Intelligent logistics scheduling method and system for sea-railway combined transportation

Abstract

The invention discloses an intelligent logistics scheduling method and system for sea-railway combined transportation, and relates to the technical field of intelligent scheduling, and the method comprises the steps: carrying out the analysis of collected sea-railway combined transportation network data and user data, constructing a sea-railway combined transportation intelligent logistics scheduling model, and formulating an intelligent logistics strategy; carrying out logistics tracking on the cargo state, obtaining cargo state data, carrying out risk prediction, and dynamically adjusting the intelligent logistics strategy; and optimizing the sea-railway combined transportation intelligent logistics scheduling model based on real-time data and user feedback. According to the invention, by constructing the sea-railway combined transportation intelligent logistics scheduling model, land transportation routes and vehicle matching are optimized, the logistics scheduling efficiency is improved, and the transportation cost is reduced; whole-course logistics tracking and risk prediction are carried out on the cargo state, and abnormal conditions are judged and processed in combination with vehicle state data, so that the safety and reliability of logistics scheduling are effectively improved, and the flexibility and the strain capacity of logistics scheduling are improved.

Description

Technical Field

[0001] The present invention relates to the field of intelligent scheduling technology, and in particular to an intelligent logistics scheduling method and system for sea-rail combined transport. Background Art

[0002] Logistics and transportation refers to a series of transportation and management processes that deliver goods from the place of shipment to the destination safely, timely and economically. With the continuous development of global trade, the demand for cross-border logistics and transportation is growing. As an important cross-border logistics and transportation mode, sea-rail transport has gradually attracted widespread attention. Sea-rail transport refers to a comprehensive transportation mode that uses the organic combination of sea and rail transportation to achieve cross-border long-distance transportation of goods. Compared with single sea or rail transportation, sea-rail transport has the advantages of short transportation time, low cost and environmental protection and energy saving. However, sea-rail transport involves the connection of multiple modes of transportation and faces many scheduling and management problems. How to efficiently coordinate multimodal transport and ensure that the status of goods in transit is visible and the risks are controllable is crucial to improving the efficiency of sea-rail transport. In recent years, with the continuous deepening of the application of emerging technologies such as artificial intelligence, big data and the Internet of Things in the field of logistics, new technical means have been provided to solve the scheduling problems of sea-rail transport. The integrated application of these advanced technologies can not only optimize the transportation efficiency and improve the transportation quality of sea-rail transport, but also realize real-time monitoring and intelligent scheduling of the entire transportation process, greatly improving the operational management level of sea-rail transport.

[0003] However, the common solutions currently available have many shortcomings, including: existing technologies mainly rely on manual experience to make logistics scheduling decisions, lack in-depth analysis and comprehensive utilization of multi-source heterogeneous data involved in the entire process of sea-rail transport, and it is difficult to fully grasp the dynamic changes in the transportation process and formulate scientific and efficient scheduling plans; existing scheduling methods usually formulate fixed scheduling strategies based on historical experience, and are unable to dynamically adjust and optimize scheduling strategies according to the real-time status of logistics transportation and weather factors, resulting in poor scheduling efficiency and risk control capabilities; relying on manual methods to track the status of cargo transportation, it is impossible to achieve full-process visual monitoring of cargo flow, and it is difficult to timely discover and warn of potential transportation risks, affecting logistics timeliness and safety. Summary of the invention

[0004] In view of the fact that it is difficult to fully grasp the dynamic changes of the transportation process in the existing technology when conducting intelligent scheduling of sea-rail transport, the scheduling efficiency and risk control capabilities are poor, it is difficult to timely discover and warn of potential transportation risks, affecting the timeliness and safety of logistics, etc., the present invention is proposed.

[0005] Therefore, the problem to be solved by the present invention is how to provide a method for intelligent logistics scheduling of sea-rail transport that optimizes land transport routes and vehicle matching, improves the efficiency of logistics scheduling, reduces transportation costs, and improves the safety and reliability of logistics scheduling by tracking the status of goods throughout the entire process and conducting risk prediction.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In the first aspect, an embodiment of the present invention provides an intelligent logistics scheduling method for sea-rail transport, which includes analyzing the collected sea-rail transport network data and user data, constructing an intelligent logistics scheduling model for sea-rail transport and formulating an intelligent logistics strategy; performing logistics tracking on the cargo status, obtaining cargo status data and performing risk prediction, and dynamically adjusting the intelligent logistics strategy; and optimizing the sea-rail transport intelligent logistics scheduling model based on real-time data and user feedback.

[0008] As a preferred solution of the intelligent logistics scheduling method for sea-rail transport of the present invention, the construction of the intelligent logistics scheduling model for sea-rail transport includes the following steps: pre-processing the sea-rail transport network data and the user data; searching for ship schedules and generating sea transport orders; judging the order type, if the container index is yes, calculating the best land transport route and generating a land transport order; if the container index is no, not generating a land transport order, the specific formula for judging the order type is as follows: ,in, is the order type. If the order type If it is 1, a land transport order is generated; if the order type If it is 0, no land transport order will be generated; Indicators for whether to make boxes; intelligently match land transport vehicles based on the land transport orders and the sea transport orders, and formulate intelligent logistics strategies.

[0009] As a preferred solution of the intelligent logistics scheduling method for sea-rail transport of the present invention, the calculation formula of the optimal land transport route is as follows: ,in, To rate the land transport routes; is the total number of shipping addresses and delivery addresses; The shipping address; is the delivery address; For shipping address To delivery address distance; Parameters for adjusting the effect of time on the score; For shipping address To delivery address Land transportation time; For shipping address To delivery address The land transport cost is calculated by scoring all land transport routes. And make a judgment, if the score of the land transport route The highest, then determine the shipping address To delivery address The land transportation route is optimal and the corresponding land transportation order is generated.

[0010] As a preferred solution of the intelligent logistics scheduling method for sea-rail transport of the present invention, the calculation formula for intelligent matching of land transport vehicles is as follows: ,in, is the matching score for land transport vehicles; is the number of vehicles; For vehicles speed; For vehicles Costs; For vehicles Available load capacity; For vehicles The available cargo length; by calculating the matching scores of all corresponding land transport vehicles according to the land transport order And make a judgment, if the matching score of the land transport vehicle The highest, then determine the vehicle Best suited for ground shipping orders as described.

[0011] As a preferred solution of the intelligent logistics scheduling method for sea-rail transport described in the present invention, the risk prediction includes the following steps: real-time logistics tracking of goods to obtain goods status data; judging the abnormal state of the vehicle based on the goods status data and calculating the vehicle detention time; constructing a risk prediction model based on the abnormal state judgment result and formulating a logistics adjustment strategy, and dynamically adjusting the intelligent logistics strategy; if the vehicle detention time If the vehicle is less than the first threshold, it is determined that the vehicle is normally detained and the original intelligent logistics strategy is maintained unchanged; if the vehicle is detained for If the vehicle detention time is greater than the second threshold, the vehicle detention is considered abnormal and the intelligent logistics strategy is re-formulated; If the vehicle is detained for a period of time greater than or equal to the first threshold and less than or equal to the second threshold, an alarm is issued to the driver and the alarm is recorded; if the vehicle is detained for a period of time greater than or equal to the first threshold and less than or equal to the second threshold, an alarm is issued to the driver and the alarm is recorded; If the number of times the vehicle stops is greater than or equal to the first threshold and less than or equal to the second threshold for N consecutive times, it is determined that the vehicle has stopped multiple times abnormally, and the intelligent logistics strategy is re-formulated.

[0012] As a preferred solution of the intelligent logistics scheduling method for sea-rail transport of the present invention, the calculation formula of the risk prediction model is as follows: ,in, is the risk assessment score; is the number of vehicle status data; For vehicles speed; For vehicles driving distance; Parameters to control the effect of residence time on risk assessment; The vehicle detention time.

[0013] As a preferred solution of the intelligent logistics scheduling method for sea-rail transport of the present invention, the dynamic adjustment of the intelligent logistics strategy includes: if the risk assessment score is If the risk assessment score is greater than the third threshold, the vehicle status is considered abnormal, an alarm is issued to the driver and maintenance personnel, and the intelligent logistics strategy is re-formulated; if the risk assessment score is If the risk assessment score is less than or equal to the third threshold and greater than or equal to the fourth threshold, the vehicle status is determined to be difficult to assess, an alarm is sent to the driver, the specific situation is collected, and the maintenance personnel are notified to perform manual operations; if the risk assessment score is If it is less than the fourth threshold, it is determined that the vehicle status is normal, the original intelligent logistics strategy is maintained unchanged, and real-time logistics tracking continues.

[0014] On the second aspect, in order to further solve the safety problems existing in sea-rail transport, the embodiment of the present invention provides an intelligent logistics scheduling system for sea-rail transport, which includes: a model building module, which is used to collect sea-rail transport network data and user data and build a sea-rail transport intelligent logistics scheduling model, judge the order type, and then generate land transport orders and sea transport orders, and calculate the best land transport route and match land transport vehicles based on the land transport order, and formulate an intelligent logistics strategy; a risk prediction module, which is used to use the Internet of Things technology to track the entire logistics of goods, and judge the abnormal status of the vehicle according to the cargo status data, and adjust the intelligent logistics strategy; a model optimization module, which is used to collect real-time data and user feedback to optimize the sea-rail transport intelligent logistics scheduling model, and adjust the model's thresholds, parameters and intelligent logistics strategies.

[0015] In a third aspect, an embodiment of the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, it implements any step of the intelligent logistics scheduling method for sea-rail transport as described in the first aspect of the present invention.

[0016] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the intelligent logistics scheduling method for sea-rail transport as described in the first aspect of the present invention.

[0017] The beneficial effects of the present invention are as follows: the present invention proposes an intelligent logistics scheduling method for sea-rail transport, which optimizes land transport routes and vehicle matching by constructing an intelligent logistics scheduling model for sea-rail transport, improves the efficiency of logistics scheduling, and reduces transportation costs; by tracking the status of goods throughout the entire logistics process and predicting risks, and combining vehicle status data to judge and handle abnormal situations, the safety and reliability of logistics scheduling are effectively improved, and the flexibility and adaptability of logistics scheduling are improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. Among them:

[0019] Figure 1 This is a flow chart for formulating the intelligent logistics strategy in Example 1.

[0020] Figure 2 This is a flow chart for dynamically adjusting the intelligent logistics strategy based on vehicle detention time in Example 1.

[0021] Figure 3 This is a schematic diagram of the basic information and route cargo information of the sea-rail transport order in Example 2.

[0022] Figure 4 This is a schematic diagram of the entrustment information and attachment information of the sea-rail transport order in Example 2.

[0023] Figure 5 This is a schematic diagram of the land transport order area in Example 2.

[0024] Figure 6 This is a diagram of the logistics tracking page in Example 2.

[0025] Figure 7 This is the order acceptance diagram in Example 2. DETAILED DESCRIPTION

[0026] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0027] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0028] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0029] Example 1

[0030] Reference Figure 1 and Figure 2 , which is the first embodiment of the present invention, and provides an intelligent logistics scheduling method for sea-rail transport, comprising the following steps:

[0031] S1: Analyze the collected sea-rail transport network data and user data, build a sea-rail transport intelligent logistics scheduling model and formulate intelligent logistics strategies.

[0032] Preferably, the sea-rail transport network data includes shipping address, delivery address, shipping time, arrival time, traffic information and vehicle information.

[0033] Preferably, the user data includes basic information of the order, route cargo information, entrustment information and attachment information.

[0034] Specifically, the basic information includes the price agreement form, whether to pack in containers, order number, ocean freight payment, trade terms, bill of lading score, operator, sales specialist, bill of lading type, customer unit, corporate organization code, contract number and shipment content.

[0035] Specifically, the route cargo information includes the port of departure, port of destination, transshipment port, notify party, marks and shipping marks, number of pieces, Chinese name of the goods, English name of the goods, volume, consignor, consignee, consignor number, and container type and quantity.

[0036] Specifically, the entrustment information includes the scheduled shipping date, contact person, telephone number, fax, email and declaration matters.

[0037] Specifically, Figure 1 The figure shows the process of formulating intelligent logistics strategies. The construction of the sea-rail transport intelligent logistics scheduling model includes the following steps: pre-processing the sea-rail transport network data and user data.

[0038] Based on the pre-allocated sailing date, the ship schedule is searched, and the sea transportation order is generated, thereby reserving sufficient time for land transportation.

[0039] The order type is judged based on the basic information. If the box is yes, the best land transportation route is calculated and a land transportation order is generated; if the box is no, no land transportation order is generated. The specific formula for judging the order type is as follows:

[0040] in, is the order type. If the order type If it is 1, a land transport order is generated; if the order type If it is 0, no land transport order will be generated; Whether to make a box indicator.

[0041] Combine land transport orders and sea transport orders to intelligently match land transport vehicles and formulate intelligent logistics strategies.

[0042] Furthermore, the calculation formula for the optimal land transport route is as follows: ,in, To rate the land transport routes; is the total number of shipping addresses and delivery addresses; The shipping address; is the delivery address; For shipping address To delivery address distance; Parameters for adjusting the effect of time on the score; For shipping address To delivery address Land transportation time; For shipping address To delivery address land transportation costs.

[0043] Specifically, the scores of all land transport routes are calculated based on the shipping address and delivery address of land transport. And make a judgment, if the score of the land transport route The highest, then determine the shipping address To delivery address The land transportation route is optimal and the corresponding land transportation order is generated.

[0044] Furthermore, the vehicle information includes vehicle speed, cost, available load capacity, and available load length.

[0045] Furthermore, the calculation formula for intelligent matching of land transport vehicles is as follows: ,in, is the matching score for land transport vehicles; is the number of vehicles; For vehicles speed; For vehicles Costs; For vehicles Available load capacity; For vehicles of available cargo length.

[0046] Specifically, by calculating the matching scores of all corresponding land transport vehicles according to the land transport orders And make a judgment, if the matching score of the land transport vehicle The highest, then determine the vehicle Best suited for ground shipping orders.

[0047] Specifically, by introducing a vehicle intelligent matching mechanism and conducting comprehensive scoring and matching based on land transport orders and vehicle conditions, the efficiency of transport capacity utilization can be effectively improved, logistics costs can be reduced, and the level of intelligence and automation in logistics scheduling can be improved.

[0048] S2: Carry out logistics tracking on the cargo status, obtain cargo status data and make risk predictions, and dynamically adjust the intelligent logistics strategy.

[0049] Preferably, the cargo status data includes vehicle location, vehicle speed, number of vehicle stops, and vehicle stop frequency.

[0050] Specifically, risk prediction includes the following steps: using Internet of Things technology to track the real-time logistics of goods and obtain goods status data.

[0051] Based on the cargo status data, the abnormal status of the vehicle is judged and the vehicle detention time is calculated. The specific formula for the vehicle detention time is as follows:

[0052] in, The vehicle detention time; For vehicles Number of stays; For vehicles In the Duration of each stop; For vehicles In the Frequency of stops; For vehicles In the The distance at the next stop.

[0053] Based on the abnormal status judgment results, a risk prediction model is built and a logistics adjustment strategy is formulated to dynamically adjust the intelligent logistics strategy.

[0054] Further, such as Figure 2The figure shows the process of dynamically adjusting the intelligent logistics strategy based on the vehicle detention time. If it is less than the first threshold, the vehicle is judged to be normally detained and the original intelligent logistics strategy is maintained unchanged.

[0055] If the vehicle is detained for a long time If it is greater than the second threshold, the vehicle detention is determined to be abnormal and the intelligent logistics strategy is re-formulated.

[0056] If the vehicle is detained for a long time If the value is greater than or equal to the first threshold and less than or equal to the second threshold, an alarm is issued to the driver and the alarm is recorded.

[0057] If the vehicle is detained for a long time If the number of times the vehicle stops is greater than or equal to the first threshold and less than or equal to the second threshold for N consecutive times, it is determined that the vehicle has stopped multiple times abnormally, and the intelligent logistics strategy is re-formulated.

[0058] Furthermore, the calculation formula of the risk prediction model is as follows:

[0059] in, is the risk assessment score; is the number of vehicle status data; For vehicles speed; For vehicles driving distance; Parameters to control the effect of residence time on risk assessment; The vehicle detention time.

[0060] Preferably, dynamically adjusting the intelligent logistics strategy includes: if the risk assessment score If it is greater than the third threshold, the vehicle status is determined to be abnormal, an alarm is issued to the driver and maintenance personnel, and the intelligent logistics strategy is re-formulated.

[0061] If the risk assessment score If the value is less than or equal to the third threshold and greater than or equal to the fourth threshold, it is determined that the vehicle status is difficult to assess, an alarm is sent to the driver, the specific situation is collected, and maintenance personnel are notified to perform manual operations.

[0062] If the risk assessment score If it is less than the fourth threshold, it is determined that the vehicle status is normal, the original intelligent logistics strategy is maintained unchanged, and real-time logistics tracking continues.

[0063] Specifically, by tracking the status of goods in real time and building a risk prediction model based on abnormal status and detention time, the logistics strategy can be dynamically adjusted, which improves the monitoring and early warning capabilities of the logistics process, accelerates logistics efficiency, and improves delivery quality and customer experience.

[0064] S3: Optimize the intelligent logistics scheduling model for sea-rail transport based on real-time data and user feedback.

[0065] Specifically, the optimization of the sea-rail transport intelligent logistics scheduling model includes the following steps: real-time collection and analysis of sea-rail transport network data, user data and cargo status data, comparison of actual data with model prediction data, and obtaining deviation data.

[0066] Regularly collect customer feedback on logistics services, including satisfaction and complaints, and then adjust the parameters and structure of the sea-rail intermodal intelligent logistics scheduling model.

[0067] Adjust model thresholds and weights based on real-time data and user feedback to optimize model results.

[0068] Optimize and adjust the intelligent logistics strategy based on the collected problems, improve the functionality of the intelligent logistics strategy, and combine manual intervention operations to deal with special situations.

[0069] The present embodiment also provides an intelligent logistics scheduling system for sea-rail transport, including: a model building module, which is used to collect sea-rail transport network data and user data and build a sea-rail transport intelligent logistics scheduling model, judge the order type, and then generate land transport orders and sea transport orders, and calculate the best land transport route and match land transport vehicles based on the land transport orders, and formulate intelligent logistics strategies; a risk prediction module, which is used to use the Internet of Things technology to track the entire logistics of goods, and judge the abnormal status of the vehicle according to the cargo status data, and adjust the intelligent logistics strategy; a model optimization module, which is used to collect real-time data and user feedback to optimize the sea-rail transport intelligent logistics scheduling model, and adjust the model's thresholds, parameters and intelligent logistics strategies.

[0070] This embodiment also provides a computer device, which is suitable for a smart logistics scheduling method for sea-rail transport, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute computer-executable instructions to implement a smart logistics scheduling method for sea-rail transport as proposed in the above embodiment.

[0071] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covered on the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0072] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, an intelligent logistics scheduling method for sea-rail transport proposed in the above embodiment is implemented; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read Only Memory, referred to as EPROM), programmable read-only memory (Programmable Red-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0073] In summary, the present invention proposes an intelligent logistics scheduling method for sea-rail transport. By constructing an intelligent logistics scheduling model for sea-rail transport, the land transport routes and vehicle matching are optimized, the efficiency of logistics scheduling is improved, and the transportation cost is reduced. By tracking the status of the goods throughout the entire logistics process and predicting the risks, and combining the vehicle status data to judge and handle abnormal situations, the safety and reliability of logistics scheduling are effectively improved, and the flexibility and adaptability of logistics scheduling are improved.

[0074] Example 2

[0075] Reference Figure 3~Figure 7, which is the second embodiment of the present invention. This embodiment is different from the first embodiment in that, in order to verify its beneficial effects, experimental comparison data between the present invention and the prior art are provided.

[0076] This example simulates 100 sea-rail transport order data and corresponding vehicle information and traffic information, and uses the method proposed by the present invention and the prior art to process the sea-rail transport order data. Figure 3 and Figure 4 The data shown is the sea-rail transport order data collected in this example, including basic order information, route cargo information, consignment information and attachment information.

[0077] Generate land transport orders and ocean transport orders based on the detailed information of the sea-rail transport order data. Select the order data on the ocean transport order acceptance page to confirm the booking, and then enter the pre-allocation information on the ocean transport order details page to complete the booking operation. If the whether to make a box indicator in the order is yes, the land transport order area will be displayed and the land transport order will be generated. Otherwise, the land transport order area will be hidden. Figure 5 As shown, after the land transport order is accepted, the logistics information can be tracked in real time. The logistics tracking page is as follows Figure 6 shown.

[0078] like Figure 7 The figure shows the order acceptance diagram after the ocean transport order and the land transport order are generated. The detailed information of the order can be seen from the figure, including the departure port and destination port of the ocean transport order, and also shows the number of land transport orders included in the corresponding order; the specific situation is shown in Table 1 compared with the order generated by the prior art by the sea-rail transport order generated according to the above steps.

[0079] Table 1 Order data comparison table

[0080] Experimental subjects Transportation time (hours) Transportation cost (yuan) Abnormal number Vehicle Utilization Method of the present invention 60 24000 5 85% Scheduling method based on historical data 68 32000 12 70%

[0081] It can be seen from the above table that compared with the prior art, the present invention effectively improves the transportation safety rate and reduces transportation costs by judging whether to generate a land transportation order based on order data and generating the best land transportation route and matching vehicles based on the land transportation order, thereby reducing the number of abnormal events. Combined with the intelligent logistics scheduling model and risk prediction model of sea-rail transport, the vehicle utilization rate is further increased, and the intelligence and automation level of logistics scheduling is improved.

[0082] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. An intelligent logistics dispatching method for sea-rail transport, characterized by: include: Analyze the collected sea-rail transport network data and user data, build a sea-rail transport intelligent logistics scheduling model and formulate intelligent logistics strategies; Carry out logistics tracking on the cargo status, obtain cargo status data and make risk predictions, and dynamically adjust the intelligent logistics strategy; The sea-rail transport intelligent logistics scheduling model is optimized based on real-time data and user feedback.

2. The intelligent logistics dispatching method for sea-rail transport as claimed in claim 1, characterized in that: The construction of the sea-rail transport intelligent logistics scheduling model includes the following steps: pre-processing the sea-rail transport network data and the user data; searching for ship schedules and generating sea transport orders; judging the order type, if the container manufacturing indicator is yes, calculating the best land transport route and generating a land transport order; if the container manufacturing indicator is no, not generating a land transport order. The specific formula for judging the order type is as follows: in, is the order type. If the order type If it is 1, a land transport order is generated; if the order type If it is 0, no land transport order will be generated; Indicators for whether to make boxes; intelligently match land transport vehicles based on the land transport orders and the sea transport orders, and formulate intelligent logistics strategies.

3. The intelligent logistics dispatching method for sea-rail transport as claimed in claim 2, characterized in that: The calculation formula of the optimal land transport route is as follows: in, To rate the land transport routes; is the total number of shipping addresses and delivery addresses; The shipping address; is the delivery address; For shipping address To delivery address distance; Parameters for adjusting the effect of time on the score; For shipping address To delivery address Land transportation time; For shipping address To delivery address The land transport cost is calculated by scoring all land transport routes. And make a judgment, if the score of the land transport route The highest, then determine the shipping address To delivery address The land transportation route is optimal and the corresponding land transportation order is generated.

4. The intelligent logistics dispatching method for sea-rail transport as claimed in claim 3 is characterized by: The calculation formula for intelligent matching of land transport vehicles is as follows: in, is the matching score for land transport vehicles; is the number of vehicles; For vehicles speed; For vehicles Costs; For vehicles Available load capacity; For vehicles The available cargo length; by calculating the matching scores of all corresponding land transport vehicles according to the land transport order And make a judgment, if the matching score of the land transport vehicle The highest, then determine the vehicle Best suited for ground shipping orders as described.

5. The intelligent logistics dispatching method for sea-rail transport as claimed in claim 4, characterized in that: The risk prediction includes the following steps: real-time logistics tracking of goods to obtain goods status data; judging the abnormal state of the vehicle based on the goods status data and calculating the vehicle detention time; building a risk prediction model based on the abnormal state judgment result and formulating a logistics adjustment strategy to dynamically adjust the intelligent logistics strategy; if the vehicle detention time If the vehicle is less than the first threshold, it is determined that the vehicle is normally detained and the original intelligent logistics strategy is maintained unchanged; if the vehicle is detained for If the vehicle detention time is greater than the second threshold, the vehicle detention is considered abnormal and the intelligent logistics strategy is re-formulated; If the vehicle is detained for a period of time greater than or equal to the first threshold and less than or equal to the second threshold, an alarm is issued to the driver and the alarm is recorded; if the vehicle is detained for a period of time greater than or equal to the first threshold and less than or equal to the second threshold, an alarm is issued to the driver and the alarm is recorded; If the number of times the vehicle stops is greater than or equal to the first threshold and less than or equal to the second threshold for N consecutive times, it is determined that the vehicle has stopped multiple times abnormally, and the intelligent logistics strategy is re-formulated.

6. The intelligent logistics dispatching method for sea-rail transport as claimed in claim 5, characterized in that: The calculation formula of the risk prediction model is as follows: in, is the risk assessment score; is the number of vehicle status data; For vehicles speed; For vehicles driving distance; Parameters to control the effect of residence time on risk assessment; The vehicle detention time.

7. The intelligent logistics dispatching method for sea-rail transport as claimed in claim 6, characterized in that: Dynamically adjust the smart logistics strategy including: if the risk assessment score If the risk assessment score is greater than the third threshold, the vehicle status is considered abnormal, an alarm is issued to the driver and maintenance personnel, and the intelligent logistics strategy is re-formulated; if the risk assessment score is If the risk assessment score is less than or equal to the third threshold and greater than or equal to the fourth threshold, the vehicle status is determined to be difficult to assess, an alarm is sent to the driver, the specific situation is collected, and the maintenance personnel are notified to perform manual operations; if the risk assessment score is If it is less than the fourth threshold, it is determined that the vehicle status is normal, the original intelligent logistics strategy is maintained unchanged, and real-time logistics tracking continues.

8. An intelligent logistics dispatching system for sea-rail transport, based on an intelligent logistics dispatching method for sea-rail transport according to any one of claims 1 to 7, characterized in that: It includes a model building module, which is used to collect sea-rail transport network data and user data and build a sea-rail transport intelligent logistics scheduling model, judge the order type, and then generate land transport orders and sea transport orders, and calculate the best land transport route and match land transport vehicles based on land transport orders, and formulate intelligent logistics strategies; The risk prediction module is used to track the entire logistics process of goods using the Internet of Things technology, and to judge the abnormal status of vehicles based on the cargo status data and adjust the intelligent logistics strategy; The model optimization module is used to collect real-time data and user feedback to optimize the sea-rail transport intelligent logistics scheduling model, and adjust the model's thresholds, parameters and intelligent logistics strategies.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the intelligent logistics scheduling method for sea-rail transport described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of an intelligent logistics scheduling method for sea-rail transport as described in any one of claims 1 to 7 are implemented.

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