Intelligent road port vehicle logistics scheduling method and system based on dynamic data

Abstract

The invention discloses an intelligent highway port vehicle logistics scheduling method and system based on dynamic data, and relates to the field of logistics scheduling, and the method comprises the steps: obtaining the feature information of to-be-transported goods and the accommodation space information of transport vehicles, generating an optimal loading scheme through the analysis of the matching degree of the two, and obtaining the real-time position information of the transport vehicles. The method comprises the steps of generating a transportation path based on a Dijkstra algorithm model in combination with a position where a transported cargo is to arrive, for a cargo receiving task, collecting feature information of the cargo to be received, analyzing the feature information of the cargo to be received and information of a current remaining accommodating space of a transportation vehicle, determining the priority, and for a high-priority loading task, obtaining information of the position where the cargo is to arrive, the position information is imported into a Dijkstra algorithm model, a to-be-selected path is generated, and an optimal path is generated in combination with real-time traffic information. The system has the advantages that a delivery system and a receiving system are combined, the no-load rate of a transport vehicle is reduced, and the transport efficiency of goods is improved.

Description

Technical Field

[0001] The present invention relates to the field of logistics scheduling, and in particular to a method and system for scheduling vehicle logistics in an intelligent highway port based on dynamic data. Background Art

[0002] Vehicle logistics dispatch refers to a series of activities such as the reasonable allocation of vehicle resources involved in logistics transportation, arranging routes, determining transportation tasks and time, etc., using various technical means and management methods. The main purpose of vehicle logistics dispatch is to reduce transportation costs, improve logistics efficiency, and enhance service quality.

[0003] With the continuous development of e-commerce, consumers have higher and higher requirements for the delivery speed and service quality of goods. The popularity of online shopping has led to a significant increase in logistics orders, especially during some large shopping festivals, the order volume will show explosive growth, which has put forward higher requirements for vehicle logistics scheduling. The data between different existing vehicle logistics scheduling systems are often unable to be shared, and the real-time data collection and processing capabilities are insufficient, resulting in information asymmetry, affecting the accuracy of scheduling decisions, causing scheduling information to lag, and unable to respond to real-time situations in a timely manner, resulting in a decrease in transportation efficiency and affecting user experience. Summary of the invention

[0004] In order to solve the above technical problems, a method and system for intelligent highway port vehicle logistics scheduling based on dynamic data is provided. This technical solution solves the problem that data between different vehicle logistics scheduling systems mentioned in the above background technology are often unable to be shared, resulting in information asymmetry, affecting the accuracy of scheduling decisions, and insufficient real-time data collection and processing capabilities.

[0005] In order to achieve the above purpose, the technical solution adopted by the present invention is:

[0006] A vehicle logistics dispatching method for an intelligent highway port based on dynamic data, comprising:

[0007] Obtaining the appearance, shape, volume and weight information of each cargo to be transported, and recording it as characteristic information of the cargo to be transported;

[0008] Obtain the total volume of the goods that can be loaded by the transport vehicle, and record it as the accommodation space information;

[0009] When loading each cargo to be transported into a transport vehicle, the characteristic information of the cargo to be transported and the space information of the transport vehicle are analyzed to generate an optimal loading plan;

[0010] Filling the storage space of the transport vehicle according to the optimal loading plan and the storage space information, and the area where the goods can be loaded is used as the loading area;

[0011] Obtain the location information to which the goods to be transported need to be delivered, which is recorded as the delivery node;

[0012] Based on wireless communication technology, real-time location information of transport vehicles is collected and combined with delivery nodes to generate transport routes;

[0013] Analyze the convenience of a single cargo to be transported in the transport route to reach the delivery node, generate a transport task list, and set a high arrival priority for tasks with high convenience in the transport task list;

[0014] When a single transport cargo delivery task is completed, the cargo feature information is released to the transport vehicle accommodation space information;

[0015] Obtain location information and feature information of the goods to be received, and generate a receiving task list;

[0016] Obtain the receiving task information in the receiving task list in sequence, and determine whether the loading area can accommodate the goods to be received based on the receiving task information and the characteristic information of the goods to be received. If so, increase the loading priority of the receiving task, otherwise reduce the loading priority;

[0017] Arrange the loading priorities from high to low, generate and analyze the loading task list.

[0018] Preferably, generating the optimal loading plan specifically includes:

[0019] The goods transported to the same delivery node are summarized and recorded as the same-location goods;

[0020] Get appearance information of goods at the same location;

[0021] According to the delivery nodes, the space information of the transport vehicles is divided into regions, which are recorded as delivery regions;

[0022] Let the available space in the delivery area be j;

[0023] The goods at the same location corresponding to the delivery area are traversed and placed through dynamic transfer equations;

[0024] Perform data analysis on the traversal results, generate and update the dynamic programming array;

[0025] Generate the optimal loading plan based on the dynamic programming array;

[0026] The dynamic transfer equation is specifically:

[0027] dp[j]=max(dp[j],dp[jV i ]+V i );

[0028] In the formula, dp[j] is the optimal way to load the transported goods into the available space in the delivery area, V i Represents the appearance parameters of the i-th transported cargo.

[0029] Preferably, the method of collecting the real-time location information of the transport vehicle and generating the transport route based on wireless communication technology specifically includes:

[0030] Obtain map information of the transport vehicle working area, generate a plane image, and establish a plane rectangular coordinate system with the dispatch center as the origin;

[0031] According to the real-time position information of the transport vehicle, the real-time position of the transport vehicle is marked in the plane rectangular coordinate system and recorded as the real-time state point of the transport vehicle;

[0032] Obtain real-time traffic data from the dispatch center, mark abnormal sections that cannot be normally passed, and isolate abnormal section information;

[0033] The road sections that can be normally driven are recorded as optional routes in the plane rectangular coordinate system, and the intersection points of the optional routes are recorded as intersection points;

[0034] Set A and Set B are set up, and the real-time status points and intersection points of the transport vehicles are stored in Set A and Set B respectively;

[0035] Add the intersection points in set B to set A in sequence, calculate the length of the road section that the real-time status point of the transport vehicle needs to pass through to reach the intersection point newly added to set A, record it as dt, and repeat this operation until set A contains all the elements in set B;

[0036] By comparing the values ​​of dt, the shortest distance from the real-time state point of the transport vehicle to each intersection point is obtained, and a real-time Dijkstra algorithm model is established;

[0037] Import the delivery nodes into the Dijkstra algorithm model;

[0038] Based on the plane image, determine the route where the delivery node is located, compare the distances from the delivery node to the intersection points at both ends of the route, and generate the shortest node path;

[0039] The shortest distance and the shortest route from the real-time status point of the transport vehicle to each intersection point are combined to generate a transport route.

[0040] Preferably, the acquiring the location information and feature information of the goods to be received and generating a receiving task list specifically includes:

[0041] Obtain spatial information of idle areas of transport vehicles;

[0042] Based on the characteristic information of the goods to be received and the spatial information of the idle area of ​​the transport vehicle, the position information of the end point of the idle area of ​​the transport vehicle and the position information of the end point of the goods to be received are obtained;

[0043] According to the endpoint position information of the two, the edge lines of each surface of the loading area of ​​the transport vehicle and the goods to be received are simulated in a three-dimensional coordinate system with the left front end point of the bottom of the transport vehicle compartment as the reference point, and recorded as edge line features;

[0044] According to the edge line features of the idle area of ​​the transport vehicle and the goods to be received, the normal vector is calculated, and the normal vector of each edge line feature is used as the current separation axis;

[0045] According to each separation axis, the projection range of the endpoints of each edge line feature of the idle area of ​​the transport vehicle and the goods to be received on the axis is calculated;

[0046] If the projection of the endpoint of the edge line feature of the idle area of ​​the transport vehicle on the axis does not overlap with the projection range of the endpoint of the goods to be received on the axis, it is determined that the idle area of ​​the transport vehicle can accommodate the goods to be received, and the assembly priority of the task to be received is increased;

[0047] Analyze assembly priorities and generate and analyze loading task lists.

[0048] Preferably, analyzing the assembly priority and generating and analyzing the loading task list specifically includes:

[0049] Make loading priority the primary priority and arrival priority the secondary priority;

[0050] Classify tasks by loading priority, into high loading priority tasks and low loading priority tasks;

[0051] Analyze the loading priority tasks of the same level, compare the high priority loading tasks with the transportation tasks, and for the low loading priority tasks, sort them according to the arrival priority after all the transportation tasks are completed.

[0052] Preferably, comparing the high priority loading task with the transport task specifically includes:

[0053] Get the location information of the goods to be received in the high loading priority task;

[0054] The transportation task is the primary task, and the assembly task is the secondary task;

[0055] Compare the location information of the goods to be received with the transportation path to determine whether they are located in the transportation path of the transport vehicle. If so, set the loading task and the transportation task corresponding to the goods to be received to the same priority task for comparison. If not, reduce the priority of the loading task corresponding to the goods to be received.

[0056] All delivery tasks are completed, and the loading tasks are arranged and prioritized according to the convenience of arrival of the location information of the goods to be received.

[0057] Preferably, the step of setting the loading task and the transport task corresponding to the goods to be received as tasks of the same priority for comparison comprises:

[0058] Update the location information of the goods to be received and the real-time location information of the transport vehicles;

[0059] According to the current transportation path, obtain the shortest distance information from the updated transportation vehicle position information to each intersection point in the plane image;

[0060] Based on the plane image, obtain the updated shortest distance information from the location information of the goods to be received to each intersection point;

[0061] Combine the shortest distance information from the updated location information of the transport vehicle to each intersection point in the plane image and the shortest distance information from the updated location information of the goods to be received to each intersection point to generate a candidate transport path;

[0062] Analyze the potential transportation routes and build the optimal route plan for the current situation.

[0063] Preferably, constructing the optimal path plan for the current situation includes:

[0064] Obtain information on transportation routes to be selected;

[0065] Analyze the transportation routes to be selected based on real-time traffic information and real-time information of transportation vehicles;

[0066] Arrange the analysis results by the shortest time;

[0067] The path that takes the shortest time to reach the target location is set as the optimal path plan.

[0068] Furthermore, a smart highway port vehicle logistics scheduling system based on dynamic data is proposed, which is used to implement the smart highway port vehicle logistics scheduling method based on dynamic data as described above, including:

[0069] A vehicle information collection module, which is used to collect real-time location information of transport vehicles and information about the space that accommodates the transport vehicles;

[0070] A cargo information collection module, which is used to obtain the appearance, shape, volume and weight information of the transported cargo;

[0071] A user terminal module, which is used by users to upload location information and send messages to the dispatch center;

[0072] The dispatch center is used to analyze the information transmitted by each module, and complete the tasks of generating models, listing tasks, setting priorities, and planning routes.

[0073] Optionally, the dispatch center specifically includes:

[0074] A data transmission unit, the data transmission unit is used to receive information transmitted by the vehicle information collection module, the cargo information collection module and the user terminal module;

[0075] A data analysis unit, which is used to organize and analyze the received transport cargo data information and transport vehicle information, generate a corresponding optimal packing algorithm model, generate a transport route by analyzing the real-time location information and node information of the transport vehicle, set task priorities by analyzing the task content, and list the task list;

[0076] A data judgment unit, the data judgment unit is used to compare the convenience of the transport vehicle arriving at the delivery node to determine the arrival priority, compare the receiving task information and the characteristic information of the goods to be received to determine the loading priority and judge the rationality of the task;

[0077] A data storage unit, the data storage unit is used to store the data transmitted after analysis by the data analysis unit and the transportation path, the selected transportation path and the optimal path generated based on the algorithm model;

[0078] A data sending unit, which is used by the dispatch center to send data to other modules in the system and to send task instructions to transport vehicles;

[0079] A real-time monitoring unit is used to obtain real-time road information and receive real-time location information of transport vehicles.

[0080] Compared with the prior art, the present invention has the following beneficial effects:

[0081] The present invention proposes a vehicle logistics scheduling scheme for an intelligent highway port based on dynamic data. The scheme designs the optimal loading scheme by collecting information on the goods to be delivered and the transport vehicles. The transport route is generated according to the location information to which the goods need to be delivered. The data information on the accommodation space of the transport vehicles is obtained in real time during the transport. The scheme analyzes whether the remaining accommodation space can complete the newly generated receiving task. The loading tasks are prioritized and the transport routes to be selected are determined in combination with the delivery arrival degree. The scheme generates the optimal route scheme according to the real-time road condition information. In this way, the empty load rate of the transport vehicles can be effectively reduced, the complexity between delivery and pickup is reduced, and the efficiency of cargo transportation is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0082] Figure 1 Flowchart of the intelligent highway port vehicle logistics scheduling method based on dynamic data proposed for this scheme;

[0083] Figure 2 A flow chart for generating the optimal loading plan proposed in this solution;

[0084] Figure 3 Generate a transportation route flow chart for this solution;

[0085] Figure 4 A flowchart for generating a receiving task list in this solution;

[0086] Figure 5 A flow chart of the method for comparing high priority loading tasks with transportation tasks in this solution;

[0087] Figure 6 A flow chart of the method for generating the transport routes to be selected in this scheme;

[0088] Figure 7 A flowchart of the optimal path plan for the current situation is constructed in this plan;

[0089] Figure 8 This is the structural diagram of the intelligent highway port vehicle logistics scheduling system based on dynamic data proposed for this plan. DETAILED DESCRIPTION

[0090] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are only examples, and those skilled in the art may think of other obvious variations.

[0091] Reference Figure 1 As shown, a vehicle logistics scheduling method for an intelligent highway port based on dynamic data includes:

[0092] Obtaining the appearance, shape, volume and weight information of each cargo to be transported, and recording it as characteristic information of the cargo to be transported;

[0093] Obtain the total volume of the goods that can be loaded by the transport vehicle, and record it as the accommodation space information;

[0094] When loading each cargo to be transported into a transport vehicle, the characteristic information of the cargo to be transported and the space information of the transport vehicle are analyzed to generate an optimal loading plan;

[0095] Filling the storage space of the transport vehicle according to the optimal loading plan and the storage space information, and the area where the goods can be loaded is used as the loading area;

[0096] Obtain the location information to which the goods to be transported need to be delivered, which is recorded as the delivery node;

[0097] Based on wireless communication technology, real-time location information of transport vehicles is collected and combined with delivery nodes to generate transport routes;

[0098] Analyze the convenience of a single cargo to be transported in the transport route to reach the delivery node, generate a transport task list, and set a high arrival priority for tasks with high convenience in the transport task list;

[0099] When a single transport cargo delivery task is completed, the cargo characteristic information of the transport vehicle accommodation space information is released. Information release means clearing the cargo characteristic information of the transport cargo in the transport vehicle accommodation space information to achieve refilling of the regional information.

[0100] Obtain location information and feature information of the goods to be received, and generate a receiving task list;

[0101] Obtain the receiving task information in the receiving task list in sequence, and determine whether the loading area can accommodate the goods to be received based on the receiving task information and the characteristic information of the goods to be received. If so, increase the loading priority of the receiving task, otherwise reduce the loading priority;

[0102] Arrange the loading priorities from high to low, generate and analyze the loading task list.

[0103] This solution designs the optimal loading plan by collecting information on the goods to be delivered and the transport vehicles, generates a transport route based on the location information where the goods need to be delivered, obtains data information on the transport vehicle's accommodation space in real time during transportation, analyzes whether the remaining accommodation space can complete the newly generated receiving task, compares the priorities of the loading tasks, determines the candidate transport routes based on the delivery arrival degree, and generates the optimal route plan based on real-time road conditions. In this way, the empty load rate of transport vehicles is effectively reduced and the repeated driving rate of transport vehicles on the road is saved. This specific implementation method takes the express logistics industry as an example. In the express logistics industry, transport vehicles have many delivery locations and the transported goods are complex and diverse. Users often temporarily change the delivery location, which makes it easy for data to be received untimely and vehicles to be empty. Express transport vehicle scheduling is a relatively complex type of logistics scheduling and has universal applicability.

[0104] Reference Figure 2 As shown, the optimal loading plan is generated, including:

[0105] The goods transported to the same delivery node are summarized and recorded as the same-location goods;

[0106] Get appearance information of goods at the same location;

[0107] According to the delivery nodes, the space information of the transport vehicles is divided into regions, which are recorded as delivery regions;

[0108] Let the available space in the delivery area be j;

[0109] The goods at the same location corresponding to the delivery area are traversed and placed through dynamic transfer equations;

[0110] Perform data analysis on the traversal results, generate and update the dynamic programming array;

[0111] Generate the optimal loading plan based on the dynamic programming array;

[0112] The dynamic transfer equation is specifically:

[0113] dp[j]=max(dp[j],dp[jV i ]+V i );

[0114] Where dp[j] is the optimal way to load the transported goods into the available space in the delivery area, V i Represents the appearance parameters of the i-th transported cargo.

[0115] It can be understood that the dynamic transfer equation mainly describes the process from one state to another. The dynamic transfer equation is used here to more clearly and intuitively show the process of generating the optimal packing algorithm model in this solution. By traversing various packing solutions to realize data analysis, it is necessary to consider the constraints. The constraints are:

[0116]

[0117] Where, X I Indicates whether the cargo i is placed in the storage space. If it is placed, then X I =1, if not put in, then X I =0, n represents the quantity of goods, M i represents the weight of cargo i, and m represents the maximum weight that the transport vehicle can carry.

[0118] See also Figure 3 As shown, based on wireless communication technology, the real-time location information of the transport vehicle is collected and the transport route is generated, which specifically includes:

[0119] Obtain map information of the transport vehicle working area, generate a plane image, and establish a plane rectangular coordinate system with the dispatch center as the origin;

[0120] According to the real-time position information of the transport vehicle, the real-time position of the transport vehicle is marked in the plane rectangular coordinate system and recorded as the real-time state point of the transport vehicle;

[0121] Obtain real-time traffic data from the dispatch center, mark abnormal sections that cannot be normally passed, and isolate abnormal section information;

[0122] The road sections that can be normally driven are recorded as optional routes in the plane rectangular coordinate system, and the intersection points of the optional routes are recorded as intersection points;

[0123] Set A and Set B are set up, and the real-time status points and intersection points of the transport vehicles are stored in Set A and Set B respectively;

[0124] Add the intersection points in set B to set A in sequence, calculate the length of the road section that the real-time status point of the transport vehicle needs to pass through to reach the intersection point newly added to set A, record it as dt, and repeat this operation until set A contains all the elements in set B;

[0125] By comparing the values ​​of dt, the shortest distance from the real-time state point of the transport vehicle to each intersection point is obtained, and a real-time Dijkstra algorithm model is established;

[0126] Import the delivery nodes into the Dijkstra algorithm model;

[0127] Based on the plane image, determine the route where the delivery node is located, compare the distances from the delivery node to the intersection points at both ends of the route, and generate the shortest node path;

[0128] The shortest distance and the shortest route from the real-time status point of the transport vehicle to each intersection point are combined to generate a transport route.

[0129] It can be understood that the intersection points in set B are added to set A in sequence, and the length of the road section calculated includes the length of the road section from the real-time status point of the transport vehicle to the intersection point and the length of the road section from the intersection point to another intersection point. Although the road structure on the map is simple and clear, it is impossible to directly determine the shortest distance. It is necessary to calculate the distance from the vehicle position to the intersection point and the distance from the intersection point to another intersection point in sequence, add the two distances, and arrange the added result values ​​in ascending order. The one with the smallest added result value is the shortest distance from the real-time status point of the transport vehicle to each intersection point.

[0130] The distance between two intersection points is stable and unchanged, so the length data of the road section from the intersection point to the other intersection point in dt is recorded and stored as the intersection point adjacency data;

[0131] A two-dimensional array is established according to the adjacent data of the intersection points. The rows and columns of the two-dimensional array represent the intersection points in the plane image. The elements in the array represent the length of the road section required to reach the intersection point, which is recorded as an adjacency matrix. Two adjacent elements in the adjacency matrix are recorded as adjacent intersection points.

[0132] Set up a distance array to record the current shortest distance from the transport vehicle's real-time status point to each intersection point;

[0133] Arrange the distance between the real-time status point of the transport vehicle and the intersection point according to priority, and set the higher priority as the priority queue with the smaller distance;

[0134] Extract the intersection point with the smallest distance to the real-time status point of the transport vehicle from the priority queue, traverse all adjacent intersection points of the intersection point, calculate the distance from the current intersection point to the adjacent intersection point, and if the distance is less than the known minimum distance, update the distance of the adjacent intersection point and insert it into the priority queue;

[0135] The process of extracting the minimum intersection point and updating the distance is repeated until the priority queue is empty or the minimum distances of all intersection points have been determined.

[0136] See also Figure 4 As shown, the location information and feature information of the goods to be received are obtained, and a receiving task list is generated, which specifically includes:

[0137] Obtain spatial information of idle areas of transport vehicles;

[0138] Based on the characteristic information of the goods to be received and the spatial information of the idle area of ​​the transport vehicle, the position information of the end point of the idle area of ​​the transport vehicle and the position information of the end point of the goods to be received are obtained;

[0139] According to the endpoint position information of the two, the edge lines of each surface of the loading area of ​​the transport vehicle and the goods to be received are simulated in a three-dimensional coordinate system with the left front end point of the bottom of the transport vehicle compartment as the reference point, and recorded as edge line features;

[0140] According to the edge line features of the idle area of ​​the transport vehicle and the goods to be received, the normal vector is calculated, and the normal vector of each edge line feature is used as the current separation axis;

[0141] According to each separation axis, the projection range of the endpoints of each edge line feature of the idle area of ​​the transport vehicle and the goods to be received on the axis is calculated;

[0142] If the projection of the endpoint of the edge line feature of the idle area of ​​the transport vehicle on the axis does not overlap with the projection range of the endpoint of the goods to be received on the axis, it is determined that the idle area of ​​the transport vehicle can accommodate the goods to be received, and the assembly priority of the task to be received is increased;

[0143] Analyze assembly priorities and generate and analyze loading task lists.

[0144] It is understandable that during the operation of the transport vehicle, the shape of the delivered goods may be irregular and the quantity may be inconsistent, so the released accommodating space is not of regular shape. In this embodiment, a separation axis is used to determine whether the goods can be accommodated. The function of the separation axis is to detect whether two objects are separated from each other. If the projection ranges of the two objects on the separation axis do not overlap, it can be determined that the two objects do not intersect in space. This method can make the judgment result more accurate, reduce the squeezing of goods, and ensure the integrity of the goods.

[0145] In spatial geometry, for a straight line, if there is a vector perpendicular to the line, then this vector is called the normal vector of the line. This solution uses a 90° clockwise rotation to determine the normal vector. The calculation formula for the endpoint projection coordinates obtained through the normal vector is:

[0146]

[0147] In the formula, represents the vector of the endpoint P relative to the reference point O, represents the separation axis, represents the modulus of the separating axis;

[0148] The projection coordinates of the two endpoints of the edge on the separating axis are P1 and P2 respectively, so the projection range of this edge on the separating axis is min(P1,P2) to max(P1,P2).

[0149] Furthermore, the assembly priority is analyzed, and a loading task list is generated and analyzed, including:

[0150] Make loading priority the primary priority and arrival priority the secondary priority;

[0151] Classify tasks by loading priority, into high loading priority tasks and low loading priority tasks;

[0152] Analyze the loading priority tasks of the same category. For high loading priority tasks, sort them according to the arrival priority. For low loading priority tasks, sort them according to the arrival priority after all transportation tasks are completed.

[0153] See also Figure 5 As shown, high priority loading tasks are compared with delivery tasks, including:

[0154] Get the location information of the goods to be received in the high loading priority task;

[0155] The transportation task is the primary task, and the assembly task is the secondary task;

[0156] Compare the location information of the goods to be received with the transportation path to determine whether it is located in the transportation path of the transport vehicle. If it is located in the transportation path, set the loading task and the delivery task corresponding to the goods to be received to the same priority task; if it is not located in the transportation path, reduce the priority of the loading task corresponding to the goods to be received;

[0157] All delivery tasks are completed, and the loading tasks are arranged and prioritized according to the convenience of arrival of the location information of the goods to be received.

[0158] It is understandable that in the process of logistics transportation, timely delivery of goods can better meet customer needs and improve customer satisfaction. The flow of goods usually depends on delivery. If delivery is delayed, subsequent transportation and warehousing arrangements may be affected, increasing overall operating costs. Therefore, transporting goods is the main task in logistics transportation.

[0159] See also Figure 6 As shown, the loading task and the delivery task corresponding to the goods to be received are set to the same priority task for comparison, including:

[0160] Update the location information of the goods to be received and the real-time location information of the transport vehicles;

[0161] According to the current transportation path, obtain the shortest distance information from the updated transportation vehicle position information to each intersection point in the plane image;

[0162] Based on the plane image, obtain the updated shortest distance information from the location information of the goods to be received to each intersection point;

[0163] Combine the shortest distance information from the updated location information of the transport vehicle to each intersection point in the plane image and the shortest distance information from the updated location information of the goods to be received to each intersection point to generate a candidate transport path;

[0164] Analyze the shortest distance information and build the optimal path plan for the current situation.

[0165] It is understandable that the position of the transport vehicle changes in real time during operation, and the user may modify the location information of the goods to be received. Therefore, the real-time location information of the transport vehicle and the location information of the goods to be received must be updated. For loading tasks, the user has set a time range for receiving the goods to be received. Therefore, the driver of the transport vehicle needs to contact the user in advance when receiving the goods to confirm that the goods can be obtained smoothly when arriving at the location of the goods to be received in the current time period. If the user cannot be contacted or the goods cannot be obtained smoothly in the current time period, the priority of the receiving task will be lowered.

[0166] See also Figure 7 As shown, the optimal path plan for the current situation is constructed, including:

[0167] Obtain information on transportation routes to be selected;

[0168] Analyze the transportation routes to be selected based on real-time traffic information and real-time information of transportation vehicles;

[0169] Arrange the analysis results by the shortest time;

[0170] The path that takes the shortest time to reach the target location is set as the optimal path plan.

[0171] For further information, see Figure 8 As shown, based on the same inventive concept as the above-mentioned intelligent highway port vehicle logistics scheduling method based on dynamic data, this solution proposes an intelligent highway port vehicle logistics scheduling system based on dynamic data, including:

[0172] A vehicle information collection module, which is used to collect real-time location information of transport vehicles and information about the space that accommodates the transport vehicles;

[0173] A cargo information collection module, which is used to obtain the appearance, shape, volume and weight information of the transported cargo;

[0174] A user terminal module, which is used by the user to upload location information and send messages to the dispatch center. In this solution, the user terminal module is the user's mobile communication device;

[0175] The dispatch center is used to analyze the information transmitted by each module, and complete the tasks of generating models, listing tasks, setting priorities, and planning routes.

[0176] In an optional embodiment, the dispatch center includes:

[0177] A data transmission unit, the data transmission unit is used to receive information transmitted by the vehicle information collection module, the cargo information collection module and the user terminal module;

[0178] A data analysis unit, which is used to organize and analyze the received transport cargo data information and transport vehicle information, generate a corresponding optimal packing algorithm model, generate a transport route by analyzing the real-time location information and node information of the transport vehicle, set task priorities by analyzing the task content, and list the task list;

[0179] A data judgment unit, the data judgment unit is used to compare the convenience of the transport vehicle arriving at the delivery node to determine the arrival priority, compare the receiving task information and the characteristic information of the goods to be received to determine the loading priority and judge the rationality of the task;

[0180] A data storage unit, the data storage unit is used to store the data transmitted after analysis by the data analysis unit and the transportation path, the selected transportation path and the optimal path generated based on the algorithm model;

[0181] A data sending unit, which is used by the dispatch center to send data to other modules in the system and to send task instructions to transport vehicles;

[0182] A real-time monitoring unit is used to obtain real-time road information and receive real-time location information of transport vehicles.

[0183] To sum up, the advantages of the present invention are: by obtaining real-time data through the dispatch center, it is possible to make rapid decisions on newly generated tasks, adjust services in a timely manner according to user needs, improve customer satisfaction, realize the interconnection between the delivery and delivery systems, maximize the use of the storage space of the transport vehicle, effectively reduce the empty load rate of the transport vehicle, and improve the transportation efficiency of goods.

[0184] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions only describe the principles of the present invention. The present invention may be subject to various changes and improvements without departing from the spirit and scope of the present invention. These changes and improvements fall within the scope of the present invention. The scope of protection claimed by the present invention is defined by the attached claims and their equivalents.

Claims

1. A method for dispatching vehicle logistics in an intelligent highway port based on dynamic data, characterized in that: include: Obtaining the appearance, shape, volume and weight information of each cargo to be transported, and recording it as characteristic information of the cargo to be transported; Obtain the total volume of the goods that can be loaded by the transport vehicle, and record it as the accommodation space information; When loading each cargo to be transported into a transport vehicle, the characteristic information of the cargo to be transported and the space information of the transport vehicle are analyzed to generate an optimal loading plan; Filling the storage space of the transport vehicle according to the optimal loading plan and the storage space information, and the area where the goods can be loaded is used as the loading area; Obtain the location information to which the goods to be transported need to be delivered, which is recorded as the delivery node; Based on wireless communication technology, real-time location information of transport vehicles is collected and combined with delivery nodes to generate transport routes; Analyze the convenience of a single cargo to be transported in the transport route to reach the delivery node, generate a transport task list, and set a high arrival priority for tasks with high convenience in the transport task list; When a single transport cargo delivery task is completed, the cargo feature information is released to the transport vehicle accommodation space information; Obtain location information and feature information of the goods to be received, and generate a receiving task list; Obtain the receiving task information in the receiving task list in sequence, and determine whether the loading area can accommodate the goods to be received based on the receiving task information and the characteristic information of the goods to be received. If so, increase the loading priority of the receiving task, otherwise reduce the loading priority; Arrange the loading priorities from high to low, generate and analyze the loading task list.

2. According to the method of intelligent highway port vehicle logistics scheduling based on dynamic data in claim 1, it is characterized in that: The generating of the optimal loading plan specifically includes: The goods transported to the same delivery node are summarized and recorded as the same-location goods; Get appearance information of goods at the same location; According to the delivery nodes, the space information of the transport vehicles is divided into regions, which are recorded as delivery regions; Let the available space in the delivery area be j; The goods at the same location corresponding to the delivery area are traversed and placed through dynamic transfer equations; Perform data analysis on the traversal results, generate and update the dynamic programming array; Generate the optimal loading plan based on the dynamic programming array; The dynamic transfer equation is specifically: dp[j]=max(dp[j],dp[j-V i ]+V i ); Where dp[j] is the optimal way to load the transported goods into the available space in the delivery area, V i Represents the appearance parameters of the i-th transported cargo.

3. According to claim 2, a method for intelligent highway port vehicle logistics scheduling based on dynamic data is characterized in that: The method of collecting the real-time location information of the transport vehicle and generating the transport route based on the wireless communication technology specifically includes: Obtain map information of the transport vehicle working area, generate a plane image, and establish a plane rectangular coordinate system with the dispatch center as the origin; According to the real-time position information of the transport vehicle, the real-time position of the transport vehicle is marked in the plane rectangular coordinate system and recorded as the real-time state point of the transport vehicle; Obtain real-time traffic data from the dispatch center, mark abnormal sections that cannot be normally passed, and isolate abnormal section information; The road sections that can be normally driven are recorded as optional routes in the plane rectangular coordinate system, and the intersection points of the optional routes are recorded as intersection points; Set A and Set B are set up, and the real-time status points and intersection points of the transport vehicles are stored in Set A and Set B respectively; Add the intersection points in set B to set A in sequence, calculate the length of the road section that the real-time status point of the transport vehicle needs to pass through to reach the intersection point newly added to set A, record it as dt, and repeat this operation until set A contains all the elements in set B; By comparing the values ​​of dt, the shortest distance from the real-time state point of the transport vehicle to each intersection point is obtained, and a real-time Dijkstra algorithm model is established; Import the delivery nodes into the Dijkstra algorithm model; Based on the plane image, determine the route where the delivery node is located, compare the distances from the delivery node to the intersection points at both ends of the route, and generate the shortest node path; The shortest distance and the shortest route from the real-time status point of the transport vehicle to each intersection point are combined to generate a transport route.

4. According to claim 3, a method for intelligent highway port vehicle logistics scheduling based on dynamic data is characterized in that: The obtaining of the location information of the goods to be received and the feature information of the goods to be received and generating a receiving task list specifically includes: Obtain spatial information of idle areas of transport vehicles; Based on the characteristic information of the goods to be received and the spatial information of the idle area of ​​the transport vehicle, the position information of the end point of the idle area of ​​the transport vehicle and the position information of the end point of the goods to be received are obtained; According to the endpoint position information of the two, the edge lines of each surface of the loading area of ​​the transport vehicle and the goods to be received are simulated in a three-dimensional coordinate system with the left front end point of the bottom of the transport vehicle compartment as the reference point, and recorded as edge line features; According to the edge line features of the idle area of ​​the transport vehicle and the goods to be received, the normal vector is calculated, and the normal vector of each edge line feature is used as the current separation axis; According to each separation axis, the projection range of the endpoints of each edge line feature of the idle area of ​​the transport vehicle and the goods to be received on the axis is calculated; If the projection of the endpoint of the edge line feature of the idle area of ​​the transport vehicle on the axis does not overlap with the projection range of the endpoint of the goods to be received on the axis, it is determined that the idle area of ​​the transport vehicle can accommodate the goods to be received, and the assembly priority of the task to be received is increased; Analyze assembly priorities and generate and analyze loading task lists.

5. The method for dispatching vehicle logistics in an intelligent highway port based on dynamic data according to claim 4 is characterized in that: The analyzing assembly priority and generating and analyzing the loading task list specifically include: Make loading priority the primary priority and arrival priority the secondary priority; Classify tasks by loading priority, into high loading priority tasks and low loading priority tasks; Analyze the loading priority tasks of the same level, compare the high priority loading tasks with the transportation tasks, and for the low loading priority tasks, sort them according to the arrival priority after all the transportation tasks are completed.

6. The method for dispatching vehicle logistics in an intelligent highway port based on dynamic data according to claim 5 is characterized in that: The comparison of the high priority loading task with the transport task specifically includes: Get the location information of the goods to be received in the high loading priority task; The transportation task is the primary task, and the assembly task is the secondary task; Compare the location information of the goods to be received with the transportation path to determine whether it is located in the transportation path of the transport vehicle. If so, set the loading task and the transportation task corresponding to the goods to be received to the same priority task for comparison. If not, reduce the priority of the loading task corresponding to the goods to be received; All delivery tasks are completed, and the loading tasks are arranged and prioritized according to the convenience of arrival of the location information of the goods to be received.

7. The method for dispatching vehicle logistics in an intelligent highway port based on dynamic data according to claim 6 is characterized in that: The step of setting the loading task and the delivery task corresponding to the goods to be received as tasks of the same priority for comparison specifically includes: Update the location information of the goods to be received and the real-time location information of the transport vehicles; According to the current transportation path, obtain the shortest distance information from the updated transportation vehicle position information to each intersection point in the plane image; Based on the plane image, obtain the updated shortest distance information from the location information of the goods to be received to each intersection point; Combine the shortest distance information from the updated location information of the transport vehicle to each intersection point in the plane image and the shortest distance information from the updated location information of the goods to be received to each intersection point to generate a candidate transport path; Analyze the potential transportation routes and build the optimal route plan for the current situation.

8. The method for intelligent highway port vehicle logistics scheduling based on dynamic data according to claim 7 is characterized in that: The constructing of the optimal path plan for the current situation specifically includes: Obtain information on transportation routes to be selected; Analyze the transportation routes to be selected based on real-time traffic information and real-time information of transportation vehicles; Arrange the analysis results by the shortest time; The path that takes the shortest time to reach the target location is set as the optimal path plan.

9. An intelligent highway port vehicle logistics dispatching system based on dynamic data, characterized in that: The method for implementing the vehicle logistics dispatching method of an intelligent highway port based on dynamic data as claimed in any one of claims 1 to 8 specifically comprises: A vehicle information collection module, which is used to collect real-time location information of transport vehicles and information about the space that accommodates the transport vehicles; A cargo information collection module, which is used to obtain the appearance, shape, volume and weight information of the transported cargo; A user terminal module, which is used by users to upload location information and send messages to the dispatch center; The dispatching center is used to analyze the data information transmitted by the vehicle information collection module, the cargo information collection module and the user-end module, generate a plane image, an optimal loading plan, a Dijkstra algorithm model, a transportation path, a candidate transportation path and an optimal path plan, and compare and sort tasks.

10. The intelligent highway port vehicle logistics dispatching system based on dynamic data according to claim 9 is characterized in that: The dispatch center specifically includes: A data transmission unit, the data transmission unit is used to receive information transmitted by the vehicle information collection module, the cargo information collection module and the user terminal module; A data analysis unit, which is used to organize and analyze the received transport cargo data information and transport vehicle information, generate a corresponding optimal packing algorithm model, generate a transport route by analyzing the real-time location information and node information of the transport vehicle, set task priorities by analyzing the task content, and list the task list; A data judgment unit, the data judgment unit is used to compare the convenience of the transport vehicle arriving at the delivery node to determine the arrival priority, compare the receiving task information and the characteristic information of the goods to be received to determine the loading priority and judge the rationality of the task; A data storage unit, the data storage unit is used to store the data transmitted after analysis by the data analysis unit and the transportation path, the selected transportation path and the optimal path generated based on the algorithm model; A data sending unit, which is used by the dispatch center to send data to other modules in the system and to send task instructions to transport vehicles; A real-time monitoring unit is used to obtain real-time road information and receive real-time location information of transport vehicles.

Images