An efficient logistics booking management method and system based on big data

Abstract

The present invention relates to the field of logistics management technology, and in particular to an efficient logistics booking management method and system based on big data. An efficient logistics booking management method based on big data, comprising the following steps: S1: obtaining logistics customer cancellation data, and obtaining customer portrait data based on the customer cancellation data; S2: obtaining logistics adjustment priority and protection adjustment priority based on the customer cancellation type and customer portrait data, and making preliminary compensation adjustments to the customer based on the logistics adjustment priority and protection adjustment priority; S3: obtaining space compensation value based on the logistics adjustment priority, protection adjustment priority and target customer booking space data. By distinguishing between active and passive customer cancellations and generating multi-dimensional customer portraits, the present invention can realize differentiated priority calculation and compensation strategies, thereby improving efficient and optimized management of customers when rebooking.

Description

Technical Field

[0001] The present invention relates to the technical field of logistics management, and in particular to an efficient logistics booking management method and system based on big data. Background Art

[0002] With the continuous expansion of global trade and the rapid growth of e-commerce, the demand for ocean freight bookings has skyrocketed. However, traditional booking management systems are mostly based on manual or simple rule-based scheduling, which cannot fully utilize massive historical data. They have the following major deficiencies: insufficient analysis of cancellation behavior. The existing system often treats customers' active and passive cancellations equally, lacks a detailed distinction between cancellation types and cancellation frequencies, and is difficult to implement differentiated compensation and priority management for different customer groups, resulting in unfair resource allocation and reduced customer satisfaction; most platforms only use simple timeliness or cargo value indicators to measure cabin priority, ignoring the deep-seated influencing factors such as the customer's historical cancellation time difference and the number of cancellation segments, making it difficult to objectively reflect the true extent of the impact on customers due to their own adjustments or insufficient transportation capacity. Summary of the Invention

[0003] In order to overcome the shortcoming of existing systems that often treat customers' active and passive cancellations equally, the present invention provides an efficient logistics booking management method and system based on big data.

[0004] The technical implementation scheme of the present invention is: an efficient logistics booking management method based on big data, comprising the following steps:

[0005] S1: Obtain logistics customer check-out data, and obtain customer profile data based on the customer check-out data;

[0006] S2: Obtaining the logistics adjustment priority and protection adjustment priority based on the customer cancellation type and customer profile data, and making preliminary compensation adjustments to the customer based on the logistics adjustment priority and protection adjustment priority;

[0007] S3: Obtain space compensation value based on logistics adjustment priority, protection adjustment priority, and target customer booking space data;

[0008] S4: Make final compensation adjustments to target customers based on the space compensation value.

[0009] Preferably, the obtaining of logistics customer check-in data and obtaining of customer portrait data based on the customer check-in data include: obtaining logistics customer check-in data, the customer check-in data including the number of customer check-ins and the types of customer check-ins, wherein the types of customer check-ins include active check-ins and passive check-ins, the active check-in being the behavior of the customer actively checking in; the passive check-in being the behavior of the customer passively checking in due to overbooking; obtaining of customer portrait data based on the types of customer check-ins and the number of customer check-ins, the customer portrait data including the logistics departure time, the customer's logistics arrangement time, the importance of the customer's goods, the number of passive check-ins by the customer within a preset time period, and the time of passive check-ins.

[0010] Preferably, before obtaining the customer portrait data according to the customer's cabin check-out type and customer's cabin check-out frequency, the method includes: performing data preprocessing on the customer portrait data by removing abnormal values, filling missing values, and removing duplicate values.

[0011] Preferably, obtaining the logistics adjustment priority and the protection adjustment priority according to the customer's cancellation type and customer profile data, and making preliminary compensation adjustments to the customer according to the logistics adjustment priority and the protection adjustment priority, includes:

[0012] If the customer's cancellation type is voluntary, the logistics adjustment formula is used based on the customer profile data to obtain the logistics adjustment priority, and the customer's logistics scheduling is adjusted based on the logistics adjustment priority;

[0013] If the customer's cancellation type is passive cancellation, the protection adjustment priority is obtained using the protection adjustment formula based on the customer portrait data, and the order of overbooked customers' cancellation is adjusted according to the protection adjustment priority.

[0014] Preferably, if the type of the customer's cabin cancellation is an active cabin cancellation, the logistics adjustment priority is obtained using a logistics adjustment formula according to the customer profile data, including: wherein the logistics adjustment formula is:

[0015] ;

[0016] Where, Adjust priorities for logistics; The first logistics departure time; Arrange the logistics time for the i-th customer; im is the importance of the customer's goods; is the adjustment factor; is the weight adjustment factor.

[0017] Preferably, if the type of the customer's cabin cancellation is passive cancellation, the protection adjustment priority is obtained using a protection adjustment formula according to the customer profile data, including: wherein the protection adjustment formula is:

[0018] ;

[0019] Where P is the protection adjustment priority; N is the number of times a customer has been forced to withdraw from the cabin within the preset time period; m is the number of segments in which a customer has been forced to withdraw from the cabin within the preset time period; is the length of the j-th run; 、 To adjust the parameters; 、 is the weight adjustment parameter of the protection adjustment formula; im is the importance of the customer's goods.

[0020] Preferably, m is the number of segments in which the customer passively unloads within a preset time period, including: mapping the customer's passive unloading situation within the preset time period into a binary sequence, taking the situation in which the passive unloading occurs as the first data; otherwise, as the second data, and taking all adjacent first data in the binary sequence as a segment of continuous operation, ultimately obtaining the number of segments in which the customer passively unloads within the preset time period, and obtaining the length of each segment according to the number of segments in which the customer passively unloads within the preset time period.

[0021] Preferably, obtaining the space compensation value according to the logistics adjustment priority, the protection adjustment priority, and the target customer booking space data includes: obtaining the space compensation value using a space compensation formula according to the logistics adjustment priority, the protection adjustment priority, and the target customer booking space data, wherein the target customer booking space data is the warehouse space size reserved by the target customer, and the space compensation formula is:

[0022] ;

[0023] Where F is the space compensation value; S is the warehouse space size reserved by the target customer; Adjust the priority for logistics of target customers; P adjusts the priority for protection of target customers; 、 is the weight adjustment coefficient of the space compensation formula.

[0024] Preferably, the final compensation adjustment for the target customer based on the space compensation value includes: setting up a virtual cabin roaming facility for the cabin space, obtaining the target customer's adjustable compensation space based on the space compensation value, showing the target customer the space layout and cargo placement effect in the cabin based on the adjustable compensation space, and accepting the target customer's layout adjustment and placement adjustment of the adjustable compensation space.

[0025] Preferably, an efficient logistics booking management system based on big data includes:

[0026] A data acquisition module is used to acquire logistics customer check-out data and obtain customer profile data based on the customer check-out data;

[0027] A preliminary adjustment module is used to obtain the logistics adjustment priority and the protection adjustment priority based on the customer's cancellation type and customer profile data, and to make preliminary compensation adjustments to the customer based on the logistics adjustment priority and the protection adjustment priority;

[0028] The first adjustment acquisition module is used to obtain the logistics adjustment priority using the logistics adjustment formula based on the customer profile data;

[0029] The second adjustment acquisition module is used to obtain the protection adjustment priority using the protection adjustment formula according to the customer profile data;

[0030] The segment acquisition module is used to obtain the number of segments that the customer has passively canceled within a preset time period;

[0031] A compensation value acquisition module is used to obtain a space compensation value based on logistics adjustment priority, protection adjustment priority, and target customer booking space data;

[0032] The final adjustment module is used to make final compensation adjustments to target customers based on the space compensation value.

[0033] The beneficial effects are:

[0034] 1. This invention distinguishes between active and passive cancellations by customers, and generates multi-dimensional customer profiles by counting the number of customer cancellations and the types of customer cancellations. This enables differentiated priority calculation and compensation strategies, improving the accuracy of classification management.

[0035] 2. This invention uses logistics adjustment formulas and protection adjustment formulas to scientifically quantify the comprehensive impact of customer profile data on customer experience, ensuring that priority assessment is more objective and reasonable;

[0036] 3. The present invention introduces a virtual cabin roaming facility and uses a space compensation formula to obtain a space compensation value. According to the space compensation value, the interpretability of the compensation scheme and user participation are improved, thereby enhancing customer satisfaction. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 This is a flow chart of the efficient logistics booking management method based on big data of the present invention;

[0038] Figure 2 This is a structural diagram of the efficient logistics booking management system based on big data of the present invention. DETAILED DESCRIPTION

[0039] The above scheme is further described below with reference to specific examples. It should be understood that these examples are intended to illustrate the present application and are not intended to limit the scope of the present application. The implementation conditions used in the examples can be further adjusted according to the conditions of the specific manufacturer. The implementation conditions not specified are generally those used in routine experiments.

[0040] Example 1: An efficient logistics booking management method based on big data, such as Figure 1 As shown, the following steps are included:

[0041] S1: Obtain logistics customer check-out data, and obtain customer profile data based on the customer check-out data;

[0042] Obtain logistics customer check-in data, the customer check-in data including the number of customer check-ins and the type of customer check-in, wherein the type of customer check-in includes active check-in and passive check-in, the active check-in being the customer's active check-in; the passive check-in being the customer's passive check-in due to overbooking; obtain customer portrait data based on the customer check-in type and the number of customer check-in, the customer portrait data including the logistics departure time, the customer's logistics arrangement time, the importance of the customer's goods, the number of passive check-ins by the customer within a preset time period, and the time of passive check-ins.

[0043] It should be explained that the target customer's customer cancellation data is obtained through real-time synchronization with the interface of the freight forwarder or shipping company's reservation system. The customer cancellation data includes the number of customer cancellations: the total number of cancellation operations submitted by customers within a predetermined time window (for example, within the past three months); the types of customer cancellations include active cancellations and passive cancellations; active cancellations: refers to the behavior of customers actively initiating cancellation applications due to their own business adjustments or cancellation needs; passive cancellations: refers to the behavior of the operator or scheduling system forcibly canceling the original space due to overbooking, insufficient space, or automatic downgrade; logistics departure time: the time of each actual loading or shipment; customer logistics scheduling time: the arrival time and loading time requirements of the customer's scheduled goods; the importance of the customer's goods: the cargo priority field filled in by the customer when placing an order or a score based on historical default costs and complaint rates; the number of passive cancellations within a preset time period: the total number of passive cancellations that occurred for the customer, for example, in the past three months or the last five orders; the time of passive cancellations: specifically the time of each passive cancellation, which is used for subsequent continuous segment analysis.

[0044] Data preprocessing is performed on customer portrait data to remove outliers, fill missing values, and remove duplicate values.

[0045] It should be explained that: outliers are removed: outliers in the number of cancellations caused by errors or repeated orders are eliminated, such as cancellation events where an order is recorded repeatedly; missing values ​​are filled: for missing cancellation timestamps or cancellation type identifiers, nearest neighbor interpolation or business rules (such as the default mark as active cancellation) are used to complete them; duplicate values ​​are removed: for multiple records of the same customer, the same order number and the same cancellation time, only one valid record is retained to prevent redundant statistics.

[0046] S2: Obtaining the logistics adjustment priority and protection adjustment priority based on the customer cancellation type and customer profile data, and making preliminary compensation adjustments to the customer based on the logistics adjustment priority and protection adjustment priority;

[0047] If the customer's cancellation type is voluntary, the logistics adjustment formula is used based on the customer profile data to obtain the logistics adjustment priority, and the customer's logistics scheduling is adjusted based on the logistics adjustment priority;

[0048] If the customer's cancellation type is passive cancellation, the protection adjustment priority is obtained using the protection adjustment formula based on the customer portrait data, and the order of overbooked customers' cancellation is adjusted according to the protection adjustment priority.

[0049] It needs to be explained that, for customers who actively cancel their cabins, the logistics adjustment formula is called according to their customer portrait data to calculate the logistics adjustment priority, and compensatory adjustments are made to the customer's logistics arrangement time according to the logistics adjustment priority to advance the customer's logistics arrangement time, so as to indirectly improve the safety of the customer's goods and reduce the risk of congestion caused by nearby departure loading; for customers who passively cancel their cabins, the protection adjustment formula is also called according to their customer portrait data to calculate the protection adjustment priority. According to the protection adjustment priority, when subsequently reallocating resources for customers who cancel their cabins due to overbooking, the space requirements of customers with the highest protection adjustment priority will be met first.

[0050] The logistics adjustment formula is:

[0051] ;

[0052] Where, Adjust priorities for logistics; is the departure time of the i-th logistics; Arrange the logistics time for the i-th customer; im is the importance of the customer's goods; is the adjustment factor; is the weight adjustment factor.

[0053] It should be explained that the proactive cancellation behavior of customers when shipment is imminent will be analyzed. If the proactive cancellation behavior is caused by temporary changes in customer demand or temporary increases in unexpected circumstances when shipment is imminent, the logistics arrangement time for such customers will be changed to increase the fault tolerance space for such customers when booking next time.

[0054] The protection adjustment formula is:

[0055] ;

[0056] Where P is the protection adjustment priority; N is the number of times a customer has been forced to withdraw from the cabin within the preset time period; m is the number of segments in which a customer has been forced to withdraw from the cabin within the preset time period; is the length of the j-th run; 、 To adjust the parameters; 、 is the weight adjustment parameter of the protection adjustment formula; im is the importance of the customer's goods.

[0057] It should be explained that N is the number of involuntary cancellations by the customer within a preset time period, which is the total number of involuntary cancellations by the customer due to overbooking and insufficient space within the preset time period (for example, the past three months or the past five order cycles). Through the protection adjustment formula, attention is paid to customers with a large number of involuntary cancellations and customers who have been involuntary cancellations continuously. ,Mapping business: When two or more cancellations occur in close proximity, it indicates a series of setbacks experienced by customers; , Mapping business: The negative impact of multiple consecutive cancellations often accumulates exponentially. For example, if there are three consecutive cancellations, customer trust will quickly collapse.

[0058] The passive cabin check-out situation of customers within a preset time period is mapped into a binary sequence, where the situation where a passive cabin check-out occurs is used as the first data; otherwise, it is used as the second data, and all adjacent first data in the binary sequence are used as a segment of continuous operation. Finally, the number of segments in which the customer passively checks-out within the preset time period is obtained, and the length of each segment is obtained based on the number of segments in which the customer passively checks-out within the preset time period.

[0059] It should be explained that the passive cancellation situation in a period of time (or several orders) is mapped into a binary sequence.

[0060] ;

[0061] In the binary sequence above, consider all adjacent "1"s as a continuous run, for example, the sequence:

[0062] ;

[0063] It can be divided into three sections for continuous operation, the first section is: 1,1,1 (length is 3); the second segment is: single 1 (length is 1); the third segment is: two 1s (length is 2); therefore, the number of segments that the customer is forced to exit during the preset time period is 3, and the length of each segment can be obtained.

[0064] S3: Obtain space compensation value based on logistics adjustment priority, protection adjustment priority, and target customer booking space data;

[0065] The space compensation value is obtained using the space compensation formula based on the logistics adjustment priority, protection adjustment priority, and target customer booking space data. The target customer booking space data is the warehouse space size reserved by the target customer. The space compensation formula is:

[0066] ;

[0067] Where F is the space compensation value; S is the warehouse space size reserved by the target customer; Adjust the priority for logistics of target customers; P adjusts the priority for protection of target customers; 、 is the weight adjustment coefficient of the space compensation formula.

[0068] It needs to be explained that the warehouse space size reserved by the customer this time is extracted from the customer order system for setting the subsequent compensation space upper limit; the logistics adjustment priority and protection adjustment priority are obtained, and the two types of priorities are combined into the input of the exponential function according to the weights to obtain the space compensation value. The space compensation value is the space size that the target customer can view and adjust based on the virtual cabin roaming facility.

[0069] S4: Make final compensation adjustments to target customers based on the space compensation value.

[0070] A virtual cabin roaming facility is set up for the cabin space, and the target customer's adjustable compensation space is obtained according to the space compensation value. The space layout and cargo placement effect in the cabin are displayed to the target customer based on the adjustable compensation space, and the target customer's layout adjustment and placement adjustment of the adjustable compensation space are accepted.

[0071] It should be explained that customers enter the virtual cabin space roaming interface through the web or client, and are automatically positioned in the compensable area, where the compensable area is a space displayed according to the size of the space compensation value, and the cabin corridors, racks and cargo placement are displayed in a dynamic roaming perspective; the initial placement plan is automatically generated according to the customer's original cargo specifications and quantity, and a three-dimensional model of the cargo is rendered and displayed in the compensable area; the customer can use the mouse or touch screen to drag, rotate and stack the cargo in the compensable space, and get real-time feedback on the space occupancy and remaining available space after loading; when the customer is satisfied with the layout, he clicks the Confirm Compensation Layout button to write the final cabin space layout plan (including the adjusted cargo position and size data) into the scheduling management module for actual booking and loading. After confirmation, the customer will rate and leave a message on the virtual roaming experience and compensation effect, and the feedback will be used as the basis for subsequent priority model and interactive interface optimization.

[0072] Example 2: Based on Example 1, an efficient logistics booking management system based on big data, such as Figure 2 Shown, including:

[0073] A data acquisition module is used to acquire logistics customer check-out data and obtain customer profile data based on the customer check-out data;

[0074] A preliminary adjustment module is used to obtain the logistics adjustment priority and the protection adjustment priority based on the customer's cancellation type and customer profile data, and to make preliminary compensation adjustments to the customer based on the logistics adjustment priority and the protection adjustment priority;

[0075] The first adjustment acquisition module is used to obtain the logistics adjustment priority using the logistics adjustment formula based on the customer profile data;

[0076] The second adjustment acquisition module is used to obtain the protection adjustment priority using the protection adjustment formula according to the customer profile data;

[0077] The segment acquisition module is used to obtain the number of segments that the customer has passively canceled within a preset time period;

[0078] A compensation value acquisition module is used to obtain a space compensation value based on logistics adjustment priority, protection adjustment priority, and target customer booking space data;

[0079] The final adjustment module is used to make final compensation adjustments to target customers based on the space compensation value.

[0080] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. An efficient logistics booking management method based on big data, characterized in that: The following steps are involved: S1: Obtain logistics customer check-out data, and obtain customer profile data based on the customer check-out data; S2: Obtaining the logistics adjustment priority and protection adjustment priority based on the customer cancellation type and customer profile data, and making preliminary compensation adjustments to the customer based on the logistics adjustment priority and protection adjustment priority; S3: Obtain space compensation value based on logistics adjustment priority, protection adjustment priority, and target customer booking space data; S4: Make final compensation adjustments to target customers based on the space compensation value; The method of obtaining the logistics adjustment priority and the protection adjustment priority based on the customer cancellation type and customer profile data, and making preliminary compensation adjustments to the customer based on the logistics adjustment priority and the protection adjustment priority, includes: If the customer's cancellation type is voluntary, the logistics adjustment formula is used based on the customer profile data to obtain the logistics adjustment priority, and the customer's logistics scheduling is adjusted based on the logistics adjustment priority; If the customer's cancellation type is passive cancellation, the protection adjustment priority is obtained using the protection adjustment formula based on the customer profile data, and the order of overbooked customers' cancellations is adjusted according to the protection adjustment priority; Including: The logistics adjustment formula is: ； Where, Adjust priorities for logistics; is the departure time of the i-th logistics; Arrange the logistics time for the i-th customer; im is the importance of the customer's goods; is the adjustment coefficient; ω is the weight adjustment factor; Including: The protection adjustment formula is: ； Where P is the protection adjustment priority; N is the number of times a customer has been forced to withdraw from the cabin within the preset time period; m is the number of segments in which a customer has been forced to withdraw from the cabin within the preset time period; For the The length of the segment run; To adjust the parameters; is the weight adjustment parameter of the protection adjustment formula; im is the importance of the customer's goods.

2. The efficient logistics booking management method based on big data according to claim 1, characterized in that: The obtaining of logistics customer check-in data and obtaining of customer portrait data based on the customer check-in data include: obtaining logistics customer check-in data, the customer check-in data including the number of customer check-ins and the type of customer check-ins, wherein the type of customer check-in includes active check-in and passive check-in, the active check-in being the behavior of the customer actively checking in; the passive check-in being the behavior of the customer passively checking in due to overbooking; obtaining of customer portrait data based on the type of customer check-in and the number of customer check-in, the customer portrait data including the logistics departure time, the customer's logistics arrangement time, the importance of the customer's goods, the number of passive check-ins by the customer within a preset time period, and the time of passive check-ins.

3. The efficient logistics booking management method based on big data according to claim 2, characterized in that: Before obtaining the customer portrait data according to the customer's cancellation type and the customer's cancellation frequency, the method includes: performing data preprocessing on the customer portrait data by removing abnormal values, filling missing values, and removing duplicate values.

4. The efficient logistics booking management method based on big data according to claim 1, characterized in that: described The number of segments in which the customer is passively unloaded within a preset time period includes: mapping the passive unloading situation of the customer within the preset time period into a binary sequence, using the situation in which the passive unloading occurs as the first data; otherwise, using the situation as the second data, and using all adjacent first data in the binary sequence as a segment of a continuous run, ultimately obtaining the number of segments in which the customer is passively unloaded within the preset time period, and obtaining the length of each run segment based on the number of segments in which the customer is passively unloaded within the preset time period.

5. The efficient logistics booking management method based on big data according to claim 1, characterized in that: The obtaining of the space compensation value according to the logistics adjustment priority, the protection adjustment priority, and the target customer booking space data includes: obtaining the space compensation value according to the logistics adjustment priority, the protection adjustment priority, and the target customer booking space data using a space compensation formula, wherein the target customer booking space data is the warehouse space size reserved by the target customer, and the space compensation formula is: ； Where F is the space compensation value; S is the warehouse space size reserved by the target customer; Adjust the priority P for the logistics of target customers; adjust the priority P for the protection of target customers; is the weight adjustment coefficient of the space compensation formula.

6. The efficient logistics booking management method based on big data according to claim 5, characterized in that: The final compensation adjustment for the target customer based on the space compensation value includes: setting up a virtual cabin roaming facility for the cabin space, obtaining the target customer's adjustable compensation space based on the space compensation value, showing the target customer the space layout and cargo placement effect in the cabin based on the adjustable compensation space, and accepting the target customer's layout adjustment and placement adjustment of the adjustable compensation space.

7. An efficient logistics booking management system based on big data, an efficient logistics booking management method based on big data according to any one of claims 1 to 6, characterized in that: Also includes: A data acquisition module is used to acquire logistics customer check-out data and obtain customer profile data based on the customer check-out data; A preliminary adjustment module is used to obtain the logistics adjustment priority and the protection adjustment priority based on the customer's cancellation type and customer profile data, and to make preliminary compensation adjustments to the customer based on the logistics adjustment priority and the protection adjustment priority; The first adjustment acquisition module is used to obtain the logistics adjustment priority using the logistics adjustment formula based on the customer profile data; The second adjustment acquisition module is used to obtain the protection adjustment priority using the protection adjustment formula according to the customer profile data; The segment acquisition module is used to obtain the number of segments that the customer has passively canceled within a preset time period; A compensation value acquisition module is used to obtain a space compensation value based on logistics adjustment priority, protection adjustment priority, and target customer booking space data; The final adjustment module is used to make final compensation adjustments to target customers based on the space compensation value.

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