Intelligent container truck scheduling data processing method for ports and stations

Abstract

The invention discloses an intelligent container truck scheduling data processing method for a port and a station, and belongs to the technical field of intelligent scheduling, and the method comprises the steps: 1, inputting data, including equipment group information, equipment information, a to-be-dispatched vehicle instruction set, operation line configuration, real-time traffic conditions, and map information; step 2, an algorithm process: converting the operation instruction information, the equipment state information, the equipment operation capability, the operation line service relationship and the equipment group information into operation line target efficiency and operation line implementation efficiency, and then according to the operation line vehicle dispatching frequency requirement, calculating the operation line vehicle dispatching frequency; based on the operation line vehicle dispatching level, the matching relation, the premise of avoiding operation hotspots, reducing the operation cost and adjusting the execution sequence, an allocated operation task is analyzed, and the matching relation is output; and step 3, executing the process to realize multi-to-be-decided vehicle processing. Through the data processing method, the overall operation efficiency of ports and stations can be improved, the operation cost can be reduced, the service quality can be improved, and more intelligent and efficient container truck scheduling can be realized.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent dispatching, and in particular to a method for processing data of intelligent container truck dispatching at ports and terminals. Background Art

[0002] At present, the intelligent scheduling of port terminals is to use the Internet of Things technology, artificial intelligence and other achievements, combine the real-time status of production site machinery and equipment, berths, vehicle transportation and other related data, and interact with the ship's driving status in real time. Through prediction, the best decision-making plan is formulated to achieve seamless connection between information system instructions and terminal machinery and equipment control functions, improve operational efficiency and accuracy, ensure the continuity, coordination, balance and economic operation of the production process, and maximize production benefits. This is the future development trend.

[0003] In view of the above-mentioned related technologies, the applicant found that in the process of container truck scheduling at ports and terminals, the commonly used scheduling methods are mostly based on the principle of shortest distance. However, this scheduling method that only considers the distance factor has exposed obvious limitations in practical applications and is difficult to meet the needs of efficient operation of modern ports and terminals. It not only limits the overall operating efficiency of ports and terminals, but also increases operating costs and reduces service quality. Therefore, a new scheduling data processing method is urgently needed to achieve more intelligent and efficient container truck scheduling. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides an intelligent container truck scheduling data processing method for ports and terminals, which can improve the overall operating efficiency of ports and terminals, reduce operating costs, improve service quality, and achieve more intelligent and efficient container truck scheduling.

[0005] The technical solution of the present invention to solve the above technical problems is as follows: A method for processing intelligent container truck dispatching data of ports and terminals comprises the following steps: Step 1: Input data, including equipment group information, equipment information, vehicle instruction set to be dispatched, operation line configuration, real-time traffic conditions, and map information; Step 2: Algorithm process, converting the operation instruction information, equipment status information, equipment operation capability, operation line service relationship and equipment group information into the operation line target efficiency and operation line implementation efficiency, and then analyzing the distribution of operation tasks and outputting the matching relationship based on the operation line dispatching frequency demand, the operation line dispatching level, matching relationship, avoiding operation hot spots, reducing operation costs and adjusting the execution order. Step 3: Execution process, through the calculation of real-time target efficiency of the operation line, the number of moves to be completed per unit time, the arrangement of instructions that need to be dispatched per unit time, the calculation of task groups to be completed per unit time, the calculation of the execution constraint time of a single task group, the calculation of the dispatch frequency of the operation line scheduling decision cycle, the determination of the dispatch level of the operation line, the extraction of the move for dispatching the operation line, the pre-matching of the vehicle to be decided and the move, and the comprehensive matching of the vehicle to be decided and the move, the processing of multiple vehicles to be decided is realized.

[0006] Furthermore, in step 1, the equipment group information is the grouping of container trucks for operation, the scheduling relationship between the operation line and the equipment group, and the configuration parameters of the minimum and maximum equipment supply quantity of the operation line given by TOS, to ensure the rationality of equipment scheduling and the binding relationship between the equipment group and the operation line; Equipment information includes the real-time status of each container truck, equipment attributes, and the command type configuration that the equipment can operate; The instruction set to be dispatched includes the size of the box, ISO code, cargo type, starting location, destination location, pairing relationship, and instruction priority parameters. All instructions currently allowed for operation are read according to the configuration in order to expand the range of instructions available and find the best matching relationship. The operation line configuration includes the expected efficiency of the operation line, the real-time efficiency of the operation line, and the start time of the operation line; Real-time traffic conditions are traffic data that provide real-time feedback on the degree of congestion of trucks at the destination of the operation and the busyness of the equipment; The map information is to read the allowed driving paths and work area information data from the map, build a time matrix based on the instructed work location and the real-time location of the container truck, and dispatch equipment by optimizing the traffic path, effectively reducing the empty driving distance and energy consumption.

[0007] Furthermore, in step 3, the real-time target efficiency calculation of the operation line is based on the current time as the reference point. According to the operation start time of the operation line, it is calculated in which efficiency cycle the current time point falls, the instructions that have been completed in the cycle are calculated, and according to the expected efficiency of the operation line and the remaining time in the current cycle, the target efficiency that the operation line should maintain in order to meet the expected efficiency is calculated; The target efficiency cannot exceed the limit efficiency, nor can it be lower than the expected efficiency, that is: .

[0008] Furthermore, in step 3, the unit time in the calculation of the number of moves to be completed per unit time is an efficiency cycle. According to the result of the real-time target efficiency calculation of the operation line, the range of the next unit time is calculated. If the efficiency cycle is not crossed, the number of moves to be completed is the target efficiency multiplied by the unit time. If the efficiency cycle is crossed, the number of moves to be completed next is the number of moves to be completed in the remaining time of the current cycle plus the number of moves completed in the next cycle occupied by the next unit time, that is: .

[0009] Furthermore, in step three, including the arrangement of instructions that need to be dispatched within a unit time, the number of instructions to be completed within the next unit has been calculated in the calculation of the number of moves to be completed within a unit time. The instructions currently in operation include instructions that have been dispatched but not yet completed. Therefore, the number of instructions that need to be dispatched within a unit time is the number of instructions that need to be completed within a unit time minus the number of instructions that have been dispatched but not yet completed. This data should be combined with the opportunity double-box and twin-crane instructions according to the dispatching rules of the operation line, that is: .

[0010] Furthermore, in step three, including the calculation of the task groups to be completed within a unit time, the number of Moves that have not yet been dispatched and have been dispatched but not completed has been calculated according to the instructions that need to be dispatched within a unit time. The instructions contained in each Move have also been calculated. According to the operation type of each instruction, it is divided into two parts: receiving boxes and delivering boxes. Each part is a WT task group. The WT contained in each Move is calculated according to the operation rules, and the total number of task groups that need to be completed within a unit time is summarized, that is: .

[0011] Furthermore, in step three, the single task group execution constraint time calculation is included. The task group to be completed next has been calculated in the calculation of the task group to be completed within the unit time. The execution constraint time of the task group is the average operation time required for each task group to complete the total number of tasks within the next operation time. Since the operation line is operated by multiple vehicles at the same time, the calculation constraint time must also consider the container trucks that have been assigned to the operation line and the number of moves that need to be dispatched next, so as to calculate the vehicle demand. During the calculation process, it will appear that the remaining instructions are less than one unit time according to the expected efficiency. In order to make the result accurate, the unit time needs to be corrected. The remaining number of moves that need to be dispatched is less than the number of container trucks that have been assigned to the current operation line. At this time, the calculation result deviates from the actual situation. The constraint time is executed according to the general task group constraint time of the yard operation, that is: .

[0012] Furthermore, in step three, including the calculation of the dispatch frequency of the operation line scheduling decision cycle, the number of Moves that need to be dispatched next is known in the calculation of the single task group execution constraint time. The number of Moves is the demand for the number of vehicles that need to be dispatched. The end time of the unit time is calculated according to the current time, unit time and efficiency cycle, which is the time when all the instructions to be dispatched are completed. From this, the estimated completion time Tlast of the last instruction can be known. The execution constraint time WCT of each task group is known. WCT is calculated by Move, so the dispatch time of the last vehicle can be calculated. The dispatch cycle time can be obtained by subtracting the dispatch time of the last instruction from the current time. The dispatch frequency is obtained by dividing the cycle time by the demand for the number of vehicles, that is: .

[0013] Furthermore, in step three, it includes determining the dispatching level of the operation line. When a container truck is idle and needs to find instructions for the container truck, all the operation lines that can be served are found according to the container truck. When the number of operation lines is greater than 1, the dispatching level of each operation line needs to be calculated. Subsequent dispatching is further determined based on this level. The instructions that can be used for operation are further determined according to the dispatching frequency calculation results of the operation line scheduling decision cycle. All operation lines with train demand in the scheduling decision cycle are taken out, and the time value of the minimum TNF in the operation line is used as the level limit. Every other TNF time is a level of the basic file, which is 1-N levels in sequence. Each operation line determines its level in the basic file according to the interval where the TNF time value is located. After the basic file level is calculated, check whether the current number of vehicles in use is less than the set minimum number of vehicles. For the operation line with the number of vehicles in use less than the minimum number of vehicles, the priority level is determined according to the difference between the "minimum number of vehicles - the number of vehicles in use", and the difference is 1-N levels from large to small. If both exist at the same time, the priority level shall prevail, that is: .

[0014] Furthermore, in step three, including the extraction of Move for dispatching vehicles to the operation line, for each operation line, the number of Moves that need to be dispatched next has been calculated. After determining the dispatch level of the operation line, according to the operation rules and system configuration, screen out which instructions among all the Moves to be dispatched can be dispatched next, and assign the following attributes to all instructions that can be operated, the attributes include one or more of the following: instruction priority attribute, instruction basic sequence attribute, instruction operation location attribute, operation box basic information attribute, entrusted cargo attribute, and the busyness of the operation area where the instruction starting point is located. After assigning the attributes, the instruction group that can be transported by the same vehicle is reintegrated according to the operation relationship, that is, Move.

[0015] In summary, compared with the prior art, the above technical solution has the following beneficial effects: (1) This data processing method can improve the overall operational efficiency of ports and terminals, reduce operating costs, improve service quality, and achieve more intelligent and efficient container truck scheduling; (2) A frequency-based dispatch strategy is adopted to optimize vehicle scheduling in the early stages of the operation and avoid the phenomenon of concentrated dispatching of vehicles due to a large number of idle trucks in the early stages of the operation. This can effectively reduce traffic congestion leading to the same operation point and the problem of excessive occupation of the operation location. By allocating dispatch instructions in an orderly manner, the dispatch process can be ensured to be more reasonable and efficient. (3) When dispatching vehicles, the system fully considers the actual operating efficiency of the operation line and automatically adjusts the allocation of idle container truck resources to balance the progress of each operation point and prevent the short board effect caused by inconsistent operation progress. This automatic resource allocation mechanism not only improves the operation efficiency, but also significantly reduces the workload of operators. There is no need to monitor the operation progress in real time and manually coordinate resources, which demonstrates the intelligence level of the system. (4) Taking into account the busyness of the command operation point, it can intelligently avoid the long waiting time caused by excessive concentration of container trucks at a certain operation point. At the same time, it also solves the problem of long-term idleness of port machinery caused by lack of container trucks at other operation points. Overall, it avoids idle waiting of equipment and saves operation costs; (5) By optimizing the sorting process of vehicle dispatch instructions, the algorithm provides a more reasonable selection range for matching instructions based on the configuration rules, effectively reducing the invalid container turning operations at the yard and the invalid waiting time of container trucks loading on the shore, thereby achieving a significant improvement in the overall operational efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A schematic diagram of a scheduling data processing method in the present invention; Figure 2 It is a schematic diagram of the algorithm flow in the present invention; Figure 3 It is a schematic diagram of pre-matching the vehicle to be decided and Move in the present invention. DETAILED DESCRIPTION

[0017] The principles and features of the present invention are described below in conjunction with all the accompanying drawings. The examples given are only used to explain the present invention and are not used to limit the scope of the present invention.

[0018] The embodiment of the invention discloses a method for processing intelligent container truck scheduling data of a port and a terminal.

[0019] At present, in the process of container truck dispatching at ports and terminals, the commonly used dispatching method is mostly based on the principle of shortest distance. However, this dispatching method that only considers distance factors has obvious limitations in practical application and is difficult to meet the needs of efficient operation of modern ports and terminals.

[0020] Specifically, the existing technology mainly has the following problems: (1) Single scheduling method: The existing scheduling algorithm mainly optimizes based on distance, ignoring other important factors, such as the actual operation progress of the terminal and the resource allocation between different operation lines.

[0021] (2) Difficulty in coordinating resources: Due to the lack of consideration of the actual operation progress of the terminal, the existing scheduling method cannot effectively coordinate the container truck resources between different operation lines, resulting in unbalanced resource utilization.

[0022] (3) Poor dispatching effect: Dispatching based solely on the principle of the shortest distance often leads to large-scale congestion of container trucks in some areas and a shortage of container trucks in other areas, which in turn causes equipment to be idle, greatly reducing the overall dispatching effect.

[0023] The existence of these problems not only limits the overall operational efficiency of ports and terminals, but also increases operating costs and reduces service quality. Therefore, a new scheduling algorithm is urgently needed to overcome the above shortcomings and achieve more intelligent and efficient container truck scheduling.

[0024] The idea of ​​this scheduling algorithm is to bind data between the pending operation instructions issued by the TOS (Terminal Operating System, hereinafter referred to as TOS system) system and the dispatched operating vehicles, and guide the container trucks to carry out container handling operations. In the calculation process, efficiency is the main driving point, and the operation status of the current operation line within a certain time range (referred to as the efficiency cycle, unit: Move / h, Move is the handling of a container) is analyzed. According to the expected efficiency of the operation line, the constraint time of the next pending operation instruction is calculated. Combined with various factors, in the binding process, a series of preset rules are strictly followed, including real-time acquisition of the current position of the container truck, evaluation of the matching degree between the container truck and the operation position, and the busyness of the instruction operation destination. A variety of intelligent algorithms are integrated, which work together to calculate the optimal binding relationship between the container truck and the instruction in an automated way.

[0025] Reference Figure 1 A method for processing intelligent container truck dispatching data of ports and terminals comprises the following steps: Step 1: Input data, including equipment group information, equipment information, vehicle instruction set to be dispatched, operation line configuration, real-time traffic conditions, map information, and some other data.

[0026] In step one, the equipment group information: TOS provides the container truck grouping for the operation, the scheduling relationship between the operation line and the equipment group, and the configuration parameters of the minimum and maximum equipment supply quantity of the operation line to ensure the rationality of the equipment scheduling and the binding relationship between the equipment group and the operation line.

[0027] Equipment information: includes the real-time status of each container truck (such as location, availability), equipment attributes, and the configuration of the types of instructions that the equipment can operate.

[0028] Instruction set for dispatched vehicles: including key parameters such as size and type of box, ISO code, cargo type, starting location, destination location, pairing relationship, instruction priority, etc. All instructions currently allowed for operation are read according to the configuration, in order to expand the range of available instructions and find the best matching relationship.

[0029] Operation line configuration: expected efficiency of the operation line, calculation of real-time efficiency of the operation line, start time of the operation line, etc.

[0030] Real-time traffic conditions: real-time feedback of traffic data such as the degree of congestion of container trucks at the destination of the operation, the busyness of the equipment, etc.

[0031] Map information: Read the map's allowed driving paths, work area information and other data, build a time matrix based on the commanded work location and the real-time location of the container truck, and schedule equipment by optimizing traffic paths, effectively reducing empty-load driving distance and energy consumption.

[0032] Other data: Customized special restrictions added based on application scenarios.

[0033] Reference Figure 2 ,Step 2: The algorithm process is to convert the operation instruction information, equipment status information, equipment operation capacity, operation line service relationship and equipment group information into the operation line target efficiency and operation line implementation efficiency according to the data information obtained in step 1, and then analyze the distribution of operation tasks based on the operation line dispatch frequency requirements, based on the operation line dispatch level, matching relationship, avoiding operation hotspots, reducing operation costs and adjusting the execution order, and output matching relationships, including automated equipment and manual equipment. Automated equipment is to send automated tasks for execution, and manual equipment is to update the information of container truck terminals.

[0034] Step 3: Execution process, through the calculation of real-time target efficiency of the operation line, the number of moves to be completed per unit time, the arrangement of instructions that need to be dispatched per unit time, the calculation of task groups to be completed per unit time, the calculation of the execution constraint time of a single task group, the calculation of the dispatch frequency of the operation line scheduling decision cycle, the determination of the dispatch level of the operation line, the extraction of the move for dispatching the operation line, the pre-matching of the vehicle to be decided and the move, and the comprehensive matching of the vehicle to be decided and the move, the processing of multiple vehicles to be decided is realized.

[0035] (1) Calculation of real-time target efficiency of the operation line Taking the current time as the reference point, according to the start time of the operation line, calculate which efficiency cycle the current time point falls into, calculate the instructions that have been completed in the cycle, and calculate the target efficiency that the operation line should maintain to meet the expected efficiency based on the expected efficiency of the operation line and the remaining time in the current cycle. The target efficiency cannot exceed the limit efficiency, nor can it be lower than the expected efficiency, that is:

[0036] (2) Calculation of the number of moves to be completed per unit time The unit time is an efficiency cycle. The range of the next unit time is calculated based on the result of the real-time target efficiency calculation of the operation line. If the efficiency cycle is not crossed, the number of moves to be completed is the target efficiency multiplied by the unit time. If the efficiency cycle is crossed, the number of moves to be completed next is the number of moves to be completed in the remaining time of the current cycle plus the number of moves completed in the next cycle occupied by the next unit time. The purpose is to allow the container truck resources to be tilted towards the operation line with lower efficiency than the expected efficiency within the unit time, so as to speed up the operation progress to the set expected efficiency, that is:

[0037] (3) Arrangement of instructions for dispatching vehicles within a unit of time The number of Moves to be completed per unit time has already been calculated. The instructions currently in operation include those that have been dispatched but not yet completed. Therefore, the number of instructions that need to be dispatched per unit time is the number of instructions that need to be completed per unit time minus the number of instructions that have been dispatched but not yet completed. This data should be combined with the opportunity double-box (instructions that can be operated on the same vehicle) and twin-crane instructions (instructions that must be operated on the same vehicle) according to the dispatch rules of the operation line, that is:

[0038] (4) Calculation of the task group to be completed per unit time According to the instructions that need to be dispatched per unit time, the number of Moves that have not been dispatched and the number of Moves that have been dispatched but not completed have been calculated. The instructions contained in each Move have also been calculated. According to the operation type of each instruction, it is divided into two parts: receiving boxes and delivering boxes. Each part is a WT task group. According to the operation rules, the WT contained in each Move is calculated, and the total number of task groups that need to be completed per unit time is summarized, that is:

[0039] (5) Calculation of single task group execution constraint time The calculation of the task groups to be completed in the unit time has already calculated all the task groups to be completed next. The execution constraint time of the task group is the average operation time required for each task group to complete the total number of tasks within the next operation time. Since the operation line is operated by multiple vehicles at the same time, the calculation of the constraint time must also consider the container trucks that have been assigned to the operation line and the number of moves that need to be dispatched next, so as to calculate the vehicle demand. During the calculation process, it may appear that the remaining instructions according to the expected efficiency are less than one unit time. In order to make the result accurate, the unit time needs to be corrected. In the case that the remaining number of moves that need to be dispatched is less than the container trucks that have been assigned to the current operation line, the calculation result deviates from the actual situation. The constraint time is executed according to the general task group constraint time of the yard operation, that is:

[0040] (6) Calculation of vehicle dispatch frequency in the operation line scheduling decision cycle In the calculation of the execution constraint time of a single task group, the number of Moves that need to be dispatched next is known. The number of Moves is the demand for the number of vehicles that need to be dispatched. The end time of the unit time is calculated based on the current time, unit time and efficiency cycle, which is the time when all the instructions to be dispatched are completed. From this, the estimated completion time Tlast of the last instruction can be known. The execution constraint time WCT of each task group is known from the fifth step. WCT is calculated by Move. From this, the dispatch time of the last vehicle can be calculated. The dispatch cycle time can be obtained by subtracting the dispatch time of the last instruction from the current time. The dispatch frequency is the cycle time divided by the demand for the number of vehicles, that is:

[0041] (7) Determine the level of vehicle dispatch for the operation line When a container truck is idle and needs to be given instructions, all the operating lines that can be served are found based on the container truck. When the number of operating lines is greater than 1, the dispatch level of each operating line needs to be calculated. Subsequent dispatches will further decide on the instructions that can be used based on this level. According to the results of the calculation of the dispatch frequency of the operating line scheduling decision cycle, all operating lines that have train demands within the scheduling decision cycle are taken out, and the time value of the minimum TNF in the operating line is used as the level limit. Every other TNF time is a level of the basic file, which is 1-N levels in sequence. Each operating line determines its level in the basic file according to the interval where the TNF time value is located. After the basic file level is calculated, check whether the current number of vehicles in use is less than the set minimum number of vehicles. For operating lines with a number of vehicles in use that is less than the minimum number of vehicles, the priority level is determined according to the difference between the "minimum number of vehicles - the number of vehicles in use", and the difference is 1-N levels from large to small. If both exist at the same time, the priority level shall prevail. That is:

[0042] (8) Extraction of Moves for the operation line For each operation line, the previous steps (1) to (7) have calculated the number of Moves that need to be dispatched next. After determining the dispatch level of the operation line, according to the operation rules and system configuration, screen out which instructions can be dispatched next among all the Moves to be dispatched, and assign the following attributes to all the instructions that can be operated. The attributes can be dynamically added or deleted according to the actual operation situation. After assigning the attributes, the instruction group that can be transported by the same vehicle is reintegrated according to the operation relationship, that is, Move, and the attributes are assigned: instruction priority attribute, instruction basic order attribute, instruction operation location attribute, operation box basic information attribute, entrusted cargo attribute, and the busyness of the operation area where the instruction starting point is located.

[0043] (9) Pre-matching of decision-making vehicles and Move Reference Figure 3 , the pre-matching step follows the following logic: ① Extract the operation line with the highest demand level.

[0044] ②Try to match the container truck with the Move within the operation line in turn.

[0045] ③If the match is successful, keep it; otherwise discard it and continue to try the next Move.

[0046] ④ If no match is found, lower the requirement level and repeat the above process until a match is found or all operation lines are traversed.

[0047] First, in the starting step, select the operation line, then process the instructions, traverse the Move, and then judge the suitable load match. If the result of the suitable load match is "yes", keep the result. If the result of the suitable load match is "no", discard it and try the next Move. Then judge the result of the traversal. If the result of the traversal is "no", continue to loop the judgment of the suitable load match. If the result of the traversal is "yes", proceed to the next step.

[0048] The next step is to determine whether there is a match. If the result of the match is "yes", output the result and end the above steps. If the result of the match is "no", continue to determine whether there are unprocessed job lines. If the result of the match is "yes", return to selecting the job line. If the result of the match is "no", output the result and end the above steps.

[0049] (10) Comprehensive matching between the decision-making vehicle and the Move After the decision car and the Move pre-matching step output the result, if there is only one instruction, the decision ends and the final matching result is output. If there are multiple instructions, the optimal instruction needs to be decided. At this time, the calculation is performed according to the following steps: ① Calculate the busy values ​​of all starting operation points that can match the instructions.

[0050] ② Evaluate the distance between each instruction and the current location of the container truck, and calculate the estimated time to arrive.

[0051] ③According to the operating efficiency and busyness value of the yard equipment, evaluate the waiting time and driving time, and calculate the earliest time to start the operation.

[0052] ④ Sort the tasks according to their estimated time from largest to smallest, and take the one with the earliest estimated time.

[0053] ⑤ If the time is still the same and the operation lines are different, the operation line with the smaller TNF will be given priority.

[0054] (11) Processing of multiple decision-making vehicles If there are multiple container trucks that need to be decided, and after the above (8) extraction of the Move for dispatching vehicles to the operation line and (9) pre-matching of the vehicles to be decided and the Move, each vehicle also has multiple instructions to be dispatched, then comprehensive processing is required.

[0055] If there is no overlap between the instructions to be dispatched for each vehicle, the final matching result can be output for each vehicle one by one according to (10) comprehensive matching of the vehicle to be decided and Move. If there is overlap in the instructions and there is an intersection, the set cards and instructions are divided into two sets, and the model of the KM algorithm is constructed. According to (9) pre-matching of the vehicle to be decided and Move and (10) comprehensive matching of the vehicle to be decided and Move, the matching relationship and corresponding weights between the two sets are calculated, and the augmenting path is continuously searched and the top mark is modified until all vehicles find suitable instructions, the maximum weight matching is found, and the overall optimal solution in the multi-decision vehicle scenario is output.

[0056] The implementation effect of an intelligent container truck scheduling data processing method for ports and terminals in an embodiment of the present invention is as follows: the data processing method takes into account the actual operating efficiency of ports and terminals, the layout of container trucks in the yard, the frequency of vehicle dispatch requirements of the operating line, the operating range when instructions are selected, etc.

[0057] This data processing method adopts a frequency-based vehicle dispatch strategy, which aims to optimize vehicle dispatch in the early stage of the operation and avoid the phenomenon of concentrated vehicle dispatch caused by a large number of idle trucks in the early stage of the operation. This can effectively reduce traffic congestion to the same operation point and the problem of excessive occupation of the operation location. By distributing vehicle dispatch instructions in an orderly manner, the vehicle dispatch process is ensured to be more reasonable and efficient.

[0058] This data processing method fully considers the actual operating efficiency of the operation line when dispatching vehicles, and automatically adjusts the allocation of idle container truck resources to balance the progress of each operation point and prevent the short board effect caused by inconsistent operation progress. This automatic resource allocation mechanism not only improves operation efficiency, but also significantly reduces the workload of operators. There is no need to monitor the operation progress in real time and manually coordinate resources, which demonstrates the intelligence level of the system.

[0059] This data processing method takes into account the busyness of the command operation point. The algorithm can intelligently avoid the long waiting time caused by excessive concentration of container trucks at a certain operation point, and also solve the problem of long idle port machinery caused by lack of container trucks at other operation points. Overall, it avoids the idle waiting of equipment and saves operation costs.

[0060] This data processing method optimizes the sorting process of vehicle dispatch instructions. The algorithm provides a more reasonable selection range for matching instructions based on configuration rules, effectively reducing invalid container turning operations in the yard and invalid waiting time for container trucks loading on the shore, thereby achieving a significant improvement in overall operational efficiency.

[0061] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for processing intelligent container truck dispatching data at ports and terminals, characterized in that: The steps include: Step 1: Input data, including equipment group information, equipment information, vehicle instruction set to be dispatched, operation line configuration, real-time traffic conditions, and map information; Step 2: Algorithm process, converting the operation instruction information, equipment status information, equipment operation capability, operation line service relationship and equipment group information into the operation line target efficiency and operation line implementation efficiency, and then analyzing the distribution of operation tasks and outputting the matching relationship based on the operation line dispatching frequency demand, the operation line dispatching level, matching relationship, avoiding operation hot spots, reducing operation costs and adjusting the execution order. Step 3: Execution process, through the calculation of real-time target efficiency of the operation line, the number of moves to be completed per unit time, the arrangement of instructions that need to be dispatched per unit time, the calculation of task groups to be completed per unit time, the calculation of the execution constraint time of a single task group, the calculation of the dispatch frequency of the operation line scheduling decision cycle, the determination of the dispatch level of the operation line, the extraction of the move for dispatching the operation line, the pre-matching of the vehicle to be decided and the move, and the comprehensive matching of the vehicle to be decided and the move, the processing of multiple vehicles to be decided is realized.

2. The method for processing intelligent container truck dispatching data for ports and terminals according to claim 1 is characterized by: In step 1, the equipment group information is the grouping of trucks for operation, the scheduling relationship between the operation line and the equipment group, and the configuration parameters of the minimum and maximum equipment supply quantity of the operation line given by TOS, to ensure the rationality of equipment scheduling and the binding relationship between the equipment group and the operation line; Equipment information includes the real-time status of each container truck, equipment attributes, and the command type configuration that the equipment can operate; The instruction set to be dispatched includes the size of the box, ISO code, cargo type, starting location, destination location, pairing relationship, and instruction priority parameters. All instructions currently allowed for operation are read according to the configuration in order to expand the range of instructions available and find the best matching relationship. The operation line configuration includes the expected efficiency of the operation line, the real-time efficiency of the operation line, and the start time of the operation line; Real-time traffic conditions are traffic data that provide real-time feedback on the degree of congestion of trucks at the destination of the operation and the busyness of the equipment; The map information is to read the allowed driving paths and work area information data from the map, build a time matrix based on the instructed work location and the real-time location of the container truck, and dispatch equipment by optimizing the traffic path, effectively reducing the empty driving distance and energy consumption.

3. The method for processing intelligent container truck dispatching data for ports and terminals according to claim 1 is characterized in that: In step 3, the real-time target efficiency calculation of the operation line is based on the current time as the reference point. According to the operation start time of the operation line, it is calculated in which efficiency cycle the current time point falls, the instructions that have been completed in the cycle are calculated, and according to the expected efficiency of the operation line and the remaining time in the current cycle, the target efficiency that the operation line should maintain in order to meet the expected efficiency is calculated; The target efficiency cannot exceed the limit efficiency, nor can it be lower than the expected efficiency, that is: 。 4. The method for processing intelligent container truck dispatching data for ports and terminals according to claim 3 is characterized by: In step 3, the unit time in the calculation of the number of moves to be completed per unit time is an efficiency cycle. According to the result of the real-time target efficiency calculation of the operation line, the range of the next unit time is calculated. If the efficiency cycle is not crossed, the number of moves to be completed is the target efficiency multiplied by the unit time. If the efficiency cycle is crossed, the number of moves to be completed next is the number of moves to be completed in the remaining time of the current cycle plus the number of moves completed in the next cycle occupied by the next unit time, that is: 。 5. The method for processing intelligent container truck dispatching data for ports and terminals according to claim 4 is characterized by: In step three, the instructions that need to be dispatched per unit time are sorted out. The number of instructions to be completed in the next unit has been calculated in the calculation of the number of moves to be completed per unit time. The instructions currently in operation include instructions that have been dispatched but not yet completed. Therefore, the number of instructions that need to be dispatched per unit time is the number of instructions that need to be completed per unit time minus the number of instructions that have been dispatched but not yet completed. This data should be combined with the opportunity double-box and twin-crane instructions according to the dispatch rules of the operation line, that is: 。 6. The method for processing intelligent container truck dispatching data for ports and terminals according to claim 5 is characterized by: In step three, the task groups to be completed within a unit time are calculated. According to the instructions that need to be dispatched within a unit time, the number of Moves that have not been dispatched and the number of Moves that have been dispatched but not completed have been calculated. The instructions contained in each Move have also been calculated. According to the operation type of each instruction, it is divided into two parts: receiving boxes and delivering boxes. Each part is a WT task group. The WT contained in each Move is calculated according to the operation rules, and the total number of task groups that need to be completed within a unit time is summarized, that is: 。 7. The method for processing intelligent container truck dispatching data for ports and terminals according to claim 6 is characterized by: In step three, the single task group execution constraint time calculation is included. The task group to be completed in the unit time calculation has been calculated. The execution constraint time of the task group is the total number of tasks to be completed in the next operation time. The average operation time required for each task group. Since the operation line is operated by multiple vehicles at the same time, the calculation constraint time must also consider the container trucks assigned to the operation line and the number of moves that need to be dispatched next, so as to calculate the vehicle demand. During the calculation process, it will appear that the remaining instructions are less than one unit time according to the expected efficiency. In order to make the result accurate, the unit time needs to be corrected. The remaining number of moves that need to be dispatched is less than the number of container trucks assigned to the current operation line. At this time, the calculation result deviates from the actual situation. The constraint time is based on the general task group execution constraint time of the yard operation, that is: 。 8. The method for processing intelligent container truck dispatching data for ports and terminals according to claim 7 is characterized by: In step three, the dispatch frequency calculation of the operation line scheduling decision cycle is included. In the single task group execution constraint time calculation, the number of Moves that need to be dispatched next is known. The number of Moves is the train demand that needs to be dispatched. According to the current time, unit time and efficiency cycle, the end time of the unit time is the time when all the instructions to be dispatched are completed. From this, the estimated completion time Tlast of the last instruction can be known. The execution constraint time WCT of each task group is known. WCT is calculated by Move. From this, the dispatch time of the last vehicle can be calculated. The dispatch cycle time can be obtained by subtracting the dispatch time of the last instruction from the current time. The dispatch frequency is obtained by dividing the cycle time by the train demand, that is: 。 9. The method for processing intelligent container truck dispatching data for ports and terminals according to claim 8 is characterized by: In step three, it includes determining the dispatch level of the operation line. When a container truck is idle and needs to find instructions for the container truck, all the operation lines that can be served are found according to the container truck. When the number of operation lines is greater than 1, the dispatch level of each operation line needs to be calculated. The subsequent dispatch is further determined according to this level. The instructions that can be used for operation are determined according to this level. According to the result of the calculation of the dispatch frequency of the operation line scheduling decision cycle, all the operation lines with train demand in the scheduling decision cycle are taken out, and the time value of the minimum TNF in the operation line is used as the level limit. Every other TNF time is a level of the basic file, which is 1-N levels in sequence. Each operation line determines its level in the basic file according to the interval where the TNF time value is located. After the basic file level is calculated, check whether the current number of vehicles in use is less than the set minimum number of vehicles. For the operation line whose number of vehicles in use is less than the minimum number of vehicles, the priority level is determined according to the difference between "minimum number of vehicles - number of vehicles in use", and the difference is 1-N levels from large to small. If both exist at the same time, the priority level shall prevail, that is: 。 10. The method for processing intelligent container truck dispatching data for ports and terminals according to claim 9 is characterized in that: In step three, including the extraction of Move for dispatching vehicles to the operation line, for each operation line, the number of Moves that need to be dispatched next has been calculated. After determining the dispatch level of the operation line, according to the operation rules and system configuration, screen out which instructions can be dispatched next among all the Moves to be dispatched, and assign the following attributes to all instructions that can be operated, including one or more of the following attributes: instruction priority attribute, instruction basic sequence attribute, instruction operation location attribute, operation box basic information attribute, entrusted cargo attribute, and the busyness of the operation area where the instruction starting point is located. After assigning the attributes, the instruction group that can be transported by the same vehicle is reintegrated according to the operation relationship, that is, Move.

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