Block chain-based cross-border logistics multimodal transport virtual simulation system

Abstract

The invention discloses a block chain-based cross-border logistics multimodal transport virtual simulation system, which comprises a logistics scheduling center, a block chain center, a user interaction unit and a score integration unit, and is characterized in that the logistics scheduling center is used for cooperating with cross-border logistics multimodal transport scheduling, analyzing and providing scheduling data; the block chain center comprises a data processing module, a data summarization module, a data grouping module and a data uploading module, the user interaction unit establishes man-machine interaction based on all data of the block chain center, and the score integration unit is used for integrating and disclosing scores of users in the user interaction unit; the user interaction unit comprises a virtual environment construction module and a daily operation practice module, the cross-border logistics multimodal transport virtual simulation system based on the block chain disclosed by the invention has the advantages that data can be fully utilized by establishing a user interaction mechanism, and in addition, by enriching a practice question library and a practice environment, the training efficiency can be improved. And the user can be effectively assisted to improve professional skills.

Description

Technical Field

[0001] This invention relates to the field of logistics, and in particular to a blockchain-based virtual simulation system for cross-border multimodal transport. Background Technology

[0002] Logistics simulation systems can help businesses select optimal transportation solutions, reduce costs, and improve efficiency by simulating different modes and routes. Regarding warehouse layout, the system improves warehouse space utilization by simulating different storage methods and racking designs. The system can also achieve intelligent scheduling based on order demand and vehicle status. International trade goods can be transported across borders via waterways, air transport, multimodal transport, and the increasingly developed international parcel express delivery. Currently, the management of various transport modes in my country, including rail, air, and water transport, remains fragmented and independent. Each logistics center and management department has its own unique management model, characterized by independence, low interrelationships, regional segmentation, and significant differences in their respective fields. Therefore, cross-border multimodal transport logistics requires the assistance of logistics simulation systems.

[0003] Since current logistics simulation systems only provide assistance for enterprise logistics scheduling and have poor interactivity with individuals, we propose a blockchain-based cross-border logistics multimodal transport virtual simulation system to fully utilize the data stored in the system and establish interaction with individuals. Summary of the Invention

[0004] This invention discloses a blockchain-based cross-border logistics multimodal transport virtual simulation system, which aims to solve the technical problem that current logistics simulation systems only provide assistance for enterprise logistics scheduling and have poor interactivity with individuals.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: A blockchain-based cross-border logistics multimodal transport virtual simulation system includes a logistics dispatch center, a blockchain center, a user interaction unit, and a performance integration unit. The logistics dispatch center is used to coordinate cross-border logistics multimodal transport scheduling, analyze and provide scheduling data. The blockchain center includes a data processing module, a data aggregation module, a data grouping module, and a data uploading module. The user interaction unit establishes human-computer interaction based on all data in the blockchain center. The performance integration unit is used to integrate and publish the user's performance in the user interaction unit. The user interaction unit includes a virtual environment construction module, a daily operation practice module, a case practice module, a VR interaction module, a data statistics module, and a data analysis module. The virtual environment construction module constructs a simulated virtual environment model based on the real logistics environment. The daily operation practice module automatically generates specific data to create daily operation practice questions. The case practice module generates case practice questions based on data aggregation from the blockchain center using real data. The VR interaction module uses VR devices to train on the constructed virtual environment model. The data statistics module is used to collect statistics on each user's practice results. The data analysis module is used to analyze the practice results of each user in the data statistics module.

[0006] By incorporating a user interaction unit, the logistics dispatch center and blockchain center can collect real dispatch data while coordinating cross-border multimodal transport dispatch. Based on the aggregated data, the system provides interactive data support for the user interaction unit. Through a daily operation practice module, practice questions are generated and established. By utilizing VR devices and a constructed virtual environment for practice, the system can assist in logistics dispatch while making full use of data through the establishment of a user interaction mechanism. In addition, by enriching the practice question bank and practice environment, it can also effectively help users improve their professional skills.

[0007] In a preferred embodiment, the data processing module is used to process real logistics data, the data aggregation module is used to aggregate real logistics data, the data grouping module is used to divide the aggregated real logistics data into normal and abnormal data, and the data uploading module is used to upload all data. The case practice module includes a normal data import module, a normal case generation module, an abnormal data import module, and an abnormal case generation module. The normal data import module is connected to the normal case generation module, and the abnormal data import module is connected to the abnormal case generation module. The ordinary data import module imports the ordinary data summarized in the data grouping module into the ordinary data import module. The ordinary case generation module extracts key information from the ordinary data summarized in the data grouping module to generate exercises, hides the logistics scheduling analysis results as the answers, and generates ordinary cases for practice. The abnormal data import module imports the abnormal data summarized in the data grouping module into the module. The abnormal case generation module extracts key information from the abnormal data summarized in the data grouping module to generate exercises, hides the abnormal logistics scheduling analysis results as auxiliary analysis data, and generates abnormal cases for practice. The data analysis module includes a derivation result integration module, a derivation path analysis module, and a result filtering module. The derivation result integration module integrates the derivation results of each user's abnormal case practice. The derivation path analysis module uses the hidden abnormal logistics scheduling analysis results as auxiliary analysis data to deduce whether the user's practice results are reasonable. The result filtering module is used to filter out reasonable derivation results and match them with user IDs.

[0008] By setting up a case study practice module and a data analysis module, exercises are generated by extracting key information from abnormal cases. The abnormal logistics scheduling analysis results are hidden as auxiliary analysis data, and abnormal cases for practice are generated. During the user's practice, the logistics scheduling can be deduced and compared with the hidden analysis results. In this way, while the user gains skills, the logical methods can also be collected and applied to the system's setting logic, so that the system can be continuously improved to better assist logistics scheduling.

[0009] In a preferred embodiment, the performance integration unit includes a performance integration module, a scoring module, a performance disclosure module, and an enterprise communication module. The performance integration module is used to integrate each user's practice frequency and practice performance. The scoring module scores based on the frequency of daily practice and the results of case practice. If abnormal cases are resolved, a large number of points are awarded. The score disclosure module is used to publish the scored scores and matching user IDs in the score ranking table. The enterprise communication module is used to provide the score ranking table to various enterprises.

[0010] By setting up a performance integration unit, the scoring module can score users based on the frequency of daily practice and the results of case studies, and publish the scores in a ranking table. This ranking is directly linked to the user's ability. Since most users of this system are logistics students, providing the ranking table to various companies can assist them in their recruitment process, making it easier for companies to select the best candidates and for capable students to find jobs quickly, achieving a win-win situation.

[0011] As shown above, the blockchain-based cross-border logistics multimodal transport virtual simulation system includes a logistics dispatch center, a blockchain center, a user interaction unit, and a performance integration unit. The logistics dispatch center is used to coordinate cross-border logistics multimodal transport scheduling, analyze and provide scheduling data. The blockchain center includes a data processing module, a data aggregation module, a data grouping module, and a data upload module. The user interaction unit establishes human-computer interaction based on all data from the blockchain center. The performance integration unit integrates and publishes user performance in the user interaction unit. The user interaction unit includes a virtual environment construction module, a daily operation practice module, a case practice module, a VR interaction module, a data statistics module, and a data analysis module. The virtual environment construction module constructs a simulated virtual environment model based on the real logistics environment. The daily operation practice module automatically generates specific data to create daily operation practice questions. The case practice module generates case practice questions based on real data aggregated from the blockchain center. The VR interaction module uses VR devices to train on practice questions in the constructed virtual environment model. The data statistics module is used to statistically analyze the practice results of each user in the data statistics module. The blockchain-based cross-border logistics multimodal transport virtual simulation system provided by this invention has the technical effect of making full use of data by establishing a user interaction mechanism, and effectively assisting users to improve their professional skills by enriching the question bank and practice environment. Attached Figure Description

[0012] Figure 1 This is a schematic diagram of the overall structure of the blockchain-based cross-border logistics multimodal transport virtual simulation system proposed in this invention.

[0013] Figure 2 This is a schematic diagram of the user interaction unit structure of the blockchain-based cross-border logistics multimodal transport virtual simulation system proposed in this invention.

[0014] Figure 3 This is a schematic diagram of the case study module structure of the blockchain-based cross-border logistics multimodal transport virtual simulation system proposed in this invention.

[0015] Figure 4 This is a schematic diagram of the data analysis module structure of the blockchain-based cross-border logistics multimodal transport virtual simulation system proposed in this invention.

[0016] Figure 5 This is a schematic diagram of the performance integration unit structure of the blockchain-based cross-border logistics multimodal transport virtual simulation system proposed in this invention. Detailed Implementation

[0017] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0018] The blockchain-based cross-border logistics multimodal transport virtual simulation system disclosed in this invention is mainly applied to scenarios where blockchain cross-border logistics simulation systems are used.

[0019] Reference Figure 1 and Figure 2 The blockchain-based cross-border logistics multimodal transport virtual simulation system includes a logistics dispatch center, a blockchain center, a user interaction unit, and a performance integration unit. The logistics dispatch center is used to coordinate cross-border logistics multimodal transport scheduling, analyze and provide scheduling data. The blockchain center includes a data processing module, a data aggregation module, a data grouping module, and a data uploading module. The user interaction unit establishes human-computer interaction based on all the data in the blockchain center. The performance integration unit is used to integrate and publish the user's performance in the user interaction unit. The user interaction unit includes a virtual environment construction module, a daily operation practice module, a case study practice module, a VR interaction module, a data statistics module, and a data analysis module. The virtual environment construction module constructs a simulated virtual environment model based on the real logistics environment. The daily operation practice module automatically generates specific data to create daily operation practice questions. The case study practice module generates case study practice questions based on data aggregation from the blockchain center using real data. The VR interaction module uses VR devices to train on practice questions within the constructed virtual environment model. The data statistics module is used to collect practice results for each user. The data analysis module analyzes the practice results for each user from the data statistics module. The logistics dispatch center and blockchain center can collect real dispatch data while coordinating cross-border multimodal transport dispatching, and provide interactive data support for the user interaction unit based on the aggregated data. The daily operation practice module generates and creates practice questions, and the system utilizes VR devices and the constructed virtual environment for practice. This allows the system to fully utilize data while assisting in logistics dispatching, and the rich practice question bank and practice environment effectively help users improve their professional skills.

[0020] Reference Figure 1 In a preferred embodiment, the data processing module is used to process real logistics data, the data aggregation module is used to aggregate real logistics data, the data grouping module is used to divide the aggregated real logistics data into normal and abnormal data, and the data upload module is used to upload all data.

[0021] Reference Figure 2In a preferred embodiment, the case practice module includes a normal data import module, a normal case generation module, an abnormal data import module, and an abnormal case generation module. The normal data import module is connected to the normal case generation module, and the abnormal data import module is connected to the abnormal case generation module.

[0022] Reference Figure 3 In a preferred embodiment, the ordinary data import module imports the ordinary data summarized in the data grouping module into the ordinary data import module, and the ordinary case generation module extracts key information from the ordinary data summarized in the data grouping module to generate exercises, hides the logistics scheduling analysis results as the answers, and generates ordinary cases for practice.

[0023] Reference Figure 3 In a preferred embodiment, the abnormal data import module imports the abnormal data summarized in the data grouping module into the module, and the abnormal case generation module extracts key information from the abnormal data summarized in the data grouping module to generate exercises, hides the abnormal logistics scheduling analysis results as auxiliary analysis data, and generates abnormal cases for practice.

[0024] Reference Figure 4 In a preferred embodiment, the data analysis module includes a derivation result integration module, a derivation path analysis module, and a result filtering module. The derivation result integration module integrates the derivation results of each user's abnormal case practice.

[0025] Reference Figure 4 In a preferred embodiment, the derivation path analysis module uses hidden abnormal logistics scheduling analysis results as auxiliary analysis data to deduce whether the user's practice results are reasonable. The result filtering module is used to filter out reasonable derivation results and match them with user IDs. In addition to generating exercises using ordinary data, the case practice module can also generate abnormal case exercises based on abnormal cases. By extracting key information from abnormal cases, exercises are generated. Hidden abnormal logistics scheduling analysis results are used as auxiliary analysis data to generate abnormal cases for practice. During the user's practice, the logistics scheduling can be deduced and compared with the hidden analysis results. If the derivation path analysis module finds the user's derivation path to be reasonable, and the details and results are better than the system's automatic derivation results, then while the user gains skills, their logical methods can also be collected simultaneously and applied to the system's set logic, enabling the system to be continuously improved to better assist logistics scheduling.

[0026] Reference Figure 5 In a preferred embodiment, the performance integration unit includes a performance integration module, a scoring module, a performance disclosure module, and an enterprise communication module. The performance integration module is used to integrate the practice frequency and practice performance of each user.

[0027] Reference Figure 5 In a preferred embodiment, the scoring module scores based on the frequency of daily practice and the results of case practice. Solving abnormal cases awards significant points. The results disclosure module publishes the scored results and matching user IDs in a ranking table. The enterprise communication module distributes the ranking table to various enterprises. In the results integration unit, scoring is based on the frequency of daily practice and the results of case practice, with the resolution of abnormal cases directly and significantly impacting the overall score. Based on the overall score and linked to the user's personal ID, a ranking table is published, directly linking the user's ability. Since most users practicing with this system are logistics students, distributing the ranking table to various enterprises assists in recruitment, making it easier for enterprises to select the best candidates and for capable students to quickly find jobs, achieving a win-win situation.

[0028] Working Principle: The logistics dispatch center and blockchain center can collect real dispatch data while coordinating cross-border multimodal transport dispatch. Based on the aggregated data, they provide interactive data support for user interaction units. A daily operation practice module generates and establishes practice questions, which are then trained using VR devices and a constructed virtual environment. This allows the system to fully utilize data while assisting in logistics dispatch, thanks to the user interaction mechanism. Furthermore, a rich question bank and practice environment effectively help users improve their professional skills. In addition to generating exercises from ordinary data, the case study practice module can also generate abnormal case exercises. By extracting key information from abnormal cases and hiding the abnormal logistics dispatch analysis results as auxiliary analysis data, abnormal cases are generated for practice. During user practice, the logistics dispatch can be deduced and compared with the hidden analysis results. By comparison, if the derivation path analysis module determines that the user's derivation path is reasonable and the details and results are better than the system's automatic derivation results, then while the user gains skills, their logical methods can also be collected and applied to the system's logic settings, enabling the system to continuously improve and better assist logistics scheduling. In the performance integration unit, the scoring system can score based on the frequency of daily practice and the results of case practice. The resolution of abnormal cases directly and significantly affects the total score. Based on the total score and the user's personal ID, a public ranking is created using a performance ranking table. This ranking is directly linked to the user's ability. Since most users practicing with this system are logistics students, providing the performance ranking table to various companies can assist them in hiring, making it easier for companies to select the best candidates and for capable students to find jobs quickly, achieving a win-win situation.

[0029] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A blockchain-based cross-border logistics multimodal transport virtual simulation system, comprising a logistics dispatch center, a blockchain center, a user interaction unit, and a performance integration unit, characterized in that: The logistics dispatch center is used to coordinate cross-border logistics multimodal transport dispatch, analyze and provide dispatch data, the blockchain center includes a data processing module, a data aggregation module, a data grouping module and a data uploading module, the user interaction unit establishes human-computer interaction based on all data in the blockchain center, and the performance integration unit is used to integrate and publish the user's performance in the user interaction unit. The user interaction unit includes a virtual environment construction module, a daily operation practice module, a case practice module, a VR interaction module, a data statistics module, and a data analysis module. The virtual environment construction module constructs a simulated virtual environment model based on the real logistics environment. The daily operation practice module automatically generates specific data to create daily operation practice questions. The case practice module generates case practice questions based on data aggregation from the blockchain center using real data. The VR interaction module uses VR devices to train on the constructed virtual environment model. The data statistics module is used to collect statistics on each user's practice results. The data analysis module is used to analyze the practice results of each user in the data statistics module.

2. The blockchain-based cross-border logistics multimodal transport virtual simulation system according to claim 1, characterized in that, The data processing module is used to process real logistics data, the data aggregation module is used to aggregate real logistics data, the data grouping module is used to divide the aggregated real logistics data into normal and abnormal data, and the data upload module is used to upload all data.

3. The blockchain-based cross-border logistics multimodal transport virtual simulation system according to claim 2, characterized in that, The case practice module includes a normal data import module, a normal case generation module, an abnormal data import module, and an abnormal case generation module. The normal data import module is connected to the normal case generation module, and the abnormal data import module is connected to the abnormal case generation module.

4. The blockchain-based cross-border logistics multimodal transport virtual simulation system according to claim 3, characterized in that, The ordinary data import module imports the ordinary data summarized in the data grouping module into the ordinary data import module. The ordinary case generation module extracts key information from the ordinary data summarized in the data grouping module to generate exercises, hides the logistics scheduling analysis results as the answers, and generates ordinary cases for practice.

5. The blockchain-based cross-border logistics multimodal transport virtual simulation system according to claim 4, characterized in that, The abnormal data import module imports the abnormal data summarized in the data grouping module into the module. The abnormal case generation module extracts key information from the abnormal data summarized in the data grouping module to generate exercises, hides the abnormal logistics scheduling analysis results as auxiliary analysis data, and generates abnormal cases for practice.

6. The blockchain-based cross-border logistics multimodal transport virtual simulation system according to claim 5, characterized in that, The data analysis module includes a derivation result integration module, a derivation path analysis module, and a result filtering module. The derivation result integration module integrates the derivation results of each user's abnormal case practice.

7. The blockchain-based cross-border logistics multimodal transport virtual simulation system according to claim 6, characterized in that, The derivation path analysis module uses the hidden abnormal logistics scheduling analysis results as auxiliary analysis data to deduce whether the user's practice results are reasonable. The result filtering module is used to filter out reasonable derivation results and match them with user IDs.

8. The blockchain-based cross-border logistics multimodal transport virtual simulation system according to claim 7, characterized in that, The performance integration unit includes a performance integration module, a scoring module, a performance disclosure module, and an enterprise communication module. The performance integration module is used to integrate each user's practice frequency and practice performance.

9. The blockchain-based cross-border logistics multimodal transport virtual simulation system according to claim 8, characterized in that, The scoring module scores based on the frequency of daily practice and the results of case practice. If abnormal cases are resolved, a large number of points are awarded. The score disclosure module is used to publish the scored scores and matching user IDs in the score ranking table. The enterprise communication module is used to provide the score ranking table to various enterprises.

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