An intelligent scheduling method and system for an air cargo terminal based on dynamic priorities of flights

Abstract

The application discloses a kind of based on flight dynamic priority air cargo station intelligent scheduling method and system, it is related to intelligent warehousing logistics technical field.The method includes: real-time docking airport flight system, obtains flight dynamic data, synchronously collects the cargo attribute data of each batch of goods, and the real-time state data of stereoscopic warehouse, each cargo equipment in warehouse, through urgency comprehensive scoring model, the basic urgency score of each batch of goods is calculated, based on the cargo attribute of each batch of goods and the real-time state data of stereoscopic warehouse, calculate layer height adjustment factor, the basic urgency score of each batch of goods is corrected, generate final urgency score, through equipment matching degree optimization algorithm, the matching degree score of each cargo equipment and each cargo task is calculated, for each cargo task matches optimal cargo equipment, executes goods in and out warehouse operation.The application can realize the intelligentization and refinement of warehousing scheduling, and comprehensively improves the overall operation management efficiency of airport cargo station.

Description

Technical Field

[0001] This invention relates to the field of intelligent warehousing and logistics technology, and in particular to an intelligent scheduling method and system for air cargo terminals based on flight dynamic priority. Background Technology

[0002] With the continuous growth of high-efficiency and high-value-added air freight volume, international airport cargo terminals have increasingly higher requirements for warehousing throughput efficiency and flight punctuality assurance capabilities. Four-way shuttle automated storage and retrieval systems have become the mainstream warehousing facilities in airport cargo areas, and the supporting warehousing scheduling technology is the core link that determines the operational capabilities of cargo terminals.

[0003] Current commonly used intelligent warehouse scheduling technologies are mostly optimized to maximize the efficiency of internal warehouse operations. They have not been customized to adapt to the special attributes and physical constraints of air cargo scenarios, resulting in several core defects: Existing technologies do not deeply integrate flight dynamic data, relying only on manual adjustment of work order or setting simple time priorities. They do not quantitatively model the core factors affecting flight support, and cannot establish a precise mapping between work priorities and flight delay risks, making it difficult to meet the rigid requirements of on-time cargo loading; they cannot adapt to the physical constraints of non-standardized warehouse floor heights, using fixed storage location allocation rules, and cannot dynamically divert operations based on floor congestion, easily causing overload of equipment on core operation floors and insufficient utilization of equipment on other floors, thus reducing the overall throughput efficiency of the warehouse; the equipment allocation logic is simplistic, only considering distance or basic load balancing, failing to achieve multi-dimensional matching of equipment capacity, real-time status, and task requirements, and cannot adapt to the differentiated operational needs of air cargo, resulting in equipment resource mismatch and low utilization.

[0004] In summary, existing scheduling technologies cannot meet the core needs of air cargo scenarios, which restricts the operational efficiency of airport cargo terminals and flight support capabilities, and targeted improvement solutions are urgently needed. Summary of the Invention

[0005] This invention provides an intelligent scheduling method for air cargo terminals based on flight dynamic priority, comprising: Step S1: Connect to the airport flight system in real time to obtain flight dynamic data, and synchronously collect cargo attribute data of each batch of goods, as well as real-time status data of the automated warehouse and various cargo equipment in the warehouse. Step S2: Based on flight dynamic data and cargo attributes of each batch of cargo, calculate the basic urgency score of each batch of cargo using the urgency comprehensive scoring model; Step S3: Based on the cargo attributes of each batch of goods and the real-time status data of the automated warehouse, calculate the floor height adjustment factor, correct the basic urgency score of each batch of goods, and generate the final urgency score. Step S4: Based on the final urgency score of each batch of goods and the real-time status data of each freight equipment, calculate the matching score between each freight equipment and each freight task through the equipment matching optimization algorithm. Step S5: Based on the matching score between each freight equipment and each freight task, match the optimal freight equipment for each freight task and execute the goods inbound and outbound operations.

[0006] The intelligent scheduling method for air cargo terminals based on flight dynamic priority, as described above, includes the following sub-steps: real-time connection to the airport flight system to obtain flight dynamic data, synchronous collection of cargo attribute data for each batch of goods, and real-time status data of the automated warehouse and various cargo equipment within the warehouse. Step S11: Connect in real time with the airport collaborative decision-making system and the airline's flight management system to obtain the flight dynamic data associated with each batch of goods; Step S12: Collect cargo attribute data for each batch of goods, fixed physical constraints of the automated warehouse, and real-time status data of the automated warehouse and various freight equipment within the warehouse.

[0007] The intelligent scheduling method for air cargo terminals based on flight dynamic priority, as described above, includes the following sub-steps for calculating the basic urgency score of each batch of cargo based on flight dynamic data and cargo attributes, using a comprehensive urgency scoring model: Step S21: Based on the flight dynamic data associated with each batch of goods, calculate the time urgency factor and delay impact factor for each batch of goods. Step S22: Based on the cargo attributes of each batch of cargo, calculate the airline weight factor and cargo type coefficient factor for each batch of cargo. Step S23: Based on the time urgency factor, delay impact factor, airline weight factor, and cargo type coefficient factor, calculate the basic urgency score for each batch of cargo using the comprehensive urgency scoring model.

[0008] The intelligent scheduling method for air cargo terminals based on flight dynamic priority, as described above, includes the following sub-steps: calculating a floor height adjustment factor based on the cargo attributes of each batch of goods and the real-time status data of the automated warehouse, correcting the basic urgency score of each batch of goods, and generating a final urgency score. Step S31: Based on the cargo attributes of each batch of goods and the fixed physical constraints of the automated warehouse, determine the compliant storage floor range for each batch of goods and synchronously match the real-time status data of the corresponding storage floor. Step S32: Based on the compliant storage floor range and the real-time status data of the corresponding storage floors for each batch of goods, calculate the floor height adjustment factor for each batch of goods through the floor height bottleneck perception scheduling strategy. Step S33: Based on the layer height adjustment factor of each batch of goods, the basic urgency score of each batch of goods is corrected to generate the final urgency score.

[0009] The intelligent scheduling method for air cargo terminals based on flight dynamic priority, as described above, involves calculating the matching score between each cargo equipment and each cargo task using an equipment matching optimization algorithm, based on the final urgency score of each batch of cargo and the real-time status data of each cargo equipment. This includes the following sub-steps: Step S41: Based on the final urgency score of each batch of goods, sort the freight tasks corresponding to each batch of goods in descending order to generate a queue of freight tasks to be executed. Step S42: Extract the task requirement parameters from each freight task in the queue of freight tasks to be executed, and filter the candidate freight equipment that meets the task requirement parameters based on the real-time status data of each freight equipment. Step S43: Based on the task requirement parameters of each freight task and the real-time status data of each corresponding candidate freight equipment, calculate the matching score between each candidate freight equipment and each freight task through the equipment matching degree optimization algorithm.

[0010] The intelligent scheduling method for air cargo terminals based on flight dynamic priority, as described above, involves matching the optimal cargo equipment for each cargo task based on the matching score between each cargo equipment and each cargo task, and executing cargo inbound and outbound operations, including the following sub-steps: Step S51: Sort all candidate freight equipment corresponding to the freight task in descending order of matching score, and select the freight equipment ranked first as the optimal freight equipment for this freight task. Step S52: Lock the association between the corresponding freight equipment and the freight task, and issue operation instructions to the equipment controller according to the order of the freight task queue to control the freight equipment to perform inbound and outbound operations.

[0011] This invention also provides an intelligent air cargo terminal scheduling system based on flight dynamic priority, comprising: The data acquisition module connects to the airport flight system in real time to obtain flight dynamic data, synchronously collect cargo attribute data of each batch of goods, and real-time status data of the automated warehouse and various cargo equipment in the warehouse. The basic urgency score generation module calculates the basic urgency score for each batch of goods based on flight dynamic data and the cargo attributes of each batch of goods through a comprehensive urgency scoring model. The final urgency score generation module calculates the floor height adjustment factor based on the cargo attributes of each batch of goods and the real-time status data of the automated warehouse, corrects the basic urgency score of each batch of goods, and generates the final urgency score. The matching score generation module calculates the matching score between each freight equipment and each freight task based on the final urgency score of each batch of goods and the real-time status data of each freight equipment through the equipment matching optimization algorithm. The optimal freight equipment matching and operation execution module matches the optimal freight equipment for each freight task based on the matching score between each freight equipment and each freight task, and executes the goods inbound and outbound operations.

[0012] As described above, an intelligent air cargo terminal scheduling system based on flight dynamic priority includes a data acquisition module, which specifically comprises: The flight dynamic data acquisition submodule connects in real time with the airport collaborative decision-making system and the airline flight management system to obtain flight dynamic data associated with each batch of cargo. The cargo and warehouse data acquisition submodule collects cargo attribute data for each batch of cargo, fixed physical constraints of the automated warehouse, and real-time status data of the automated warehouse and various freight equipment within the warehouse.

[0013] The aforementioned intelligent air cargo terminal scheduling system based on flight dynamic priority includes a basic urgency score generation module, specifically comprising: The time urgency factor and delay impact factor calculation submodule calculates the time urgency factor and delay impact factor for each batch of goods based on the flight dynamic data associated with each batch of goods. The airline weight factor and cargo type coefficient factor calculation submodule calculates the airline weight factor and cargo type coefficient factor for each batch of cargo based on the cargo attributes of each batch of cargo. The basic urgency score calculation submodule calculates the basic urgency score for each batch of goods based on the time urgency factor, delay impact factor, airline weight factor, and cargo type coefficient factor, using a comprehensive urgency scoring model.

[0014] The intelligent air cargo terminal scheduling system based on flight dynamic priority, as described above, includes a final urgency score generation module that specifically comprises: The floor-determination submodule determines the compliant floor range for each batch of goods based on the goods attributes and the fixed physical constraints of the automated warehouse, and synchronously matches the real-time status data of the corresponding storage floor. The floor height adjustment factor calculation submodule calculates the floor height adjustment factor for each batch of goods based on the compliant storage floor range and the real-time status data of the corresponding storage floor, through a floor height bottleneck perception scheduling strategy. The final urgency score calculation submodule corrects the basic urgency score of each batch of goods based on the layer height adjustment factor, and generates the final urgency score.

[0015] The intelligent air cargo terminal scheduling system based on flight dynamic priority, as described above, includes a matching score generation module that specifically comprises: The pending freight task queue generation submodule sorts the freight tasks corresponding to each batch of goods in descending order based on the final urgency score of each batch of goods, and generates a pending freight task queue. The candidate freight equipment screening submodule extracts the task requirement parameters from each freight task in the queue of freight tasks to be executed, and filters candidate freight equipment that meets the task requirement parameters based on the real-time status data of each freight equipment. The matching score calculation submodule calculates the matching score between each candidate freight equipment and each freight task based on the task requirement parameters of each freight task and the real-time status data of each corresponding candidate freight equipment through the equipment matching optimization algorithm.

[0016] The intelligent air cargo terminal scheduling system based on flight dynamic priority, as described above, includes an optimal cargo equipment matching and operation execution module, specifically comprising: The optimal freight equipment matching submodule sorts all candidate freight equipment corresponding to the freight task in descending order of matching score, and selects the freight equipment ranked first as the optimal freight equipment for this freight task. The inbound / outbound operation execution submodule locks the association between the corresponding freight equipment and freight tasks, and issues operation instructions to the equipment controller according to the order of the freight task queue to control the freight equipment to perform inbound / outbound operations.

[0017] The beneficial effects achieved by this invention are as follows: This invention can significantly improve the on-time guarantee capability of air cargo flights, effectively avoid flight delays due to high-priority cargo, and fully meet the high-efficiency operation requirements of special cargo such as fresh produce, live animals, and dangerous goods; it can accurately alleviate congestion bottlenecks on the core floors of the warehouse, balance the equipment load of each storage floor, and significantly improve the overall throughput efficiency and storage resource utilization of the automated warehouse; it achieves optimal matching between cargo tasks and equipment, significantly improves the comprehensive utilization rate of equipment such as shuttle cars and hoists, and effectively shortens the operation response time; at the same time, it reduces reliance on manual intervention, reduces human error, realizes intelligent and refined warehouse scheduling, and comprehensively improves the overall operation and management efficiency of airport cargo terminals. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0019] Figure 1 This is a flowchart of an intelligent air cargo terminal scheduling method based on flight dynamic priority provided in Embodiment 1 of this application; Figure 2 This is a schematic diagram of an intelligent air cargo terminal scheduling system based on flight dynamic priority, provided in Embodiment 2 of this application. Detailed Implementation

[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] Example 1 like Figure 1 As shown, Embodiment 1 of this application provides an intelligent scheduling method for air cargo terminals based on flight dynamic priority. The method includes the following steps: Step S1: Connect to the airport flight system in real time to obtain flight dynamic data, and synchronously collect cargo attribute data of each batch of goods, as well as real-time status data of the automated warehouse and various cargo equipment in the warehouse. Furthermore, real-time connection to the airport flight system is required to obtain flight dynamic data, synchronously collect cargo attribute data for each batch of goods, and real-time status data of the automated warehouse and various cargo equipment within the warehouse, including the following sub-steps: Step S11: Connect in real time with the airport collaborative decision-making system and the airline's flight management system to obtain the flight dynamic data associated with each batch of goods; Specifically, the system connects in real time with the airport's collaborative decision-making system and the flight management system of the corresponding airline to which the air waybill belongs. Using the air waybill as the sole basis for association, the system collects real-time flight dynamic data of the flights bound to each batch of goods. The flight dynamic data includes, but is not limited to, the estimated departure time, the scheduled departure time, and the cut-off time of the flight.

[0022] Step S12: Collect cargo attribute data for each batch of goods, fixed physical constraints of the automated warehouse, and real-time status data of the automated warehouse and each freight equipment within the warehouse. Specifically, collect the goods attribute data of each batch of goods to be warehoused and shipped out. The goods attribute data includes, but is not limited to, the height of the goods, the type of goods marked on the air waybill, the airline to which the goods belong, the flight information bound to the waybill, the type of goods in and out of the warehouse operation, and the special equipment capacity requirements for the operation. The stereoscopic warehouse is a fixed four - layer structure. The first layer is the conveyor line entrance and exit layer, and the second to fourth layers are storage layers. The goods are transferred between layers by a hoist. The fixed physical constraints of the stereoscopic warehouse are: the maximum allowable height of the goods that can be stored on the third layer is the standard height of the goods layer H3, the maximum allowable height of the goods that can be stored on the second layer is the intermediate layer height H2, and the maximum allowable height of the goods that can be stored on the fourth layer is the standard low - goods layer height H4, and the three satisfy H4 < H2 < H3. The real - time status data of the stereoscopic warehouse includes the real - time occupancy rate of each storage floor. The real - time status data of each freight equipment includes, but is not limited to, the real - time load rate, the floors where the equipment can operate, the real - time position of the equipment, the available status of the equipment, and the special ability attributes of the equipment for each floor shuttle car, hoist and other freight equipment.

[0023] Step S2: Based on the flight dynamic data and the goods attributes of each batch of goods, calculate the basic urgency score of each batch of goods through the urgency comprehensive scoring model. Further, calculating the basic urgency score of each batch of goods based on the flight dynamic data and the goods attributes of each batch of goods through the urgency comprehensive scoring model includes the following sub - steps: Step S21: Based on the flight dynamic data associated with each batch of goods, calculate the time urgency factor and the delay impact factor of each batch of goods respectively. Specifically, taking the current system time as a reference, combined with the cut - off time of the flight corresponding to each batch of goods, through the segmented time urgency function , calculate the time urgency factor of the corresponding goods, where is the time urgency factor of the goods, and the value range is . The less the remaining cut - off time, the higher the score. is the remaining cut - off time, with the unit of hour. , is the cut - off time of the flight. is the current system time. represents converting the time difference between the flight cut - off time and the current system time into the total number of seconds. When , it means that the goods have exceeded the flight cut - off time, and the time urgency factor takes the maximum value of 100.0. When , If the value is greater than 100.0, the time urgency factor takes the maximum value of 100.0. If it is less than 100.0, the time urgency factor takes . When At that time, the factor of time urgency Values ,when At that time, the factor of time urgency Values .

[0024] Based on the current system time, when each batch of goods corresponds to a flight with an estimated departure time, the delay reference time is the estimated departure time. When each batch of goods corresponds to a flight without an estimated departure time, the delay reference time is the scheduled departure time. Based on the delay reference time, the delay is calculated using the formula... Calculate the number of hours the flight has been delayed, among which, This represents the number of hours the flight has been delayed. The current system time. To delay the reference time, The system displays the time difference between the current system time and the delay reference time, converted into a total number of seconds; based on the flight delay duration corresponding to each batch of goods. Through the delay impact function Calculate the delay impact factor for the corresponding goods, where, The delay factor for goods, with a value range of [value range missing]. , This represents the number of hours the flight has been delayed. When the result of the function calculation is greater than 100.0, the maximum value of 100.0 is taken.

[0025] Step S22: Based on the cargo attributes of each batch of cargo, calculate the airline weight factor and cargo type coefficient factor for each batch of cargo. Specifically, based on the importance of the airlines to which each batch of cargo belongs at the airport, corresponding basic weight coefficients are matched. The basic weight coefficient for the base airline is 3.0, for the major airline it is 2.5, for the partner airline it is 1.5, and for other airlines it is 1.0 by default. Through a linear normalization mapping rule, each basic weight coefficient is mapped to the 0-100 range to obtain the airline weight factor for each batch of cargo. .

[0026] Based on the cargo types indicated on each batch of air waybills, and combined with business support priorities, corresponding base coefficients are matched. Specifically, the base coefficient for live animals requiring survival support is 3.0; for fresh goods requiring preservation, the base coefficient is 2.5; for dangerous goods requiring safety supervision and express shipments with time-definite commitments, the base coefficient is 2.0; for high-value goods with security requirements, the base coefficient is 1.8; and for other general cargo undergoing routine processing, the base coefficient is 1.0 by default. A linear normalization mapping rule is used to map each base coefficient to the 0-100 range, obtaining the cargo type coefficient factor for each batch of goods. .

[0027] Step S23: Based on the time urgency factor, delay impact factor, airline weight factor and cargo type coefficient factor, calculate the basic urgency score of each batch of cargo through the comprehensive urgency scoring model; Specifically, based on the impact of time urgency, delay impact, airline weighting, and cargo type coefficient on flight operations, fixed weight percentages are set: time urgency 50%, delay impact 30%, airline weighting 15%, and cargo type coefficient 5%. Based on the time urgency, delay impact, airline weighting, and cargo type coefficient factors for each batch of cargo, and their corresponding weights, an urgency-based comprehensive scoring model is used. Calculate the basic urgency score for each batch of goods, where, For the first The basic urgency score for a batch of goods. , The total quantity of goods. For the first The time urgency factor of batch goods, For the first The impact factor of delays in shipments. For the first Airlines' weighting factors for batches of goods. For the first The commodity type coefficient factor for a batch of goods.

[0028] Step S3: Based on the cargo attributes of each batch of goods and the real-time status data of the automated warehouse, calculate the floor height adjustment factor, correct the basic urgency score of each batch of goods, and generate the final urgency score. Furthermore, based on the cargo attributes of each batch of goods and the real-time status data of the automated warehouse, a floor height adjustment factor is calculated to correct the basic urgency score of each batch of goods, generating the final urgency score, including the following sub-steps: Step S31: Based on the goods attributes of each batch of goods and the fixed physical constraints of the stereoscopic warehouse, determine the compliant and available storage floor ranges for each batch of goods, and synchronously match the real-time status data of the corresponding storage floors; Specifically, taking the physical height constraints of the stereoscopic warehouse where H4 < H2 < H3 as the benchmark, and combining the height attributes of each batch of goods, determine the compliant and available storage floor ranges for each batch of goods. For medium-high goods with a height greater than H2, they can only be compliantly stored on the third floor, and there are no other optional floors. For short-medium goods with a height less than or equal to H2 and greater than H4, they can be compliantly stored on the second and third floors, with priority given to the second floor. For low goods with a height less than or equal to H4, they can be compliantly stored on the fourth, second, and third floors, with priority given to the fourth floor and the second floor as the second choice; synchronously retrieve the real-time warehouse location utilization rate data of each corresponding storage floor.

[0029] Step S32: Based on the compliant and available storage floor ranges of each batch of goods and the real-time status data of the corresponding storage floors, calculate the height adjustment factor for each batch of goods through the height bottleneck perception scheduling strategy; Specifically, based on the compliant and available storage floor ranges of each batch of goods, and combining the scheduling strategies of congestion diversion and idle priority, determine the target storage floors for each batch of goods. Among them, for medium-high goods, the target storage floor is fixed at 3; for short-medium goods, when the real-time warehouse location utilization rate of the second floor is less than 60%, the target storage floor is fixed at 2, and when the real-time warehouse location utilization rate of the second floor is greater than or equal to 60%, the target storage floor is fixed at 3. For low goods, when the real-time warehouse location utilization rate of the fourth floor is less than 80%, the target storage floor is fixed at 4, and when the real-time warehouse location utilization rate of the fourth floor is greater than or equal to 80% and the second floor is less than 60%, the target storage floor is fixed at 2, and in other cases it is fixed at 3; based on the target storage floors of each batch of goods and the real-time utilization rate data of the corresponding storage floors, through the height bottleneck perception scheduling algorithm calculate the height adjustment factor for each batch of goods and dynamically adjust the goods storage strategy, where is the height adjustment factor, which represents imposing a priority penalty on goods that must enter the congested third floor and giving a priority reward to goods that can enter the idle second floor. is the target storage floor of the goods. is the real-time warehouse location utilization rate of the third floor. is the real-time warehouse location utilization rate of the second floor, 0.8. represents goods that must be stored on the third floor and the real-time utilization rate of the third floor is greater than 80%. Reduce their priority, and the height adjustment factor is taken as 0.8. represents goods stored on the second floor and the real-time warehouse location utilization rate of the second floor is less than 60%. Increase their priority, and the height adjustment factor is taken as 1.2. represents all other scenarios, and the height adjustment factor is defaulted to 1.0.

[0030] Step S33: Based on the layer height adjustment factor of each batch of goods, correct the basic urgency score of each batch of goods and generate the final urgency score. Specifically, the floor height adjustment factor is only used to correct the basic urgency score of inbound operations. For outbound operations, since the goods are stored on fixed floors and there is no space for diversion or adjustment, the floor height adjustment factor is uniformly set to 1.0. Based on the floor height adjustment factor of each batch of goods, the urgency correction formula is used. The basic urgency score for each batch of goods. The following corrections were made: For the first The final urgency score of the batch of goods. For the first The basic urgency score for a batch of goods. For the first The layer height adjustment factor for a batch of goods.

[0031] Step S4: Based on the final urgency score of each batch of goods and the real-time status data of each freight equipment, calculate the matching score between each freight equipment and each freight task through the equipment matching optimization algorithm. Furthermore, based on the final urgency score of each batch of goods and the real-time status data of each freight equipment, the matching score between each freight equipment and each freight task is calculated using an equipment matching optimization algorithm, including the following sub-steps: Step S41: Based on the final urgency score of each batch of goods, sort the freight tasks corresponding to each batch of goods in descending order to generate a queue of freight tasks to be executed. Specifically, freight tasks are generated based on the type of goods inbound / outbound operations, with each batch of goods corresponding to at least one freight task. When the weight or size of a single batch of goods exceeds the rated load limit or operational limit of freight equipment such as shuttles and elevators, the batch of goods is split into multiple freight tasks, with the goods parameters of each task matched to the equipment's operational capacity. The starting point of an inbound task is the designated inbound location on the first-level conveyor line, and the ending point is the compliant storage location on the target storage floor. The starting point of an outbound task is the storage location on the current storage floor, and the ending point is the designated outbound location on the first-level conveyor line. The inbound / outbound freight tasks corresponding to each batch of goods are scored according to the final urgency of each batch of goods. The tasks are sorted in descending order from high to low to generate a queue of freight tasks to be executed. The freight task information includes the task inbound / outbound type, the task start location, the task end location, the special operational capabilities required for the task, and the compliant storage floor range for the goods associated with the task.

[0032] Step S42: Extract the task requirement parameters from each freight task in the queue of freight tasks to be executed, and filter the candidate freight equipment that meets the task requirement parameters based on the real-time status data of each freight equipment. Specifically, from each freight task in the queue of pending freight tasks, the task requirement parameters corresponding to each freight task are extracted sequentially according to the order of the tasks in the queue. The task requirement parameters include, but are not limited to, the starting position of the task, the special operational capability requirements of the task, and the compliant operation floor of the goods associated with the task. Simultaneously, the real-time status data of each freight equipment in the automated warehouse is retrieved, and the freight equipment whose availability status is normal and whose operation floor matches the compliant operation floor of the goods associated with the task is selected as the candidate freight equipment for the corresponding freight task.

[0033] Step S43: Based on the task requirement parameters of each freight task and the real-time status data of each candidate freight equipment, calculate the matching score between each candidate freight equipment and each freight task through the equipment matching degree optimization algorithm. Specifically, based on the task requirements parameters of each freight mission and the real-time status data of each corresponding candidate freight equipment, an equipment matching degree optimization algorithm is used. Calculate the matching score between each candidate freight equipment and each freight task. The maximum matching score is 100 points. For freight missions With freight equipment The matching score, For task indexing, , This represents the total number of freight shipments. For freight equipment indexing, , This represents the number of candidate freight equipment corresponding to each freight mission. To maximize the function, ensure that the matching score is not negative. For freight missions With freight equipment The working distance between them, in meters. For every meter the freight equipment is further from the starting point of the mission, 10 points will be deducted. For freight missions Special operational capabilities and freight equipment required The matching value of the equipment's special capability attributes, when the freight task... Special operational capabilities and freight equipment required When matching the special capability attributes of the device The value is 50, when the freight task Special operational capabilities and freight equipment required When the special capability attributes of the equipment do not match The value is 0. For freight equipment Real-time load rate, This means that 2 points will be deducted for every 10% increase in equipment load.

[0034] Step S5: Based on the matching score between each freight equipment and each freight task, match the optimal freight equipment for each freight task and execute the goods inbound and outbound operations. Furthermore, based on the matching score between each freight equipment and each freight task, the optimal freight equipment is matched for each freight task, and the execution of cargo inbound and outbound operations includes the following sub-steps: Step S51: Sort all candidate freight equipment corresponding to the freight task in descending order of matching score, and select the freight equipment ranked first as the optimal freight equipment for this freight task. Specifically, for a single freight task, all candidate freight equipment are sorted in descending order of their matching scores. The freight equipment with the highest matching score is the optimal freight equipment for this freight task. When multiple freight equipment have the same matching score, the freight equipment with the lowest real-time load rate is selected first.

[0035] Step S52: Lock the association between the corresponding freight equipment and the freight task, and send the operation instruction to the equipment controller according to the order of the freight task queue to control the freight equipment to perform inbound and outbound operations. Specifically, after matching the optimal freight equipment for a single task, the operation permission of the freight equipment corresponding to this task is locked until the task is completed, actively canceled, or abnormally terminated. According to the priority order of the queue of freight tasks to be executed, operation instructions are sent to the warehouse equipment control system in sequence. After the equipment receives the operation instruction, after the equipment completes the operation, or when the task is canceled or abnormally terminated, the real-time status data of the automated warehouse and each freight equipment in the warehouse are dynamically updated.

[0036] Example 2 like Figure 2 As shown, Embodiment 2 of this application provides an intelligent air cargo terminal scheduling system based on flight dynamic priority, including: Data acquisition module 21 connects to the airport flight system in real time to obtain flight dynamic data, synchronously collect cargo attribute data of each batch of goods, and real-time status data of the automated warehouse and various cargo equipment in the warehouse. Furthermore, the data acquisition module 21 includes the following sub-modules: The flight dynamic data acquisition submodule connects in real time with the airport collaborative decision-making system and the airline flight management system to obtain flight dynamic data associated with each batch of cargo. The cargo and warehouse data acquisition submodule collects cargo attribute data for each batch of cargo, fixed physical constraints of the automated warehouse, and real-time status data of the automated warehouse and various freight equipment within the warehouse. The basic urgency score generation module 22 calculates the basic urgency score of each batch of goods based on flight dynamic data and the cargo attributes of each batch of goods through a comprehensive urgency scoring model. Furthermore, the basic urgency score generation module 22 includes the following sub-modules: The time urgency factor and delay impact factor calculation submodule calculates the time urgency factor and delay impact factor for each batch of goods based on the flight dynamic data associated with each batch of goods. The airline weight factor and cargo type coefficient factor calculation submodule calculates the airline weight factor and cargo type coefficient factor for each batch of cargo based on the cargo attributes of each batch of cargo. The basic urgency score calculation submodule calculates the basic urgency score for each batch of goods based on the time urgency factor, delay impact factor, airline weight factor, and cargo type coefficient factor, using a comprehensive urgency scoring model. The final urgency score generation module 23 calculates the floor height adjustment factor based on the cargo attributes of each batch of goods and the real-time status data of the automated warehouse, corrects the basic urgency score of each batch of goods, and generates the final urgency score. Furthermore, the final urgency score generation module 23 includes the following sub-modules: The floor-determination submodule determines the compliant floor range for each batch of goods based on the goods attributes and the fixed physical constraints of the automated warehouse, and synchronously matches the real-time status data of the corresponding storage floor. The floor height adjustment factor calculation submodule calculates the floor height adjustment factor for each batch of goods based on the compliant storage floor range and the real-time status data of the corresponding storage floor, through a floor height bottleneck perception scheduling strategy. The final urgency score calculation submodule corrects the basic urgency score of each batch of goods based on the layer height adjustment factor and generates the final urgency score. The matching score generation module 24 calculates the matching score between each freight equipment and each freight task based on the final urgency score of each batch of goods and the real-time status data of each freight equipment through the equipment matching optimization algorithm. Furthermore, the matching score generation module 24 includes the following sub-modules: The pending freight task queue generation submodule sorts the freight tasks corresponding to each batch of goods in descending order based on the final urgency score of each batch of goods, and generates a pending freight task queue. The candidate freight equipment screening submodule extracts the task requirement parameters from each freight task in the queue of freight tasks to be executed, and filters candidate freight equipment that meets the task requirement parameters based on the real-time status data of each freight equipment. The matching score calculation submodule calculates the matching score between each candidate freight equipment and each freight task based on the task requirement parameters of each freight task and the real-time status data of each corresponding candidate freight equipment through the equipment matching optimization algorithm. The optimal freight equipment matching and operation execution module 25 matches the optimal freight equipment for each freight task based on the matching score between each freight equipment and each freight task, and executes the goods in and out of the warehouse. Furthermore, the optimal freight equipment matching and operation execution module 25 includes the following sub-modules: The optimal freight equipment matching submodule sorts all candidate freight equipment corresponding to the freight task in descending order of matching score, and selects the freight equipment ranked first as the optimal freight equipment for this freight task. The inbound / outbound operation execution submodule locks the association between the corresponding freight equipment and freight tasks, and sends operation instructions to the equipment controller according to the order of the freight tasks to be executed, thereby controlling the freight equipment to perform inbound / outbound operations. Corresponding to the above embodiments, the present invention provides a computer storage medium, including: at least one memory and at least one processor; The memory is used to store one or more program instructions; A processor is used to run one or more program instructions to execute an intelligent air cargo terminal scheduling method based on flight dynamic priority.

[0037] Corresponding to the above embodiments, this embodiment of the invention provides a computer-readable storage medium containing one or more program instructions, which are used by a processor to provide an intelligent scheduling method for air cargo terminals based on flight dynamic priority.

[0038] The embodiments disclosed in this invention provide a computer-readable storage medium storing computer program instructions. When the computer program instructions are executed on a computer, the computer executes the above-described intelligent scheduling method for air cargo terminals based on flight dynamic priority.

[0039] In this embodiment of the invention, the processor can be an integrated circuit chip with signal processing capabilities. The processor can be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0040] The various methods, steps, and logic diagrams disclosed in the embodiments of this invention can be implemented or executed. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this invention can be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software modules can reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. The processor reads information from the storage medium and, in conjunction with its hardware, completes the steps of the above methods.

[0041] The storage medium can be memory, such as volatile memory or non-volatile memory, or may include both volatile and non-volatile memory.

[0042] Among them, non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory.

[0043] Volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of RAM are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous linked dynamic random access memory (Synchlink DRAM, SLDRAM), and direct memory bus RAM (DRRAM).

[0044] The storage media described in the embodiments of the present invention are intended to include, but are not limited to, these and any other suitable types of memory.

[0045] Those skilled in the art will recognize that, in one or more of the examples above, the functions described in this invention can be implemented using a combination of hardware and software. When applied as software, the corresponding functions can be stored in a computer-readable medium or transmitted as one or more instructions or code on a computer-readable medium. Computer-readable media include computer storage media and communication media, wherein communication media include any medium that facilitates the transmission of computer programs from one place to another. Storage media can be any available medium that can be accessed by a general-purpose or special-purpose computer.

[0046] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made on the basis of the technical solution of the present invention should be included within the scope of protection of the present invention.

Claims

1. An intelligent scheduling method for an air cargo terminal based on dynamic priority of flights, characterized in that, include: Step S1: Connect to the airport flight system in real time to obtain flight dynamic data, and synchronously collect cargo attribute data of each batch of goods, as well as real-time status data of the automated warehouse and various cargo equipment in the warehouse. Step S2: Based on flight dynamic data and the cargo attributes of each batch of cargo, calculate the basic urgency score of each batch of cargo using the comprehensive urgency scoring model; this includes the following sub-steps: Step S21: Based on the flight dynamic data associated with each batch of goods, calculate the time urgency factor and delay impact factor for each batch of goods. Using a piecewise time urgency function Calculate the time urgency factor for the corresponding goods, where, For the time urgency factor of the goods, For the remaining cut-off time, , This is the flight's cut-off time. The current system time. This indicates that the time difference between the flight's cut-off time and the current system time is converted into a total number of seconds; Through the delay impact function Calculate the delay impact factor for the corresponding goods, where, This is a factor affecting cargo delays. This represents the number of hours the flight has been delayed. , To delay the reference time, This indicates that the time difference between the current system time and the delay reference time is converted into a total number of seconds; Step S22: Based on the cargo attributes of each batch of cargo, calculate the airline weight factor and cargo type coefficient factor for each batch of cargo. Step S23: Based on the time urgency factor, delay impact factor, airline weight factor and cargo type coefficient factor, calculate the basic urgency score of each batch of cargo through the comprehensive urgency scoring model; Step S3: Based on the cargo attributes of each batch of goods and the real-time status data of the automated warehouse, calculate the floor height adjustment factor, correct the basic urgency score of each batch of goods, and generate the final urgency score; including the following sub-steps: Step S31: Based on the cargo attributes of each batch of goods and the fixed physical constraints of the automated warehouse, determine the compliant storage floor range for each batch of goods and synchronously match the real-time status data of the corresponding storage floor. Step S32: Based on the compliant storage floor range and the real-time status data of the corresponding storage floors for each batch of goods, calculate the floor height adjustment factor for each batch of goods through the floor height bottleneck perception scheduling strategy. Using a floor height bottleneck-aware scheduling algorithm Calculate the floor height adjustment factor for each batch of goods, where, This is the floor height adjustment factor. The target storage layer for goods. This refers to the real-time storage space utilization rate at the third level. This refers to the real-time storage space utilization rate at the second level. This indicates that the goods must be stored on the third floor, and the real-time utilization rate of the third floor is greater than 80%. This indicates that the goods must be stored on the second floor, and the real-time storage space utilization rate on the second floor is less than 60%. Step S33: Based on the layer height adjustment factor of each batch of goods, correct the basic urgency score of each batch of goods and generate the final urgency score. Urgency correction formula The basic urgency score for each batch of goods. Make corrections, among which... For the first The final urgency score of the batch of goods. For the first The basic urgency score for a batch of goods. For the first The layer height adjustment factor for a batch of goods; Step S4: Based on the final urgency score of each batch of goods and the real-time status data of each freight equipment, calculate the matching score between each freight equipment and each freight task through the equipment matching optimization algorithm. Step S5: Based on the matching score between each freight equipment and each freight task, match the optimal freight equipment for each freight task and execute the goods inbound and outbound operations.

2. The intelligent scheduling method for air cargo terminal based on dynamic priority of flights according to claim 1, wherein, Real-time connection to the airport flight system to obtain flight dynamic data, synchronous collection of cargo attribute data for each batch of goods, and real-time status data of the automated warehouse and various cargo equipment within the warehouse includes the following sub-steps: Step S11: Connect in real time with the airport collaborative decision-making system and the airline's flight management system to obtain the flight dynamic data associated with each batch of goods; Step S12: Collect cargo attribute data for each batch of goods, fixed physical constraints of the automated warehouse, and real-time status data of the automated warehouse and various freight equipment within the warehouse.

3. The method of claim 1, wherein, Based on the final urgency score of each batch of goods and the real-time status data of each freight equipment, the matching score between each freight equipment and each freight task is calculated using an equipment matching optimization algorithm, including the following sub-steps: Step S41: Based on the final urgency score of each batch of goods, sort the freight tasks corresponding to each batch of goods in descending order to generate a queue of freight tasks to be executed. Step S42: Extract the task requirement parameters from each freight task in the queue of freight tasks to be executed, and filter the candidate freight equipment that meets the task requirement parameters based on the real-time status data of each freight equipment. Step S43: Based on the task requirement parameters of each freight task and the real-time status data of each corresponding candidate freight equipment, calculate the matching score between each candidate freight equipment and each freight task through the equipment matching degree optimization algorithm.

4. An intelligent scheduling system for an air cargo terminal based on dynamic priority of flights, characterized in that, include: The data acquisition module connects to the airport flight system in real time to obtain flight dynamic data, synchronously collect cargo attribute data of each batch of goods, and real-time status data of the automated warehouse and various cargo equipment in the warehouse. The basic urgency score generation module, based on flight dynamic data and cargo attributes of each batch of goods, calculates the basic urgency score for each batch of goods using a comprehensive urgency scoring model; specifically including: The time urgency factor and delay impact factor calculation submodule calculates the time urgency factor and delay impact factor for each batch of goods based on the flight dynamic data associated with each batch of goods. Using a piecewise time urgency function Calculate the time urgency factor for the corresponding goods, where, For the time urgency factor of the goods, For the remaining cut-off time, , This is the flight's cut-off time. The current system time. This indicates that the time difference between the flight's cut-off time and the current system time is converted into a total number of seconds; Through the delay impact function Calculate the delay impact factor for the corresponding goods, where, This is a factor affecting cargo delays. This represents the number of hours the flight has been delayed. , To delay the reference time, This indicates that the time difference between the current system time and the delay reference time is converted into a total number of seconds; The airline weight factor and cargo type coefficient factor calculation submodule calculates the airline weight factor and cargo type coefficient factor for each batch of cargo based on the cargo attributes of each batch of cargo. The basic urgency score calculation submodule calculates the basic urgency score for each batch of goods based on the time urgency factor, delay impact factor, airline weight factor, and cargo type coefficient factor, using a comprehensive urgency scoring model. The final urgency score generation module calculates the floor height adjustment factor based on the cargo attributes of each batch of goods and the real-time status data of the automated warehouse, corrects the basic urgency score of each batch of goods, and generates the final urgency score; specifically including: The floor-determination submodule determines the compliant floor range for each batch of goods based on the goods attributes and the fixed physical constraints of the automated warehouse, and synchronously matches the real-time status data of the corresponding storage floor. The floor height adjustment factor calculation submodule calculates the floor height adjustment factor for each batch of goods based on the compliant storage floor range and the real-time status data of the corresponding storage floor, through a floor height bottleneck perception scheduling strategy. Using a floor height bottleneck-aware scheduling algorithm Calculate the floor height adjustment factor for each batch of goods, where, This is the floor height adjustment factor. The target storage layer for goods. This refers to the real-time storage space utilization rate at the third level. This refers to the real-time storage space utilization rate at the second level. This indicates that the goods must be stored on the third floor, and the real-time utilization rate of the third floor is greater than 80%. This indicates that the goods must be stored on the second floor, and the real-time storage space utilization rate on the second floor is less than 60%. The final urgency score calculation submodule corrects the basic urgency score of each batch of goods based on the layer height adjustment factor and generates the final urgency score. by the urgency modifier formula the base urgency score for each batch of goods is modified, wherein the final urgency score for the batch of goods, the base urgency score for the batch of goods, the height adjustment factor for the batch of goods; The matching score generation module calculates the matching score between each freight equipment and each freight task based on the final urgency score of each batch of goods and the real-time status data of each freight equipment through the equipment matching optimization algorithm. The optimal freight equipment matching and operation execution module matches the optimal freight equipment for each freight task based on the matching score between each freight equipment and each freight task, and executes the goods inbound and outbound operations.

5. The intelligent scheduling system for an air cargo terminal based on dynamic priority of flights as claimed in claim 4 wherein, The data acquisition module specifically includes: The flight dynamic data acquisition submodule connects in real time with the airport collaborative decision-making system and the airline flight management system to obtain flight dynamic data associated with each batch of cargo. The cargo and warehouse data acquisition submodule collects cargo attribute data for each batch of cargo, fixed physical constraints of the automated warehouse, and real-time status data of the automated warehouse and various freight equipment within the warehouse.

6. The intelligent scheduling system for an air cargo terminal based on dynamic priority of flights as claimed in claim 4 wherein, The matching score generation module specifically includes: The pending freight task queue generation submodule sorts the freight tasks corresponding to each batch of goods in descending order based on the final urgency score of each batch of goods, and generates a pending freight task queue. The candidate freight equipment screening submodule extracts the task requirement parameters from each freight task in the queue of freight tasks to be executed, and filters candidate freight equipment that meets the task requirement parameters based on the real-time status data of each freight equipment. The matching score calculation submodule calculates the matching score between each candidate freight equipment and each freight task based on the task requirement parameters of each freight task and the real-time status data of each corresponding candidate freight equipment through the equipment matching optimization algorithm.

Images