Object scheduling method for logistics service and related device

Abstract

The embodiment of the present specification provides a logistics service object scheduling method, which obtains service data of a target object; then performs data statistics on the service data through a service evaluation index to obtain a service evaluation parameter; and performs data statistics on the service data through a parameter influence index to obtain an influence factor; then adjusts the service evaluation parameter based on the influence factor to obtain a regional familiarity of the target object to a target region; and further schedules a service process corresponding to the target object according to the regional familiarity. Thus, the logistics service capacity resource dynamic scheduling process is realized. Since the multi-dimensional comprehensive service evaluation index is used to quantify the logistics service execution effect, and the multi-dimensional influence factor is used for quantitative correction, the evaluation accuracy of the service effect of the target object in a specific region is improved, and the service process is scheduled based on the obtained regional familiarity, so that the execution efficiency of the logistics service is improved.

Description

Technical Field

[0001] This specification relates to the field of computer application technology, specifically to object scheduling technology for logistics services within the field of computer application technology, and more specifically to object scheduling methods and related apparatus for logistics services. Background Technology

[0002] With the development of internet technology, logistics services are becoming increasingly common in people's lives. In the execution of logistics services, couriers play an important role as a transportation resource. However, considering the regional differences in logistics services, the utilization rate of different transportation resources varies in different regions. Therefore, in order to improve the efficiency of logistics services, how to allocate different transportation resources to different regions has become a challenge. Summary of the Invention

[0003] This specification provides an object scheduling method and related apparatus for logistics services, thereby improving the execution efficiency of logistics services.

[0004] To achieve the above technical objectives, the embodiments of this specification provide the following technical solutions: Firstly, one embodiment of this specification provides an object scheduling method for logistics services, comprising: Acquire service data of the target object performing logistics services within the target area, wherein the service data is collected based on a statistical time period; The service evaluation indicators and parameter impact indicators associated with the logistics service are determined, and the service data is statistically analyzed using the service evaluation indicators to obtain service evaluation parameters. The service evaluation indicators are configured based on at least one of the following: task execution items, regional distribution items, and timeliness statistics items. The service data is statistically analyzed using the parameter influence indicators to obtain the influence factors. The parameter influence indicators include at least one of the following: time decay factor, customer evaluation factor, and regional complexity factor. The service evaluation parameters are adjusted based on the influencing factors to obtain the target object's familiarity with the target area. The service process corresponding to the target object is scheduled based on the area familiarity, and the service process is used to instruct the target object to perform the logistics service in the target area.

[0005] Secondly, one embodiment of this specification provides an object scheduling device for logistics services, comprising: The acquisition unit is used to acquire service data of the target object performing logistics services in the target area, and the service data is collected based on a statistical time period. The processing unit is used to determine the service evaluation indicators and parameter impact indicators associated with the logistics service, and to perform data statistics on the service data through the service evaluation indicators to obtain service evaluation parameters. The service evaluation indicators are configured based on at least one of the task execution items, regional distribution items, and timeliness statistics items. The processing unit is further configured to perform data statistics on the service data through the parameter influence indicators to obtain influence factors. The parameter influence indicators include at least one of time decay factor, customer evaluation factor and regional complexity factor. The processing unit is further configured to adjust the service evaluation parameters based on the influencing factors to obtain the target object's regional familiarity with the target area; The scheduling unit is used to schedule the service process corresponding to the target object based on the area familiarity, and the service process is used to instruct the target object to perform the logistics service in the target area.

[0006] Optionally, in one possible implementation, the processing unit is specifically used to determine the regional participation and waybill ratio of the target object in the target area through the service data when the service evaluation index indicates a task execution item; The processing unit is specifically used to determine the regional coverage of the target object when performing logistics services in the target area by using the service data when the service evaluation index indicates the regional distribution item. The processing unit is specifically used to determine the on-time delivery rate and delivery efficiency score of the target object in the target area through the service data when the service evaluation indicator indicates the timeliness statistics item. The processing unit is specifically used to perform a weighted combination based on the regional participation rate, the waybill ratio, the regional coverage, the on-time delivery rate, and the delivery efficiency score to obtain the service evaluation parameters.

[0007] Optionally, in one possible implementation, the processing unit is specifically configured to determine the service task type of the target object based on the service data; The processing unit is specifically used to determine the weight distribution information corresponding to the service task type; The processing unit is specifically used to weight and combine the regional participation, the waybill ratio, the regional coverage, the on-time delivery rate, and the delivery efficiency score according to the weight distribution information to obtain the service evaluation parameters.

[0008] Optionally, in one possible implementation, the processing unit is specifically used to determine the service cutoff time information and current time information of the target object in the target area through the service data when the parameter influence index indicates the time decay factor; The processing unit is specifically used to determine the interval time information based on the service deadline information and the current time information; The processing unit is specifically used to configure the parameters of the attenuation coefficient based on the interval time information to obtain the influence factor.

[0009] Optionally, in one possible implementation, the processing unit is specifically used to determine the item type information corresponding to the target object based on the service data; The processing unit is specifically used to obtain the attenuation coefficient corresponding to the item type information; The processing unit is specifically used to configure the parameters of the attenuation coefficient based on the interval time information to obtain the influence factor.

[0010] Optionally, in one possible implementation, the processing unit is specifically used to determine the evaluation information of the target object in the target area through the service data when the parameter influence index indicates the customer evaluation factor; The processing unit is specifically used to obtain the smoothing coefficient for the logistics service configuration; The processing unit is specifically used to configure the evaluation information parameters using the smoothing coefficient to obtain the influence factor.

[0011] Optionally, in one possible implementation, the scheduling unit is specifically used to compare the area familiarity with a preset threshold. The scheduling unit is specifically used to assign a helper object to the target object if the area familiarity is greater than the preset threshold. The scheduling unit is specifically used to schedule the service process corresponding to the target object based on the task requirements corresponding to the help object.

[0012] Thirdly, one embodiment of this specification also provides a computing device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the object scheduling method for logistics services as described above.

[0013] Fourthly, one embodiment of this specification also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the object scheduling method for logistics services as described above.

[0014] Fifthly, embodiments of this specification provide a computer program product or computer program, the computer program product including a computer program that can be stored in a computer-readable storage medium or in the cloud; the processor of the computer device reads the computer program, and when the processor executes the computer program, it implements the steps of the above-described object scheduling method for logistics services.

[0015] As can be seen from the above technical solution, the object scheduling method for logistics services provided in the embodiments of this specification obtains service data of the target object performing logistics services in the target area. This service data is collected based on a statistical time period. Then, it determines the service evaluation indicators and parameter influence indicators associated with the logistics service, and performs data statistics on the service data through the service evaluation indicators to obtain service evaluation parameters. These service evaluation indicators are configured based on at least one of the task execution items, regional distribution items, and timeliness statistics items. It also performs data statistics on the service data through the parameter influence indicators to obtain influence factors. These parameter influence indicators include at least one of the time decay factor, customer evaluation factor, and regional complexity factor. Then, it adjusts the service evaluation parameters based on the influence factors to obtain the target object's regional familiarity with the target area. Finally, it schedules the service process corresponding to the target object according to the regional familiarity. The service process is used to instruct the target object to perform logistics services in the target area. This enables dynamic scheduling of logistics service capacity resources. By using multi-dimensional and comprehensive service evaluation indicators to quantify the performance of logistics services and combining them with multi-dimensional influencing factors for quantitative correction, the accuracy of the evaluation of the service performance of the target object in a specific area is improved. Furthermore, the scheduling of service processes is based on the regional familiarity obtained from the evaluation, thereby improving the utilization of capacity resources and thus improving the execution efficiency of logistics services. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments or prior art of this specification, the drawings used in the description of the embodiments or prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of this specification. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0017] Figure 1 Network architecture diagram of the object scheduling system that serves logistics.

[0018] Figure 2 This is a flowchart illustrating the object scheduling process of a logistics service provided in an embodiment of this application.

[0019] Figure 3This is a flowchart illustrating an object scheduling method for a logistics service, provided as one embodiment of this specification.

[0020] Figure 4 This is a schematic diagram illustrating a scenario of an object scheduling method for a logistics service provided as one embodiment of this specification.

[0021] Figure 5 A schematic diagram of the functional modules of an object scheduling device for logistics services provided in one embodiment of this specification.

[0022] Figure 6 This is a schematic diagram of the structure of a computing device provided for one embodiment of this specification. Detailed Implementation

[0023] Unless otherwise defined, the technical or scientific terms used in the embodiments of this specification shall have the ordinary meaning understood by one of ordinary skill in the art to which this specification pertains. The terms "first," "second," and similar terms used in the embodiments of this specification do not indicate any order, quantity, or importance, but are merely used to avoid confusion of constituent elements.

[0024] Unless the context otherwise requires, throughout this specification, "a plurality of" means "at least two," and "including" is interpreted as open-ended or encompassing, that is, "including, but not limited to." In the description of this specification, terms such as "one embodiment," "some embodiments," "exemplary embodiment," "example," "specific example," or "some examples" are intended to indicate that a particular feature, structure, material, or characteristic associated with that embodiment or example is included in at least one embodiment or example of this specification. The illustrative representations of the above terms do not necessarily refer to the same embodiment or example.

[0025] It should be understood that the object scheduling method for logistics services provided in this application can be applied to systems or programs in terminal devices that include object scheduling functions for logistics services, such as logistics management applications. Specifically, the object scheduling system for logistics services can run in environments such as... Figure 1 In the network architecture shown, such as Figure 1 The diagram shown illustrates the network architecture of a logistics service object scheduling system. As can be seen, this system can provide object scheduling processes for logistics services with multiple information sources. This is achieved through interaction with logistics services on the terminal side, triggering the server to perform corresponding service evaluation and scheduling processes. It can be understood that... Figure 1 The document illustrates various terminal devices, which can be computer devices. In real-world scenarios, more or fewer types of terminal devices may participate in the object scheduling process of logistics services. The specific number and types depend on the actual scenario and are not limited here. Figure 1The example shows one server, but in real-world scenarios, multiple servers can be involved, especially in multidisciplinary output scenarios. The specific number of servers depends on the actual scenario.

[0026] In this embodiment, the server can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms. The terminal can be a smartphone, tablet, laptop, desktop computer, smart speaker, smartwatch, etc., but is not limited to these. The terminal and server can be directly or indirectly connected via wired or wireless communication, and the terminal and server can be connected to form a blockchain network; this application does not impose any restrictions.

[0027] It is understandable that the aforementioned logistics service object scheduling system can run on personal mobile terminals, such as as a logistics management application, or it can run on a server, or it can run on third-party devices to provide object scheduling for logistics services, so as to obtain the object scheduling processing results of the logistics services from the information source. Specifically, the logistics service object scheduling system can run in the aforementioned devices as a program, or it can run as a system component in the aforementioned devices, or it can run as a cloud service program. The specific operating mode depends on the actual scenario and is not limited here.

[0028] With the development of internet technology, logistics services are becoming increasingly common in people's lives. In the execution of logistics services, couriers play a crucial role as a transportation resource. However, considering the regional differences in logistics services, the utilization rate of different transportation resources varies in different areas. Common methods for assessing the familiarity of delivery personnel with a specific region rely solely on single-dimensional data such as delivery frequency or working hours, lacking a precise evaluation system. This makes it difficult to effectively measure the regional proficiency of delivery personnel and fails to provide strong data support for management decisions such as personnel scheduling, customer service optimization, and training management for courier companies. Therefore, how to allocate different transportation resources to different regions has become a challenge in improving the efficiency of logistics services.

[0029] To address the aforementioned problems, this application proposes an object scheduling method for logistics services, which is applied to... Figure 2 In the object scheduling process framework of the logistics service shown, such as Figure 2The diagram shown is a flowchart of the object scheduling process for a logistics service provided in this application embodiment. Through the service interaction process of the terminal, the corresponding service data is sent to the server, enabling the server to perform a multi-dimensional service evaluation parameter quantification process. This method is used for the target object area familiarity assessment in the express delivery industry, and in particular, it is a scientific modeling method that combines multi-dimensional business data to accurately reflect the target object's proficiency in business in a specified geographical area.

[0030] It is understood that the object scheduling method for logistics services provided in this application can be a program written as processing logic in a hardware system, or it can be an object scheduling device for logistics services, implementing the above processing logic through integration or external connection. As one implementation, the object scheduling device for logistics services acquires service data of the target object performing logistics services within the target area. This service data is collected based on a statistical time period. Then, it determines the service evaluation indicators and parameter influence indicators associated with the logistics service, and performs data statistics on the service data using the service evaluation indicators to obtain service evaluation parameters. These service evaluation indicators are configured based on at least one of task execution items, regional distribution items, and timeliness statistics items. It also performs data statistics on the service data using the parameter influence indicators to obtain influence factors. These parameter influence indicators include at least one of time decay factors, customer evaluation factors, and regional complexity factors. Then, it adjusts the service evaluation parameters based on the influence factors to obtain the target object's regional familiarity with the target area. Finally, it schedules the service process corresponding to the target object based on the regional familiarity. The service process is used to instruct the target object to perform logistics services within the target area. This enables dynamic scheduling of logistics service capacity resources. By using multi-dimensional and comprehensive service evaluation indicators to quantify the performance of logistics services and combining them with multi-dimensional influencing factors for quantitative correction, the accuracy of the evaluation of the service performance of the target object in a specific area is improved. Furthermore, the scheduling of service processes is based on the regional familiarity obtained from the evaluation, thereby improving the utilization of capacity resources and thus improving the execution efficiency of logistics services.

[0031] Based on the above process architecture, the object scheduling method for logistics services in this application will be described below. Please refer to [link / reference]. Figure 3 , Figure 3 A flowchart illustrating an object scheduling method for logistics services provided in this application embodiment is included. This application embodiment includes at least the following steps: 301. Obtain service data of the target object performing logistics services within the target area. The service data is collected based on statistical time periods.

[0032] In this embodiment, the target object is the object that performs logistics services. Specifically, it can be a courier, driver, warehouse manager, courier equipment, or other transportation resources with the ability to transport goods. This embodiment uses a courier as an example for explanation. Correspondingly, the target area is the area where the target object performs logistics services. Specifically, it can be a delivery area, a collection area, or other areas involved in the logistics process. The division of the area can be done in different granularities such as residential area, community, service point, and transit station. The specific form depends on the actual scenario.

[0033] Specifically, for service data collected based on statistical time periods, that is, the data set of couriers performing logistics services in the target area, including but not limited to actions, time, and evaluation information.

[0034] In one possible scenario, let the courier be c, the target area be r, and the statistical time period be t (days). Then the service data can include the following data: : The number of delivery orders delivered by courier c in area r within a unit of time t.

[0035] The total number of delivery orders made by courier c within a unit of time t.

[0036] Total number of all delivery orders in region r within a unit time t.

[0037] The number of different POIs (Points of Interest / Delivery Points) served by courier c in area r within a unit of time t.

[0038] Total number of POIs for all express deliveries in the region.

[0039] : The number of courier c delivers packages on time within area r within a unit of time t.

[0040] : The baseline or average delivery time per order for region r.

[0041] : The average delivery time per order for courier C in area r.

[0042] The number of positive reviews for courier C in area R.

[0043] The number of negative reviews for delivery orders in area r by courier c.

[0044] : Number of positive reviews for region r.

[0045] : Number of negative reviews for shipments in region r.

[0046] Current date.

[0047] The date on which courier C last made a delivery in area R.

[0048] : The total number of days that courier c provides delivery services in area r within a unit of time t.

[0049] 302. Determine the service evaluation indicators and parameter impact indicators associated with logistics services, and conduct data statistics on service data through service evaluation indicators to obtain service evaluation parameters. The service evaluation indicators are configured based on at least one of the task execution items, regional distribution items, and timeliness statistics items.

[0050] In this embodiment, the service evaluation index is used to intuitively quantify the service performance of couriers, while the parameter influence index is used to simulate factors that may affect the service performance of couriers.

[0051] Specifically, the process of statistically analyzing service data through service evaluation indicators can be configured with at least one of the following: task execution items, regional distribution items, and timeliness statistics items. That is, it can be one of them or a combination of multiple items.

[0052] Among them, when the service evaluation indicators indicate the task execution items, the service data is used to determine the regional participation and order ratio of the courier in the target area; the task execution items are used to indicate the quantitative representation of the courier's business volume during the task execution process.

[0053] This refers to regional engagement, which is the proportion of a courier's business output in a specific area relative to their total business volume. It measures the level of attention and effort a courier invests in their work within that area.

[0054] This refers to regional coverage, which is the share of a courier's business volume in a given area out of the total business volume in that area. It reflects the courier's level of involvement in the business within that area, and indirectly indicates the frequency of contact between the courier and customers in that area, as well as their familiarity with the business in that area.

[0055] When the service evaluation indicator indicates the regional distribution item, the service data is used to determine the regional coverage corresponding to the courier's performance of logistics services within the target area; the regional distribution item is used to indicate the area involved by the courier during the performance of the task.

[0056] This refers to area coverage, or POI coverage rate, which measures the breadth of different locations a courier actually delivers to or picks up in a certain area. The higher the coverage rate, the better the courier's familiarity with the terrain and customer distribution in the area.

[0057] When the service evaluation indicators indicate the timeliness statistics, the on-time delivery rate and delivery efficiency score of the courier in the target area are determined by the service data; the timeliness statistics are used to indicate the courier's on-time performance in the process of performing the task.

[0058] On-time delivery rate: the percentage of packages delivered on time by a courier within a given area.

[0059] This refers to the delivery efficiency score, which is a standardized average delivery time for a region. It compares the actual average delivery time of each courier with the average delivery time of their colleagues in the region. Whoever is relatively faster gets a higher score, eliminating regional differences and reflecting individual efficiency.

[0060] After obtaining the above data, the service evaluation parameters are obtained by weighting and combining the scores of regional participation, waybill ratio, regional coverage, on-time delivery rate, and delivery efficiency.

[0061] In one possible scenario, the weighted combination of scores based on regional participation, waybill share, regional coverage, on-time delivery rate, and delivery efficiency can be achieved through a dynamically configured weight distribution. This involves introducing a dynamic weight adjustment mechanism, assigning different weights to the five evaluation dimensions based on the service task type (e.g., ordinary parcels, fresh cold chain, valuables, etc.). This allows familiarity assessment to adapt to different types of logistics service needs, achieving more precise capability matching.

[0062] Specifically, the system first determines the courier's service task type based on service data; then, it determines the weight distribution information corresponding to the service task type; and finally, it weights and combines regional participation, order percentage, regional coverage, on-time delivery rate, and delivery efficiency scores according to the weight distribution information to obtain service evaluation parameters. For example, if a courier needs to be dispatched to deliver fresh cold chain products, the system will identify the task type as 'time-sensitive'. In this case, the weight of the on-time delivery rate and delivery efficiency scores will be increased (e.g., each accounting for 30%), while the weight of regional coverage will be appropriately reduced (e.g., reduced to 10%). This is because for fresh food delivery, timeliness is more important than geographical breadth. Conversely, if dispatching large furniture deliveries, the system may increase the weight of regional coverage, as this type of delivery requires more familiarity with the neighborhood's roads and parking conditions.

[0063] 303. Perform data statistics on service data through parameter impact indicators to obtain impact factors. The parameter impact indicators include at least one of the following: time decay factor, customer evaluation factor, and regional complexity factor.

[0064] In this embodiment, the parameter influence index is used to simulate factors that may affect the courier's service execution process. It is obtained by configuring at least one of the time decay factor, customer evaluation factor and regional complexity factor, which can be a combination of one or more of them.

[0065] Specifically, considering the interval between the courier's last service time in the target area and the current time, an exponential decay function is used for calculation. The longer the interval, the more significant the decay, reflecting the skill rust effect. Therefore, when the parameter influencing the time decay factor is determined, the courier's service deadline and current time in the target area can be determined using service data; then, the interval time information can be determined based on the service deadline and current time information; and the decay coefficient can be configured based on the interval time information to obtain the influencing factor.

[0066] Combining the data from step 301, the time decay factor can be obtained. : ; Wherein, λ is the attenuation coefficient (its value range can be 0.01 to 0.1). This indicates the service duration, which is the total number of days the courier has served in the area (e.g., the number of days from the first delivery to the last delivery); a longer service time range indicates greater familiarity with the area. It indicates the last delivery time, meaning whether the courier was still serving in the area recently. The more recent the time, the fresher the courier's familiarity with the area, and it will not diminish due to a long period of inactivity.

[0067] Furthermore, the time decay mechanism dynamically adjusts the decay coefficient based on the type of delivered goods. Different types of logistics services have different requirements for experience freshness: high-efficiency, high-value services require more recent experience, while ordinary parcels can accept a longer time interval.

[0068] Therefore, the process of configuring the attenuation coefficient based on interval time information can begin by first determining the item type information corresponding to the courier based on service data; then obtaining the attenuation coefficient corresponding to the item type information; and finally configuring the attenuation coefficient based on the interval time information to obtain the influencing factor. For example, for ordinary parcel delivery, the system might set the attenuation coefficient to 0.02, indicating slow experience attenuation; while for medical emergency supplies delivery, the attenuation coefficient might be set to 0.1, indicating rapid experience attenuation; even if someone served in the area a month ago, they are no longer familiar with it. When dispatching emergency medicine delivery, the system will identify the item type as high-time-sensitive and high-value, using a larger attenuation coefficient to ensure that only couriers with recent service experience in the area are dispatched.

[0069] Furthermore, a smoothing coefficient was specifically introduced into the calculation method for customer evaluation factors to address the issue of data sparsity. By averaging, individual evaluation data is combined with the overall regional evaluation level, avoiding evaluation distortion caused by small sample sizes.

[0070] Therefore, when the parameter influence index indicates the customer evaluation factor, the evaluation information of the courier in the target area is determined through service data; then, a smoothing coefficient for logistics service configuration is obtained; and the evaluation information is parameter-configured using the smoothing coefficient to obtain the influence factor. Combined with the data representation in step 301, the customer evaluation factor can be obtained. The calculation process is as follows: Smooth positive review rate = Smoothing out negative review penalties = = Smoothed positive review rate × Smoothed negative review penalty The smoothing coefficient α controls the smoothing effect and can range from 0.1 to 1, adjustable based on business experience. Smoothing the positive review rate uses the overall regional positive review trend to flatten fluctuations in individual positive review rates, determining whether a courier's positive review performance in the region is above average. A high smoothed positive review rate score indicates more stable and reliable service quality from the courier in that region. Smoothing the negative review penalty uses the overall regional negative review trend to dilute the penalty for individual negative reviews, determining whether a courier's negative review performance in the region is below average, indicating fewer service issues and higher familiarity with the service.

[0071] For example, a new courier, Xiao Li, only delivered 20 orders in a certain area and received one negative review. If calculated directly, his negative review rate is as high as 5%. The system will apply a smoothing coefficient (e.g., set to 10), combined with the overall negative review rate of 2% for that area, to smooth the data: Smoothed negative review rate = (1 + 10) / 2. 2%) / (20 + 10) = 3.3%. This reflects the fact that Xiao Li did indeed receive negative reviews, while avoiding the excessive influence of individual negative reviews on the evaluation of new employees, thus providing a fairer assessment of his service quality.

[0072] Furthermore, when the parameter influences the regional complexity factor, the regional type complexity factor is also relevant. You can directly use business experience to determine the value for complexity marking; that is, the more complex the area, the greater the impact of the value.

[0073] By configuring the above-mentioned influencing factors from different dimensions, we can simulate the decay of a courier's familiarity with a region, and thus more accurately assess the courier's familiarity with the region.

[0074] 304. Adjust the service evaluation parameters based on the influencing factors to obtain the target object's familiarity with the target area.

[0075] In this embodiment, the service evaluation parameters are adjusted based on the influencing factor, which simulates the decay process of the courier's familiarity with the area.

[0076] Based on the data representation in step 301, the calculation process for regional familiarity can be obtained as follows: The calculation process described above is explained below with a specific example. Courier Li delivers packages within area A, and the statistical period is 90 days. Based on the given formula and data points, Li's familiarity with area A can be calculated: The calculation of task execution items involves the following data: (Number of delivery orders made by Xiao Li in Region A within 90 days) = 1200 orders (Number of delivery orders made by Xiao Li within 90 days) = 3000 orders (Total number of delivery orders in Region A within 90 days) = 5000 orders The calculation of the regional distribution term involves the following data: (Number of different Points of Interest (POIs) that Xiao Li served in Region A within 90 days) = 8 (Total number of POIs for all express deliveries in the region) = 10 The calculation of timeliness statistics involves the following data: (Number of on-time delivery orders by Xiao Li in Region A within 90 days) = 1100 orders (Timeliness statistics, on-time delivery rate and delivery efficiency score) (Average delivery time per order for all couriers in Region A over 90 days) = 30 minutes (Average delivery time per order for Xiao Li in Region A over 90 days) = 25 minutes The calculation process for multi-dimensional influencing factors involves the following data: (Number of positive-review orders from Xiao Li in Region A within 90 days) = 1 order (Number of negative reviews for Xiao Li's orders in Region A within 90 days) = 1 order (Number of positive-reviewed orders in Region A within 90 days) = 5 orders (Number of negative reviews for orders in Region A within 90 days) = 5 orders (Current date) = 2025 / 06 / 05 (The last date Xiao Li made a delivery in Area A) = 2025 / 05 / 26 (Total number of days Xiao Li provided delivery service in Area A within 90 days) = 15 days (Time decay factor) = = 0.1011 (Customer rating factor) = (Complexity of region A) = 0.8 (Regional participation weight) = 0.3 (Waybill weighting) = 0.3 (Regional coverage weight) = 0.2 (On-time delivery rate weight) = 0.1 (Delivery efficiency score weight) = 0.1 In summary, we can conclude that: That is, the courier's familiarity with the area within region A is... .

[0077] 305. Schedule the service process of the courier based on the courier's familiarity with the area. The service process is used to instruct the courier to perform logistics services in the target area.

[0078] In this embodiment, the regional familiarity calculated based on the above steps can reflect the efficiency of the courier in performing logistics services within that region, and targeted improvements can be made to increase the utilization rate of the courier as a transportation resource.

[0079] In one possible scenario, the process of scheduling the service process of couriers based on their regional familiarity can be a targeted optimization process of transportation resources. That is, the regional familiarity of couriers in different areas is calculated, and the areas with the highest regional familiarity reaching the threshold or the highest data are selected for task dispatch / service process scheduling, thereby improving the utilization of transportation resources.

[0080] In another possible scenario, when a courier's familiarity with a region exceeds a preset threshold, the system will assign them a "helping partner" (who could be a new employee, a temporary worker, or an employee unfamiliar with the area), forming a collaborative model of "familiarity with the unfamiliar." This ensures service quality while promoting the transfer of experience.

[0081] Therefore, the process of scheduling a courier's service progress can be achieved by comparing the courier's familiarity with the area with a preset threshold. If the familiarity with the area is greater than the preset threshold, a helper is assigned to the courier. Then, the courier's service progress is scheduled based on the task requirements corresponding to the helper.

[0082] For example, if courier 1's familiarity score with area A reaches 0.02 (threshold set at 0.01), the system identifies him as an expert courier for that area. When a new employee, courier 2, is assigned to that area, the system will mark courier 2 as courier 1's helper and coordinate their scheduling and task allocation. For instance, courier 1 might be responsible for deliveries to complex office buildings, while courier 2 handles relatively simple residential areas, or they might be assigned to work together on certain complex delivery tasks, thereby improving the utilization of delivery resources.

[0083] In addition, there can be one or more help objects, such as Figure 4 As shown, Figure 4This diagram illustrates a scenario of an object scheduling method for logistics services, provided as one embodiment of this specification. The diagram shows that object 1 has reached a familiarity threshold in region A, allowing it to assist objects 4 and 5 in providing services within region A, thus linking their service processes. Object 2, however, has not reached a familiarity threshold in region B, therefore it cannot be assigned an assisting object. Object 3 has reached a familiarity threshold in region C, allowing it to assist object 6 in providing services within region C, thus linking their service processes. By using familiarity to schedule the service processes of different objects, the utilization rate of transportation resources is improved while completing logistics services.

[0084] Understandably, the process of scheduling the service process for couriers, besides addressing training resource allocation and new employee mentoring, can also be used for service quality evaluation and targeted optimization of customer retention. This involves targeted improvements for couriers with lower familiarity with their area, or reassigning them to areas with higher familiarity. It can also be used for risk assessment and anomaly warnings in high-frequency business areas, issuing risk warnings when a courier's familiarity with a specific area falls below a threshold. Therefore, by constructing a courier area capability knowledge graph, implicit experience is transformed into explicit, quantifiable, and actionable data assets, providing data support for refined management and intelligent scheduling of logistics services.

[0085] In summary, this embodiment acquires service data on the logistics services performed by the target object within the target area, which is collected based on a statistical time period. Then, it determines service evaluation indicators and parameter impact indicators associated with the logistics services, and performs data statistics on the service data using the service evaluation indicators to obtain service evaluation parameters. These service evaluation indicators are configured based on at least one of task execution items, regional distribution items, and timeliness statistics items. Furthermore, it performs data statistics on the service data using parameter impact indicators to obtain impact factors, which include at least one of time decay factors, customer evaluation factors, and regional complexity factors. Then, it adjusts the service evaluation parameters based on the impact factors to obtain the target object's familiarity with the target area. Finally, it schedules the service process corresponding to the target object based on the regional familiarity, and the service process is used to instruct the target object to perform logistics services within the target area. This enables dynamic scheduling of logistics service capacity resources. By using multi-dimensional and comprehensive service evaluation indicators to quantify the performance of logistics services and combining them with multi-dimensional influencing factors for quantitative correction, the accuracy of the evaluation of the service performance of the target object in a specific area is improved. Furthermore, the scheduling of service processes is based on the regional familiarity obtained from the evaluation, thereby improving the utilization of capacity resources and thus improving the execution efficiency of logistics services.

[0086] It should be noted that the various embodiments described in this specification emphasize the parts that differ from other embodiments, and the embodiments can be explained by comparison with each other. Any combination of the various embodiments described in this specification based on general technical knowledge is covered within the scope of this specification.

[0087] In one exemplary embodiment of this specification, an object scheduling device 500 for logistics services is also provided, such as... Figure 5 As shown, Figure 5 A functional module diagram of a logistics service object scheduling device provided in one embodiment of this specification is shown. The scheduling device 500 includes: The acquisition unit 501 is used to acquire service data of the target object performing logistics services in the target area, wherein the service data is collected based on a statistical time period. The processing unit 502 is used to determine the service evaluation indicators and parameter impact indicators associated with the logistics service, and to perform data statistics on the service data through the service evaluation indicators to obtain service evaluation parameters. The service evaluation indicators are configured based on at least one of the task execution items, regional distribution items, and timeliness statistics items. The processing unit 502 is further configured to perform data statistics on the service data through the parameter influence index to obtain the influence factor. The parameter influence index includes at least one of the following: time decay factor, customer evaluation factor, and regional complexity factor. The processing unit 502 is further configured to adjust the service evaluation parameters based on the influencing factors to obtain the target object's regional familiarity with the target area; The scheduling unit 503 is used to schedule the service process corresponding to the target object according to the area familiarity, and the service process is used to instruct the target object to perform the logistics service in the target area.

[0088] Optionally, in one possible implementation, the processing unit 502 is specifically used to determine the regional participation and waybill ratio of the target object in the target area through the service data when the service evaluation index indicates the task execution item; The processing unit 502 is specifically used to determine the regional coverage of the target object when performing logistics services in the target area by using the service data when the service evaluation index indicates the regional distribution item. The processing unit 502 is specifically used to determine the on-time delivery rate and delivery efficiency score of the target object in the target area through the service data when the service evaluation index indicates the timeliness statistics item. The processing unit 502 is specifically used to perform a weighted combination based on the regional participation, the waybill ratio, the regional coverage, the on-time delivery rate, and the delivery efficiency score to obtain the service evaluation parameters.

[0089] Optionally, in one possible implementation, the processing unit 502 is specifically used to determine the service task type of the target object based on the service data; The processing unit 502 is specifically used to determine the weight distribution information corresponding to the service task type; The processing unit 502 is specifically used to weight and combine the regional participation, the waybill ratio, the regional coverage, the on-time delivery rate, and the delivery efficiency score according to the weight distribution information to obtain the service evaluation parameters.

[0090] Optionally, in one possible implementation, the processing unit 502 is specifically used to determine the service cutoff time information and current time information of the target object in the target area through the service data when the parameter influence index indicates the time decay factor; The processing unit 502 is specifically used to determine the interval time information based on the service deadline information and the current time information; The processing unit 502 is specifically used to configure the parameters of the attenuation coefficient based on the interval time information to obtain the influence factor.

[0091] Optionally, in one possible implementation, the processing unit 502 is specifically used to determine the item type information corresponding to the target object based on the service data; The processing unit 502 is specifically used to obtain the attenuation coefficient corresponding to the item type information; The processing unit 502 is specifically used to configure the parameters of the attenuation coefficient based on the interval time information to obtain the influence factor.

[0092] Optionally, in one possible implementation, the processing unit 502 is specifically used to determine the evaluation information of the target object in the target area through the service data when the parameter influence index indicates the customer evaluation factor; The processing unit 502 is specifically used to obtain the smoothing coefficient for the logistics service configuration; The processing unit 502 is specifically used to configure the evaluation information parameters through the smoothing coefficient to obtain the influence factor.

[0093] Optionally, in one possible implementation, the scheduling unit 503 is specifically used to compare the area familiarity with a preset threshold. The scheduling unit 503 is specifically used to assign a helper object to the target object if the area familiarity is greater than the preset threshold. The scheduling unit 503 is specifically used to schedule the service process corresponding to the target object based on the task requirements corresponding to the help object.

[0094] Specifically, the acquisition unit and processing unit in this embodiment can correspond to physical components. For example, the processing unit can be a processing module such as a CPU, GPU, or FPGA. The specific physical component can be any component or combination of components with the above functions. The specific method depends on the actual scenario and is not limited here.

[0095] The aforementioned scheduling device acquires service data on the logistics services performed by the target object within the target area. This service data is collected based on a statistical time period. It then determines service evaluation indicators and parameter impact indicators associated with the logistics services. The service evaluation indicators are used to statistically analyze the service data to obtain service evaluation parameters, which are configured based on at least one of task execution items, regional distribution items, and timeliness statistics items. Furthermore, the device uses parameter impact indicators to statistically analyze the service data to obtain impact factors, which include at least one of time decay factors, customer evaluation factors, and regional complexity factors. The device then adjusts the service evaluation parameters based on these impact factors to determine the target object's familiarity with the target area. Finally, it schedules the service processes corresponding to the target object based on this regional familiarity. These service processes instruct the target object to perform logistics services within the target area. This enables dynamic scheduling of logistics service capacity resources. By using multi-dimensional and comprehensive service evaluation indicators to quantify the performance of logistics services and combining them with multi-dimensional influencing factors for quantitative correction, the accuracy of the evaluation of the service performance of the target object in a specific area is improved. Furthermore, the scheduling of service processes is based on the regional familiarity obtained from the evaluation, thereby improving the utilization of capacity resources and thus improving the execution efficiency of logistics services.

[0096] Specific limitations regarding the object scheduling device for logistics services can be found in the limitations regarding the object scheduling method for logistics services described above, and will not be repeated here. Each unit module in the aforementioned object scheduling device for logistics services can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0097] Another embodiment of this application also proposes a computing device, see [link to relevant documentation] Figure 6As shown, an exemplary embodiment of this specification also provides a computing device, including: a memory and a processor, the memory storing a computer program, the processor executing the computer program to perform steps in the object scheduling method for logistics services according to various embodiments of this specification described above.

[0098] The internal structure of the computing device can be as follows: Figure 6 As shown, the computing device includes a processor, memory, network interface, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The network interface is used to communicate with external terminals via a network connection. When the computer program is executed by the processor, it follows the steps of the object scheduling method for logistics services according to various embodiments of this specification as described in the above embodiments.

[0099] The processor may include the main processor, as well as baseband chips, modems, etc.

[0100] The memory stores a program that executes the technical solution of this invention, and may also store an operating system and other critical business functions. Specifically, the program may include program code, which includes computer operation instructions. More specifically, the memory may include read-only memory (ROM), other types of static storage devices capable of storing static information and instructions, random access memory (RAM), other types of dynamic storage devices capable of storing information and instructions, disk storage, flash memory, etc.

[0101] The processor can be a general-purpose processor, such as a general-purpose central processing unit (CPU), a microprocessor, etc., or an application-specific integrated circuit (ASIC), or one or more integrated circuits used to control the execution of the program of the present invention. It can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), an off-the-shelf programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0102] Input devices may include devices that receive data and information input by the user, such as keyboards, mice, cameras, scanners, light pens, voice input devices, touch screens, pedometers, or gravity sensors.

[0103] Output devices may include devices that allow information to be output to the user, such as displays, printers, speakers, etc.

[0104] The communication interface may include any transceiver-like device for communicating with other devices or communication networks, such as Ethernet, Radio Access Network (RAN), Wireless Local Area Network (WLAN), etc.

[0105] The processor executes the program stored in the memory and calls other devices, which can be used to implement the various steps of the object scheduling method for any logistics service provided in the above embodiments of this application.

[0106] The computing device may also include a display component and a voice component. The display component may be a liquid crystal display screen or an e-ink display screen. The input device of the computing device may be a touch layer covering the display component, or a button, trackball or touchpad set on the casing of the computing device, or an external keyboard, touchpad or mouse, etc.

[0107] Those skilled in the art will understand that Figure 6 The structures shown are merely block diagrams of some structures related to the solutions in this specification and do not constitute a limitation on the computing devices on which the solutions in this specification are applied. Specific computing devices may include more or fewer components than those shown in the figures, or combine certain components, or have different component arrangements.

[0108] In addition to the methods and devices described above, the object scheduling method for logistics services provided in the embodiments of this specification can also be a computer program product, which includes a computer program that, when run by a processor, causes the processor to perform the steps in the object scheduling method for logistics services according to various embodiments of this specification as described in the "Exemplary Methods" section above.

[0109] The computer program product described herein can be written in any combination of one or more programming languages ​​to perform the operations of the embodiments described herein. These programming languages ​​include object-oriented programming languages ​​such as Java and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0110] Furthermore, embodiments of this specification also provide a computer-readable storage medium having a computer program stored thereon, the computer program being executed by a processor of the steps in the object scheduling method for logistics services according to various embodiments of this specification as described in the "Exemplary Methods" section above.

[0111] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this specification can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0112] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0113] The embodiments described above are merely illustrative of several implementation methods outlined in this specification. While the descriptions are specific and detailed, they should not be construed as limiting the scope of the solutions provided in this specification. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this specification, and these all fall within the scope of protection of this specification. Therefore, the scope of protection for this patent should be determined by the appended claims.

Claims

1. A method for scheduling objects in a logistics service, characterized in that, include: Acquire service data of the target object performing logistics services within the target area, wherein the service data is collected based on a statistical time period; The service evaluation indicators and parameter impact indicators associated with the logistics service are determined, and the service data is statistically analyzed using the service evaluation indicators to obtain service evaluation parameters. The service evaluation indicators are configured based on at least one of the following: task execution items, regional distribution items, and timeliness statistics items. The service data is statistically analyzed using the parameter influence indicators to obtain the influence factors. The parameter influence indicators include at least one of the following: time decay factor, customer evaluation factor, and regional complexity factor. The service evaluation parameters are adjusted based on the influencing factors to obtain the target object's familiarity with the target area. The service process corresponding to the target object is scheduled based on the area familiarity, and the service process is used to instruct the target object to perform the logistics service in the target area.

2. The method according to claim 1, characterized in that, The step of performing data statistics on the service data using the service evaluation indicators to obtain service evaluation parameters includes: When the service evaluation indicator indicates a task execution item, the service data is used to determine the target object's regional participation and waybill percentage within the target area; When the service evaluation indicator indicates the regional distribution item, the service data is used to determine the regional coverage corresponding to the target object performing logistics services in the target region; When the service evaluation indicator indicates the timeliness statistics item, the on-time delivery rate and delivery efficiency score of the target object in the target area are determined by the service data. The service evaluation parameters are obtained by weighting and combining the regional participation rate, the order proportion, the regional coverage, the on-time delivery rate, and the delivery efficiency score.

3. The method according to claim 2, characterized in that, The service evaluation parameters are obtained by weighting and combining the regional participation rate, the order proportion, the regional coverage, the on-time delivery rate, and the delivery efficiency score, including: The service task type of the target object is determined based on the service data; Determine the weight distribution information corresponding to the service task type; The service evaluation parameters are obtained by weighting the regional participation, the order proportion, the regional coverage, the on-time delivery rate, and the delivery efficiency score according to the weight distribution information.

4. The method according to claim 1, characterized in that, The step of performing data statistics on the service data using the parameter influence indicators to obtain influence factors includes: When the parameter influences the indicator time decay factor, the service cutoff time information and current time information of the target object in the target area are determined by the service data. The interval time information is determined based on the service cutoff time information and the current time information; The attenuation coefficient is configured based on the interval time information to obtain the influence factor.

5. The method according to claim 4, characterized in that, The step of configuring parameters for the attenuation coefficient based on the interval time information to obtain the influencing factor includes: The item type information corresponding to the target object is determined based on the service data; Obtain the attenuation coefficient corresponding to the item type information; The attenuation coefficient is configured with parameters based on the interval time information to obtain the influence factor.

6. The method according to claim 1, characterized in that, The step of performing data statistics on the service data using the parameter influence indicators to obtain influence factors includes: When the parameter influences the customer evaluation factor, the evaluation information of the target object in the target area is determined through the service data; Obtain the smoothing coefficient for the logistics service configuration; The evaluation information is parameterized using the smoothing coefficient to obtain the influence factor.

7. The method according to claim 1, characterized in that, The step of scheduling the service process corresponding to the target object based on the region familiarity includes: Compare the area familiarity with a preset threshold. If the familiarity with the region is greater than the preset threshold, then a helper object is assigned to the target object; The service process corresponding to the target object is scheduled based on the task requirements corresponding to the help object.

8. A scheduling device for logistics services, characterized in that, include: The acquisition unit is used to acquire service data of the target object performing logistics services in the target area, and the service data is collected based on a statistical time period. The processing unit is used to determine the service evaluation indicators and parameter impact indicators associated with the logistics service, and to perform data statistics on the service data through the service evaluation indicators to obtain service evaluation parameters. The service evaluation indicators are configured based on at least one of the task execution items, regional distribution items, and timeliness statistics items. The processing unit is further configured to perform data statistics on the service data through the parameter influence indicators to obtain influence factors. The parameter influence indicators include at least one of time decay factor, customer evaluation factor and regional complexity factor. The processing unit is further configured to adjust the service evaluation parameters based on the influencing factors to obtain the target object's regional familiarity with the target area; The scheduling unit is used to schedule the service process corresponding to the target object based on the area familiarity, and the service process is used to instruct the target object to perform the logistics service in the target area.

9. A computing device, characterized in that, The system includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the object scheduling method for logistics services as described in any one of claims 1 to 7.

10. A computer program product, characterized in that, include: A computer program, when executed by a processor, implements the object scheduling method for logistics services as described in any one of claims 1 to 7.

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