E-commerce system based on network

Abstract

The invention discloses an electronic commerce system based on a network, and relates to the technical field of logistics selection, and the system comprises the steps: recording a current to-be-selected logistics distribution cargo as a first cargo, recording a cargo type corresponding to the first cargo as a target cargo type, and recording each cargo which is subjected to logistics distribution and corresponds to the target cargo type as a second cargo; obtaining order state data of the first cargo and order state data of each second cargo, calculating a similarity value of the order state data of the first cargo and the order state data of each second cargo, and screening out a target logistics type set; obtaining order distribution state data corresponding to each target logistics type, and calculating a logistics distribution priority coefficient; the first goods are distributed according to the target logistics type corresponding to the maximum logistics distribution priority coefficient; in this way, a proper logistics distribution company can be selected according to the actual state of the new order, goods damage or additional cost in the distribution process is reduced, and losses of merchants and customers are reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of logistics selection, and in particular to an e-commerce system based on a network. Background Art

[0002] Web-based e-commerce systems usually refer to systems that use the Internet platform to conduct transactions, payments, and distribution of goods or services. Such systems not only include basic functions such as product browsing, selection, and payment, but also involve multiple links such as order management, customer information processing, inventory management, and distribution scheduling; greatly improving the operational efficiency of merchants. In the traditional e-commerce model, logistics and distribution usually rely on the courier companies that merchants choose to cooperate with; with the surge in order volume and the diversification of customer needs, a single logistics option can no longer meet the delivery requirements of all orders. In the existing web-based e-commerce system, more and more merchants choose to cooperate with multiple logistics companies to achieve high-quality delivery of orders.

[0003] In the actual selection of logistics distribution companies for orders, network-based e-commerce systems often default to selecting the main logistics company that the merchant cooperates with or a lower-cost logistics service provider for new orders; however, different orders have different statuses. If the appropriate logistics distribution company cannot be selected according to the actual status of the new order, it may cause damage to the goods during the delivery process or incur additional costs, causing greater losses to both merchants and customers. Summary of the invention

[0004] The purpose of the present invention is to solve the above-mentioned problem that if the appropriate logistics distribution company is not selected according to the actual status of the new order, it may cause damage to the goods during the distribution process or additional costs, causing great losses to both merchants and customers, and to provide a network-based e-commerce system.

[0005] The present invention provides a network-based e-commerce system, the system comprising:

[0006] Marking module: record the goods currently to be selected for logistics distribution as the first goods, record the goods type corresponding to the first goods as the target goods type, and record each goods corresponding to the target goods type that have passed logistics distribution as the second goods;

[0007] Screening module: obtaining the order status data of the first goods and the order status data of each second goods, calculating the similarity value between the order status data of the first goods and the order status data of each second goods, and screening out a logistics type set that can deliver the first goods according to the similarity value, recorded as a target logistics type set;

[0008] Delivery priority module: for each target logistics type in the target logistics type set, obtain the corresponding order delivery status data, and calculate the logistics delivery priority coefficient of each target logistics type based on the order delivery status data and the corresponding similarity value; the order delivery status data includes the delivery efficiency coefficient, the delivery time instability coefficient, the damage coefficient and the cost coefficient;

[0009] Distribution module: The target logistics type corresponding to the largest logistics distribution priority coefficient in the target logistics type set is taken as the final logistics type, and the first cargo is distributed.

[0010] Optionally, the similarity values ​​of the order status data of the first goods and the order status data of each second goods are calculated, and a set of logistics types that can deliver the first goods is screened out according to the similarity values, which is recorded as the target logistics type set including:

[0011] Acquire the order status data of the first goods and the order status data of each second goods, and pre-process the order status data of the first goods and the order status data of each second goods;

[0012] Construct a distance matrix, in which each element is the distance between the order status data point of the first product and the order status data of the second product;

[0013] Calculating the shortest path in the distance matrix using a dynamic programming algorithm to find the best match between the order status data sequence of the first product and the order status data sequence of the second product;

[0014] Aligning the order status data sequence of the first product with the order status data sequence of the second product according to the calculated shortest path;

[0015] Calculate the similarity value between the aligned order status data sequence of the first product and the order status data sequence of the second product; and use the calculated similarity value as the similarity value corresponding to the second product;

[0016] According to the similarity values ​​of each second cargo, a set of logistics types that can deliver the first cargo is screened out and recorded as a target logistics type set.

[0017] Optionally, a set of logistics types that can deliver the first goods is screened out according to the similarity values ​​of each second goods, and is recorded as a target logistics type set including:

[0018] Compare the similarity value of each second product with a preset similarity value threshold, and if the similarity value is not less than the preset similarity value threshold, record the corresponding second product as a conforming product;

[0019] Each logistics type corresponding to the logistics distribution of the goods is recorded as the logistics type of the first deliverable goods, and all logistics types of the first deliverable goods are integrated to obtain a target logistics type set.

[0020] Optionally, the delivery efficiency coefficient includes;

[0021] Get the total number of times the second cargo is delivered for each target logistics type, and get the actual total time spent on each delivery and the corresponding preset total time spent, and mark the actual total time and the corresponding preset total time spent as D respectively. m and S m , m represents the sequence number of the number of times the second cargo is delivered by the target logistics type, m=1, 2, 3, 4, ..., n, n represents the total number of times the second cargo is delivered by the target logistics type, and n is a positive integer;

[0022] According to D m and S m Calculate the distribution efficiency coefficient corresponding to the target logistics type. The calculation formula is: Where DF is the distribution efficiency coefficient.

[0023] Optionally, the delivery time instability factor includes:

[0024] Get the total number of times the second cargo is delivered for each target logistics type, and get the total time actually spent on each delivery, and mark the total time actually spent as D m , m represents the sequence number of the number of times the second cargo is delivered by the target logistics type, m=1, 2, 3, 4, ..., n, n represents the total number of times the second cargo is delivered by the target logistics type, and n is a positive integer;

[0025] Calculate the average total time actually spent The calculation formula is:

[0026] Calculate the delivery time instability coefficient of the target logistics type. The calculation formula is:

[0027]

[0028] Among them, HT is the delivery time instability coefficient of the target logistics type.

[0029] Optionally, the damage factor includes:

[0030] Obtain the total number of times the second cargo is delivered for each target logistics type, and obtain the total number of times the cargo is damaged after each delivery. Divide the total number of times the cargo is damaged by the total number of times the second cargo is delivered to obtain the damage rate of the delivered cargo of the target logistics type.

[0031] Obtain the preset damage severity score corresponding to each time the goods are damaged, and calculate the average of the preset damage severity scores as the damage severity value of the target logistics type;

[0032] The damage coefficient of the target logistics type is calculated according to the damage rate and damage severity value of the distribution goods of the target logistics type. The calculation formula is: ER=a1×zx+a2×cv, where ER is the damage coefficient of the target logistics type, zx and cv are the damage rate and damage severity value of the distribution goods of the target logistics type, a1 and a2 are the preset proportional coefficients of zx and cv, respectively, and a1 and a2 are both greater than 0.

[0033] Optionally, calculating the logistics distribution priority coefficient of each target logistics type according to the order distribution status data and the corresponding similarity value includes:

[0034] Obtain the total number of times the second cargo is delivered for each target logistics type, calculate the average cost of each delivery, and use the average cost as the cost coefficient of the target logistics type;

[0035] The logistics distribution priority coefficient of each target logistics type is calculated based on the distribution efficiency coefficient, distribution time instability coefficient, damage coefficient, cost coefficient and corresponding similarity values. The calculation formula is:

[0036]

[0037] Where Ycv is the logistics distribution priority coefficient, DF, HT, ER, RF, and KU are the distribution efficiency coefficient, distribution time instability coefficient, damage coefficient, and cost coefficient and their corresponding similarity values, respectively; f1, f2, f3, f4, and f5 are the preset proportional coefficients of DF, HT, ER, RF, and KU, respectively, and f1, f2, f3, f4, and f5 are all greater than 0.

[0038] Beneficial effects of the present invention:

[0039] 1. The present invention proposes a network-based e-commerce system, which records the goods currently to be selected for logistics distribution as the first goods, records the goods type corresponding to the first goods as the target goods type, and records each of the goods corresponding to the target goods type that have been distributed through logistics as the second goods; obtains the order status data of the first goods and the order status data of each second goods, calculates the similarity value of the order status data of the first goods and the order status data of each second goods, and screens out a set of logistics types that can deliver the first goods according to the similarity value, and records it as the target logistics type set; this method can select the logistics method that best matches the current order according to the actual performance of historical orders, which not only improves the accuracy of the selection, but also ensures that the logistics distribution plan is more in line with actual needs, thereby improving distribution efficiency and cargo safety.

[0040] 2. For each target logistics type in the target logistics type set, obtain its corresponding order delivery status data, and calculate the logistics delivery priority coefficient of each target logistics type based on the order delivery status data and the corresponding similarity value; take the target logistics type corresponding to the largest logistics delivery priority coefficient in the target logistics type set as the final logistics type, and deliver the first cargo; in this way, the appropriate logistics delivery company can be selected according to the actual status of the new order, thereby reducing cargo damage or additional costs during the delivery process, and reducing losses to merchants and customers. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] The present invention will be further described below in conjunction with the accompanying drawings.

[0042] Figure 1 A framework diagram of a network-based e-commerce system. DETAILED DESCRIPTION

[0043] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0044] Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in the field without making any creative work shall fall within the scope of protection of the present invention.

[0045] The embodiment of the present invention provides a network-based e-commerce system. Figure 1 , Figure 1 A framework diagram of a network-based e-commerce system provided by an embodiment of the present invention, the system includes:

[0046] Marking module: record the goods currently to be selected for logistics distribution as the first goods, record the goods type corresponding to the first goods as the target goods type, and record each goods corresponding to the target goods type that have passed logistics distribution as the second goods;

[0047] Screening module: obtaining the order status data of the first goods and the order status data of each second goods, calculating the similarity value between the order status data of the first goods and the order status data of each second goods, and screening out a logistics type set that can deliver the first goods according to the similarity value, recorded as a target logistics type set;

[0048] Delivery priority module: for each target logistics type in the target logistics type set, obtain the corresponding order delivery status data, and calculate the logistics delivery priority coefficient of each target logistics type based on the order delivery status data and the corresponding similarity value; the order delivery status data includes the delivery efficiency coefficient, the delivery time instability coefficient, the damage coefficient and the cost coefficient;

[0049] Distribution module: The target logistics type corresponding to the largest logistics distribution priority coefficient in the target logistics type set is taken as the final logistics type, and the first cargo is distributed.

[0050] Based on a network-based e-commerce system provided by an embodiment of the present invention, through the above method, a suitable logistics distribution company can be selected according to the actual status of the new order, thereby reducing damage to goods or additional costs during the distribution process and reducing losses for merchants and customers.

[0051] In one embodiment, the similarity values ​​of the order status data of the first goods and the order status data of each second goods are calculated, and a set of logistics types that can deliver the first goods is screened out according to the similarity values, which is recorded as the target logistics type set and includes:

[0052] Acquire the order status data of the first goods and the order status data of each second goods, and pre-process the order status data of the first goods and the order status data of each second goods;

[0053] Construct a distance matrix, in which each element is the distance between the order status data point of the first product and the order status data of the second product;

[0054] Calculating the shortest path in the distance matrix using a dynamic programming algorithm to find the best match between the order status data sequence of the first product and the order status data sequence of the second product;

[0055] Aligning the order status data sequence of the first product with the order status data sequence of the second product according to the calculated shortest path;

[0056] Calculate the similarity value between the aligned order status data sequence of the first product and the order status data sequence of the second product; and use the calculated similarity value as the similarity value corresponding to the second product;

[0057] According to the similarity values ​​of each second cargo, a set of logistics types that can deliver the first cargo is screened out and recorded as a target logistics type set.

[0058] In one embodiment, a set of logistics types that can deliver the first goods is screened out according to the similarity values ​​of each second goods, and is recorded as a target logistics type set, including:

[0059] Compare the similarity value of each second product with a preset similarity value threshold, and if the similarity value is not less than the preset similarity value threshold, record the corresponding second product as a conforming product;

[0060] Each logistics type that meets the logistics distribution corresponding to the goods is recorded as the logistics type of the first deliverable goods, and all logistics types of the first deliverable goods are integrated to obtain a target logistics type set.

[0061] It should be noted that the order status data may include the length of delivery time of the goods in the order, the volume and weight of the goods, the total delivery distance, the ambient temperature and humidity during delivery, etc. These data not only help to evaluate the complexity of logistics distribution, but also reflect the special needs that the goods may encounter during transportation. For example, if the goods are perishable, the system will pay special attention to changes in temperature and humidity during delivery to ensure that they are transported in a suitable environment to avoid damage. In addition, the total delivery distance and the weight of the goods also directly affect the cost and timeliness requirements of transportation, so these factors are particularly important when choosing the most suitable logistics company. By comprehensively analyzing these order status data, the system can accurately match the appropriate logistics type to ensure that the goods are delivered on time and safely; the selection of specific order status data can be determined by professional personnel based on actual conditions, and there is no specific limitation here;

[0062] It should be noted that there are many ways to preprocess the order status data and calculate similarity values, depending on the type and complexity of the order status data. In the data preprocessing stage, the original data can be standardized, for example, the weight, volume, delivery time and other data of the goods can be converted into a unified dimension to facilitate subsequent comparison and calculation. In addition, data cleaning is also an important step, including processing missing values, outliers and noise data to ensure the accuracy and completeness of the data. For similarity value calculation, methods such as dynamic time warping (DTW) or Euclidean distance can be used to measure the similarity between order status data. Dynamic time warping (DTW) is particularly suitable for time series data because it can find the best match between different time points, thereby reducing errors caused by time offset. This method can efficiently evaluate the similarity between different orders, thereby more accurately selecting the most suitable logistics distribution method.

[0063] It should be noted that the order status data of the first goods can be obtained from the order management system of the e-commerce platform. Usually, after the customer places an order, the system will automatically generate and record all information related to the order, including the type of goods, delivery requirements, order status, timeliness, destination, etc. The order status data of the second goods can be obtained from historical order data. These data are usually stored in the system's database or data warehouse and contain relevant information in the execution process of orders for similar goods or similar delivery requirements, such as delivery time, distance, cost, environmental conditions, etc. These historical data can be extracted through data mining and analysis tools and used to compare with the status of the current order to help the system select the most appropriate logistics distribution method;

[0064] It should be noted that the preset similarity value threshold is set by professionals according to actual conditions, and no specific limitation or elaboration is made;

[0065] In one implementation, the order status data is optimally aligned and matched through a dynamic programming algorithm, thereby effectively eliminating the time and data order differences that may exist during the order execution process. This method can accurately capture the similarities in logistics requirements of different orders and avoid the deviations that may be caused by simple direct comparisons. By calculating the similarity value, the system can select the logistics method that best matches the current order based on the actual performance of historical orders, which not only improves the accuracy of the selection, but also ensures that the logistics distribution plan is more in line with actual needs, improving distribution efficiency and cargo safety. Therefore, this method based on dynamic programming and similarity calculation can find the optimal logistics distribution plan in a complex order environment and effectively improve the intelligence level of the system.

[0066] In one implementation, the benefit of the target logistics type set selected by the above method is that it can accurately match the most suitable logistics company and delivery method according to the specific status data of the order. By considering factors such as cargo volume, weight, delivery time, environmental requirements, etc., the system can select those logistics types that have performed well in historical orders and can meet specific needs. This not only improves the timeliness and safety of delivery, reduces damage or delays to goods caused by improper logistics, but also reduces costs because the system will select the logistics service provider with the highest cost-effectiveness. In addition, intelligent screening based on historical data can also continuously optimize logistics selection strategies, improve delivery efficiency and customer satisfaction, and thus enhance the merchant's operational capabilities and market competitiveness.

[0067] In one embodiment, the delivery efficiency coefficient includes:

[0068] Get the total number of times the second cargo is delivered for each target logistics type, and get the actual total time spent on each delivery and the corresponding preset total time spent, and mark the actual total time and the corresponding preset total time spent as D respectively.m and S m , m represents the sequence number of the number of times the second cargo is delivered by the target logistics type, m=1, 2, 3, 4, ..., n, n represents the total number of times the second cargo is delivered by the target logistics type, and n is a positive integer;

[0069] According to D m and S m Calculate the distribution efficiency coefficient corresponding to the target logistics type. The calculation formula is: Where DF is the distribution efficiency coefficient.

[0070] It should be noted that the total number of times the target logistics type delivers the second cargo and the actual time and preset time for each delivery can be obtained in real time through synchronization with the interface of the logistics company system; specifically, the system will obtain the delivery time requirement (preset time) of the order from the order management module, and obtain the actual time for each delivery from the tracking system of the logistics service provider; after each delivery is completed, the logistics company will provide real-time feedback of relevant delivery information (such as delivery time, start and end time, etc.) to the e-commerce platform, and the system will record this data for subsequent analysis; in addition, the preset time is determined according to the actual situation and is not limited or elaborated.

[0071] It should be noted that the distribution efficiency coefficient of the target logistics type refers to the difference between the actual time spent on the distribution of the second cargo and the preset time spent by each target logistics type; if the distribution efficiency coefficient is smaller, that is, the difference between the actual time spent on the distribution of the second cargo and the preset time spent by the target logistics type is smaller, the corresponding target logistics type is more suitable for the current cargo to be selected for logistics distribution, that is, the distribution of the first cargo, and the corresponding distribution selection priority is higher. The reason is that the smaller the distribution efficiency coefficient, the smaller the difference between the time spent by the target logistics type in the actual distribution process and the preset time, indicating that the logistics company can accurately and stably distribute according to the expected time. This accuracy and stability are crucial for merchants, especially in terms of efficiency and cost control. For example, the preset time is usually set based on multiple factors such as order type, destination, traffic conditions, etc., which can better predict the delivery time. If the target logistics type can accurately match this time expectation, it means that it has high efficiency and reliability in planning and execution, and can effectively avoid delays and additional costs, thereby improving the efficiency of the overall logistics process and customer satisfaction. Therefore, logistics types with lower delivery efficiency coefficients usually mean higher service quality and can better meet the timeliness requirements of order delivery. Merchants will also give priority to these logistics types to improve transportation accuracy and efficiency, ensure on-time delivery and reduce potential risks.

[0072] In one implementation, the benefit of analyzing the distribution efficiency coefficient of the target logistics type for selecting which target logistics type to select for the current selected logistics distribution of goods, i.e., the distribution of a cargo, is that it can help the system accurately evaluate the performance of each logistics type in historical orders, especially the ability to control time. A logistics type with a smaller distribution efficiency coefficient usually means that it can better control the delivery time during the execution process, avoiding additional costs or customer dissatisfaction caused by delays. By selecting a logistics type with a smaller distribution efficiency coefficient, merchants can improve the accuracy and reliability of distribution, ensure that the goods arrive at the destination on time, and reduce losses caused by inaccurate transportation time. At the same time, this selection helps to optimize transportation resources, reduce unnecessary expenses, and improve the overall efficiency of logistics operations. Ultimately, this selection based on the distribution efficiency coefficient can improve customer experience, enhance the competitiveness of merchants, and further promote customer loyalty and repurchase rate.

[0073] In one embodiment, the delivery time instability coefficient includes:

[0074] Get the total number of times the second cargo is delivered for each target logistics type, and get the total time actually spent on each delivery, and mark the total time actually spent as D m , m represents the sequence number of the number of times the second cargo is delivered by the target logistics type, m=1, 2, 3, 4, ..., n, n represents the total number of times the second cargo is delivered by the target logistics type, and n is a positive integer;

[0075] Calculate the average total time actually spent The calculation formula is:

[0076] Calculate the delivery time instability coefficient of the target logistics type. The calculation formula is:

[0077]

[0078] Among them, HT is the delivery time instability coefficient of the target logistics type.

[0079] It should be noted that the delivery time instability coefficient of the target logistics type refers to the degree of instability of the time actually spent by each target logistics type when delivering the second cargo each time; if the degree of instability is higher, that is, the larger the delivery time instability coefficient of the target logistics type, the less suitable the corresponding target logistics type is for the cargo to be selected for logistics delivery, that is, the delivery of the first cargo, and the lower the corresponding delivery selection priority. The reason is that the larger the delivery time instability coefficient, the greater the fluctuation of the time spent by the target logistics type during the delivery process, which usually indicates that the logistics company has strong instability in time control, and there may be changes in delivery time due to various factors; for e-commerce systems, especially when dealing with high-value or time-sensitive cargo, unstable delivery time may cause customer dissatisfaction and even affect customer experience and brand reputation. If there is a large uncertainty in the delivery time of the target logistics type, merchants will face a higher risk because they cannot accurately predict when the cargo will arrive at the destination, which may lead to delays, out-of-stock or other service errors, thereby affecting the overall operational efficiency. Therefore, the system should give priority to those logistics types with stronger time stability and less volatility to ensure that the cargo can be delivered on time and avoid potential customer loss and additional costs caused by instability.

[0080] In one implementation, analyzing the delivery time instability coefficient of the target logistics type has the benefit of selecting which target logistics type to use for the goods to be currently selected for logistics delivery, i.e., the delivery of a certain number of goods, in that it can help merchants avoid selecting logistics service providers that have large time fluctuations in the delivery process. The stability of delivery time is crucial to customer satisfaction, especially when orders need to be delivered within a specific time limit. The uncertainty of delivery time may lead to customer dissatisfaction and complaints, affecting the merchant's reputation. By selecting a logistics type with a smaller delivery time instability coefficient, merchants can ensure that the timeliness of the delivery process is more reliable, reduce the additional costs and risks caused by delivery delays or uncertainties, and improve the reliability and controllability of the overall delivery; ultimately, stable delivery time can improve customer experience and reduce volatility in logistics operations.

[0081] In one embodiment, the damage factor includes:

[0082] Obtain the total number of times the second cargo is delivered for each target logistics type, and obtain the total number of times the cargo is damaged after each delivery. Divide the total number of times the cargo is damaged by the total number of times the second cargo is delivered to obtain the damage rate of the delivered cargo of the target logistics type.

[0083] Obtain the preset damage severity score corresponding to each time the goods are damaged, and calculate the average of the preset damage severity scores as the damage severity value of the target logistics type;

[0084] The damage coefficient of the target logistics type is calculated according to the damage rate and damage severity value of the distribution goods of the target logistics type. The calculation formula is: ER=a1×zx+a2×cv, where ER is the damage coefficient of the target logistics type, zx and cv are the damage rate and damage severity value of the distribution goods of the target logistics type, a1 and a2 are the preset proportional coefficients of zx and cv, respectively, and a1 and a2 are both greater than 0.

[0085] It should be noted that a1 and a2 are set by professionals based on actual conditions. Generally, the sum of a1 and a2 is 1. For example, a1 and a2 can be 0.5 and 0.5 respectively, and the specific values ​​are not limited. In addition, before calculating the damage coefficient, the damage rate and damage severity value of the distributed goods of each target logistics type need to be normalized. Commonly used normalization methods include Min-Max normalization, Z-Score normalization, etc. The specific method is selected and determined by professionals based on the actual situation and is not limited.

[0086] It should be noted that the data on the total number of deliveries and the total number of damaged goods can be obtained through the logistics management system or warehouse management system, which records the details of each delivery of goods, including whether damage occurred and the number of damages. Secondly, the data on the damage severity score usually comes from the evaluation of each damage event, and a severity score can be assigned to each damage event by staff or an automated detection system based on the actual situation of the damage. This score can be a preset standard, and a classification assessment is conducted based on the degree of damage (for example, minor damage, moderate damage, severe damage, etc.); the specific degree of damage and damage severity score are determined by professionals based on the actual situation and are not limited or elaborated.

[0087] It should be noted that the damage coefficient refers to the probability and severity of damage to the goods when the target logistics type delivers the second goods; if the probability of damage to the goods is greater and the severity of damage to the goods is greater, that is, the larger the damage coefficient, the less suitable the corresponding target logistics type is for the delivery of the goods to be selected for logistics delivery, that is, the first goods, and the lower the priority of the corresponding delivery selection. The reason is that when the damage coefficient of the target logistics type is larger, it means that the probability and severity of damage to the goods during the delivery process of this logistics type are both higher. During the delivery process, damage to the goods may lead to additional losses and unnecessary expenses, such as replacing goods, compensating customers or conducting subsequent processing, which will increase the costs of merchants and consumers. More importantly, damage to the goods will have a negative impact on the customer experience, which may lead to customer dissatisfaction, damage to the brand image and customer loss. Therefore, the logistics type with a larger damage coefficient has a higher risk, and its priority should naturally decrease. In order to ensure that the goods can be delivered safely and intact, reducing the risk of damage becomes a key consideration when choosing logistics and delivery services. Based on this, merchants will tend to choose logistics types with lower damage coefficients to ensure that the probability of damage to the goods during the delivery process is lower, while avoiding additional economic losses and customer complaints caused by severe damage.

[0088] In one implementation, the benefit of analyzing the damage coefficient of the target logistics type for selecting which target logistics type to be selected for the current logistics distribution of the goods, i.e., the distribution of a certain goods, is that a logistics type with a higher damage coefficient means that during the distribution process, the probability of damage to the goods is higher, and the severity of the damage is higher, which will directly affect the safety of the goods and the reputation of the merchant. Therefore, by calculating and analyzing the damage coefficient, those logistics methods with lower risks and capable of ensuring the intact distribution of goods can be effectively screened out. When a logistics type with a lower damage coefficient is selected, it means that the logistics service provider can better protect the goods during transportation, reduce the possibility of damage to the goods, and ensure that the customer receives intact goods, which not only improves customer satisfaction, but also reduces the merchant's compensation and after-sales costs. In addition, the analysis of the damage coefficient is also helpful to optimize the logistics distribution strategy, avoid logistics companies with high damage risks, thereby reducing the economic losses that may occur during the logistics process and improving the overall distribution efficiency and reliability.

[0089] In one embodiment, calculating the logistics delivery priority coefficient of each target logistics type according to the order delivery status data and the corresponding similarity value includes:

[0090] Obtain the total number of times the second cargo is delivered for each target logistics type, calculate the average cost of each delivery, and use the average cost as the cost coefficient of the target logistics type;

[0091] The logistics distribution priority coefficient of each target logistics type is calculated based on the distribution efficiency coefficient, distribution time instability coefficient, damage coefficient, cost coefficient and corresponding similarity values. The calculation formula is:

[0092]

[0093] Where Ycv is the logistics distribution priority coefficient, DF, HT, ER, RF, and KU are the distribution efficiency coefficient, distribution time instability coefficient, damage coefficient, and cost coefficient and their corresponding similarity values, respectively; f1, f2, f3, f4, and f5 are the preset proportional coefficients of DF, HT, ER, RF, and KU, respectively, and f1, f2, f3, f4, and f5 are all greater than 0.

[0094] It should be noted that f1, f2, f3, f4, and f5 are set by professionals according to actual circumstances. Generally, the sum of f1, f2, f3, f4, and f5 is 1. For example, f1, f2, f3, f4, and f5 can be 0.2, 0.2, 0.2, 0.2, and 0.2, respectively. They can also be other numbers and are not limited to any specific number. In addition, before calculating the logistics distribution priority coefficient, it is necessary to normalize the distribution efficiency coefficient, distribution time instability coefficient, damage coefficient, cost coefficient and corresponding similarity values. Commonly used normalization methods include Min-Max normalization, Z-Score standardization, etc. The specific method is selected and determined by professionals according to the actual situation and is not limited to any specific number.

[0095] It should be noted that the calculation of the cost coefficient involves the cost of each delivery, and the data can be obtained by analyzing historical delivery records. These costs usually include transportation costs, labor costs, fuel costs, vehicle maintenance costs, storage costs, etc. Specifically, merchants can extract the actual cost of each delivery from the logistics management system, the bills and contract data of the transportation service provider, or obtain the cost data of the delivery by cooperating with a third-party logistics company. In addition, by regularly tracking and recording various expenses, merchants can accurately calculate the average cost of each delivery, thereby deriving the cost coefficient for each target logistics type.

[0096] It should be noted that, from the above calculation expression, when the distribution efficiency coefficient of the target logistics type is smaller, the distribution time instability coefficient is smaller, the damage coefficient is smaller, the cost coefficient is smaller and the corresponding similarity value is larger, the corresponding target logistics type is more suitable for the distribution of the first goods. The reason is that the smaller the distribution efficiency coefficient, the smaller the distribution time instability ...

[0097] In one implementation method, the target logistics type corresponding to the largest logistics distribution priority coefficient in the target logistics type set is used as the final logistics type to distribute the first cargo; the advantage of this method is that the type with the largest logistics distribution priority coefficient in the target logistics type is selected to ensure that the selected logistics method performs best in terms of distribution efficiency, stability, safety and cost. By comprehensively considering various key factors such as distribution efficiency, time stability, damage risk and distribution cost, the distribution quality is optimized to the maximum extent, avoiding the bias of a single factor. Selecting this type of logistics can effectively reduce the risk in the distribution process, improve the safety of cargo, and improve the overall logistics operation efficiency by reducing costs, so that the first cargo can be distributed in the best way. The application of this method can not only improve customer satisfaction, but also save logistics resources for enterprises and achieve long-term sustainable development.

[0098] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be used to artificially limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A network-based e-commerce system, characterized in that: The system includes: Marking module: record the goods currently to be selected for logistics distribution as the first goods, record the goods type corresponding to the first goods as the target goods type, and record each goods corresponding to the target goods type that have passed logistics distribution as the second goods; Screening module: obtaining the order status data of the first goods and the order status data of each second goods, calculating the similarity value between the order status data of the first goods and the order status data of each second goods, and screening out a logistics type set that can deliver the first goods according to the similarity value, recorded as a target logistics type set; Delivery priority module: for each target logistics type in the target logistics type set, obtain the corresponding order delivery status data, and calculate the logistics delivery priority coefficient of each target logistics type based on the order delivery status data and the corresponding similarity value; the order delivery status data includes the delivery efficiency coefficient, the delivery time instability coefficient, the damage coefficient and the cost coefficient; Distribution module: The target logistics type corresponding to the largest logistics distribution priority coefficient in the target logistics type set is taken as the final logistics type, and the first cargo is distributed.

2. A network-based e-commerce system according to claim 1, characterized in that: The similarity values ​​of the order status data of the first goods and the order status data of each second goods are calculated, and the logistics type set that can deliver the first goods is screened out according to the similarity values, which is recorded as the target logistics type set including: Acquire the order status data of the first goods and the order status data of each second goods, and pre-process the order status data of the first goods and the order status data of each second goods; Construct a distance matrix, in which each element is the distance between the order status data point of the first product and the order status data of the second product; Calculating the shortest path in the distance matrix using a dynamic programming algorithm to find the best match between the order status data sequence of the first product and the order status data sequence of the second product; Aligning the order status data sequence of the first product with the order status data sequence of the second product according to the calculated shortest path; Calculate the similarity value between the aligned order status data sequence of the first product and the order status data sequence of the second product; and use the calculated similarity value as the similarity value corresponding to the second product; According to the similarity values ​​of each second cargo, a set of logistics types that can deliver the first cargo is screened out and recorded as a target logistics type set.

3. A network-based e-commerce system according to claim 2, characterized in that: According to the similarity values ​​of each second cargo, a set of logistics types that can deliver the first cargo is selected, which is recorded as the target logistics type set and includes: Compare the similarity value of each second product with a preset similarity value threshold, and if the similarity value is not less than the preset similarity value threshold, record the corresponding second product as a conforming product; Each logistics type corresponding to the logistics distribution of the goods is recorded as the logistics type of the first deliverable goods, and all logistics types of the first deliverable goods are integrated to obtain a target logistics type set.

4. A network-based e-commerce system according to claim 1, characterized in that: The distribution efficiency coefficients include; Get the total number of times the second cargo is delivered for each target logistics type, and get the actual total time spent on each delivery and the corresponding preset total time spent, and mark the actual total time and the corresponding preset total time spent as D respectively. m and S m , m represents the sequence number of the number of times the second cargo is delivered by the target logistics type, m=1, 2, 3, 4, ..., n, n represents the total number of times the second cargo is delivered by the target logistics type, and n is a positive integer; According to D m and S m Calculate the distribution efficiency coefficient corresponding to the target logistics type. The calculation formula is: Where DF is the distribution efficiency coefficient.

5. A network-based e-commerce system according to claim 1, characterized in that: The delivery time instability coefficient includes: Get the total number of times the second cargo is delivered for each target logistics type, and get the total time actually spent on each delivery, and mark the total time actually spent as D m , m represents the sequence number of the number of times the second cargo is delivered by the target logistics type, m=1, 2, 3, 4, ..., n, n represents the total number of times the second cargo is delivered by the target logistics type, and n is a positive integer; Calculate the average total time actually spent The calculation formula is: Calculate the delivery time instability coefficient of the target logistics type. The calculation formula is: Among them, HT is the delivery time instability coefficient of the target logistics type.

6. A network-based e-commerce system according to claim 1, characterized in that: Damage factors include: Obtain the total number of times the second cargo is delivered for each target logistics type, and obtain the total number of times the cargo is damaged after each delivery. Divide the total number of times the cargo is damaged by the total number of times the second cargo is delivered to obtain the damage rate of the delivered cargo of the target logistics type. Obtain the preset damage severity score corresponding to each time the goods are damaged, and calculate the average of the preset damage severity scores as the damage severity value of the target logistics type; The damage coefficient of the target logistics type is calculated according to the damage rate and damage severity value of the distribution goods of the target logistics type. The calculation formula is: ER=a1×zx+a2×cv, where ER is the damage coefficient of the target logistics type, zx and cv are the damage rate and damage severity value of the distribution goods of the target logistics type, a1 and a2 are the preset proportional coefficients of zx and cv, respectively, and a1 and a2 are both greater than 0.

7. A network-based e-commerce system according to claim 1, characterized in that: The logistics distribution priority coefficients of each target logistics type are calculated based on the order delivery status data and the corresponding similarity values, including: Obtain the total number of times the second cargo is delivered for each target logistics type, calculate the average cost of each delivery, and use the average cost as the cost coefficient of the target logistics type; The logistics distribution priority coefficient of each target logistics type is calculated based on the distribution efficiency coefficient, distribution time instability coefficient, damage coefficient, cost coefficient and corresponding similarity values. The calculation formula is: Where Ycv is the logistics distribution priority coefficient, DF, HT, ER, RF, and KU are the distribution efficiency coefficient, distribution time instability coefficient, damage coefficient, and cost coefficient and their corresponding similarity values, respectively; f1, f2, f3, f4, and f5 are the preset proportional coefficients of DF, HT, ER, RF, and KU, respectively, and f1, f2, f3, f4, and f5 are all greater than 0.

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