Digital logistics freight tracking method and system

Abstract

The invention discloses a digital logistics freight tracking method and system, and relates to the technical field of logistics transportation, and the method comprises the steps: collecting logistics data in real time through an Internet of Things device, sending the collected data to a central data processing center for processing and storage, enabling a user to enter a user interface, filling a corresponding query condition, and carrying out the tracking of the logistics freight. And after a backtracking request of a user or a system is received, backtracking analysis is carried out on the logistics data extracted from the backtracking module by utilizing an intelligent algorithm, a detailed logistics historical track is formed, and a backtracking analysis result is displayed to the user in a visual form. The central processing center screens data collected by the Internet of Things equipment, key data used for backtracking are reserved in the cache region so as to be flexibly called during order backtracking, if a user is not satisfied with freight service, a backtracking request can be initiated for a waybill, and after the key data which are originally screened out and related to backtracking are analyzed, the backtracking request can be sent to the user. And the information is transmitted to a user terminal in a visual form.

Description

Technical Field

[0001] The present invention relates to the field of logistics and transportation technology, and in particular to a digital logistics freight tracking method and system. Background Art

[0002] With the rapid development of the logistics industry, the demand for real-time tracking and management of logistics freight is increasing. Digital freight refers to a new freight model that uses digital technology to optimize and improve the freight process in order to improve freight efficiency, reduce freight costs, and improve freight safety and service quality. This freight model enables freight companies and users to understand the freight transportation situation more quickly and accurately.

[0003] However, the existing digital logistics tracking still has some shortcomings. Although the location and distance of the goods can be refreshed in real time during the logistics transportation process, after the order is completed and the goods are delivered to the destination, it does not mean that there are no problems in the entire freight process. In particular, when problems such as late delivery and damaged goods occur, users cannot find the key to the problem in time, and the accuracy of the corresponding data cannot be guaranteed, which often leads to missing the backtracking cycle and reducing user favorability. Summary of the invention

[0004] The purpose of the present invention is to provide a digital logistics freight tracking method and system to solve the deficiencies in the above-mentioned prior art.

[0005] In order to achieve the above object, the present invention provides the following technical solutions:

[0006] A digital logistics freight tracking method, the method comprising:

[0007] Collect logistics data in real time through IoT devices, and use big data analysis technology to track and process the collected data;

[0008] The collected data is sent to the central data processing center for processing and storage, and the key data related to the backtracking tracking is screened and cached using the backtracking module in the central data processing center;

[0009] The user enters the user interface and fills in the corresponding query conditions. After receiving the traceback request from the user or the system, the intelligent algorithm is used to perform a traceback analysis on the logistics data extracted from the traceback module. The traceback analysis includes the path optimization changes, timeline arrangement, and cargo status changes, thereby forming a detailed logistics history track;

[0010] The results of the retrospective analysis are presented to the user in a visual form, and abnormal data found by the user or the system are marked;

[0011] During the tracing process and after marking abnormal data, a multi-source data verification mechanism is introduced to achieve data sharing and collaboration with other related systems in the supply chain, ensure data accuracy, and conduct a secondary comparison of the marked data.

[0012] Preferably, the IoT devices include but are not limited to RFID tags, GPS locators, temperature sensors and humidity sensors.

[0013] Preferably, the data collected by the material network device includes but is not limited to location information, timestamp, and cargo status information.

[0014] Preferably, the visualization forms include but are not limited to charts, maps, trajectory animations, etc., so that users can intuitively understand the logistics process and historical trajectory.

[0015] Preferably, the central processing center regularly evaluates and optimizes the logistics efficiency of the freight process of the traced orders.

[0016] Preferably, key data related to the backtracking tracking is cleared from the buffer area in the backtracking module after the order is completely completed.

[0017] Preferably, the obtained retrospective analysis data and the retrospective data previously retained and screened by the system are encrypted using different data encryption methods, and the user's retrospective analysis data cannot be known by non-user accounts without the user's authentication authorization.

[0018] A digital logistics freight tracking system, comprising:

[0019] IoT device units, which are sensors and RFID tags deployed on freight vehicles, cargo packaging, and origin and transit locations to collect real-time data on item status and environment;

[0020] Data processing cache unit: The central data processing center uses big data technology to conduct in-depth mining and analysis of logistics information, extract valuable information, and filter out the data required for backtracking through the backtracking module, and then transfer the data to the cache center;

[0021] Intelligent analysis unit: after the user logs in, he / she enters the user request interface in the backtracking module. After sending a backtracking request to the system, the intelligent analysis unit analyzes the logistics data in the cache center and transmits the analysis results to the user terminal in a visual form;

[0022] The backtracking data verification unit introduces multi-party data to verify and compare the backtracking data during the backtracking data analysis phase, and simultaneously shares and collaborates on the data from all parties. After the user marks and submits the abnormal data, the backtracking data verification unit compares the introduced data with the marked abnormal data, and then performs a secondary comparison on the backtracking data in the cache center and the marked abnormal data.

[0023] Preferably, the data processing cache unit includes a data encryption module, which contains at least two data encryption methods, which are respectively applied to the backtracking data in the buffer center and the backtracking data displayed by the user terminal.

[0024] Preferably, the intelligent analysis unit includes a self-check analysis module, which regularly performs self-check analysis on the orders requested to be backtracked by the user and transmits the detailed analysis results to the operation and maintenance center.

[0025] In the above technical solution, the data collected by the IoT devices are screened by the central processing center, and the key data used for backtracking is retained in the cache area for flexible call-up during order backtracking. If the user is not satisfied with the freight service, he can initiate a backtracking request for the waybill. The key data related to the backtracking that was originally screened out will be analyzed and transmitted to the user terminal in a visual form. The user can also mark and annotate the data in the backtracking results. The central processing center then performs multi-source data verification on the backtracking data previously cached in the backtracking module and the marked data, and then performs a second comparison on the two sets of verified backtracking data to ensure the accuracy and reliability of the backtracking data, so that the user can obtain transparent and accurate backtracking data after issuing a backtracking request.

[0026] It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.

[0027] This application document provides an overview of various implementations or examples of the technology described in the present disclosure, and is not a comprehensive disclosure of the entire scope or all features of the disclosed technology.

[0028] Additional aspects and advantages of the present application will be partially given in the following description, which will become apparent from the following description, or will be understood through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0030] Figure 1 A process framework diagram of a digital logistics freight tracking method of the present invention;

[0031] Figure 2 It is an overall schematic diagram of a digital logistics freight tracking system of the present invention; DETAILED DESCRIPTION

[0032] In order to make the purpose, technical solution and advantages of the embodiments of the present disclosure clearer, the technical solution of the embodiments of the present disclosure will be clearly and completely described below in conjunction with the drawings of the embodiments of the present disclosure. Obviously, the described embodiments are part of the embodiments of the present disclosure, not all of the embodiments. Based on the described embodiments of the present disclosure, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present disclosure.

[0033] See also Figure 1-2 , an embodiment of the present invention provides a digital logistics freight tracking method, the method comprising:

[0034] Collect logistics data in real time through IoT devices, and use big data analysis technology to track and process the collected data;

[0035] The collected data is sent to the central data processing center for processing and storage, and the key data related to the backtracking tracking is screened and cached using the backtracking module in the central data processing center;

[0036] The user enters the user interface and fills in the corresponding query conditions. After receiving the traceback request from the user or the system, the intelligent algorithm is used to perform a traceback analysis on the logistics data extracted from the traceback module. The traceback analysis includes the path optimization changes, timeline arrangement, and cargo status changes, thereby forming a detailed logistics history track;

[0037] The results of the retrospective analysis are presented to the user in a visual form, and abnormal data found by the user or the system are marked;

[0038] During the tracing process and after marking abnormal data, a multi-source data verification mechanism is introduced to achieve data sharing and collaboration with other related systems in the supply chain, ensure data accuracy, and conduct a secondary comparison of the marked data.

[0039] Specifically, first install the corresponding IoT devices on freight vehicles and goods, use the IoT to remotely monitor the transportation status of goods, and use the big data model to track and update the data in real time. The data collected during the freight process will be stored in the central data processing center, and then the data will be screened and processed through the backtracking module, so that the key data related to the backtracking can enter the cache area of ​​the central data processing center. After the order is completed, a cycle is preset to wait for the user to send a backtracking request from the terminal. During this cycle, the backtracking request from the user or the system is received, and the intelligent algorithm of the central data processing center is used to perform backtracking analysis on the logistics data extracted from the backtracking module. The number of transport route changes, specific route maps, logistics time nodes, temperature and humidity inside the truck, etc. are comprehensively analyzed, and the analysis results are sent to the user terminal in a visual form. The user can also directly mark the data that is considered abnormal in the analysis results on the personal terminal, and verify the backtracking data in the buffer area and the marked abnormal data through a multi-source verification mechanism. The data is verified by multi-party system data sharing and collaborative operations to ensure that the data is true and accurate. After the verification is passed, the marked abnormal data is compared with the backtracking data in the cache area to avoid data tampering, so that the backtracking results obtained by the user are transparent and accurate.

[0040] Compared with the prior art, the central processing center of the embodiment of the present invention screens the data collected by the IoT devices, and retains the key data used for backtracking in the cache area so as to be flexibly called when the order is backtracked. If the user is not satisfied with the freight service, a backtracking request can be initiated for the waybill. The key data related to the backtracking that was originally screened out will be analyzed and transmitted to the user terminal in a visual form. The user can also mark and annotate the data in the backtracking result. The central processing center then performs multi-source data verification on the backtracking data previously cached in the backtracking module and the marked data, and then performs a second comparison on the two sets of backtracking data that have been verified to be correct, so as to ensure the accuracy and reliability of the backtracking data, so that the user can obtain transparent and accurate backtracking data after issuing a backtracking request.

[0041] In a further embodiment of the present invention, the Internet of Things devices include but are not limited to RFID tags, GPS locators, temperature sensors and humidity sensors, and the sensors are installed on logistics vehicles.

[0042] In a further embodiment of the present invention, the data collected by the material network device includes but is not limited to location information, timestamp, and cargo status information. Specifically, through the arrangement of Internet of Things devices, the vehicle's location, speed, acceleration and other information are collected in real time to identify the identity and status of the cargo, making the collected data more diverse.

[0043] In a further embodiment of the present invention, the visualization form includes but is not limited to charts, maps, trajectory animations, etc., so that users can intuitively understand the logistics process and historical trajectory. Specifically, the visualization form is adapted to PC and mobile terminals. After the user chooses to trace back the order, the visualization analysis result is sent to the user by email.

[0044] In a further embodiment of the present invention, the central processing center regularly evaluates and optimizes the logistics efficiency of the freight process of the backtracked orders. Specifically, based on the real-time data of logistics tracking, combined with historical data and market demand, the transportation capacity and transportation route are intelligently adjusted.

[0045] In a further embodiment of the present invention, key data related to backtracking will be cleared from the cache area in the backtracking module after the order is completely completed. Specifically, the complete completion of the order means that the order has been completed and the cycle for initiating the backtracking request has passed. At this time, the backtracking cache data belonging to the order is automatically cleared to release space in the central data processing center.

[0046] In a further embodiment of the present invention, the obtained retrospective analysis data and the retrospective data previously retained and screened out by the system are encrypted using different data encryption methods, and the user's retrospective analysis data cannot be known by non-user accounts without the user's authentication authorization. Specifically, the previously screened retrospective data and the retrospective data completed by subsequent analysis are encrypted using different encryption methods to prevent the retrospective data from being easily changed after logging into the system account.

[0047] A digital logistics freight tracking system, comprising:

[0048] IoT device units, which are sensors and RFID tags deployed on freight vehicles, cargo packaging, and origin and transit locations to collect real-time data on item status and environment;

[0049] Data processing cache unit: The central data processing center uses big data technology to conduct in-depth mining and analysis of logistics information, extract valuable information, and filter out the data required for backtracking through the backtracking module, and then transfer the data to the cache center;

[0050] Intelligent analysis unit: after the user logs in, he / she enters the user request interface in the backtracking module. After sending a backtracking request to the system, the intelligent analysis unit analyzes the logistics data in the cache center and transmits the analysis results to the user terminal in a visual form;

[0051] The backtracking data verification unit introduces multi-party data to verify and compare the backtracking data during the backtracking data analysis phase, and simultaneously shares and collaborates on the data from all parties. After the user marks and submits the abnormal data, the backtracking data verification unit compares the introduced data with the marked abnormal data, and then performs a secondary comparison on the backtracking data in the cache center and the marked abnormal data.

[0052] Specifically, the IoT device unit, which is deployed on freight vehicles, cargo packaging, and sensors and RFID tags at the origin and transit points, is used to collect item status and environmental data in real time. Multiple sensors on freight vehicles are used to collect vehicle location, speed, acceleration and other information in real time. The data processing cache unit, the central data processing center uses big data technology to conduct in-depth mining and analysis of logistics information, including data mining, pattern recognition, etc. For example, by analyzing the transportation route and transportation time of the goods, the arrival time of the goods can be predicted. By analyzing the environmental parameters of the goods, the safety status of the goods can be determined, and valuable information can be extracted. The data required for backtracking can be screened out through the backtracking module, and then the data is transferred to the cache center for caching. The intelligent analysis unit, after the user logs in, enters the user request interface in the backtracking module. After sending a backtracking request to the system, the intelligent analysis unit analyzes the logistics data of the corresponding order in the cache center, and transmits the analysis results to the user terminal in a visual form. The user can Through the web page or mobile application, you can view the different locations, transportation routes, arrival times and other information of the entire transportation stage of the goods. At the same time, it will also provide a historical trajectory change query function for the goods, so that users can understand the complete transportation process of the goods before. The traceability data verification unit introduces multi-party data to verify and compare the traceability data during the traceability data analysis stage, and at the same time shares and collaborates on the data of all parties. After the user marks and submits the abnormal data, the traceability data verification unit compares the introduced data with the marked abnormal data, and then compares the traceability data of the cache center and the marked abnormal data for the second time. The entire logistics process is traced back and analyzed based on the data in the logistics information database and the data in the supply chain database. By comparing and analyzing the data in different time periods, different links and different locations, the problems in the logistics order process are found out. The traceability data in different forms at different stages are verified and compared with each other, so that users have more confidence in the accuracy of the traceability data.

[0053] In a further embodiment of the present invention, the data processing cache unit includes a data encryption module, which contains at least two data encryption methods, which are respectively applied to the backtracking data in the buffer center and the backtracking data displayed by the user terminal. The data encryption module can prevent the backtracking data of the two stages from being tampered with. At the same time, the two sets of data encryption methods are different, which can reduce the possibility of synchronous tampering of the data.

[0054] In a further embodiment of the present invention, the intelligent analysis unit includes a self-inspection analysis module, which regularly performs self-inspection analysis on orders requested to be backtracked by users, and transmits detailed analysis results to the operation and maintenance center. The staff manually analyzes the analysis results received by the operation and maintenance center, finds out the reasons why the orders are backtracked and compiles statistics, so as to improve the logistics services in a targeted manner.

[0055] The above description is only by way of illustration of certain exemplary embodiments of the present invention. It is undoubted that, for those skilled in the art, the described embodiments can be modified in various ways without departing from the spirit and scope of the present invention. Therefore, the above description is illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

Claims

1. A digital logistics freight tracking method, characterized in that: The method comprises: Collect logistics data in real time through IoT devices, and use big data analysis technology to track and process the collected data; The collected data is sent to the central data processing center for processing and storage, and the key data related to the backtracking tracking is screened and cached using the backtracking module in the central data processing center; The user enters the user interface and fills in the corresponding query conditions. After receiving the traceback request from the user or the system, the intelligent algorithm is used to perform a traceback analysis on the logistics data extracted from the traceback module. The traceback analysis includes the path optimization changes, timeline arrangement, and cargo status changes, thereby forming a detailed logistics history track; The results of the retrospective analysis are presented to the user in a visual form, and abnormal data found by the user or the system are marked; During the tracing process and after marking abnormal data, a multi-source data verification mechanism is introduced to achieve data sharing and collaboration with other related systems in the supply chain, ensure data accuracy, and conduct a secondary comparison of the marked data.

2. A digital logistics freight tracking method according to claim 1, characterized in that: The IoT devices include but are not limited to RFID tags, GPS locators, temperature sensors and humidity sensors.

3. A digital logistics freight tracking method according to claim 1, characterized in that: The data collected by the material network device includes but is not limited to location information, timestamp, and cargo status information.

4. A digital logistics freight tracking method according to claim 1, characterized in that: The visualization forms include but are not limited to charts, maps, trajectory animations, etc., so that users can intuitively understand the logistics process and historical trajectory.

5. A digital logistics freight tracking method according to claim 1, characterized in that: The central processing center regularly evaluates and optimizes the logistics efficiency of the freight process of the traced orders.

6. A digital logistics freight tracking method according to claim 1, characterized in that: Key data related to backtracking will be cleared from the buffer in the backtracking module after the order is completely closed.

7. A digital logistics freight tracking method according to claim 1, characterized in that: The retrospective analysis data obtained by the user and the retrospective data previously retained and screened by the system are encrypted using different data encryption methods, and the user's retrospective analysis data cannot be obtained by non-user accounts without the user's authentication authorization.

8. A digital logistics freight tracking system, characterized in that: include: IoT device units, which are sensors and RFID tags deployed on freight vehicles, cargo packaging, and origin and transit locations to collect real-time data on item status and environment; Data processing cache unit: The central data processing center uses big data technology to conduct in-depth mining and analysis of logistics information, extract valuable information, and filter out the data required for backtracking through the backtracking module, and then transfer the data to the cache center; Intelligent analysis unit: after the user logs in, he / she enters the user request interface in the backtracking module. After sending a backtracking request to the system, the intelligent analysis unit analyzes the logistics data in the cache center and transmits the analysis results to the user terminal in a visual form; The backtracking data verification unit introduces multi-party data to verify and compare the backtracking data during the backtracking data analysis phase, and simultaneously shares and collaborates on the data from all parties. After the user marks and submits the abnormal data, the backtracking data verification unit compares the introduced data with the marked abnormal data, and then performs a secondary comparison of the backtracking data in the cache center and the marked abnormal data.

9. A digital logistics freight tracking system according to claim 8, characterized in that: The data processing cache unit includes a data encryption module, which contains at least two data encryption methods, which are respectively applied to the backtracking data in the buffer center and the backtracking data displayed by the user terminal.

10. A digital logistics freight tracking system according to claim 8, characterized in that: The intelligent analysis unit includes a self-check analysis module, which regularly performs self-check analysis on the orders requested for backtracking by users and transmits detailed analysis results to the operation and maintenance center.

Images