Commodity distribution logistics estimation management system adaptive to e-commerce platform

Abstract

The invention belongs to the technical field of e-commerce distribution management, and particularly relates to a commodity distribution logistics estimation management system adapted to an e-commerce platform, which comprises an order acquisition and processing module, a distribution logistics estimation module, a logistics real-time tracking module, a distribution logistics evaluation module, a user interaction module and a distribution logistics management end. According to the invention, the order acquisition and processing module automatically acquires order information and generates a delivery task, the delivery logistics estimation module estimates the commodity delivery time, and the logistics real-time tracking module tracks the delivery logistics of the order in real time, so that accurate logistics estimation and real-time delivery state updating are realized, the trust and satisfaction of customers are enhanced, and the customer experience is improved. And step-by-step analysis is carried out through the distribution logistics evaluation module and the optimization reanalysis module to judge whether the distribution logistics estimation module needs to be optimized, and the distribution logistics estimation module is correspondingly optimized when an estimation optimization signal or an optimization alarm signal is generated, so that the subsequent operation performance and operation safety of the distribution logistics estimation module are ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of e-commerce distribution management, and specifically to a commodity distribution logistics estimation management system adapted to an e-commerce platform. Background Art

[0002] An e-commerce platform is a platform that provides online transactions for enterprises or individuals. It not only provides information about buyers and sellers and helps realize transactions of products or services, but also has a series of functions and services to promote the smooth progress of e-commerce. At present, when managing the delivery logistics of goods on e-commerce platforms, the delivery arrival time is generally estimated through the delivery estimation method. However, it is difficult to accurately estimate the delivery time and reasonably judge the estimated operation performance. Management personnel cannot make corresponding optimization measures in time, which is not conducive to improving user satisfaction and system performance. In view of the above technical defects, a solution is now proposed. Summary of the invention

[0003] The purpose of the present invention is to provide a commodity distribution logistics estimation management system adapted to e-commerce platforms, which solves the problem that the prior art is difficult to accurately estimate the delivery time and reasonably judge the estimated operating performance, and the management personnel are unable to make corresponding optimization measures in time, which is not conducive to improving user satisfaction and system performance.

[0004] To achieve the above object, the present invention provides the following technical solutions: The commodity distribution logistics estimation management system adapted to the e-commerce platform includes an order collection and processing module, a distribution logistics estimation module, a logistics real-time tracking module, a distribution logistics evaluation module, a user interaction module and a distribution logistics management terminal; when the e-commerce platform receives a customer order, the order collection and processing module automatically obtains the order information, generates a picking list and a distribution task according to the order requirements, performs picking and outbound operations according to the picking list, updates the inventory information in real time, and sends the distribution task to the distribution logistics estimation module and the logistics real-time tracking module; The distribution logistics estimation module uses machine learning algorithms to build a logistics estimation model. The logistics estimation model estimates the delivery time of goods based on the distribution tasks and the traffic data and weather information of the distribution routes, and sends the estimation information to the user interaction module and the distribution logistics evaluation module; The logistics real-time tracking module uses GPS positioning and Internet of Things technology to track the delivery logistics of the order in real time, sends the location and status information of the delivery vehicle to the user interaction module, the delivery logistics evaluation module and the delivery logistics management end, and reminds the user to pick up the goods when they arrive at the destination; the delivery logistics evaluation module analyzes the estimated performance of delivery logistics based on the estimated information and delivery logistics tracking information, and generates an estimated optimization signal or an estimated qualified signal based on this, and sends the estimated optimization signal or the estimated qualified signal to the delivery logistics management end, which issues an early warning when receiving the estimated optimization signal.

[0005] Furthermore, the specific analysis process of the distribution logistics evaluation module includes: Set the analysis period to W1 days. When the number of days reaches W1, obtain all delivery tasks within the analysis period, collect the estimated delivery time and actual delivery time of the corresponding delivery tasks, and mark the interval between the estimated delivery time and the actual delivery time as the distance duration. Compare the distance duration with the preset distance duration threshold, and if the distance duration exceeds the preset distance duration threshold, mark the corresponding distance duration as divorce duration; obtain the number of divorce durations in the analysis period and mark it as the divorce detection value, and calculate the ratio of the divorce detection value to the number of distance durations in the analysis period to obtain the divorce ratio value; The departure time decision value is obtained by calculating the mean of all distance durations within the analysis period, the estimated deviation coefficient is obtained by numerically calculating the departure time decision value, the divorce detection value and the divorce ratio value, and the estimated deviation coefficient is numerically compared with the preset estimated deviation coefficient threshold. If the estimated deviation coefficient exceeds the preset estimated deviation coefficient threshold, an estimated optimization signal is generated; if the estimated deviation coefficient does not exceed the preset estimated deviation coefficient threshold, an estimated qualified signal is generated.

[0006] Furthermore, the distribution logistics evaluation module is communicatively connected to the optimization re-analysis module. The distribution logistics evaluation module sends the estimated qualified signal to the optimization re-analysis module. When the optimization re-analysis module receives the estimated qualified signal, it continues to analyze whether it is necessary to optimize the distribution logistics estimation module, and determines whether to generate an optimization alarm signal accordingly, and sends the optimization alarm signal to the distribution logistics management end. When the distribution logistics management end receives the optimization alarm signal, it issues an early warning.

[0007] Furthermore, the specific analysis process of optimizing the reanalysis module is as follows: When the distribution logistics estimation module receives the corresponding distribution task, the corresponding moment is marked as the first moment, and the moment when the distribution logistics estimation module outputs the corresponding estimation information is marked as the second moment, and the interval between the first moment and the second moment is marked as the estimated duration; The average value of all estimated durations within the analysis period is obtained and marked as the estimated efficiency value. The high-pressure operation value of the logistics estimation model within the analysis period is obtained through analysis, and the time difference between the current moment and the last time the logistics estimation model was optimized is calculated to obtain the optimized separation value. And obtain the protection alarm value of the distribution logistics evaluation module, obtain the optimized urgency value by numerically calculating the protection alarm value, estimated efficiency value, high-pressure operation value and optimized separation value, and compare the optimized urgency value with the preset optimized urgency threshold. If the optimized urgency value exceeds the preset optimized urgency threshold, an optimized alarm signal is generated.

[0008] Furthermore, the specific analysis process of the high-pressure operation value is as follows: The real-time quantity of the delivery tasks executed by the logistics estimation model is collected and marked as the estimated actual inspection value, and the estimated actual inspection value is numerically compared with the preset estimated actual inspection threshold. If the estimated actual inspection value exceeds the preset estimated actual inspection threshold, it is judged that the logistics estimation model is in a high-pressure operation state; The duration that the logistics estimation model is in a high-pressure operation state on the corresponding date within the analysis period is obtained and marked as the high-pressure daily measurement value, the high-pressure daily measurement values ​​of all dates within the analysis period are averaged to obtain the high-pressure performance value, and the proportion of days in which the high-pressure daily measurement value exceeds the preset high-pressure daily measurement threshold within the analysis period is marked as the high-pressure daily variation value, and the high-pressure daily measurement value with the largest value is marked as the high-pressure measurement amplitude value; the high-pressure operation value is obtained by numerically calculating the high-pressure performance value, the high-pressure daily variation value and the high-pressure amplitude measurement value.

[0009] Furthermore, the optimization re-analysis module is communicatively connected to the operation protection analysis module, and the operation protection analysis module analyzes the operation protection performance of the distribution logistics estimation module during the analysis period, obtains the protection alarm value accordingly, and sends the protection alarm value to the optimization re-analysis module.

[0010] Furthermore, the specific analysis process of running the protection analysis module includes: The total number of times the distribution logistics estimation module is attacked by network attacks and virus invasions during the analysis period is collected and marked as a damaged risk value, and all durations during which the distribution logistics estimation module cannot operate normally due to network attacks or virus invasions during the analysis period are obtained. The sum of all durations during which the module cannot operate normally due to network attacks or virus invasions during the analysis period is calculated to obtain a poor protection value, and the number of times during which the module cannot operate normally due to network attacks or virus invasions during the analysis period exceeds a preset duration threshold is marked as a dangerous protection value, and the protection alarm value is obtained by numerically calculating the damaged risk value, the poor protection value and the dangerous protection value.

[0011] Furthermore, the distribution logistics management end is connected to the alarm supervision module in communication. Before the distribution logistics management end issues an alarm, the alarm supervision module obtains the manager of the monitoring area where the distribution logistics management end is located through the monitoring camera. If there is no manager in the monitoring area, the alarm supervision module sends an alarm message to the smart terminal of the corresponding manager; If there is a manager in the monitoring area, the shortest distance between the manager and the distribution logistics management end is collected and marked as the alarm short distance value, and the noise decibel value of the monitoring area is collected and marked as the alarm noise detection value; the alarm phonetic value is obtained by numerically calculating the alarm short distance value and the alarm noise detection value; Several groups of preset alarm phonetic symbol value ranges are set in advance, and each group of preset alarm phonetic symbol value ranges corresponds to a group of volume standard values. The alarm phonetic symbol value is compared with all preset alarm phonetic symbol value ranges one by one, and the preset alarm phonetic symbol value range containing the corresponding alarm phonetic symbol value is marked as the optimal range, and the volume standard value corresponding to the optimal range is marked as the volume matching value, so that the distribution logistics management end can issue an early warning according to the volume matching value.

[0012] Furthermore, the operation process of the alarm monitoring module also includes: When the distribution logistics management end issues an early warning, the actual volume is collected, the difference between the actual volume and the corresponding volume matching value is calculated and the absolute value is taken to obtain the alarm sound abnormality value, all the alarm sound abnormality values ​​of the corresponding early warning process are averaged to obtain the alarm sound condition value, and the proportion of the duration of the alarm sound abnormality value exceeding the preset alarm sound abnormality threshold in the corresponding early warning process is marked as the alarm abnormality time value; The distribution logistics management end also determines the delay time of the corresponding warning and marks it as the alarm delay value. The alarm analysis value is obtained by numerically calculating the alarm sound value, the alarm abnormal time value and the alarm delay value. The alarm analysis value is numerically compared with the preset alarm analysis threshold. If the alarm analysis value exceeds the preset alarm analysis threshold, the corresponding warning process is marked as an abnormal alarm process. A supervision period is set, the number of abnormal alarm processes within the supervision period is obtained and the ratio is calculated with the total number of warning processes to obtain the abnormal alarm detection value, and the abnormal alarm detection value is numerically compared with the preset abnormal alarm detection threshold. If the abnormal alarm detection value exceeds the preset abnormal alarm detection threshold, an alarm non-optimal signal is generated and sent to the intelligent terminal of the corresponding management personnel.

[0013] Compared with the prior art, the present invention has the following beneficial effects: 1. In the present invention, the order collection and processing module automatically obtains order information and generates delivery tasks, the delivery logistics estimation module estimates the delivery time of goods, and the logistics real-time tracking module tracks the delivery logistics of the order in real time, so as to achieve accurate logistics estimation and real-time delivery status update, enhance customer trust and satisfaction, and gradually analyze through the delivery logistics evaluation module and the optimization re-analysis module to determine whether it is necessary to optimize the delivery logistics estimation module, and optimize the delivery logistics estimation module accordingly when generating the estimation optimization signal or optimization alarm signal, so as to ensure its subsequent operation performance and operation safety; 2. In the present invention, the alarm supervision module is used to identify the management personnel in the monitoring area where the distribution logistics management end is located before the distribution logistics management end issues an early warning. When there are management personnel in the monitoring area, the early warning volume of the distribution logistics management end is reasonably and automatically controlled to ensure the early warning effect and reduce energy consumption. It is also possible to conduct a comprehensive evaluation of the early warning status of the distribution logistics management end during the supervision period, and to inspect and repair the distribution logistics management end when an alarm non-optimal signal is generated, thereby reducing the management difficulty of the management personnel and ensuring the early warning performance of the distribution logistics management end. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to facilitate understanding by those skilled in the art, the present invention is further described below in conjunction with the accompanying drawings; Figure 1 This is a system block diagram of Embodiment 1 of the present invention; Figure 2 This is a system block diagram of Embodiment 2 of the present invention. DETAILED DESCRIPTION

[0015] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0016] Embodiment 1: Figure 1 As shown, the commodity distribution logistics estimation management system adapted to the e-commerce platform proposed in the present invention includes an order collection and processing module, a distribution logistics estimation module, a logistics real-time tracking module, a distribution logistics evaluation module, an optimization and re-analysis module, a user interaction module and a distribution logistics management terminal; When the e-commerce platform receives a customer order, the order collection and processing module automatically obtains the order information (including but not limited to the order number, product name, quantity, weight, volume, delivery address, delivery time requirements, etc.), and generates a picking list and delivery tasks based on the order requirements; Carry out picking and delivery operations according to the picking list and update the inventory information in real time to monitor the inventory status so as to replenish the goods in time and avoid out-of-stock or backlog. The delivery tasks are sent to the delivery logistics estimation module and the logistics real-time tracking module.

[0017] The distribution logistics estimation module uses machine learning algorithms (such as neural networks, support vector machines, etc.) to build a logistics estimation model. The logistics estimation model estimates the delivery time of goods based on the distribution task and the traffic data and weather information of the distribution route, and sends the estimation information to the user interaction module and the distribution logistics evaluation module; among them, the estimation information is mainly the estimated arrival time of the delivery task.

[0018] The logistics real-time tracking module uses GPS positioning and Internet of Things technology to track the delivery logistics of orders in real time, and sends the location and status information of the delivery vehicle to the user interaction module, the delivery logistics evaluation module and the delivery logistics management terminal. It also reminds users to pick up the goods when they arrive at the destination. Customers can understand the order status and delivery progress at any time. Through accurate logistics estimates and real-time delivery status updates, customer trust and satisfaction are enhanced.

[0019] The distribution logistics evaluation module will analyze the distribution logistics estimation performance based on the estimation information and distribution logistics tracking information, and generate an estimation optimization signal or an estimation qualification signal accordingly, and send the estimation optimization signal or the estimation qualification signal to the distribution logistics management end. When the distribution logistics management end receives the estimation optimization signal, it will issue an early warning to remind the management personnel to optimize the distribution logistics estimation module in time to ensure the accuracy of its subsequent estimation. The specific analysis process of the distribution logistics evaluation module is as follows: Set the analysis period to W1, preferably, W1 is ten days; when the number of days reaches W1, obtain all the delivery tasks within the analysis period, collect the estimated delivery time and the actual delivery time of the corresponding delivery tasks, and mark the interval between the estimated delivery time and the actual delivery time as the distance time; wherein, the larger the value of the distance time, the less accurate the estimated result for the corresponding delivery task; Compare the distance duration with the preset distance duration threshold. If the distance duration exceeds the preset distance duration threshold, it indicates that the estimated result for the corresponding delivery task is inaccurate, and the corresponding distance duration is marked as divorce duration. The number of divorce durations in the analysis period is obtained and marked as a divorce detection value, and the divorce detection value is calculated by ratio with the number of distance durations in the analysis period to obtain a divorce ratio value. And the departure time decision value is obtained by calculating the mean of all distance durations within the analysis period, and the departure time decision value YR, the divorce detection value YS and the divorce proportion value YZ are numerically calculated by the formula YL=(wq1*YR+wq2*YS) / wq3+wq3*YZ to obtain the estimated deviation coefficient YL; wherein wq1, wq2, wq3 are preset proportional coefficients with values ​​greater than zero, and the larger the value of the estimated deviation coefficient YL, the worse the estimation accuracy of the distribution logistics estimation module within the analysis period; The estimated deviation coefficient is numerically compared with the preset estimated deviation coefficient threshold. If the estimated deviation coefficient exceeds the preset estimated deviation coefficient threshold, it indicates that the estimation accuracy of the distribution logistics estimation module within the analysis period is poor, and an estimation optimization signal is generated; if the estimated deviation coefficient does not exceed the preset estimated deviation coefficient threshold, it indicates that the estimation accuracy of the distribution logistics estimation module within the analysis period is good, and an estimation qualification signal is generated.

[0020] The distribution logistics evaluation module sends the estimated qualified signal to the optimization re-analysis module. When the optimization re-analysis module receives the estimated qualified signal, it continues to analyze whether the distribution logistics estimation module needs to be optimized, and determines whether to generate an optimization alarm signal based on this, and sends the optimization alarm signal to the distribution logistics management end. When the distribution logistics management end receives the optimization alarm signal, it issues an early warning to remind the management personnel to optimize the distribution logistics estimation module in time, so as to further ensure its subsequent operation performance and operation safety. The specific analysis process of the optimization re-analysis module is as follows: When the distribution logistics estimation module receives the corresponding distribution task, the corresponding moment is marked as the first moment, and the moment when the distribution logistics estimation module outputs the corresponding estimation information is marked as the second moment, and the interval between the first moment and the second moment is marked as the estimated duration; wherein, the larger the value of the estimated duration is, the slower the estimated efficiency for the corresponding distribution task is; The high-pressure operation value of the logistics estimation model within the analysis period is obtained through analysis, specifically: the real-time number of delivery tasks executed by the logistics estimation model (that is, the real-time number of delivery tasks undergoing estimation analysis) is collected and marked as the estimated actual inspection value, and the estimated actual inspection value is numerically compared with the preset estimated actual inspection threshold. If the estimated actual inspection value exceeds the preset estimated actual inspection threshold, it indicates that the real-time operation load of the logistics estimation model is large, and it is judged that the logistics estimation model is in a high-pressure operation state; The duration of the logistics prediction model in the corresponding date within the analysis period being in the high-pressure operation state is obtained and marked as the high-pressure daily measurement value, the high-pressure daily measurement values ​​of all dates within the analysis period are averaged to obtain the high-pressure performance value, and the proportion of days within the analysis period in which the high-pressure daily measurement value exceeds the preset high-pressure daily measurement threshold is marked as the high-pressure daily abnormal value, and the high-pressure daily measurement value with the largest value within the analysis period is marked as the high-pressure measurement amplitude value; The high-pressure performance value LX, the high-pressure daily variation value LF and the high-pressure amplitude measurement value LW are numerically calculated by the formula LP=+c2*LF+(c1*LXc3*LW) / 2 to obtain the high-pressure operation value LP; wherein c1, c2 and c3 are preset proportional coefficients, c2>c1>c3>0.362; and the larger the value of the high-pressure operation value LP is, the greater the operation load of the logistics prediction model in the analysis period is, and the more difficult it is to operate effectively; The average value of all estimated durations within the analysis period is obtained and marked as the estimated efficiency value, and the time difference between the current moment and the last moment when the logistics estimation model was optimized is calculated to obtain the optimized separation value; and the protection alarm value of the distribution logistics evaluation module is obtained; The protection alarm value TP, the estimated efficiency value NY, the high-pressure operation value LP and the optimized separation value WS are numerically calculated by the formula XL=(eg1*TP+eg2*NY+eg3*LP+eg4*WS) / 4 to obtain the optimized urgency value XL, wherein eg1, eg2, eg3 and eg4 are preset proportional coefficients whose values ​​are greater than zero, and the larger the value of the optimized urgency value XL is, the more timely it is necessary to optimize the distribution logistics estimation module; the optimized urgency value XL is numerically compared with the preset optimized urgency threshold value, and if the optimized urgency value XL exceeds the preset optimized urgency threshold value, it indicates that the distribution logistics estimation module needs to be optimized in time, and an optimized alarm signal is generated.

[0021] Furthermore, the optimization reanalysis module is connected to the operation protection analysis module in communication. The operation protection analysis module analyzes the operation protection performance of the distribution logistics estimation module during the analysis period, and obtains the protection alarm value TP based on this. The protection alarm value TP is sent to the optimization reanalysis module, which can not only reasonably analyze and accurately feedback the degree of safety hazards in the distribution logistics estimation module, but also provide data support for the analysis process of the optimization reanalysis module to ensure the accuracy of its analysis results. The specific analysis process of the operation protection analysis module is as follows: The total number of times the distribution logistics estimation module is attacked by network attacks and virus invasions during the analysis period is collected and marked as a damage risk value, and all durations during which the distribution logistics estimation module cannot operate normally due to network attacks or virus invasions during the analysis period are obtained, and all durations during which the distribution logistics estimation module cannot operate normally due to network attacks or virus invasions during the analysis period are summed up to obtain a poor protection value, and the number of occurrences during which the duration during which the module cannot operate normally due to network attacks or virus invasions during the analysis period exceeds a preset duration threshold is marked as a protection risk value; By formula The damaged risk value SY, the poor protection value FX and the dangerous protection value HP are numerically calculated to obtain the protection alarm value TP; among them, uy1, uy2, and uy3 are preset proportional coefficients, uy3>uy1>uy2>0; and, the larger the value of the protection alarm value TP is, the greater the security risks in the distribution logistics estimation module during the analysis period, and the more timely it is necessary to strengthen defense optimization.

[0022] Embodiment 2: Figure 2 As shown, the difference between this embodiment and the first embodiment is that the distribution logistics management end is connected to the alarm supervision module in communication. The alarm supervision module obtains the management personnel of the monitoring area where the distribution logistics management end is located through the monitoring camera before the distribution logistics management end issues an early warning. If there is no management personnel in the monitoring area, the early warning information is sent to the smart terminal of the corresponding management personnel; If there is a manager in the monitoring area, the shortest distance between the manager and the distribution logistics management terminal is collected and marked as the alarm short distance value, and the noise decibel value of the monitoring area is collected and marked as the alarm noise detection value; The alarm short-distance value PK and the alarm noise detection value PF are numerically calculated by the formula PM=ku1*PK+ku2*PF to obtain the alarm sound mark value PM; wherein ku1 and ku2 are preset proportional coefficients, ku2>ku1>0; and the larger the value of the alarm sound mark value PM is, the more it is necessary to increase the alarm volume of the distribution logistics management end to remind the management personnel in time; Several groups of preset alarm phonetic symbol value ranges are set in advance, and each group of preset alarm phonetic symbol value ranges corresponds to a group of volume standard values. It should be noted that the larger the value of the preset alarm phonetic symbol value range is, the larger the corresponding volume standard value is; The alarm phonetic symbol value is compared with all preset alarm phonetic symbol value ranges one by one, the preset alarm phonetic symbol value range containing the corresponding alarm phonetic symbol value is marked as the optimal range, and the volume standard value corresponding to the optimal range is marked as the volume matching value, so that the distribution logistics management end can issue an early warning according to the volume matching value, and the early warning volume of the distribution logistics management end can be reasonably and automatically controlled, thereby ensuring the early warning effect and reducing energy consumption.

[0023] Furthermore, the operation process of the alarm supervision module also includes: when an early warning is issued at the distribution logistics management end, the actual volume is collected, the difference between the actual volume and the corresponding volume matching value is calculated and the absolute value is taken to obtain the alarm sound abnormality value, all the alarm sound abnormality values ​​of the corresponding early warning process are averaged to obtain the alarm sound condition value, and the proportion of the time when the alarm sound abnormality value exceeds the preset alarm sound abnormality threshold value in the corresponding early warning process is marked as the alarm abnormality time value; and the delay time for the distribution logistics management end to issue a corresponding warning and mark it as the alarm delay value (i.e., the interval between the actual start time of the warning at the distribution logistics management end and the time when the corresponding warning signal is received); By formula The alarm sound condition value GY, the alarm time difference value DW and the alarm delay value ZF are numerically calculated to obtain the alarm analysis value HM; wherein ng1, ng2 and ng3 are preset proportional coefficients with values ​​greater than zero, and the larger the value of the alarm analysis value HM is, the worse the warning performance of the corresponding warning process of the distribution logistics management end is; The alarm analysis value HM is numerically compared with the preset alarm analysis threshold. If the alarm analysis value HM exceeds the preset alarm analysis threshold, it indicates that the warning performance of the corresponding warning process of the distribution logistics management end is poor, and the corresponding warning process is marked as an abnormal alarm process; A supervision period is set, preferably, the supervision period is thirty days; the number of abnormal alarm processes within the supervision period is obtained and the ratio thereof is calculated to the total number of early warning processes to obtain the abnormal alarm detection value, the abnormal alarm detection value is numerically compared with the preset abnormal alarm detection threshold, if the abnormal alarm detection value exceeds the preset abnormal alarm detection threshold, indicating that the early warning status of the distribution logistics management end during the supervision period is generally poor, then an alarm non-optimal signal is generated, and the alarm non-optimal signal is sent to the intelligent terminal of the corresponding management personnel to remind the management personnel to inspect and repair the distribution logistics management end in time, reduce the management difficulty of the management personnel, and further ensure the early warning performance and operation stability of the distribution logistics management end.

[0024] The working principle of the present invention is as follows: when in use, the order information is automatically obtained and a picking list and delivery tasks are generated through the order collection and processing module, and picking and outbound operations are performed according to the picking list. The delivery logistics estimation module estimates the delivery time of goods according to the delivery tasks and the traffic data and weather information of the delivery path through the logistics estimation model. The logistics real-time tracking module tracks the delivery logistics of the order in real time, and enhances the customer's trust and satisfaction through accurate logistics estimation and real-time delivery status updates. The delivery logistics evaluation module analyzes the delivery logistics estimation performance based on the estimation information and delivery logistics tracking information. When an estimation qualified signal is generated, the optimization re-analysis module continues to analyze whether the delivery logistics estimation module needs to be optimized. When an estimation optimization signal or an optimization alarm signal is generated, the delivery logistics estimation module is optimized accordingly to ensure its subsequent operation performance and operation safety, and the intelligence level is high.

[0025] The above formulas are all dimensionless and numerical calculations. The formula is a formula obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formula are set by technicians in this field according to actual conditions. The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the contents of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that technicians in the relevant technical field can understand and use the present invention well. The present invention is only limited by the claims and their full scope and equivalents.

Claims

1. A commodity distribution logistics estimation management system adapted to e-commerce platforms, characterized by: It includes order collection and processing module, distribution logistics estimation module, logistics real-time tracking module, distribution logistics evaluation module, user interaction module and distribution logistics management terminal; when the e-commerce platform receives a customer order, the order collection and processing module automatically obtains the order information, generates a picking list and delivery tasks according to the order requirements, performs picking and outbound operations according to the picking list, updates the inventory information in real time, and sends the delivery tasks to the distribution logistics estimation module and the logistics real-time tracking module; The distribution logistics estimation module uses machine learning algorithms to build a logistics estimation model. The logistics estimation model estimates the delivery time of goods based on the distribution tasks and the traffic data and weather information of the distribution routes, and sends the estimation information to the user interaction module and the distribution logistics evaluation module; The real-time logistics tracking module uses GPS positioning and Internet of Things technology to track the delivery logistics of the order in real time, sends the location and status information of the delivery vehicle to the user interaction module, the delivery logistics evaluation module and the delivery logistics management terminal, and reminds the user to pick up the goods when they arrive at the destination; The distribution logistics evaluation module analyzes the distribution logistics estimated performance based on the estimated information and distribution logistics tracking information, generates an estimated optimization signal or an estimated qualified signal accordingly, and sends the estimated optimization signal or the estimated qualified signal to the distribution logistics management end.

2. According to claim 1, the commodity distribution logistics estimation management system adapted to the e-commerce platform is characterized in that: The specific analysis process of the distribution logistics evaluation module includes: Set the analysis period to W1 days. When the number of days reaches W1, obtain all delivery tasks within the analysis period, collect the estimated delivery time and actual delivery time of the corresponding delivery tasks, and mark the interval between the estimated delivery time and the actual delivery time as the distance duration. If the distance duration exceeds the preset distance duration threshold, the corresponding distance duration is marked as the divorce duration; the number of divorce durations in the analysis period is obtained and marked as the divorce detection value, and the divorce detection value is calculated by ratio with the number of distance durations in the analysis period to obtain the divorce ratio value; The departure time decision value is obtained by calculating the mean of all distance durations within the analysis period, and the estimated deviation coefficient is obtained by numerically calculating the departure time decision value, the divorce detection value and the divorce ratio value. If the estimated deviation coefficient exceeds the preset estimated deviation coefficient threshold, an estimated optimization signal is generated; if the estimated deviation coefficient does not exceed the preset estimated deviation coefficient threshold, an estimated qualified signal is generated.

3. The commodity distribution logistics estimation management system adapted to the e-commerce platform according to claim 2 is characterized in that: The distribution logistics evaluation module is communicatively connected to the optimization re-analysis module. The distribution logistics evaluation module sends the estimated qualified signal to the optimization re-analysis module. When the optimization re-analysis module receives the estimated qualified signal, it continues to analyze whether it is necessary to optimize the distribution logistics estimation module, and determines whether to generate an optimization alarm signal based on this, and sends the optimization alarm signal to the distribution logistics management end.

4. The commodity distribution logistics estimation management system adapted to the e-commerce platform according to claim 3 is characterized in that: The specific analysis process of optimizing the reanalysis module is as follows: When the distribution logistics estimation module receives the corresponding distribution task, the corresponding moment is marked as the first moment, and the moment when the distribution logistics estimation module outputs the corresponding estimation information is marked as the second moment, and the interval between the first moment and the second moment is marked as the estimated duration; The average value of all estimated durations within the analysis period is obtained and marked as the estimated efficiency value. The high-pressure operation value of the logistics estimation model within the analysis period is obtained through analysis, and the time difference between the current moment and the last time the logistics estimation model was optimized is calculated to obtain the optimized separation value. And obtain the protection alarm value of the distribution logistics evaluation module, and obtain the optimized urgency value by numerically calculating the protection alarm value, estimated efficiency value, high-pressure operation value and optimized separation value. If the optimized urgency value exceeds the preset optimized urgency threshold, an optimized alarm signal is generated.

5. According to claim 4, the commodity distribution logistics estimation management system adapted to the e-commerce platform is characterized in that: The specific analysis process of high-pressure operation value is as follows: The real-time quantity of the delivery tasks executed by the logistics estimation model is collected and marked as the estimated actual inspection value. If the estimated actual inspection value exceeds the preset estimated actual inspection threshold, it is judged that the logistics estimation model is in a high-pressure operation state; The duration that the logistics estimation model is in a high-pressure operation state on the corresponding date within the analysis period is obtained and marked as the high-pressure daily measurement value, the high-pressure daily measurement values ​​of all dates within the analysis period are averaged to obtain the high-pressure performance value, and the proportion of days in which the high-pressure daily measurement value exceeds the preset high-pressure daily measurement threshold within the analysis period is marked as the high-pressure daily variation value, and the high-pressure daily measurement value with the largest value is marked as the high-pressure measurement amplitude value; the high-pressure operation value is obtained by numerically calculating the high-pressure performance value, the high-pressure daily variation value and the high-pressure amplitude measurement value.

6. The commodity distribution logistics estimation management system adapted to the e-commerce platform according to claim 4 is characterized in that: The optimization re-analysis module is communicatively connected to the operation protection analysis module. The operation protection analysis module analyzes the operation protection performance of the distribution logistics estimation module during the analysis period, obtains the protection alarm value accordingly, and sends the protection alarm value to the optimization re-analysis module.

7. The commodity distribution logistics estimation management system adapted to the e-commerce platform according to claim 6 is characterized in that: The specific analysis process of running the protection analysis module includes: The total number of times the distribution logistics estimation module is attacked by network attacks and virus invasions during the analysis period is collected and marked as a damaged risk value, and all durations during which the distribution logistics estimation module cannot operate normally due to network attacks or virus invasions during the analysis period are obtained. The sum of all durations during which the module cannot operate normally due to network attacks or virus invasions during the analysis period is calculated to obtain a poor protection value, and the number of times during which the module cannot operate normally due to network attacks or virus invasions during the analysis period exceeds a preset duration threshold is marked as a dangerous protection value, and the protection alarm value is obtained by numerically calculating the damaged risk value, the poor protection value and the dangerous protection value.

8. The commodity distribution logistics estimation management system adapted to the e-commerce platform according to claim 1 is characterized in that: The distribution logistics management end is connected to the alarm supervision module through communication. Before the distribution logistics management end issues an alarm, the alarm supervision module obtains the management personnel in the monitoring area where the distribution logistics management end is located through the monitoring camera. If there is no management personnel in the monitoring area, the alarm information is sent to the smart terminal of the corresponding management personnel; If there is a manager in the monitoring area, the shortest distance between the manager and the distribution logistics management end is collected and marked as the alarm short distance value, and the noise decibel value of the monitoring area is collected and marked as the alarm noise detection value; the alarm phonetic value is obtained by numerically calculating the alarm short distance value and the alarm noise detection value; Several groups of preset alarm phonetic symbol value ranges are set in advance, and each group of preset alarm phonetic symbol value ranges corresponds to a group of volume standard values. The preset alarm phonetic symbol value ranges containing corresponding alarm phonetic symbol values ​​are marked as optimal ranges, and the volume standard values ​​corresponding to the optimal ranges are marked as volume matching values, so that the distribution logistics management end can issue early warnings according to the volume matching values.

9. The commodity distribution logistics estimation management system adapted to the e-commerce platform according to claim 8 is characterized in that: The operation process of the alarm supervision module also includes: When the distribution logistics management end issues an early warning, the difference between the actual volume and the corresponding volume matching value is calculated and the absolute value is taken to obtain the alarm sound abnormality value. The average of all the alarm sound abnormality values ​​in the corresponding early warning process is calculated to obtain the alarm sound condition value. The proportion of the duration of the alarm sound abnormality value exceeding the preset alarm sound abnormality threshold in the corresponding early warning process is marked as the alarm abnormality time value. The distribution logistics management end also determines the delay time of the corresponding warning and marks it as the alarm delay value. The alarm analysis value is obtained by numerically calculating the alarm sound value, the alarm abnormal time value and the alarm delay value. If the alarm analysis value exceeds the preset alarm analysis threshold, the corresponding warning process is marked as an abnormal alarm process. A supervision period is set, and the number of abnormal alarm processes within the supervision period is obtained and the ratio is calculated with the total number of warning processes to obtain the abnormal alarm detection value. If the abnormal alarm detection value exceeds the preset abnormal alarm detection threshold, an alarm non-optimal signal is generated and sent to the smart terminal of the corresponding management personnel.

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