Logistics monitoring method and system for e-commerce

Abstract

The invention relates to the technical field of logistics monitoring, and particularly discloses a logistics monitoring method and system for e-commerce, and the method comprises the steps: obtaining historical logistics whole process information, and constructing an aging prediction model based on the historical logistics whole process information; inputting current logistics basic information to the aging prediction model; obtaining logistics whole process information in real time, inputting the logistics whole process information into the aging prediction model, and correcting the predicted standard aging; determining the current process time according to the logistics whole process information, and calculating the deviation rate of the current process time compared with the predicted standard time efficiency; according to the invention, the logistics circulation condition can be supervised by monitoring the whole logistics process, so that the logistics abnormity can be found in time, the logistics automatic monitoring becomes a first finding subject of the abnormity, and the logistics monitoring efficiency is improved. Therefore, the experience feeling of consumers can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of logistics monitoring, and in particular to a logistics monitoring method and system for e-commerce. Background Art

[0002] In the existing e-commerce logistics monitoring environment, the main body of logistics anomalies is still the e-commerce users, that is, consumers. After discovering logistics anomalies, they contact the merchant customer service or logistics customer service. The first to participate in the reception is still the intelligent customer service. When the intelligent customer service learns about the logistics situation, the handling method is only to urge the delivery. However, logistics anomalies are not only slow logistics, but also the loss and missing of items, which cause the logistics information to remain in the transfer station or the collection process or the delivery process. It is necessary to complete the digestion of the logistics anomalies reported by consumers. Therefore, in this process, manual customer service is still needed. Naturally, there needs to be a process of conversion from intelligent customer service to manual customer service. Therefore, consumers, as the main body of anomaly discovery, have to go through the process of contacting customer service, switching to manual customer service, and waiting for manual customer service to verify the feedback from discovery to feedback, resulting in a poor consumer experience and cumbersome anomaly handling. Summary of the invention

[0003] The purpose of the present invention is to provide a logistics monitoring method and system for e-commerce to solve the problems raised in the above background technology.

[0004] To achieve the above object, the present invention provides the following technical solution: a logistics monitoring method for e-commerce, the method comprising:

[0005] Acquire historical logistics full process information, wherein the historical logistics full process information includes basic logistics information, and construct a time efficiency prediction model based on the historical logistics full process information;

[0006] Input the current basic logistics information into the time efficiency prediction model, wherein the basic logistics information includes logistics transportation, logistics route, and logistics order number, and generate a predicted standard time efficiency;

[0007] Obtain the whole process information of logistics in real time. When the whole process information of logistics is inconsistent with the input basic logistics information, input the whole process information of logistics into the time efficiency prediction model and correct the prediction standard time efficiency;

[0008] Determine the time taken for the current process based on the information of the entire logistics process, and calculate the deviation rate between the time taken for the current process and the predicted standard time;

[0009] Generate abnormality levels and abnormality reports based on the deviation rate, and determine the copy personnel based on the abnormality level;

[0010] Copy the exception report to the cc personnel.

[0011] As a further solution of the present invention, the step of building a time efficiency prediction model based on historical logistics full process information specifically includes:

[0012] Classify express delivery agencies according to historical logistics full process information, and generate multiple logistics full process information sample libraries corresponding to the express delivery agencies;

[0013] Constructing influencing factors, including holidays, route traffic volume, and the number of online transaction items;

[0014] The historical logistics process information is divided into collection section, transfer section, transportation section and distribution section;

[0015] Construct a timeliness prediction model and train the model based on influencing factors and multiple logistics full-process information sample libraries corresponding to express delivery agencies.

[0016] As a further solution of the present invention, the input of current basic logistics information into the time efficiency prediction model, wherein the basic logistics information includes logistics transportation, logistics route, and logistics order number, and the step of generating the predicted standard time efficiency specifically includes:

[0017] Generate a forecast logistics chain based on basic logistics information;

[0018] Input the predicted logistics chain into the timeliness prediction model to generate the predicted standard timeliness.

[0019] As a further solution of the present invention, the step of obtaining the whole process information of logistics in real time, and inputting the whole process information of logistics into the time efficiency prediction model and correcting the prediction standard time efficiency when the whole process information of logistics is inconsistent with the input basic logistics information specifically includes:

[0020] Obtain the whole process information of logistics in real time, and extract the logistics chain based on the whole process information of logistics;

[0021] Intercept the logistics chain based on the current time node;

[0022] Compare the intercepted logistics chain with the predicted logistics chain;

[0023] The prediction standard time is revised according to the comparison results.

[0024] As a further solution of the present invention, the step of determining the current process time according to the full logistics process information and calculating the deviation rate of the current process time compared with the predicted standard time specifically includes:

[0025] Determine the current process based on the full logistics process information;

[0026] Backtrack based on the time node of the current process to determine the time taken by the current process;

[0027] Calculate the deviation rate between the current process time and the predicted standard time.

[0028] As a further solution of the present invention, it also includes obtaining logistics reporting information from the consumer side, verifying and copying it based on the reported information.

[0029] As a further solution of the present invention, the steps of obtaining the logistics reporting information of the consumer side, verifying and copying the reported information specifically include:

[0030] Obtain the logistics reporting information from the consumer side, and determine the deviation rate of the current process based on the logistics reporting information;

[0031] Generate timeliness report or copy instruction based on deviation rate and identify the copy personnel;

[0032] Send the aging report to the consumer end or send the cc instruction to the cc personnel.

[0033] As a further solution of the present invention, it also includes generating a receipt according to the copy feedback result, and sending the receipt to the merchant end and the consumer end.

[0034] The present invention also provides a logistics monitoring system for e-commerce, which is used to implement a logistics monitoring method for e-commerce, and the system includes:

[0035] A model building module is used to obtain historical logistics full process information, wherein the historical logistics full process information includes basic logistics information, and to build a time efficiency prediction model based on the historical logistics full process information;

[0036] A prediction result generation module is used to input the current basic logistics information into the time efficiency prediction model, wherein the basic logistics information includes logistics transportation, logistics route, and logistics order number, and generate a prediction standard time efficiency;

[0037] The correction module is used to obtain the whole process information of logistics in real time. When the whole process information of logistics is inconsistent with the input basic logistics information, the whole process information of logistics is input into the timeliness prediction model and the prediction standard timeliness is corrected;

[0038] The deviation rate calculation module is used to determine the current process time based on the full logistics process information and calculate the deviation rate between the current process time and the predicted standard time;

[0039] The CC determination module is used to generate anomaly levels and anomaly reports according to the deviation rate, and determine the CC personnel according to the anomaly level;

[0040] The CC module is used to send the exception report to the CC personnel.

[0041] As a further solution of the present invention, the model building module specifically includes:

[0042] A sample classification unit is used to classify express delivery agencies according to historical logistics full process information and generate multiple logistics full process information sample libraries corresponding to the express delivery agencies;

[0043] An influencing factor establishment unit, used to construct influencing factors, wherein the influencing factors include holidays, route traffic volume, and the number of online transaction items;

[0044] The segmentation unit is used to segment the historical logistics process information into collection segment, transfer segment, transportation segment, and distribution segment.

[0045] A construction unit is used to construct a timeliness prediction model and train the model based on influencing factors and multiple logistics full-process information sample libraries corresponding to express delivery agencies.

[0046] Compared with the prior art, the beneficial effects of the present invention are: by monitoring the entire logistics process, the logistics flow situation can be supervised, so that logistics anomalies can be discovered in time, making automatic logistics monitoring the first discovery entity of anomalies, thereby improving consumers' experience; secondly, by constructing a timeliness prediction model, the standard timeliness can be predicted while considering multiple factors, so that abnormal situations can be judged in time, thereby determining different copy personnel; secondly, the deviation rate can be corrected according to the actual logistics route, so that the prediction of timeliness can be more accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention.

[0048] Figure 1 A flowchart of a logistics monitoring method for e-commerce provided in an embodiment of the present invention.

[0049] Figure 2 A flowchart of the steps of building a time efficiency prediction model based on historical logistics full process information provided by an embodiment of the present invention.

[0050] Figure 3 The embodiment of the present invention provides a flowchart of the steps of inputting current logistics basic information into a time efficiency prediction model to generate a prediction standard time efficiency step.

[0051] Figure 4 The embodiment of the present invention provides a flowchart of the steps of obtaining the whole logistics process information in real time, inputting the whole logistics process information into the timeliness prediction model and correcting the prediction standard timeliness when the whole logistics process information is inconsistent with the input logistics basic information.

[0052] Figure 5 A flowchart of the steps of determining the current process time according to the full logistics process information and calculating the deviation rate of the current process time compared with the predicted standard time provided by the embodiment of the present invention.

[0053] Figure 6 A structural block diagram of a logistics monitoring system for e-commerce provided in an embodiment of the present invention.

[0054] Figure 7 A structural block diagram of a model building module of a logistics monitoring system for e-commerce provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0055] In order to make the technical problems, technical solutions and beneficial effects to be solved by the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0056] Figure 1 The flowchart of a logistics monitoring method for e-commerce is shown in the following figure. In an embodiment of the present invention, a logistics monitoring method for e-commerce includes steps S100 to S600: A logistics monitoring method for e-commerce includes:

[0057] Step S100, obtaining historical logistics full process information, wherein the historical logistics full process information includes basic logistics information, and building a time efficiency prediction model based on the historical logistics full process information;

[0058] Step S200, inputting current logistics basic information into the time efficiency prediction model, wherein the logistics basic information includes logistics transportation, logistics route, and logistics order number, and generating a predicted standard time efficiency;

[0059] Step S300, real-time acquisition of logistics process information. When the logistics process information is inconsistent with the input logistics basic information, the logistics process information is input into the time efficiency prediction model and the prediction standard time efficiency is corrected;

[0060] Step S400, determining the current process time according to the logistics process information, and calculating the deviation rate between the current process time and the predicted standard time;

[0061] Step S500, generating an abnormality level and an abnormality report according to the deviation rate, and determining the copy person according to the abnormality level;

[0062] Step S600, copy the abnormal report to the copy personnel.

[0063] In this embodiment, a time efficiency prediction model is constructed based on historical logistics full process information, so that the time required for each process segment in the entire logistics process can be predicted, so that abnormal situations can be identified. In addition, the predicted standard time efficiency can be generated before the express is dispatched, so that the delivery time of the items can be roughly estimated. In addition, considering the logistics flow process, taking into account factors such as road conditions, the temporary change of routes can also correct the predicted standard time efficiency. In addition, through the preset deviation rate range, the abnormal situation can be further divided into levels, and the abnormal level includes level one and level two, so that designated personnel are copied according to different processes. When the logistics stops at the transfer station and an abnormality occurs, the corresponding copy personnel for the first level are merchants and transfer station staff, and the corresponding copy personnel for the second level are merchants, transfer station staff, and transfer station managers. When the logistics is abnormal during the distribution process, the corresponding copy personnel for the first level are merchants and delivery personnel, and the corresponding copy personnel for the second level are merchants, delivery personnel, and distribution station managers, so that the abnormal report is copied to the copy personnel.

[0064] By monitoring the entire logistics process, the logistics flow can be supervised, so that logistics anomalies can be discovered in time, making automatic logistics monitoring the first entity to discover anomalies, thereby improving consumers' experience. Secondly, by building a time prediction model, the standard time can be predicted while considering multiple factors, so that abnormal situations can be judged in time, thereby determining different copy personnel. Secondly, the deviation rate can be corrected according to the actual logistics route, so that the prediction of time can be more accurate.

[0065] like Figure 2 As shown, as a preferred embodiment of the present invention, the step of building a time efficiency prediction model based on historical logistics full process information specifically includes:

[0066] Step S101, classifying express delivery agencies according to historical logistics full process information, and generating multiple logistics full process information sample libraries corresponding to the express delivery agencies;

[0067] Step S102, constructing influencing factors, wherein the influencing factors include holidays, route traffic volume, and the number of items sold online;

[0068] Step S103, dividing the historical logistics process information into collection section, transfer section, transportation section, and distribution section;

[0069] Step S104, constructing a time efficiency prediction model, and training the model based on influencing factors and multiple logistics full-process information sample libraries corresponding to express delivery agencies.

[0070] In this embodiment, by constructing a time efficiency prediction model, the time efficiency under different routes and different means of transportation can be predicted. First, by classifying different express delivery agencies, the express delivery agencies include Shentong, ZTO, SF Express, etc. Different express delivery agencies have completely different logistics circulation and time efficiency. Therefore, the express delivery agencies are classified to establish a single logistics full-process information sample library corresponding to the express delivery agencies. In addition, influencing factors are established. The influencing factors are factors that affect the express delivery time efficiency, including holidays. Considering that the merchant side will rest on holidays and thus cannot collect the goods, the route traffic volume is also included. By considering the route traffic volume, the time used in the transportation process can be accurately considered. Secondly, it also includes the number of items sold online. During large-scale promotional activities, there will be a short-term warehouse explosion, which will delay the time efficiency. Therefore, the delivery time efficiency can be accurately predicted through the influencing factors. In addition, the model is trained and iterated through several samples in the logistics full-process information sample library, so that a time efficiency prediction model can be generated.

[0071] like Figure 3 As shown, as a preferred embodiment of the present invention, the current logistics basic information is input into the time efficiency prediction model, and the logistics basic information includes logistics transportation, logistics route, and logistics order number. The step of generating the predicted standard time efficiency specifically includes:

[0072] Step S201, generating a predicted logistics chain according to basic logistics information;

[0073] Step S202, input the predicted logistics chain into the time efficiency prediction model to generate a predicted standard time efficiency.

[0074] In this embodiment, in the process of existing e-commerce express delivery, an express number is generally generated first, and the express number contains the information of the place of shipment and the place of receipt. Secondly, the express number also corresponds to a corresponding price, and the corresponding price also corresponds to the choice of logistics transportation. The place of shipment and the place of receipt are the logistics routes. The logistics transportation tools include trucks, three-wheeled carts (collection and distribution process), high-speed railways or motor vehicles, airplanes, etc. A predicted logistics chain can be generated through the selection of logistics routes and logistics transportation tools. For example, a predicted material chain is collector-distribution station-Shanghai transit station-Wuxi transit station-distribution station-delivery personnel. By inputting the predicted logistics chain into the time efficiency prediction model, the predicted standard time efficiency of each process segment can be generated. The process segments include collection segment, transit segment, transportation segment, and distribution segment.

[0075] like Figure 4 As shown, as a preferred embodiment of the present invention, the real-time acquisition of the whole process information of logistics, when the whole process information of logistics is inconsistent with the input logistics basic information, the whole process information of logistics is input into the time efficiency prediction model and the prediction standard time efficiency is corrected. Specifically, the steps include:

[0076] Step S301, obtaining the whole logistics process information in real time, and extracting the logistics chain based on the whole logistics process information;

[0077] Step S302, intercepting the logistics chain based on the current time node;

[0078] Step S303, comparing the intercepted logistics chain with the predicted logistics chain;

[0079] Step S304, correcting the predicted standard timeliness according to the comparison result.

[0080] In this embodiment, the current full-process information of the logistics is obtained in real time, so that the logistics chain can be extracted. When "about to be sent to Shanghai Transshipment Center" appears in the current logistics, the future logistics chain can be clearly known. By intercepting the future logistics chain based on the current time node, the current future logistics chain is obtained, so that the current future logistics chain can be compared with the predicted logistics chain. When the current future logistics chain is inconsistent with the predicted logistics chain, it means that the logistics chain has changed. Therefore, the predicted logistics chain is corrected to generate a corrected prediction standard time limit. When the current future logistics chain is consistent with the predicted logistics chain, the original prediction standard time limit is kept unchanged.

[0081] like Figure 5 As shown, as a preferred embodiment of the present invention, the step of determining the current process time according to the logistics full process information and calculating the deviation rate of the current process time compared with the predicted standard time specifically includes:

[0082] Step S401, determining the current process according to the full logistics process information;

[0083] Step S402, backtracking based on the time node of the current process to determine the time taken by the current process;

[0084] Step S403, calculating the deviation rate between the current process time and the predicted standard time.

[0085] In this embodiment, the current time node is determined, and then the current logistics process is determined based on the full logistics process information. The current process time is back-calculated based on the current time node. For example, the current process is in transit at a transfer station. The current process time is calculated from the current time node to the time when the express just arrives at the transfer station. The deviation rate is calculated by dividing the current process time by the predicted standard time.

[0086] As a preferred embodiment of the present invention, it also includes obtaining logistics reporting information from the consumer side, verifying and copying it according to the reported information.

[0087] In this embodiment, in addition to the use of intelligent logistics monitoring, based on the consumer's perspective, there is still a need for a consumer reporting procedure. Therefore, in this embodiment, a solution for consumers to report is provided. Consumers may have the need to urge delivery, or believe that the logistics has not been updated for a long time and there are abnormalities. However, considering the normal operation of logistics, it is necessary to verify the consumer's needs. When there is indeed a delay or abnormality, the next step of copying is carried out.

[0088] As a preferred embodiment of the present invention, the steps of obtaining the logistics reporting information of the consumer side, verifying and copying the reported information specifically include:

[0089] Obtain the logistics reporting information from the consumer side, and determine the deviation rate of the current process based on the logistics reporting information;

[0090] Generate timeliness report or copy instruction based on deviation rate and identify the copy personnel;

[0091] Send the aging report to the consumer end or send the cc instruction to the cc personnel.

[0092] In this embodiment, firstly, the logistics reporting information of the consumer side is obtained, and then the reporting type is determined according to the logistics reporting information, and the reporting type includes abnormal logistics delays and urging, and the deviation rate of the current process is obtained, and then the deviation rate result is used to judge whether the current logistics is normal transportation or there is a delay. If there is a slight delay, a copy instruction is generated, and the deviation rate is lower than the first-level deviation rate range. At this time, the copy personnel is the next transfer station. By speeding up the transfer speed at the transfer station, the time for the next process can be reduced. When the current logistics is judged as normal transportation by the deviation rate result, a timeliness report is generated. The timeliness report includes the whole logistics process information and the predicted standard timeliness information. The timeliness report can be regarded as an explanation report. The timeliness report is sent to the consumer side, which can prove that the logistics is normal transportation.

[0093] As a preferred embodiment of the present invention, it also includes generating a receipt according to the copy feedback result, and sending the receipt to the merchant end and the consumer end.

[0094] In this embodiment, the abnormal report copied to the cc personnel is fed back, and the cc personnel handle the abnormal situation according to the abnormal report. The handling method can be to find out the missing items, re-scan the lost items for re-warehousing and delivery, and generate a receipt based on the processing result, which is sent to the merchant side and the consumer side through the receipt. While improving the consumer side experience, it can also respond to the logistics situation, avoiding the consumer side or the merchant side from reporting the logistics abnormality again.

[0095] As a preferred embodiment of the present invention, it also includes activating a reward mechanism when an item is lost and cannot be found in time.

[0096] When existing express delivery agencies encounter lost items during the circulation process, they basically contact the merchant to resend the item and then compensate them with double the shipping fee. However, the actual value of the goods is far greater than the double shipping fee. In response to this situation, a reward for recovery is offered by sending a reward invitation to the staff at the place where the item was lost. The place where the item was lost is generally a transfer station, distribution station, collection point, etc. The reward mechanism can increase the enthusiasm of logistics staff, thereby recovering the merchant's losses as much as possible.

[0097] like Figure 6 As shown, an embodiment of the present invention further provides a logistics monitoring system for e-commerce, the system comprising:

[0098] A model building module 100 is used to obtain historical logistics full process information, wherein the historical logistics full process information includes basic logistics information, and to build a time efficiency prediction model based on the historical logistics full process information;

[0099] The prediction result generation module 200 is used to input the current basic logistics information into the time efficiency prediction model, wherein the basic logistics information includes logistics transportation, logistics route, and logistics order number, and generate the prediction standard time efficiency;

[0100] The correction module 300 is used to obtain the whole process information of logistics in real time. When the whole process information of logistics is inconsistent with the input basic logistics information, the whole process information of logistics is input into the time efficiency prediction model and the prediction standard time efficiency is corrected;

[0101] The deviation rate calculation module 400 is used to determine the current process time according to the logistics process information, and calculate the deviation rate of the current process time compared with the predicted standard time;

[0102] The copy determination module 500 is used to generate an abnormality level and an abnormality report according to the deviation rate, and determine the copy personnel according to the abnormality level;

[0103] The copy module 600 is used to copy the abnormality report to the copy personnel.

[0104] like Figure 7 As shown, as a preferred embodiment of the present invention, the model building module 100 specifically includes:

[0105] The sample classification unit 101 is used to classify express delivery agencies according to historical logistics full process information and generate multiple logistics full process information sample libraries corresponding to the express delivery agencies;

[0106] An influencing factor establishment unit 102 is used to construct influencing factors, wherein the influencing factors include holidays, route traffic volume, and the number of items sold online;

[0107] The segmentation unit 103 is used to segment the historical logistics process information into a collection section, a transfer section, a transportation section, and a distribution section;

[0108] The construction unit 104 is used to construct a time efficiency prediction model and train the model based on influencing factors and multiple logistics full-process information sample libraries corresponding to express delivery agencies.

[0109] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A logistics monitoring method for e-commerce, characterized in that: The method comprises: Acquire historical logistics full process information, wherein the historical logistics full process information includes basic logistics information, and construct a time efficiency prediction model based on the historical logistics full process information; Input the current basic logistics information into the time efficiency prediction model, wherein the basic logistics information includes logistics transportation, logistics route, and logistics order number, and generate a predicted standard time efficiency; Obtain the whole process information of logistics in real time. When the whole process information of logistics is inconsistent with the input basic logistics information, input the whole process information of logistics into the time efficiency prediction model and correct the prediction standard time efficiency; Determine the time taken for the current process based on the information of the entire logistics process, and calculate the deviation rate between the time taken for the current process and the predicted standard time; Generate abnormality levels and abnormality reports based on the deviation rate, and determine the copy personnel based on the abnormality level; Copy the exception report to the cc personnel.

2. A logistics monitoring method for e-commerce according to claim 1, characterized in that: The steps of constructing a time efficiency prediction model based on historical logistics full process information specifically include: Classify express delivery agencies according to historical logistics full process information, and generate multiple logistics full process information sample libraries corresponding to the express delivery agencies; Constructing influencing factors, including holidays, route traffic volume, and the number of online transaction items; The historical logistics process information is divided into collection section, transfer section, transportation section and distribution section; Construct a timeliness prediction model and train the model based on influencing factors and multiple logistics full-process information sample libraries corresponding to express delivery agencies.

3. A logistics monitoring method for e-commerce according to claim 1, characterized in that: The input of current logistics basic information into the time efficiency prediction model, wherein the logistics basic information includes logistics transportation, logistics route, and logistics order number, and the step of generating the predicted standard time efficiency specifically includes: Generate a forecast logistics chain based on basic logistics information; Input the predicted logistics chain into the timeliness prediction model to generate the predicted standard timeliness.

4. A logistics monitoring method for e-commerce according to claim 1, characterized in that: The step of obtaining the full logistics process information in real time and inputting the full logistics process information into the time efficiency prediction model and correcting the prediction standard time efficiency when the full logistics process information is inconsistent with the input logistics basic information specifically includes: Obtain the whole process information of logistics in real time, and extract the logistics chain based on the whole process information of logistics; Intercept the logistics chain based on the current time node; Compare the intercepted logistics chain with the predicted logistics chain; The prediction standard time is revised according to the comparison results.

5. A logistics monitoring method for e-commerce according to claim 1, characterized in that: The steps of determining the current process time according to the logistics process information and calculating the deviation rate between the current process time and the predicted standard time specifically include: Determine the current process based on the full logistics process information; Backtrack based on the time node of the current process to determine the time taken by the current process; Calculate the deviation rate between the current process time and the predicted standard time.

6. A logistics monitoring method for e-commerce according to claim 1, characterized in that: It also includes obtaining logistics reporting information from the consumer side, verifying and copying the reported information.

7. A logistics monitoring method for e-commerce according to claim 1, characterized in that: The steps of obtaining the logistics reporting information from the consumer side, verifying and copying the reported information specifically include: Obtain the logistics reporting information from the consumer side, and determine the deviation rate of the current process based on the logistics reporting information; Generate timeliness report or copy instruction based on deviation rate and identify the copy personnel; Send the aging report to the consumer end or send the cc instruction to the cc personnel.

8. A logistics monitoring method for e-commerce according to claim 1, characterized in that: It also includes generating a receipt based on the CC feedback result, and sending the receipt to the merchant side and the consumer side.

9. A logistics monitoring system for e-commerce, used to implement a logistics monitoring method for e-commerce according to any one of claims 1 to 8, characterized in that: The system comprises: A model building module is used to obtain historical logistics full process information, wherein the historical logistics full process information includes basic logistics information, and to build a time efficiency prediction model based on the historical logistics full process information; A prediction result generation module is used to input the current basic logistics information into the time efficiency prediction model, wherein the basic logistics information includes logistics transportation, logistics route, and logistics order number, and generate a prediction standard time efficiency; The correction module is used to obtain the whole process information of logistics in real time. When the whole process information of logistics is inconsistent with the input basic logistics information, the whole process information of logistics is input into the timeliness prediction model and the prediction standard timeliness is corrected; The deviation rate calculation module is used to determine the current process time based on the full logistics process information and calculate the deviation rate between the current process time and the predicted standard time; The CC determination module is used to generate anomaly levels and anomaly reports according to the deviation rate, and determine the CC personnel according to the anomaly level; The CC module is used to send the exception report to the CC personnel.

10. A logistics monitoring system for e-commerce according to claim 9, characterized in that: The model building module specifically includes: A sample classification unit is used to classify express delivery agencies according to historical logistics full process information and generate multiple logistics full process information sample libraries corresponding to the express delivery agencies; An influencing factor establishment unit, used to construct influencing factors, wherein the influencing factors include holidays, route traffic volume, and the number of online transaction items; The segmentation unit is used to segment the historical logistics process information into collection segment, transfer segment, transportation segment, and distribution segment. A construction unit is used to construct a timeliness prediction model and train the model based on influencing factors and multiple logistics full-process information sample libraries corresponding to express delivery agencies.

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