Cross-border e-commerce logistics intelligent scheduling method and system based on cloud computing

Abstract

The application relates to the technical field of logistics scheduling, and discloses a cross-border e-commerce logistics intelligent scheduling method and system based on cloud computing, which comprises the following steps: after collecting unit operation data of each symbiotic unit in a cross-border e-commerce logistics system, the equivalent of the transport capacity resources corresponding to the symbiotic unit is calculated; the unit symbiotic coefficient between the symbiotic units is calculated; a symbiotic interface between a logistics service object and the symbiotic units in the cross-border e-commerce logistics system is constructed, a collaborative mechanism between the symbiotic units is established after analyzing the symbiotic mode corresponding to the symbiotic units; the collaborative benefits generated in the collaborative scheduling process of the symbiotic units are calculated, a risk sharing mechanism between the symbiotic units is constructed after determining the benefit distribution coefficient corresponding to the symbiotic units, the scheduling and distribution of logistics tasks in the cross-border e-commerce logistics system are executed, and the logistics intelligent scheduling result is obtained. The application can improve the efficiency of cross-border e-commerce logistics intelligent scheduling based on cloud computing.

Description

Technical Field

[0001] This invention relates to a cloud-based intelligent scheduling method and system for cross-border e-commerce logistics, belonging to the field of logistics scheduling technology. Background Technology

[0002] Cross-border e-commerce logistics scheduling refers to the process of coordinating and allocating resources for multiple logistics links involved in cross-border trade, such as domestic warehousing, cross-border transportation, customs clearance, and overseas delivery, in order to achieve efficient order fulfillment and effective cost control.

[0003] Current cross-border e-commerce logistics scheduling often adopts a single-entity independent planning method, with a single entity such as a cross-border logistics company or e-commerce platform as the core to complete the entire process scheduling. After receiving the order fulfillment requirements of its own channels, the entity plans the transportation route and allocates warehousing and trunk transportation resources independently based on its own transportation capacity, warehousing resources and historical transportation data. This approach only considers its own cost and timeliness goals throughout the entire process, without analyzing the symbiotic adaptation relationship and benefits among multiple entities in cross-border e-commerce. Ultimately, this leads to an imbalance in resource matching during the scheduling process, reducing the efficiency of cross-border e-commerce logistics scheduling. Summary of the Invention

[0004] This invention provides a cloud computing-based intelligent scheduling method and system for cross-border e-commerce logistics, the main purpose of which is to improve the efficiency of cloud computing-based intelligent scheduling of cross-border e-commerce logistics.

[0005] To achieve the above objectives, the present invention provides a cloud computing-based intelligent scheduling method for cross-border e-commerce logistics, comprising: After collecting the unit operation data of each symbiotic unit in the cross-border e-commerce logistics system, the equivalent transportation capacity resources corresponding to the symbiotic unit are calculated. Extract the unit quality parameters of the symbiotic units, and calculate the unit co-occurrence coefficient between the symbiotic units based on the unit quality parameters; Based on a pre-set cloud computing platform, a symbiotic interface is constructed between the logistics service objects and the symbiotic units in the cross-border e-commerce logistics system. Based on the symbiotic interface, the symbiotic mode corresponding to the symbiotic unit is analyzed, and a collaborative mechanism between the symbiotic units is established. Based on the aforementioned collaborative mechanism, the collaborative benefits generated by the symbiotic units during the collaborative scheduling process are calculated. After determining the benefit allocation coefficient corresponding to the symbiotic units based on the collaborative benefits, a risk-sharing mechanism among the symbiotic units is constructed. By combining the aforementioned transport capacity equivalent, the aforementioned unit symbiosis coefficient, and the aforementioned risk-sharing mechanism, the scheduling and allocation of logistics tasks in the cross-border e-commerce logistics system are executed to obtain intelligent logistics scheduling results.

[0006] Optionally, after collecting the unit operation data of each symbiotic unit in the cross-border e-commerce logistics system, the equivalent transportation capacity resources corresponding to the symbiotic unit are calculated, including: Extract the job timeliness data and job saturation data from the unit operation data; Based on the aforementioned job timeliness data, the unit job efficiency of the symbiotic unit is determined; Obtain the unit configuration information of the symbiotic unit, and determine the unit baseline capability corresponding to the symbiotic unit based on the unit configuration information; Based on the job saturation data and the unit baseline capability, the effective job capability of the symbiotic unit is calculated. Based on the unit's operational efficiency and effective operational capability, the equivalent of the transportation resources corresponding to the symbiotic unit is calculated.

[0007] Optionally, calculating the equivalent transport capacity resources corresponding to the symbiotic unit based on the unit's operational efficiency and effective operational capability includes: Measure the standard working hours corresponding to the unit workload of the work object in the symbiotic unit; Based on the effective operational capabilities of the unit, calculate the resource conversion factor corresponding to the symbiotic unit; Retrieve the historical task completion volume corresponding to the symbiotic unit, and calculate the task completion rate corresponding to the symbiotic unit based on the historical task completion volume and the preset benchmark task volume; The equivalent of transportation resources corresponding to the symbiotic unit is calculated by combining the standard operating hours, the resource conversion factor, the operation completion rate, the unit operation efficiency, and the unit effective operation capacity.

[0008] Optionally, the extraction of the unit quality parameters of the co-occurring unit includes: Determine the initial index pool of the symbiotic unit, and filter out candidate indicators of the symbiotic unit from the initial index pool; The candidate indicators are dimensionless to obtain standard indicators; the correlation coefficient between the standard indicators is calculated. Based on the correlation coefficient of the aforementioned indicators, the core indicators among the candidate indicators are selected. Based on the aforementioned core indicators, the unit quality parameters of the symbiotic unit are determined.

[0009] Optionally, calculating the co-occurrence coefficient between the co-occurring units based on the unit quality parameter includes: Collect the original time series data of the symbiotic unit with respect to the unit quality parameter, and perform alignment processing on the original time series data according to the preset time interval to obtain the synchronization time series data; Based on the synchronous time-series data, the relative rate of change of the unit mass parameter within the preset time interval is calculated; Based on the relative rate of change and the unit quality parameter, the degree of unit co-occurrence between the co-occurring units is calculated; Based on the unit co-occurrence degree, the unit co-occurrence coefficient between the co-occurring units is calculated.

[0010] Optionally, the construction of the symbiotic interface between the logistics service object and the symbiotic unit in the cross-border e-commerce logistics system based on a preset cloud computing platform includes: Obtain the historical business records of the logistics service object and the historical service records of the symbiotic unit; The historical business records and the historical service records are matched to obtain a business-service association set; Interaction nodes are extracted from the business-service association set to obtain a node distribution map; Frequency clustering is performed on the node distribution map to obtain densely interactive regions; By combining the densely interactive area with the preset cloud computing platform, a symbiotic interface is constructed between the logistics service object and the symbiotic unit.

[0011] Optionally, the step of analyzing the symbiotic mode corresponding to the symbiotic unit based on the symbiotic interface includes: The interaction records generated on the symbiotic interface are processed to identify the business flow characteristics. Assign the interaction dependency degree corresponding to the business flow characteristics; By combining the business flow characteristics and the interaction dependency, the symbiotic mode corresponding to the symbiotic unit is determined.

[0012] Optionally, establishing a collaborative mechanism among the symbiotic units includes: The business interactions of the symbiotic units under the symbiotic mode are decomposed into nodes to obtain the collaborative operation link; The task connection paths of the collaborative task chain are analyzed, the adaptable resources of the collaborative task chain are identified, and a resource configuration list is obtained. By combining the collaborative operation link, the operation connection path, and the resource configuration list, a collaborative mechanism is established among the symbiotic units.

[0013] Optionally, the step of constructing a risk-sharing mechanism among the symbiotic units based on the benefit allocation coefficient includes: Based on the benefit allocation coefficient, the risk-bearing ratio of the symbiotic unit in the collaborative scheduling is determined; Analyze the risk triggering scenarios corresponding to the symbiotic unit, and determine the risk responsibility fulfillment method of the symbiotic unit based on the risk triggering scenarios; By combining the risk-sharing ratio and the risk responsibility fulfillment method, a risk-sharing mechanism is constructed among the symbiotic units.

[0014] To address the above problems, the present invention also provides a cloud-based intelligent scheduling system for cross-border e-commerce logistics, the system comprising: The resource equivalent calculation module is used to collect the unit operation data of each symbiotic unit in the cross-border e-commerce logistics system and then calculate the transportation capacity resource equivalent corresponding to the symbiotic unit. The co-occurrence coefficient calculation module is used to extract the unit quality parameters of the co-occurring units and calculate the unit co-occurrence coefficient between the co-occurring units based on the unit quality parameters. The collaboration mechanism establishment module is used to construct a symbiotic interface between the logistics service object and the symbiotic unit in the cross-border e-commerce logistics system based on a preset cloud computing platform. Based on the symbiotic interface, after analyzing the symbiotic mode corresponding to the symbiotic unit, a collaboration mechanism is established between the symbiotic units. The risk-sharing mechanism construction module is used to calculate the collaborative benefits generated by the symbiotic units during the collaborative scheduling process based on the collaborative mechanism, and after determining the benefit allocation coefficient corresponding to the symbiotic units according to the collaborative benefits, construct the risk-sharing mechanism among the symbiotic units. The intelligent scheduling and allocation module is used to combine the equivalent of the transportation capacity resources, the unit symbiosis coefficient, and the risk-sharing mechanism to execute the scheduling and allocation of logistics tasks in the cross-border e-commerce logistics system, and obtain intelligent logistics scheduling results.

[0015] To address the above problems, the present invention also provides an electronic device, the electronic device comprising: A memory for storing at least one instruction; and a processor for executing the instructions stored in the memory to implement the cloud computing-based intelligent scheduling method for cross-border e-commerce logistics described above.

[0016] To address the aforementioned issues, the present invention also provides a computer-readable storage medium storing at least one instruction, which is executed by a processor in an electronic device to implement the cloud computing-based intelligent scheduling method for cross-border e-commerce logistics described above.

[0017] Compared to the problems described in the background art, this invention, by calculating the equivalent transport capacity resources corresponding to the symbiotic unit based on the unit's operational data, can understand the actual operational carrying capacity of the symbiotic unit in the logistics network, providing a basis for the scheduling and allocation of logistics tasks in the subsequent cross-border e-commerce logistics system. Furthermore, by extracting the unit quality parameters of the symbiotic unit, this invention can obtain the essential attribute set of the symbiotic unit, thus providing a basis for the subsequent calculation of the unit symbiosis coefficient. Finally, by constructing a symbiotic interface between the logistics service objects and the symbiotic units in the cross-border e-commerce logistics system based on a preset cloud computing platform, this invention can achieve cross-entity connectivity of logistics data resources and service capabilities. This invention forms an interactive platform for multi-party collaborative operations, facilitating subsequent analysis and processing of symbiotic modes. Then, based on the collaborative mechanism, the invention calculates the collaborative benefits generated by the symbiotic units during collaborative scheduling, measuring the overall gain level of the symbiotic units in joint operation, thus providing a basis for determining the benefit allocation coefficients corresponding to the symbiotic units. Finally, by combining the equivalent of transportation resources, the unit symbiotic coefficient, and the risk-sharing mechanism, the invention executes the scheduling and allocation of logistics tasks in the cross-border e-commerce logistics system, thereby improving the scheduling efficiency of cross-border e-commerce logistics. Therefore, the invention can improve the efficiency of intelligent scheduling of cloud-based cross-border e-commerce logistics. Attached Figure Description

[0018] Figure 1 A flowchart illustrating a cloud-based intelligent scheduling method for cross-border e-commerce logistics provided in an embodiment of the present invention; Figure 2 A schematic diagram showing the correlation coefficient and index correspondence of a cloud-based intelligent scheduling method for cross-border e-commerce logistics provided in this application embodiment; Figure 3 A schematic diagram of the change curves of cross-border e-commerce logistics and supplier quality parameters for a cloud-based intelligent scheduling method for cross-border e-commerce logistics provided in this application embodiment; Figure 4 A schematic diagram of modules for implementing a cloud computing-based intelligent scheduling method for cross-border e-commerce logistics, provided in an embodiment of the present invention; Figure 5 A schematic diagram of the structure of an electronic device for a cloud computing-based intelligent scheduling method for cross-border e-commerce logistics provided in an embodiment of the present invention; The objectives, features, and advantages of this invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0019] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0020] This application provides a cloud-based intelligent scheduling method for cross-border e-commerce logistics. The executing entity of this cloud-based intelligent scheduling method includes, but is not limited to, at least one of the following electronic devices that can be configured to execute the method provided in this application: a server, a terminal, etc. In other words, the cloud-based intelligent scheduling method for cross-border e-commerce logistics can be executed by software or hardware installed on a terminal device or a server device. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster.

[0021] Reference Figure 1 The diagram shown is a flowchart illustrating a cloud-based intelligent scheduling method for cross-border e-commerce logistics according to an embodiment of the present invention. In this embodiment, the cloud-based intelligent scheduling method for cross-border e-commerce logistics includes: Reference Figure 1 The diagram shown is a flowchart illustrating a cloud-based intelligent scheduling method for cross-border e-commerce logistics according to an embodiment of the present invention. In this embodiment, the cloud-based intelligent scheduling method for cross-border e-commerce logistics includes: S1. After collecting the unit operation data of each symbiotic unit in the cross-border e-commerce logistics system, calculate the equivalent transportation capacity resources corresponding to the symbiotic unit.

[0022] This invention calculates the equivalent of the transport capacity resources corresponding to the symbiotic unit based on the unit's operating data, thereby understanding the actual operational carrying capacity of the symbiotic unit in the logistics network and providing a basis for the scheduling and allocation of logistics tasks in the subsequent cross-border e-commerce logistics system.

[0023] The cross-border e-commerce logistics system is a collaborative operation system that supports the flow of cross-border goods from the shipping end to the receiving end, including links such as warehousing, transportation, and customs clearance. The symbiotic units are the basic components of the cross-border e-commerce logistics system that undertake specific operational functions, such as overseas warehouses, transportation fleets, and customs clearance agencies. The unit operation data are the operation records of the symbiotic units, such as the throughput, timeliness, and equipment utilization rate. Furthermore, the unit operation data of each symbiotic unit in the cross-border e-commerce logistics system can be collected through business system interfaces, such as order fulfillment and warehousing management systems.

[0024] In detail, after collecting the unit operation data of each symbiotic unit in the cross-border e-commerce logistics system, the equivalent transportation capacity resources corresponding to the symbiotic unit are calculated, including: Extract the job timeliness data and job saturation data from the unit operation data; Based on the aforementioned job timeliness data, the unit job efficiency of the symbiotic unit is determined; Obtain the unit configuration information of the symbiotic unit, and determine the unit baseline capability corresponding to the symbiotic unit based on the unit configuration information; Based on the job saturation data and the unit baseline capability, the effective job capability of the symbiotic unit is calculated. Based on the unit's operational efficiency and effective operational capability, the equivalent of the transportation resources corresponding to the symbiotic unit is calculated.

[0025] The operational timeliness data refers to the set of time records for the symbiotic unit to complete a single operation, including processing times for different orders or batches. For example, the average processing time from order acceptance to shipment in an overseas warehouse is 4.5 hours. The operational saturation data refers to the ratio of the actual workload to the available workload of the symbiotic unit within a statistical period, used to determine whether the unit is overloaded or idle. For example, a daily operational saturation maintained between 75% and 85% for a consecutive week. The unit operational efficiency refers to the number of operations completed by the symbiotic unit per unit of time. The unit configuration information refers to information representing the inherent operational capabilities of the symbiotic unit. Technical parameters include manpower allocation, number of equipment, and site area. For example, an overseas warehouse is equipped with 20 pickers and 5,000 shelf positions. The unit baseline capacity refers to the maximum processing capacity of the symbiotic unit under standard working conditions, which is determined by combining the unit configuration information with industry benchmarks. For example, a customs clearance company's standard daily clearance capacity is 200 shipments. The unit effective operating capacity refers to the actual amount of work that the symbiotic unit can call upon under the current operating saturation state. It is the product of the baseline capacity and the saturation coefficient. For example, a transportation fleet's baseline capacity is 100 shipments per day. When the current saturation is 80%, the effective operating capacity is 80 shipments.

[0026] Furthermore, the timeliness data of the operations can be collected through business system logs. For example, the order processing system can automatically record the timestamps of each order at each stage, and the average processing time can be summarized every 2 hours. The operation saturation can be continuously monitored through the operation scheduling platform. When the saturation fluctuation range is ≤5% for 3 consecutive days, it is determined to be a stable operation state. The unit operation efficiency can be obtained by comparing the amount of work completed in the same period with the operation time through the efficiency calculation module. For example, if an overseas warehouse has 480 outbound orders on a certain day and the total operation time is 160 hours, the efficiency value of 3 orders / person can be calculated. The unit configuration information can be obtained by reviewing unit design documents or conducting on-site verification, such as retrieving data on warehouse area, number of shelves, and staffing from warehouse management files; the unit's baseline capacity can be calculated by summing the theoretical maximum processing capacity of each work node within the unit, for example, the maximum sorting capacity per hour can be calculated for a certain distribution center based on the conveyor belt design speed and the number of sorting ports; the effective work capacity of the unit can be obtained by converting the product of work saturation and actual work volume, for example, when a customs clearance company's baseline capacity is 200 shipments and the current saturation is 75%, the effective work capacity is 150 shipments.

[0027] Optionally, calculating the equivalent transport capacity resources corresponding to the symbiotic unit based on the unit's operational efficiency and effective operational capacity includes: Measure the standard working hours corresponding to the unit workload of the work object in the symbiotic unit; Based on the effective operational capabilities of the unit, calculate the resource conversion factor corresponding to the symbiotic unit; Retrieve the historical task completion volume corresponding to the symbiotic unit, and calculate the task completion rate corresponding to the symbiotic unit based on the historical task completion volume and the preset benchmark task volume; The equivalent of transportation resources corresponding to the symbiotic unit is calculated by combining the standard operating hours, the resource conversion factor, the operation completion rate, the unit operation efficiency, and the unit effective operation capacity.

[0028] The standard operating time refers to the baseline time required to complete a single unit of work, reflecting the work consumption quota of the work object. For example, the standard operating time for processing a cross-border order is 0.5 hours. The resource conversion factor is the ratio of the unit's effective operating capacity to the unit's baseline capacity, reflecting the proportion of the unit's actual available operating space. For example, the conversion factor is 0.75 when the effective operating capacity is 150 orders and the baseline capacity is 200 orders. The historical work completion volume refers to the actual work completion record of the symbiotic unit over a period of time, including work volume data for different time periods, such as the daily order processing volume completed in the past 30 days. The preset baseline work volume refers to the work completion volume that the symbiotic unit should achieve under standard configuration, determined by the unit configuration and industry standards. For example, the daily processing baseline for an overseas warehouse is 500 orders based on area and staffing. The work completion rate is the ratio of the historical average work completion volume to the preset baseline work volume. For example, the work completion rate is 0.9 when the historical average daily processing volume is 450 orders.

[0029] Furthermore, the standard operating hours can be obtained through operation measurement methods, such as using a stopwatch to continuously observe the operation process and taking the median value of multiple measurements as the standard operating hours; the preset benchmark operating volume can be obtained by consulting industry operation quota standards, such as referring to the cross-border logistics industry warehousing operation specifications to determine the benchmark processing volume for warehouses of different areas; the historical operation completion volume can be obtained by retrieving the operation data of the past 6 months from the business database, such as extracting the operation completion volume value recorded at the top of each hour of each day; the preset benchmark operating volume can be calculated by unit benchmark capacity, standard operating hours, and operation saturation, for example, when a transportation fleet has a benchmark capacity of 100 shipments and a standard working hour of 2 hours / shipment, the preset benchmark operating volume is 200 working hours; the operation completion rate can be calculated by dividing the average historical operation completion volume by the preset benchmark operating volume, for example, when the historical average completion volume is 450 shipments and the preset benchmark is 500 shipments, the operation completion rate = 450 / 500 = 0.9.

[0030] Optionally, the calculation of the equivalent transport capacity resources corresponding to the symbiotic unit using the following formula, combining the standard operating hours, the resource conversion factor, the operation completion rate, the unit operation efficiency, and the unit's effective operation capacity, includes:

[0031] in, This represents the equivalent of transportation resources corresponding to the symbiotic unit. Indicates standard operating hours. Indicates the unit's effective operational capacity. Indicates unit operation efficiency. This represents the resource conversion factor. Indicates the completion rate of the assignment. Indicates the additional coefficient for work collaboration. This indicates the idle depreciation correction value. This indicates the adjustment value for operational fluctuations.

[0032] The operational collaboration additional coefficient represents the extra operational capacity gain generated by the connection and cooperation when multiple coexisting units work together, reflecting the positive impact of the level of cooperation between units on the overall transportation capacity. It is obtained by analyzing the operational matching degree between units and fitting it with historical collaboration data. The idle loss correction value represents the negative impact of equipment or personnel idleness caused by under-saturation of operations on the actual operational capacity of the unit, reflecting the capacity loss when the operational saturation is low. It is calculated by monitoring the distribution of idle time periods of units and the operation start-up response time. The operational fluctuation correction value represents the dynamic compensation for the stability of unit operational capacity caused by changes in order peaks and valleys, reflecting the deviation impact of fluctuations in actual operational volume. It is calculated by combining the ratio of the standard deviation to the mean of operational volume within the statistical period with industry experience thresholds.

[0033] Optionally, in the above formula, standard working hours The unit of measurement is time, and the unit's effective work capacity. The units of measurement are the number of tasks and the efficiency of a unit task. The dimension of the product of the three is the quantity of work per unit time, and the dimension of the product of the three is the square of the quantity of work, while the resource conversion factor is... Homework completion rate Additional coefficient for work collaboration Idle depreciation correction value Operational fluctuation correction value All are dimensionless coefficients. From a dimensional analysis perspective, the equivalent of transport capacity resources... In this formula, a dimensionless relative comparison index is defined, rather than an absolute physical quantity, by... , , Multiplying three parameters with physical dimensions and then multiplying them with a dimensionless coefficient yields a result that, in physical units, represents the square of the number of operations. However, in practical applications, this calculation result is treated as an index value characterizing the overall capacity level of a unit, without being assigned a specific physical unit. This approach makes symbiotic units of different sizes and types comparable. The formula outputs a relative value for horizontal comparison. In specific applications, by setting the calculated value of a benchmark unit as a reference and comparing the calculated results of other units with the benchmark value, the relative level of capacity resources of each unit can be determined.

[0034] Furthermore, the above formula will include standard operating hours. Unit effective operation capability With unit operation efficiency The product of these three factors represents the basic operational output of the unit under standard operating conditions, reflecting the inherent level of the unit's configuration and operating speed; resource conversion factor. Adjust the unit's basic capacity from its theoretical maximum to the actual available value under current saturation conditions; task completion rate. The stability of the unit's historical performance is adjusted to reflect the deviation between the unit's actual performance and design capabilities; the operational collaboration additional coefficient. Introducing the positive gain from inter-unit collaboration to adjust the impact on overall capacity during calculation; idle capacity loss correction value. and operational fluctuation correction value The impact of external disturbances on the stability of unit operation capacity is quantified from two aspects: insufficient operation saturation and uneven operation volume. The above formula shows that the impact of each factor on the final transport capacity resource equivalent is independent and has a multiplicative superposition characteristic.

[0035] S2. Extract the unit quality parameters of the symbiotic units, and calculate the unit co-occurrence coefficient between the symbiotic units based on the unit quality parameters.

[0036] This invention extracts the unit quality parameters of the symbiotic unit to obtain the set of essential attributes of the symbiotic unit, thereby providing a basis for the subsequent calculation of the unit symbiosis coefficient. The unit quality parameters are core key variables used to characterize the essential attributes of the symbiotic unit, such as the annual cooling load of a building or the peak energy load of a production workshop.

[0037] Specifically, the extraction of the unit quality parameters of the co-occurring unit includes: Determine the initial index pool of the symbiotic unit, and filter out candidate indicators of the symbiotic unit from the initial index pool; The candidate indicators are dimensionless to obtain standard indicators; the correlation coefficient between the standard indicators is calculated. Based on the correlation coefficient of the aforementioned indicators, the core indicators among the candidate indicators are selected. Based on the aforementioned core indicators, the unit quality parameters of the symbiotic unit are determined.

[0038] The initial indicator pool refers to a set of various indicators related to the attributes of the symbiotic unit, initially collected based on the characteristics of the domain to which the symbiotic unit belongs. For example, the cross-border e-commerce market size and export value of cross-border e-commerce platforms, and the express delivery volume, port cargo throughput, and air cargo and mail volume of cross-border e-commerce logistics. The candidate indicators refer to the qualitative parameters that have the potential to characterize the essential attributes of the unit and are initially selected from the initial indicator pool. The standard indicators refer to the numerical sequence of indicators after dimensionless processing, and the indicators are comparable. The indicator correlation coefficient is a quantitative value used to measure the degree of correlation between different standard indicators.

[0039] Furthermore, an initial indicator pool for the symbiotic unit can be determined by consulting relevant literature, such as classic literature on material flow analysis in industrial ecology. Candidate indicators for the symbiotic unit can be screened from the initial indicator pool using a combination of expert scoring and statistical frequency analysis. For example, authoritative experts in industrial ecology and systems engineering can be invited to conduct multiple rounds of inquiry and scoring, simultaneously statistically analyzing the frequency and significance of indicators in existing core studies, eliminating invalid indicators, and finally locking in candidate indicators. The candidate indicators can be dimensionlessly processed using initialization methods to obtain standard indicators. The correlation coefficient between the standard indicators can be calculated using grey relational analysis. Based on the correlation coefficient, the correlation coefficients of each candidate indicator at each time point are arithmetically averaged to obtain the comprehensive correlation of each candidate indicator within a preset time interval. The comprehensive correlation coefficients are then sorted from highest to lowest, and the top-ranked candidate indicators are extracted as core indicators. For example, the average correlation coefficients over 10 years from 2014 to 2023 can be used as the comprehensive correlation of each indicator, and the five indicators with the highest comprehensive correlation can be selected as core indicators. For more details, please refer to the following: Figure 2 This diagram illustrates the correlation coefficients and corresponding indicators provided in this embodiment of the invention. It includes the five indicators with the highest correlation coefficients and their specific values. These indicators, ranked from highest to lowest correlation, are: express delivery volume (0.958), cross-border e-commerce express delivery quantity (0.939), enterprise e-commerce procurement amount (0.904), express delivery revenue (0.894), and cross-border e-commerce market size (0.873). All coefficients are greater than 0.85, indicating that these indicators have a very strong correlation with the system under study.

[0040] Furthermore, based on the core indicators, principal component analysis is used to determine the unit quality parameters of the symbiotic unit, such as the first principal component and the second principal component characterizing the core attributes of the symbiotic unit. This method can retain the effective information of the original core indicators to the greatest extent, achieving dimensionality reduction and accurate characterization of multi-dimensional indicators. For details, please refer to the following: Figure 3 This is a schematic diagram of the change curves of cross-border e-commerce logistics and supplier quality parameters provided in an embodiment of the present invention. The diagram intuitively shows the dynamic evolution trend of the unit quality parameters of the logistics symbiotic unit and the supplier symbiotic unit over time from 2016 to 2023. The upper line represents cross-border e-commerce logistics, and the lower line corresponds to the cross-border e-commerce platform.

[0041] Furthermore, as shown in the figure, the unit quality parameters of both logistics symbiotic units and supplier symbiotic units exhibit a continuous upward evolutionary characteristic. Initially, both were approximately 0.10 in 2016, increasing to approximately 1.00 by 2023, indicating that the core development level of both types of symbiotic units gradually improved over time. From 2016 to 2021, the unit quality parameters of supplier symbiotic units were consistently slightly higher than those of logistics symbiotic units, with the difference remaining in the range of 0.10-0.15, reflecting the first-mover advantage of suppliers in the symbiotic relationship. From 2021 to 2023, the growth rate of unit quality parameters of logistics symbiotic units accelerated significantly, and the difference with supplier symbiotic units gradually narrowed, reaching near parity by 2023. This indicates a significant catch-up effect in the development of logistics symbiotic units, with the symbiotic relationship evolving towards a more balanced direction.

[0042] This invention calculates the co-existence coefficient between the co-existing units based on the unit quality parameters, thereby obtaining the degree of interrelation between different units during collaborative operation, providing data support for subsequent scheduling and allocation processing; wherein, the unit co-existence coefficient refers to a quantitative value used to measure the interdependence and collaborative effect between the co-existing units.

[0043] Specifically, the calculation of the co-occurrence coefficient between the co-occurring units based on the unit quality parameters includes: Collect the original time series data of the symbiotic unit with respect to the unit quality parameter, and perform alignment processing on the original time series data according to the preset time interval to obtain the synchronization time series data; Based on the synchronous time-series data, the relative rate of change of the unit mass parameter within the preset time interval is calculated; Based on the relative rate of change and the unit quality parameter, the degree of unit co-occurrence between the co-occurring units is calculated; Based on the unit co-occurrence degree, the unit co-occurrence coefficient between the co-occurring units is calculated.

[0044] Wherein, the original time-series data is the numerical data of the symbiotic units corresponding to the unit quality parameters arranged in chronological order; the preset time interval is a uniform time span selected for symbiotic relationship analysis; the synchronous time-series data is the same-dimensional data formed by aligning the original time-series data with time nodes; the relative rate of change is calculated based on the synchronous time-series data, showing the magnitude of change of the unit quality parameters within the preset time interval; and the unit symbiosis degree is calculated based on the relative rate of change and the unit quality parameters, showing the ratio of the rates of change between the symbiotic units.

[0045] Furthermore, the original time-series data corresponding to the unit quality parameters of the symbiotic unit can be collected by consulting statistical yearbooks, industry bulletins, or connecting to the business system database; according to the preset time interval, the original time-series data from different sources can be aligned to the same year node to obtain synchronized time-series data; based on the synchronized time-series data, the relative rate of change of the unit quality parameters within the preset time interval can be calculated by dividing the difference between the start and end period values ​​by the start period value.

[0046] Optionally, calculating the degree of co-occurrence between the co-occurring units based on the relative rate of change and the unit quality parameter includes:

[0047] in, Indicates the degree of co-occurrence between co-occurring units. This represents the relative rate of change of the p-th co-occurring unit. This represents the element quality parameter of the p-th co-occurring element. This represents the relative rate of change of the j-th co-existing unit. p represents the unit quality parameter of the j-th co-occurring unit, and p and j represent the sequence number corresponding to the co-occurring unit.

[0048] Optionally, because and Having the same dimensions, the ratio of the two is a dimensionless pure numerical value. Similarly, the denominator is also a dimensionless numerical value. Therefore, the unit co-occurrence degree E obtained by dividing the two is also dimensionless and is not affected by the original unit of measurement, thus ensuring the comparability and universality of the calculation results.

[0049] Furthermore, this formula originates from the ratio of the rate of change of mass parameters in the symbiotic theory, reflecting the sensitivity of the two symbiotic units to each other during the system evolution process. When the E value is close to 1, it indicates that the two units have strong synchronicity in change, while a deviation from 1 reflects that there is a difference in the magnitude of change between the two.

[0050] Optionally, calculating the cell co-occurrence coefficient between the co-occurring cells based on the cell co-occurrence degree includes: Based on the unit co-occurrence degree, the reverse co-occurrence degree between the co-occurring units is determined; Combining the unit co-occurrence degree and the reverse co-occurrence degree, the unit co-occurrence coefficient between the co-occurring units is calculated using the following formula:

[0051] in, This represents the co-occurrence coefficient between co-occurring units. This represents the absolute value of the co-occurrence degree between the p-th and j-th co-occurring units. This represents the absolute value of the reverse co-occurrence degree between the p-th co-occurring unit and the j-th co-occurring unit.

[0052] Wherein, the reverse co-occurrence degree is the reciprocal of the unit co-occurrence degree.

[0053] Optionally, in the above formula for calculating the co-occurrence coefficient of the unit, The value is dimensionless. It is also a dimensionless value, therefore the unit co-occurrence coefficient G calculated by this formula is also dimensionless.

[0054] Furthermore, based on the principle of symmetry reciprocity in symbiosis theory, this formula normalizes the degree of symbiosis of a unit and its inverse symbiosis to reflect the relative contribution of two symbiotic units in the cooperative relationship. The closer the G value is to 1, the more dominant the p-th symbiotic unit is in the symbiotic relationship, and the closer it is to 0, the more subordinate it is.

[0055] S3. Based on a preset cloud computing platform, construct a symbiotic interface between the logistics service object and the symbiotic unit in the cross-border e-commerce logistics system. Based on the symbiotic interface, analyze the symbiotic mode corresponding to the symbiotic unit, and then establish a collaborative mechanism between the symbiotic units.

[0056] This invention constructs a symbiotic interface between logistics service objects and symbiotic units in the cross-border e-commerce logistics system based on a preset cloud computing platform. This enables cross-entity connectivity of logistics data resources and service capabilities, forming an interactive carrier for multi-party collaborative operations, which facilitates subsequent analysis and processing of symbiotic modes.

[0057] The cloud computing platform is a remote service platform with data storage and computing capabilities, used to aggregate and process interactive information from various participants in the logistics system, such as logistics collaborative management platforms deployed on Alibaba Cloud or Tencent Cloud; the logistics service recipients are the terminal entities receiving logistics services in the cross-border e-commerce logistics system, such as overseas consumers placing orders on cross-border e-commerce platforms and cross-border sellers joining the platform; the symbiotic interface is the connecting carrier for information exchange and business docking between the logistics service recipients and the symbiotic units, carrying the content of exchanges between the two parties in order processing, cargo tracking, inventory synchronization, etc.

[0058] In detail, the symbiotic interface between the logistics service object and the symbiotic unit in the cross-border e-commerce logistics system, constructed based on a preset cloud computing platform, includes: Obtain the historical business records of the logistics service object and the historical service records of the symbiotic unit; The historical business records and the historical service records are matched to obtain a business-service association set; Interaction nodes are extracted from the business-service association set to obtain a node distribution map; Frequency clustering is performed on the node distribution map to obtain densely interactive regions; By combining the densely interactive area with the preset cloud computing platform, a symbiotic interface is constructed between the logistics service object and the symbiotic unit.

[0059] The historical business records are a collection of business behavior data generated by logistics service recipients during cross-border transactions, including time points and status information of order generation, payment confirmation, customs declaration, and other stages. The historical service records are a collection of service behavior data provided by symbiotic units during logistics execution, including operational information of warehousing operations, trunk transportation, last-mile delivery, and other stages. The business-service association set is a paired data chain of business behaviors and service behaviors arranged in chronological order after time-series alignment, reflecting the correspondence between the two parties in the time dimension. The node distribution map is a visual or structured representation obtained through the extraction of interaction nodes, presenting a map of the location distribution of interactions between logistics service recipients and symbiotic units in various business stages. The interaction density interval is a period of time or business stage with highly concentrated interaction behavior obtained through frequency clustering, reflecting the active area of ​​collaborative operation between the two parties.

[0060] Furthermore, historical business records of the logistics service object and historical service records of the symbiotic unit can be obtained by accessing the order database of the cross-border e-commerce platform and the operating system of the logistics service provider; the historical business records and historical service records can be time-series aligned by matching business events and service events according to timestamps to obtain a business-service association set, for example, matching the order payment completion time with the warehouse picking order generation time; key nodes of state change in the business-service association set can be identified, and interaction nodes can be extracted to obtain a node distribution map; the frequency of business and service occurrences at each interaction node can be statistically analyzed and clustered to obtain interaction-intensive intervals, for example, the daily order peak period from 14:00 to 16:00 and the warehouse processing peak period can be clustered into a high-density interval; the interaction-intensive intervals can be mapped to the service interface configuration of the cloud computing platform, and a symbiotic interface between the logistics service object and the symbiotic unit can be constructed by combining the interaction-intensive intervals with the preset cloud computing platform, for example, configuring elastic interface resources for peak periods on the cloud platform to carry high-frequency matching of orders and transportation capacity.

[0061] This invention analyzes the symbiotic mode corresponding to the symbiotic unit based on the symbiotic interface, which can identify the cooperation type and dependency characteristics between the logistics service object and the symbiotic unit, facilitating the establishment and processing of subsequent collaborative mechanisms.

[0062] The symbiotic mode refers to the type of interactive behavior between logistics service objects and symbiotic units on the symbiotic interface, reflecting the structural characteristics of resource exchange and business cooperation between the two parties, such as the symbiotic mode of order push and capacity undertaking formed between the platform and logistics providers.

[0063] In detail, the analysis of the symbiotic mode corresponding to the symbiotic unit based on the symbiotic interface includes: The interaction records generated on the symbiotic interface are processed to identify the business flow characteristics. Assign the interaction dependency degree corresponding to the business flow characteristics; By combining the business flow characteristics and the interaction dependency, the symbiotic mode corresponding to the symbiotic unit is determined.

[0064] The interaction record refers to the business transaction data records generated between the logistics service object and the symbiotic unit on the symbiotic interface; the business flow characteristic is an interaction direction description obtained after flow identification processing, reflecting the transmission path of resources or information between the subjects, for example, order information flowing from the platform to the logistics provider is dominant, and status feedback flowing from the logistics provider to the platform is supplementary; the interaction dependency is a quantitative value obtained after the business flow characteristic is processed by weight allocation, reflecting the degree of interdependence between the logistics service object and the symbiotic unit.

[0065] Furthermore, by analyzing the initiator and receiver information in the interface call records on the symbiotic interface, the interaction records generated on the symbiotic interface can be processed to identify the flow direction and obtain business flow characteristics. The interaction dependency can be obtained by weighting the interaction frequency and business volume of each flow in the business flow characteristics. For example, if the platform pushes orders to logistics providers 1000 times / day and the logistics provider sends status feedback to the platform 800 times / day, the weighted dependency of the platform on the logistics provider is 0.55. By setting a flow dominance threshold and dependency level, and combining the business flow characteristics and the interaction dependency, the symbiotic mode corresponding to the symbiotic unit can be determined. For example, when the business flow characteristics show that the platform initiates more than 60% and the interaction dependency is greater than 0.5, it is determined to be a platform-dominated symbiotic mode.

[0066] By establishing a collaborative mechanism among the symbiotic units, this invention can standardize the collaboration process between subsequent logistics service recipients and symbiotic units in the symbiotic mode, thereby improving the operational efficiency and resource utilization of the entire cross-border logistics chain.

[0067] The aforementioned collaborative mechanism is an operational method that ensures orderly cooperation among symbiotic units. It is used to clarify interaction rules and responsibility boundaries, such as the standard process and exception handling rules for order-triggered warehousing operations.

[0068] In detail, the establishment of the collaborative mechanism between the symbiotic units includes: The business interactions of the symbiotic units under the symbiotic mode are decomposed into nodes to obtain the collaborative operation link; The task connection paths of the collaborative task chain are analyzed, the adaptable resources of the collaborative task chain are identified, and a resource configuration list is obtained. By combining the collaborative operation link, the operation connection path, and the resource configuration list, a collaborative mechanism is established among the symbiotic units.

[0069] The collaborative operation link is an ordered set of business interaction links obtained after node decomposition, reflecting the tasks that the symbiotic unit needs to complete sequentially during the collaboration process, such as order receipt, inventory locking, picking and outbound, transportation scheduling, and last-mile delivery. The operation connection path is a concrete representation of the interaction rules, flow order, and docking standards between nodes in the collaborative operation link. The resource configuration list is a summary table of the resource elements required for each operation link obtained after resource matching, including the types and quantities of resources such as manpower, equipment, and system interfaces. For example, the picking and outbound link requires two operators and one handheld terminal.

[0070] Furthermore, by decomposing the business activities involved in the symbiotic model into processes and breaking down the business interactions of the symbiotic units in the symbiotic model into nodes, a collaborative operation link can be obtained. For example, cross-border e-commerce logistics can be broken down into five operation nodes: order generation, customs declaration, overseas warehousing, cross-border transportation, and last-mile delivery. The operation connection path of the collaborative operation link can be sorted out through time sequence analysis, and the order of succession between preceding and subsequent operations can be clarified based on the sequential dependencies and business triggering conditions of each operation node. By taking stock of the personnel, equipment, and system permissions required for each operation link and performing resource matching processing on the operation connection path, a resource configuration list can be obtained. For example, the customs declaration link requires one specialist familiar with the regulations of the destination country, and the cross-border transportation link requires reserved container space. The collaborative operation link can be integrated as a process framework, the operation connection path as a flow rule, and the resource configuration list as a supporting condition. By combining the collaborative operation link, the operation connection path, and the resource configuration list, a collaborative mechanism between the symbiotic units can be established. For example, a standard operating procedure covering the collaborative operation of cross-border e-commerce platforms, logistics service providers, and overseas warehouse operators can be formed.

[0071] S4. Based on the aforementioned collaborative mechanism, calculate the collaborative benefits generated by the symbiotic units during the collaborative scheduling process. After determining the benefit allocation coefficient corresponding to the symbiotic units based on the collaborative benefits, construct a risk-sharing mechanism among the symbiotic units.

[0072] This invention calculates the collaborative benefits generated by the symbiotic unit during the collaborative scheduling process based on the aforementioned collaborative mechanism, which can measure the overall gain level of the symbiotic unit in joint operation, and thus provide a basis for determining the benefit allocation coefficient corresponding to the symbiotic unit in the future.

[0073] The synergistic benefit refers to the additional gain generated by the symbiotic unit during coordinated scheduling compared to independent operation. Furthermore, based on the synergistic mechanism, the synergistic benefit can be calculated by comparing and analyzing the changes in key indicators of the symbiotic unit before and after coordination. For example, the baseline benefit value of each symbiotic unit in independent operation can be obtained, along with the actual benefit value achieved after participating in coordinated scheduling. The positive difference between the two can be summed as the total synergistic benefit generated by the coordinated scheduling process. This method, through quantitative comparison, objectively reflects the incremental value brought about by coordination.

[0074] This invention determines the benefit allocation coefficient corresponding to each symbiotic unit based on the synergistic benefits, thereby reasonably dividing the overall gain generated by collaborative scheduling among the symbiotic units and reflecting the differences in contribution of each unit in the collaboration process. The benefit allocation coefficient reflects the proportion of the share that each symbiotic unit should receive in the synergistic benefits. Furthermore, the invention collects the input level, resource consumption, and execution efficiency of each symbiotic unit in the collaborative scheduling process, calculates the contribution weight of each unit, and allocates the synergistic benefits among the symbiotic units proportionally according to the contribution weight, thus obtaining the benefit allocation coefficient corresponding to each symbiotic unit.

[0075] This invention reduces potential losses caused by uncertainties in collaboration by constructing a risk-sharing mechanism among the symbiotic units. The risk-sharing mechanism is an agreement among the symbiotic units to jointly bear the losses or responsibilities that may occur during the collaborative scheduling process, and is used to balance the interests and responsibilities of each party in the collaboration.

[0076] Specifically, the risk-sharing mechanism among the symbiotic units, based on the benefit allocation coefficient, includes: Based on the benefit allocation coefficient, the risk-bearing ratio of the symbiotic unit in the collaborative scheduling is determined; Analyze the risk triggering scenarios corresponding to the symbiotic unit, and determine the risk responsibility fulfillment method of the symbiotic unit based on the risk triggering scenarios; By combining the risk-sharing ratio and the risk responsibility fulfillment method, a risk-sharing mechanism is constructed among the symbiotic units.

[0077] The risk-bearing ratio is the share of responsibility that each symbiotic unit should bear when a loss occurs during collaborative scheduling, calculated based on the benefit distribution coefficient, to achieve a symmetrical match between benefits and risks; the risk triggering scenario is the specific scenario or condition that may cause a loss event during collaborative scheduling, including situations such as logistics delays, customs clearance anomalies, insufficient transportation capacity, and warehouse cargo damage; the risk responsibility fulfillment method is the specific practice of symbiotic units in fulfilling their responsibilities under specific risk triggering scenarios, including methods such as advance compensation, proportional sharing, and priority of responsibility.

[0078] Furthermore, risk points can be identified throughout the entire collaborative scheduling process, and the risk triggering scenarios corresponding to the symbiotic units can be analyzed. For example, customs inspection anomalies, trunk line transportation delays, and last-mile delivery failures in cross-border logistics can be considered as risk triggering scenarios. Responsibility initiation conditions and execution sequences can be preset for various risk triggering scenarios. Based on these scenarios, the risk responsibility fulfillment methods of the symbiotic units can be determined. For example, it can be agreed that customs brokers will handle customs inspection anomalies first, and the portion exceeding their share of responsibility will be shared by other symbiotic units according to their risk-sharing ratio. The risk-sharing ratio and risk responsibility fulfillment methods can be compiled into collaborative agreement clauses. Combining these elements, a risk-sharing mechanism among the symbiotic units can be constructed. For example, the initiation conditions, responsibility order, and sharing ratio for various risk events can be clearly stated in a multi-party cooperation agreement, serving as the execution basis in collaborative scheduling.

[0079] S5. Combining the transport capacity equivalent, the unit symbiosis coefficient, and the risk-sharing mechanism, the logistics task scheduling and allocation in the cross-border e-commerce logistics system is executed to obtain the logistics intelligent scheduling result.

[0080] This invention improves the scheduling efficiency of cross-border e-commerce logistics by combining the equivalent of transportation capacity resources, the unit symbiosis coefficient, and the risk-sharing mechanism to perform the scheduling and allocation of logistics tasks in the cross-border e-commerce logistics system.

[0081] Furthermore, by combining the aforementioned transport capacity equivalent, the aforementioned unit symbiosis coefficient, and the aforementioned risk-sharing mechanism, the scheduling and allocation of logistics tasks in the cross-border e-commerce logistics system are executed to obtain intelligent logistics scheduling results. The specific steps are as follows: First, collect the currently available transport capacity equivalent of each logistics node to clarify the actual carrying capacity of different transportation modes; then, determine the degree of cooperation between warehousing, transshipment, and delivery links based on the unit symbiosis coefficient to form a task coordination sequence; next, introduce the risk-sharing mechanism to calculate the alternative support capability of a node for other nodes under abnormal circumstances and determine the task allocation priority; finally, match the transport capacity equivalent, coordination sequence, and priority to generate intelligent logistics scheduling results.

[0082] Compared to the problems described in the background art, this invention, by calculating the equivalent transport capacity resources corresponding to the symbiotic unit based on the unit's operational data, can understand the actual operational carrying capacity of the symbiotic unit in the logistics network, providing a basis for the scheduling and allocation of logistics tasks in the subsequent cross-border e-commerce logistics system. Furthermore, by extracting the unit quality parameters of the symbiotic unit, this invention can obtain the essential attribute set of the symbiotic unit, thus providing a basis for the subsequent calculation of the unit symbiosis coefficient. Finally, by constructing a symbiotic interface between the logistics service objects and the symbiotic units in the cross-border e-commerce logistics system based on a preset cloud computing platform, this invention can achieve cross-entity connectivity of logistics data resources and service capabilities. This invention forms an interactive platform for multi-party collaborative operations, facilitating subsequent analysis and processing of symbiotic modes. Then, based on the collaborative mechanism, the invention calculates the collaborative benefits generated by the symbiotic units during collaborative scheduling, measuring the overall gain level of the symbiotic units in joint operation, thus providing a basis for determining the benefit allocation coefficients corresponding to the symbiotic units. Finally, by combining the equivalent of transportation resources, the unit symbiotic coefficient, and the risk-sharing mechanism, the invention executes the scheduling and allocation of logistics tasks in the cross-border e-commerce logistics system, thereby improving the scheduling efficiency of cross-border e-commerce logistics. Therefore, the invention can improve the efficiency of intelligent scheduling of cloud-based cross-border e-commerce logistics.

[0083] like Figure 4 The diagram shown is a functional module diagram of the intelligent scheduling system for cross-border e-commerce logistics based on cloud computing according to the present invention.

[0084] The cloud-based intelligent scheduling system 400 for cross-border e-commerce logistics described in this invention can be installed in an electronic device. Depending on the functions implemented, the cloud-based intelligent scheduling system 400 may include a resource equivalent calculation module 401, a symbiotic coefficient calculation module 402, a collaborative mechanism establishment module 403, a risk-sharing mechanism construction module 404, and an intelligent scheduling and allocation module 405. The modules described in this invention can also be referred to as units, which are a series of computer program segments that can be executed by the processor of an electronic device and perform a fixed function, stored in the memory of the electronic device.

[0085] In this embodiment of the invention, the functions of each module / unit are as follows: The resource equivalent calculation module 401 is used to collect the unit operation data of each symbiotic unit in the cross-border e-commerce logistics system and then calculate the transportation capacity resource equivalent corresponding to the symbiotic unit. The co-occurrence coefficient calculation module 402 is used to extract the unit quality parameters of the co-occurring units and calculate the unit co-occurrence coefficient between the co-occurring units based on the unit quality parameters. The collaboration mechanism establishment module 403 is used to construct a symbiotic interface between the logistics service object and the symbiotic unit in the cross-border e-commerce logistics system based on a preset cloud computing platform. Based on the symbiotic interface, after analyzing the symbiotic mode corresponding to the symbiotic unit, a collaboration mechanism between the symbiotic units is established. The risk-sharing mechanism construction module 404 is used to calculate the collaborative benefits generated by the symbiotic units during the collaborative scheduling process based on the collaborative mechanism, and after determining the benefit allocation coefficient corresponding to the symbiotic units according to the collaborative benefits, construct a risk-sharing mechanism among the symbiotic units. The intelligent scheduling and allocation module 405 is used to combine the equivalent of the transportation capacity resources, the unit symbiosis coefficient and the risk-sharing mechanism to execute the scheduling and allocation of logistics tasks in the cross-border e-commerce logistics system, and obtain intelligent logistics scheduling results.

[0086] In detail, the modules in the cloud-based intelligent dispatching system 400 for cross-border e-commerce logistics described in this embodiment of the invention employ the same methods as described above. Figure 1 The method uses the same technical means as the cloud computing-based intelligent scheduling method for cross-border e-commerce logistics described in the article and can produce the same technical effect, so it will not be elaborated here.

[0087] like Figure 5 The diagram shown is a structural schematic of an electronic device for implementing a cloud computing-based intelligent scheduling method for cross-border e-commerce logistics, according to an embodiment of the present invention.

[0088] The electronic device 1 may include a processor 10, a memory 11 and a bus 12, and may also include a computer program stored in the memory 11 and capable of running on the processor 10, such as a cloud computing-based intelligent scheduling method program for cross-border e-commerce logistics.

[0089] The memory 11 includes at least one type of readable storage medium, such as flash memory, portable hard drive, multimedia card, card-type memory (e.g., SD or DX memory), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 11 can be an internal storage unit of the electronic device 1, such as the portable hard drive of the electronic device 1. In other embodiments, the memory 11 can be an external storage device of the electronic device 1, such as a plug-in portable hard drive, smart media card (SMC), secure digital card (SD), flash card, etc., equipped on the electronic device 1. Furthermore, the memory 11 includes both internal storage units and external storage devices of the electronic device 1. The memory 11 can be used not only to store application software and various types of data installed on the electronic device 1, such as the code of a cloud-based intelligent scheduling method program for cross-border e-commerce logistics, but also to temporarily store data that has been output or will be output.

[0090] In some embodiments, the processor 10 may be composed of integrated circuits, such as a single packaged integrated circuit or multiple integrated circuits with the same or different functions, including combinations of one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and various control chips. The processor 10 is the control unit of the electronic device, connecting various components of the entire electronic device through various interfaces and lines. It executes programs or modules stored in the memory 11 (e.g., a cloud-based intelligent scheduling method for cross-border e-commerce logistics) and calls data stored in the memory 11 to perform various functions of the electronic device 1 and process data.

[0091] The bus 12 can be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus 12 can be divided into an address bus, a data bus, a control bus, etc. The bus 12 is configured to realize the connection and communication between the memory 11 and at least one processor 10, etc.

[0092] Figure 5 Only electronic devices with components are shown; it will be understood by those skilled in the art that... Figure 5The structure shown does not constitute a limitation on the electronic device 1, and may include fewer or more components than shown, or combine certain components, or have different component arrangements.

[0093] For example, although not shown, the electronic device 1 may also include a power supply (such as a battery) to power the various components. Preferably, the power supply can be logically connected to the at least one processor 10 through a power management device, thereby enabling functions such as charging management, discharging management, and power consumption management. The power supply may also include one or more DC or AC power supplies, recharging devices, power fault detection circuits, power converters or inverters, power status indicators, and other arbitrary components. The electronic device 1 may also include various sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be described in detail here.

[0094] Furthermore, the electronic device 1 may also include a network interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a Wi-Fi interface, a Bluetooth interface, etc.), which is typically used to establish communication connections between the electronic device 1 and other electronic devices.

[0095] Optionally, the electronic device 1 may further include a user interface, which may be a display, an input unit (such as a keyboard), and optionally, a standard wired interface or a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an OLED (Organic Light-Emitting Diode) touchscreen, etc. The display may also be appropriately referred to as a screen or display unit, used to display information processed in the electronic device 1 and to display a visual user interface.

[0096] The cloud-based intelligent scheduling method program for cross-border e-commerce logistics stored in the memory 11 of the electronic device 1 is a combination of multiple instructions. When run in the processor 10, it can achieve the following: After collecting the unit operation data of each symbiotic unit in the cross-border e-commerce logistics system, the equivalent transportation capacity resources corresponding to the symbiotic unit are calculated. Extract the unit quality parameters of the symbiotic units, and calculate the unit co-occurrence coefficient between the symbiotic units based on the unit quality parameters; Based on a pre-set cloud computing platform, a symbiotic interface is constructed between the logistics service objects and the symbiotic units in the cross-border e-commerce logistics system. Based on the symbiotic interface, the symbiotic mode corresponding to the symbiotic unit is analyzed, and a collaborative mechanism between the symbiotic units is established. Based on the aforementioned collaborative mechanism, the collaborative benefits generated by the symbiotic units during the collaborative scheduling process are calculated. After determining the benefit allocation coefficient corresponding to the symbiotic units based on the collaborative benefits, a risk-sharing mechanism among the symbiotic units is constructed. By combining the aforementioned transport capacity equivalent, the aforementioned unit symbiosis coefficient, and the aforementioned risk-sharing mechanism, the scheduling and allocation of logistics tasks in the cross-border e-commerce logistics system are executed to obtain intelligent logistics scheduling results.

[0097] Specifically, the processor 10's implementation method for the above instructions can be found in [reference needed]. Figures 1 to 4 The descriptions of the relevant steps in the corresponding embodiments are not repeated here.

[0098] Furthermore, if the modules / units integrated in the electronic device 1 are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. The computer-readable storage medium can be volatile or non-volatile. For example, the computer-readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, or a read-only memory (ROM).

[0099] The present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor of an electronic device, can perform the following: After collecting the unit operation data of each symbiotic unit in the cross-border e-commerce logistics system, the equivalent transportation capacity resources corresponding to the symbiotic unit are calculated. Extract the unit quality parameters of the symbiotic units, and calculate the unit co-occurrence coefficient between the symbiotic units based on the unit quality parameters; Based on a pre-set cloud computing platform, a symbiotic interface is constructed between the logistics service objects and the symbiotic units in the cross-border e-commerce logistics system. Based on the symbiotic interface, the symbiotic mode corresponding to the symbiotic unit is analyzed, and a collaborative mechanism between the symbiotic units is established. Based on the aforementioned collaborative mechanism, the collaborative benefits generated by the symbiotic units during the collaborative scheduling process are calculated. After determining the benefit allocation coefficient corresponding to the symbiotic units based on the collaborative benefits, a risk-sharing mechanism among the symbiotic units is constructed. By combining the aforementioned transport capacity equivalent, the aforementioned unit symbiosis coefficient, and the aforementioned risk-sharing mechanism, the scheduling and allocation of logistics tasks in the cross-border e-commerce logistics system are executed to obtain intelligent logistics scheduling results.

[0100] In the embodiments provided by this invention, it should be understood that the disclosed devices, systems, and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative, and actual implementations may have other classification methods.

[0101] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0102] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0103] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0104] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A cloud computing-based intelligent scheduling method for cross-border e-commerce logistics, characterized in that: The method includes: After collecting the unit operation data of each symbiotic unit in the cross-border e-commerce logistics system, the equivalent transportation capacity resources corresponding to the symbiotic unit are calculated. Extract the unit quality parameters of the symbiotic units, and calculate the unit co-occurrence coefficient between the symbiotic units based on the unit quality parameters; Based on a pre-set cloud computing platform, a symbiotic interface is constructed between the logistics service objects and the symbiotic units in the cross-border e-commerce logistics system. Based on the symbiotic interface, the symbiotic mode corresponding to the symbiotic unit is analyzed, and a collaborative mechanism between the symbiotic units is established. Based on the aforementioned collaborative mechanism, the collaborative benefits generated by the symbiotic units during the collaborative scheduling process are calculated. After determining the benefit allocation coefficient corresponding to the symbiotic units based on the collaborative benefits, a risk-sharing mechanism among the symbiotic units is constructed. By combining the aforementioned transport capacity equivalent, the aforementioned unit symbiosis coefficient, and the aforementioned risk-sharing mechanism, the scheduling and allocation of logistics tasks in the cross-border e-commerce logistics system are executed to obtain intelligent logistics scheduling results.

2. The cloud computing-based intelligent scheduling method for cross-border e-commerce logistics as described in claim 1, characterized in that, After collecting the unit operation data of each symbiotic unit in the cross-border e-commerce logistics system, the equivalent transportation capacity resources corresponding to the symbiotic unit are calculated, including: Extract the job timeliness data and job saturation data from the unit operation data; Based on the aforementioned job timeliness data, the unit job efficiency of the symbiotic unit is determined; Obtain the unit configuration information of the symbiotic unit, and determine the unit baseline capability corresponding to the symbiotic unit based on the unit configuration information; Based on the job saturation data and the unit baseline capability, the effective job capability of the symbiotic unit is calculated. Based on the unit's operational efficiency and effective operational capability, the equivalent of the transportation resources corresponding to the symbiotic unit is calculated.

3. The cloud computing-based intelligent scheduling method for cross-border e-commerce logistics as described in claim 2, characterized in that, The calculation of the equivalent transportation resources corresponding to the symbiotic unit based on the unit's operational efficiency and effective operational capacity includes: Measure the standard working hours corresponding to the unit workload of the work object in the symbiotic unit; Based on the effective operational capabilities of the unit, calculate the resource conversion factor corresponding to the symbiotic unit; Retrieve the historical task completion volume corresponding to the symbiotic unit, and calculate the task completion rate corresponding to the symbiotic unit based on the historical task completion volume and the preset benchmark task volume; The equivalent of transportation resources corresponding to the symbiotic unit is calculated by combining the standard operating hours, the resource conversion factor, the operation completion rate, the unit operation efficiency, and the unit effective operation capacity.

4. The cloud computing-based intelligent scheduling method for cross-border e-commerce logistics as described in claim 1, characterized in that, The extraction of the unit quality parameters of the co-occurring unit includes: Determine the initial index pool of the symbiotic unit, and filter out candidate indicators of the symbiotic unit from the initial index pool; The candidate indicators are dimensionless to obtain standard indicators; the correlation coefficient between the standard indicators is calculated. Based on the correlation coefficient of the aforementioned indicators, the core indicators among the candidate indicators are selected. Based on the aforementioned core indicators, the unit quality parameters of the symbiotic unit are determined.

5. The cloud computing-based intelligent scheduling method for cross-border e-commerce logistics as described in claim 1, characterized in that, The calculation of the co-occurrence coefficient between the co-occurring units based on the unit quality parameters includes: Collect the original time series data of the symbiotic unit with respect to the unit quality parameter, and perform alignment processing on the original time series data according to the preset time interval to obtain the synchronization time series data; Based on the synchronous time-series data, the relative rate of change of the unit mass parameter within the preset time interval is calculated; Based on the relative rate of change and the unit quality parameter, the degree of unit co-occurrence between the co-occurring units is calculated; Based on the unit co-occurrence degree, the unit co-occurrence coefficient between the co-occurring units is calculated.

6. The cloud computing-based intelligent scheduling method for cross-border e-commerce logistics as described in claim 1, characterized in that, The symbiotic interface between the logistics service object and the symbiotic unit in the cross-border e-commerce logistics system, constructed based on a preset cloud computing platform, includes: Obtain the historical business records of the logistics service object and the historical service records of the symbiotic unit; The historical business records and the historical service records are matched to obtain a business-service association set; Interaction nodes are extracted from the business-service association set to obtain a node distribution map; Frequency clustering is performed on the node distribution map to obtain densely interactive regions; By combining the densely interactive area with the preset cloud computing platform, a symbiotic interface is constructed between the logistics service object and the symbiotic unit.

7. The cloud computing-based intelligent scheduling method for cross-border e-commerce logistics as described in claim 1, characterized in that, The analysis of the symbiotic mode corresponding to the symbiotic unit based on the symbiotic interface includes: The interaction records generated on the symbiotic interface are processed to identify the business flow characteristics. Assign the interaction dependency degree corresponding to the business flow characteristics; By combining the business flow characteristics and the interaction dependency, the symbiotic mode corresponding to the symbiotic unit is determined.

8. The cloud computing-based intelligent scheduling method for cross-border e-commerce logistics as described in claim 1, characterized in that, The establishment of a collaborative mechanism among the symbiotic units includes: The business interactions of the symbiotic units under the symbiotic mode are decomposed into nodes to obtain the collaborative operation link; The task connection paths of the collaborative task chain are analyzed, the adaptable resources of the collaborative task chain are identified, and a resource configuration list is obtained. By combining the collaborative operation link, the operation connection path, and the resource configuration list, a collaborative mechanism is established among the symbiotic units.

9. The cloud computing-based intelligent scheduling method for cross-border e-commerce logistics as described in claim 1, characterized in that, The risk-sharing mechanism among the symbiotic units, based on the benefit allocation coefficient, includes: Based on the benefit allocation coefficient, the risk-bearing ratio of the symbiotic unit in the collaborative scheduling is determined; Analyze the risk triggering scenarios corresponding to the symbiotic unit, and determine the risk responsibility fulfillment method of the symbiotic unit based on the risk triggering scenarios; By combining the risk-sharing ratio and the risk responsibility fulfillment method, a risk-sharing mechanism is constructed among the symbiotic units.

10. A cloud-based intelligent scheduling system for cross-border e-commerce logistics, characterized in that: The system includes: The resource equivalent calculation module is used to collect the unit operation data of each symbiotic unit in the cross-border e-commerce logistics system and then calculate the transportation capacity resource equivalent corresponding to the symbiotic unit. The co-occurrence coefficient calculation module is used to extract the unit quality parameters of the co-occurring units and calculate the unit co-occurrence coefficient between the co-occurring units based on the unit quality parameters. The collaboration mechanism establishment module is used to construct a symbiotic interface between the logistics service object and the symbiotic unit in the cross-border e-commerce logistics system based on a preset cloud computing platform. Based on the symbiotic interface, after analyzing the symbiotic mode corresponding to the symbiotic unit, a collaboration mechanism is established between the symbiotic units. The risk-sharing mechanism construction module is used to calculate the collaborative benefits generated by the symbiotic units during the collaborative scheduling process based on the collaborative mechanism, and after determining the benefit allocation coefficient corresponding to the symbiotic units according to the collaborative benefits, construct the risk-sharing mechanism among the symbiotic units. The intelligent scheduling and allocation module is used to combine the equivalent of the transportation capacity resources, the unit symbiosis coefficient, and the risk-sharing mechanism to execute the scheduling and allocation of logistics tasks in the cross-border e-commerce logistics system, and obtain intelligent logistics scheduling results.

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