Bulk logistics intelligent scheduling method and device, equipment and storage medium

Abstract

This disclosure provides a method, apparatus, equipment, and storage medium for intelligent scheduling of bulk logistics. The method includes: obtaining unprocessed transportation tasks from a task pool, along with their loading locations and specified vehicle types; selecting transportation capacity within a preset radius of the loading location and matching the specified vehicle type from the unprocessed capacity as first candidate capacity; obtaining and, based on the carrier, historical route preferences, departure time range, existing task information, arrival location, and real-time road conditions and weather of the first candidate capacity, selecting target capacity suitable for the unprocessed transportation tasks and generating a target capacity list; creating transportation orders based on the target capacity list and pushing the transportation orders to each target capacity for processing. This method can improve the scientific nature of scheduling decisions and avoid the waste of transportation resources.

Description

Technical Field

[0001] This disclosure relates to the field of data processing technology, and in particular to a method, apparatus, equipment and storage medium for intelligent scheduling of bulk logistics. Background Technology

[0002] Traditional bulk logistics scheduling relies heavily on the historical experience of dispatchers, manually assigning transportation tasks to designated vehicles. Because dispatchers lack comprehensive access to massive amounts of transportation task information, vehicle resource information, and complex external environmental information, scheduling decisions lack scientific data support and are prone to waste of transportation resources due to subjective judgment biases. Therefore, improving the scientific nature of scheduling decisions and avoiding waste of transportation resources has become a pressing technical problem for those skilled in the art. Summary of the Invention

[0003] In view of this, this disclosure proposes a method, device, equipment and storage medium for intelligent scheduling of bulk logistics, which can improve the scientific nature of scheduling decisions and avoid the waste of transportation resources.

[0004] According to a first aspect of this disclosure, a method for intelligent scheduling of bulk logistics is provided, comprising: Obtain pending transportation tasks from the task pool and obtain the loading location and specified vehicle type of the pending transportation tasks; The first candidate transport capacity is selected from the transport capacity to be dispatched, which is located within a preset radius of the loading location and matches the specified vehicle type. Based on the carrier, historical route preference, departure time range, existing task information, pick-up location, and real-time traffic and weather of the first candidate capacity, target capacity that is suitable for the transportation task to be processed is selected and a target capacity list is generated. Based on the target capacity list, a carrier order is created and the carrier order is pushed to each target capacity for processing.

[0005] In one possible implementation, retrieving transport tasks from the task pool includes: Calculate the dynamic priority score of each of the transport tasks to be processed in the task pool; The tasks to be processed are sorted based on their dynamic priority scores to obtain a scheduling queue of tasks to be processed. The transportation tasks to be processed are read sequentially from the scheduling queue and intelligently scheduled.

[0006] In one possible implementation, after generating the target capacity list, the method further includes: Determine whether the target capacity on the target capacity list meets the minimum departure quantity of the transportation task to be processed; If the minimum departure volume is not met, the preset radius range is expanded by a preset ratio to supplement the target capacity screening.

[0007] In one possible implementation, after pushing the shipping order to the target capacity, the method further includes: Determine whether the target transport capacity accepts the transport order; If the carrier order is accepted, an electronic route book corresponding to the carrier order is generated and the electronic route book is pushed to the target transport capacity. The electronic route book includes at least one forecast of dangerous road section warnings, unloading site queuing conditions, and weather along the route.

[0008] In one possible implementation, if it is determined that the shipping order is not accepted, the transportation task corresponding to the shipping order is returned to the task pool for rescheduling.

[0009] In one possible implementation, if it is determined that the target capacity accepts the carrier order, the method further includes: Acquire vehicle transportation status data, energy consumption model, and energy replenishment point data along the route during the execution of the transportation order by the target transportation capacity; Based on the vehicle transportation status data, energy consumption model, and data on energy replenishment points along the route, combined with real-time road conditions and weather, an energy replenishment plan is generated and pushed to the target transportation capacity.

[0010] In one possible implementation, if it is determined that the target capacity accepts the carrier order, the method further includes: Anomaly monitoring is performed on the vehicle transportation status data and the completion status of the transportation orders during the execution of the transportation orders by the target transportation capacity; When an anomaly is detected, trigger an anomaly alarm and / or anomaly handling.

[0011] According to a second aspect of this disclosure, a bulk logistics intelligent scheduling device is provided, comprising: The data acquisition module is used to acquire unprocessed transportation tasks from the task pool and to acquire the loading location and specified vehicle type of the unprocessed transportation tasks; The candidate transport capacity screening module is used to select transport capacity located within a preset radius of the loading location and matching the specified vehicle type from the transport capacity to be dispatched as the first candidate transport capacity; The target capacity matching module is used to obtain and, based on the carrier to which the first candidate capacity belongs, historical route preferences, departure time range, existing task information, pick-up location, and real-time road conditions and weather, filter out target capacity that is suitable for the transportation task to be processed and generate a target capacity list. The task push module is used to create a carrier order based on the target capacity list and push the carrier order to each target capacity for processing.

[0012] According to a third aspect of this disclosure, a bulk logistics intelligent scheduling device is provided, comprising: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to perform the method described in the first aspect of this disclosure.

[0013] According to a fourth aspect of this disclosure, a non-volatile computer-readable storage medium is provided that stores computer program instructions thereon, wherein the computer program instructions, when executed by a processor, implement the method described in the first aspect of this disclosure.

[0014] This disclosure provides a method, apparatus, equipment, and storage medium for intelligent scheduling of bulk logistics. The method includes: obtaining unprocessed transportation tasks from a task pool, along with their loading locations and specified vehicle types; selecting transportation capacity within a preset radius of the loading location and matching the specified vehicle type from the unprocessed capacity as first candidate capacity; obtaining and, based on the carrier, historical route preferences, departure time range, existing task information, arrival location, and real-time road conditions and weather of the first candidate capacity, selecting target capacity suitable for the unprocessed transportation tasks and generating a target capacity list; creating transportation orders based on the target capacity list and pushing the transportation orders to each target capacity for processing. This method can improve the scientific nature of scheduling decisions and avoid the waste of transportation resources.

[0015] Other features and aspects of this disclosure will become clear from the following detailed description of exemplary embodiments with reference to the accompanying drawings. Attached Figure Description

[0016] The accompanying drawings, which are included in and form part of this specification, illustrate exemplary embodiments, features, and aspects of this disclosure together with the specification and serve to explain the principles of this disclosure.

[0017] Figure 1 A flowchart is shown for a bulk logistics intelligent scheduling method according to an embodiment of the present disclosure; Figure 2 A schematic block diagram of a bulk logistics intelligent scheduling device according to an embodiment of the present disclosure is shown. Figure 3 A schematic block diagram of a bulk logistics intelligent scheduling device according to an embodiment of the present disclosure is shown. Detailed Implementation

[0018] Various exemplary embodiments, features, and aspects of this disclosure will now be described in detail with reference to the accompanying drawings. The same reference numerals in the drawings denote elements that have the same or similar functions. Although various aspects of the embodiments are shown in the drawings, they are not necessarily drawn to scale unless specifically indicated otherwise.

[0019] The term “exemplary” as used herein means “serving as an example, embodiment, or illustration.” Any embodiment illustrated herein as “exemplary” is not necessarily to be construed as superior to or better than other embodiments.

[0020] Furthermore, to better illustrate this disclosure, numerous specific details are set forth in the following detailed description. Those skilled in the art will understand that this disclosure can be practiced without certain specific details. In some instances, methods, means, components, and circuits well known to those skilled in the art have not been described in detail in order to highlight the main points of this disclosure.

[0021] <Method Implementation> Figure 1 A flowchart illustrating a bulk logistics intelligent scheduling method according to an embodiment of the present disclosure is shown. Figure 1 As shown, the method includes steps S1100-S1400.

[0022] S1100 retrieves pending transportation tasks from the task pool and obtains the loading location and specified vehicle type for each task.

[0023] First, it should be noted that the system executing the methods disclosed herein (hereinafter referred to as the system) pre-constructs a task pool, which stores multiple pending transportation tasks, each with its corresponding detailed task information. This task information includes at least one of the following: route information, vehicle requirements information, and customer demand information. Specifically, the route information includes at least one of the following: loading location, unloading location, standard operating route, standard loading time, standard unloading time, standard travel time, and standard road and bridge toll information. The vehicle requirements information includes at least one of the required vehicle type and the designated carrier. The customer demand information includes at least one of the following: customer priority, timeliness urgency, and the minimum number of departures required per day.

[0024] In one possible implementation, the pending transportation tasks in the task pool are automatically constructed and updated based on customer order and inventory information. Specifically, customer order and inventory information are first obtained. Order information includes goods requirements, quantity, delivery time, location, vehicle requirements, customer priority, and the minimum daily dispatch volume. Inventory information includes the inventory quantity and storage location of the goods. Specifically, the latest dispatch time is calculated by working backward from the order delivery time. The urgency is determined based on the difference between the latest dispatch time and the current time. The minimum daily dispatch volume is calculated based on the minimum daily dispatch volume in the order information. Then, the minimum daily dispatch volume is compared with the current inventory quantity of the products. If the inventory is sufficient and the current time is earlier than the latest dispatch time, the route information, vehicle requirements, and customer demand information for the pending transportation task are automatically matched and calculated to directly generate the pending transportation task. If inventory is insufficient, the production plan will be further queried to assess whether the missing goods can be completed and put into storage before the latest departure time. If so, an inactive pending transportation task will be generated and temporarily stored, and will be automatically activated as a pending transportation task after the missing goods are produced and put into storage. If not, a stockout warning or an exception handling process will be triggered. The automatically generated pending transportation task will be automatically added to the task pool to await scheduling.

[0025] This method enables the automated generation and dynamic updating of pending transportation tasks, reduces manual intervention, improves the response speed and accuracy of transportation tasks, and optimizes the collaborative management of inventory and transportation, which can significantly improve overall logistics efficiency.

[0026] In one possible implementation, the process of retrieving a transport task to be processed from the task pool may include the following steps: First, calculate the dynamic priority score for each pending transportation task in the task pool. Specifically, iterate through all pending transportation tasks in the task pool. For each currently pending task, extract the customer priority and timeliness urgency from its customer demand information. Calculate the dynamic priority score for the current pending transportation task based on these factors. Once the iteration is complete, the dynamic priority score for each pending transportation task is obtained. It should be noted that higher customer priority and higher timeliness result in a higher calculated dynamic priority score.

[0027] In one possible implementation, the urgency of each pending transportation task is dynamically adjusted based on the current time. Specifically, within the task pool, the urgency of each pending transportation task is recalculated based on the latest current time to ensure the effectiveness of its urgency level.

[0028] Second, the tasks to be processed are sorted based on their dynamic priority scores to obtain a scheduling queue. Specifically, the tasks to be processed are sorted in descending order of their dynamic priority scores to obtain the scheduling queue.

[0029] Third, the transportation tasks to be processed are read sequentially from the scheduling queue and intelligently scheduled. Specifically, the top-ranked transportation task (i.e., the one with the highest current dynamic priority score) is extracted from the scheduling queue each time for intelligent scheduling, thereby ensuring that urgent transportation tasks are processed first.

[0030] After obtaining the transportation task to be processed, you can directly read its corresponding task information and obtain the corresponding loading location and the specified vehicle type from the task information.

[0031] S1200 selects the first candidate transport capacity from the available transport capacity, choosing those located within a preset radius of the loading location of the transport task to be processed and matching the specified vehicle type.

[0032] The capacity awaiting dispatch refers to the capacity that is currently online and where both drivers and vehicles are available. Specifically, when drivers and vehicles are available, drivers can go online by raising their hands. Once online, the driver and their vehicle constitute a usable capacity awaiting dispatch, which is recorded in the system. The driver's "raising hand" action can be performed by the driver themselves or by the team leader of the driver's fleet during the shift scheduling; there is no specific limitation here.

[0033] Furthermore, for each available transport capacity that has raised its hand and is waiting to be dispatched, its identifier, specific vehicle type, affiliated carrier, departure time range, arrival location, historical route preferences, existing task information, average mileage of completed tasks, vehicle energy consumption model (trained based on historical energy consumption data), commission information, and real-time location information are obtained. This information is then stored as transport capacity data and linked to the available transport capacity recorded in the system for subsequent capacity screening. The departure time range and arrival location can be dynamically adjusted based on drivers' real-time needs. Historical route preferences cover one of the following types: long-distance, medium-distance, and short-distance transport. Historical route preferences can be determined based on the average mileage of currently completed tasks. Specifically, the average mileage of completed tasks is compared with the mileage ranges corresponding to long-distance, medium-distance, and short-distance transport to determine which historical route preference range it falls into, thus clarifying its specific historical route preference. Existing task information refers to all pending transportation tasks that the scheduled capacity has accepted on the same day. Each pending transportation task has the task information mentioned above and its completion status is recorded. The completion status includes one of the following: completed, in progress, and waiting to be processed.

[0034] After obtaining the loading location and specified vehicle type of the transportation task to be processed, the system can filter out the transportation capacity located within a preset radius centered on the loading location of the transportation task to be processed and matching the vehicle type specified in the transportation task to be processed, based on the transportation capacity information of each transportation capacity to be dispatched recorded in the system, as the first candidate transportation capacity.

[0035] After selecting the first candidate capacity, step S1300 can be executed to obtain and, based on the carrier to which the first candidate capacity belongs, historical route preferences, departure time range, existing task information, pick-up location, and real-time location, road conditions and weather, select the target capacity that is suitable for the transportation task to be processed and generate a target capacity list.

[0036] In one possible implementation, when selecting target capacity suitable for the transportation task to be processed based on the carrier of the first candidate capacity, historical route preferences, departure time range, existing task information, pick-up location, and real-time location, road conditions, and weather, the following steps may be included: First, based on the departure time range, existing task information, pick-up location, real-time location, road conditions, and weather of each first-candidate transport capacity, calculate the maximum number of trips that each first-candidate transport capacity can complete today. The maximum number of trips that can be completed today refers to the maximum number of trips that the first-candidate transport capacity can complete for the pending transport tasks today.

[0037] It should be noted that the calculation method for the maximum number of trips that can be completed today is the same for each first-candidate capacity. Therefore, the following explanation uses one first-candidate capacity as an example to illustrate the calculation process for its maximum number of trips that can be completed today. The specific steps are as follows: First, based on the existing task information of the first candidate transport capacity, as well as its real-time location, road conditions, and weather, the time for the first candidate transport capacity to complete all existing tasks is calculated. Specifically, each existing task is traversed, and for each currently traversed existing task, its completion status is identified. An algorithm matching the completion status is used to determine the completion time for the first candidate transport capacity to complete the current existing task. After the traversal is complete, the completion time for the first candidate transport capacity to complete all existing tasks is obtained. The sum of the completion times of all existing tasks is then calculated as the total time for the first candidate transport capacity to complete all existing tasks.

[0038] In one possible implementation, when identifying the completion status of an existing task and using an algorithm matching its completion status to determine the completion time of the first candidate transport capacity for the existing task, it can be done as follows: When its completion status is "completed," the actual completion time recorded by the system is the completion time of the existing task. When its completion status is "in execution," the current location of the first candidate transport capacity is obtained. Based on the current location of the first candidate transport capacity, the unloading location of the pending transport task, the standard operating route of the pending transport task, and the road conditions and weather on the standard operating route, the time for the first candidate transport capacity to complete the remaining mileage is calculated. The sum of the time to complete the remaining mileage, the execution time of the existing task recorded by the system, and the standard unloading time of the pending transport task is taken as the completion time of the existing task. When its completion status is "awaiting processing," based on the loading location, unloading location, standard operating route, and the road conditions and weather on the standard operating route of the pending transport task, the time for the first candidate transport capacity to complete all mileage of the existing task is calculated. The sum of the time to complete all mileage of the existing task, the standard loading time, and the standard unloading time is taken as the completion time of the existing task. After calculating the completion time of each existing task, the time for the first candidate capacity to complete all existing tasks can be obtained by calculating the completion time of all existing tasks.

[0039] Secondly, based on the departure time range and the time to complete existing tasks of the first candidate transport capacity, the working time of the first candidate transport capacity is calculated. Specifically, based on the departure time range, the total departure time of the first candidate transport capacity in a day is calculated, and the difference between the total departure time and the time to complete existing tasks is calculated as the working time of the first candidate transport capacity.

[0040] Next, based on the first candidate transport vehicle's arrival location, the unloading location of the last existing task, and road and weather conditions, the return time of the first candidate transport vehicle is calculated. Specifically, an optimal driving route is planned based on the arrival location and the unloading location of the last existing task, and the return time of the first candidate transport vehicle is calculated based on the optimal driving route combined with the road and weather conditions under the optimal driving route.

[0041] Next, based on the route information of the transportation task to be processed, the one-trip transport time for the first candidate transport capacity to complete the transportation task to be processed is calculated. The calculation method for the one-trip transport time is the same as the calculation method for the completion time of existing tasks that are in the waiting state, so it will not be described again here.

[0042] Finally, based on the available working time, return time, and one-way transport time of the first candidate transport capacity, the maximum number of trips that the first candidate transport capacity can complete today is calculated. Specifically, the ratio of (available working time + return time) / (one-way transport time + return time) is calculated, and then the calculated ratio is rounded down. The rounded value is the maximum number of trips that the first candidate transport capacity can complete today.

[0043] Second, based on the carriers, historical route preferences, and maximum number of trips available today for each first-candidate capacity, target capacity suitable for the transportation tasks to be processed is selected from all first-candidate capacity. This can specifically include the following multiple rounds of selection steps: In the first round of screening, it is determined whether the transportation task to be processed has a designated carrier. If it is determined that there is a designated carrier, the capacity belonging to the designated carrier is selected from the first candidate capacity as the designated carrier capacity, and the target capacity suitable for the transportation task to be processed is selected from the designated carrier capacity.

[0044] In one possible implementation, when selecting target capacity suitable for the transportation task from the capacity of a designated carrier, the following steps may be included: calculating the total mileage of the transportation task; determining the task type based on the total mileage, wherein the task type includes one of long-distance, medium-distance, and short-distance; selecting capacity matching the task type of the transportation task from the designated carrier's capacity based on historical route preferences as second candidate capacity; obtaining the weight of each second candidate capacity; extracting second candidate capacity sequentially in descending order of weight; calculating the sum of the maximum number of trips that can be completed today for all extracted second candidate capacity after each extraction, until the sum of the maximum number of trips that can be completed today is greater than or equal to the minimum departure volume of the transportation task; and then extracting all extracted second candidate capacity as target capacity. If, after all second-candidate capacity has been extracted, the sum of the maximum number of trips that can be completed today is still less than the minimum number of departures for the pending transportation task, then it will be further determined whether the pending transportation task can only be exclusively used by the designated carrier. If so, the current target capacity will be directly output as the final result. If not, a second round of screening will be initiated to supplement the target capacity.

[0045] In one possible implementation, the weight of each transport capacity is dynamically determined based on its cumulative commission, transport efficiency, and number of order rejections within a recently set time period (e.g., one month). Transport efficiency is the average time it takes for a capacity to complete a set mileage; a shorter average time indicates higher efficiency. The number of order rejections refers to the number of times a capacity refuses to assign pending transport tasks. Specifically, the weight of each transport capacity is obtained by weighted summing of cumulative commission, transport efficiency, and number of order rejections. It should be noted that there are default weighting coefficients for cumulative commission, transport efficiency, and number of order rejections. To ensure drivers' commissions, after obtaining their commissions, it is determined whether their commissions are below a guaranteed threshold (e.g., 4000 yuan). If not, the cumulative commission is weighted according to the default coefficient; if below, the default coefficient is first increased by a preset multiple, and then subsequent weighting calculations are performed based on this increased coefficient. This appropriately increases the weight of transport capacities with commissions below the guaranteed threshold, thereby increasing their chances of receiving pending transport tasks.

[0046] The second round of screening determines whether the minimum number of vehicles required to supplement external carriers is met. If not, a third candidate capacity is selected from the first candidate capacity that belongs to other external carriers (excluding the designated carrier) to supplement the target capacity selected in the first round. If the requirement is met, the third round of screening proceeds directly.

[0047] In one possible implementation, when selecting third candidate capacity from the first candidate capacity belonging to other external carriers (excluding the designated carrier) to supplement the target capacity selected in the first round, the following steps may be included: First, the capacity belonging to other external carriers in the first candidate capacity is categorized by carrier, and the carriers are sorted from low to high according to the cost of undertaking the transportation task to be processed. Second, for the capacity to be dispatched under each other external carrier, capacity whose historical route preferences match the task type of the transportation task to be processed is selected as the third candidate capacity, and then these third candidate capacity are sorted from high to low according to capacity weight, finally forming a list of third candidate capacity. Next, the minimum departure volume of the transportation task to be processed is calculated as the first difference between the sum of the maximum number of trips that can be completed today for all target capacities selected in the first round. Third-candidate capacities are then extracted sequentially from the third-candidate capacity list. For each extracted third-candidate capacity, the sum of the maximum number of trips that can be completed today for all extracted third-candidate capacities is calculated, until the sum of the maximum number of trips that can be completed today is greater than or equal to the calculated first difference. All extracted third-candidate capacities are then added to the target capacities selected in the first round as target capacities, resulting in the target capacities for the second round of selection. This second round of selection is then output as the result of the target capacities selection. If, after all capacities in the third-candidate capacity list have been extracted, the sum of the calculated maximum number of trips that can be completed today is still less than the calculated first difference, then the third round of selection is initiated.

[0048] The third round of screening involves selecting self-operated capacity from the first candidate capacity, and then selecting a fourth candidate capacity from among these self-operated capacity. This fourth candidate capacity supplements the target capacity selected in the first or second round. Specifically, this may include the following steps: First, determine the route strategy of the self-operated capacity. The route strategy can be either profit-first or self-operated vehicle attendance-first, and this strategy can be pre-configured according to demand. If the route strategy prioritizes profit, calculate the profit margin for each self-operated capacity to complete the transportation task. Self-operated capacity with a profit margin meeting a preset threshold is selected as the fourth candidate capacity. If the route strategy prioritizes self-operated vehicle attendance-first, all self-operated capacity is directly selected as the fourth candidate capacity. After obtaining the fourth candidate capacity, capacity whose historical route preferences match the task type of the transportation task is selected from the fourth candidate capacity. These capacity are then sorted by weight to obtain the fourth candidate capacity list. Calculate the second difference between the minimum departure volume of the transportation task to be processed and the sum of the maximum number of trips that can be completed today for all target capacities selected in the previous round (round one or round two). Then, sequentially extract fourth candidate capacities from the fourth candidate capacity list. For each fourth candidate capacity extracted, calculate the sum of the maximum number of trips that can be completed today for all extracted fourth candidate capacities, until the sum of the maximum number of trips that can be completed today is greater than or equal to the calculated second difference. At this point, add all extracted fourth candidate capacities as target capacities to the target capacities selected in the first or second round, resulting in the target capacities for the third round of selection. This third round of selection is then output as the result of the target capacities selection. If, after extracting all capacities from the fourth candidate capacity list, the sum of the calculated maximum number of trips that can be completed today is still less than the calculated second difference, then the fourth round of selection is initiated.

[0049] In one possible implementation, calculating the profit margin for self-operated capacity to complete the pending transportation task may include the following steps: First, calculate the energy cost of using self-operated transport capacity to complete the transportation task. Specifically, obtain the task information of the transportation task; extract the standard operating route from the route information from the task information, and calculate the standard operating mileage based on the standard operating route information; based on the standard operating mileage, and combined with the vehicle energy consumption model of self-operated transport capacity, calculate the energy cost of using self-operated transport capacity to complete the transportation task.

[0050] Secondly, calculate the usage cost of self-operated capacity to complete the pending transportation task. Specifically, obtain the task information of the pending transportation task; extract route information from the task information; and calculate the usage cost of self-operated capacity to complete the pending transportation task based on the route information and the commission information of self-operated capacity.

[0051] Next, the toll fees for completing the transport task to be processed by the self-operated transport capacity are calculated. Specifically, the task information of the transport task to be processed is obtained, and the standard toll fees in the route information are extracted from the task information as the toll fees for completing the transport task by the self-operated transport capacity.

[0052] Next, the sum of energy consumption cost, usage fee, and road and bridge tolls is used as the cost of completing the pending transportation task using self-operated transportation capacity. That is, the cost of completing the pending candidate task using self-operated transportation capacity = energy consumption cost + usage fee + road and bridge tolls.

[0053] Finally, based on the revenue and cost of completing the pending candidate task using self-operated capacity, the profit margin of completing the pending candidate task using self-operated capacity is calculated.

[0054] The fourth round of screening continues by supplementing the target capacity from the remaining external carriers' capacity that was not allocated in the second round. Specifically, the unallocated capacity of other external carriers is sorted according to the process of the second round, resulting in a fifth candidate capacity list. The third difference between the minimum departure volume of the pending transportation task and the sum of the maximum number of trips that can be completed today for all target capacity selected in the third round is calculated. Fifth candidate capacity is extracted sequentially from the fifth candidate capacity list. For each extracted fifth candidate capacity, the sum of the maximum number of trips that can be completed today for all extracted fifth candidate capacity is calculated, until the sum of the maximum number of trips that can be completed today is greater than or equal to the calculated third difference. All extracted fifth candidate capacity are then added to the target capacity selected in the third round as target capacity, resulting in the target capacity for the fourth round of screening, which is then output as the screening result of the target capacity. If, after all the capacity in the fifth candidate capacity list has been extracted, the sum of the calculated maximum number of trips that can be completed today is still less than the calculated third difference, all the selected fifth candidate capacity will be directly added to the target capacity selected in the third round to obtain the target capacity for the fourth round of selection, and this will be output as the selection result of the target capacity.

[0055] After selecting the target capacity suitable for the transportation tasks to be processed, a target capacity list can be generated based on the selected target capacity. Specifically, the selected target capacity is sorted in descending order of its weight to obtain the target capacity list.

[0056] In one possible implementation, after generating the target capacity list, the following steps are also included: determining whether the target capacity on the target capacity list meets the minimum departure volume for the transportation task to be processed; if the minimum departure volume is not met, expanding the preset radius range by a preset percentage (e.g., 50%) and continuing to supplement the target capacity screening. The specific process is the same as the process of screening target capacity within the preset radius range described above, and will not be repeated here.

[0057] S1400 creates carrier orders based on the target capacity list and pushes the carrier orders to each target capacity for processing.

[0058] In one possible implementation, when creating a transport order based on the target capacity list and pushing the transport order to the target capacity for processing, there are two modes: automatic push mode and manual push mode. In automatic mode, after generating the target capacity list, a transport order is directly created based on the target capacity list and sent to the target capacity's app. In manual push mode, after generating the target capacity list, the target capacity list is pushed to the dispatcher for final decision-making. The decision includes at least one of approval, rejection, or manual modification. Then, a transport order is created based on the dispatcher's decision on the target capacity list and sent to the target capacity's app.

[0059] In one possible implementation, after pushing the shipping order to the target capacity, it also includes: The system determines whether the target transportation capacity will accept the assigned transport orders. Specifically, drivers of the target transportation capacity can view the transport orders assigned by the system in the app and choose to accept or reject them. The decision to accept or reject the orders determines whether the target transportation capacity will accept the assigned transport orders.

[0060] Once it is determined that the target transport capacity accepts the assigned transport order, the corresponding transport task is marked as "accepted" and the execution phase begins. After the execution phase, a corresponding electronic route book is generated for the transport order and pushed to the target transport capacity. The electronic route book includes warnings of dangerous road sections, queue conditions at the unloading site, and at least one weather forecast along the route. Pushing the electronic route book helps drivers plan their driving strategies in advance, effectively avoiding potential risks, improving the safety and smoothness of transport, and allowing drivers to have a more comprehensive understanding of key information during transport, enabling them to better manage their transport schedule.

[0061] If the system determines that the target capacity does not accept the recommended task, it will release the transportation task occupied by the target capacity and return the transportation task corresponding to the carrier order assigned to it to the task pool for rescheduling. Simultaneously, when rejecting the recommended task, a reason for rejection must be provided, and the system will also perform the following operations: 1) Record the rejection and deduct the credit score of the capacity to be reassigned. 2) Trigger a cooldown period (e.g., no more recommended tasks will be given to the target capacity for 30 minutes).

[0062] In one possible implementation, if it is determined that the target capacity accepts the carrier order, the process may also include an operation to generate a replenishment plan. The specific operation to generate a replenishment plan may be as follows: First, acquire vehicle transportation status data, energy consumption model, and route-connected charging point data from the target capacity's fulfillment of transport orders. The vehicle transportation status data is dynamic and includes: real-time vehicle location, remaining fuel / electricity, real-time load, and mileage traveled relative to the total mileage of the transport order. The energy consumption model is constructed and updated based on dynamic data from the target capacity's vehicle type, remaining mileage of historical transport orders, historical fuel consumption data (fuel consumption differences under different loads), real-time road conditions (congestion coefficient, gradient), and weather. The route-connected charging point data includes the location, closure status, available capacity, current queue time, and at least one of the following: fuel / electricity price.

[0063] Second, based on vehicle transport status data, energy consumption models, and data on refueling points along the route, combined with real-time road conditions and weather, a refueling plan is generated and pushed to the target transport capacity. Specifically, this may include the following steps: First, vehicle energy consumption prediction and refueling demand determination are performed. Specifically, the vehicle's current mileage is obtained, and the remaining mileage is calculated based on the total mileage of the transport order and the current mileage. The remaining mileage, real-time road conditions and weather, and the vehicle's current load are input into the most recently updated energy consumption model, which predicts the energy consumption required for the vehicle to reach the destination of the transport order. It is then determined whether this predicted energy consumption value is lower than a preset safe energy consumption threshold. If it is lower, the following refueling planning steps are triggered. The safe energy consumption threshold can be set according to specific scenarios; preferably, it can be set to 1.2 times the predicted energy consumption value. Because the energy consumption prediction is based on the target capacity's historical data, the energy consumption model accurately reflects the energy consumption characteristics of the target capacity in actual transportation scenarios, effectively improving the accuracy of the predicted energy consumption value and providing a reliable basis for subsequent refueling demand determination.

[0064] Secondly, energy replenishment points are screened and prioritized. This may include the following steps: Step 1: Perform geographical screening. Specifically, screen charging stations within ±5 kilometers of the current driving route's real-time location, excluding those that are closed or at full capacity, to obtain candidate charging stations. 2) Conduct cost evaluations on each candidate charging station to obtain a comprehensive score. Specifically, multiple factors need to be considered to calculate the comprehensive score of each candidate charging station. First, there is the detour cost, which refers to the cost of the extra mileage traveled due to choosing a certain charging station. It is calculated by multiplying the extra detour mileage by the unit energy consumption cost per kilometer. This cost reflects the increased energy consumption caused by the route change. Second, there is the charging time cost, which consists of two parts: one is the waiting time at the charging station, and the other is the actual time required for refueling or charging. These two together determine the total time cost spent at the charging station. Finally, the cost also needs to be considered, which involves multiplying the total energy required for charging by the corresponding energy price, such as the amount of fuel multiplied by the fuel price when refueling, or the amount of electricity multiplied by the electricity price when charging. By comprehensively evaluating detour costs, refueling time costs, and expense costs, a comprehensive score is derived for each candidate refueling point, providing a scientific basis for decision-making. 3) Preference Weight Adjustment: When adjusting the scores of each candidate refueling point, it is necessary to fully consider the driver's historical refueling preferences, such as the refueling stations they frequently choose, which may be places they trust based on past experience. At the same time, it is also necessary to consider the refueling points stipulated in the carrier's agreement, as drivers can enjoy certain discounts or preferential policies at these locations, which are highly attractive to drivers. By comprehensively analyzing the drivers' frequently used stations and the preferential refueling points in the carrier's agreement, we can reasonably adjust the initial scores of each candidate refueling point, thereby calculating the final score of each candidate refueling point, so as to provide drivers with choices that better meet their needs and interests.

[0065] Next, the refueling plan is generated and updated. Specifically, the refueling point with the highest final score is recommended, and a refueling plan including "estimated arrival time, suggested refueling, and estimated stay duration" is generated and pushed to the driver's terminal. If energy consumption is abnormal during driving (such as a sudden surge in fuel consumption) or the status of the refueling point changes (such as an increase in queue time), the system will recalculate and update the refueling plan in real time.

[0066] Finally, the latest generated or updated energy replenishment plan will be pushed to the target capacity.

[0067] This approach allows for the automatic generation and dynamic updating of refueling plans during the journey, effectively ensuring the continuity and timeliness of transportation tasks. Specifically, by collecting real-time vehicle transportation status data, dynamically updating the energy consumption model, and integrating real-time information from refueling points along the route, the system can dynamically adjust and optimize refueling plans based on constantly changing road conditions, weather, vehicle load, and the operational status of refueling points during actual vehicle operation. For example, when a vehicle encounters sudden congestion leading to increased energy consumption, or when the previously recommended refueling point experiences a sudden increase in queue time or temporary closure, the system can quickly respond to these changes, reassess the current energy consumption and refueling needs, and perform secondary screening and prioritization based on the latest refueling point data, promptly pushing updated refueling plans to the target capacity. This dynamic update mechanism avoids the risk of transportation delays or energy depletion that may result from the initial refueling plan being out of sync with the actual situation, ensuring that drivers always receive the most economical, efficient, and reliable refueling guidance, thereby significantly improving energy management efficiency and the reliability of transportation task completion in bulk logistics transportation.

[0068] In one possible implementation, if it is determined that the target capacity accepts the shipping order, the implementation may further include an operation to monitor the anomaly status of the shipping order. This anomaly monitoring operation may include the following steps: First, anomaly monitoring is conducted on the vehicle transportation status of the target transport capacity and the completion status of transport orders. Specifically, 1) Task monitoring: The planned completion time and actual completion time of each node in the transport order (such as arrival at the loading point, completion of loading and unloading, arrival at the unloading point, etc.) are compared. If the deviation between the calculated time and the actual completion time exceeds a preset time threshold, the system determines that an anomaly has been identified. 2) Anomaly detection: Anomaly information reported by the target transport capacity or provided by third-party APIs (weather, road conditions) is monitored. When anomaly information is detected, the system determines that an anomaly has been identified.

[0069] Second, upon detecting an anomaly, an anomaly alarm and / or anomaly handling will be triggered. Specifically, an anomaly alarm will be triggered upon detection. This alarm will be pushed to the dispatcher to facilitate necessary manual intervention (such as manually assigning vehicles or suspending tasks). In the event of a serious anomaly (such as prolonged delays or vehicle malfunctions), in addition to triggering an anomaly alarm, the affected transportation task will be automatically returned to the task pool. If the minimum requirement for the corresponding transport order is not met, a new round of matching calculations will be automatically triggered.

[0070] During anomaly monitoring, the monitored vehicle transportation status, order completion status, and alarm information are simultaneously pushed to the large screen in the dispatch and command center for display. This allows dispatchers to grasp the overall operational status of the transportation task in real time and intuitively, promptly identify the location, type, and severity of abnormal events, and thus make rapid response decisions, improving the overall control and handling efficiency of abnormal situations. At the same time, the historical data and trend analysis displayed on the large screen can also provide data support for dispatchers to optimize subsequent dispatch strategies and evaluate capacity performance.

[0071] This disclosure provides a method for intelligent scheduling of bulk logistics, including: obtaining unprocessed transportation tasks from a task pool and acquiring the loading location and specified vehicle type for each task; selecting transportation capacity within a preset radius of the loading location and matching the specified vehicle type from the unprocessed transportation capacity as first candidate capacity; acquiring and, based on the carrier, historical route preferences, departure time range, existing task information, pick-up location, real-time location, road conditions, and weather of the first candidate capacity, selecting target capacity suitable for the unprocessed transportation tasks and generating a target capacity list; creating transportation orders based on the target capacity list and pushing the transportation orders to each target capacity for processing. Compared with existing scheduling methods that rely on human experience, the technical solution provided in this disclosure achieves significant beneficial effects by constructing an intelligent scheduling closed loop of "data collection - multi-dimensional filtering - dynamic adaptation - automatic order dispatch". Specifically, this solution first obtains key attributes of the transportation tasks to be processed and performs automated initial screening based on geographical location and vehicle type, overcoming the bottleneck of massive information processing in manual dispatching. Then, by deeply integrating multi-dimensional dynamic and static data such as the carrier of the first candidate transport capacity, historical route preferences, departure time range, existing task information, pick-up location, and real-time road conditions and weather, it solves the problems of information silos and the inability to perceive changes in the external environment in real time in traditional dispatching. Finally, based on the above objective data, the transport capacity is quantitatively evaluated and screened, automatically generating a target transport capacity list and creating push orders. This process transforms dispatching decisions from relying on subjective experience to scientific calculation based on comprehensive data. It not only avoids the misallocation and waste of transport resources caused by subjective judgment biases but also effectively improves the utilization rate of transport resources and the speed of dispatching response, realizing the intelligence and precision of bulk logistics dispatching. Thus, while improving the scientific nature of dispatching decisions, it effectively reduces the waste of transport resources.

[0072] <Device Embodiment> Figure 2 A schematic block diagram of a bulk logistics intelligent scheduling device according to an embodiment of the present disclosure is shown. Figure 2 As shown, the device 100 includes: Data acquisition module 110 is used to acquire pending transportation tasks from the task pool and to acquire the loading location and specified vehicle type of the pending transportation tasks; The candidate transport capacity screening module 120 is used to screen the transport capacity located within a preset radius of the loading location and matching the specified vehicle type from the transport capacity to be dispatched as the first candidate transport capacity; The target capacity matching module 130 is used to obtain and filter target capacity that matches the transportation task to be processed based on the carrier, historical route preference, departure time range, existing task information, pick-up location, real-time location, road conditions and weather of the first candidate capacity and generate a target capacity list. The task push module 140 is used to create carrier orders based on the target capacity list and push the carrier orders to each target capacity for processing.

[0073] <Equipment Example> Figure 3 A schematic block diagram of a bulk logistics intelligent scheduling device according to an embodiment of the present disclosure is shown. Figure 3 As shown, the bulk logistics intelligent scheduling device 200 includes a processor 210 and a memory 220 for storing executable instructions of the processor 210. The processor 210 is configured to implement any of the aforementioned bulk logistics intelligent scheduling methods when executing the executable instructions.

[0074] It should be noted here that the number of processors 210 can be one or more. Furthermore, the bulk logistics intelligent scheduling device 200 in this embodiment may also include an input device 230 and an output device 240. The processors 210, memory 220, input device 230, and output device 240 can be connected via a bus or other means, without specific limitations here.

[0075] The memory 220, as a computer-readable storage medium, can be used to store software programs, computer-executable programs, and various modules, such as the program or module corresponding to the bulk logistics intelligent scheduling method of this disclosure embodiment. The processor 210 executes various functional applications and data processing of the bulk logistics intelligent scheduling device 200 by running the software program or module stored in the memory 220.

[0076] Input device 230 can be used to receive input digital numbers or signals. These signals may include key signals related to user settings and function control of the device / terminal / server. Output device 240 may include a display device such as a screen.

[0077] <Storage Medium Examples> According to a fourth aspect of this disclosure, a non-volatile computer-readable storage medium is also provided, on which computer program instructions are stored, which, when executed by processor 210, implement any of the preceding bulk logistics intelligent scheduling methods.

[0078] The various embodiments of this disclosure have been described above. These descriptions are exemplary and not exhaustive, and are not limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen to best explain the principles, practical applications, or technical improvements to the technology in the market, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. A method for intelligent scheduling of bulk logistics, characterized in that, include: Obtain pending transportation tasks from the task pool and obtain the loading location and specified vehicle type of the pending transportation tasks; The first candidate transport capacity is selected from the transport capacity to be dispatched, which is located within a preset radius of the loading location and matches the specified vehicle type. Based on the carrier, historical route preference, departure time range, existing task information, pick-up location, real-time location, road conditions and weather of the first candidate capacity, target capacity that is suitable for the transportation task to be processed is selected and a target capacity list is generated. Based on the target capacity list, a carrier order is created and the carrier order is pushed to each target capacity for processing.

2. The method according to claim 1, characterized in that, When retrieving transport tasks to be processed from the task pool, the following is included: Calculate the dynamic priority score of each of the transport tasks to be processed in the task pool; The tasks to be processed are sorted based on their dynamic priority scores to obtain a scheduling queue of tasks to be processed. The transportation tasks to be processed are read sequentially from the scheduling queue and intelligently scheduled.

3. The method according to claim 1, characterized in that, After generating the target capacity list, the following is also included: Determine whether the target capacity on the target capacity list meets the minimum departure quantity of the transportation task to be processed; If the minimum departure volume is not met, the preset radius range is expanded by a preset ratio to supplement the target capacity screening.

4. The method according to claim 1, characterized in that, After pushing the carrier order to the target capacity, the process also includes: Determine whether the target transport capacity accepts the transport order; If the carrier order is accepted, an electronic route book corresponding to the carrier order is generated and the electronic route book is pushed to the target transport capacity. The electronic route book includes at least one forecast of dangerous road section warnings, unloading site queuing conditions, and weather along the route.

5. The method according to claim 4, characterized in that, If it is determined that the shipment order is not accepted, the transportation task corresponding to the shipment order is returned to the task pool for rescheduling.

6. The method according to claim 4, characterized in that, If it is determined that the target capacity accepts the carrier order, the method further includes: Acquire vehicle transportation status data, energy consumption model, and energy replenishment point data along the route during the execution of the transportation order by the target transportation capacity; Based on the vehicle transportation status data, energy consumption model, and data on energy replenishment points along the route, combined with real-time road conditions and weather, an energy replenishment plan is generated and pushed to the target transportation capacity.

7. The method according to claim 4, characterized in that, If it is determined that the target capacity accepts the carrier order, the method further includes: Anomaly monitoring is performed on the vehicle transportation status data and the completion status of the transportation orders during the execution of the transportation orders by the target transportation capacity; When an anomaly is detected, trigger an anomaly alarm and / or anomaly handling.

8. A bulk logistics intelligent scheduling device, characterized in that, include: The data acquisition module is used to acquire unprocessed transportation tasks from the task pool and to acquire the loading location and specified vehicle type of the unprocessed transportation tasks; The candidate transport capacity screening module is used to select transport capacity located within a preset radius of the loading location and matching the specified vehicle type from the transport capacity to be dispatched as the first candidate transport capacity; The target capacity matching module is used to obtain and filter target capacity that is suitable for the transportation task to be processed based on the carrier, historical route preference, departure time range, existing task information, pick-up location, real-time location, road conditions and weather of the first candidate capacity, and generate a target capacity list. The task push module is used to create a carrier order based on the target capacity list and push the carrier order to each target capacity for processing.

9. A bulk logistics intelligent dispatching device, characterized in that, include: processor; Memory used to store processor-executable instructions; The processor is configured to implement the method of any one of claims 1 to 7 when executing the executable instructions.

10. A non-volatile computer-readable storage medium storing computer program instructions thereon, characterized in that, When the computer program instructions are executed by the processor, they implement the method described in any one of claims 1 to 7.

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