Monitoring management method and system for flight luggage transportation data

Abstract

The invention relates to the technical field of modern aviation logistics, and discloses a flight luggage transportation data monitoring management method and system, and the method comprises the steps: obtaining fluctuation data of the number of luggage in an airport and upper limit data borne by transportation resources; performing grouping processing on the two tasks, and verifying and splitting according to a grouping result to obtain a preliminary task allocation scheme; monitoring the position and progress of a luggage batch, calculating the instant updating frequency of the state change data, and if the instant updating frequency is lower than a threshold value, adjusting to obtain a state deviation correction value; correcting the state data, and performing adjustment in combination with transportation resource available slot position distribution data to obtain an adjustment task allocation scheme; calculating a potential loss risk in combination with historical delay mode data; if the risk threshold value is exceeded, sorting high-risk batches to obtain a task circulation sequence; and updating the instruction, collecting real-time feedback data, and outputting a final transportation confirmation report after confirming that all batches finish dynamic balance connection. According to the method, dynamic accurate monitoring and resource optimization configuration of flight luggage transportation data can be realized.

Description

Technical Field

[0001] This invention relates to the field of modern aviation logistics technology, and in particular to a method and system for monitoring and managing flight baggage transportation data. Background Technology

[0002] Currently, in the field of modern air logistics, with the rapid development of the air transport industry and the continuous growth of passenger travel demand, the scale of flight baggage transportation is constantly expanding and the complexity of the process is significantly increasing. As a key link to ensure transportation efficiency and reduce the risk of delays and loss, baggage transportation data monitoring and management is directly related to the service quality of airlines and the travel experience of passengers.

[0003] Existing technologies, such as the authorized patent CN120509809B, simplify airport baggage transportation into a route network optimization problem. However, this solution neglects the refined management of the main transportation entity, namely the baggage batches themselves. It cannot track and assess the status and risks of specific baggage batches in real time. The scheduling logic mainly relies on mathematical formulas to calculate route scores, failing to deeply integrate with key business rules such as flight transfers. This results in system response delays, lack of risk warnings, and a disconnect between scheduling strategies and actual operations. This approach is clearly inadequate in complex operating environments, unable to adapt to dynamic fluctuations in baggage volume, and prone to overloading or idle transportation resources. Furthermore, it suffers from monitoring lag, making it difficult to quickly respond to deviations in baggage processing progress. Simultaneously, it fails to fully integrate historical delay data to predict risks, making it impossible to prioritize high-risk batches, leading to a higher probability of loss or delays. Especially in scenarios with dense flights and complex transfer links, it is difficult to achieve efficient coordination between various stages.

[0004] In summary, existing technologies are insufficient for dynamic and accurate monitoring and resource optimization of flight baggage transportation data, and cannot meet the efficiency and security requirements of baggage transportation in large-scale, highly dynamic scenarios. Summary of the Invention

[0005] This invention provides a method and system for monitoring and managing flight baggage transportation data, so as to realize dynamic and accurate monitoring and resource optimization of flight baggage transportation data, and meet the needs of efficiency and security of baggage transportation in large-scale and highly dynamic scenarios.

[0006] Firstly, to address the aforementioned technical problems, this invention provides a method for monitoring and managing flight baggage transportation data, comprising: acquiring fluctuation data of baggage quantity and upper limit data of transportation resource capacity within the airport; grouping the fluctuation data and the upper limit data; performing baggage capacity upper limit verification and overload splitting based on the grouping results to obtain a preliminary task allocation scheme; monitoring the location and processing progress of the current baggage batch according to the preliminary task allocation scheme; calculating the real-time update frequency of the baggage batch status change data; if the real-time update frequency is lower than a preset lower frequency threshold, calculating the status deviation and a correction value for the status deviation; and correcting the status change data according to the correction value to obtain the actual transportation demand. The system simultaneously acquires the distribution data of available transportation slots. Based on the actual transportation demand and the distribution data, it adjusts the initial task allocation scheme to obtain an adjusted task allocation scheme. Based on the adjusted task allocation scheme and pre-acquired historical delay pattern data, it calculates the potential loss risk for each baggage batch. If the potential loss risk exceeds a preset risk assessment threshold, the corresponding baggage batch is identified as a high-risk batch, and the high-risk batches are prioritized to obtain a task flow sequence. The system updates baggage handling instructions based on the task flow sequence and collects real-time feedback data. If the real-time feedback data shows that all baggage batches have achieved dynamic balance and connection, a final transportation confirmation report is generated and output.

[0007] In one optional implementation, obtaining the fluctuation data of the number of bags in the airport and the upper limit data of the transportation resource capacity includes: collecting baggage number data in the airport baggage handling area in real time, calculating the increase or decrease of the number of bags per unit time to obtain the fluctuation data of the number of bags; collecting the rated capacity data of transportation resources and the current operating load parameters, and subtracting the rated capacity data from the operating load parameters to obtain the upper limit data of the transportation resource capacity.

[0008] In one optional implementation, the step of grouping the fluctuation data and the upper limit data, and performing baggage capacity limit verification and excess baggage splitting based on the grouping results to obtain a preliminary task allocation scheme includes: removing outliers and standardizing the format of the fluctuation data and the upper limit data to obtain standardized fluctuation datasets and upper limit datasets; determining the total amount of baggage to be transported in each time period based on the fluctuation datasets; extracting the current capacity limit of each transportation resource from the upper limit datasets; splitting the total amount of baggage to be transported in the corresponding time period according to the current capacity limit of the transportation resources based on the current capacity limit, and allocating it to each device to form a grouping result; if the amount of baggage allocated to any transportation resource in the grouping result exceeds the current capacity limit, then splitting the excess baggage and redistributing it to baggage handling devices with sufficient capacity in the transportation resources to obtain a preliminary task allocation scheme.

[0009] In one optional implementation, the step of monitoring the location and processing progress of the current baggage batch according to the preliminary task allocation scheme, calculating the real-time update frequency of the baggage batch status change data, and calculating the status deviation and the correction value of the status deviation if the real-time update frequency is lower than a preset lower limit threshold, includes: locating the transportation resources corresponding to each baggage batch according to the task allocation scheme, and calculating the real-time update frequency of the baggage batch status change data; wherein the status change data includes: the number of changes in the location or processing progress of the baggage batch.

[0010] If the real-time update frequency is lower than a preset lower frequency threshold, historical state change data from the same period is extracted and compared with the state change data to obtain the state deviation; a correction value for the state deviation is calculated based on the state deviation.

[0011] In one optional implementation, the step of correcting the state change data according to the correction value to obtain the actual transportation demand, and simultaneously acquiring the distribution data of available slots for transportation resources, and adjusting the preliminary task allocation scheme according to the actual transportation demand and the distribution data to obtain an adjusted task allocation scheme, includes: correcting the state change data according to the correction value to determine the actual number and pace of luggage to be transported, thereby obtaining the actual transportation demand; wherein, the pace includes: the amount of luggage transported per unit time and the transportation schedule; acquiring the distribution data of available slots for transportation resources, the distribution data including the number of empty slots on conveyor belts and the available carrying capacity of transportation vehicles; comparing the actual transportation demand with the distribution data, and if the distribution data cannot meet the actual transportation demand, then readjusting the luggage allocation amount of each transportation resource in the preliminary task allocation scheme to obtain an adjusted task allocation scheme.

[0012] In one optional implementation, the step of calculating the potential loss risk of each baggage batch based on the adjusted task allocation scheme and in conjunction with pre-acquired historical delay pattern data includes: pre-extracting historical delay pattern data from a baggage delay record database, wherein the historical delay pattern data includes delay time periods, delayed transportation resource types, and the proportion of delayed baggage; determining the current transportation resource type and transportation time period based on the adjusted task allocation scheme, filtering out the historical delay pattern data that matches the current situation, statistically analyzing the probability of baggage loss in the matching data, and calculating the potential loss risk of each transportation link in conjunction with the transportation volume of the current baggage batch.

[0013] In one optional implementation, prioritizing the high-risk batches to obtain a task flow sequence includes: extracting urgency information of baggage transportation from the flight scheduling system, prioritizing the high-risk batches according to their urgency requirements, determining the processing order of each high-risk batch based on the prioritization results, and obtaining a task flow sequence.

[0014] In one optional implementation, the step of updating baggage handling instructions according to the task flow sequence and collecting real-time feedback data, and generating and outputting a final transportation confirmation report if the real-time feedback data shows that all baggage batches have achieved dynamic balance and connection, includes: issuing baggage handling instructions to each transportation resource according to the task flow sequence, collecting the processing status of each baggage batch in real time, and obtaining real-time feedback data; comparing the actual processing progress of each baggage batch with the planned progress in the adjusted task allocation scheme based on the real-time feedback data to obtain the progress deviation; if the progress deviation is lower than a preset progress deviation threshold, determining that all baggage batches have achieved dynamic balance and connection, integrating the transportation records, risk control results, and resource usage data of each baggage batch, and generating a final transportation confirmation report.

[0015] Secondly, the present invention provides a monitoring and management system for flight baggage transportation data, comprising: a data acquisition module for acquiring fluctuation data of baggage quantity and upper limit data of transportation resource capacity within the airport; a grouping processing module for grouping the fluctuation data and the upper limit data, performing baggage capacity upper limit verification and overload splitting based on the grouping results, and obtaining a preliminary task allocation scheme; a deviation calculation module for monitoring the location and processing progress of the current baggage batch according to the preliminary task allocation scheme, calculating the real-time update frequency of the baggage batch status change data, and calculating the status deviation and a correction value for the status deviation if the real-time update frequency is lower than a preset lower limit threshold; and a resource reallocation module for correcting the status change data according to the correction value to obtain the actual transportation demand. Simultaneously, the system acquires the distribution data of available slots for transportation resources, adjusts the initial task allocation scheme based on the actual transportation demand and the distribution data, and obtains an adjusted task allocation scheme. A risk calculation module calculates the potential loss risk of each baggage batch based on the adjusted task allocation scheme and pre-acquired historical delay pattern data. A priority sorting module identifies the corresponding baggage batch as a high-risk batch if the potential loss risk exceeds a preset risk judgment threshold, prioritizes the high-risk batches, and obtains a task flow sequence. A report generation module updates baggage handling instructions based on the task flow sequence and collects real-time feedback data. If the real-time feedback data shows that all baggage batches have achieved dynamic balance and connection, a final transportation confirmation report is generated and output.

[0016] Compared with the prior art, the present invention has the following beneficial effects: (1) This invention collects real-time data on the fluctuation of baggage quantity and the upper limit of transportation resource carrying capacity in the airport baggage handling area. After outlier removal and format unification, the baggage quantity is standardized and allocated according to the upper limit of transportation resource carrying capacity. If the baggage quantity of a certain group exceeds the upper limit, it is split into resources with sufficient carrying capacity to obtain a preliminary task allocation scheme. This invention breaks through the limitation that the traditional fixed task allocation cannot adapt to the dynamic fluctuation of baggage quantity. It fully explores the matching characteristics of baggage flow and resource carrying capacity, eliminates the imbalance interference of resource overload or idleness, provides a high-precision allocation basis for transportation scheduling, effectively improves the overall utilization rate of transportation resources, and solves the problem of uneven resource allocation in the existing technology. (2) According to the preliminary task allocation scheme, the present invention tracks the location and processing progress of the transportation resources corresponding to each baggage batch in real time, calculates the update frequency by counting the number of state changes per unit time, and extracts the difference between the historical state data and the current data if the frequency is lower than the lower limit threshold. The state deviation correction value is obtained by fitting and adjusting to eliminate the lag effect. The actual baggage transportation demand is determined by combining the correction value. The distribution data of available slots for transportation resources is compared and the task allocation is readjusted to match the fluctuation. This invention breaks through the limitation of traditional single-dimensional state tracking with monitoring lag, accurately captures the dynamic changes of baggage transportation status, provides multi-dimensional basis for task adjustment, significantly improves the response time of transportation scheduling under complex working conditions, and makes up for the deficiency of the existing technology in terms of insufficient state deviation correction capability. (3) Based on the adjusted task allocation scheme, this invention extracts historical delay pattern data from the delay record database, filters data that matches the current transportation resource type and time period to calculate the probability of loss, and calculates the probability distribution of potential loss risk in each link in combination with the current baggage volume. If the risk threshold is exceeded, the high-risk batches are prioritized according to the transportation time limit and baggage value level to generate a task flow sequence. Instructions are issued according to the sequence and the processing status is collected in real time. The deviation between the actual progress and the planned progress is compared. After the target is met, the transportation records, risk results and resource data are integrated to generate a transportation confirmation report. This invention breaks through the limitations of traditional monitoring that does not combine historical data and lacks risk prediction and priority processing. It provides maintenance personnel with accurate risk levels and scheduling basis, solves the problems of high baggage loss or delay rates and inefficient transfer connections, takes into account both transportation efficiency and safety, and meets the needs of flight baggage transportation management in large-scale and highly dynamic scenarios. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of a monitoring and management method for flight baggage transportation data provided in the first embodiment of the present invention; Figure 2 This is a schematic diagram of the monitoring and management system structure for flight baggage transportation data provided in the second embodiment of the present invention. Detailed Implementation

[0018] 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 embodiments of the present invention, and not all embodiments. 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.

[0019] Reference Figure 1 The first embodiment of the present invention provides a method for monitoring and managing flight baggage transportation data, including the following steps: S101, obtain data on fluctuations in the number of bags in the airport and the upper limit of transportation resource capacity; S102 performs grouping processing on the fluctuation data and the upper limit data, and performs baggage carrying limit verification and overload splitting based on the grouping results to obtain a preliminary task allocation scheme; S103, according to the preliminary task allocation scheme, monitor the location and processing progress of the current baggage batch, calculate the real-time update frequency of the baggage batch status change data, and if the real-time update frequency is lower than the preset frequency lower limit threshold, adjust the status change data to obtain the correction value of the status deviation. S104, Based on the correction value, correct the state change data to obtain the actual transportation demand, and at the same time obtain the distribution data of available slots for transportation resources. Adjust the preliminary task allocation scheme according to the actual transportation demand and the distribution data to obtain an adjusted task allocation scheme. S105, Based on the adjusted task allocation scheme and combined with the previously acquired historical delay pattern data, calculate the potential loss risk of each baggage batch; S106, if the potential loss risk exceeds the preset risk judgment threshold, the corresponding baggage batch is judged as a high-risk batch, and the high-risk batch is prioritized to obtain a task flow sequence; S107, update baggage handling instructions according to the task flow sequence and collect real-time feedback data. If the real-time feedback data shows that all baggage batches have achieved dynamic balance and connection, then generate and output the final transportation confirmation report.

[0020] In step S101, obtaining the fluctuation data of the number of bags in the airport and the upper limit data of the transportation resource carrying capacity includes: collecting baggage number data in the airport baggage handling area in real time, calculating the increase or decrease of the number of bags per unit time to obtain the fluctuation data of the number of bags; collecting the rated carrying capacity data of transportation resources and the current operating load parameters, and subtracting the rated carrying capacity data from the operating load parameters to obtain the upper limit data of the transportation resource carrying capacity.

[0021] It should be noted that, firstly, when collecting real-time data on the number of bags in the airport's baggage handling area, sensors are deployed at key nodes such as the baggage check-in area, sorting area, and conveyor belts to continuously capture information on the number of bags passing through. Then, the increase or decrease in the number of bags within a fixed time interval is statistically analyzed to obtain data on baggage quantity fluctuations. For example, during the morning rush hour at a certain airport, the sensors analyze the data every 10 minutes, finding that the number of bags increased from 280 to 410 pieces between 8:00 and 8:10, and further increased to 530 pieces between 8:10 and 8:20, thus obtaining data on the rapid increase in baggage quantity during that period.

[0022] Next, when collecting the rated carrying capacity data of transportation resources, the maximum carrying capacity data is extracted from the technical parameter documents of transportation resources such as conveyor belts, transport vehicles, and sorting equipment. Then, the current operating load of each resource is obtained through real-time monitoring to determine the upper limit data that the transportation resources can still carry. For example, if the rated carrying capacity data of a transport vehicle is 300 pieces of luggage per trip, and the current operating load shows that 180 pieces have been loaded, then the upper limit data of the vehicle's carrying capacity is determined to be 120 pieces.

[0023] In step S102, the fluctuation data and upper limit data are grouped, and baggage carrying capacity limit verification and excess baggage splitting are performed based on the grouping results to obtain a preliminary task allocation scheme. This includes: removing outliers and standardizing the format of the fluctuation data and upper limit data to obtain a standardized fluctuation dataset and upper limit dataset; determining the total amount of baggage to be transported in each time period based on the fluctuation dataset; extracting the current carrying capacity limit of each transportation resource from the upper limit dataset; splitting the total amount of baggage to be transported in the corresponding time period according to the current carrying capacity of the transportation resource based on the current carrying capacity limit, and allocating it to each device to form a grouping result; if the amount of baggage allocated to any transportation resource in the grouping result exceeds the current carrying capacity limit, the excess baggage is split and redistributed to baggage handling devices with sufficient carrying capacity in the transportation resources to obtain a preliminary task allocation scheme.

[0024] It should be noted that, firstly, when removing outliers and standardizing the format of fluctuating and upper limit data, outlier removal adopts the 3σ principle (normal distribution statistical method). Values ​​exceeding the mean ± 3σ are identified as outliers. This criterion is set based on the normal fluctuation range of airport baggage transportation data over the past year, and is verified with no less than 80,000 data points per quarter, achieving a confidence level of over 95%. Simultaneously, the σ coefficient can be fine-tuned according to the data fluctuation amplitude; when data fluctuations are severe, it can be adjusted to ± 2.5σ. Format standardization involves standardizing the time unit (unified to hours) and quantity unit (unified to pieces) of data from different sources, resulting in standardized fluctuating and upper limit datasets. For example, if the original fluctuating data shows 1500 outliers in a certain hour (mean 800 pieces, σ=200, exceeding the 3σ range), after removal, all data are standardized to the format "XX hour: XX pieces," forming a standardized dataset.

[0025] To determine the total number of bags to be transported in each time period based on the fluctuation dataset, the time periods are divided according to a preset time interval (usually 1 hour). The cumulative value of the number of bags in each time period is then calculated, and this cumulative value is the total number of bags to be transported in that time period. For example, the standardized fluctuation dataset shows that the number of bags in each 10-minute interval from 9:00 to 10:00 is 120, 130, 110, 140, 120, and 130, with a cumulative total of 650 bags. Based on this, the total number of bags to be transported in that time period is determined to be 650 bags.

[0026] Next, based on the upper limit dataset, when allocating the total amount of luggage according to the carrying capacity limit of transportation resources, the maximum carrying capacity of each transportation resource (conveyor belt, transport vehicle, etc.) is first determined. Then, the total amount of luggage for the corresponding time period is allocated to different transportation resources according to the carrying capacity ratio, ensuring that the initial allocation of each resource does not exceed its own carrying capacity limit, thus obtaining the grouping result. For example, the upper limit dataset shows that the carrying capacity limit of conveyor belt A is 300 pieces / hour and that of conveyor belt B is 350 pieces / hour. 650 pieces need to be transported from 9:00 to 10:00. Therefore, 300 pieces are allocated to conveyor belt A and 350 pieces are allocated to conveyor belt B according to the carrying capacity ratio, forming the grouping result.

[0027] Finally, if the baggage volume allocated to any transportation resource in the grouping results exceeds the carrying capacity limit, the excess baggage quantity is first calculated. Then, the excess portion is split into smaller batches and redistributed to transportation resources whose current carrying capacity has not reached the limit and still has sufficient surplus, until all resource allocation quantities are within the carrying capacity range, resulting in a preliminary task allocation plan. For example, if 450 pieces of baggage are mistakenly allocated to conveyor belt C with a carrying capacity limit of 400 pieces during a certain period, resulting in an excess of 50 pieces, and conveyor belt D is only allocated 280 pieces (capacity 350 pieces, surplus 70 pieces), then these 50 pieces are split and allocated to conveyor belt D, ultimately determining the preliminary task allocation plan.

[0028] In step S103, according to the preliminary task allocation scheme, monitoring the location and processing progress of the current baggage batch, calculating the real-time update frequency of the baggage batch status change data, and if the real-time update frequency is lower than a preset lower limit threshold, calculating the status deviation and the correction value of the status deviation, includes: locating the transportation resources corresponding to each baggage batch according to the task allocation scheme, and calculating the real-time update frequency of the baggage batch status change data; wherein the status change data includes: the number of changes in the location or processing progress of the baggage batch.

[0029] If the real-time update frequency is lower than a preset lower frequency threshold, historical state change data from the same period is extracted and compared with the state change data to obtain the state deviation; a correction value for the state deviation is calculated based on the state deviation.

[0030] It should be noted that, firstly, when locating the transportation resources corresponding to each baggage batch according to the task allocation plan, RFID radio frequency identification technology, specifically UHF-RFID, is used. A unique RFID tag is attached to each baggage batch, and RFID readers are deployed at key nodes of transportation resources such as conveyor belts and transport vehicles to capture tag signals in real time to determine the baggage's location. Simultaneously, photoelectric sensors monitor whether the baggage has entered the next processing stage and record processing progress information synchronously. For example, if the task allocation plan shows that batch A corresponds to conveyor belt number 3, the UHF-RFID reader tracks batch A in real time to the entrance of the loading area, and the photoelectric sensor shows that it has completed sorting, with a processing progress of 80%.

[0031] When calculating the frequency of changes in baggage batch location or processing progress within a unit of time, the unit of time is set to 5 minutes based on the airport's average baggage processing cycle over the past year (verified with no less than 80,000 processing records per quarter; this time interval balances real-time performance and data stability). The frequency of effective changes within 5 minutes, such as baggage location changes (e.g., from the sorting area to the loading area) or increases in processing progress (e.g., from 50% to 70%), is calculated. The number of changes is divided by 5 minutes to obtain the frequency of real-time status updates (unit: times / minute). For example, if batch B changes location from the check-in area to the sorting area and its progress increases from 30% to 60% within 5 minutes, a total of 2 effective changes, the calculated real-time update frequency is 0.4 times / minute.

[0032] If the frequency is lower than the preset lower threshold, when comparing historical status change data from the same period with the current data, the lower threshold is set based on the airport's status update frequency statistics for the same period over the past year, such as the morning peak from 7:00-9:00 and the evening peak from 17:00-19:00. The initial threshold is set using a percentile statistical method, taking the lower limit of the frequency range of 95% of normal transport cases. After verification with no less than 100,000 baggage transport records per quarter, the threshold confidence level reaches over 95%. It can also be fine-tuned by time period; during peak baggage handling surges, the threshold is lowered by 0.2 times / minute (e.g., from 1 time / minute to 0.8 times / minute), and increased by 0.2 times / minute during off-peak periods. Historical status change frequency data for the same period and transport resource type from the previous week is extracted and compared with the current frequency to obtain the data difference. For example, if the current frequency is 0.8 times / minute, the preset threshold is 1 time / minute, and the frequency for the same period last week was 1.2 times / minute, the calculated data difference is 0.4 times / minute.

[0033] Next, when fitting and adjusting the current state change data based on the differences, the least squares method is used to construct a linear fitting model with time as the independent variable and the number of changes as the dependent variable. Using the time-number of changes relationship of historical state change data as a benchmark, the differences between the current data and historical data are substituted into the model to adjust the time nodes of the current state change data, such as supplementing the delayed progress update time, and the magnitude of the change, such as correcting the underestimated progress increase, eliminating the impact of data lag, and obtaining the correction value for the state deviation. For example, after fitting using the least squares method, if it is determined that the current batch progress update is delayed by 1 minute and the progress value is underestimated by 10%, the corresponding correction value for the state deviation is "supplementing the 1-minute delayed progress, correcting the current progress from 60% to 70%".

[0034] In step S104, the process of correcting the state change data according to the correction value to obtain the actual transportation demand, and simultaneously acquiring the distribution data of available slots for transportation resources, and adjusting the preliminary task allocation scheme according to the actual transportation demand and the distribution data to obtain an adjusted task allocation scheme, includes: correcting the state change data according to the correction value to determine the actual number and pace of luggage to be transported, thereby obtaining the actual transportation demand; wherein, the pace includes: the amount of luggage transported per unit time and the transportation schedule; acquiring the distribution data of available slots for transportation resources, the distribution data including the number of empty slots on conveyor belts and the available carrying capacity of transportation vehicles; comparing the actual transportation demand with the distribution data, and if the distribution data cannot meet the actual transportation demand, then readjusting the luggage allocation amount of each transportation resource in the preliminary task allocation scheme to obtain an adjusted task allocation scheme.

[0035] It should be noted that, firstly, when correcting the status change data of the current baggage batch based on the correction value, linear interpolation is used to supplement lagging status information, such as processing progress that has not been updated in a timely manner or the number of baggage items that were missed in the statistics. By correcting lagging data and supplementing missing data, the total number of baggage items that actually needs to be transported is determined. At the same time, the transportation rhythm is determined based on the transportation volume that needs to be completed per hour. The rhythm is calculated based on the ratio of the corrected total number to the remaining transportation time. For example, before the correction, it was shown that 500 pieces needed to be transported in a certain period of time at a rhythm of 100 pieces / hour. After the correction, it was found that 80 pieces were underestimated due to data lag. Therefore, it was determined that 580 pieces needed to be transported at a rhythm of 116 pieces / hour, thus obtaining the actual transportation demand.

[0036] Next, when acquiring data on the distribution of available slots for transportation resources, infrared sensors are used to monitor vacant slots on conveyor belts, and weight sensors are used to calculate the remaining carrying capacity of transport vehicles. The number of unoccupied slots on each conveyor belt and the number of luggage items each transport vehicle can still carry are collected in real time, forming distribution data containing specific values. For example, after retrieving the data, it was found that conveyor belt number 2 has 120 vacant slots, and the remaining carrying capacity of the three transport vehicles is 80, 60, and 70 items respectively.

[0037] Then, when comparing actual transportation demand with distribution data, a preset matching threshold is established: the total number of available slots and spare space must be 10% more than the actual number. This threshold is set based on statistical data on the fluctuation range of baggage volume at the airport over the past year, using percentile statistics to determine the upper limit of fluctuation for 95% of normal transportation scenarios, ensuring sufficient margin to cope with sudden fluctuations. Verified with no less than 60,000 transportation records per quarter, the threshold has a confidence level of over 95%. Simultaneously, it can be fine-tuned by time period, with the threshold increased to 15% during peak hours and decreased to 5% during off-peak hours. If the total available quantity is lower than the sum of the actual quantity and the threshold, i.e., it cannot match fluctuations, the allocation of some resources is reduced, and the allocation of resources with sufficient spare space is increased, resulting in an adjusted task allocation scheme. For example, if the actual demand is 580 pieces, a matching threshold of 10% corresponds to a required available quantity of 638 pieces, but the current available quantity is only 530 pieces, the original allocation of 150 pieces to conveyor belt 1 is reduced to 120 pieces, and the extra 30 pieces are allocated to vehicles with 70 spare pieces, resulting in an adjusted task allocation scheme.

[0038] In step S105, the step of calculating the potential loss risk of each baggage batch according to the adjusted task allocation scheme and in combination with the pre-acquired historical delay pattern data includes: pre-extracting historical delay pattern data from the baggage delay record database, wherein the historical delay pattern data includes delay period, delayed transportation resource type, and delayed baggage quantity percentage; determining the current transportation resource type and transportation period according to the adjusted task allocation scheme, filtering out the historical delay pattern data that matches the current situation, statistically analyzing the probability of baggage loss in the matching data, and calculating the potential loss risk of each transportation link in combination with the transportation volume of the current baggage batch.

[0039] It should be noted that, firstly, when extracting historical delay pattern data from the baggage delay record database, SQL structured query technology is used to retrieve delay records from the past three years. Three types of information are extracted: delay time period, type of delayed transportation resource, and percentage of delayed baggage quantity, forming a historical delay pattern dataset. For example, it was found that during the evening peak hours of the past three months, the percentage of delayed baggage using sorting machines was 15%, and the percentage using transport trailers was 9%. The baggage delay record database contains all records and data on past baggage delays at the airport, including the three types of data: delay time period, type of delayed transportation resource, and percentage of delayed baggage quantity.

[0040] Next, after determining the current transportation resource type and transportation time period based on the adjusted task allocation plan, the Jaccard similarity algorithm is used to filter and match historical delay pattern data. The delay time period, transportation resource type, and baggage quantity ratio of the historical delay pattern data are encoded as discrete features, forming a feature set. The Jaccard similarity threshold is set to 0.8. This threshold is based on the statistical matching accuracy rate of historical data over the past year, requiring a matching accuracy rate of over 90%. After verification with no less than 50,000 matching cases per quarter, the threshold confidence level reaches over 95%. During peak hours, the threshold can be lowered to 0.7 to expand the matching range, and during off-peak hours, it can be raised to 0.9 to improve matching accuracy. The probability of baggage loss in the matched data is statistically analyzed using a frequency statistics method. The number of lost cases is divided by the total number of matching cases to obtain the potential loss risk for each transportation stage. For example, if a sorting machine is currently used for transportation during the evening peak, the matched data shows a loss probability of 7%, meaning the potential loss risk for this stage is 7%.

[0041] In step S106, if the potential loss risk exceeds a preset risk assessment threshold, the corresponding baggage batch is identified as a high-risk batch, and the high-risk batch is prioritized to obtain a task flow sequence. This includes: prioritizing the high-risk batch to obtain a task flow sequence, which involves: extracting baggage transportation urgency information from the flight scheduling system, prioritizing the high-risk batches according to their urgency requirements, determining the processing order of each high-risk batch based on the prioritization results, and obtaining the task flow sequence.

[0042] It should be noted that, firstly, when comparing the potential risks of each baggage batch with the preset risk assessment thresholds, the risk assessment thresholds are based on the airport's baggage loss case statistics over the past two years, calculating the actual loss rate corresponding to different risk values, and the baggage transportation security service specifications issued by the Civil Aviation Administration of China, clearly defining the maximum allowable upper limit of loss risk. The threshold is set using a percentile statistical method, selecting the risk value range corresponding to 95% of normal transportation cases, and taking the upper limit of this range as the initial threshold. After verification with no less than 100,000 baggage transportation records every quarter, the threshold confidence level reaches over 95%. Simultaneously, it supports fine-tuning by time period; during peak holiday periods when baggage volume surges, the threshold is increased by 1%, such as from 6% to 7%, and decreased by 1%, such as from 6% to 5%. If the risk value of a baggage batch exceeds this threshold, it is judged as a high-risk batch. For example, if the potential loss risk of a baggage batch is calculated to be 8%, exceeding the preset threshold of 6%, this batch is judged as a high-risk batch.

[0043] Next, the urgency information of baggage transportation is extracted from the flight scheduling system, specifically including the baggage transportation deadline for high-risk batches, flight type, and baggage transfer connection time. Priorities are then assigned based on these three types of timeliness-related information. The flight transportation deadline is determined based on the corresponding flight's departure time, combined with the standard operating time for airport baggage sorting, transportation, and loading. This standard operating time, based on historical operational data, averages 1.5 hours. The transportation deadline is then calculated by working backwards, typically set 2 hours before flight departure. During peak flight traffic, this can be shortened to 1.8 hours, accurate to the minute. For example, if a flight departs at 14:30, the transportation deadline is 12:30. Regarding flight type and connection time, direct flights are judged based on the pre-departure transport deadline, while connecting flights are judged based on the interval between the landing time of the preceding flight and the departure time of the subsequent flight, which is the connection time. Connections with a time ≤ 45 minutes are judged as high-urgency connections, 45-90 minutes as medium-urgency connections, and > 90 minutes as low-urgency connections. This classification standard complies with the requirements of the Civil Aviation Administration's "Transit Baggage Transportation Guarantee Specifications".

[0044] When prioritizing, a time-sensitive hierarchical sorting principle is followed. The first step prioritizes batches with shorter remaining deadlines, such as batches with 1 hour remaining before those with 1.5 hours remaining. The second step, if the remaining deadlines are the same, prioritize batches with shorter connecting flight times, such as high-urgency connecting flight batches before medium-urgency connecting flight batches. The third step, if the first two steps are the same, prioritize direct flight batches, as direct flights have no secondary transfer pressure and must be prioritized for loading. Finally, the processing order for each high-risk batch is determined, resulting in a task flow sequence. For example, among high-risk batches, batch A is a direct flight with 1.5 hours remaining, batch B is a connecting flight with 1 hour remaining and a 30-minute connecting flight, and batch C is a connecting flight with 2 hours remaining and a 40-minute connecting flight. The priority is batch B before batch A, and batch A before batch C, forming the task flow sequence.

[0045] In step S107, updating baggage handling instructions according to the task flow sequence and collecting real-time feedback data, and generating and outputting a final transportation confirmation report if the real-time feedback data shows that all baggage batches have achieved dynamic balance and connection, includes: issuing baggage handling instructions to each transportation resource according to the task flow sequence, collecting the processing status of each baggage batch in real time, and obtaining real-time feedback data; comparing the actual processing progress of each baggage batch with the planned progress in the adjusted task allocation scheme according to the real-time feedback data to obtain the progress deviation; if the progress deviation is lower than a preset progress deviation threshold, it is determined that all baggage batches have achieved dynamic balance and connection, integrating the transportation records, risk control results and resource usage data of each baggage batch, and generating a final transportation confirmation report.

[0046] It should be noted that, firstly, when baggage processing instructions are issued to various transportation resources according to the task flow sequence, sorting and transportation-related instructions are sent to the control terminals of resources such as conveyor belts and transport vehicles based on the processing order of each baggage batch in the sequence. Simultaneously, the real-time processing status of each baggage batch is continuously acquired through the status acquisition system on the resources, thus obtaining updated real-time feedback data. For example, if the task flow sequence indicates that batch E should be processed first, a priority sorting instruction is issued to the conveyor belt responsible for batch E, and the status of batch E entering transportation is collected in real time and updated in the real-time feedback data.

[0047] Next, when comparing the actual processing progress of each baggage batch with the planned progress in the adjusted task allocation plan based on real-time feedback data, the plan's planned completion percentage for each batch at the corresponding time node is first extracted from the plan. Then, the actual completion percentage in the real-time feedback data is compared, and the difference between the two is calculated. This difference is the progress deviation. For example, in the adjusted task allocation plan, the planned progress of batch F at the current time node is 80%, while the real-time feedback data shows that its actual progress is 78%, resulting in a calculated progress deviation of 2%.

[0048] Finally, when determining whether the schedule deviation is lower than the preset schedule deviation threshold, the threshold needs to be set based on the airport's baggage transportation schedule deviation statistics for the past two years, the on-time baggage loading rate requirements issued by the Civil Aviation Administration of China, and the efficiency standards of the baggage handling process. The threshold is set using a percentile statistical method to select the schedule deviation range of 95% of normal transportation cases. The upper limit of this range is taken as the initial threshold, and then verified by operational data from no less than 80,000 baggage handling records each quarter to ensure a confidence level of over 95%. The threshold also supports fine-tuning based on monthly operational fluctuations; for example, during peak baggage handling periods, an adjustment of ±0.5% is allowed to adapt to actual working conditions. If the schedule deviation is lower than the threshold, all baggage batches are considered to have achieved dynamic balance. Subsequently, the transportation time records, risk control results, and usage data of each batch are integrated to generate the final transportation confirmation report. For example, if the preset schedule deviation threshold is 3%, and the schedule deviation of a certain batch is 2%, which is lower than the threshold, dynamic balance is considered achieved, and the final transportation confirmation report is generated by integrating the entire process data of that batch.

[0049] In summary, this invention discloses a method for monitoring and managing flight baggage transportation data, comprising: acquiring fluctuation data of baggage quantity and upper limit data of transportation resource capacity within the airport; grouping the fluctuation data and the upper limit data, performing baggage capacity upper limit verification and overload splitting based on the grouping results to obtain a preliminary task allocation scheme; monitoring the location and processing progress of the current baggage batch according to the preliminary task allocation scheme, calculating the real-time update frequency of baggage batch status change data, and if the real-time update frequency is lower than a preset lower limit threshold, calculating the status deviation and a correction value for the status deviation; correcting the status change data according to the correction value to obtain the actual transportation demand, and simultaneously acquiring... The distribution data of available slots for transportation resources is used to adjust the initial task allocation scheme based on the actual transportation demand and the distribution data, resulting in an adjusted task allocation scheme. Based on the adjusted task allocation scheme and pre-acquired historical delay pattern data, the potential loss risk of each baggage batch is calculated. If the potential loss risk exceeds a preset risk assessment threshold, the corresponding baggage batch is identified as a high-risk batch, and these high-risk batches are prioritized to obtain a task flow sequence. Baggage handling instructions are updated according to the task flow sequence, and real-time feedback data is collected. If the real-time feedback data shows that all baggage batches have achieved dynamic balance and connection, a final transportation confirmation report is generated and output. This achieves dynamic and accurate monitoring and resource optimization of flight baggage transportation data, meeting the efficiency and security requirements of baggage transportation in large-scale, highly dynamic scenarios.

[0050] Reference Figure 2The second embodiment of the present invention provides a monitoring and management system for flight baggage transportation data, comprising: a data acquisition module for acquiring fluctuation data of baggage quantity and upper limit data of transportation resource capacity within the airport; a grouping processing module for grouping the fluctuation data and the upper limit data, performing baggage capacity upper limit verification and overload splitting based on the grouping results, and obtaining a preliminary task allocation scheme; a deviation calculation module for monitoring the location and processing progress of the current baggage batch according to the preliminary task allocation scheme, calculating the real-time update frequency of the baggage batch status change data, and calculating the status deviation and correction value if the real-time update frequency is lower than a preset lower limit threshold; and a resource reallocation module for correcting the status change data according to the correction value to obtain the actual transportation demand. Simultaneously, the system acquires the distribution data of available slots for transportation resources, adjusts the initial task allocation scheme based on the actual transportation demand and the distribution data, and obtains an adjusted task allocation scheme. A risk calculation module calculates the potential loss risk of each baggage batch based on the adjusted task allocation scheme and pre-acquired historical delay pattern data. A priority sorting module identifies the corresponding baggage batch as a high-risk batch if the potential loss risk exceeds a preset risk judgment threshold, prioritizes the high-risk batches, and obtains a task flow sequence. A report generation module updates baggage handling instructions based on the task flow sequence and collects real-time feedback data. If the real-time feedback data shows that all baggage batches have achieved dynamic balance and connection, a final transportation confirmation report is generated and output.

[0051] It should be noted that the flight baggage transportation data monitoring and management system provided in this embodiment of the invention is used to execute all the process steps of the flight baggage transportation data monitoring and management method in the above embodiment. The working principles and beneficial effects of the two are one-to-one, so they will not be described again.

[0052] This invention also provides an electronic device. The electronic device includes a processor, a memory, and a computer program stored in the memory and executable on the processor, such as a packet processing program. When the processor executes the computer program, it implements the steps described in the embodiments of the flight baggage transportation data monitoring and management method above, for example... Figure 1 The step S101 shown. Alternatively, when the processor executes the computer program, it implements the functions of each module / unit in the above system embodiments, such as the data acquisition module.

[0053] For example, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules / units may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the electronic device.

[0054] The electronic device may be a desktop computer, laptop, handheld computer, or smart tablet, etc. The electronic device may include, but is not limited to, a processor and memory. Those skilled in the art will understand that the above components are merely examples of electronic devices and do not constitute a limitation on the electronic device. It may include more or fewer components than described above, or combine certain components, or different components. For example, the electronic device may also include input / output devices, network access devices, buses, etc.

[0055] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the electronic device, connecting all parts of the electronic device via various interfaces and lines.

[0056] The memory can be used to store the computer programs and / or modules. The processor implements various functions of the electronic device by running or executing the computer programs and / or modules stored in the memory and by calling data stored in the memory. The memory may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created according to the use of the mobile phone (such as audio data, phonebook, etc.). In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0057] If the modules / units integrated into the electronic device are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or system capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electrical carrier signals and telecommunication signals.

[0058] It should be noted that the system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the system embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can be specifically implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0059] 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 descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. In particular, it should be noted that any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention for those skilled in the art.

Claims

1. A method for monitoring and managing flight baggage transportation data, characterized in that, include: Obtain data on fluctuations in the number of bags at the airport and the upper limit of transportation resource capacity; The fluctuation data and the upper limit data are grouped and processed. Based on the grouping results, the baggage carrying limit is checked and excess baggage is split to obtain a preliminary task allocation scheme. According to the preliminary task allocation scheme, monitor the location and processing progress of the current baggage batch, calculate the real-time update frequency of the baggage batch status change data, and if the real-time update frequency is lower than the preset lower limit threshold, calculate the status deviation and the correction value of the status deviation. Based on the correction value, the state change data is corrected to obtain the actual transportation demand. Simultaneously, the distribution data of available transportation slots is acquired. The preliminary task allocation scheme is adjusted based on the actual transportation demand and the distribution data to obtain an adjusted task allocation scheme. Based on the adjusted task allocation scheme and pre-acquired historical delay pattern data, the potential loss risk of each baggage batch is calculated. If the potential loss risk exceeds a preset risk assessment threshold, the corresponding baggage batch is identified as a high-risk batch. The high-risk batches are prioritized to obtain a task flow sequence. Baggage handling instructions are updated based on the task flow sequence, and real-time feedback data is collected. If the real-time feedback data shows that all baggage batches have achieved dynamic balance and connection, a final transportation confirmation report is generated and output.

2. The method for monitoring and managing flight baggage transportation data according to claim 1, characterized in that, The acquisition of data on the fluctuation of baggage quantity and the upper limit of transportation resource capacity within the airport includes: real-time collection of baggage quantity data in the airport baggage handling area, statistical analysis of the increase or decrease in baggage quantity per unit time to obtain baggage quantity fluctuation data; collection of rated capacity data of transportation resources and current operating load parameters, and subtraction of the rated capacity data and the operating load parameters to obtain the upper limit of transportation resource capacity.

3. The method for monitoring and managing flight baggage transportation data according to claim 1, characterized in that, The process of grouping the fluctuation data and the upper limit data, and then performing baggage capacity limit verification and excess baggage splitting based on the grouping results to obtain a preliminary task allocation scheme includes: removing outliers and standardizing the format of the fluctuation data and the upper limit data to obtain a standardized fluctuation dataset and upper limit dataset; determining the total amount of baggage to be transported in each time period based on the fluctuation dataset; extracting the current capacity limit of each transportation resource from the upper limit dataset; splitting the total amount of baggage to be transported in the corresponding time period according to the current capacity limit of the transportation resource according to the current capacity limit of the equipment, and allocating it to each equipment to form a grouping result; if the amount of baggage allocated to any transportation resource in the grouping result exceeds the current capacity limit, then splitting the excess baggage and redistributing it to baggage handling equipment with sufficient capacity in the transportation resources to obtain a preliminary task allocation scheme.

4. The method for monitoring and managing flight baggage transportation data according to claim 1, characterized in that, The process of monitoring the location and processing progress of the current baggage batch according to the preliminary task allocation scheme, calculating the real-time update frequency of the baggage batch status change data, and calculating the status deviation and correction value of the status deviation if the real-time update frequency is lower than a preset lower frequency threshold, includes: locating the transportation resources corresponding to each baggage batch according to the task allocation scheme, and calculating the real-time update frequency of the baggage batch status change data; wherein the status change data includes: the number of changes in the location or processing progress of the baggage batch; if the real-time update frequency is lower than the preset lower frequency threshold, extracting historical status change data from the same period and comparing it with the status change data to obtain the status deviation; and calculating the correction value of the status deviation based on the status deviation.

5. The method for monitoring and managing flight baggage transportation data according to claim 1, characterized in that, The process involves correcting the state change data based on the correction value to obtain the actual transportation demand, and simultaneously acquiring the distribution data of available transportation slots. The preliminary task allocation scheme is then adjusted based on the actual transportation demand and the distribution data to obtain an adjusted task allocation scheme. This includes: correcting the state change data based on the correction value to determine the actual number and pace of luggage to be transported, thus obtaining the actual transportation demand; wherein the pace includes: the amount of luggage transported per unit time and the transportation schedule; acquiring the distribution data of available transportation slots, including the number of empty conveyor belt slots and the available carrying capacity of transportation vehicles; comparing the actual transportation demand with the distribution data, and if the distribution data cannot meet the actual transportation demand, then readjusting the luggage allocation amount of each transportation resource in the preliminary task allocation scheme to obtain an adjusted task allocation scheme.

6. The method for monitoring and managing flight baggage transportation data according to claim 1, characterized in that, The step of calculating the potential loss risk of each baggage batch based on the adjusted task allocation scheme and in conjunction with pre-acquired historical delay pattern data includes: pre-extracting historical delay pattern data from the baggage delay record database, wherein the historical delay pattern data includes delay period, delayed transportation resource type, and delayed baggage quantity percentage; determining the current transportation resource type and transportation period based on the adjusted task allocation scheme, filtering out the historical delay pattern data that matches the current situation, statistically analyzing the probability of baggage loss in the matching data, and calculating the potential loss risk of each transportation link in conjunction with the current baggage batch transportation volume.

7. The method for monitoring and managing flight baggage transportation data according to claim 1, characterized in that, The step of prioritizing the high-risk batches to obtain a task flow sequence includes: extracting urgency information of baggage transportation from the flight scheduling system, prioritizing the high-risk batches according to their urgency requirements, determining the processing order of each high-risk batch based on the prioritization results, and obtaining a task flow sequence.

8. The method for monitoring and managing flight baggage transportation data according to claim 1, characterized in that, The process of updating baggage handling instructions according to the task flow sequence and collecting real-time feedback data, and generating and outputting a final transportation confirmation report if the real-time feedback data shows that all baggage batches have achieved dynamic balance and connection, includes: issuing baggage handling instructions to each transportation resource according to the task flow sequence, collecting the processing status of each baggage batch in real time, and obtaining real-time feedback data; comparing the actual processing progress of each baggage batch with the planned progress in the adjusted task allocation scheme based on the real-time feedback data to obtain the progress deviation; if the progress deviation is lower than a preset progress deviation threshold, determining that all baggage batches have achieved dynamic balance and connection, integrating the transportation records, risk control results, and resource usage data of each baggage batch, and generating a final transportation confirmation report.

9. A monitoring and management system for flight baggage transportation data, characterized in that, include: The data acquisition module is used to obtain data on fluctuations in the number of bags in the airport and the upper limit of transportation resource capacity. The grouping processing module is used to group the fluctuation data and the upper limit data, and perform baggage carrying limit verification and overload splitting based on the grouping results to obtain a preliminary task allocation scheme. The deviation calculation module is used to monitor the location and processing progress of the current baggage batch according to the preliminary task allocation scheme, calculate the real-time update frequency of the baggage batch status change data, and if the real-time update frequency is lower than the preset frequency lower limit threshold, calculate the status deviation and the correction value of the status deviation; the resource reallocation module is used to correct the status change data according to the correction value to obtain the actual transportation demand, and at the same time obtain the distribution data of available slots of transportation resources, and adjust the preliminary task allocation scheme according to the actual transportation demand and the distribution data to obtain an adjusted task allocation scheme. The risk calculation module is used to calculate the potential loss risk of each baggage batch based on the adjusted task allocation scheme and in combination with the pre-acquired historical delay pattern data. The priority sorting module is used to determine the corresponding baggage batch as a high-risk batch if the potential loss risk exceeds a preset risk judgment threshold, and to sort the high-risk batches by priority to obtain a task flow sequence. The report generation module is used to update baggage handling instructions and collect real-time feedback data according to the task flow sequence. If the real-time feedback data shows that all baggage batches have achieved dynamic balance and connection, then the final transportation confirmation report is generated and output.

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