Big data driven shipping lightering prediction and scheduling system

Abstract

The invention relates to the technical field of shipping and lightering point scheduling, in particular to a big data-driven shipping lightering prediction and scheduling system, which comprises a scheduling center, a big data analysis module and an intelligent planning platform. The scheduling center, the big data analysis module and the intelligent planning platform are used as a framework of the shipping lightering prediction and scheduling system, the big data analysis module carries out big data analysis according to shipping real-time information and lightering point real-time information, and the intelligent planning platform is used for planning the shipping lightering prediction and scheduling system according to system big data and business. An optimal lightering scheme for arriving at the lightering place is recommended, and the shipping cost is reduced; the big data analysis module is used for importing and setting abnormal data statistics according to real-time information synchronized by the dispatching center, predicting a predicted port arrival date according to a line, and providing the latest condition of a destination port, recommending a port changing plan and reducing the lag condition and the operation cost for a cargo owner in combination with weather information, water regimen, wharf announcement and the like.

Description

Technical Field

[0001] The present invention relates to the technical field of shipping and transshipment point scheduling, and in particular to a big data driven shipping transshipment prediction and scheduling system. Background Art

[0002] In inland waterway shipping, due to the different water levels in different river basins, multiple ships are required to cooperate in transporting goods in sections. The shipping schedule is discontinuous during the transportation process, which increases the cargo transportation time and makes it impossible to deliver goods on time. When the ship arrives at the unloading port, the cargo is often piled up at the port or unloading is crowded due to reasons such as mismatch between the customer's cargo plan and the arrival plan, dock maintenance, and weather, causing ship demurrage and increasing transportation costs.

[0003] Therefore, a big data driven shipping transshipment prediction and scheduling system is proposed to address the above problems. Summary of the invention

[0004] The purpose of the present invention is to provide a big data driven shipping transshipment prediction and scheduling system to solve the problems raised in the above background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A big data driven shipping transshipment prediction and scheduling system, including:

[0007] Dispatching center: The dispatching center includes shipping order import, real-time information import, abnormal data verification, order data summary and shipping and transshipment point dispatch; shipping order import is used to connect and input shipping orders, and the input content includes the starting and ending points, shipping time and cargo information; real-time information import is used to receive the shipping vessel's track information, transshipment information, and weather information and water conditions of each shipping channel section to obtain real-time shipping information; order data summary is used to synchronize shipping orders and real-time shipping information and then summarize the data to obtain the shipping data package corresponding to each shipping order;

[0008] Big data analysis module: The big data analysis module includes data collection, data classification and sorting, data feature analysis, data settlement and storage, dynamic learning and optimization, abnormal data statistics and abnormal data correction; data collection synchronizes the shipping data packet of each shipping order, marks it according to the time axis, and obtains the shipping data packet marked with time; data classification and sorting After receiving the shipping data packet marked with time, the shipping track information and transshipment information in each shipping data packet are extracted, and the shipping track in each shipping track information is segmented, and each section of the shipping channel is marked, and the cargo information, passing time, weather information and water conditions corresponding to the shipping channel section are recorded, and the shipping channel sections with the same mark in all shipping orders are sorted and summarized to obtain multiple passage data of different shipping vessels passing through each shipping channel section. At the same time, the transshipment information in each shipping data packet is extracted to obtain the transshipment data of each transshipment point; data feature analysis After receiving multiple passage data of different shipping vessels passing through each shipping channel section, they are classified according to cargo information. And sort out the traffic data of the same category, summarize multiple traffic data of the same category in the same navigation channel section, and sort out multiple traffic data of the same category in the same navigation channel section with date as the axis, eliminate traffic data with large differences, and obtain traffic change data of the same navigation channel section with date as the axis. At the same time, the data feature analysis receives the transshipment data of each transshipment point, extracts the transshipment flow, transshipment time arrangement, and transshipment vessel tonnage data of each transshipment point, summarizes multiple transshipment data of the same transshipment point, and prepares a transshipment data table for each transshipment point; after data calculation and storage, synchronize the traffic change data of the same navigation channel section with date as the axis, divide the date into intervals according to the solar terms, and calculate the travel time of the same navigation channel section in each interval, obtain the average travel time of the same navigation channel section in the same cargo information classification in different solar terms, and store it in a unified manner in combination with weather information and water conditions information, and obtain the shipping time data table of each navigation channel section in the service coverage area in different cargo information and different solar terms;

[0009] Intelligent planning platform: The intelligent planning platform includes information import, shipping and transshipment plan planning, established plan revision, planning plan data statistics, transportation time analysis, shipping plan and resource scheduling and data synchronization; information import receives the starting and ending locations, shipping time and cargo information from the shipping order import; shipping and transshipment plan synchronization information import receives the starting and ending locations, shipping time and cargo information, and designs several shipping routes and transshipment plans based on the starting and ending locations and shipping time; planning plan data statistics After receiving several shipping routes and transshipment plans of the same shipping order, the shipping time and cargo information of the shipping order are used as the basic data to extract each shipping route and transshipment plan One by one, and then compare it with the corresponding shipping time data table in the data calculation and storage to obtain the corresponding shipping route and transshipment The planning scheme data of the plan; after analyzing the transportation time and synchronizing the planning scheme data of several shipping routes and transshipment plans of the same shipping order, the passage time and toll are compared to generate comparison data; after determining the comparison data corresponding to the synchronization of several shipping routes and transshipment plans in the shipping plan, the shipping routes and transshipment plans with short passage time and low tolls are selected to determine the shipping plan; the resource scheduling and data are set up with a transport vessel scheduling database. After the resource scheduling and data synchronization receives the determined shipping routes and transshipment plans, the vessel information applicable to the shipping order is retrieved, and the shipping and transshipment point scheduling instructions are generated in combination with the determined shipping routes and transshipment plans, and the shipping and transshipment point scheduling instructions are synchronized to the scheduling center. After the scheduling center synchronizes the shipping and transshipment point scheduling instructions, the shipping and transshipment point scheduling performs the ship scheduling and shipping order synchronization.

[0010] As a preferred solution, cargo information includes cargo type, cargo tonnage and tonnage of the vessel transporting the cargo; weather information includes wind direction, wind speed, weather conditions and driving visibility; and water information includes water flow speed and water flow direction.

[0011] As a preferred solution, during data classification and sorting, the navigation channel sections are marked according to the starting and ending points of the river section. When a section is marked as AB when it departs, it will be marked as BA when it returns through the section.

[0012] As a preferred solution, in the data feature analysis, the classification according to the cargo information is specifically: classification according to the tonnage of the ship transporting the cargo.

[0013] As a preferred solution, during data calculation and storage, when the average travel time of a navigation channel section within the same solar term interval is stored, the weather information and water conditions information of the corresponding navigation channel section within the same solar term interval are synchronously stored, and when the weather information and water conditions information are stored, the difference in the impact of wind direction, wind speed, weather conditions, driving visibility, water flow velocity, and water flow direction in the weather information and water conditions information on the travel time of the navigation channel section will be recorded.

[0014] As a preferred solution, real-time information is imported to receive the trajectory information of shipping vessels, transshipment information, and weather and water information of each navigation channel section. At the same time, it receives announcement information from each transshipment point and passage announcement information of each navigation channel section. When an abnormality occurs at a transshipment point and the transshipment service cannot be provided, or a navigation channel section is impassable, the dispatch center uses abnormal data verification to determine. When the abnormal data verification determines that the corresponding transshipment point or navigation channel section has an abnormality, the abnormal data verification synchronizes the information to the abnormal data statistics in the big data analysis module.

[0015] As a preferred solution, the big data analysis module also includes dynamic learning and optimization, abnormal data statistics and abnormal data correction. After the abnormal data statistics receive the abnormal information of the transfer point and the navigation channel section from the abnormal data verification, the abnormal information of the transfer point and the navigation channel section is synchronized to the dynamic learning and optimization. The dynamic learning and optimization retrieves the navigation channel section and transfer point information with corresponding marks in the data calculation and storage according to the navigation channel section mark, and marks the navigation channel section and the transfer point as abnormal.

[0016] As a preferred solution, abnormal data correction synchronizes the abnormal navigation channel section and transshipment point data recorded in the abnormal data statistics. When the real-time information is imported and the abnormal navigation channel section and transshipment point can be used normally, the abnormal data correction will synchronize the navigation channel section and transshipment point information that can be used normally to the dynamic learning and optimization. The dynamic learning and optimization retrieves the corresponding navigation channel section and transshipment point in the data calculation and storage, and cancels the abnormal mark of the navigation channel section and transshipment point.

[0017] As a preferred solution, the shipping and transshipment plan avoids abnormal shipping channel sections and transshipment points recorded in abnormal data statistics when planning shipping routes and transshipment plans. When executing the shipping corresponding to the shipping order, if the abnormal shipping channel sections and transshipment points appearing in the abnormal data statistics happen to fall on the shipping route and transshipment point corresponding to the shipping order, the established plan is corrected to receive the abnormal shipping channel section information, and determine the real-time positioning of the corresponding transport vessel through real-time information import. When the real-time positioning of the transport vessel is not at the abnormal shipping channel section and transshipment point, the established plan is corrected to retrieve the shipping channel section and transshipment point that can replace the abnormal shipping channel section and transshipment point in the data calculation and storage, and correct the planned shipping route and transshipment plan, and synchronize the correction information to resource scheduling and data synchronization. After the resource scheduling and data synchronization modify the shipping and transshipment point scheduling instructions according to the correction information, they are synchronized to the shipping and transshipment point scheduling.

[0018] It can be seen from the technical solution provided by the present invention that the big data driven shipping transshipment prediction and scheduling system provided by the present invention has the following beneficial effects:

[0019] 1. By using the dispatch center, big data analysis module and intelligent planning platform as the framework of the shipping transshipment prediction and dispatching system, the big data analysis module conducts big data analysis based on real-time shipping information and transshipment point information, and uses the intelligent planning platform to recommend the optimal transshipment plan for the expected arrival at the transshipment location based on the system big data and business, thereby reducing shipping costs;

[0020] 2. The big data analysis module is set up with abnormal data statistics based on the real-time information imported by the dispatching center. It provides the latest status of the destination port to the shipper based on the estimated arrival date of the route forecast, combined with weather information, water conditions, terminal announcements, etc., and recommends port change plans to reduce demurrage and operating costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 The figure is a schematic diagram of the overall structure of a big data driven shipping transshipment prediction and scheduling system of the present invention. DETAILED DESCRIPTION

[0022] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0023] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0024] like Figure 1 As shown, an embodiment of the present invention provides a big data driven shipping transshipment prediction and scheduling system, including a scheduling center, a big data analysis module and an intelligent planning platform:

[0025] The dispatch center includes shipping order import, real-time information import, abnormal data verification, order data aggregation and shipping and transshipment point dispatch;

[0026] Shipping order import is used to connect and input shipping orders. The input content includes the starting and ending locations, shipping time and cargo information. The cargo information includes cargo type, cargo tonnage and tonnage of the vessel transporting the cargo. The starting and ending locations include the shipping starting location and shipping end point. The shipping time is specifically the customer's cargo plan and arrival plan.

[0027] Real-time information import is used to receive the track information, transshipment information of shipping vessels, and weather information and water conditions information of each navigation channel section to obtain real-time shipping information. Among them, weather information includes wind direction, wind speed, weather conditions, and driving visibility. Water conditions information includes water flow rate and water flow direction. Real-time information import is used to connect with transshipment points (ports, etc.) in real time to synchronize the weather, water conditions, terminal announcements, queues of ships waiting for transshipment, and dispatch of transshipment ships at transshipment points;

[0028] Order data aggregation: After synchronizing shipping orders and real-time shipping information, data aggregation is performed to obtain the shipping data package corresponding to each shipping order;

[0029] The big data analysis module includes data collection, data classification and sorting, data feature analysis, data settlement and storage, dynamic learning and optimization, abnormal data statistics and abnormal data correction;

[0030] Data collection synchronizes the shipping data packets of each shipping order, marks them according to the time axis, and obtains the shipping data packets marked with time;

[0031] After receiving the shipping data packets marked with time, the shipping track information and transshipment information in each shipping data packet are extracted, the shipping track in each shipping track information is segmented, and each shipping channel section is marked, and the cargo information, passing time, weather information and water conditions corresponding to the shipping channel section are recorded. The shipping channel sections with the same mark in all shipping orders are sorted and summarized to obtain multiple passage data of different shipping vessels passing through each shipping channel section. At the same time, the transshipment information in each shipping data packet is extracted to obtain the transshipment data of each transshipment point;

[0032] Specifically, the marking of the navigable river section is carried out according to the starting and ending points of the river section. When a river section is marked as AB when it departs, it is marked as BA when it returns through the river section;

[0033] After receiving multiple passage data of different shipping vessels passing through each shipping channel section, the data feature analysis classifies them according to the cargo information, and sorts out the passage data of the same category, summarizes multiple passage data of the same category in the same shipping channel section, and sorts out multiple passage data of the same category in the same shipping channel section with date as the axis, removes the passage data with large differences, and obtains the passage change data of the same shipping channel section with date as the axis. At the same time, the data feature analysis receives the transshipment data of each transshipment point, extracts the transshipment flow, transshipment schedule, and transshipment vessel tonnage data of each transshipment point, and summarizes multiple transshipment data of the same transshipment point to prepare a transshipment data table for each transshipment point, wherein the classification according to cargo information is specifically: divided according to the tonnage of the cargo transporting vessel;

[0034] After synchronizing the traffic change data of the same navigation channel section with the date as the axis, the date is divided into intervals according to the solar terms, and the travel time of the same navigation channel section in each interval is calculated to obtain the average travel time of the same navigation channel section in the same cargo information classification in different solar terms, and store them in a unified manner in combination with weather information and water conditions information, to obtain a shipping time data table of each navigation channel section in the service coverage area in different cargo information and different solar terms. Specifically, when the average travel time of the navigation channel section in the same solar term interval is stored, the weather information and water conditions information of the corresponding navigation channel section in the solar term interval are synchronously stored, and when the weather information and water conditions information are stored, the difference in the influence of wind direction, wind speed, weather conditions, driving visibility, water flow velocity, and water flow direction in the weather information and water conditions information on the travel time of the navigation channel section will be recorded;

[0035] The intelligent planning platform includes information import, shipping and transshipment plan planning, established plan revision, plan data statistics, transportation time efficiency analysis, shipping plan determination, resource scheduling and data synchronization;

[0036] Information import receives the origin and destination, shipping time and cargo information from the shipping order import;

[0037] Shipping and transshipment plan synchronization information imports the received starting and ending locations, shipping time and cargo information, and designs several shipping routes and transshipment plans based on the starting and ending locations and shipping time;

[0038] After receiving several shipping routes and transshipment plans of the same shipping order, the data statistics of the planning scheme are as follows: based on the shipping time and cargo information of the shipping order, each shipping channel section in the several shipping routes and transshipment plans is extracted one by one, and then compared with the corresponding shipping time data table in the data calculation and storage, and the planning scheme data corresponding to each shipping route and transshipment plan is obtained;

[0039] After analyzing the transportation efficiency and synchronizing the planning scheme data of several shipping routes and transshipment plans of the same shipping order, the transit time and toll are compared to generate comparison data; after determining the shipping plan and synchronizing the comparison data corresponding to several shipping routes and transshipment plans, the shipping route and transshipment plan with short transit time and low toll are selected to determine the shipping plan;

[0040] Resource scheduling and data are equipped with a transport vessel scheduling database. After resource scheduling and data synchronization receives the determined shipping route and transshipment plan, it retrieves the vessel information applicable to the shipping order, and generates shipping and transshipment point scheduling instructions in combination with the determined shipping route and transshipment plan, and synchronizes the shipping and transshipment point scheduling instructions to the scheduling center. After the scheduling center synchronizes the shipping and transshipment point scheduling instructions, the shipping and transshipment point scheduling will perform vessel scheduling and shipping order synchronization.

[0041] In this embodiment, real-time information is imported and received, including the trajectory information of shipping vessels, transshipment information, and weather information and water conditions of each navigation channel section. At the same time, announcement information from each transshipment point and passage announcement information of each navigation channel section are received. When an abnormality occurs at a transshipment point and the transshipment service is unable to be provided, or a navigation channel section is impassable, the dispatch center uses abnormal data verification to determine. When the abnormal data verification determines that the corresponding transshipment point or navigation channel section has an abnormality, the abnormal data verification synchronizes the information to the abnormal data statistics in the big data analysis module.

[0042] In this embodiment, the big data analysis module also includes dynamic learning and optimization, abnormal data statistics and abnormal data correction. After the abnormal data statistics receive the abnormal information of the transfer point and the navigation channel section from the abnormal data verification, the abnormal information of the transfer point and the navigation channel section is synchronized to the dynamic learning and optimization. The dynamic learning and optimization retrieves the navigation channel section and the transfer point information corresponding to the mark in the data calculation and storage according to the navigation channel section mark, and marks the navigation channel section and the transfer point as abnormal.

[0043] Furthermore, the abnormal data correction synchronizes the abnormal navigation channel section and the transshipment point data recorded in the abnormal data statistics. When the real-time information is imported and the abnormal navigation channel section and the transshipment point can be used normally, the abnormal data correction synchronizes the navigation channel section and the transshipment point information that can be used normally to the dynamic learning and optimization. The dynamic learning and optimization retrieves the corresponding navigation channel section and the transshipment point in the data calculation and storage, and cancels the abnormal mark of the navigation channel section and the transshipment point.

[0044] Furthermore, when planning shipping routes and transshipment plans, the shipping and transshipment plan avoids abnormal shipping channel sections and transshipment points recorded in abnormal data statistics. When executing the shipping corresponding to the shipping order, if the abnormal shipping channel sections and transshipment points appearing in the abnormal data statistics happen to fall on the shipping route and transshipment point corresponding to the shipping order, the established plan is corrected to receive the abnormal shipping channel section information, and determine the real-time positioning of the corresponding transport vessel through real-time information import. When the real-time positioning of the transport vessel is not at the abnormal shipping channel section and transshipment point, the established plan is corrected to retrieve the shipping channel section and transshipment point that can replace the abnormal shipping channel section and transshipment point in the data calculation and storage, and correct the planned shipping route and transshipment plan, and synchronize the correction information to the resource scheduling and data synchronization. After the resource scheduling and data synchronization modify the shipping and transshipment point scheduling instructions according to the correction information, they are synchronized to the shipping and transshipment point scheduling.

[0045] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A big data driven shipping transshipment prediction and scheduling system, characterized by: include: Dispatching center: The dispatching center includes shipping order import, real-time information import, abnormal data verification, order data summary and shipping and transfer point dispatch; the shipping order import is used to connect and input shipping orders, and the input content includes the starting and ending locations, shipping time and cargo information; The real-time information import is used to receive the track information, transshipment information and weather information and water condition information of each shipping channel section of the shipping vessel to obtain real-time shipping information; the order data aggregation simultaneously synchronizes the shipping order and the real-time shipping information and then aggregates the data to obtain the shipping data packet corresponding to each shipping order; Big data analysis module: The big data analysis module includes data collection, data classification and sorting, data feature analysis, data settlement and storage, dynamic learning and optimization, abnormal data statistics and abnormal data correction; the data collection synchronizes the shipping data packet of each shipping order, marks it according to the time axis, and obtains the shipping data packet marked with time; after the data classification and sorting receives the shipping data packet marked with time, it extracts the shipping track information and transshipment information in each shipping data packet, segments the shipping track in each shipping track information, and marks each section of the shipping channel, records the cargo information, passing time, weather information and water conditions corresponding to the shipping channel, and sorts and summarizes the shipping channel sections with the same mark in all shipping orders to obtain multiple passage data of different shipping vessels passing through each shipping channel section, and at the same time, extracts the transshipment information in each shipping data packet to obtain the transshipment data of each transshipment point; after the data feature analysis receives the multiple passage data of different shipping vessels passing through each shipping channel section, it sorts according to the cargo information Classify and sort out the traffic data of the same category, summarize multiple traffic data of the same category of the same navigation channel section, and sort out multiple traffic data of the same category of the same navigation channel section with date as the axis, eliminate the traffic data with large differences, and obtain the traffic change data of the same navigation channel section with date as the axis. At the same time, the data feature analysis receives the transshipment data of each transshipment point, extracts the transshipment flow, transshipment time arrangement, and transshipment vessel tonnage data of each transshipment point, and summarizes multiple transshipment data of the same transshipment point to form a transshipment data table for each transshipment point; after the data calculation and storage synchronize the traffic change data of the same navigation channel section with date as the axis, the date is divided into intervals according to the solar terms, and the travel time of the same navigation channel section in each interval is calculated to obtain the average of the travel time of the same navigation channel section in different solar terms in the same cargo information classification, and is uniformly stored in combination with weather information and water conditions information to obtain the shipping time data table of each navigation channel section in the service coverage area with different cargo information and different solar terms; Intelligent planning platform: The intelligent planning platform includes information import, shipping and transshipment plan planning, established plan revision, planning plan data statistics, transportation time efficiency analysis, shipping plan and resource scheduling and data synchronization; The information import receives the starting and ending locations, shipping time and cargo information from the shipping order import; the shipping and transshipment plan synchronization information import receives the starting and ending locations, shipping time and cargo information, and designs a plurality of shipping routes and transshipment plans according to the starting and ending locations and shipping time; the planning scheme data statistics receives a plurality of shipping routes and transshipment plans of the same shipping order, and extracts each shipping channel section in the plurality of shipping routes and transshipment plans one by one based on the shipping time and cargo information of the shipping order, and then compares them with the corresponding shipping time data table in the data calculation and storage, so as to obtain the planning scheme data corresponding to each shipping route and transshipment plan; After analyzing the transportation time efficiency and synchronizing the planning scheme data of several shipping routes and transshipment planning of the same shipping order, the passage time and toll are compared to generate comparison data; After the shipping plan is determined and the comparison data corresponding to several shipping routes and transshipment plans are synchronized, the shipping route and transshipment plan with short travel time and low toll are selected to determine the shipping plan; the resource scheduling and data are provided with a transport vessel scheduling database, and after the resource scheduling and data synchronization receives the determined shipping route and transshipment plan, after retrieving the vessel information applicable to the shipping order, the shipping and transshipment point scheduling instructions are generated in combination with the determined shipping route and transshipment plan, and the shipping and transshipment point scheduling instructions are synchronized to the scheduling center. After the scheduling center synchronizes the shipping and transshipment point scheduling instructions, the shipping and transshipment point scheduling performs the ship scheduling and shipping order synchronization.

2. A big data driven shipping transshipment prediction and scheduling system according to claim 1, characterized in that: The cargo information includes cargo type, cargo tonnage and tonnage of the cargo transporting vessel; the weather information includes wind direction, wind speed, weather conditions and driving visibility; and the water condition information includes water flow speed and water flow direction.

3. The big data driven shipping transshipment prediction and scheduling system according to claim 1, characterized in that: In the data classification and sorting, the marking of the navigation channel section is carried out according to the starting and ending points of the river section. When a section of the river is marked as AB when it departs, it is marked as BA when it returns through the section of the river.

4. The big data driven shipping transshipment prediction and scheduling system according to claim 1, characterized in that: In the data feature analysis, the classification according to cargo information is specifically: classification according to the tonnage of the ship transporting the cargo.

5. The big data driven shipping transshipment prediction and scheduling system according to claim 1, characterized in that: During the data calculation and storage, when the average travel time of the navigation channel section within the same solar term interval is stored, the weather information and water conditions information of the corresponding navigation channel section within the solar term interval are synchronously stored, and when the weather information and water conditions information are stored, the difference in the influence of wind direction, wind speed, weather conditions, driving visibility, water flow velocity, and water flow direction in the weather information and water conditions information on the travel time of the navigation channel section will be recorded.

6. The big data driven shipping transshipment prediction and scheduling system according to claim 1, characterized in that: The real-time information is imported and received, including the trajectory information, transshipment information, weather information and water condition information of each navigation channel section of the shipping vessel, and at the same time, announcement information from each transshipment point and passage announcement information of each navigation channel section is received. When an abnormality occurs at a transshipment point and the transshipment service cannot be provided, or a navigation channel section is impassable, the dispatch center uses abnormal data verification to determine. When the abnormal data verification determines that the corresponding transshipment point or navigation channel section has an abnormality, the abnormal data verification synchronizes the information to the abnormal data statistics in the big data analysis module.

7. The big data driven shipping transshipment prediction and scheduling system according to claim 1, characterized in that: The big data analysis module also includes dynamic learning and optimization, abnormal data statistics and abnormal data correction. After the abnormal data statistics receive the abnormal information of the transfer point and the navigation channel section from the abnormal data verification, the abnormal information of the transfer point and the navigation channel section is synchronized to the dynamic learning and optimization. The dynamic learning and optimization retrieves the navigation channel section and transfer point information with corresponding marks in the data calculation and storage according to the navigation channel section mark, and marks the navigation channel section and the transfer point as abnormal.

8. A big data driven shipping transshipment prediction and scheduling system according to claim 7, characterized in that: The abnormal data correction synchronizes the abnormal navigation channel section and the transshipment point data of the abnormal data statistical record. When the real-time information is imported and the abnormal navigation channel section and the transshipment point can be used normally, the abnormal data correction synchronizes the navigation channel section and the transshipment point information that can be used normally to the dynamic learning and optimization. The dynamic learning and optimization retrieves the corresponding navigation channel section and the transshipment point in the data calculation and storage, and cancels the abnormal mark of the navigation channel section and the transshipment point.

9. The big data driven shipping transshipment prediction and scheduling system according to claim 7, characterized in that: The shipping and transshipment plan planning avoids abnormal shipping channel sections and transshipment points recorded in abnormal data statistics when planning shipping routes and transshipment plans. When executing the shipping corresponding to the shipping order, if the abnormal shipping channel sections and transshipment points appearing in the abnormal data statistics happen to fall on the shipping route and transshipment point corresponding to the shipping order, the established plan is corrected to receive the abnormal shipping channel section information, and determine the real-time positioning of the corresponding transport vessel through real-time information import. When the real-time positioning of the transport vessel is not at the abnormal shipping channel section and transshipment point, the established plan is corrected to retrieve the shipping channel section and transshipment point that can replace the abnormal shipping channel section and transshipment point in the data calculation and storage, and the planned shipping route and transshipment plan are corrected, and the correction information is synchronized to the resource scheduling and data synchronization. After the resource scheduling and data synchronization modify the shipping and transshipment point scheduling instructions according to the correction information, they are synchronized to the shipping and transshipment point scheduling.

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