Automatic logistics management system based on big data

Abstract

The invention discloses an automatic logistics management system based on big data, and belongs to the technical field of logistics management. The method comprises the following steps: performing statistics on target types in logistics cargoes and corresponding logistics sorting states to obtain corresponding archive logs, and tracking historical records and current logistics states of the cargoes; by monitoring the same type of target objects in real time, whether the object sorting state accords with the expected process or not is judged, the system can automatically mark an abnormal result as an abnormal label, and an early warning prompt is sent to the corresponding target object; the degree of influence of abnormal influence coefficients of different sorting nodes on type problems existing in the sorting process is calculated and analyzed, and targeted management measures are implemented; the system is used for solving the problem that an automatic logistics management system in an existing scheme cannot supervise retention and loss of goods transfer at different stations and cannot actively give an alarm for prompt.

Description

Technical Field

[0001] The present invention relates to the technical field of logistics management, and particularly relates to an automated logistics management system based on big data. Background Art

[0002] In recent years, with the rapid development of technologies such as big data and artificial intelligence, it has become a reality to achieve the automation of real-time monitoring of logistics goods and warehousing management through technical means.

[0003] In logistics management, the problems of goods detention and loss are still common and important issues in logistics transportation. Most management systems cannot actively analyze and prompt for the supervision of goods detention and loss, nor can they actively monitor and warn the logistics transportation status, making it impossible for managers to timely discover and handle the problems of goods detention and loss. Summary of the Invention

[0004] The purpose of the present invention is to provide an automated logistics management system based on big data, which is used to solve the technical problems of inability to actively supervise, analyze, prompt, and warn about goods detention and loss.

[0005] The purpose of the present invention can be achieved through the following technical solutions: An automated logistics management system based on big data, including a logistics information data statistics module, which is used to count different types of goods entering the logistics site, and upload the logistics goods and file logs corresponding to several same-type targets to the automated logistics management system according to the target types of the logistics goods; A logistics system automated sorting module, which is used to monitor the same-type target items in real time, record the arrival time and departure time of the target items at each node; mark them as the first identifier and the second identifier according to the target type and the corresponding logistics sorting status in the logistics goods, and update and upload the file log; analyze and judge the item sorting status, and issue a warning prompt to the corresponding target item according to the abnormal label in the judgment result; An intelligent logistics management module, which is used to achieve deep coordination between various links of the logistics chain. Through data integration technology, various types of data are unified into the logistics database. On the basis of data integration, through monthly periodic supervision and evaluation of abnormal data at different sorting nodes, targeted supervision and optimization of the system are carried out, realizing real-time sharing of information and collaborative operation.

[0006] Preferably, the target types of the logistics goods include electronics, food, daily necessities, clothing, and fragile items; the file log includes the target goods information, logistics sorting status, recipient, recipient address, and contact information; among them, the goods information includes type, goods specifications, goods weight, and goods quantity.

[0007] Preferably, the logistics sorting status includes: summary picking status, in-transit sorting status, temporary storage status, transfer status, and sorting and outbound status.

[0008] Preferably, conduct active supervision and analysis on the summary picking status, pick items from the inventory area for classification and packing, record the sorting volume in a preset time period, where the preset time period is in hours; according to the minimum value and the maximum value of the sorting volume corresponding to all time periods in the historical record that are the same as the preset time period , as well as the average value of the sorting volume , determine the range of the sorting volume corresponding to each historical time period through the range formula; calculate the error range formula to determine the upper limit and lower limit of the error range. ; The formula for the average value of the sorting volume is: ; In the formula, is the total number of sorting volumes taken, is the range in the th time period taken; The difference formula is: ; Among them, the upper limit and lower limit of the error range are: ; In the formula, is the upper limit of the error range, is the lower limit of the error range, is the confidence level , that is, the error range is ; Analyze the sorting volume of the summary sorting monitored and statistically counted within the preset time period: If the sorting volume of the summary sorting within the preset time period satisfies , then mark the corresponding summary sorted items with a normal sorting label; If the sorting volume of the summary sorting within the preset time period does not satisfy , then mark the corresponding summary sorted items with an abnormal sorting label and issue a summary picking exception warning prompt.

[0009] Preferably, conduct active supervision and analysis on the in-transit sorting status. Based on the historical in-transit sorting data of the same target items in the past, determine the maximum and minimum values of the historical sorting time and set up a time fluctuation range. Traverse and scan the records to obtain the actual in-transit sorting time of the target items, and compare the actual in-transit sorting time with the time fluctuation range. Label the items corresponding to the actual goods distribution time within the time fluctuation range with normal goods distribution labels, label the items corresponding to the actual goods distribution time that does not conform to the time fluctuation range with abnormal goods distribution labels, and give an early warning prompt for abnormal goods distribution along the line for the items corresponding to the abnormal goods distribution labels.

[0010] Preferably, conduct active supervision and analysis on the temporary storage status to obtain the temporary storage time of the target items. Different types of goods have different requirements for the temporary storage time. Obtain the corresponding temporary storage time threshold according to the goods type; Implement the judgment of the temporary storage time of the target items and the storage time threshold. If the temporary storage time is less than the storage time threshold, label the temporarily stored normal items with normal temporary storage labels. If the temporary storage time is not less than the storage time threshold, label the temporarily stored abnormal items with abnormal temporary storage labels, and issue an early warning prompt for abnormal temporary storage status.

[0011] Preferably, conduct active supervision and analysis on the transfer status. Classify the target items according to the different cities where the receiving addresses are located, and verify whether the receiving address of the target items after transfer is consistent with the corresponding city information during transfer processing. If the receiving address of the target items after transfer is consistent with the corresponding city information during transfer processing, it indicates that the item transfer is normal and label it with a normal transfer label; Otherwise, it indicates that the item transfer is abnormal. Label it with an abnormal transfer label and issue an early warning prompt for abnormal transfer status.

[0012] Preferably, conduct active supervision and analysis on the sorting and outbound status. Traverse the outbound instructions issued by the system to obtain the target items and quantities required for sorting and outbound, use a barcode scanner to assist in confirming the target item information, and verify the outbound items against the outbound list; among them, the outbound list includes the outbound item number, name, specification, quantity, and item quality. If the target item information of the sorted and outbound items is consistent with the outbound list, it is determined as a standard outbound and a normal outbound label is generated; if the target item information is inconsistent with the outbound list, it is determined as a non-standard outbound, an abnormal outbound label is generated and an early warning prompt for abnormal sorting and outbound is issued.

[0013] Preferably, the intelligent logistics management module includes: using data integration technology to uniformly integrate various types of data into the logistics database; the system conducts a regular evaluation cycle for abnormal data on a monthly basis; counts the total number of items of all abnormal types that appear at different sorting nodes, as well as the abnormal weights corresponding to different abnormal types, and sequentially calculates the corresponding abnormal influence coefficients for the total number of items of all abnormal types and the abnormal weights corresponding to different abnormal types counted for different sorting nodes through the integration influence formula ; Abnormal influence coefficient The calculation formula is: ; In the formula, is the abnormal weight corresponding to the retention type, is the abnormal weight corresponding to the lost item type, is the total number of the corresponding retention type counted at this sorting node, is the total number of the corresponding lost item type counted at this sorting node; Determine the abnormal influence threshold according to the data required by the actual application scenario, and judge the abnormal influence coefficient and the preset abnormal influence threshold; If the abnormal influence coefficient of a certain sorting node is not greater than the preset abnormal influence threshold, the overall influence caused by different abnormal types is relatively light, and the sorting node needs to be regularly inspected and maintained; If the abnormal influence coefficient of a certain sorting node is greater than the preset abnormal influence threshold, the overall influence caused by different abnormal types is relatively heavy, and targeted optimization management needs to be carried out on this sorting node.

[0014] Compared with the existing scheme, the beneficial effects achieved by the present invention are as follows: The present invention obtains the corresponding file log by counting the target type and the corresponding logistics sorting status in the logistics goods, which helps to track the historical record of the goods and the current logistics status; by real-time monitoring of the same type of target items, it is judged whether the sorting status of the items meets the expected process, and the system will automatically mark the abnormal results as abnormal labels and issue early warning prompts for the corresponding target items, which helps to actively supervise the logistics sorting status; by calculating and analyzing the influence degree of the abnormal influence coefficient of different sorting nodes on the type problems existing in the sorting process and implementing targeted management measures, the automated logistics management system is optimized and upgraded in a targeted manner. Description of the Drawings

[0015] The present invention will be further described below with reference to the accompanying drawings.

[0016] Figure 1 is the module block diagram of an automated logistics management system based on big data according to the present invention.

[0017] Figure 2 is the flow block diagram of the analysis of the logistics sorting status in the present invention. Specific Embodiments

[0018] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0019] Embodiment 1: AsFigure 1 As shown in the figure, the present invention is an automated logistics management system based on big data, including a logistics information data statistics module and an automated sorting module for the logistics system; The logistics information data statistics module is used to count different types of goods entering the logistics site, and at the same time upload the logistics goods and file logs corresponding to several identical type targets to the automated logistics sorting system according to the target type of the logistics goods and in the order of real-time Beijing time; among them, the target types include but are not limited to electronics, food, daily necessities, clothing, and fragile items; The file log includes the target goods information, logistics sorting status, recipient, recipient address, and contact information; among them, the goods information includes type, goods specifications, goods weight, and goods quantity; In the embodiment of the present invention, through the statistics of logistics goods information data, data support and sharing are provided for subsequent analysis of logistics goods sorting status, and it is possible to actively supervise and analyze logistics sorting operations according to logistics information.

[0020] The automated sorting module of the logistics system is used to monitor the arrival time and departure time of target items at each node by recording the time points of real-time scanning of the same type of target items, obtain the goods type and corresponding logistics sorting status in the logistics goods, and mark them as the first identifier and the second identifier, and then update and upload them to the automated logistics management system; Among them, as Figure 2 shown, the analysis of the logistics sorting status is implemented; The logistics sorting status includes: summary picking status, in-line sorting status, temporary storage status, transfer status, and sorting and outbound status; For summary picking, traverse and obtain the information of the target items, pick out the items from the inventory area for classification and packing, and record the sorting volume in a preset time period. The preset time period is one hour as a unit, and according to the minimum value and the maximum value of the sorting volume corresponding to all historical time periods the same as the preset time period in the historical records, as well as the average value of the sorting volume, through the range formula, determine the range of the sorting volume corresponding to each historical time period; calculate the error range formula to determine the upper limit and lower limit of the error range; The range formula is: The calculation formula for the average value of the sorting volume is: ; In the formula, is the total number of sorting volumes taken, is the range in the th time period taken; The difference formula is: ; Among them, the upper and lower limits of the error range are: ; In the formula, is the upper limit of the error range, is the lower limit of the error range, is the confidence level , that is, the error range is ; Analyze the sorting volume of the aggregated sorting monitored and statistically summarized within the preset time period: If the sorting volume of the aggregated sorting within the preset time period satisfies , then mark the corresponding aggregated sorting items with normal sorting labels; If the sorting volume of the aggregated sorting within the preset time period does not satisfy , then mark the corresponding aggregated sorting items with abnormal sorting labels and issue an early warning prompt for abnormal aggregated picking; The in-line sorting status refers to the distribution of goods from the production line to the corresponding containers or shelves, and real-time monitoring of the distribution situation and time points of the target items on the transportation line; Based on the past in-line sorting data corresponding to the same target items, determine the maximum and minimum sorting times and set up a time fluctuation range, traverse the scan records to obtain the actual in-line sorting time of the target items, and compare the actual in-line sorting time with the time fluctuation range; Mark the items corresponding to the actual in-line sorting time that conforms to the time fluctuation range with normal sorting labels, mark the items corresponding to the actual sorting time that does not conform to the time fluctuation range with abnormal sorting labels, and issue an early warning prompt for abnormal in-line sorting for the items corresponding to the abnormal sorting labels; The temporary storage status means that the target items are temporarily stored in a designated area because the previous process or the subsequent process has not been completed, waiting for further processing or transfer; Different types of goods have different requirements for the temporary storage time, and obtain the corresponding temporary storage time threshold according to the goods type; This threshold refers to the maximum time that the items can be stored in the temporary storage area without affecting the normal operation; Implement the judgment of the temporary storage time of the target items and the storage time threshold. If the temporary storage time is less than the storage time threshold, mark the temporarily stored normal items with normal temporary storage labels. If the temporary storage time is not less than the storage time threshold, mark the temporarily stored abnormal items with abnormal temporary storage labels and issue an early warning prompt for abnormal temporary storage status; The transfer status is to correctly transfer the target items from one place to another through classification and information verification; Classify the target items according to the different cities where the receiving addresses are located, and verify whether the receiving address of the target items after transfer is consistent with the corresponding city information during the transfer process. If the receiving address of the target items after transfer is consistent with the corresponding city information during the transfer process, it means that the item transfer is normal and mark it with a normal transfer label; If the receiving address of the target item after transfer is inconsistent with the corresponding city information during transfer processing, it indicates that the item transfer is abnormal. Mark it with an abnormal transfer label and issue a transfer status abnormal warning prompt; Sorting and outbound status refers to the process of sorting the target item from the storage area and preparing for shipment. Traverse the outbound instructions issued by the system to obtain the target items and quantities to be sorted and outbound, use a barcode scanner to assist in confirming the target item information, and verify the outbound items against the outbound list; among them, the outbound list includes the outbound item number, name, specification, quantity, and item quality; If the target item information of the sorted and outbound items is consistent with the outbound list, it is determined as a standard outbound, and a normal outbound label is generated; if the target item information is inconsistent with the outbound list, it is determined as a non-standard outbound, and an abnormal outbound label is generated and an abnormal warning prompt for sorting and outbound is issued; Upload the normal outbound label or abnormal outbound label to form the sorting and outbound verification data to the system archive log. The system automatically issues an abnormal warning prompt for sorting and outbound according to the abnormal outbound label, and notifies the management personnel for review and processing; In the embodiment of the present invention, through high-degree technical integration and intelligent management, the different sorting states of the target items in the logistics goods are analyzed to identify whether there are abnormalities in the sorting process of the target items. If there are abnormalities, the specific abnormal type of goods and the corresponding abnormal sorting nodes can be obtained, which is conducive to timely handling of the sorting problems of the goods, and changing from the traditional result-oriented management method to a process-oriented management method.

[0021] Embodiment 2: On the basis of Embodiment 1, it further includes: The intelligent logistics management module uses data integration technology, such as ETL (Extract, Transform, Load), to uniformly integrate various types of data into the logistics database to ensure real-time update and sharing of information. This integration not only promotes the automated transmission of logistics information, but also greatly improves the efficiency and accuracy of logistics operations; The intelligent logistics management module also includes periodic supervision and evaluation, which refers to the system's regular evaluation cycle for abnormal data on a monthly basis; count the total number of all abnormal type items that appear at different sorting nodes, as well as the abnormal weights corresponding to different abnormal types, and clean the counted data; among them, the abnormal weight is comprehensively determined according to factors such as historical data, severity, and occurrence frequency of various abnormalities; Successively calculate the corresponding abnormal influence coefficients by integrating the influence formula for the total number of all abnormal type items corresponding to different sorting nodes and the abnormal weights corresponding to different abnormal types ; Abnormal influence coefficient The calculation formula of is: ; In the formula, is the exception weight corresponding to the retention type, is the exception weight corresponding to the lost item type, is the total number of the corresponding retention type counted at this sorting node, is the total number of the corresponding lost item type counted at this sorting node; This formula comprehensively considers the quantity and weight of different exception types and obtains a quantitative index, which helps managers comprehensively understand the exception impact degree of each sorting node; Judge the exception impact coefficient with a preset exception impact threshold, and then conduct targeted management optimization for different sorting nodes; Among them, the exception impact threshold can be determined according to the data requirements of the actual application scenario; If the exception impact coefficient of a certain sorting node is not greater than the preset exception impact threshold, the overall impact caused by different exception types is relatively light, and the sorting node needs to be regularly inspected and maintained; If the exception impact coefficient of a certain sorting node is greater than the preset exception impact threshold, the overall impact caused by different exception types is relatively heavy, and targeted optimization management needs to be carried out on this sorting node. Logistics information management and training of sorting personnel can be strengthened, an employee reward and punishment mechanism can be formulated to improve the work accuracy and responsibility of employees, and the configuration of sorting personnel or equipment can be increased to improve the sorting efficiency and reduce the frequency of goods of different exception types; In the embodiments of the present invention, the intelligent logistics management module integrates data information, supervises and evaluates different exception types, updates the comprehensive information data of logistics goods, and conducts targeted optimization management on different fans according to nodes, improves the efficiency and reliability of the logistics system operation, gives early warning prompts for potential exception problems in the logistics system, and provides a strong guarantee for the sustainable development of the enterprise.

[0022] In several embodiments provided by the present invention, it should be understood that the disclosed system can be implemented in other ways. For example, the above-described embodiments of the invention are merely illustrative. For example, the division of modules is only a logical function division, and there may be other division methods in actual implementation.

[0023] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, that is, they may be located in one place, or may be distributed to multiple network modules. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0024] In addition, in each embodiment of the present invention, each functional module can be integrated into a processing module, or each module can exist physically alone, or two or more modules can be integrated into one module. The above-mentioned integrated module can be implemented in the form of hardware, or in the form of a combination of hardware and software functional modules.

[0025] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above-mentioned exemplary embodiments, and without departing from the basic characteristics of the present invention, the present invention can be implemented in other specific forms.

[0026] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. An automated logistics management system based on big data, characterized in that: It includes a logistics information data statistics module, which is used to scan and count the different types of goods entering the logistics site through QR code scanning technology, and upload several logistics goods and file logs corresponding to the same type of targets to the automated logistics management system according to the target type of the logistics goods; The automated sorting module of the logistics system is used to monitor the same type of target items in real time, record the arrival time and departure time of the target items at each node; mark the target items as the first identification and the second identification according to the target type and the corresponding logistics sorting status in the logistics goods, and update the uploaded archive log; analyze and judge the sorting status of the items, and issue early warning prompts to the corresponding target items according to the abnormal labels in the judgment results; The intelligent logistics management module is used to achieve deep collaboration between all links in the logistics chain. Through data integration technology, various types of data are unified into the logistics database. On the basis of data integration, through monthly periodic supervision and evaluation of abnormal data of different sorting nodes, targeted supervision and optimization of the system are carried out, realizing real-time information sharing and collaborative operation.

2. According to claim 1, an automated logistics management system based on big data is characterized in that: The target types of logistics goods include electronics, food, daily necessities, clothing and fragile items; the archive log contains the target cargo information, logistics sorting status, recipient, receiving address and contact information; among them, the cargo information includes type, cargo specifications, cargo weight and cargo quantity.

3. The automated logistics management system based on big data according to claim 1, characterized in that: The logistics sorting status includes: summary picking status, distribution along the line status, temporary storage status, transfer status, and sorting outbound status.

4. The automated logistics management system based on big data according to claim 3 is characterized in that: Actively monitor and analyze the summary picking status, pick items from the inventory area for classification and packaging, and record the sorting volume in the preset time period; according to the minimum value of the sorting volume corresponding to all time periods that are the same as the preset time period in the historical records and maximum value , and the average sorting amount , determine the range of sorting volume corresponding to each historical time period through the range formula; calculate the error range formula to determine the upper limit and lower limit of the error range; The range formula is: ; The average calculation formula of sorting volume is: ; In the formula, is the total number of sorting quantities taken, For the The extreme difference within a time period; The difference formula is: ; The upper and lower limits of the error range are: ; In the formula, is the upper limit of the error range, is the lower limit of the error range, Confidence , that is, the error range is ; Analyze the sorting volume of the summary sorting monitored and counted within the preset time period: If the sorting volume of the summary sorting within the preset time period meets , then the corresponding summary sorting items are marked with normal sorting labels; If the sorting volume of the summary sorting within the preset time period does not meet , the corresponding summary sorting items are marked with abnormal sorting labels and a summary picking abnormal warning prompt is issued.

5. The big data-based automated logistics management system according to claim 3, characterized in that: Actively monitor and analyze the status of goods sorting along the line. Based on the past data of goods sorting along the line corresponding to the same target items, determine the maximum and minimum values ​​of the historical sorting time and set the time fluctuation range. Traverse the scanning records to obtain the actual time of goods sorting along the line for the target items, and compare the actual time of goods sorting along the line with the time fluctuation range. The items corresponding to the actual distribution time along the line that is within the time fluctuation range are marked with normal distribution labels, and the items corresponding to the actual distribution time that is not within the time fluctuation range are marked with abnormal distribution labels, and the items corresponding to the abnormal distribution labels are given abnormal distribution warning prompts along the line.

6. The automated logistics management system based on big data according to claim 3 is characterized in that: Actively monitor and analyze the temporary storage status, obtain the temporary storage time of the target items, and obtain the corresponding temporary storage time threshold according to the type of goods; The temporary storage time of the target item is judged against the storage time threshold. If the temporary storage time is less than the storage time threshold, the normal temporarily stored items will be marked with a normal temporary storage label. If the temporary storage time is not less than the storage time threshold, the abnormal temporarily stored items will be marked with an abnormal temporary storage label, and an abnormal temporary storage status warning will be issued.

7. The automated logistics management system based on big data according to claim 3 is characterized in that: Actively monitor and analyze the transfer status, classify the target items according to the different cities where the delivery addresses are located, and verify whether the delivery addresses of the target items after transfer are consistent with the city information corresponding to the transfer process. If the delivery addresses of the target items after transfer are consistent with the city information corresponding to the transfer process, it means that the transfer of the items is normal and a normal operation label is generated; Otherwise, it means that the item transfer is abnormal, and an abnormal transfer label is generated and an abnormal transfer status warning is issued.

8. The big data-based automated logistics management system according to claim 3, characterized in that: Actively monitor and analyze the sorting and outbound status, traverse the outbound instructions issued by the system to obtain the target items and quantities required for sorting and outbound, use a scanner to assist in confirming the target item information, and verify the outbound items against the outbound list; the outbound list includes the outbound item number, name, specification, quantity, and item quality; If the target item information of the sorted outbound items is consistent with the outbound list, it is judged as a standard outbound and a normal outbound label is generated; if the target item information is inconsistent with the outbound list, it is judged as an irregular outbound, an abnormal outbound label is generated and an abnormal sorting outbound warning prompt is issued.

9. The automated logistics management system based on big data according to claim 1, characterized in that: The intelligent logistics management module includes: using data integration technology to integrate various types of data into the logistics database; the system conducts regular evaluation cycles on abnormal data with a monthly time unit; counting the total number of all abnormal types of items appearing at different sorting nodes, as well as the abnormal weights corresponding to different abnormal types, and calculating the corresponding abnormal impact coefficients by integrating the impact formula to calculate the total number of all abnormal types of items and the abnormal weights corresponding to different abnormal types corresponding to different sorting nodes. ; Abnormal influence coefficient The calculation formula is: ; In the formula, is the abnormal weight corresponding to the detention type, is the abnormal weight corresponding to the lost item type, is the total number of corresponding retention types counted at the sorting node, The total number of the corresponding lost item type counted at the sorting node; The abnormal impact coefficient is compared with the preset abnormal impact threshold. If the abnormal impact coefficient of a certain sorting node is not greater than the preset abnormal impact threshold, the overall impact of different abnormal types is relatively light, and the sorting node needs to be regularly inspected and maintained. If the abnormal impact coefficient of a certain sorting node is greater than the preset abnormal impact threshold, the overall impact of different abnormal types is heavier, and targeted optimization management of the sorting node is required.

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