacity scheduling method as described in any of the above embodiments.

[0126] Reference Figure 5 The diagram illustrates a schematic representation of an electronic device structure according to an embodiment of this disclosure. Figure 5 As shown, the electronic device includes a processor 501, a communication interface 502, a memory 503, and a communication bus 504. The processor 501, communication interface 502, and memory 503 communicate with each other via the communication bus 504.

[0127] Memory 503 is used to store computer programs.

[0128] The processor 501, when executing the program stored in the memory 503, implements the capacity scheduling method of any of the above embodiments.

[0129] Communication interface 502 is used for communication between the above-mentioned electronic device and other devices.

[0130] The aforementioned communication bus 504 can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This communication bus can be divided into address bus, data bus, control bus, etc. For ease of illustration, it is represented by only one thick line in the diagram, but this does not indicate that there is only one bus or one type of bus.

[0131] The processor 501 mentioned above may include, but is not limited to: a central processing unit (CPU), a network processor (NP), a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0132] The aforementioned memory 503 may include, but is not limited to: Read Only Memory (ROM), Random Access Memory (RAM), Compact Disc Read Only Memory (CD-ROM), Electronic Erasable Programmable Read Only Memory (EEPROM), Hard Disk, Floppy Disk, Flash Memory, etc.

[0133] In embodiments of this disclosure, a non-transitory computer-readable storage medium is also provided, on which a computer program is stored, which can be executed by a processor of an electronic device, and when the computer program is executed by the processor, causes the processor to perform the capacity scheduling method as described in any of the foregoing embodiments.

[0134] The algorithms and displays provided herein are not inherently related to any particular computer, virtual system, or other device. Various general-purpose systems can also be used in conjunction with the teachings herein. The required structure for constructing such systems is apparent from the above description. Furthermore, the embodiments of this disclosure are not directed to any particular programming language. It should be understood that the embodiments of this disclosure described herein can be implemented using various programming languages, and the above description of specific languages ​​is for the purpose of disclosing the best mode of implementation of the embodiments of this disclosure.

[0135] Numerous specific details are set forth in the specification provided herein. However, it will be understood that embodiments of this disclosure may be practiced without these specific details. In some instances, well-known methods, structures, and techniques have not been shown in detail so as not to obscure the understanding of this specification.

[0136] Similarly, it should be understood that, in order to simplify this disclosure and aid in understanding one or more of the various inventive aspects, in the foregoing description of exemplary embodiments of this disclosure, various features of embodiments of this disclosure are sometimes grouped together in a single embodiment, figure, or description thereof. However, this approach to disclosure should not be construed as reflecting an intention that the claimed embodiments of this disclosure require more features than expressly recited in each claim. Rather, as reflected in the following claims, inventive aspects lie in fewer than all features of a single foregoing disclosed embodiment. Therefore, the claims following the detailed description are hereby expressly incorporated into that detailed description, wherein each claim itself is a separate embodiment of the present disclosure.

[0137] Those skilled in the art will understand that modules in the device of the embodiments can be adaptively changed and placed in one or more devices different from that embodiment. Modules, units, or components in the embodiments can be combined into a single module, unit, or component, and further, they can be divided into multiple sub-modules, sub-units, or sub-components. Except where at least some of such features and / or processes or units are mutually exclusive, any combination can be used to combine all features disclosed in this specification (including the accompanying claims, abstract, and drawings) and all processes or units of any method or device so disclosed. Unless expressly stated otherwise, each feature disclosed in this specification (including the accompanying claims, abstract, and drawings) may be replaced by an alternative feature serving the same, equivalent, or similar purpose.

[0138] The various component embodiments of this disclosure can be implemented in hardware, or as software modules running on one or more processors, or a combination thereof. Those skilled in the art will understand that microprocessors or digital signal processors (DSPs) can be used in practice to implement some or all of the functions of some or all of the components in the motion picture generation device according to the embodiments of this disclosure. Embodiments of this disclosure can also be implemented as device or apparatus programs for performing some or all of the methods described herein. Such programs implementing embodiments of this disclosure can be stored on a computer-readable medium or can take the form of one or more signals. Such signals can be downloaded from an Internet website, provided on a carrier signal, or provided in any other form.

[0139] It should be noted that the above embodiments are illustrative of embodiments of this disclosure and not restrictive of embodiments of this disclosure, and that those skilled in the art can devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses should not be construed as limiting the claims. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. Embodiments of this disclosure can be implemented by means of hardware comprising several different elements and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by the same item of hardware. The use of the words first, second, and third, etc., does not indicate any order. These words can be interpreted as names.

[0140] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0141] The above description is merely a specific implementation of the embodiments of this disclosure, but the protection scope of the embodiments of this disclosure is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the embodiments of this disclosure should be covered within the protection scope of the embodiments of this disclosure.

Claims

1. A transportation capacity scheduling method, characterized in that, include: The process involves: obtaining a first quantity of capacity of a specific capacity type required for each region and a second quantity of capacity of the specific capacity type already assigned; determining a first region with insufficient capacity and a second region without insufficient capacity based on the first quantity and the second quantity; allocating target waybills to the capacity of the specific capacity type in the second region, wherein the target waybill is a waybill that enables the capacity of the specific capacity type to pass through the first region within a first preset time period; wherein obtaining the first quantity of capacity of a specific capacity type required for any region includes: obtaining the number of waybills of a specific capacity type in the current region within a future second preset time period; matching the specific waybill type with the specific capacity type; obtaining the historical delivery efficiency of the capacity of the specific capacity type; and calculating the first quantity of capacity of the specific capacity type required for the current region based on the number of waybills and the historical delivery efficiency.

2. The method according to claim 1, characterized in that, Assigning target waybills to the specific capacity type in the second region includes: selecting candidate waybills and candidate capacity; repeating the following steps until all candidate waybills or candidate capacity are assigned: determining the effect score of each remaining candidate waybill after being assigned to each candidate capacity; determining whether the candidate capacity with the highest effect score corresponding to the current candidate waybill meets the acceptance criteria; if it does, assigning the current candidate waybill to the candidate capacity with the highest effect score, and deleting the current candidate waybill and the candidate capacity with the highest effect score.

3. The method according to claim 2, characterized in that, The selection of candidate waybills includes: selecting waybills from those carried by the specific capacity type that pass through the first region and whose capacity still passes through the first region after removal as candidate waybills; and / or selecting waybills from those carried by capacity of other capacity types besides the specific capacity type that pass through the first region as candidate waybills; and / or selecting waybills from those carried by capacity of the specific capacity type that do not pass through the first region as candidate waybills.

4. The method according to claim 2, characterized in that, Selecting candidate transport capacity includes: selecting transport capacity that has not passed through the first region within the first preset time period from the transport capacity of the specific transport capacity type in the second region as the candidate transport capacity; or, selecting transport capacity that has not passed through the first region within the first preset time period from the transport capacity of the specific transport capacity type in the second region, and using the selected transport capacity and the original transport capacity of the candidate waybill as the candidate transport capacity.

5. The method according to claim 2, characterized in that, Determining the effectiveness score after assigning any candidate waybill to any candidate capacity includes: obtaining the scores and weights of each preset indicator after assigning the current candidate waybill to the current candidate capacity, as well as the number of first regions that the current candidate capacity can pass through within the first preset time period and the capacity shortage parameter; performing a weighted calculation on the scores and weights of each preset indicator, calculating the product of the number of first regions and the capacity shortage parameter, and adding the weighted calculation result to the product to obtain the effectiveness score after assigning the current candidate waybill to the current candidate capacity.

6. The method according to claim 2, characterized in that, Determining whether the candidate transport capacity with the highest performance score corresponding to the current candidate waybill meets the acceptance conditions includes: determining whether the candidate transport capacity with the highest performance score can pass through the first area within the first preset time period; obtaining the first matching score between the current candidate waybill and its original transport capacity, and the first matching score between the current candidate waybill and the candidate transport capacity with the highest performance score; if the candidate transport capacity with the highest performance score can pass through the first area within the first preset time period, and the first matching score and the first matching score meet the preset conditions, then it is determined that the candidate transport capacity with the highest performance score meets the acceptance conditions.

7. A transportation capacity scheduling device, characterized in that, include: The acquisition module is used to acquire a first quantity of capacity of a specific capacity type required by each region and a second quantity of capacity of the specific capacity type that has been assigned, and to acquire the number of waybills of a specific waybill type in the current region within a second preset time period in the future and the historical delivery efficiency of the capacity of the specific capacity type; the specific waybill type is matched with the specific capacity type; The determining module is used to determine, based on the first quantity and the second quantity, a first area with insufficient capacity and a second area without insufficient capacity, and to calculate, based on the order volume and the historical delivery efficiency, a first quantity of capacity of a specific capacity type required for the current area; the allocating module is used to allocate target orders to the capacity of the specific capacity type in the second area, wherein the target order is an order that enables the capacity of the specific capacity type to pass through the first area within a first preset time period.

8. An electronic device, characterized in that, include: One or more processors; and one or more computer-readable storage media thereon storing instructions; when the instructions are executed by the one or more processors, the processors cause the processors to perform the capacity scheduling method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed by a processor, causes the processor to perform the capacity scheduling method as described in any one of claims 1 to 6.

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