Intelligent dispatching method, system and storage medium for road-rail intermodal container

Abstract

The present invention relates to the technical field of intelligent manufacturing equipment industry, and more specifically to an intelligent scheduling method, system and storage medium for road-rail intermodal containers, including: obtaining historical environmental data of containers from a memory, preprocessing the historical environmental data, establishing a machine learning model, obtaining internal environmental data, calculating the length of time that materials in the container can be stored, prioritizing all containers, and arranging the stacking positions of containers in the road-rail intermodal logistics park yard based on the priority of the containers; selecting a genetic algorithm framework, creating a scheduling plan for the container, and taking different control measures for the container crane when the container crane executes the scheduling plan to prevent collisions between the container cranes. The present invention can complete the handling of the container before the materials in the container reach the critical conditions for storage.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent manufacturing equipment industry, and more specifically to an intelligent scheduling method, system and storage medium for road-rail intermodal transport containers. Background Art

[0002] As an efficient multimodal transport mode, road-rail transport is of great significance for improving logistics efficiency and reducing transportation costs. However, since containers involve the conversion of multiple modes of transportation during road-rail transport, its scheduling problem becomes complex and changeable. Traditional scheduling methods rely on manual decision-making, lack flexibility and real-time performance, and are difficult to adapt to the needs of modern logistics for high efficiency and high reliability.

[0003] Similar prior art includes a Chinese patent application with publication number CN116205472A, which provides a method and system for formulating a raw material container transportation plan based on production tasks, belonging to the field of smart production and logistics distribution. The method steps are to obtain the production tasks and corresponding raw material requirements and designated container requirements within a predetermined time period; at the same time, obtain the container working conditions, capacity limitation information and raw material inventory information; according to the raw material requirements and raw material inventory, modify the number of raw material requirements that cannot be met to the current total inventory; according to the designated container requirements and container working conditions, update the container requirements to ensure that all requirements can be met after the update, and screen out useful containers; score the completion of raw material transportation according to the demand level of raw materials; construct a container transportation plan generation model; solve the container transportation plan generation model, and give the optimal container transportation plan without capacity constraints. However, this application only considers production efficiency, and does not consider the problem of how to dispatch containers when the materials transported by containers are affected by the environment.

[0004] Similar prior art includes a Chinese patent application with publication number CN114997786A, which provides a method, device, and electronic device for container scheduling, and relates to the field of automated terminal technology, including obtaining yard storage data, including the proportion of containers that have been released; receiving a reservation for container release request; judging whether there is a target yard based on the reservation for container release request, and the target yard includes a yard that meets the reservation for container release request; when it is judged that there is a target yard based on the reservation for container release request, the target yard is arranged in reverse order based on the proportion of containers that have been released; and the target yard that is ranked first in reverse order is pushed. However, this application only considers how to improve the turnover rate of containers, and does not consider the problem of how to schedule containers when the materials transported by containers are affected by the environment. Summary of the invention

[0005] To solve the above problems, the present invention provides an intelligent scheduling method, system and storage medium for road-rail intermodal containers, which can monitor the status of the containers in real time and complete the handling of the containers before the materials in the containers reach the critical storage conditions.

[0006] In order to achieve the above-mentioned object of the invention, an intelligent dispatching method for road-rail intermodal containers is provided, wherein environmental sensors are respectively installed inside and outside each container, and a storage unit is also installed inside each container, and a position sensor and an operating unit are installed on the container crane, and the operating unit is used to receive and execute instructions from the control unit, and is implemented by executing the following steps:

[0007] Step S1: acquiring historical environmental data of a container from a memory and preprocessing the historical environmental data, wherein the historical environmental data includes internal environmental data of the container, external environmental data of the container, and collection time points corresponding to the internal environmental data and the external environmental data, respectively; the internal environmental data includes temperature, humidity, and gas concentration monitored by an internal environmental sensor of the container; and the external environmental data includes outdoor temperature and outdoor humidity monitored by an external environmental sensor of the container;

[0008] Step S2: Establish a machine learning model, train the machine learning model based on the historical environmental data, so as to learn the corresponding relationship between the internal environmental data of the container and the external environmental data of the container that changes over time, obtain new internal environmental data and external environmental data of the container, and input them into the machine learning model together with the critical conditions for storing materials in the container, obtain the maximum storage time length of the materials in the container predicted by the machine learning model, and prioritize all the containers based on the maximum storage time length of the materials in each container, and obtain the priority of the sorted containers, and also arrange the stacking position of the containers in the road-rail combined transport logistics park yard based on the priority of the containers;

[0009] Step S3: Select a genetic algorithm framework, create a scheduling plan for the container based on the priority of the container and the stacking position of the container in the road-rail intermodal logistics park, and control the container crane to execute the scheduling plan. When the container crane executes the scheduling plan, different control measures are taken for the container crane based on the current positions of the multiple container cranes and the priorities of the containers being transported, so as to prevent collisions between the container cranes.

[0010] As a preferred technical solution of the present invention, in step S2, all the containers are prioritized based on the maximum storage time of the materials in each container, including:

[0011] The shorter the storage time of the materials in the container, the higher the corresponding priority. Based on the maximum storage time of the materials in each container, all the containers are prioritized;

[0012] The new internal and external environmental data of the container are periodically acquired, and the maximum storage time of the materials in the container is re-predicted in combination with the critical conditions for storing materials in the container, and the priority ranking of all the containers is updated based on the new maximum storage time. At the same time, the storage unit of the container updates the priority of the container.

[0013] As a preferred technical solution of the present invention, different control measures are taken on the container cranes based on the current positions of the multiple container cranes and the priorities of the containers being transported, including:

[0014] Based on the appearance structure of the container crane and the volume of the container, the internal safety area and the geometric center of the first container crane are calculated, and the external safety area of ​​the first container crane is also calculated based on the geometric center. The internal safety area and the external safety area of ​​the second container crane are calculated in the same way. When the external safety area of ​​the first container crane touches the internal safety area or the external safety area of ​​the second container crane, control measures are taken on the container cranes to prevent the first container crane and the second container crane from colliding, wherein the first container crane and the second container crane are any two of the multiple container cranes executing the scheduling plan.

[0015] As a preferred technical solution of the present invention, when the external safety area of ​​the first container crane touches the internal safety area or the external safety area of ​​the second container crane, taking control measures on the container crane includes:

[0016] Generate directed line segments in the advancing directions of the first container crane and the second container crane, and based on the directed line segments, respectively generate a first extension line in the advancing direction of the first container crane and a second extension line in the advancing direction of the second container crane, and determine whether the first extension line intersects with the second extension line;

[0017] If the first extension line intersects with the second extension line, the intersection point is determined; if the first extension line does not intersect with the second extension line, it is detected whether the external safety area of ​​the first container crane touches the internal safety area of ​​the second container crane.

[0018] As a preferred technical solution of the present invention, calculating the external safety area of ​​the first container crane includes:

[0019] acquiring the size of the first container crane and the size of the container, and when the width of the container is greater than the width of the first container crane, calculating the internal safety area of ​​the first container crane based on a preset safety distance and the size of the container, and when the width of the container is less than or equal to the width of the first container crane, calculating the internal safety area of ​​the first container crane based on the preset safety distance and the size of the first container crane, and acquiring the geometric center of the first container crane based on the internal safety area of ​​the first container crane;

[0020] Based on the geometric center, current speed and decelerated speed of the first container crane, the external safety area of ​​the first container crane is calculated by Formula 1:

[0021] (Formula 1)

[0022] in, represents the outer safety area of ​​the first container crane, represents the preset coefficient, Indicates the preset parameters. represents the current speed of the first container crane, represents the speed of the first container crane after deceleration.

[0023] As a preferred technical solution of the present invention, after determining the intersection point, the method includes:

[0024] Determine whether the intersection point is in the direction in which the first container crane or the second container crane is traveling; if the intersection point is in the direction in which the first container crane and the second container crane are traveling at the same time, calculate a first distance from the first container crane to the intersection point and a second distance from the second container crane to the intersection point; when the first distance is greater than the second distance, obtain the current speed of the first container crane and the current speed of the second container crane; when the current speed of the first container crane is less than the current speed of the second container crane, do not take control measures, and both the first container crane and the second container crane continue to travel at the current speed; when the current speed of the first container crane is greater than or equal to the first distance, obtain the current speed of the first container crane and the current speed of the second container crane; When the current speed of the two container cranes is determined, the priority of the containers handled by the first container crane and the priority of the containers handled by the second container crane are obtained; if the priority of the containers handled by the first container crane is greater than the priority of the containers handled by the second container crane, the second container crane is controlled to stop, the first container crane travels normally at the current speed, and when the first container crane passes the intersection, the speed of the second container crane is controlled to travel at the speed before stopping; otherwise, the first container crane is controlled to decelerate or stop, the second container crane travels normally at the current speed, and when the second container crane passes the intersection, the speed of the first container crane is controlled to return to the speed before deceleration or stopping.

[0025] As a preferred technical solution of the present invention, detecting whether the external safety area of ​​the first container crane touches the internal safety area of ​​the second container crane includes:

[0026] If the external safety area of ​​the first container crane does not touch the internal safety area of ​​the first container crane, the current speed is maintained and the crane continues to travel. If the external safety area of ​​the first container crane touches the internal safety area of ​​the second container crane, the priority of the container carried by the first container crane and the priority of the container carried by the second container crane are obtained. If the priority of the container carried by the first container crane is greater than the priority of the container carried by the second container crane, the second container crane is controlled to decelerate or stop, the first container crane travels normally, and after the external safety area of ​​the first container crane does not touch the internal safety area of ​​the second container crane, the speed of the second container crane is controlled to be restored to the speed before deceleration or stopping. Otherwise, the first container crane is controlled to decelerate or stop, the second container crane travels normally, and after the external safety area of ​​the second container crane does not touch the internal safety area of ​​the first container crane, the speed of the first container crane is controlled to be restored to the speed before deceleration or stopping.

[0027] As a preferred technical solution of the present invention, judging whether the intersection point is in the advancing direction of the first container crane or the second container crane further includes:

[0028] If the intersection point is only in the direction of the first container crane or only in the direction of the second container crane, and the external safety areas of the two container cranes do not touch each other, the current speed of the first container crane is determined. If the current speed of the first container crane is equal to 0, the second container crane is controlled to stop, and after the first container crane leaves the external safety area of ​​the second container crane, the second container crane is controlled to travel normally. If the current speed of the first container crane is greater than 0, the second container crane is controlled to decelerate or stop, and the first container crane maintains the current speed and travels normally.

[0029] If the intersection point is only in the direction of the first container crane or only in the direction of the second container crane, when the external safety area of ​​the first container crane touches the internal safety area of ​​the second container crane, the second container crane is controlled to stop; after the first container crane leaves the external safety area of ​​the second container crane, the second container crane is controlled to travel normally.

[0030] The invention also provides an intelligent dispatching system for road-rail transport containers as described above, wherein environmental sensors are installed inside and outside each container, and each container is also equipped with a storage unit. A position sensor and an operating unit are installed on the container crane, and the operating unit is used to receive and execute instructions from the control unit. The operating unit is used to receive and execute instructions from the control unit, and includes the following modules:

[0031] an acquisition unit, configured to acquire historical environmental data of the container from a memory and preprocess the historical environmental data, wherein the historical environmental data includes internal environmental data of the container, external environmental data of the container, and collection time points corresponding to the internal environmental data and the external environmental data, respectively; the internal environmental data includes temperature, humidity, and gas concentration monitored by an internal environmental sensor of the container; and the external environmental data includes outdoor temperature and outdoor humidity monitored by an external environmental sensor of the container;

[0032] A learning unit, used for establishing a machine learning model, training the machine learning model based on the historical environmental data to learn the corresponding relationship between the internal environmental data of the container and the external environmental data of the container that changes over time, obtaining new internal environmental data and external environmental data of the container, and inputting them into the machine learning model together with the critical conditions for storing materials in the container, obtaining the maximum storage time length of the materials in the container predicted by the machine learning model, and prioritizing all the containers based on the maximum storage time length of the materials in each container, and obtaining the priority of the sorted containers, and also arranging the stacking position of the containers in the road-rail combined transport logistics park yard based on the priority of the containers;

[0033] The scheduling unit is used to select a genetic algorithm framework, create a scheduling plan for the container based on the priority of the container and the stacking position of the container in the road-rail combined transport logistics park, and control the container crane to execute the scheduling plan. When the container crane executes the scheduling plan, different control measures are taken for the container crane based on the current positions of multiple container cranes and the priorities of the containers being transported, so as to prevent collisions between the container cranes.

[0034] The present invention also provides a storage medium, which stores computer-executable instructions. When the computer-executable instructions are executed by a processor, the above-mentioned intelligent scheduling method for road-rail combined transport containers is implemented.

[0035] Compared with the prior art, the beneficial effects of the present invention are at least as follows:

[0036] 1. The technical solution of the present invention reveals the impact of external environmental changes on the internal environment of the container by analyzing the historical environmental data of the container stored in the memory, and predicts the maximum storage time of materials in the container under the premise of ensuring the quality of the materials based on these data. This prediction not only provides a scientific basis for optimizing the container scheduling plan, but also effectively prevents the decay or deterioration of materials due to long-term turnover. By establishing and training a machine learning model, the present invention learns the changing patterns of environmental data inside and outside the container over time, and combines the newly collected environmental data with the critical conditions for material storage, and inputs them into the model to predict the appropriate storage time of the materials. Based on the prediction results, the present invention prioritizes all containers and arranges their optimal stacking positions in the road-rail combined transport logistics park yard accordingly, ensuring that the materials can be transported and stored in the most suitable time, thereby reducing damage caused by adverse environmental conditions.

[0037] 2. The technical solution of the present invention uses a genetic algorithm framework to intelligently create a container scheduling plan based on the priority and stacking position of the container. The control unit accurately controls the container crane to execute these scheduling plans, and during the execution process, a differentiated control strategy is implemented according to the real-time position of the container crane and the priority of the container being transported, thereby effectively avoiding collisions between container cranes. By reducing the risk of collisions between container cranes, the present invention ensures that the container can be transported as early as possible, further reducing the risk of material damage caused by poor environmental conditions. Through the coordination between the above steps, the present invention can efficiently complete the handling of containers before the materials reach the critical storage conditions, significantly improving the efficiency of road-rail transport and the safety of materials. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0039] Figure 1 is a flowchart of the steps of the intelligent dispatching method for road-rail intermodal container in the present invention;

[0040] Figure 2 It is a schematic diagram of the road-rail intermodal container in the present invention;

[0041] Figure 3 It is a schematic diagram of the first container crane and the second container crane in the present invention having no intersection in the advancing direction;

[0042] Figure 4It is a schematic diagram showing that the first container crane and the second container crane in the present invention have an intersection in the advancing direction;

[0043] Figure 5 It is a structural diagram of the intelligent dispatching system for road-rail intermodal container in the present invention;

[0044] In the figure: 11, first container crane; 12, second container crane; 21, internal safety area of ​​the first container crane; 22, external safety area of ​​the first container crane; 31, internal safety area of ​​the second container crane; 32, external safety area of ​​the second container crane; L1, first extension line; L2, second extension line; Q, intersection of L1 and L2. DETAILED DESCRIPTION

[0045] 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.

[0046] It is understood that the terms "first", "second", etc. used in this application may be used herein to describe various elements, but unless otherwise specified, these elements are not limited by these terms. These terms are only used to distinguish a first element from another element. For example, without departing from the scope of this application, a first xx script may be referred to as a second xx script, and similarly, a second xx script may be referred to as a first xx script.

[0047] As an efficient multimodal transport method, road-rail transport is of great significance for improving logistics efficiency and reducing transportation costs. However, since containers involve the conversion of multiple modes of transportation during road-rail transport, its scheduling problem becomes complex and changeable. The existing scheduling methods do not consider how to schedule the materials transported by containers when they are affected by the environment.

[0048] In view of the above technical problems, the present invention proposes Figure 1 The intelligent dispatching method for road-rail intermodal containers shown in the figure is to install environmental sensors inside and outside each container, and to install a storage unit inside each container. A position sensor and an operating unit are installed on the container crane. The operating unit is used to receive and execute instructions from the control unit, and is implemented by executing the following steps:

[0049] Step S1: Acquire historical environmental data of the container from the memory and preprocess the historical environmental data, wherein the historical environmental data includes internal environmental data of the container, external environmental data of the container, and collection time points corresponding to the internal environmental data and the external environmental data, respectively. The internal environmental data includes the temperature, humidity, and gas concentration monitored by the internal environmental sensor of the container, and the external environmental data includes the outdoor temperature and outdoor humidity monitored by the external environmental sensor of the container.

[0050] Specifically, if Figure 2 The example of a road-rail intermodal container is described. By installing temperature, humidity, and gas concentration sensors inside each container and temperature and humidity sensors outside, environmental data can be collected in real time and stored together in the storage unit of the container, and then uploaded to the storage of the server. After extracting the historical environmental data from the storage, the first step is to clean the data, remove invalid or erroneous data records, such as data during sensor failure, readings outside the normal range, etc., and convert data from different sources and formats into a unified format. For missing data, interpolation, mean value substitution or other statistical methods are used to supplement it. After cleaning, formatting, missing value processing and outlier detection, the data is organized into a structured data set for subsequent intelligent scheduling. Historical environmental data is used to analyze the correspondence between the internal environmental data of containers and the external environmental data, so as to identify the impact of external environmental changes on the internal environment of containers. For example, the container is stacked with frozen products that are severely affected by temperature. When the outdoor temperature is 35 degrees, the internal ambient temperature of the container monitored at 11 o'clock is -10 degrees. As time goes by, the internal ambient temperature of the containers stacked in the container yard is gradually increasing. The internal ambient temperature monitored at 12 o'clock is -7 degrees, the internal ambient temperature monitored at 13 o'clock is -2 degrees, and the internal ambient temperature monitored at 15:30 is 5 degrees. Assuming that the materials stored in the container will deteriorate after the ambient temperature exceeds 5 degrees, the container cannot be placed in the container yard for more than 15:30. For the current time of 11 o'clock, the maximum turnover time of the container cannot exceed 3.5 hours. By analyzing the changes in the temperature inside the container when the outdoor temperature rises, as well as the impact of changes in outdoor humidity on the humidity inside the container, the machine learning model can learn this changing trend and use it to predict the maximum storage time of materials in the container while ensuring quality based on the current internal and external environmental data of the container, thereby optimizing the container scheduling plan to prevent materials from rotting or deteriorating due to turnover.

[0051] Step S2: Establish a machine learning model, train the machine learning model based on historical environmental data to learn the corresponding relationship between the internal environmental data of the container and the external environmental data of the container that changes over time, obtain new internal environmental data and external environmental data of the container, and input them into the machine learning model together with the critical conditions for storing materials in the container, obtain the maximum storage time length of the materials in the container predicted by the machine learning model, and prioritize all containers based on the maximum storage time length of the materials in each container, and obtain the priority of the sorted containers, and also arrange the stacking position of the containers in the road-rail intermodal logistics park yard based on the priority of the containers.

[0052] Specifically, a machine learning model suitable for time series prediction, such as a long short-term memory network (LSTM) or a random forest model for time series prediction, is selected to predict the maximum length of time that materials in a container can be stored while ensuring quality. Features are extracted from historical environmental data, including temperature, humidity, gas concentration, etc. The preprocessed historical environmental data is divided into a training set and a test set, usually in chronological order, to ensure that the model can learn the changing trend of environmental data over time. Through sensors inside and outside the container, new internal and external environmental data of the container are collected in real time, and the critical conditions for storing materials in the container, which refer to the conditions under which the materials are about to deteriorate, such as the shelf life, storage temperature, humidity range, etc. of food, are also input into the machine learning model as input features. The newly collected data and material storage conditions are input into the trained machine learning model, and the model will predict the maximum length of time that materials can be stored in the container. All containers are prioritized according to the maximum length of time that materials can be stored predicted by the model. The shorter the time length, that is, the more urgent the storage time of materials, the higher the priority of the container. The stacking location of the container in the road-rail intermodal logistics park is also determined based on the priority of the container. High-priority containers may need to be placed in locations that are easy to load and unload quickly to ensure timely processing of materials. Through intelligent scheduling methods, materials can be ensured to be transported and stored within the optimal time frame, improve scheduling efficiency, and reduce damage caused by poor environmental conditions; by giving priority to materials that are about to expire, the cost of emergency processing and material loss can also be reduced.

[0053] Step S3: Select a genetic algorithm framework, create a container scheduling plan based on the priority of the containers and the stacking position of the containers in the road-rail intermodal logistics park, and control the container crane to execute the scheduling plan. When the container crane executes the scheduling plan, different control measures are taken for the container crane based on the current positions of multiple container cranes and the priorities of the containers being transported to prevent collisions between the container cranes.

[0054] It should be noted that a container crane is a movable crane, or a container reach crane, which is used for loading, unloading and handling of containers.

[0055] Specifically, a suitable genetic algorithm (GA) framework, such as DEAP, is selected, and a fitness function is defined to evaluate the quality of the scheduling scheme. The fitness function takes into account factors such as the priority, stacking location, handling distance and time of the container. The genetic algorithm can generate efficient scheduling schemes to reduce the handling time and distance of the container crane. A set of random scheduling schemes is generated as the initial population, and each scheduling scheme is represented as a chromosome, which contains the handling order and path of the container. A selection operation (such as roulette selection) is used to select chromosomes from the population, and then a crossover operation (such as single-point crossover) is performed to generate new chromosomes; a mutation operation (such as exchanging the positions of two genes) is performed on the newly generated chromosomes to increase the diversity of the population. The fitness of each chromosome in the new population is evaluated, and the chromosome with the highest fitness is selected as the next generation population. The selection, crossover, mutation and evaluation process is repeated until the preset number of iterations or fitness threshold is reached. The chromosome with the highest fitness is selected from the final population and converted into an actual scheduling scheme. The control unit controls the container crane to perform tasks according to the generated scheduling scheme. During the execution of the container crane, the position of the container crane and the priority of the container being handled are monitored in real time. When it is detected that two container cranes may collide, different control measures are taken, such as adjusting the speed or suspending a container crane. A collision will prolong the transportation time of the container. Therefore, the risk of collision between container cranes is reduced to complete the handling of the container as soon as possible, thereby reducing the damage to materials caused by poor environmental conditions. At the same time, by optimizing the scheduling plan, container cranes and storage space can be used more effectively and resource utilization can be improved. Among them, the scheduling plan can be dynamically adjusted according to the execution of the container crane and environmental changes.

[0056] Through the coordination of the above steps, the present invention can complete the container handling work before the materials in the container reach the critical storage condition.

[0057] Furthermore, in the above step S2, all containers are prioritized based on the maximum storage time of materials in each container, including:

[0058] The shorter the storage time of materials in the container, the higher the corresponding priority. Based on the maximum storage time of materials in each container, all containers are prioritized;

[0059] Periodically obtain new internal and external environmental data of the container, and combine the critical conditions for storing materials in the container to re-predict the maximum storage time of materials in the container, and update the priority ranking of all containers based on the new maximum storage time. At the same time, the storage unit of the container updates the priority of the container.

[0060] Specifically, the machine learning model trained in step S1 is used to input the current internal environment data and external environment data, and combined with the critical conditions of material storage, the maximum storage time length of materials in each container is predicted. A priority rule is defined, that is, the shorter the storage time length of materials, the higher the priority of the container. That is, those containers predicted to be about to expire will be given a higher priority. For example, if the storage time of materials in container A is 0.5 days, and the storage time of materials in container B is 1 day, then container A has a higher priority than container B. Data reflecting the latest environmental status of the container is regularly obtained from the internal and external sensors of the container, and input into the machine learning model to re-predict the maximum storage time length of materials in each container. According to the re-predicted maximum time length, the priority of each container is adjusted. If the predicted storage time of a container is shortened, its priority will be increased, and the storage unit of the container will update the priority information of each container so that it can be given priority during the handling process. By periodically updating the priority sorting, the environmental changes and material status are responded to in real time to ensure that the containers that need the most attention are given priority, reducing the risk of expired or damaged materials. The control unit controls the container crane to perform tasks according to the updated scheduling plan. For example, if the priority of container A is increased, the container crane may need to change the handling order and handle container A first. When the container crane executes the updated scheduling plan, collision avoidance measures continue to be taken to ensure safety between container cranes. Specific collision avoidance measures will be described below.

[0061] Further, based on the current positions of the multiple container cranes and the priorities of the containers being transported, different control measures are taken for the container cranes, including:

[0062] Based on the appearance structure of the container crane and the volume of the container, the internal safety area 21 and the geometric center of the first container crane 11 are calculated, and the external safety area 22 of the first container crane 11 is also calculated based on the geometric center. The internal safety area 31 and the external safety area 32 of the second container crane 12 are calculated in the same way. When the external safety area 22 of the first container crane 11 touches the internal safety area 31 or the external safety area 32 of the second container crane 12, control measures are taken on the container cranes to prevent the first container crane 11 and the second container crane 12 from colliding, wherein the first container crane 11 and the second container crane 12 are any two of the multiple container cranes executing the scheduling plan.

[0063] Specifically, based on the appearance structure of each container crane and the volume of the container, the internal safety area and geometric center of each container crane are calculated, which can be completed by geometric and physical size measurement. Then, based on the geometric center of the container crane, its external safety area is calculated to ensure that there is enough space to avoid collision during movement. The internal safety area is determined based on the appearance outline of the container crane and is the last barrier to protect the container crane from collision. The external safety area plays a warning role in preventing collision and controls the container crane to take measures. For the first container crane 11 and the second container crane 12, the above method is used to calculate their respective internal and external safety areas. The positions of the two container cranes are monitored in real time. When the external safety area 22 of the first container crane 11 touches the internal safety area 31 or the external safety area 32 of the second container crane 12, the situation is identified. Once the contact of the safety area is detected, control measures are taken immediately, including: adjusting the container crane to slow down and stop, thereby reducing the risk of collision, completing the handling of the container as soon as possible, and reducing the damage to materials caused by poor environmental conditions. The position of the container crane and the priority of the container are continuously monitored, and the control measures are dynamically adjusted according to real-time data to ensure the safety and efficiency between the container cranes.

[0064] Furthermore, when the external safety area 22 of the first container crane 11 touches the internal safety area 31 or the external safety area 32 of the second container crane 12, the control measures taken on the container cranes include:

[0065] Generate a directed line segment in the forward direction of the first container crane 11 and the second container crane 12, and generate a first extension line L1 in the forward direction of the first container crane 11 and a second extension line L2 in the forward direction of the second container crane 12 based on the directed line segment, and determine whether the first extension line L1 intersects with the second extension line L2;

[0066] If the first extension line L1 intersects with the second extension line L2 , the intersection point Q is determined; if the first extension line L1 does not intersect with the second extension line L2 , it is detected whether the external safety area 22 of the first container crane 11 touches the internal safety area 31 of the second container crane 12 .

[0067] Specifically, if Figure 3 and Figure 4 As shown, for the first container crane 11 and the second container crane 12, two directed line segments are generated according to their current positions and directions of travel, and the line segments represent the expected directions of movement of the container cranes. Based on the directed line segment of the first container crane 11, a first extension line L1 is generated, and the line segment extends along the direction of travel of the first container crane 11. Similarly, based on the directed line segment of the second container crane 12, a second extension line L2 is generated, and the line segment extends along the direction of travel of the second container crane 12. A geometric algorithm is used to determine whether the first extension line L1 and the second extension line L2 intersect in order to predict and avoid potential collisions in advance. If the two extension lines intersect, as shown in FIG. Figure 4 As shown, the position of the intersection point Q is calculated, and according to the position of the intersection point Q, the moving path or speed of the container crane is adjusted to avoid collision at this point. Figure 3 As shown, it is further detected whether the outer safety area 22 of the first container crane 11 touches the inner safety area 31 of the second container crane 12. If there is a touch, corresponding control measures are taken, such as controlling the container crane to stop, to ensure safety and complete the container handling work as soon as possible.

[0068] Furthermore, calculating the external safety area 22 of the first container crane 11 includes:

[0069] The size of the first container crane 11 and the size of the container are obtained. When the width of the container is greater than the width of the first container crane 11, the internal safety area 21 of the first container crane 11 is calculated based on the preset safety distance and the size of the container. When the width of the container is less than or equal to the width of the first container crane 11, the internal safety area 21 of the first container crane 11 is calculated based on the preset safety distance and the size of the first container crane 11. The geometric center of the first container crane 11 is also obtained based on the internal safety area 21 of the first container crane 11.

[0070] Based on the geometric center, current speed and decelerated speed of the first container crane 11, the external safety area 22 of the first container crane 11 is calculated by Formula 1:

[0071] (Formula 1)

[0072] in, represents the outer safety area 22 of the first container crane 11, represents the preset coefficient, Indicates the preset parameters. represents the current speed of the first container crane 11, It indicates the speed of the first container crane 11 after deceleration.

[0073] Specifically, by accurately calculating the internal and external safety areas of the container crane, collisions can be avoided more effectively and the safety of the container handling process can be improved. Among them, when the operating conditions of the road-rail combined transport logistics park yard are relatively narrow or limited, the safety requirements are more stringent, and the preset coefficient is set high, and the value of the preset coefficient can be 0.5 to 1.5; when the weight of the container transported by the first container crane 11 is relatively heavy and the volume is large, the preset parameter is set large, and the value of the preset parameter can be 0.8 to 2.0.

[0074] Further, determining the intersection point Q includes:

[0075] Determine whether the intersection point Q is in the direction in which the first container crane 11 or the second container crane 12 is advancing. If the intersection point Q is in the direction in which the first container crane 11 and the second container crane 12 are advancing at the same time, calculate the first distance from the first container crane 11 to the intersection point Q and the second distance from the second container crane 12 to the intersection point Q. When the first distance is greater than the second distance, obtain the current speed of the first container crane 11 and the current speed of the second container crane 12. When the current speed of the first container crane 11 is less than the current speed of the second container crane 12, no control measures are taken. The first container crane 11 and the second container crane 12 continue to travel at the current speed. When the current speed of the first container crane 11 is greater than or equal to the current speed of the second container crane 12, the control measures are not taken. When the first container crane 11 and the second container crane 12 are moved to the intersection point Q, the priority of the container handled by the first container crane 11 and the priority of the container handled by the second container crane 12 are obtained. If the priority of the container handled by the first container crane 11 is greater than the priority of the container handled by the second container crane 12, the second container crane 12 is controlled to stop, and the first container crane 11 travels normally at the current speed. When the first container crane 11 passes the intersection point Q, the speed of the second container crane 12 is controlled to travel at the speed before stopping. Otherwise, the first container crane 11 is controlled to decelerate or stop, and the second container crane 12 travels normally at the current speed. When the second container crane 12 passes the intersection point Q, the speed of the first container crane 11 is controlled to return to the speed before deceleration or stopping, thereby ensuring the safe operation of the container cranes.

[0076] Further, detecting whether the external safety area 22 of the first container crane 11 touches the internal safety area 31 of the second container crane 12 includes:

[0077] If the external safety area 22 of the first container crane 11 does not touch the internal safety area 21 of the first container crane 11, the current speed is maintained and the crane continues to travel. If the external safety area 22 of the first container crane 11 touches the internal safety area 31 of the second container crane 12, the priority of the container carried by the first container crane 11 and the priority of the container carried by the second container crane 12 are obtained. If the priority of the container carried by the first container crane 11 is greater than the priority of the container carried by the second container crane 12, the second container crane 12 is controlled to decelerate or stop, the first container crane 11 travels normally, and after the external safety area 22 of the first container crane 11 and the internal safety area 31 of the second container crane 12 do not touch, the speed of the second container crane 12 is controlled to be restored to the speed before deceleration or stopping. Otherwise, the first container crane 11 is controlled to decelerate or stop, the second container crane 12 travels normally, and after the external safety area 32 of the second container crane 12 and the internal safety area 21 of the first container crane 11 do not touch, the speed of the first container crane 11 is controlled to be restored to the speed before deceleration or stopping, thereby ensuring the safe operation of the container cranes.

[0078] Further, judging whether the intersection point Q is in the advancing direction of the first container crane 11 or the second container crane 12 further includes:

[0079] If the intersection point Q is only in the direction of the first container crane 11 or only in the direction of the second container crane 12, and the external safety areas of the two container cranes do not touch each other, the current speed of the first container crane 11 is determined. If the current speed of the first container crane 11 is equal to 0, the second container crane 12 is controlled to stop, and after the first container crane 11 leaves the external safety area 32 of the second container crane 12, the second container crane 12 is controlled to travel normally, thereby ensuring the safe operation of the container cranes; if the current speed of the first container crane 11 is greater than 0, the second container crane 12 is controlled to decelerate or stop, and the first container crane 11 maintains the current speed and travels normally;

[0080] If the intersection point Q is only in the direction of the first container crane 11 or only in the direction of the second container crane 12, when the external safety area 22 of the first container crane 11 touches the internal safety area 31 of the second container crane 12, the second container crane 12 is controlled to stop, and after the first container crane 11 leaves the external safety area 32 of the second container crane 12, the second container crane 12 is controlled to travel normally, thereby ensuring the safe operation of the container cranes.

[0081] The present invention also provides a Figure 5 The intelligent dispatching system for road-rail transport containers shown in the figure has environmental sensors installed inside and outside each container, and a storage unit installed inside each container. A position sensor and an operating unit are installed on the container crane. The operating unit is used to receive and execute instructions from the control unit. The system includes the following modules:

[0082] an acquisition unit, used to acquire historical environmental data of the container from a memory and preprocess the historical environmental data, wherein the historical environmental data includes internal environmental data of the container, external environmental data of the container, and collection time points corresponding to the internal environmental data and the external environmental data, respectively; the internal environmental data includes temperature, humidity, and gas concentration monitored by an internal environmental sensor of the container; and the external environmental data includes outdoor temperature and outdoor humidity monitored by an external environmental sensor of the container;

[0083] A learning unit is used to establish a machine learning model, train the machine learning model based on historical environmental data, learn the corresponding relationship between the internal environmental data of the container and the external environmental data of the container that changes over time, obtain new internal environmental data and external environmental data of the container, and input them into the machine learning model together with the critical conditions for storing materials in the container, obtain the maximum storage time length of the materials in the container predicted by the machine learning model, prioritize all containers based on the maximum storage time length of the materials in each container, and obtain the priority of the sorted containers, and also arrange the stacking position of the containers in the road-rail intermodal logistics park based on the priority of the containers;

[0084] The scheduling unit is used to select the genetic algorithm framework, create a container scheduling plan based on the priority of the containers and the stacking position of the containers in the road-rail intermodal logistics park, and the control unit controls the container crane to execute the scheduling plan. When the container crane executes the scheduling plan, different control measures are taken for the container crane based on the current positions of multiple container cranes and the priorities of the containers being transported to prevent collisions between the container cranes.

[0085] The present invention also provides a computer storage medium, which stores program instructions. When the program instructions are executed, the device where the computer storage medium is located is controlled to execute the above-mentioned intelligent scheduling method for road-rail combined transport containers.

[0086] In summary, the present invention reveals the impact of external environmental changes on the internal environment of the container by analyzing the historical environmental data of the container stored in the memory, and predicts the maximum storage time of the materials in the container under the premise of ensuring the quality of the materials based on these data. This prediction not only provides a scientific basis for optimizing the container scheduling plan, but also effectively prevents the decay or deterioration of materials due to long-term turnover. By establishing and training a machine learning model, the present invention learns the changing laws of the environmental data inside and outside the container over time, and combines the newly collected environmental data with the critical conditions for material storage, and inputs it into the model to predict the appropriate storage time of the materials. Based on the prediction results, the present invention prioritizes all containers and arranges their optimal stacking positions in the road-rail combined transport logistics park yard accordingly, ensuring that the materials can be transported and stored in the most suitable time, thereby reducing damage caused by adverse environmental conditions.

[0087] In addition, the present invention adopts a genetic algorithm framework to intelligently create a scheduling plan for containers based on the priority and stacking position of the containers. The control unit accurately controls the container crane to execute these scheduling plans, and during the execution process, a differentiated control strategy is implemented based on the real-time position of the container crane and the priority of the container being transported, thereby effectively avoiding collisions between container cranes. By reducing the risk of collisions between container cranes, the present invention ensures that the containers can be transported as early as possible, further reducing the risk of material damage caused by poor environmental conditions. Through the coordination between the above steps, the present invention can efficiently complete the handling of containers before the materials reach the critical storage conditions, significantly improving the efficiency of road-rail transport and the safety of materials.

[0088] It should be understood that, although each step in the flow chart of each embodiment of the present invention is shown in sequence according to the indication of the arrow, these steps are not necessarily performed in sequence according to the order indicated by the arrow. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be performed in other orders. Moreover, at least a portion of the steps in each embodiment may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these sub-steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with at least a portion of other steps or sub-steps or stages of other steps.

[0089] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the above-mentioned program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0090] The technical features of the above-mentioned embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above-mentioned embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0091] The above-mentioned embodiments only express several implementation methods of the present invention, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

[0092] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. An intelligent dispatching method for road-rail intermodal containers, wherein environmental sensors are installed inside and outside each container, each container is also equipped with a storage unit, and a position sensor and an operating unit are installed on the container crane, wherein the operating unit is used to receive and execute instructions from a control unit, characterized in that: The method comprises: Step S1: acquiring historical environmental data of a container from a memory and preprocessing the historical environmental data, wherein the historical environmental data includes internal environmental data of the container, external environmental data of the container, and collection time points corresponding to the internal environmental data and the external environmental data, respectively; the internal environmental data includes temperature, humidity, and gas concentration monitored by an internal environmental sensor of the container; and the external environmental data includes outdoor temperature and outdoor humidity monitored by an external environmental sensor of the container; Step S2: Establish a machine learning model, train the machine learning model based on the historical environmental data, so as to learn the corresponding relationship between the internal environmental data of the container and the external environmental data of the container that changes over time, obtain new internal environmental data and external environmental data of the container, and input them into the machine learning model together with the critical conditions for storing materials in the container, obtain the maximum storage time length of the materials in the container predicted by the machine learning model, and prioritize all the containers based on the maximum storage time length of the materials in each container, and obtain the priority of the sorted containers, and also arrange the stacking position of the containers in the road-rail combined transport logistics park yard based on the priority of the containers; Step S3: Selecting a genetic algorithm framework, creating a dispatching scheme for the container based on the priority of the container and the stacking position of the container in the road-rail combined transport logistics park yard, a control unit controlling a container crane to execute the dispatching scheme, and taking different control measures for the container crane based on the current positions of the plurality of container cranes and the priorities of the containers being transported when the container crane executes the dispatching scheme, so as to prevent collisions between the container cranes; Based on the current positions of the plurality of container cranes and the priorities of the containers being transported, taking different control measures on the container cranes includes: calculating the internal safety area and the geometric center of the first container crane based on the appearance structure of the container crane and the volume of the container, and also calculating the external safety area of ​​the first container crane based on the geometric center, and calculating the internal safety area and the external safety area of ​​the second container crane in the same way, and taking control measures on the container cranes to prevent the first container crane and the second container crane from colliding when the external safety area of ​​the first container crane touches the internal safety area or the external safety area of ​​the second container crane, wherein the first container crane and the second container crane are any two of the plurality of container cranes executing the scheduling scheme; Calculating the external safety area of ​​the first container crane includes: acquiring the size of the first container crane and the size of the container, and when the width of the container is greater than the width of the first container crane, calculating the internal safety area of ​​the first container crane based on a preset safety distance and the size of the container, and when the width of the container is less than or equal to the width of the first container crane, calculating the internal safety area of ​​the first container crane based on the preset safety distance and the size of the first container crane, and acquiring the geometric center of the first container crane based on the internal safety area of ​​the first container crane; Based on the geometric center, current speed and decelerated speed of the first container crane, the external safety area of ​​the first container crane is calculated by Formula 1: S1 = π[(β * v + λ) / σ] 2 (Formula 1) Wherein, S1 represents the external safety area of ​​the first container crane, β represents a preset coefficient, λ represents a preset parameter, v represents the current speed of the first container crane, and σ represents the speed of the first container crane after deceleration.

2. The method according to claim 1, characterized in that: In step S2, all the containers are prioritized based on the maximum storage time of the materials in each container, including: The shorter the storage time of the materials in the container, the higher the corresponding priority. Based on the maximum storage time of the materials in each container, all the containers are prioritized; The new internal and external environmental data of the container are periodically acquired, and the maximum storage time of the materials in the container is re-predicted in combination with the critical conditions for storing materials in the container, and the priority ranking of all the containers is updated based on the new maximum storage time. At the same time, the storage unit of the container updates the priority of the container.

3. The method according to claim 1, characterized in that When the external safety area of ​​the first container crane touches the internal safety area or the external safety area of ​​the second container crane, taking control measures on the container crane includes: Generate directed line segments in the advancing directions of the first container crane and the second container crane, and based on the directed line segments, respectively generate a first extension line in the advancing direction of the first container crane and a second extension line in the advancing direction of the second container crane, and determine whether the first extension line intersects with the second extension line; If the first extension line intersects with the second extension line, the intersection point is determined; if the first extension line does not intersect with the second extension line, it is detected whether the external safety area of ​​the first container crane touches the internal safety area of ​​the second container crane.

4. The method according to claim 3, characterized in that: After determining the intersection point, include: Determine whether the intersection point is in the direction in which the first container crane or the second container crane is traveling; if the intersection point is in the direction in which the first container crane and the second container crane are traveling at the same time, calculate a first distance from the first container crane to the intersection point and a second distance from the second container crane to the intersection point; when the first distance is greater than the second distance, obtain the current speed of the first container crane and the current speed of the second container crane; when the current speed of the first container crane is less than the current speed of the second container crane, do not take control measures, and both the first container crane and the second container crane continue to travel at the current speed; when the current speed of the first container crane is greater than or equal to the first distance, obtain the current speed of the first container crane and the current speed of the second container crane; When the current speed of the two container cranes is determined, the priority of the containers handled by the first container crane and the priority of the containers handled by the second container crane are obtained; if the priority of the containers handled by the first container crane is greater than the priority of the containers handled by the second container crane, the second container crane is controlled to stop, the first container crane travels normally at the current speed, and when the first container crane passes the intersection, the speed of the second container crane is controlled to travel at the speed before stopping; otherwise, the first container crane is controlled to decelerate or stop, the second container crane travels normally at the current speed, and when the second container crane passes the intersection, the speed of the first container crane is controlled to return to the speed before deceleration or stopping.

5. The method according to claim 3, characterized in that: Detecting whether the external safety area of ​​the first container crane touches the internal safety area of ​​the second container crane includes: If the external safety area of ​​the first container crane does not touch the internal safety area of ​​the first container crane, the current speed is maintained and the crane continues to travel. If the external safety area of ​​the first container crane touches the internal safety area of ​​the second container crane, the priority of the container carried by the first container crane and the priority of the container carried by the second container crane are obtained. If the priority of the container carried by the first container crane is greater than the priority of the container carried by the second container crane, the second container crane is controlled to decelerate or stop, the first container crane travels normally, and after the external safety area of ​​the first container crane does not touch the internal safety area of ​​the second container crane, the speed of the second container crane is controlled to be restored to the speed before deceleration or stopping. Otherwise, the first container crane is controlled to decelerate or stop, the second container crane travels normally, and after the external safety area of ​​the second container crane does not touch the internal safety area of ​​the first container crane, the speed of the first container crane is controlled to be restored to the speed before deceleration or stopping.

6. The method according to claim 4, characterized in that The step of judging whether the intersection point is in the advancing direction of the first container crane or the second container crane further includes: If the intersection point is only in the direction of the first container crane or only in the direction of the second container crane, and the external safety areas of the two container cranes do not touch each other, the current speed of the first container crane is determined. If the current speed of the first container crane is equal to 0, the second container crane is controlled to stop, and after the first container crane leaves the external safety area of ​​the second container crane, the second container crane is controlled to travel normally. If the current speed of the first container crane is greater than 0, the second container crane is controlled to decelerate or stop, and the first container crane maintains the current speed and travels normally. If the intersection point is only in the direction of the first container crane or only in the direction of the second container crane, when the external safety area of ​​the first container crane touches the internal safety area of ​​the second container crane, the second container crane is controlled to stop; after the first container crane leaves the external safety area of ​​the second container crane, the second container crane is controlled to travel normally.

7. An intelligent dispatching system for road-rail intermodal containers, used to implement the method according to any one of claims 1 to 6, wherein environmental sensors are installed inside and outside each container, each container is also equipped with a storage unit, and a position sensor and an operating unit are installed on the container crane, wherein the operating unit is used to receive and execute instructions from the control unit, characterized in that: The system includes the following modules: an acquisition unit, configured to acquire historical environmental data of the container from a memory and preprocess the historical environmental data, wherein the historical environmental data includes internal environmental data of the container, external environmental data of the container, and collection time points corresponding to the internal environmental data and the external environmental data, respectively; the internal environmental data includes temperature, humidity, and gas concentration monitored by an internal environmental sensor of the container; and the external environmental data includes outdoor temperature and outdoor humidity monitored by an external environmental sensor of the container; A learning unit, used for establishing a machine learning model, training the machine learning model based on the historical environmental data to learn the corresponding relationship between the internal environmental data of the container and the external environmental data of the container that changes over time, obtaining new internal environmental data and external environmental data of the container, and inputting them into the machine learning model together with the critical conditions for storing materials in the container, obtaining the maximum storage time length of the materials in the container predicted by the machine learning model, and prioritizing all the containers based on the maximum storage time length of the materials in each container, and obtaining the priority of the sorted containers, and also arranging the stacking position of the containers in the road-rail combined transport logistics park yard based on the priority of the containers; A scheduling unit is used to select a genetic algorithm framework, create a scheduling plan for the container based on the priority of the container and the stacking position of the container in the road-rail combined transport logistics park, control the container crane to execute the scheduling plan, and when the container crane executes the scheduling plan, take different control measures for the container crane based on the current positions of the plurality of container cranes and the priorities of the containers being transported, so as to prevent collisions between the container cranes; The scheduling unit is further configured to: calculate the internal safety area and geometric center of the first container crane based on the appearance structure of the container crane and the volume of the container, and also calculate the external safety area of ​​the first container crane based on the geometric center, and calculate the internal safety area and external safety area of ​​the second container crane in the same way, and take control measures on the container crane when the external safety area of ​​the first container crane touches the internal safety area or the external safety area of ​​the second container crane to prevent the first container crane and the second container crane from colliding, wherein the first container crane and the second container crane are any two of the multiple container cranes executing the scheduling scheme; obtain the size of the first container crane and the size of the container, and when the width of the container is greater than the width of the first container crane, calculate the internal safety area of ​​the first container crane based on the preset safety distance and the size of the container, and when the width of the container is less than or equal to the width of the first container crane, calculate the internal safety area of ​​the first container crane based on the preset safety distance and the size of the first container crane, and also obtain the geometric center of the first container crane based on the internal safety area of ​​the first container crane; and calculate the external safety area of ​​the first container crane by formula 1 based on the geometric center, current speed and decelerated speed of the first container crane: S1 = π[(β * v + λ) / σ] 2 (Formula 1) Wherein, S1 represents the external safety area of ​​the first container crane, β represents a preset coefficient, λ represents a preset parameter, v represents the current speed of the first container crane, and σ represents the speed of the first container crane after deceleration.

8. A computer storage medium, characterized in that: The storage medium stores program instructions, wherein when the program instructions are executed, the device where the storage medium is located is controlled to execute the intelligent scheduling method for road-rail intermodal containers according to any one of claims 1 to 6.

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