E-commerce logistics parcel delivery system and method

Abstract

The invention relates to an e-commerce parcel delivery system and method, in particular to the technical field of logistics transportation, and the system comprises an information acquisition module which is used for acquiring user information, express delivery information, reservation information and feedback information, a user analysis module which is used for carrying out the priority analysis of users, and adjusting the process of carrying out the priority analysis of the users. The reservation analysis module is used for analyzing the effectiveness of user reservation, the reservation recommendation module is used for recommending a reservation time period to the user and optimizing the process of recommending the reservation time period to the user, and the pushing module is used for pushing a recommendation result of the reservation time period to the user. And according to the user selection result, the effective user reservation and the user information, pushing the reservation result to the deliverer, and updating the analysis process of the user priority according to the obtained feedback information. According to the invention, the efficiency of pushing information to the user is improved.

Description

Technical Field

[0001] The present invention relates to the field of logistics and transportation technology, and in particular to an e-commerce logistics package delivery system and method. Background Art

[0002] The rapid growth of the e-commerce industry has had a huge impact on the express delivery industry. Consumers are increasingly shopping online, which has led to a sharp increase in the volume of packages, prompting a significant increase in demand for express delivery and delivery services.

[0003] The e-commerce logistics package delivery system analyzes users, analyzes user reservation information based on the analysis results of users, and pushes delivery information to delivery personnel based on the analysis results of user reservation information, thereby improving the efficiency of pushing information to users.

[0004] Chinese Patent Publication No.: CN107527173A discloses a courier delivery method, comprising the following steps: S1: courier points receive express parcels assigned by courier companies; S2: courier points assign express parcels according to community locations; S3: community properties accept corresponding express parcels assigned by courier points; S4: community property personnel deliver express parcels to households. Since the courier points assign express parcels according to community locations and the community property personnel assign them to households, travel, image and safety issues caused by couriers entering and leaving the community can be avoided, ensuring the normal operation of the community, and the delivery task of the "last mile" of the express logistics industry can be transferred to the community property personnel. Since the community property personnel are more familiar with the community conditions in the community, the express delivery efficiency is higher and the express delivery quality can be better guaranteed. It can be seen that the solution does not recommend appointment time periods to users when performing express delivery, and there is a problem of low efficiency in pushing information to users. Summary of the invention

[0005] To this end, the present invention provides an e-commerce logistics package delivery system and method to overcome the problem of low efficiency in pushing information to users in the prior art.

[0006] To achieve the above-mentioned purpose, the present invention provides an e-commerce logistics package delivery system, the system comprising: Information acquisition module, used to obtain user information, express information, appointment information and feedback information; A user analysis module is used to perform a priority analysis on users according to the obtained user levels and user waiting times, and to adjust the process of performing a priority analysis on users according to the obtained time of user cancellations and the historical number of user cancellations; The appointment analysis module is used to analyze the validity of user appointments based on the analysis results of user priority, the obtained express information, appointment date and appointment time period; The appointment recommendation module is used to recommend appointment time periods to users according to preset time periods when the user's appointment is invalid, and is also used to optimize the process of recommending appointment time periods to users according to the number of historical appointments made by the user in each appointment time period and the delivery availability of each appointment time period; The push module is used to push the recommended results of the appointment time period to the user, and to push the appointment results to the delivery person based on the user selection results, valid user appointments and user information, and to update the analysis process of user priorities based on the feedback information obtained.

[0007] Furthermore, the user analysis module includes a priority analysis unit, a first user analysis unit and a second user analysis unit. The priority analysis unit classifies users according to the acquired user levels, and compares the user waiting time t0 with each preset waiting time according to the classification results, and analyzes the user priority according to the comparison results to obtain the user priority Aa.

[0008] Furthermore, the first user analysis unit calculates the difference between the time T0 when the user cancels the appointment and the start time T1 of the user's appointment period, and compares the calculation result with the preset time difference T2. According to the comparison result, the abnormality of the time interval between the user's cancellation of the appointment is analyzed. The abnormality of the time interval between the user's cancellation of the appointment includes normal and abnormal. When the time interval between the user's cancellation of the appointment is abnormal, the user's priority Aa is adjusted to Aa'.

[0009] Furthermore, the second user analysis unit compares the historical number of times the user cancels an appointment L0 with the preset number L1, and analyzes the abnormality of the historical number of times the user cancels an appointment based on the comparison result. The abnormality of the historical number of times the user cancels an appointment includes normal and abnormal. When the historical number of times the user cancels an appointment is abnormal, the preset time difference is adjusted to T2'.

[0010] Furthermore, the appointment analysis module is provided with a date analysis unit, which adds the express order time b1 and the express delivery time b2, and compares them with the user appointment time b0, and analyzes the validity of the user appointment according to the comparison result, wherein: When b1+b2<b0, the date analysis unit determines that the user's reservation date is invalid; When b1+b2≥b0, the date analysis unit determines that the user's reservation date is valid; The reservation analysis module is further provided with a time period analysis unit. When the user reservation date is valid, the time period analysis unit analyzes the validity of the user reservation according to the vacancy k0 of the preset time period of the user reservation period and the user's priority, wherein: When k0=0, the time period analysis unit determines that the user's reservation time period is invalid; When k0>0, if multiple users reserve the reservation time period at the same time, the number of users is set to m. If m≤k0, the time period analysis unit determines that the user's reservation time period is valid. If m>k0, the time period analysis unit sorts the users in descending order of priority, and selects k0 users with the highest priority, determines that the reservation time periods of the k0 users are valid, and determines that the reservation time periods of the remaining users are invalid; if a single user reserves the reservation time period at the same time, the time period analysis unit determines that the user's reservation time period is valid.

[0011] Furthermore, the reservation recommendation module includes a reservation recommendation unit, a reservation frequency analysis unit and a vacancy analysis unit. When the user's reservation period is invalid, the reservation recommendation unit analyzes the vacancy p0 of the preset period dispatch in the user's reservation date to obtain a pre-set period that can be reserved, and sets the start time W of the pre-set period that can be reserved to the time zone. i Calculate the difference between the start time W0 of the user's scheduled time period and the recommended degree H of each preset time period according to the result of the difference calculation. When H≤h1, the reservation recommendation unit determines that the preset time period is a non-recommended preset time period; when H>h1, the reservation recommendation unit determines that the preset time period is a recommended preset time period; Among them, h1 is the preset recommendation degree, W i is the start time of the i-th preset period.

[0012] Furthermore, the reservation number analysis unit compares the historical reservation number z0 of the user in each preset time period with the preset number z1, and analyzes the abnormality of the user's reservation number based on the comparison result. The abnormality of the user's reservation number includes normal and abnormal. When the user's reservation number in the preset time period is abnormal, the recommendation degree of the preset time period is optimized to H'.

[0013] Furthermore, the availability analysis unit compares the delivery availability Si of the preset time period that can be reserved in the user's reservation date with the preset availability s0, and analyzes the delivery availability status based on the comparison result. The delivery availability status includes normal and abnormal. When the delivery availability of the preset time period is abnormal, the preset number of times is optimized to z1'.

[0014] Furthermore, the push module includes a trust analysis unit, which calculates the trust of the user based on the number of historical deliveries made by the courier to the user and the number of historical failed pickups by the user, and compares the user's trust with a preset trust Y0 to analyze the user's trust status. The user's trust status includes normal and abnormal. When the user's trust status is abnormal, the preset waiting time is updated to tα', where tα is the preset waiting time.

[0015] On the other hand, the present invention also provides a method for delivering an e-commerce logistics package, comprising: Step S1: Obtain user information, express information, reservation information and feedback information; Step S2: performing a priority analysis on the user according to the obtained user level and user waiting time, and adjusting the process of performing a priority analysis on the user according to the obtained time of user cancellation and the historical number of user cancellations; Step S3: Analyzing the validity of the user's reservation based on the analysis result of the user's priority, the obtained express information, the reservation date and the reservation period; Step S4: when the user's reservation is invalid, recommending a reservation time period to the user according to the preset time period, and optimizing the process of recommending a reservation time period to the user according to the number of historical reservations made by the user in each reservation time period and the delivery availability in each reservation time period; Step S5: pushes the recommended result of the appointment time period to the user, pushes the appointment result to the delivery person according to the user selection result, the valid user appointment and the user information, and updates the analysis process of the user priority according to the feedback information obtained.

[0016] Compared with the prior art, the beneficial effects of the present invention are that the priority analysis unit improves the accuracy of the user priority calculation result by setting a preset waiting time, thereby improving the accuracy of the appointment analysis, thereby improving the accuracy of the information pushed to the user and the delivery person, and finally improving the efficiency of pushing information to the user; the first user analysis unit improves the accuracy of the adjustment coefficient by setting a preset time difference, thereby improving the accuracy of the user priority, thereby improving the accuracy of the appointment analysis, thereby improving the accuracy of the information pushed to the user and the delivery person, and finally improving the efficiency of pushing information to the user; the second user analysis unit improves the accuracy of the correction coefficient by setting a preset number of times, thereby improving the accuracy of the user priority, thereby improving the accuracy of the appointment analysis, thereby improving the accuracy of the information pushed to the user and the delivery person, and finally improving the efficiency of pushing information to the user; the date analysis unit and the time period analysis unit analyze the validity of the appointment date and the appointment time period to obtain the validity of the user appointment, thereby improving the accuracy of the appointment analysis, thereby improving the efficiency of pushing information to the user and the delivery person. The accuracy of information sent, and ultimately improves the efficiency of pushing information to users. The appointment recommendation module improves the judgment efficiency of the preset time period that can be booked by setting a preset delivery vacancy, and improves the judgment efficiency of the recommended preset time period by setting a preset recommendation degree, thereby improving the accuracy of information pushed to users and delivery personnel, and ultimately improves the efficiency of pushing information to users. The appointment number analysis unit improves the accuracy of the first optimization coefficient by setting a preset number of times, thereby improving the accuracy of the recommendation degree of the preset time period, and thereby improving the accuracy of information pushed to users and delivery personnel, and ultimately improves the efficiency of pushing information to users. The vacancy analysis unit improves the accuracy of the second optimization coefficient by setting a preset vacancy, thereby improving the accuracy of the recommendation degree of the preset time period, and thereby improving the accuracy of information pushed to users and delivery personnel, and ultimately improves the efficiency of pushing information to users. The trust analysis unit improves the accuracy of the adjustment coefficient by setting a preset trust degree, thereby improving the accuracy of the user priority, and thereby improving the accuracy of information pushed to users and delivery personnel, and ultimately improves the efficiency of pushing information to users. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 This is a schematic diagram of the structure of the e-commerce logistics package delivery system of this embodiment; Figure 2 This is a schematic diagram of the structure of the user analysis module of this embodiment; Figure 3 This is a schematic diagram of the structure of the reservation analysis module of this embodiment; Figure 4 This is a schematic diagram of the structure of the appointment recommendation module of this embodiment; Figure 5 This is a schematic diagram of the structure of the push module in this embodiment; Figure 6Schematic diagram of the process of the e-commerce package delivery method of this embodiment. DETAILED DESCRIPTION

[0018] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0019] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the protection scope of the present invention.

[0020] It should be noted that in the description of the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected" and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, or it can be the internal communication of two components. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0021] See also Figure 1 As shown, it is a schematic diagram of the structure of the e-commerce logistics package delivery system of this embodiment, the system includes: The information acquisition module is used to obtain user information, express information, reservation information and feedback information. The user information is user ID, user level and user delivery address. The user level is level one and level two. Level one is ordinary user and level two is member user. The express information includes express volume, express order time and express delivery time. The reservation information includes reservation date, reservation time, user cancellation time, user waiting time, delivery vacancies in the preset time period and the number of people who make reservations at the same time. The delivery vacancies in the preset time period are the number of express deliveries that can be scheduled in the preset time period. The feedback information includes the historical information provided by the deliveryman to the user. The number of historical deliveries and the number of historical uncollected items by the user; in this embodiment, several preset time periods are set. In this embodiment, the setting of the preset time periods is not specifically limited. Technical personnel in this field can set them freely, and they only need to meet the setting requirements of the preset time periods. For example, 9 a.m. to 6 p.m. can be set as a preset time period every two hours, etc. In this embodiment, the method of obtaining user information, express information and appointment information is not specifically limited. Technical personnel in this field can set it freely, and they only need to meet the requirements for obtaining user information, express information, appointment information and feedback information. For example, user information, express information and appointment information can be imported through the background information of the shopping platform.

[0022] Please continue reading Figure 1As shown, the system includes a user analysis module for performing priority analysis on users according to the obtained user levels and user waiting times, and adjusting the process of performing priority analysis on users according to the obtained time of user cancellation and the historical number of user cancellations. The user analysis module is connected to the information acquisition module.

[0023] See also Figure 2 As shown, the user analysis module includes a priority analysis unit, which is used to classify users according to the acquired user levels, and compare the user waiting time t0 with each preset waiting time according to the classification results, and analyze the user priority according to the comparison results, wherein: When the user level is level 1, if t0<t1, the priority analysis unit determines that the priority of the user is 0, and if t0≥t1, the priority analysis unit sets the user's priority to A1, setting A1=3×[(t0-t1) / t1]2-2×[(t0-t1) / t1]3, wherein when t0-t1≥t1, the value of t0-t1 is t1; When the user level is level two, if t0<t2, the priority analysis unit determines that the priority of the user is 0; if t0≥t2, the priority analysis unit sets the user's priority to A2, and sets A2=3×[(t0-t2) / t2]2-2×[(t0-t2) / t2]3, wherein, when t0-t2≥t2, the value of t0-t2 is t2; t1>t2, t1 is the first preset waiting time, and t2 is the second preset waiting time.

[0024] Specifically, the priority analysis unit improves the accuracy of the user priority calculation results by setting a preset waiting time, thereby improving the accuracy of the appointment analysis, and further improving the accuracy of information pushed to users and delivery personnel, and ultimately improving the efficiency of pushing information to users; in this embodiment, there is no specific limitation on the value of the preset waiting time, and technical personnel in this field can set it freely as long as the value requirements of the preset waiting time are met. Among them, the best value of t1 is 5h, and the best value of t2 is 3h.

[0025] Please continue reading Figure 2 As shown, the user analysis module includes a first user analysis unit, which is connected to the priority analysis unit, and is used to calculate the difference between the time T0 when the user cancels the reservation and the start time T1 of the user's reservation period, and compare the calculation result with the preset time difference T2, analyze the abnormality of the time interval between the user canceling the reservation according to the comparison result, and adjust the analysis process of the user priority, wherein: When T1-T0≤T2, the first user analysis unit determines that the user's reservation cancellation time interval is abnormal, and adjusts the user's priority Aa to Aa', setting Aa'=Aa×exp[-(T2-△T) / (T2+△T)], △T=T1-T0; When T1-T0>T2, the first user analysis unit determines that the time interval for canceling the reservation by the user is normal and does not make any adjustment.

[0026] Specifically, the first user analysis unit improves the accuracy of the adjustment coefficient by setting a preset time difference, thereby improving the accuracy of the user priority, and further improving the accuracy of the appointment analysis, and further improving the accuracy of information pushed to users and delivery personnel, and ultimately improving the efficiency of pushing information to users; in this embodiment, the value of the preset time difference is not specifically limited, and technical personnel in this field can set it freely, as long as the value requirements of the preset time difference are met, among which the optimal value of the preset time difference is 4h.

[0027] Please continue reading Figure 2 As shown, the user analysis module includes a second user analysis unit, which is connected to the first user analysis unit and is used to compare the historical number of times L0 of user cancellations with the preset number L1, analyze the abnormality of the historical number of times the user cancels appointments according to the comparison result, and adjust the analysis process of the abnormality of the time interval between user cancellations, wherein: When L0≤L1, the second user analysis unit determines that the number of times the user cancels reservations is normal and does not make any adjustments; When L0>L1, the second user analysis unit determines that the number of times the user cancels reservations is abnormal, and sets the adjusted preset time difference as T2', setting T2'=T2×{1-ln[(L0-L1) / (L1+L0)+1] / ln2}.

[0028] Specifically, the second user analysis unit improves the accuracy of the correction coefficient by setting a preset number of times, thereby improving the accuracy of user priority, and further improving the accuracy of appointment analysis, and further improving the accuracy of information pushed to users and delivery personnel, and ultimately improving the efficiency of pushing information to users; in this embodiment, the value of the preset number of times is not specifically limited, and technical personnel in this field can set it freely, as long as the value requirements of the preset number are met, among which the optimal value of the preset number of times is 5.

[0029] Please continue reading Figure 1 As shown, the system includes an appointment analysis module for analyzing the validity of user appointments based on the analysis results of user priority, the acquired express information, appointment date and appointment period, and the appointment analysis module is connected to the user analysis module.

[0030] See also Figure 3 As shown, the appointment analysis module includes a date analysis unit, which is used to add the express order time b1 and the express delivery time b2, and compare them with the user appointment time b0, and analyze the validity of the user appointment according to the comparison result, wherein: When b1+b2<b0, the date analysis unit determines that the user's reservation date is invalid; When b1+b2≥b0, the date analysis unit determines that the user reservation date is valid.

[0031] Please continue reading Figure 3 As shown, the reservation analysis module includes a time period analysis unit, which is connected to the date analysis unit. When the user reservation date is valid, the time period analysis unit analyzes the validity of the user reservation according to the vacancy k0 of the preset time period of the user reservation period and the user's priority, wherein: When k0=0, the time period analysis unit determines that the user's reservation time period is invalid; When k0>0, if multiple users reserve the reservation time period at the same time, the number of users is set to m. If m≤k0, the time period analysis unit determines that the user's reservation time period is valid. If m>k0, the time period analysis unit sorts the users in descending order of priority, and selects k0 users with the highest priority, determines that the reservation time periods of the k0 users are valid, and determines that the reservation time periods of the remaining users are invalid; if a single user reserves the reservation time period at the same time, the time period analysis unit determines that the user's reservation time period is valid.

[0032] Specifically, the date analysis unit and the time period analysis unit analyze the validity of the reservation date and the reservation time period to obtain the validity of the user's reservation, thereby improving the accuracy of the reservation analysis, thereby improving the accuracy of pushing information to users and delivery personnel, and ultimately improving the efficiency of pushing information to users.

[0033] Please continue reading Figure 1 As shown, the system includes an appointment recommendation module, which is used to recommend an appointment time period to the user according to a preset time period when the user's appointment is invalid, and is also used to optimize the process of recommending an appointment time period to the user according to the user's historical number of appointments in each appointment time period and the delivery vacancies in each appointment time period. The appointment recommendation module is connected to the appointment analysis module.

[0034] See also Figure 4 As shown, the reservation recommendation module includes a reservation recommendation unit. When the user's reservation period is invalid, the reservation recommendation unit analyzes the preset period delivery vacancy p0 in the user's reservation date to obtain a pre-set time period that can be reserved, and sets the start time W of the pre-set time period that can be reserved. iThe difference between the start time W0 of the user's reservation period is calculated, and the recommendation degree of each preset period is calculated according to the result of the difference calculation, and the reservation period is recommended to the user according to the recommendation degree, where: When p0≤p1, the reservation recommendation unit determines that the preset time period is a non-reservable preset time period; When p0>p1, the reservation recommendation unit determines that the preset time period is a reservationable preset time period; The reservation recommendation unit sets the recommendation degree to H, setting H=1 / [|W i -W0| / t 预 ],in: When H≤h1, the reservation recommendation unit determines that the preset time period is a non-recommended preset time period; When H>h1, the reservation recommendation unit determines that the preset time period is a recommended preset time period; Among them, p1 is the preset delivery margin, h1 is the preset recommendation degree, W i is the starting time of the i-th preset period, t 预 The duration of the preset time period.

[0035] Specifically, the reservation recommendation unit improves the efficiency of judging the preset time periods that can be reserved by setting the preset delivery vacancies, and improves the efficiency of judging the recommended preset time periods by setting the preset recommendation degree, thereby improving the accuracy of information pushed to users and delivery personnel, and ultimately improving the efficiency of pushing information to users. In this embodiment, there is no specific limitation on the setting of the preset delivery vacancies and the preset recommendation degree, and those skilled in the art can set them freely as long as the setting requirements of the preset delivery vacancies and the preset recommendation degree are met. Among them, the optimal value of the preset delivery vacancies is 6, and the optimal value of the preset recommendation degree is 0.5.

[0036] Please continue reading Figure 4 As shown, the appointment recommendation module includes an appointment number analysis unit, which is connected to the appointment recommendation unit and is used to compare the historical appointment number z0 of the user in each preset time period with the preset number z1, analyze the abnormality of the user's appointment number according to the comparison result, and optimize the recommendation process of the appointment time period, wherein: When z0<z1, the reservation number analysis unit determines that the reservation number of the user in the preset time period is normal and does not perform optimization; When z0≥z1, the reservation number analysis unit determines that the reservation number of the user in the preset time period is abnormal, and optimizes the recommendation degree of the preset time period to H', setting H'=H×{1+[e 4×(z0-z1) / (z0+z1) -1] / [e 4 -1]}.

[0037] Specifically, the appointment frequency analysis unit improves the accuracy of the first optimization coefficient by setting a preset number of times, thereby improving the accuracy of the recommendation degree of the preset time period, and further improving the accuracy of information pushed to users and delivery personnel, and ultimately improving the efficiency of pushing information to users; in this embodiment, there is no specific limitation on the setting of the preset number of times, and technical personnel in this field can set it freely, as long as the setting requirements of the preset number are met, among which the optimal value of the preset number of times is 8.

[0038] Please continue reading Figure 4 As shown, the reservation recommendation module includes a vacancy analysis unit, which is connected to the reservation number analysis unit and is used to calculate the delivery vacancy S of the preset time period available for reservation on the user's reservation date. i Compare with the preset s0, analyze the delivery slack status according to the comparison result, and optimize the analysis process of the abnormality of the number of user reservations, where: When S i ≤s0, the vacancy analysis unit determines that the delivery vacancy of the preset time period is abnormal, and optimizes the preset number to z1', setting z1'=z1×[1+(s0-S i ) / (S i +s0)]; When S i ≥s0, the vacancy analysis unit determines that the delivery vacancy in the preset time period is normal and does not perform optimization.

[0039] Specifically, the margin analysis unit improves the accuracy of the second optimization coefficient by setting a preset margin, thereby improving the accuracy of the recommendation degree for the preset time period, and further improving the accuracy of information pushed to users and delivery personnel, and ultimately improving the efficiency of pushing information to users; in this embodiment, there is no specific limitation on the setting of the preset margin, and technical personnel in this field can set it freely as long as the setting requirements of the preset number are met, among which the optimal value of the preset minimum margin is 3, and the optimal value of the preset maximum margin is 6.

[0040] Please continue reading Figure 1 As shown, the system includes a push module for pushing the recommended results of the appointment time period to the user, and pushing the appointment results to the delivery person based on the user selection results, valid user appointments and user information, and also for updating the analysis process of user priority based on the feedback information obtained. The push module is connected to the appointment recommendation module.

[0041] Please continue reading Figure 5 As shown, the push module includes a push unit, which is used to push the recommended results of the appointment time period to the user for the user to choose, and is also used to push the appointment result to the delivery person according to the user selection result and the valid user appointment and user information.

[0042] Please continue reading Figure 5 As shown, the push module includes a trust analysis unit, which is connected to the push unit, and is used to calculate the trust of the user according to the historical number of deliveries by the deliveryman to the user and the historical number of uncollected items by the user, and compare the trust of the user with the preset trust Y0, and update the analysis process of the user priority according to the comparison result. The trust analysis unit sets the trust of the user to Y, and sets Y=1-y1 / y0, where: When Y≤Y0, the trust analysis unit determines that the user's trust status is abnormal, and sets the preset waiting time to tα', setting tα'=tα×{1+{[(Y0-Y) / (Y0+Y)] 3 +e 2×[(Y0-Y) / (Y0+Y)] -1} / e 2}, tα is the preset waiting time, α=1,2; When Y>Y0, the trust analysis unit determines that the user trust status is normal and does not update.

[0043] Specifically, the trust analysis unit improves the accuracy of the adjustment coefficient by setting a preset trust, thereby improving the accuracy of the user priority, and further improving the accuracy of information pushed to users and delivery personnel, and ultimately improving the efficiency of pushing information to users; in this implementation, there is no specific limitation on the value of the preset trust, and technical personnel in this field can set it freely as long as the value requirements of the preset trust are met. Among them, the optimal value of the preset trust is 0.3.

[0044] Specifically, the e-commerce logistics parcel delivery system described in this embodiment is applied to the delivery of express delivery to community residents. It analyzes the users and analyzes the user reservation information based on the analysis results of the users. The delivery information is pushed to the delivery personnel based on the analysis results of the user reservation information, thereby improving the efficiency of pushing information to users.

[0045] See also Figure 6 As shown, it is a flow chart of the e-commerce package delivery method of this embodiment, and the method includes: Step S1: Obtain user information, express information, reservation information and feedback information; Step S2: performing a priority analysis on the user according to the obtained user level and user waiting time, and adjusting the process of performing a priority analysis on the user according to the obtained time of user cancellation and the historical number of user cancellations; Step S3: Analyzing the validity of the user's reservation based on the analysis result of the user's priority, the obtained express information, the reservation date and the reservation period; Step S4: when the user's reservation is invalid, recommending a reservation time period to the user according to the preset time period, and optimizing the process of recommending a reservation time period to the user according to the number of historical reservations made by the user in each reservation time period and the delivery availability in each reservation time period; Step S5: pushes the recommended result of the appointment time period to the user, pushes the appointment result to the delivery person according to the user selection result, the valid user appointment and the user information, and updates the analysis process of the user priority according to the feedback information obtained.

[0046] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

Claims

1. An e-commerce package delivery system, characterized in that: include, Information acquisition module, used to obtain user information, express information, appointment information and feedback information; A user analysis module is used to perform a priority analysis on users according to the obtained user levels and user waiting times, and to adjust the process of performing a priority analysis on users according to the obtained time of user cancellations and the historical number of user cancellations; The appointment analysis module is used to analyze the validity of user appointments based on the analysis results of user priority, the obtained express information, appointment date and appointment time period; The appointment recommendation module is used to recommend appointment time periods to users according to preset time periods when the user's appointment is invalid, and is also used to optimize the process of recommending appointment time periods to users according to the number of historical appointments made by the user in each appointment time period and the delivery availability of each appointment time period; The push module is used to push the recommended results of the appointment time period to the user, and to push the appointment results to the delivery person based on the user selection results, valid user appointments and user information, and to update the analysis process of user priorities based on the feedback information obtained.

2. The e-commerce package delivery system according to claim 1, characterized in that: The user analysis module includes a priority analysis unit, a first user analysis unit and a second user analysis unit. The priority analysis unit classifies users according to the acquired user levels, and compares the user waiting time t0 with each preset waiting time according to the classification results, and analyzes the user priority according to the comparison results to obtain the user priority Aa.

3. The e-commerce package delivery system according to claim 2, characterized in that: The first user analysis unit calculates the difference between the time T0 when the user cancels the appointment and the start time T1 of the user's appointment period, and compares the calculation result with the preset time difference T2. According to the comparison result, the abnormality of the time interval between the user's cancellation of the appointment is analyzed. The abnormality of the time interval between the user's cancellation of the appointment includes normal and abnormal. When the time interval between the user's cancellation of the appointment is abnormal, the user's priority Aa is adjusted to Aa'.

4. The e-commerce package delivery system according to claim 3, characterized in that: The second user analysis unit compares the historical number of times L0 that the user cancels an appointment with the preset number L1, and analyzes the abnormality of the historical number of times the user cancels an appointment based on the comparison result. The abnormality of the historical number of times the user cancels an appointment includes normal and abnormal. When the historical number of times the user cancels an appointment is abnormal, the preset time difference is adjusted to T2'.

5. The e-commerce package delivery system according to claim 1, characterized in that: The appointment analysis module is provided with a date analysis unit, which adds the express order time b1 and the express delivery time b2, and compares them with the user appointment time b0, and analyzes the validity of the user appointment according to the comparison result, wherein: When b1+b2<b0, the date analysis unit determines that the user's reservation date is invalid; When b1+b2≥b0, the date analysis unit determines that the user's reservation date is valid; The reservation analysis module is further provided with a time period analysis unit. When the user reservation date is valid, the time period analysis unit analyzes the validity of the user reservation according to the vacancy k0 of the preset time period of the user reservation period and the user's priority, wherein: When k0=0, the time period analysis unit determines that the user's reservation time period is invalid; When k0>0, if multiple users reserve the reservation time period at the same time, the number of users is set to m. If m≤k0, the time period analysis unit determines that the user's reservation time period is valid. If m>k0, the time period analysis unit sorts the users in descending order of priority, and selects k0 users with the highest priority, determines that the reservation time periods of the k0 users are valid, and determines that the reservation time periods of the remaining users are invalid; if a single user reserves the reservation time period at the same time, the time period analysis unit determines that the user's reservation time period is valid.

6. The e-commerce package delivery system according to claim 5, characterized in that: The reservation recommendation module includes a reservation recommendation unit, a reservation frequency analysis unit and a vacancy analysis unit. When the user's reservation period is invalid, the reservation recommendation unit analyzes the vacancy p0 of the preset period dispatch in the user's reservation date to obtain a preset period that can be reserved, and sets the start time W of the preset period that can be reserved. i Calculate the difference between the start time W0 of the user's scheduled time period and the recommended degree H of each preset time period according to the result of the difference calculation. When H≤h1, the reservation recommendation unit determines that the preset time period is a non-recommended preset time period; when H>h1, the reservation recommendation unit determines that the preset time period is a recommended preset time period; Among them, h1 is the preset recommendation degree, W i is the start time of the i-th preset period.

7. The e-commerce package delivery system according to claim 6, characterized in that: The reservation number analysis unit compares the historical reservation number z0 of the user in each preset time period with the preset number z1, and analyzes the abnormality of the user's reservation number according to the comparison result. The abnormality of the user's reservation number includes normal and abnormal. When the user's reservation number in the preset time period is abnormal, the recommendation degree of the preset time period is optimized to H'.

8. The e-commerce package delivery system according to claim 7, characterized in that: The availability analysis unit calculates the delivery availability S of the preset time period available for reservation on the user's reservation date. i Compare with the preset spare quantity s0, and analyze the delivery spare status according to the comparison result. The delivery spare status includes normal and abnormal. When the delivery spare quantity in the preset time period is abnormal, the preset number of times is optimized to z1'.

9. The e-commerce package delivery system according to claim 2, characterized in that: The push module includes a trust analysis unit, which calculates the trust of the user based on the number of historical deliveries made by the courier to the user and the number of historical failed pickups by the user, and compares the user's trust with a preset trust Y0 to analyze the user's trust status. The user's trust status includes normal and abnormal. When the user's trust status is abnormal, the preset waiting time is updated to tα', where tα is the preset waiting time.

10. An e-commerce package delivery method, applied to the e-commerce package delivery system according to any one of claims 1 to 9, characterized in that: include, Step S1: Obtain user information, express information, reservation information and feedback information; Step S2: performing a priority analysis on the user according to the obtained user level and user waiting time, and adjusting the process of performing a priority analysis on the user according to the obtained time of user reservation cancellation and the historical number of user reservation cancellations; Step S3: Analyzing the validity of the user's reservation based on the analysis result of the user's priority, the obtained express information, the reservation date and the reservation period; Step S4: when the user's reservation is invalid, recommending a reservation time period to the user according to the preset time period, and optimizing the process of recommending a reservation time period to the user according to the number of historical reservations made by the user in each reservation time period and the delivery availability in each reservation time period; Step S5: pushes the recommended result of the appointment time period to the user, pushes the appointment result to the delivery person according to the user selection result, the valid user appointment and the user information, and updates the analysis process of the user priority according to the feedback information obtained.

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