An intelligent retail order dynamic scheduling method based on multi-source data fusion

Abstract

The present invention discloses an intelligent retail order dynamic scheduling method based on multi-source data fusion, which relates to the technical field of order dynamic scheduling. The present invention determines the delivery location of the target order by monitoring the change of the order location information during the order transportation process, and analyzes multiple optional paths and time limits based on a preset path model; First, identify whether the order is a time limit order and calculate the transportable time limit; Then, compare the time limit required for the optimal path with the transportable time limit of the order to determine whether it is an abnormal order; If it is an abnormal order, then determine whether the transport time limit can be extended through protective measures, and finally decide whether the delivery can be completed according to the optimal path; The refined management of the present invention not only effectively prevents the delivery risks caused by external interference, but also provides an intelligent, flexible and highly adaptable scheduling solution for logistics enterprises, thereby improving the economic benefits while greatly improving the customer experience and satisfaction.

Description

Technical Field

[0001] The present invention relates to the technical field of order dynamic scheduling, and particularly to an intelligent retail order dynamic scheduling method based on multi-source data fusion. Background Art

[0002] With the rapid development of the e-commerce and retail industries, the surge in the number of orders and the continuous improvement of consumers' requirements for delivery timeliness and service quality have put forward higher requirements for the intelligent and refined management of the logistics distribution system.

[0003] In the existing retail order distribution system, once an order is generated and enters the distribution process, the order information is often solidified. If a user finds that the address is filled in incorrectly during the distribution process, it is often difficult to change it in a timely manner. Due to the lack of real-time dynamic monitoring of the order status and a flexible scheduling mechanism in the traditional system, it is very difficult to effectively modify an order with an incorrect address midway. This not only makes it impossible to reconfirm the correct delivery address before distribution, but also causes the subsequent distribution links to be unable to be executed according to the original plan due to the incorrect address, thus directly affecting the timeliness and accuracy of the distribution.

[0004] In addition, even if some systems allow address modification under certain conditions, since the order has passed through multiple transfer nodes or is in transit during transportation, there may be a large deviation between the modified new address and the current geographical location of the order. The existing scheduling technology is difficult to integrate real-time traffic information and transportation status in a timely manner to dynamically optimize and adjust the route. This situation of lacking an effective response mechanism often causes further delays in the transportation process of the order, increases the complexity of logistics scheduling and the risk of distribution failure, thus seriously affecting the user experience and the overall distribution efficiency. Summary of the Invention

[0005] Aiming at the deficiencies of the prior art, the present invention provides an intelligent retail order dynamic scheduling method based on multi-source data fusion, which solves the problems in the background art.

[0006] To achieve the above objectives, the present invention is realized through the following technical solutions: An intelligent retail order dynamic scheduling method based on multi-source data fusion, including:

[0007] Step 1: Monitor the position information of the order during the transportation process, obtain the changed content of the target order position information, obtain the new delivery position of the target order, and determine the specific position where the current target order is located by judging whether the current target order is in the transportation state;

[0008] Step 2: Obtain the specific location of the current target order, and at the same time obtain the new delivery location of the target order. Based on the specific location and the new delivery location, determine several alternative routes through a preset path model; combine the analysis of the delivery time limits required for several alternative routes to determine the optimal route and the transportation duration required for the optimal route;

[0009] Step 3: Obtain the specific item information of the current target order. Based on the specific item information, identify whether the current target order is a time limit order, and determine the transportable time limit of the current target order according to the identification result;

[0010] Step 4: Obtain the transportation duration required for the optimal route and the transportable time limit of the current target order, and judge their magnitudes. If the transportable time limit of the current target order is less than or equal to the transportation duration required for the optimal route, mark the current target order as an abnormal order;

[0011] Step 5: Conduct a secondary judgment on the abnormal order to identify whether protective measures can be taken during the transportation of the optimal route to increase the transportable time limit of the current target order. If it is identified that the transportable time limit of the current target order can be increased, determine the increased transportable time limit of the current target order. Based on the comparison between the optimal transportable time limit and the transportation duration required for the optimal route, determine the target order that can be delivered and transported along the optimal route.

[0012] As a further solution of the present invention: In the above Step 1, the specific method for determining the specific location where the current target order is located by judging whether the current target order is in the transportation state is as follows:

[0013] If the current target order is in the transportation state, confirm the next station that the current target order arrives at, and determine the location of this station as the specific location where the current target order is located;

[0014] If the current target order is not in the transportation state, confirm the station where the current target order is located, and determine the location of this station as the specific location where the current target order is located.

[0015] As a further solution of the present invention: In the above Step 2, the specific method for determining the optimal route by combining the analysis of the delivery time limits required for several alternative routes is as follows:

[0016] AS1: Analyze each alternative route as the target route in turn as follows:

[0017] AS2: Confirm the specific location of the target order as the station where the target order is located, and at the same time determine the current time point as Td;

[0018] AS3: Then obtain the scanning, loading and departure time point T1 of the station where it is located in the target route;

[0019] Simultaneously determine the distance between the current station and the next target station and the average speed of the transport vehicle per unit time, denoted as L1 and V1 respectively;

[0020] According to the ratio of the two: t1 = L1 / V1; obtain the time t1 taken for the target order to reach the next target station from the current station. Combine with the time point T1 to obtain the specific time point when the target order reaches the next target station, denoted as T2, T2 = T1 + t1;

[0021] AS4: Whenever the target order reaches the next target station, define this target station as the current station of the target order, and then repeat the above step AS3 to determine the specific time point when the target order reaches the next target station;

[0022] And so on until the time point for determining the new delivery location is obtained, denoted as Tn;

[0023] AS5: Obtain the current time point Td and the time point Tn, calculate the interval duration Ts between the two: Ts = Tn - Td; use the interval duration Ts as the delivery time limit required for transporting the target order on the target path.

[0024] As a further solution of the present invention: after the step AS5, it further includes:

[0025] AS6: Repeat steps AS1 - AS3 to obtain the delivery time limits required for transporting the target order on several alternative paths, and determine the alternative path with the minimum delivery time limit as the optimal path.

[0026] As a further solution of the present invention: in the third step, the specific method for identifying whether the current target order is a time - limit order based on the specific item information and determining the transportable time limit of the current target order according to the identification result is:

[0027] When it is identified that the current target order is a time - limit order: calculate the transportable time limit through the following formula:

[0028]

[0029] Where: is the transportable time limit of the current order; is the specified delivery time limit of the time - limit order; is the time between receiving the order and starting transportation; is the actual time already taken for transportation; is the time the item stays during warehousing, sorting or transfer; is a preset value representing the delay time due to unforeseen factors;

[0030] When it is recognized that the current target order is not a time-limited order: uniformly set the available transportation time limit to .

[0031] As a further solution of the present invention: in the fourth step, it further includes:

[0032] If the available transportation time limit of the current target order is greater than the transportation time required for the optimal path, then the current target order is delivered and transported through the optimal path.

[0033] As a further solution of the present invention: in the fifth step, to determine the available transportation time limit after the increase of the current target order, and based on the comparison between the best available transportation time limit and the transportation time required for the optimal path, the specific content of determining the target order that can be delivered and transported through the optimal path includes:

[0034] BS1: Identify whether the stations on the optimal path have protection measures:

[0035] If none of the stations on the optimal path have protection measures, then determine the abnormal order as a non-conforming order;

[0036] If the stations on the optimal path have protection measures, determine the number of stations with protection measures;

[0037] BS2: Obtain the increased available transportation time limit for the current target order to take one protection measure, and based on the number of stations with protection measures, obtain the total increased available transportation time limit during the transportation of the current target order on the optimal path, denoted as Tz;

[0038] BS3: Then calculate the sum Ta of Tz and , Ta = Tz + :

[0039] If Ta is greater than or equal to the transportation time required for the optimal path, then determine that the current target order can be delivered and transported through the optimal path;

[0040] As a further solution of the present invention: in the step BS3, it further includes:

[0041] If Ta is less than the transportation time required for the optimal path, then determine that the current target order cannot be delivered and transported through the optimal path.

[0042] The present invention provides an intelligent retail order dynamic scheduling method based on multi-source data fusion. Compared with the prior art, it has the following beneficial effects:

[0043] The present invention realizes the full-process intelligent scheduling from order collection, transportation status judgment, route optimization to exception handling by integrating real-time logistics monitoring, advanced route planning and precise time limit management; by generating multiple optional routes and selecting the optimal solution based on dynamically calculating the transportation duration, the risk of delay is effectively reduced in a complex transportation environment, and the logistics efficiency and resource utilization rate are improved.

[0044] Meanwhile, the present invention distinguishes time-limited orders from ordinary orders by deeply identifying order attributes, and combines protection measures to conduct secondary evaluation and remedy for abnormal orders, further enhancing the security and time limit guarantee of orders during transportation; this refined management not only effectively prevents the distribution risks caused by external interference, but also provides a smart, flexible and highly adaptable scheduling solution for logistics enterprises, thus improving economic benefits while significantly enhancing the customer experience and satisfaction. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] The present invention will be further described below with reference to the accompanying drawings.

[0046] Figure 1 is the step flowchart of an intelligent retail order dynamic scheduling method based on multi-source data fusion according to the present invention;

[0047] Figure 2 is the logical structure diagram of an intelligent retail order dynamic scheduling method based on multi-source data fusion according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0048] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0049] Embodiment 1

[0050] Please refer to Figure 1 - Figure 2 , the present invention provides an intelligent retail order dynamic scheduling method based on multi-source data fusion, including;

[0051] Step 1: Monitor the order location information during the order transportation process, obtain the changed content of the target order location information, obtain the new delivery location of the target order, and determine the specific location where the current target order is located by judging whether the current target order is in the transportation state;

[0052] The specific method for determining the specific location where the current target order is located by judging whether the current target order is in the transportation state is:

[0053] When the current target order is in the transportation state, confirm the next station that the current target order arrives at, and determine the location of this station as the specific location where the current target order is located;

[0054] When the current target order is not in the transportation state, confirm the station where the current target order is located, and determine the location of this station as the specific location where the current target order is located;

[0055] It should be noted that the logistics monitoring of the prior art can judge in real time whether the current order is in transportation or at the location of a certain station. Specifically, generally, whenever a package passes through an important transportation node (such as a warehouse, a transfer center, a distribution station, etc.), the staff or the automated system will scan the bar code or QR code on the package; these scan information will be updated to the logistics system in real time to mark the specific location where the package is located; consumers can view the latest status of the package through the logistics information on platforms such as Taobao, including whether it is in transit or has arrived at a certain station;

[0056] This step can accurately capture the state change of the target order during the transportation process through the real-time monitoring of the order location information, confirm its location or the next destination station according to whether the order is in the transportation state, so as to update the specific location information of the order in real time; this method not only ensures that the logistics system can immediately reflect the current state of the package, but also provides reliable basic data for subsequent path planning and scheduling decisions, reduces the risk of transportation arrangement errors caused by delayed or inaccurate location information, and improves the response speed and accuracy of the entire logistics monitoring system at the same time;

[0057] Step 2: Obtain the specific location of the current target order, and at the same time obtain the new delivery location of the target order. Based on the specific location and the new delivery location, determine several optional paths through a preset path model; combine the analysis of the delivery time required for several optional paths to determine the optimal path and the required time limit (transportation duration) of the optimal path.

[0058] Specifically, when obtaining the specific location of the current target order, the target order is already at the specific location;

[0059] It should be noted that the preset path model is constructed by the prior art and will not be elaborated in detail here. Through the early model construction by the staff, it mainly includes the shortest path algorithms based on graph theory (such as Dijkstra algorithm and A* algorithm), real-time traffic information and dynamic path update, machine learning methods, and multi-objective optimization algorithms. The graph theory algorithm calculates the shortest path by modeling the road network as a graph, and the real-time traffic information dynamically adjusts the path selection according to the current road conditions. The machine learning method learns from historical data to predict the optimal path, while the multi-objective optimization algorithm optimizes the path selection considering various factors (such as time, cost, etc.). These technologies work together to calculate several optional paths from the current target location to the new delivery location.

[0060] The specific method for determining the optimal route by combining the analysis of the delivery time limit required for several optional paths is as follows:

[0061] AS1: Analyze each optional path as the target path in turn and perform the following steps:

[0062] AS2: Confirm the specific location of the target order as the station where the target order is located, and at the same time determine the current time point as Td.

[0063] AS3: Then obtain the time point T1 when the order is scanned, loaded, and dispatched at the station on the target path.

[0064] At the same time, determine the distance between the station and the next target station and the average speed of the transport vehicle per unit time, denoted as L1 and V1 respectively.

[0065] According to the ratio of the two: t1 = L1 / V1; obtain the time t1 spent by the target order from the current station to the next target station, and combine it with the time point T1 to obtain the specific time point when the target order arrives at the next target station, denoted as T2, T2 = T1 + t1.

[0066] It should be noted that in the actual delivery process, in the real scenario, the time points for scanning, loading, and dispatching at each station are pre-determined and strictly regulated. This means that each link in the transportation process has a clear time arrangement. From the goods scanning, loading to the vehicle dispatching, it follows the pre-determined schedule to ensure the efficiency and punctuality of transportation. These regulated time points are usually based on the optimization of operation requirements, traffic conditions, and logistics plans to ensure the smooth connection and timely progress of each link, thereby avoiding unnecessary delays and waste and ensuring the efficient operation of the delivery process.

[0067] AS4: Whenever the target order arrives at the next target station, define this target station as the station where the target order is located, and then repeat the above step AS3 to determine the specific time point when the target order arrives at the next target station.

[0068] And so on until the time point when the new delivery location is determined, denoted as Tn;

[0069] AS5: Obtain the current time point Td and the time point Tn, and calculate the interval duration Ts between them: Ts = Tn - Td; Use the interval duration Ts as the delivery time limit required for the transportation target order of the target path;

[0070] AS6: Repeat steps AS1 - AS3 to obtain the delivery time limits required for the transportation target orders of several alternative paths, and determine the alternative path with the minimum delivery time limit among them as the optimal path;

[0071] By integrating the specific location of the current order and the new delivery location, and using the pre - constructed path model, several feasible paths are generated, and a comprehensive analysis is carried out based on the transportation time limits of each path. Finally, the optimal route is selected; This not only makes the path selection more scientific and reasonable, can dynamically adjust the route in a complex and changeable transportation environment, but also significantly improves the transportation efficiency and timeliness, ensures that the order is delivered in the shortest time in the optimal state, thereby optimizing the allocation of the overall logistics resources;

[0072] Step three: Obtain the specific item information of the current target order, identify whether the current target order is a time - limit order based on the specific item information, and determine the transportable time limit of the current target order according to the identification result;

[0073] The specific method for identifying whether the current target order is a time - limit order based on the specific item information and determining the transportable time limit of the current target order according to the identification result is as follows:

[0074] When it is identified that the current target order is a time - limit order: Calculate the transportable time limit through the following formula:

[0075]

[0076] Where: is the transportable time limit of the current order; is the specified delivery time limit of the time - limit order; is the time between receiving the order and starting transportation (such as preparation time, inventory confirmation time, etc.); is the actual time already required for transportation; is the time the item stays during warehousing, sorting or transfer; is a preset value, representing the delay time caused by unforeseen factors (such as weather, traffic accidents, etc.);

[0077] When it is identified that the current target order is not a time - limit order: Uniformly set the transportable time limit to ;

[0078] It should be noted that the scope of time-limited orders is specifically set by professional staff. In this embodiment, time-limited orders include fresh-keeping products, urgent orders, perishable foods, medicines, flowers, frozen goods, emergency medical supplies, etc. Such orders need to be delivered within a specified time;

[0079] In this step, by parsing the item information in the order in detail, it is judged whether the order belongs to a time-limited order and the transportable time limit is calculated according to its specific delivery requirements. Such identification and time limit calculation not only achieve refined management of different types of orders, but also can set a reasonable transport time window in advance according to the attributes of the order itself, so as to ensure that the time limit requirements of various orders can be fully met during the subsequent transportation process, reduce the possibility of delivery delays, and improve customer experience and satisfaction at the same time;

[0080] Step Four: Obtain the transport duration required for the optimal path and the transportable time limit of the current target order, judge the size of the two, and determine the abnormal order according to the judgment result;

[0081] The specific method of obtaining the transport duration required for the optimal path and the transportable time limit of the current target order, judging the size of the two, and determining the abnormal order according to the judgment result is as follows:

[0082] If the transportable time limit of the current target order is less than or equal to the transport duration required for the optimal path, mark the current target order as an abnormal order:

[0083] If the transportable time limit of the current target order is greater than the transport duration required for the optimal path, deliver and transport the current target order through the optimal path;

[0084] This step can timely identify abnormal orders with time limit risks by comparing the transport duration required for the optimal path with the preset transportable time limit of the order. It not only constructs a key risk warning mechanism, enabling the logistics system to discover orders that may not be able to be delivered on time due to insufficient time limit before delivery, but also provides a decision-making basis for whether additional protection measures need to be taken subsequently, thus avoiding potential problems caused by time limit mismatch during transportation and improving the safety and reliability of the overall transportation process;

[0085] Step Five: Make a secondary judgment on the abnormal order to identify whether protective measures can be taken during the transportation process of the optimal path to increase the transportable time limit of the current target order. If it is identified that the transportable time limit of the current target order can be increased, determine the increased transportable time limit of the current target order, and judge whether the current target order can be delivered and transported through the optimal path based on the comparison between the best transportable time limit and the transport duration required for the optimal path;

[0086] It should be noted that, according to the judgment of whether each site has protective measures, it is identified whether the transportable time limit of the current target order can be increased by taking protective measures during transportation. The protective measures for the above-mentioned abnormal orders include temperature control, humidity control, packaging protection, real-time monitoring, etc., to ensure that the items are delivered to customers in the best condition within the specified time; through these additional protective measures, not only can the transportable time limit be extended, but also the risks caused by external factors can be reduced during transportation;

[0087] For the abnormal orders determined in step 4, this step uses secondary judgment to identify whether the transportable time limit of the order can be increased through temperature control, humidity adjustment, packaging protection, real-time monitoring and other measures during transportation, and calculates whether the total time limit after the increase meets the time required for the optimal path; such protection measures not only provide a remedy for orders with time pressure, but also strive for more delivery time under the premise of ensuring transportation safety, thereby greatly improving the order delivery success rate and the flexibility of the transportation process, and also providing strong support for the logistics system to achieve efficient and accurate scheduling management;

[0088] Embodiment 2

[0089] In the specific implementation process of this embodiment, based on the first embodiment, and different from the first embodiment, the content of step five is described in detail:

[0090] The second judgment of the abnormal order is performed to identify whether protective measures can be taken during the optimal path transportation process to increase the transportable time limit of the current target order. If it is identified that the transportable time limit of the current target order can be increased, the increased transportable time limit of the current target order is determined. Based on the comparison between the increased transportable time limit and the time limit transportation length required for the optimal path, the specific contents of determining whether the current target order is transported by the optimal path include:

[0091] BS1: Identify whether the site on the optimal path has protection measures:

[0092] If all stations on the optimal path have no protection measures, the abnormal order will be determined as an unqualified order;

[0093] If the sites on the optimal path have protection measures, determine the number of sites with protection measures;

[0094] BS2: Obtain the increased transportable time limit of the current target order when a protection measure is taken, and according to the number of stations of the protection measure, obtain the total increased transportable time limit of the current target order during the optimal path transportation process, recorded as Tz;

[0095] Specifically, in the process of obtaining the increased transportable time limit for taking a protection measure for the current target order, according to the specific category in the time limit order, determine the increased transportable time limit for the specific protection measure. Generally, the staff estimates in advance to determine the increased transportable time limit. For different target orders, the determined increased transportable time limit for the specific protection measure is different, and it is specifically determined by professional staff;

[0096] BS3: Then calculate the sum Ta of Tz and Ta = Tz + :

[0097] If Ta is greater than or equal to the transport time required for the optimal path, it is determined that the current target order can be transported by the optimal path;

[0098] If Ta is less than the transport time required for the optimal path, it is determined that the current target order cannot be transported by the optimal path;

[0099] It should be noted that when it is determined that the current target order cannot be transported by the optimal path, subsequent processing of communication and compensation on the platform is carried out.

[0100] Embodiment 3

[0101] In the specific implementation process of this embodiment, it includes all the implementation processes of the above two groups of embodiments.

[0102] Some data in the above formula are all numerically calculated by removing their dimensions, and the content not described in detail in this specification belongs to the prior art well-known to those skilled in the art.

[0103] The above embodiments are only used to illustrate the technical method of the present invention and not to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical method of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical method of the present invention.

Claims

1. An intelligent retail order dynamic scheduling method based on multi-source data fusion, characterized in that, Including: Step 1: Monitor the location information of the order during the order transportation process, obtain the changed content of the target order location information, obtain the new delivery location of the target order, and determine the specific location where the current target order is located by judging whether the current target order is in the transportation state; Step 2: Obtain the specific location of the current target order, and at the same time obtain the new delivery location of the target order. Based on the specific location and the new delivery location, determine several alternative paths through a preset path model; combine the analysis of the delivery required time limit for several alternative paths to determine the optimal path and the required time limit for the optimal path (transportation duration); Step 3: Obtain the specific item information of the current target order, identify whether the current target order is a time limit order based on the specific item information, and determine the transportable time limit of the current target order according to the identification result; The specific method is: When it is identified that the current target order is a time limit order: Calculate the transportable time limit through the following formula: Wherein: is the transportable time limit for the current order; is the specified delivery time limit for the time limit order; is the time between receiving the order and starting transportation; is the actual time required for transportation; is the time the item stays during warehousing, sorting or transfer; is a preset value representing the delay time due to unforeseeable factors; When it is recognized that the current target order is not a time-limited order: uniformly set the transportable time limit to ; Step 4: Obtain the required time limit for the optimal path (transportation duration) and the transportable time limit of the current target order, and judge the size of the two. If the transportable time limit of the current target order is less than or equal to the required time limit for the optimal path (transportation duration), mark the current target order as an abnormal order; Step 5: Perform a secondary judgment on the abnormal order to identify whether protective measures can be taken during the transportation of the optimal path to increase the transportable time limit of the current target order. If it is identified that the transportable time limit of the current target order can be increased, determine the increased transportable time limit of the current target order. Based on the comparison between the best transportable time limit and the required time limit for the optimal path (transportation duration), determine the target order that can be delivered and transported along the optimal path; The specific content includes: BS1: Identify whether the stations on the optimal path have protective measures: If all stations on the optimal path have no protective measures, determine this abnormal order as a non-conforming order; If the stations on the optimal path have protective measures, determine the number of stations with protective measures; BS2: Obtain the increased transportable time limit when the current target order takes a single protective measure, and based on the number of stations with protective measures, obtain the total increased transportable time limit of the current target order during the transportation of the optimal path, denoted as Tz; BS3: Next, calculate the sum Ta of Tz and Ta = Tz + : If Ta is greater than or equal to the required time limit for the optimal path (transportation duration), determine that the current target order can be delivered and transported along the optimal path.

2. The intelligent retail order dynamic scheduling method based on multi-source data fusion according to claim 1, characterized in that In the above Step 1, the specific method for determining the specific location where the current target order is located by judging whether the current target order is in the transportation state is: When the current target order is in the transportation state, confirm the next station that the current target order arrives at, and determine the location of this station as the specific location where the current target order is located; When the current target order is not in the transportation state, confirm the station where the current target order is located, and determine the location of this station as the specific location where the current target order is located.

3. An intelligent retail order dynamic scheduling method based on multi-source data fusion according to claim 2, characterized in that, In the above Step 2, the specific method for combining the analysis of the delivery required time limit for several alternative paths to determine the optimal path and the required time limit for the optimal path (transportation duration) is: AS1: Analyze each alternative path as the target path in turn as follows: AS2: Confirm the specific location of the target order as the station where the target order is located, and at the same time determine the current time point as Td; AS3: Then obtain the scanning, loading, and departure time point T1 of the station where the target order is located in the target path; At the same time, determine the distance between the station where the target order is located and the next target station and the average speed of the transport vehicle per unit time, denoted as L1 and V1 respectively; According to the ratio of the two: t1 = L1 / V1; Obtain the time t1 taken for the target order to reach the next target station from the station where the target order is located. Combine with the time point T1 to obtain the specific time point when the target order reaches the next target station, denoted as T2, T2 = T1 + t1; AS4: Whenever the target order reaches the next target station, define this target station as the station where the target order is located, and then repeat the above step AS3 to determine the specific time point when the target order reaches the next target station; And so on until the time point when the new delivery location is determined, denoted as Tn; AS5: Obtain the current time point Td and the time point Tn, and calculate the interval duration Ts between the two: Ts = Tn - Td; Use the interval duration Ts as the delivery time limit required for transporting the target order on the target path.

4. An intelligent retail order dynamic scheduling method based on multi-source data fusion according to claim 3, characterized in that After the step AS5, it further includes: AS6: Repeat steps AS1 - AS3 to obtain the delivery time limits required for transporting the target order on several alternative paths, and determine the alternative path with the minimum delivery time limit as the optimal path.

5. The intelligent retail order dynamic scheduling method based on multi-source data fusion according to claim 1, characterized in that In the fourth step, it further includes: If the transportable time limit of the current target order is greater than the transport duration required for the optimal path, then transport and deliver the current target order through the optimal path.

6. The intelligent retail order dynamic scheduling method based on multi-source data fusion according to claim 1, characterized in that In the step BS3, it further includes: If Ta is less than the transport duration required for the optimal path, then determine that the current target order cannot be transported and delivered through the optimal path.

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