A multi-platform order collaborative fulfillment automated scheduling method and system

Abstract

The present application belongs to the technical field of order scheduling, and discloses a multi-platform order collaborative fulfillment automated scheduling method and system, the method comprising: obtaining a reception timestamp when receiving inventory data of each warehouse and transportation capacity information of each logistics company, and based on the reception timestamp, calculating the timeliness score of each inventory data and each transportation capacity information in real time, which are recorded as a first timeliness score and a second timeliness score respectively; according to the first timeliness score and the second timeliness score, adjusting the allocated orders and allocating new orders to each platform to obtain order scheduling results; adjusting the allocated orders and allocating new orders to each platform according to the timeliness score of the inventory data and transportation capacity information determined by the reception timestamp when the information is received, thereby improving the scheduling efficiency of orders.

Description

Technical Field

[0001] The present application relates to the technical field of order scheduling, and more specifically, to a method and system for automated scheduling of collaborative fulfillment of multi-platform orders. Background Art

[0002] Existing automated order fulfillment scheduling systems are used to process orders from multiple external e-commerce platforms and internal sales channels. The system's goal is to aggregate and process these dispersed orders and, through automated scheduling, drive the fulfillment of orders using the company's distributed warehousing and logistics resources.

[0003] However, in actual order fulfillment scheduling systems, due to technical and business reasons, warehouse inventory data and logistics company capacity information are not always fully synchronized in real time. This data originates from different systems, and the synchronization mechanisms and frequencies between different data sources vary. Synchronization intervals can be several minutes or even tens of minutes. Furthermore, data formats and fields vary significantly between different data sources, requiring time for data processing and synchronization. This leads to data source latency in order fulfillment scheduling systems, specifically, data synchronization delays from multiple sources leading to inconsistent data freshness. This data freshness discrepancy can affect the accuracy of scheduling decisions in scenarios with large order volumes, frequent inventory changes (for example, rapid inventory reduction of a particular product), or abnormal logistics conditions (for example, weather-related suspension of pickup in a certain area). For example, an order may be assigned to a warehouse that is actually out of stock but the system indicates it still has inventory, resulting in oversold or incorrect allocations, requiring manual intervention or rescheduling, increasing processing time and operating costs.

[0004] Therefore, in order to solve the technical problem that the existing order fulfillment scheduling system is difficult to ensure the accuracy of order fulfillment decisions in complex and changing scenarios due to varying degrees of data source delays, there is an urgent need for an automated scheduling method and system for multi-platform order collaborative fulfillment. Summary of the Invention

[0005] The purpose of this application is to provide an automated scheduling method and system for collaborative fulfillment of orders on multiple platforms. By determining the timeliness score of inventory data and transportation capacity information based on the reception timestamp when the information is received, the allocated orders are adjusted and new orders on each platform are allocated to obtain order scheduling results. This solves the problem that the existing order fulfillment scheduling system is difficult to ensure the accuracy of order fulfillment decisions in complex and changing scenarios due to varying degrees of data source delays. Adjusting orders and allocating new orders based on timeliness scores ensures that the scheduling system can give priority to using more reliable and closer to real-time data for decision-making, thereby improving the accuracy and efficiency of order fulfillment.

[0006] In a first aspect, the present application provides a multi-platform order collaborative fulfillment automated scheduling method, comprising:

[0007] Obtain the timestamp of receiving inventory data from each warehouse and transportation capacity information from each logistics company;

[0008] Based on the reception timestamp, calculating in real time a timeliness score of each of the inventory data and each of the transportation capacity information, and recording them as a first timeliness score and a second timeliness score respectively;

[0009] According to the first timeliness score and the second timeliness score, the allocated orders are adjusted and the new orders of each platform are allocated to obtain an order scheduling result.

[0010] The multi-platform order collaborative fulfillment automated scheduling method provided in this application can realize the automated scheduling of order fulfillment. By determining the timeliness score of inventory data and transportation capacity information based on the reception timestamp when the information is received, the allocated orders are adjusted and new orders on each platform are allocated to obtain order scheduling results. This solves the problem that the existing order fulfillment scheduling system is difficult to ensure the accuracy of order fulfillment decisions in complex and changing scenarios due to varying degrees of data source delays. Adjusting orders and allocating new orders based on the timeliness score ensures that the scheduling system can give priority to using more reliable and closer to real-time data for decision-making, thereby improving the accuracy and efficiency of order fulfillment.

[0011] Optionally, obtain the timestamp of when the inventory data of each warehouse and the transportation capacity information of each logistics company are received, including:

[0012] Obtain the initial timestamp of inventory data from each warehouse and capacity information from each logistics company upon receipt;

[0013] Determine respectively whether the update time of the source data is recorded in each of the inventory data and each of the capacity information; if so, determine the update time as the receiving timestamp of the corresponding inventory data or the corresponding capacity information; if not, determine the preliminary timestamp as the receiving timestamp of the corresponding inventory data or the corresponding capacity information.

[0014] Optionally, based on the reception timestamp, calculating in real time a timeliness score for each of the inventory data and each of the transport capacity information, including:

[0015] Calculate the time difference between the current moment and the received timestamp;

[0016] According to the inventory data and the transportation capacity information, a corresponding inventory weight and a corresponding transportation capacity weight are set;

[0017] Based on the time difference, combined with the inventory weight and the capacity weight, a timeliness score for each inventory data and each capacity information is calculated; wherein, the greater the time difference, the lower the timeliness score, the higher the inventory weight and the capacity weight, the higher the timeliness score, and the impact of the time difference on the timeliness score is much greater than the impact of the inventory weight and the capacity weight on the timeliness score.

[0018] The multi-platform order collaborative fulfillment automated scheduling method provided in this application can realize the automated scheduling of order fulfillment. By clarifying that the time difference is inversely proportional to the timeliness score, the weight (inventory weight and capacity weight) is directly proportional to the score, and emphasizing that the impact of the time difference on the score is far greater than the impact of the weight, a timeliness score that comprehensively reflects the age and importance of the data is calculated. This timeliness score more accurately quantifies the availability of current inventory and capacity data, provides a more reliable basis for subsequent order adjustments and new order allocation, and thus improves the accuracy of scheduling decisions.

[0019] Optionally, after calculating the time difference between the current moment and the received timestamp, the method further includes:

[0020] When the time difference is greater than or equal to a preset maximum time difference threshold, an update reminder is sent to the corresponding warehouse or logistics company to update the inventory data or transportation capacity information in a timely manner.

[0021] Optionally, according to the first timeliness score and the second timeliness score, the allocated orders are adjusted and new orders on each platform are allocated to obtain an order scheduling result, including:

[0022] Calculating a risk assessment result of the assigned order based on the first timeliness score and the second timeliness score;

[0023] Based on the risk assessment result, the allocated order is adjusted to obtain an order adjustment result;

[0024] selecting, based on the inventory data and the transportation capacity information, the warehouse with the highest first timeliness score and the logistics company with the highest second timeliness score, and allocating new orders to the respective platforms to obtain a new order allocation result;

[0025] The order adjustment results and the new order allocation results are summarized to obtain an order scheduling result.

[0026] The multi-platform order collaborative fulfillment automated scheduling method provided in this application can realize the automated scheduling of order fulfillment. For the assigned orders, the risk assessment results of these orders are calculated based on the first timeliness score of their associated warehouses and the second timeliness score of the logistics companies. The assigned orders are adjusted according to the risk assessment results to re-evaluate or assign orders with higher risks to avoid incorrect fulfillment due to outdated data. For newly received orders, the timeliness score is used to give priority to the warehouses and logistics companies with the freshest data (i.e., the highest timeliness score) for allocation, ensuring that the initial allocation of new orders is based on the most reliable data currently. By distinguishing between the processing of assigned orders and new orders, the need for incorrect allocation and manual intervention is effectively reduced in the case of delays and inconsistencies in data sources, thereby improving order scheduling efficiency.

[0027] Optionally, based on the risk assessment result, the allocated order is adjusted to obtain an order adjustment result, including:

[0028] Determining whether the risk assessment result of each of the allocated orders is greater than or equal to a preset risk threshold;

[0029] If so, for the allocated order whose risk assessment result is greater than or equal to the preset risk threshold, the warehouse with the highest first timeliness score or the logistics company with the highest second timeliness score is selected from each warehouse or logistics company as the adjusted warehouse or logistics company based on the inventory data and the transportation capacity information, to obtain adjusted order information;

[0030] If not, for the allocated order whose risk assessment result is less than the preset risk threshold, select the original warehouse and logistics company as the adjusted warehouse or logistics company to obtain adjusted order information;

[0031] Based on the adjusted order information, an order adjustment result is determined.

[0032] Optionally, the inventory data includes the inventory quantity of goods; the transportation capacity information includes the available logistics transportation capacity; the order information includes the order quantity of goods and the required logistics transportation capacity of goods; the warehouse or logistics company with the highest timeliness score is selected from each of the warehouses or logistics companies as the adjusted warehouse or logistics company, and the adjusted order information is obtained, including:

[0033] Based on the inventory data and the transportation capacity information, a warehouse whose inventory quantity is greater than the order quantity is determined as a backup warehouse, and a logistics company whose spare logistics capacity is greater than the required logistics capacity is determined as a backup logistics company, and a list of backup warehouses and backup logistics companies to which orders have been allocated is obtained;

[0034] The standby warehouse with the highest first timeliness score or the standby logistics company with the highest second timeliness score is selected from the standby warehouse list or the standby logistics list as the adjusted warehouse or logistics company to obtain adjusted order information.

[0035] In a second aspect, the present application provides a multi-platform order collaborative fulfillment automated scheduling system, comprising:

[0036] The acquisition module is used to obtain the receipt timestamp of the inventory data of each warehouse and the transportation capacity information of each logistics company;

[0037] A scheduling module, configured to calculate, in real time, a timeliness score for each of the inventory data and each of the transport capacity information based on the reception timestamp, and record the scores as a first timeliness score and a second timeliness score, respectively;

[0038] The scheduling module is used to adjust the allocated orders and allocate new orders to each platform according to the first time limit score and the second time limit score to obtain an order scheduling result.

[0039] This multi-platform order collaborative fulfillment automated scheduling system adjusts assigned orders and allocates new orders to each platform through the timeliness score of inventory data and transportation capacity information determined by the reception timestamp when the information is received, and obtains order scheduling results. It solves the problem that the existing order fulfillment scheduling system is difficult to ensure the accuracy of order fulfillment decisions in complex and changing scenarios due to varying degrees of data source delays. Adjusting orders and allocating new orders based on timeliness scores ensures that the scheduling system can give priority to using more reliable and closer to real-time data for decision-making, thereby improving the accuracy and efficiency of order fulfillment.

[0040] Beneficial effects: The multi-platform order collaborative fulfillment automated scheduling method and system provided in this application adjusts the allocated orders and allocates new orders to each platform through the timeliness score of inventory data and transportation capacity information determined by the reception timestamp when the information is received, and obtains order scheduling results. It solves the problem that the existing order fulfillment scheduling system is difficult to ensure the accuracy of order fulfillment decisions in complex and changing scenarios due to varying degrees of data source delays. Order adjustments and new order allocations based on timeliness scores ensure that the scheduling system can give priority to using more reliable and closer to real-time data for decision-making, thereby improving the accuracy and efficiency of order fulfillment. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 Flowchart of the multi-platform order collaborative fulfillment automated scheduling method provided in an embodiment of the present application.

[0042] Figure 2A schematic diagram of the structure of the multi-platform order collaborative fulfillment automated scheduling system provided in an embodiment of the present application.

[0043] Explanation of numbers: 1. Acquisition module; 2. Calculation module; 3. Scheduling module. DETAILED DESCRIPTION

[0044] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. The components of the embodiments of the present application generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work fall within the scope of protection of the present application.

[0045] It should be noted that similar reference numerals and letters represent similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined or explained in subsequent drawings. At the same time, in the description of this application, the terms "first", "second", etc. are only used to distinguish the description and should not be understood as indicating or implying relative importance.

[0046] Please refer to Figure 1 , Figure 1 In some embodiments of the present application, a multi-platform order collaborative fulfillment automated scheduling method is provided for scheduling multi-platform order collaborative fulfillment, including the following steps:

[0047] Step S101, obtaining a receiving timestamp when receiving inventory data from each warehouse and transportation capacity information from each logistics company;

[0048] Step S102: Based on the received timestamp, a timeliness score of each inventory data and each transport capacity information is calculated in real time, and recorded as a first timeliness score and a second timeliness score respectively;

[0049] Step S103 : According to the first timeliness score and the second timeliness score, the allocated orders are adjusted and the new orders of each platform are allocated to obtain an order scheduling result.

[0050] This multi-platform order collaborative fulfillment automated scheduling method adjusts assigned orders and allocates new orders to each platform through the timeliness score of inventory data and transportation capacity information determined by the reception timestamp when the information is received, and obtains order scheduling results. It solves the problem that the existing order fulfillment scheduling system is difficult to ensure the accuracy of order fulfillment decisions in complex and changing scenarios due to varying degrees of data source delays. Adjusting orders and allocating new orders based on timeliness scores ensures that the scheduling system can give priority to using more reliable and closer to real-time data for decision-making, thereby improving the accuracy and efficiency of order fulfillment.

[0051] Specifically, in step S101, the receiving timestamp of the inventory data of each warehouse and the transportation capacity information of each logistics company is obtained, including:

[0052] Obtain the initial timestamp of inventory data from each warehouse and capacity information from each logistics company upon receipt;

[0053] Determine whether the update time of the source data is recorded in each inventory data and each capacity information respectively; if so, determine the update time as the receiving timestamp of the corresponding inventory data or the corresponding capacity information; if not, determine the preliminary timestamp as the receiving timestamp of the corresponding inventory data or the corresponding capacity information.

[0054] In step S101, due to the diverse data sources and different synchronization mechanisms for warehouse inventory data and logistics capacity information, data arrival at the scheduling system can experience delays and inconsistent freshness. This data delay impacts the accuracy of order scheduling decisions based on this data, potentially leading to incorrect assignments or the need for manual intervention. Therefore, by obtaining a preliminary timestamp upon data receipt and further determining whether the data itself contains the source system's update time, the source data's update time is prioritized, improving timestamp accuracy. If the data contains the source data's update time, such as a clear "last update time" field in the data packet, this source data's update time is used as the data's receipt timestamp. This timestamp is closer to the actual time when the data's status changed in the source system and better reflects the data's true freshness than the time it arrived at the scheduling system. If the data does not contain the source data's update time field, the time the data was received by the scheduling system is used as the receipt timestamp. This provides a more accurate time reference for the subsequent calculation of the data's timeliness score, improving the effectiveness of the timeliness score, and ultimately, improving the accuracy of order scheduling decisions while reducing incorrect assignments and manual intervention caused by outdated data.

[0055] Specifically, in step S102, based on the received timestamp, the timeliness score of each inventory data and each transportation capacity information is calculated in real time, including:

[0056] Calculate the time difference between the current time and the received timestamp;

[0057] According to inventory data and transportation capacity information, set the corresponding inventory weight and corresponding transportation capacity weight;

[0058] Based on the time difference, combined with the inventory weight and capacity weight, the timeliness score of each inventory data and each capacity information is calculated; among them, the greater the time difference, the lower the timeliness score, and the higher the inventory weight and capacity weight, the higher the timeliness score, and the impact of the time difference on the timeliness score is far greater than the impact of the inventory weight and capacity weight on the timeliness score.

[0059] In step S102, by quantifying the freshness and importance of inventory data and capacity information, the issue of inconsistent data "freshness" caused by delays in multi-source data synchronization is resolved. First, the data's reception timestamp is obtained and the time difference between the current moment and this timestamp is calculated (typically in minutes). This directly measures the data's timeliness. The larger the time difference, the older the data and the less accurately it reflects actual conditions. Second, weights are assigned to data of different types or importance. For example, inventory data for key commodities can be assigned a higher weight, or warehouses with larger inventory quantities can be assigned a larger weight. The time difference and weight are then combined to calculate a timeliness score. This calculation method ensures that even if data is assigned a high weight, if it becomes outdated (with a large time difference), its timeliness score will be significantly reduced, accurately reflecting its reduced availability. Conversely, even if data is assigned a low weight, if it is very fresh (with a small time difference), its score will be relatively high. In this way, the system can obtain a quantitative indicator that comprehensively reflects the age and importance of the data, namely the timeliness score. This score more accurately assesses the reliability of current inventory and capacity data, providing a more reliable basis for subsequent order adjustments and new order allocations. This improves the accuracy of scheduling decisions, reduces misallocations or overselling caused by using outdated data, and reduces the need for manual intervention and rescheduling.

[0060] Specifically, a function can be set to calculate the time limit score. For example, the function can be set as: ;in, Time-sensitive scoring (i.e., first time-sensitive scoring or second time-sensitive scoring); The preset highest possible score (the preset highest possible score for inventory timeliness when calculating the first timeliness score, and the preset highest possible score for logistics timeliness when calculating the second timeliness score); is the time decay rate, which can be set according to the historical inventory change rate or the historical logistics capacity change rate (when calculating the first time-limited score, it is the inventory time decay rate; when calculating the second time-limited score, it is the logistics time decay rate); T is the time difference; is the weight gain coefficient (when calculating the first time-limit score, it is the inventory weight gain coefficient; when calculating the second time-limit score, it is the logistics weight gain coefficient). The weight gain coefficient is a very small decimal value and can be set according to actual needs. It is generally set to 0.001. W is the weight (inventory weight or transportation weight. When calculating the first time-limit score, it is the inventory weight; when calculating the second time-limit score, it is the transportation weight). When setting is 100, is 0.1, When it is 0.001, assuming is 10, and =100, calculated , assuming is 1, and =100, calculated , assuming is 1, and =300, calculated It can be seen that an increase in the time difference from 1 minute to 10 minutes leads to a decrease in the timeliness score of about 53.7 points, while an increase in the weight from 1 to 10 only leads to an increase in the timeliness score of about 0.2 points when the time difference is 1 minute, which reflects the dominance of the time difference in affecting the score.

[0061] Specifically, in step S102, after calculating the time difference between the current moment and the received timestamp, the process further includes:

[0062] When the time difference is greater than or equal to the preset maximum time difference threshold, an update reminder is sent to the corresponding warehouse or logistics company to update the inventory data or transportation capacity information in a timely manner.

[0063] In step S102, after calculating the time difference between the corresponding receipt timestamps of inventory data and capacity information and the current moment, a maximum time difference threshold is set to identify severely outdated data. Once the time difference for a data source reaches or exceeds the set maximum time difference threshold, the data is immediately deemed severely outdated and no longer suitable for accurate scheduling. At this point, a data update reminder is proactively sent to the data source (for example, a warehouse's WMS system or a logistics company's capacity management system). This reminder prompts the data source system to promptly push the latest inventory or capacity information to the order fulfillment scheduling system. In this way, relatively "fresh" data can be obtained and used in a timely manner for subsequent order allocation and adjustments, avoiding scheduling errors caused by the use of outdated data, improving the accuracy and efficiency of scheduling decisions, and reducing the need for manual intervention and secondary scheduling. The maximum time difference threshold can be set according to actual needs.

[0064] Specifically, in step S103, the allocated orders are adjusted and the new orders of each platform are allocated according to the first timeliness score and the second timeliness score, and the order scheduling result is obtained, including the following steps:

[0065] Calculate the risk assessment results of the allocated orders based on the first timeliness score and the second timeliness score;

[0066] Based on the risk assessment results, the allocated orders are adjusted to obtain order adjustment results;

[0067] Based on inventory data and transportation capacity information, the warehouse with the highest first timeliness score and the logistics company with the highest second timeliness score are selected to allocate new orders to each platform and obtain the new order allocation results;

[0068] Summarize the order adjustment results and new order allocation results to obtain the order scheduling results.

[0069] Specifically, in step S103, the risk assessment result of the allocated order is calculated based on the first timeliness score and the second timeliness score, including:

[0070] Get order information of assigned orders;

[0071] Determine a first timeliness score of a corresponding warehouse and a second timeliness score of a corresponding logistics company in the order information of the assigned order;

[0072] Through the preset risk scoring rules, based on the first timeliness score of the corresponding warehouse and the second timeliness score of the corresponding logistics company in the order information of the allocated order, the risk assessment result of the allocated order is calculated.

[0073] In step S103, detailed information about the assigned order requiring risk assessment is obtained. This information includes the specific warehouse and logistics company to which the order is assigned. Based on the warehouse and logistics company specified in the order information, the aforementioned calculation method is used to query and obtain their respective first and second timeliness scores. Based on the pre-set risk scoring rules, a numerical value representing the risk level of the assigned order's current fulfillment path is calculated, taking into account the first timeliness score of the corresponding warehouse and the second timeliness score of the corresponding logistics company. This results in a risk assessment result for the assigned order. For example, the lower the timeliness score (the less fresh the data), the higher the calculated risk assessment result is likely to be. In this way, the impact of data timeliness on the fulfillment of assigned orders can be quantified, providing objective and actionable evidence for determining whether the order's assignment needs to be adjusted later, thereby reducing fulfillment errors and additional costs caused by scheduling using outdated data.

[0074] Specifically, in step S103, a risk assessment result of the allocated order is calculated based on the first timeliness score of the warehouse and the second timeliness score of the logistics company in the order information of the allocated order using the preset risk scoring rules, including:

[0075] Obtain pre-built risk assessment parameters, warehouse scoring weights, and logistics company scoring weights based on preset risk scoring rules;

[0076] The risk assessment result of the allocated order is calculated based on the first timeliness score of the corresponding warehouse and the second timeliness score of the corresponding logistics company in the order information of the allocated order, combined with the risk assessment parameters, the warehouse score weight and the logistics company score weight.

[0077] In step S103, pre-set calculation elements for risk assessment of assigned orders are obtained. These elements include risk assessment parameters, warehouse scoring weights, and logistics company scoring weights. These elements are determined based on the understanding of the impact of data timeliness on fulfillment risk. For example, the impact weight of warehouse data timeliness on risk can be set to be higher than that of logistics data timeliness.

[0078] For each assigned order, the system searches for the currently assigned warehouse and logistics company, obtains their respective first and second timeliness scores, and then performs a calculation based on these scores, along with pre-set risk assessment parameters, warehouse score weights, and logistics company score weights. This calculation implements the pre-set risk scoring rules. For example, it could be a weighted summation model, multiplying the two timeliness scores by their corresponding weights, adding the results, and adjusting them based on risk assessment parameters to ultimately generate a numerical value representing the order's risk level: "Risk Assessment Result = Base Risk Value - (First Timeliness Score × Warehouse Score Weight + Second Timeliness Score × Logistics Company Score Weight)." This solution provides a standardized and repeatable method for converting the abstract concept of data timeliness into concrete risk values, thus informing subsequent order adjustment decisions. This quantitative assessment helps the system identify orders that may face fulfillment issues due to insufficiently fresh underlying data, improving the accuracy of scheduling decisions.

[0079] In some optional embodiments, the risk assessment results can be divided into warehouse risk assessment results and logistics company risk assessment results, where the warehouse risk assessment result = basic risk value - first time limit score × warehouse score weight, and the logistics company risk assessment result = basic risk value - second time limit score × logistics company score weight.

[0080] Specifically, in step S103, based on the risk assessment result, the allocated order is adjusted to obtain an order adjustment result, including:

[0081] Determine whether the risk assessment result of each allocated order is greater than or equal to the preset risk threshold;

[0082] If so, for the assigned orders whose risk assessment results are greater than or equal to the preset risk threshold, based on inventory data and transportation capacity information, the warehouse with the highest first timeliness score or the logistics company with the highest second timeliness score is selected from each warehouse or logistics company as the adjusted warehouse or logistics company, and the adjusted order information is obtained;

[0083] If not, for the allocated orders whose risk assessment results are less than the preset risk threshold, the original warehouse and logistics company are selected as the adjusted warehouse or logistics company to obtain the adjusted order information;

[0084] Based on the adjusted order information, an order adjustment result is determined.

[0085] In step S103, the risk assessment results for each assigned order are obtained and compared with a preset risk threshold (the preset risk threshold can be set as needed). If the order's risk assessment result is equal to or higher than the risk threshold, it indicates that the current assignment carries a high risk and may result in fulfillment failure due to outdated data. In this case, the original assignment is no longer trusted. Instead, based on the latest inventory data and transportation capacity information, warehouses with inventory quantities greater than the ordered quantity and logistics companies with available transportation capacity greater than the required transportation capacity are selected. Among these selected warehouses or logistics companies, those with the highest timeliness scores are prioritized, as high timeliness scores indicate the most up-to-date and reliable data. By selecting resources with the freshest data for reallocation, fulfillment risks caused by outdated data are reduced. If the order's risk assessment result is lower than the risk threshold, it indicates that the current assignment's risk is within an acceptable range and that the data freshness meets the requirements. In this case, the original warehouse and logistics company assignments are maintained without adjustment to maintain scheduling stability. After the above judgment and processing, each assigned order receives updated assignment information (which may be the original assignment or a newly selected resource). This updated order information is aggregated to form the order adjustment results. By introducing risk thresholds and a resource selection mechanism based on timeliness scores, this method provides a way to intelligently adjust assigned orders, improving the accuracy and robustness of order fulfillment scheduling and reducing fulfillment anomalies caused by data delays.

[0086] In some optional embodiments, when the risk assessment results are divided into warehouse risk assessment results and logistics company risk assessment results, the preset risk thresholds can be divided into preset warehouse assessment risk thresholds and preset logistics company assessment risk thresholds (the preset warehouse assessment risk thresholds and the preset logistics company assessment risk thresholds can be set according to actual needs), and the warehouse risk assessment results are compared with the preset warehouse assessment risk thresholds, and the logistics company risk assessment results are compared with the preset logistics company assessment risk thresholds. When the warehouse risk assessment result is greater than or equal to the preset warehouse assessment risk threshold, the original warehouse is adjusted, and the warehouse with the highest timeliness score is preferentially selected as the adjusted warehouse. When the logistics company risk assessment result is greater than or equal to the preset logistics company assessment risk threshold, the original logistics company is adjusted, and the logistics company with the highest timeliness score is preferentially selected as the adjusted logistics company.

[0087] Specifically, inventory data includes the inventory quantity of goods; transportation capacity information includes available logistics transportation capacity; and order information includes the order quantity of goods and the logistics transportation capacity required for the goods. In step S103, the warehouse or logistics company with the highest timeliness score is selected from each warehouse or logistics company as the adjusted warehouse or logistics company, and the adjusted order information is obtained, including:

[0088] Based on inventory data and transportation capacity information, warehouses with more inventory than ordered are identified as backup warehouses, and logistics companies with more spare capacity than required are identified as backup logistics companies. This results in a list of backup warehouses and backup logistics companies to which orders have been allocated.

[0089] The backup warehouse with the highest first timeliness score or the backup logistics company with the highest second timeliness score is selected from the backup warehouse list or the backup logistics list as the adjusted warehouse or logistics company to obtain adjusted order information.

[0090] In step S103, when an assigned order is assessed as high-risk and requires adjustment, the required quantity of goods and logistics capacity for the order are obtained. The inventory data of all warehouses is traversed to identify which warehouses have the corresponding inventory that can meet the order quantity requirements. These warehouses are added to the list of backup warehouses. The transportation capacity information of all logistics companies is traversed to identify which logistics companies have spare capacity that can meet the logistics requirements of the order. These logistics companies are added to the list of backup logistics companies. After determining the set of backup resources with fulfillment capabilities, warehouses or logistics companies that do not have fulfillment capabilities are no longer considered, even if their data timeliness scores are high. The warehouse with the highest timeliness score is searched in the list of backup warehouses, and the logistics company with the highest timeliness score is searched in the list of backup logistics companies. The found warehouse and logistics company are used as the adjusted fulfillment parties for the high-risk order, and the order information is updated. This method solves the problem of allocating resources without actual fulfillment capabilities based solely on data freshness, ensuring that the adjusted allocation plan is feasible and based on the latest data.

[0091] In step S103, for each new order received, the system retrieves warehouses with inventory quantities exceeding the ordered quantity and logistics companies with available capacity exceeding the required capacity based on the latest inventory data and transportation capacity information. The system then queries the first timeliness scores of the currently retrieved warehouses and the second timeliness scores of the currently retrieved logistics companies. The system then prioritizes the warehouse with the highest first timeliness score and the logistics company with the highest second timeliness score as the fulfillment resource for the new order. This allocation strategy based on the highest timeliness score ensures that new orders can be fulfilled while utilizing the latest and most reliable inventory and transportation capacity data.

[0092] Adjustments to assigned orders (including maintaining the original assignment or reallocating them) are integrated with the allocations to new orders to generate a final scheduling plan encompassing all orders. This approach distinguishes between assigned and new orders, managing risk and adjusting assigned orders while optimizing the allocation of new orders based on the latest data. This improves the accuracy and robustness of overall scheduling decisions and reduces errors and manual intervention caused by data timeliness.

[0093] From the above, it can be seen that the multi-platform order collaborative fulfillment automated scheduling method obtains the receiving timestamp when receiving the inventory data of each warehouse and the transportation capacity information of each logistics company, and based on the receiving timestamp, calculates the timeliness score of each inventory data and each transportation capacity information in real time, which are recorded as the first timeliness score and the second timeliness score respectively. According to the first timeliness score and the second timeliness score, the allocated orders are adjusted and the new orders of each platform are allocated to obtain the order scheduling result; thus, the allocated orders are adjusted and the new orders of each platform are allocated through the timeliness score of the inventory data and transportation capacity information determined by the receiving timestamp when the information is received, and the order scheduling result is obtained, which solves the problem that the existing order fulfillment scheduling system is difficult to ensure the accuracy of order fulfillment decisions in complex and changing scenarios due to different degrees of data source delay. Order adjustment and new order allocation based on the timeliness score ensure that the scheduling system can give priority to using more reliable and closer to real-time data for decision-making, thereby improving the accuracy and efficiency of order fulfillment.

[0094] refer to Figure 2 This application provides a multi-platform order collaborative fulfillment automated scheduling system for scheduling multi-platform order collaborative fulfillment, including:

[0095] Acquisition module 1 is used to obtain the receiving timestamp when receiving the inventory data of each warehouse and the transportation capacity information of each logistics company;

[0096] Calculation module 2, for calculating the timeliness score of each inventory data and each transport capacity information in real time based on the received timestamp, and recording them as a first timeliness score and a second timeliness score respectively;

[0097] The scheduling module 3 is used to adjust the allocated orders and allocate new orders to each platform according to the first time limit score and the second time limit score to obtain an order scheduling result.

[0098] This multi-platform order collaborative fulfillment automated scheduling system adjusts assigned orders and allocates new orders to each platform through the timeliness score of inventory data and transportation capacity information determined by the reception timestamp when the information is received, and obtains order scheduling results. It solves the problem that the existing order fulfillment scheduling system is difficult to ensure the accuracy of order fulfillment decisions in complex and changing scenarios due to varying degrees of data source delays. Adjusting orders and allocating new orders based on timeliness scores ensures that the scheduling system can give priority to using more reliable and closer to real-time data for decision-making, thereby improving the accuracy and efficiency of order fulfillment.

[0099] Specifically, when acquiring the timestamp of receiving the inventory data of each warehouse and the transportation capacity information of each logistics company, the acquisition module 1 executes:

[0100] Obtain the initial timestamp of inventory data from each warehouse and capacity information from each logistics company upon receipt;

[0101] Determine whether the update time of the source data is recorded in each inventory data and each capacity information respectively; if so, determine the update time as the receiving timestamp of the corresponding inventory data or the corresponding capacity information; if not, determine the preliminary timestamp as the receiving timestamp of the corresponding inventory data or the corresponding capacity information.

[0102] When acquiring module 1 is executed, warehouse inventory data and logistics capacity information come from diverse data sources and differing synchronization mechanisms, leading to data delays and inconsistent freshness when they arrive at the scheduling system. This data delay affects the accuracy of order scheduling decisions based on this data, potentially leading to incorrect assignments or requiring manual intervention. Therefore, by obtaining a preliminary timestamp when the data is received and further determining whether the data itself contains the source system's update time, the source data's update time is prioritized, improving timestamp accuracy. If the data contains the source data's update time, such as a clear "last update time" field in the data packet, this source data's update time is used as the data's receipt timestamp. This timestamp is closer to the actual time when the data's state changed in the source system and better reflects the data's true freshness than the time it arrived at the scheduling system. If the data does not contain the source data's update time field, the time the data was received by the scheduling system is used as the receipt timestamp. This provides a more accurate time reference for the subsequent calculation of the data's timeliness score, improving the effectiveness of the timeliness score, and ultimately enhancing the accuracy of order scheduling decisions while reducing incorrect assignments and manual intervention caused by stale data.

[0103] Specifically, when calculating the timeliness score of each inventory data and each transportation capacity information in real time based on the received timestamp, the calculation module 2 executes:

[0104] Calculate the time difference between the current time and the received timestamp;

[0105] According to inventory data and transportation capacity information, set the corresponding inventory weight and corresponding transportation capacity weight;

[0106] Based on the time difference, combined with the inventory weight and capacity weight, the timeliness score of each inventory data and each capacity information is calculated; among them, the greater the time difference, the lower the timeliness score, and the higher the inventory weight and capacity weight, the higher the timeliness score, and the impact of the time difference on the timeliness score is far greater than the impact of the inventory weight and capacity weight on the timeliness score.

[0107] During execution, calculation module 2 quantifies the freshness and importance of inventory data and transportation capacity information, addressing the issue of inconsistent data "freshness" caused by delays in multi-source data synchronization. First, it obtains the data's reception timestamp and calculates the time difference between the current moment and that timestamp (typically in minutes). This directly measures the data's timeliness. The larger the time difference, the older the data and the less accurately it reflects actual conditions. Second, it assigns weights to data of different types or importance. For example, inventory data for key commodities can be assigned a higher weight, or warehouses with larger inventory quantities can be assigned a larger weight. The time difference and weight are then combined to calculate a timeliness score. This calculation method ensures that even if data is assigned a high weight, if it becomes outdated (with a large time difference), its timeliness score will be significantly reduced, accurately reflecting its reduced availability. Conversely, even if data is assigned a low weight, if it is very fresh (with a small time difference), its score will be relatively high. In this way, the system can obtain a quantitative indicator that comprehensively reflects the age and importance of the data, namely the timeliness score. This score more accurately assesses the reliability of current inventory and capacity data, providing a more reliable basis for subsequent order adjustments and new order allocations. This improves the accuracy of scheduling decisions, reduces misallocations or overselling caused by using outdated data, and reduces the need for manual intervention and rescheduling.

[0108] Specifically, a function can be set to calculate the time limit score. For example, the function can be set as: ;in, Time-sensitive scoring (i.e., first time-sensitive scoring or second time-sensitive scoring); The preset highest possible score (the preset highest possible score for inventory timeliness when calculating the first timeliness score, and the preset highest possible score for logistics timeliness when calculating the second timeliness score); is the time decay rate, which can be set according to the historical inventory change rate or the historical logistics capacity change rate (when calculating the first time-limited score, it is the inventory time decay rate; when calculating the second time-limited score, it is the logistics time decay rate); T is the time difference; is the weight gain coefficient (when calculating the first time-limit score, it is the inventory weight gain coefficient; when calculating the second time-limit score, it is the logistics weight gain coefficient). The weight gain coefficient is a very small decimal value and can be set according to actual needs. It is generally set to 0.001. W is the weight (inventory weight or transportation weight. When calculating the first time-limit score, it is the inventory weight; when calculating the second time-limit score, it is the transportation weight). When setting is 100, is 0.1, When it is 0.001, assuming is 10, and =100, calculated , assuming is 1, and =100, calculated , assuming is 1, and =300, calculated It can be seen that an increase in the time difference from 1 minute to 10 minutes leads to a decrease in the timeliness score of about 53.7 points, while an increase in the weight from 1 to 10 only leads to an increase in the timeliness score of about 0.2 points when the time difference is 1 minute, which reflects the dominance of the time difference in affecting the score.

[0109] Specifically, after calculating the time difference between the current moment and the received timestamp, the calculation module 2 further executes:

[0110] When the time difference is greater than or equal to the preset maximum time difference threshold, an update reminder is sent to the corresponding warehouse or logistics company to update the inventory data or transportation capacity information in a timely manner.

[0111] During execution, calculation module 2 calculates the time difference between the corresponding receipt timestamps of inventory data and capacity information and the current moment. It then sets a maximum time difference threshold to identify severely outdated data. Once the time difference for a data source reaches or exceeds the set maximum time difference threshold, the data is immediately deemed severely outdated and no longer suitable for accurate scheduling. At this point, a data update reminder is proactively sent to the data source (e.g., a warehouse's WMS system or a logistics company's capacity management system). This reminder prompts the data source system to promptly push the latest inventory or capacity information to the order fulfillment scheduling system. This approach enables timely acquisition and use of relatively "fresh" data for subsequent order allocation and adjustments, avoiding scheduling errors caused by the use of outdated data, improving the accuracy and efficiency of scheduling decisions, and reducing the need for manual intervention and secondary scheduling. The maximum time difference threshold can be set based on actual needs.

[0112] Specifically, the scheduling module 3 adjusts the allocated orders and allocates new orders to each platform according to the first timeliness score and the second timeliness score, and when obtaining the order scheduling result, executes:

[0113] Calculate the risk assessment results of the allocated orders based on the first timeliness score and the second timeliness score;

[0114] Based on the risk assessment results, the allocated orders are adjusted to obtain order adjustment results;

[0115] Based on inventory data and transportation capacity information, the warehouse with the highest first timeliness score and the logistics company with the highest second timeliness score are selected to allocate new orders to each platform and obtain the new order allocation results;

[0116] Summarize the order adjustment results and new order allocation results to obtain the order scheduling results.

[0117] Specifically, when the scheduling module 3 calculates the risk assessment result of the allocated order based on the first timeliness score and the second timeliness score, it executes:

[0118] Get order information of assigned orders;

[0119] Determine a first timeliness score of a corresponding warehouse and a second timeliness score of a corresponding logistics company in the order information of the assigned order;

[0120] Through the preset risk scoring rules, based on the first timeliness score of the corresponding warehouse and the second timeliness score of the corresponding logistics company in the order information of the allocated order, the risk assessment result of the allocated order is calculated.

[0121] During execution, Scheduling Module 3 obtains detailed information about assigned orders requiring risk assessment. This information includes the specific warehouses and logistics companies to which the orders are assigned. Based on the warehouses and logistics companies specified in the order information, the module uses the aforementioned calculation method to query and obtain their respective first and second timeliness scores. Based on pre-set risk scoring rules, the module comprehensively considers the first timeliness score of the corresponding warehouse and the second timeliness score of the corresponding logistics company to calculate a numerical value representing the risk level of the assigned order's current fulfillment path, thereby obtaining a risk assessment result for the assigned order. For example, the lower the timeliness score (the less fresh the data), the higher the calculated risk assessment result is likely to be. In this way, the impact of data timeliness on the fulfillment of assigned orders can be quantified, providing objective and actionable evidence for determining whether the order's assignment needs to be adjusted later, thereby reducing fulfillment errors and additional costs caused by using outdated data for scheduling.

[0122] Specifically, when the scheduling module 3 calculates the risk assessment result of the assigned order based on the first timeliness score of the warehouse and the second timeliness score of the logistics company in the order information of the assigned order according to the preset risk scoring rules, it executes:

[0123] Obtain pre-built risk assessment parameters, warehouse scoring weights, and logistics company scoring weights based on preset risk scoring rules;

[0124] The risk assessment result of the allocated order is calculated based on the first timeliness score of the corresponding warehouse and the second timeliness score of the corresponding logistics company in the order information of the allocated order, combined with the risk assessment parameters, the warehouse score weight and the logistics company score weight.

[0125] When the scheduling module 3 is executed, it obtains the pre-set calculation elements for the risk assessment of the assigned orders. These elements include risk assessment parameters, warehouse scoring weights and logistics company scoring weights. These elements are determined based on the understanding of the impact of data timeliness on fulfillment risk. For example, the impact weight of warehouse data timeliness on risk can be set to be higher than that of logistics data timeliness.

[0126] For each assigned order, the system searches for the currently assigned warehouse and logistics company, obtains their respective first and second timeliness scores, and then performs a calculation based on these scores, along with pre-set risk assessment parameters, warehouse score weights, and logistics company score weights. This calculation implements the pre-set risk scoring rules. For example, it could be a weighted summation model, multiplying the two timeliness scores by their corresponding weights, adding the results, and adjusting them based on risk assessment parameters to ultimately generate a numerical value representing the order's risk level: "Risk Assessment Result = Base Risk Value - (First Timeliness Score × Warehouse Score Weight + Second Timeliness Score × Logistics Company Score Weight)." This solution provides a standardized and repeatable method for converting the abstract concept of data timeliness into concrete risk values, thus informing subsequent order adjustment decisions. This quantitative assessment helps the system identify orders that may face fulfillment issues due to insufficiently fresh underlying data, improving the accuracy of scheduling decisions.

[0127] In some optional embodiments, the risk assessment results can be divided into warehouse risk assessment results and logistics company risk assessment results, where the warehouse risk assessment result = basic risk value - first time limit score × warehouse score weight, and the logistics company risk assessment result = basic risk value - second time limit score × logistics company score weight.

[0128] Specifically, the scheduling module 3 adjusts the allocated orders based on the risk assessment results. When the order adjustment results are obtained, the scheduling module 3 executes:

[0129] Determine whether the risk assessment result of each allocated order is greater than or equal to the preset risk threshold;

[0130] If so, for the assigned orders whose risk assessment results are greater than or equal to the preset risk threshold, based on inventory data and transportation capacity information, the warehouse with the highest first timeliness score or the logistics company with the highest second timeliness score is selected from each warehouse or logistics company as the adjusted warehouse or logistics company, and the adjusted order information is obtained;

[0131] If not, for the allocated orders whose risk assessment results are less than the preset risk threshold, the original warehouse and logistics company are selected as the adjusted warehouse or logistics company to obtain the adjusted order information;

[0132] Based on the adjusted order information, an order adjustment result is determined.

[0133] During execution, Scheduling Module 3 obtains the risk assessment results for each assigned order and compares them with a preset risk threshold (which can be set based on actual needs). If the order's risk assessment result is equal to or higher than the risk threshold, the current assignment carries a high risk and may result in fulfillment failure due to outdated data. At this point, the original assignment is no longer trusted. Instead, based on the latest inventory data and capacity information, warehouses with inventory quantities greater than the ordered quantity and logistics companies with available capacity greater than the required capacity are selected. Among these selected warehouses or logistics companies, those with the highest timeliness scores are prioritized, as high timeliness scores indicate the most up-to-date and reliable data. By reallocating resources with the freshest data, fulfillment risks caused by outdated data are reduced. If the order's risk assessment result is lower than the risk threshold, the current assignment's risk is within an acceptable range and the data freshness meets the requirements. The original warehouse and logistics company assignments are maintained without adjustment to maintain scheduling stability. After this judgment and processing, each assigned order receives updated assignment information (which may be the original assignment or a newly selected resource). This updated order information is aggregated to form the order adjustment results. By introducing risk thresholds and a resource selection mechanism based on timeliness scores, this method provides a way to intelligently adjust assigned orders, improving the accuracy and robustness of order fulfillment scheduling and reducing fulfillment anomalies caused by data delays.

[0134] In some optional embodiments, when the risk assessment results are divided into warehouse risk assessment results and logistics company risk assessment results, the preset risk thresholds can be divided into preset warehouse assessment risk thresholds and preset logistics company assessment risk thresholds (the preset warehouse assessment risk thresholds and the preset logistics company assessment risk thresholds can be set according to actual needs), and the warehouse risk assessment results are compared with the preset warehouse assessment risk thresholds, and the logistics company risk assessment results are compared with the preset logistics company assessment risk thresholds. When the warehouse risk assessment result is greater than or equal to the preset warehouse assessment risk threshold, the original warehouse is adjusted, and the warehouse with the highest timeliness score is preferentially selected as the adjusted warehouse. When the logistics company risk assessment result is greater than or equal to the preset logistics company assessment risk threshold, the original logistics company is adjusted, and the logistics company with the highest timeliness score is preferentially selected as the adjusted logistics company.

[0135] Specifically, inventory data includes the inventory quantity of goods; transportation capacity information includes available logistics transportation capacity; and order information includes the order quantity and the logistics transportation capacity required for the goods. When the scheduling module 3 selects the warehouse or logistics company with the highest timeliness score from among the warehouses or logistics companies as the adjusted warehouse or logistics company and obtains the adjusted order information, it executes:

[0136] Based on inventory data and transportation capacity information, warehouses with more inventory than ordered are identified as backup warehouses, and logistics companies with more spare capacity than required are identified as backup logistics companies. This results in a list of backup warehouses and backup logistics companies to which orders have been allocated.

[0137] The backup warehouse with the highest first timeliness score or the backup logistics company with the highest second timeliness score is selected from the backup warehouse list or the backup logistics list as the adjusted warehouse or logistics company to obtain adjusted order information.

[0138] When scheduling module 3 executes and an assigned order is assessed as high-risk and requires adjustment, it obtains the required quantity and logistics capacity for the order, traverses the inventory data of all warehouses, identifies which warehouses have the inventory of the corresponding items that can meet the order quantity requirements, and adds these warehouses to the list of backup warehouses. It then traverses the capacity information of all logistics companies, identifies which logistics companies have available capacity that can meet the logistics requirements of the order, and adds these logistics companies to the list of backup logistics companies. After determining the set of backup resources with fulfillment capabilities, warehouses or logistics companies that lack fulfillment capabilities are discontinued, even if their data timeliness scores are high. The backup warehouse list is searched for the warehouse with the highest timeliness score, and the backup logistics company list is searched for the logistics company with the highest timeliness score. These warehouses and logistics companies are designated as the adjusted fulfillment parties for the high-risk order, and the order information is updated. This approach solves the problem of allocating resources without actual fulfillment capabilities based solely on data freshness, ensuring that the adjusted allocation plan is feasible and based on the latest data.

[0139] When executing, Scheduling Module 3, based on the latest inventory data and transportation capacity information, retrieves warehouses with inventory quantities greater than the ordered quantity and logistics companies with available transportation capacity greater than the required transportation capacity. It then queries the first timeliness scores of the retrieved warehouses and the second timeliness scores of the retrieved logistics companies, prioritizing the warehouse with the highest first timeliness score and the logistics company with the highest second timeliness score as the fulfillment resource for the new order. This allocation strategy based on the highest timeliness score ensures that new orders can be fulfilled while utilizing the latest and most reliable inventory and transportation capacity data.

[0140] Adjustments to assigned orders (including maintaining the original assignment or reallocating them) are integrated with the allocations to new orders to generate a final scheduling plan encompassing all orders. This approach distinguishes between assigned and new orders, managing risk and adjusting assigned orders while optimizing the allocation of new orders based on the latest data. This improves the accuracy and robustness of overall scheduling decisions and reduces errors and manual intervention caused by data timeliness.

[0141] From the above, it can be seen that the multi-platform order collaborative fulfillment automated scheduling system obtains the receiving timestamp when receiving the inventory data of each warehouse and the transportation capacity information of each logistics company, and calculates the timeliness score of each inventory data and each transportation capacity information in real time based on the receiving timestamp, which are recorded as the first timeliness score and the second timeliness score respectively. According to the first timeliness score and the second timeliness score, the allocated orders are adjusted and the new orders of each platform are allocated to obtain the order scheduling result; thus, the allocated orders are adjusted and the new orders of each platform are allocated through the timeliness score of the inventory data and transportation capacity information determined by the receiving timestamp when the information is received, and the order scheduling result is obtained, which solves the problem that the existing order fulfillment scheduling system is difficult to ensure the accuracy of order fulfillment decisions in complex and changing scenarios due to different degrees of data source delay. Order adjustment and new order allocation based on the timeliness score ensure that the scheduling system can give priority to using more reliable and closer to real-time data for decision-making, thereby improving the accuracy and efficiency of order fulfillment.

[0142] In the embodiments provided in this application, it should be understood that the disclosed systems and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. There may be other division methods in actual implementation. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed may be through some communication interface, indirect coupling or communication connection of devices or units, which may be electrical, mechanical or other forms.

[0143] In addition, the units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, and may be located in one place or distributed across multiple network units. Some or all of these units may be selected based on actual needs to achieve the purpose of the solution of this embodiment.

[0144] Furthermore, the functional modules in each embodiment of the present application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0145] In this document, relational terms such as first and second, etc. are used merely to distinguish one entity or operation from another entity or operation, but do not necessarily require or imply any actual relationship or order between these entities or operations.

[0146] The above description is merely an embodiment of the present application and is not intended to limit the scope of protection of the present application. For those skilled in the art, various modifications and variations of the present application are possible. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the scope of protection of the present application.

Claims

1. A multi-platform order collaborative fulfillment automated scheduling method for scheduling multi-platform order collaborative fulfillment, characterized in that: Including steps: Obtain the timestamp of receiving inventory data from each warehouse and transportation capacity information from each logistics company; Based on the reception timestamp, calculating in real time a timeliness score of each of the inventory data and each of the transport capacity information, and recording them as a first timeliness score and a second timeliness score respectively; Adjusting the allocated orders and allocating new orders to each platform based on the first timeliness score and the second timeliness score to obtain an order scheduling result; Based on the receiving timestamp, a timeliness score of each of the inventory data and each of the transport capacity information is calculated in real time, including: Calculate the time difference between the current moment and the received timestamp; According to the inventory data and the transportation capacity information, a corresponding inventory weight and a corresponding transportation capacity weight are set; Based on the time difference, combined with the inventory weight and the transportation capacity weight, a timeliness score for each piece of inventory data and each piece of transportation capacity information is calculated; wherein, the greater the time difference, the lower the timeliness score; the higher the inventory weight and the transportation capacity weight, the higher the timeliness score; and the influence of the time difference on the timeliness score is far greater than the influence of the inventory weight and the transportation capacity weight on the timeliness score; The calculation formula for the time limit score is: ； in, It is a time-limited score, i.e., a first time-limited score or a second time-limited score; The highest possible score is the preset score. When calculating the first timeliness score, it is the highest possible score for inventory timeliness. When calculating the second timeliness score, it is the highest possible score for logistics timeliness. It is the time decay rate, which can be set according to the historical inventory change rate or the historical logistics capacity change rate. When calculating the first time-limited score, it is the inventory time decay rate, and when calculating the second time-limited score, it is the logistics time decay rate. T is the time difference; is the weight gain coefficient. When calculating the first time limit score, it is the inventory weight gain coefficient. When calculating the second time limit score, it is the logistics weight gain coefficient. The weight gain coefficient is a very small decimal. W is the weight, that is, the inventory weight or the capacity weight. When calculating the first time limit score, it is the inventory weight. When calculating the second time limit score, it is the capacity weight.

2. The multi-platform order collaborative fulfillment automated scheduling method according to claim 1 is characterized in that: Get the timestamp of receiving inventory data from each warehouse and capacity information from each logistics company, including: Obtain the initial timestamp of inventory data from each warehouse and capacity information from each logistics company upon receipt; Determine respectively whether the update time of the source data is recorded in each of the inventory data and each of the capacity information; if so, determine the update time as the receiving timestamp of the corresponding inventory data or the corresponding capacity information; if not, determine the preliminary timestamp as the receiving timestamp of the corresponding inventory data or the corresponding capacity information.

3. The multi-platform order collaborative fulfillment automated scheduling method according to claim 1 is characterized in that: After calculating the time difference between the current moment and the received timestamp, the method further includes: When the time difference is greater than or equal to a preset maximum time difference threshold, an update reminder is sent to the corresponding warehouse or logistics company to update the inventory data or transportation capacity information in a timely manner.

4. The multi-platform order collaborative fulfillment automated scheduling method according to claim 1, characterized in that: According to the first timeliness score and the second timeliness score, the allocated orders are adjusted and the new orders on each platform are allocated to obtain an order scheduling result, including: Calculating a risk assessment result of the assigned order based on the first timeliness score and the second timeliness score; Based on the risk assessment result, the allocated order is adjusted to obtain an order adjustment result; selecting, based on the inventory data and the transportation capacity information, the warehouse with the highest first timeliness score and the logistics company with the highest second timeliness score, and allocating new orders to the respective platforms to obtain a new order allocation result; The order adjustment results and the new order allocation results are summarized to obtain an order scheduling result.

5. The multi-platform order collaborative fulfillment automated scheduling method according to claim 4 is characterized in that: A risk assessment result of the allocated order is calculated based on the first timeliness score and the second timeliness score, including: Get order information of assigned orders; Determine a first timeliness score of a corresponding warehouse and a second timeliness score of a corresponding logistics company in order information of an assigned order; The risk assessment result of the allocated order is calculated by using a preset risk scoring rule and based on the first timeliness score of the corresponding warehouse and the second timeliness score of the corresponding logistics company in the order information of the allocated order.

6. The multi-platform order collaborative fulfillment automated scheduling method according to claim 5 is characterized in that: The risk assessment result of the allocated order is calculated based on the first timeliness score of the warehouse and the second timeliness score of the logistics company in the order information of the allocated order according to the preset risk scoring rules, including: Obtaining pre-constructed risk assessment parameters, warehouse scoring weights, and logistics company scoring weights based on the preset risk scoring rules; The risk assessment result of the allocated order is calculated based on the first timeliness score of the corresponding warehouse and the second timeliness score of the corresponding logistics company in the order information of the allocated order, combined with the risk assessment parameters, the warehouse score weight and the logistics company score weight.

7. The multi-platform order collaborative fulfillment automated scheduling method according to claim 4 is characterized in that: Based on the risk assessment results, the allocated orders are adjusted to obtain order adjustment results, including: Determining whether the risk assessment result of each of the allocated orders is greater than or equal to a preset risk threshold; If so, for the allocated order whose risk assessment result is greater than or equal to the preset risk threshold, based on the inventory data and the transportation capacity information, select the warehouse with the highest first timeliness score or the logistics company with the highest second timeliness score from among the warehouses or logistics companies as the adjusted warehouse or logistics company to obtain adjusted order information; If not, for the allocated order whose risk assessment result is less than the preset risk threshold, select the original warehouse and logistics company as the adjusted warehouse or logistics company to obtain adjusted order information; Based on the adjusted order information, an order adjustment result is determined.

8. The multi-platform order collaborative fulfillment automated scheduling method according to claim 7 is characterized in that: The inventory data includes the inventory quantity of goods; the transportation capacity information includes the available logistics transportation capacity; the order information includes the order quantity of goods and the logistics transportation capacity required for the goods; the warehouse or logistics company with the highest timeliness score is selected from each of the warehouses or logistics companies as the adjusted warehouse or logistics company, and the adjusted order information is obtained, including: Based on the inventory data and the transportation capacity information, a warehouse whose inventory quantity is greater than the order quantity is determined as a backup warehouse, and a logistics company whose spare logistics capacity is greater than the required logistics capacity is determined as a backup logistics company, and a list of backup warehouses and backup logistics companies to which orders have been allocated is obtained; The standby warehouse with the highest first timeliness score or the standby logistics company with the highest second timeliness score is selected from the standby warehouse list or the standby logistics list as the adjusted warehouse or logistics company to obtain adjusted order information.

9. A multi-platform order collaborative fulfillment automated scheduling system for scheduling multi-platform order collaborative fulfillment, characterized by: include: The acquisition module is used to obtain the receipt timestamp of the inventory data of each warehouse and the transportation capacity information of each logistics company; A calculation module, configured to calculate, in real time, a timeliness score of each of the inventory data and each of the transport capacity information based on the reception timestamp, and record the scores as a first timeliness score and a second timeliness score, respectively; a scheduling module, configured to adjust the allocated orders and allocate new orders to each platform based on the first timeliness score and the second timeliness score, thereby obtaining an order scheduling result; A calculation module, configured to calculate, in real time, a timeliness score for each of the inventory data and each of the transport capacity information based on the reception timestamp, including: Calculate the time difference between the current moment and the received timestamp; According to the inventory data and the transportation capacity information, a corresponding inventory weight and a corresponding transportation capacity weight are set; Based on the time difference, combined with the inventory weight and the transportation capacity weight, a timeliness score for each piece of inventory data and each piece of transportation capacity information is calculated; wherein, the greater the time difference, the lower the timeliness score; the higher the inventory weight and the transportation capacity weight, the higher the timeliness score; and the influence of the time difference on the timeliness score is far greater than the influence of the inventory weight and the transportation capacity weight on the timeliness score; The calculation formula for the time limit score is: ； in, It is a time-limited score, i.e., a first time-limited score or a second time-limited score; The highest possible score is the preset score. When calculating the first timeliness score, it is the highest possible score for inventory timeliness. When calculating the second timeliness score, it is the highest possible score for logistics timeliness. It is the time decay rate, which can be set according to the historical inventory change rate or the historical logistics capacity change rate. When calculating the first time-limited score, it is the inventory time decay rate, and when calculating the second time-limited score, it is the logistics time decay rate. T is the time difference; is the weight gain coefficient. When calculating the first time limit score, it is the inventory weight gain coefficient. When calculating the second time limit score, it is the logistics weight gain coefficient. The weight gain coefficient is a very small decimal. W is the weight, that is, the inventory weight or the capacity weight. When calculating the first time limit score, it is the inventory weight. When calculating the second time limit score, it is the capacity weight.

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