Reverse express status identification and tracking management method and system based on big data

Abstract

The present invention provides a method and system for reverse express status identification and tracking management based on big data, which relates to the technical field of reverse express tracking management. The tracking management method includes: S1, obtaining the commodity information, location information and commodity status of reverse express; S2, obtaining the return process and cost of reverse express return; S3, conducting non-return risk assessment and comprehensive value assessment on damaged goods, and making processing decisions; S4, obtaining return status data of each return link in the return process; S5, conducting reverse express loss risk prediction, and obtaining the loss risk probability of reverse express; S6, setting a corresponding tracking management plan according to the loss risk probability. The present invention makes more reasonable damaged commodity disposal decisions by conducting non-return risk assessment and comprehensive value assessment on reverse express, thereby minimizing overall losses. By predicting the loss risk probability of reverse express and taking differentiated tracking management measures, the loss risk is effectively reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of reverse express status identification and tracking management, and in particular to a reverse express status identification and tracking management method based on big data, and a reverse express status identification and tracking management system based on big data. Background Art

[0002] As a vital component of the modern service industry, the express delivery industry plays an increasingly important role in economic and social development. The widespread use of the internet and the rapid development of information technology have enabled the real-time and accurate transmission of logistics information. Customers can easily place orders and check the logistics status of express deliveries online, allowing businesses to efficiently track and manage their shipments. The continuous development and optimization of transportation modes such as road, rail, and air have built a vast and efficient transportation network. This provides a solid foundation for rapid express delivery and significantly shortens cargo transportation times. The application of automated warehousing systems and intelligent sorting equipment has improved the efficiency of cargo storage and handling. The use of technologies such as barcodes and RFID enables rapid identification and sorting of goods, reducing manual errors and time costs. With the booming development of e-commerce, consumer demand for delivery of goods has increased dramatically. The convenience of online shopping has driven the rapid expansion of the express delivery industry to meet consumer expectations for fast and accurate delivery. The government has introduced a series of policies to support the development of the logistics industry, regulate market order, and promote the healthy development of express delivery companies. At the same time, the continuous improvement of relevant regulations has also protected the legitimate rights and interests of consumers and businesses.

[0003] Returns are an inevitable part of express delivery. For example, when consumers shop online, they can't touch and feel the products themselves, leading to dissatisfaction, wrong size, or wrong color, leading to returns. Some products may be damaged during production, transportation, or storage, leading consumers to request returns. To boost consumer confidence, some businesses offer unconditional returns, which has increased the number of returns. Errors in address entries, inaccurate recipient information, and delivery delays can prevent recipients from receiving their goods in a timely manner, leading to returns.

[0004] As the express delivery industry continues to develop, tracking and managing returned express items has become increasingly important. Many companies rely primarily on manual intervention in reverse logistics management and lack systematic data collection and analysis mechanisms. This makes it difficult to fully grasp the specific dynamic information of returned items at each stage, making it difficult to identify and resolve problems in a timely manner. For damaged goods, a simple disposal strategy of direct return or scrapping is often adopted. The lack of comprehensive consideration of factors such as the degree of damage and residual value makes it difficult to make optimal decisions and minimize losses. Furthermore, reverse express delivery is more susceptible to loss during transportation, but many companies have failed to establish effective loss risk prediction mechanisms. The lack of differentiated control measures for different risk levels makes it difficult to reduce the loss risk level in a targeted manner. Summary of the Invention

[0005] The present invention provides a method and system for reverse express status identification and tracking management based on big data, which is used to solve the defects of the existing technology that it is difficult to minimize losses and is easy to be lost.

[0006] On the one hand, the present invention provides a method for identifying and tracking reverse express delivery status based on big data, which is used to identify the status of reverse express delivery and perform tracking management, including:

[0007] S1. Obtain the product information, current location information, and product status of the reverse express delivery. The product information includes product type, volume, and weight. The product status includes damaged products and intact products.

[0008] S2. Obtain the return process and return cost of reverse express return through current location information and product information.

[0009] S3. For reverse express deliveries of damaged goods, a non-return risk assessment and a comprehensive value assessment will be conducted, and a handling decision will be made based on the comprehensive value assessment results. The handling decisions include direct abandonment, discounted sales, and return via the original route.

[0010] S4. Track the reverse express delivery according to the preset cycle and obtain the return status data of each return link in the return process. The return status data includes the actual time data, transportation route information and weight change information of each return link.

[0011] S5. Construct a loss prediction model based on a multi-layer perceptron to predict the loss risk of reverse express delivery, and optimize the hyperparameters of the loss prediction model through the Hippo algorithm. Input the return status data and output the loss risk probability of reverse express delivery.

[0012] S6. Classify the loss risk level of reverse express delivery according to the loss risk probability, and set up a corresponding tracking management plan for each loss risk level.

[0013] According to a method for reverse express status identification and tracking management based on big data provided by the present invention, in step S2, the return cost includes the return logistics fee and the management time cost. The calculation formula of the return cost is:

[0014]

[0015] In the formula, g represents the return logistics cost, represents the unit time cost coefficient, and H represents the management time.

[0016] According to a method for reverse express delivery status identification and tracking management based on big data provided by the present invention, in step S3, the process of performing non-return risk assessment includes:

[0017] Collect historical relevant data, including product information, damage status and corresponding non-return risks.

[0018] The relevant data are coded and normalized to obtain standardized data.

[0019] Build an evaluation model based on the AdaBoost algorithm, input product information and damage conditions, and output the corresponding non-return risk.

[0020] The evaluation model is trained and the model parameters that meet the test accuracy are retained.

[0021] The product information and damage status of reverse express delivery are coded and standardized, and then input into the trained evaluation model to output the non-return risk of reverse express delivery, which includes no risk and risk.

[0022] Conduct a comprehensive value assessment on risk-free damaged goods, and return risky damaged goods along the original route.

[0023] According to a method for reverse express delivery status identification and tracking management based on big data provided by the present invention, in step S3, the process of performing comprehensive value assessment includes:

[0024] S31. Obtain an image of the damaged location, and evaluate the damaged product in combination with customer feedback information to obtain a damage ratio.

[0025] S32. Calculate the residual value based on the damage ratio.

[0026] S33. Comprehensive value assessment of reverse express delivery is performed using the following formula:

[0027]

[0028] Where, It represents the inventory pressure conversion coefficient, Y represents the inventory quantity of the product, S represents the cost required to process the product, and B represents the residual utilization value.

[0029] According to a method for reverse express delivery status identification and tracking management based on big data provided by the present invention, the process of making a processing decision includes:

[0030] If C≤0, choose to give up directly.

[0031] If P>C>0, choose discount sales, where P represents the expected value that can be obtained from discount sales, and the formula is:

[0032]

[0033] Where, Indicates the discount percentage.

[0034] If C≥P, the product will be marked as damaged and returned along the original route.

[0035] According to a big data-based reverse express status identification and tracking management method provided by the present invention, when a discount sale is selected, it is determined whether the customer can purchase according to the expected value. If so, a discount sale is made; otherwise, the product is marked as damaged and returned along the original route.

[0036] According to a method for reverse express delivery status identification and tracking management based on big data provided by the present invention, the process of constructing a loss prediction model based on a multi-layer perceptron includes:

[0037] S51. Collect historical return status data of reverse express and mark whether it is lost.

[0038] S52. Preprocess the historical return status data. The preprocessing includes supplementing missing values, cleaning outliers, and standardizing data to obtain preprocessed data. The preprocessed data is divided into a training set, a test set, and a validation set.

[0039] S53. Construct a multi-layer perceptron model, including an input layer for receiving return status data, multiple hidden layers, each of which includes a preset number of neurons, and an output layer for outputting the loss risk probability of the reverse delivery.

[0040] S54. Select the activation function and use the Sigmoid function as the activation function of the multilayer perceptron model. The expression formula of the Sigmoid function is:

[0041]

[0042] Here, the input value x is between 0 and 1, and the output value f(x) varies smoothly between 0 and 1.

[0043] S55. Train the multilayer perceptron model on the training set, use the test set to evaluate the trained multilayer perceptron model, calculate the accuracy, retain the multilayer perceptron model parameters with the highest accuracy, and obtain the loss prediction model.

[0044] According to a method for reverse express delivery status identification and tracking management based on big data provided by the present invention, in step S5, the process of optimizing the hyperparameters of the loss prediction model by the Hippo algorithm includes:

[0045] The hyperparameter combination is composed of the learning rate, the number of hidden layer neurons and the regularization parameter.

[0046] A population of hippos is randomly generated, where each individual hippo represents a set of hyperparameters.

[0047] The validation set data is used to evaluate the performance of the loss prediction model under the hyperparameter combination represented by each hippo individual as the fitness value.

[0048] According to the fitness value, the individual optimal position of each hippopotamus and the global optimal position of the entire hippopotamus are updated. The hippopotamus position update formula is:

[0049]

[0050] in, and are the positions of the i-th hippopotamus at the t+1th and tth iterations in the j-th dimension, respectively. is the weight that controls the approach to the optimal individual, is the weight that controls random exploration. is the position of the best hippopotamus in the current iteration in the jth dimension, is the position of a hippopotamus randomly selected from the population in the jth dimension.

[0051] According to a method for reverse express delivery status identification and tracking management based on big data provided by the present invention, in step S6, the loss risk level includes a low loss risk level, a medium loss risk level, and a high loss risk level. Setting a corresponding tracking management plan includes:

[0052] For reverse express deliveries with low loss risk levels, tracking will be carried out according to the preset cycle.

[0053] For reverse express deliveries with a medium loss risk level, increase the tracking frequency.

[0054] For reverse express deliveries with high loss risk levels, real-time tracking is carried out and an emergency response mechanism is established.

[0055] On the other hand, the present invention also provides a reverse express status identification and tracking management system based on big data, comprising:

[0056] The data collection module is used to obtain the product information, current location information and product status of reverse express delivery.

[0057] The return path and cost analysis module is used to obtain the return process and return cost of reverse express returns.

[0058] The damaged goods processing module is used to conduct non-return risk assessment and comprehensive value assessment on the reverse express delivery of damaged goods, and make processing decisions based on the comprehensive value assessment results.

[0059] The reverse express loss prediction module is used to track reverse express according to a preset cycle, obtain the return status data of each link, build a loss prediction model based on a multi-layer perceptron to predict the loss risk of reverse express, and optimize the hyperparameters of the loss prediction model through the Hippo algorithm. It inputs the return status data and outputs the loss risk probability of the reverse express.

[0060] The hierarchical management and control module is used to classify the loss risk level of reverse express delivery according to the loss risk probability, and set up a corresponding tracking management plan for each loss risk level.

[0061] The big data-based reverse express status identification and tracking management method and system provided by the present invention can make more reasonable decisions on the disposal of damaged goods by conducting non-return risk assessment and comprehensive value assessment on reverse express, taking into account both the value of the goods and the disposal costs, and can also prevent subsequent losses caused by the failure to return damaged goods, thereby minimizing overall losses. By constructing a loss prediction model, the loss risk probability of reverse express can be accurately predicted, and differentiated tracking and management measures can be taken for different loss risk levels to effectively reduce the loss risk. The big data-based method can effectively solve the key pain points in reverse logistics management, has significant advantages in improving operational efficiency, reducing costs and risks, and provides strong support for the intelligent transformation of corporate reverse logistics. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] In order to more clearly illustrate the technical solutions in the present invention or the prior art, a brief introduction is given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0063] Figure 1 1 is a flow chart of a method for reverse express delivery status identification and tracking management based on big data provided by an embodiment of the present invention;

[0064] Figure 2It is a structural diagram of a reverse express status identification and tracking management system based on big data provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0065] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0066] The following combination Figure 1-Figure 2 The present invention describes a method and system for reverse express status identification and tracking management based on big data.

[0067] Figure 1 It is a flow chart of a method for reverse express status identification and tracking management based on big data provided by an embodiment of the present invention.

[0068] like Figure 1 As shown, the embodiment of the present invention provides a method and system for identifying and tracking reverse express delivery status based on big data. The execution subject may be a method for identifying and tracking reverse express delivery status based on big data. The method includes:

[0069] S1. Obtain the reverse express order number, product information, current location information, and product status. Product information includes product type, volume, and weight. Product status includes damaged products and intact products.

[0070] In this embodiment, real-time reverse courier tracking numbers, product information, and current location data are obtained from the courier company's system interface or app. A unified product information coding system is established, with standardized definitions for product types, dimensions, and weight attributes. A comprehensive data verification mechanism is also established to identify and correct potential errors or omissions in the data, and data sources are regularly reviewed and evaluated to ensure data accuracy and completeness.

[0071] By classifying the status of goods, we can determine the reason for reverse delivery. If the customer returns the goods because they are damaged, we can consider selling them at a discount.

[0072] S2. Obtain the return process and return cost of reverse express return through current location information and product information.

[0073] The return cost includes return logistics costs and management time costs. The calculation formula for return costs is:

[0074]

[0075] In the formula, g represents the return logistics cost, represents the unit time cost coefficient, and H represents the management time.

[0076] Calculate the specific return logistics costs based on the weight and volume of the product, combined with the return shipping rates of different courier companies. Also consider the cost differences of different return methods, including in-person pickup and door-to-door collection. For remote areas or special products, additional surcharges may be required.

[0077] Administrative time includes the time required for processes such as receiving returns, inspecting them, recording them, and issuing refunds. The unit time cost coefficient is calculated based on the company's labor budget and reflects the value of labor input. For different return processing processes, the time cost of each step is calculated separately.

[0078] S3. For reverse express deliveries of damaged goods, a non-return risk assessment and a comprehensive value assessment will be conducted, and a handling decision will be made based on the comprehensive value assessment results. The handling decisions include direct abandonment, discounted sales, and return via the original route.

[0079] The process of conducting a non-return risk assessment includes:

[0080] Collect historical relevant data, including product information, damage status and corresponding non-return risks.

[0081] The relevant data are coded and normalized to obtain standardized data.

[0082] Build an evaluation model based on the AdaBoost algorithm, input product information and damage conditions, and output the corresponding non-return risk.

[0083] The evaluation model is trained and the model parameters that meet the test accuracy are retained.

[0084] The product information and damage status of reverse express delivery are coded and standardized, and then input into the trained evaluation model to output the non-return risk of reverse express delivery, which includes no risk and risk.

[0085] Conduct a comprehensive value assessment on risk-free damaged goods, and return risky damaged goods along the original route.

[0086] In this embodiment, non-return risk assessment is performed based on the type and damage of the product, which can prevent subsequent losses caused by damaged products not being returned. For example, special products such as chemicals and batteries may pose safety hazards and environmental pollution risks even if they are slightly damaged.

[0087] The process of conducting a comprehensive value assessment includes:

[0088] S31. Obtain an image of the damaged location and evaluate the damaged product in combination with customer feedback information to obtain a damage ratio. The calculation formula is:

[0089]

[0090] In the formula, A represents the damage ratio, N represents the value of the damaged part, and M represents the value of the complete product.

[0091] S32. Calculate the residual value based on the damage ratio. The calculation formula is:

[0092]

[0093] Where B represents the residual utilization value.

[0094] S33. Comprehensive value assessment of reverse express delivery is performed using the following formula:

[0095]

[0096] Where, It represents the inventory pressure conversion coefficient, Y represents the inventory quantity of the product, S represents the cost required to process the product, and B represents the residual utilization value.

[0097] In this embodiment, the inventory pressure conversion coefficient can be dynamically adjusted based on the company's inventory management strategy and cost accounting system. When the damaged goods are large in size and heavy in weight, S represents the cost of transporting the damaged goods to the shipping point or recycling point, including labor costs, transportation costs, etc.

[0098] The process for making treatment decisions includes:

[0099] If C≤0, choose to give up directly.

[0100] If P>C>0, choose discount sales, where P represents the expected value that can be obtained from discount sales, and the formula is:

[0101]

[0102] Where, Indicates the discount percentage.

[0103] If C≥P, the product will be marked as damaged and returned along the original route.

[0104] When discount sales are selected, determine whether the customer can purchase at the expected value. If so, discount sales are made. Otherwise, mark the product as damaged and return it along the original route.

[0105] S4. Track the reverse express delivery according to the preset cycle and obtain the return status data of each return link in the return process. The return status data includes the actual time data, transportation route information and weight change information of each return link.

[0106] S5. Construct a loss prediction model based on a multi-layer perceptron to predict the loss risk of reverse express delivery, and optimize the hyperparameters of the loss prediction model through the Hippo algorithm. Input the return status data and output the loss risk probability of reverse express delivery.

[0107] The process of building a loss prediction model based on a multi-layer perceptron includes:

[0108] S51. Collect historical return status data of reverse express and mark whether it is lost.

[0109] S52. Preprocess the historical return status data. The preprocessing includes supplementing missing values, cleaning outliers, and standardizing data to obtain preprocessed data. The preprocessed data is divided into a training set, a test set, and a validation set.

[0110] Use the z-score to check for outliers in historical return status data, remove data points outside the reasonable range and obvious erroneous data, fill missing values ​​with the average value, and eliminate duplicates in the data to reduce redundancy. The z-score formula is expressed as:

[0111]

[0112] Where d is the data point, is the sample mean, is the sample standard deviation, when When the value is greater than the set threshold, the data point is considered an outlier.

[0113] The mean value filling formula is:

[0114]

[0115] Here, m is the number of non-missing values ​​in the dataset.

[0116] S53. Construct a multi-layer perceptron model, including an input layer for receiving return status data, multiple hidden layers, each of which includes a preset number of neurons, and an output layer for outputting the loss risk probability of the reverse delivery.

[0117] S54. Select the activation function and use the Sigmoid function as the activation function of the multilayer perceptron model. The expression formula of the Sigmoid function is:

[0118]

[0119] Here, the input value x is between 0 and 1, and the output value f(x) varies smoothly between 0 and 1.

[0120] S55. Train the multilayer perceptron model on the training set, use the test set to evaluate the trained multilayer perceptron model, calculate the accuracy, retain the multilayer perceptron model parameters with the highest accuracy, and obtain the loss prediction model.

[0121] The process of optimizing the hyperparameters of the loss prediction model through the Hippo algorithm includes:

[0122] The hyperparameter combination is composed of the learning rate, the number of hidden layer neurons and the regularization parameter.

[0123] A population of hippos is randomly generated, where each individual hippo represents a set of hyperparameters.

[0124] The validation set data is used to evaluate the performance of the loss prediction model under the hyperparameter combination represented by each hippo individual as the fitness value.

[0125] According to the fitness value, the individual optimal position of each hippopotamus and the global optimal position of the entire hippopotamus are updated. The hippopotamus position update formula is:

[0126]

[0127] in, and are the positions of the i-th hippopotamus at the t+1th and tth iterations in the j-th dimension, respectively. is the weight that controls the approach to the optimal individual, is the weight that controls random exploration. is the position of the best hippopotamus in the current iteration in the jth dimension, is the position of a hippopotamus randomly selected from the population in the jth dimension.

[0128] S6. Classify the loss risk level of reverse express delivery according to the loss risk probability, and set up a corresponding tracking management plan for each loss risk level.

[0129] The loss risk levels include low, medium and high. The corresponding tracking management plans include:

[0130] For reverse express deliveries with low loss risk levels, tracking will be carried out according to the preset cycle.

[0131] For reverse express deliveries with a medium loss risk level, increase the tracking frequency.

[0132] For reverse express deliveries with high loss risk levels, real-time tracking is carried out and an emergency response mechanism is established.

[0133] In this embodiment, for reverse express shipments with a low risk of loss, regular tracking is performed at a predetermined interval (e.g., every 3-5 days). Tracking methods may include making phone inquiries and checking logistics information. This allows for timely monitoring of shipping status and timely resolution of any issues.

[0134] Increase tracking frequency for reverse express deliveries with a medium loss risk level, such as conducting active tracking every 1-2 days. Strengthen collaboration with logistics service providers and closely monitor logistics node dynamics. Establish an early warning mechanism to promptly initiate handling procedures should an anomaly occur.

[0135] We implement real-time, full-process tracking for reverse express deliveries with a high risk of loss, monitoring product movements throughout the entire process. We maintain close communication with logistics service providers and develop emergency response plans. When necessary, we implement special measures such as manual tracking to ensure product safety.

[0136] In summary, this embodiment provides a method for reverse express status identification and tracking management based on big data. By conducting a risk assessment of non-return items and a comprehensive value assessment on reverse express, a more reasonable decision on the disposal of damaged goods is made, which takes into account both the value of the goods and the disposal costs. It can also prevent subsequent losses caused by the failure to return damaged goods, thereby minimizing overall losses. By constructing a loss prediction model, the probability of loss risk of reverse express can be accurately predicted, and differentiated tracking and management measures can be taken for different loss risk levels to effectively reduce the risk of loss. The big data-based method can effectively solve the key pain points in reverse logistics management, and has significant advantages in improving operational efficiency, reducing costs and risks, and provides strong support for the intelligent transformation of corporate reverse logistics.

[0137] Based on the same general inventive concept, the present invention also protects a reverse express status identification and tracking management system based on big data. The reverse express status identification and tracking management system based on big data provided by the present invention is described below. The reverse express status identification and tracking management system based on big data described below and the reverse express status identification and tracking management method based on big data described above can be referenced to each other.

[0138] Figure 2 It is a structural diagram of a reverse express status identification and tracking management system based on big data provided by an embodiment of the present invention.

[0139] like Figure 2 As shown, the reverse express status identification and tracking management system based on big data includes a data collection module, a return path and cost analysis module, a damaged goods processing module, a reverse express loss prediction module and a hierarchical management and control module.

[0140] The data collection module is used to obtain the order number, product information, current location information and product status of reverse express delivery.

[0141] The return path and cost analysis module is used to obtain the return process and return cost of reverse express returns.

[0142] The damaged goods processing module is used to conduct non-return risk assessment and comprehensive value assessment on the reverse express delivery of damaged goods, and make processing decisions based on the comprehensive value assessment results.

[0143] The reverse express loss prediction module is used to track reverse express according to a preset cycle, obtain the return status data of each link, build a loss prediction model based on a multi-layer perceptron to predict the loss risk of reverse express, and optimize the hyperparameters of the loss prediction model through the Hippo algorithm. The return status data is input and the loss risk probability of the reverse express is output.

[0144] The hierarchical management and control module is used to classify the loss risk level of reverse express delivery according to the loss risk probability, and set up a corresponding tracking management plan for each loss risk level.

[0145] Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented using software plus a necessary general-purpose hardware platform, or of course, hardware. Based on this understanding, the essence of the above technical solution, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, or an optical disk, and includes a number of instructions for causing a computer device (such as a personal computer, server, or network device) to execute the methods described in each embodiment or certain portions of the embodiments.

[0146] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A method for identifying and tracking reverse express delivery status based on big data, which is used to identify the status of reverse express delivery and conduct tracking management, characterized in that: include: S1. Obtaining reverse express product information, current location information, and product status, wherein the product information includes product type, volume, and weight, and the product status includes whether the product is damaged or intact; S2. Obtaining the return process and return cost of the reverse express return based on the current location information and the product information; S3. Conduct a non-return risk assessment and a comprehensive value assessment on the reverse express delivery of damaged goods, and make a handling decision based on the comprehensive value assessment results, including direct abandonment, discounted sales, and return via the original route; S4. Track the reverse express delivery according to a preset period and obtain return status data for each return link in the return process, wherein the return status data includes actual time data, transportation route information, and weight change information of each return link; S5. Construct a loss prediction model based on a multi-layer perceptron to predict the loss risk of reverse express delivery, optimize the hyperparameters of the loss prediction model using the Hippo algorithm, input the return status data, and output the loss risk probability of the reverse express delivery; The process of building a loss prediction model based on a multi-layer perceptron includes: S51. Collect historical return status data of reverse express and mark whether it is lost; S52: Preprocess the historical return status data, wherein the preprocessing includes supplementing missing values, cleaning outliers, and normalizing data to obtain preprocessed data, and dividing the preprocessed data into a training set, a test set, and a validation set; S53, constructing a multi-layer perceptron model, comprising an input layer for receiving the return status data; a plurality of hidden layers, each hidden layer comprising a preset number of neurons; and an output layer for outputting the loss risk probability of the reverse express delivery; S54, select an activation function, and use the Sigmoid function as the activation function of the multilayer perceptron model. The expression formula of the Sigmoid function is: Among them, the input value x is between 0 and 1, and the output value f(x) changes smoothly between 0 and 1; S55, training the multilayer perceptron model on the training set, evaluating the trained multilayer perceptron model using the test set, calculating the accuracy, retaining the multilayer perceptron model parameters with the highest accuracy, and obtaining a loss prediction model; S6. Classifying the reverse express delivery into loss risk levels according to the loss risk probability, and setting a corresponding tracking management plan for each loss risk level; the loss risk levels include a low loss risk level, a medium loss risk level, and a high loss risk level; setting the corresponding tracking management plan includes: For reverse express with low loss risk level, tracking shall be carried out according to the preset period; For reverse express deliveries with the medium loss risk level, the tracking frequency will be increased; For reverse express deliveries with a high risk of loss, real-time tracking will be carried out and an emergency response mechanism will be established.

2. The method for reverse express delivery status identification and tracking management based on big data according to claim 1, characterized in that: In step S2, the return cost includes return logistics fees and management time costs. The calculation formula of the return cost is: σ=g-α*H Where g represents the return logistics cost, α represents the unit time cost coefficient, and H represents the management time.

3. The method for reverse express status identification and tracking management based on big data according to claim 1, characterized in that: In step S3, the process of conducting non-return risk assessment includes: Collect historical data, including product information, damage status, and corresponding non-return risk; Performing code conversion and numerical standardization on the relevant data to obtain standardized data; Construct an evaluation model based on the AdaBoost algorithm, input the product information and damage condition, and output the corresponding non-return risk; Training the evaluation model and retaining model parameters that meet the test accuracy; The product information and damage status of the reverse express are coded and standardized, and the information is input into the trained evaluation model to output the non-return risk of the reverse express, where the non-return risk includes no risk and risk; Conduct a comprehensive value assessment on the risk-free damaged goods, and return the risky damaged goods along the original route.

4. The method for reverse express delivery status identification and tracking management based on big data according to claim 1, characterized in that: In step S3, the process of performing the comprehensive value assessment includes: S31. Obtaining an image of the damaged location and evaluating the damaged product in combination with customer feedback to obtain a damage ratio; S32. Calculating the residual utilization value based on the damage ratio; S33. Perform a comprehensive value assessment on the reverse express delivery, using the following calculation formula: C=B-σ-β*YS Where β represents the inventory pressure conversion coefficient, Y represents the inventory quantity of the product, S represents the cost required to process the product, and B represents the residual utilization value.

5. The method for reverse express status identification and tracking management based on big data according to claim 1, characterized in that: The process for making such processing decisions includes: If C≤0, choose to give up directly; If P>C>0, choose discount sales, where P represents the expected value that can be obtained from discount sales, and the formula is: P=M*θ Where θ represents the discount ratio; If C≥P, the product will be marked as damaged and returned along the original route.

6. The method for reverse express status identification and tracking management based on big data according to claim 5 is characterized in that: When the discount sale is selected, it is determined whether the customer can purchase at the expected value. If so, the discount sale is performed; otherwise, the product is marked as damaged and returned along the original path.

7. The method for reverse express status identification and tracking management based on big data according to claim 1, characterized in that: In step S5, the process of optimizing the hyperparameters of the loss prediction model by the Hippo algorithm includes: The hyperparameter combination is formed by the learning rate, the number of hidden layer neurons and the regularization parameter; Randomly generate a population of hippos, where each individual hippo represents a set of hyperparameters; Using the validation set data to evaluate the performance of the loss prediction model under the hyperparameter combination represented by each hippopotamus individual as a fitness value; According to the fitness value, the individual optimal position of each hippopotamus and the global optimal position of the entire hippopotamus are updated. The hippopotamus position update formula is: Among them, X ij (t+1) and X ij (t) are the positions of the i-th hippopotamus individual in the j-th dimension at the t+1th and tth iterations, respectively; is the weight that controls the approach to the optimal individual, δ is the weight that controls random exploration; X best,j (t) is the position of the best hippopotamus in the current iteration in the jth dimension, X random,j (t) is the position of a hippopotamus randomly selected from the population in the jth dimension.

8. A reverse express status identification and tracking management system based on big data, using the reverse express status identification and tracking management method based on big data according to any one of claims 1 to 7, characterized in that: The reverse express status identification and tracking management system based on big data includes: Data collection module, used to obtain reverse express product information, current location information and product status; The return path and cost analysis module is used to obtain the return process and return cost of reverse express returns; The damaged goods processing module is used to conduct non-return risk assessment and comprehensive value assessment on reverse express delivery of damaged goods, and make processing decisions based on the comprehensive value assessment results; The reverse express delivery loss prediction module is used to track reverse express delivery at a preset period, obtain return status data at each link, build a loss prediction model based on a multi-layer perceptron to predict the loss risk of reverse express delivery, and optimize the hyperparameters of the loss prediction model using the Hippo algorithm. It inputs return status data and outputs the loss risk probability of reverse express delivery. The hierarchical management and control module is used to classify the loss risk level of reverse express delivery according to the loss risk probability, and set up a corresponding tracking management plan for each loss risk level.

Images