Air port airside cargo flow process optimization management method based on RFID and big data

Abstract

This invention discloses a method for optimizing and managing airside cargo flow at air ports based on RFID and big data, belonging to the field of air logistics information technology. The method includes the following steps: collecting reading records of the same RFID tag within a continuous time window in the apron area; sorting the reading records according to a unified time scale to construct a reading time sequence; and marking densely read segments that appear consecutively within a preset time span and whose reading frequency meets a preset density condition within the reading time sequence. This invention constructs a reading time sequence and performs time difference compression analysis and displacement process calculation. Combined with the time range required for normal apron movement, it constrains and corrects abnormal jump behaviors to avoid misjudging real displacement as multiple jumps. Simultaneously, by associating electromagnetic fluctuation records and dynamically adjusting the judgment window, it achieves rhythm rearrangement during interference and post-incident recovery, ensuring continuous and stable cargo flow.

Description

Technical Field

[0001] This invention relates to the field of information technology in aviation logistics, specifically to a method for optimizing the management of airside cargo flow at air ports based on RFID and big data. Background Technology

[0002] Airside cargo flow optimization management based on RFID and big data at air ports refers to the automatic collection and real-time transmission of data such as cargo identity information, spatial location, and flow status within the airside area of ​​air ports by deploying RFID tags and reading / writing devices on cargo, vehicles, and key operational nodes. This unifies the collection of data scattered across various stages such as arrival, inspection, distribution, temporary storage, and loading, constructing a continuous cargo flow data sequence on a unified timeline. Based on this, and relying on big data processing and analysis technologies, massive, multi-source, and heterogeneous data are cleaned, integrated, correlated, modeled, and trend-mined. Dynamic evaluation and scheduling optimization are conducted around cargo flow rhythm, node dwell time, path deviation trajectory, and regulatory status, forming a standardized full-process operation control logic. This enables traceability of cargo status, closed-loop operation, and early identification and intervention of risks throughout the entire process from domestic arrival to international departure. This solves the problems of process fragmentation, data breakage, and regulatory lag in the traditional model, promoting the deep integration of airside transshipment business and the port's information system, and achieving synergistic development of improved transshipment efficiency and enhanced safety supervision capabilities.

[0003] The existing technology has the following shortcomings: In existing technologies, during airside cargo transfer at air ports, when cargo is unloaded from the aircraft and briefly re-transferred in the tarmac area, the activation of auxiliary power units of surrounding aircraft, the operation of ground support vehicles, and the concentrated operation of wireless communication equipment generate instantaneous electromagnetic fluctuations. Under the influence of electromagnetic interference, RFID readers and writers are prone to repeatedly reading the same tag within a very short period of time, and even signal superposition may occur. Without interference identification mechanisms, the background data processing program often interprets a single real displacement as multiple consecutive jumps, generating an abnormal flow trajectory. This abnormal trajectory is easily identified as abnormal transfer behavior, triggering an automatic freeze process, causing the transfer of that batch of cargo to be suspended, affecting the loading schedule, and in severe cases, causing flight delays.

[0004] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0005] The purpose of this invention is to provide a method for optimizing the management of airside cargo flow at air ports based on RFID and big data, so as to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a method for optimizing and managing airside cargo flow at air ports based on RFID and big data, comprising the following steps: Collect reading records of the same RFID tag in the apron area within a continuous time window, sort the reading records according to a unified time scale, construct a reading time series, and mark the dense reading segments that appear continuously within a preset time span and whose reading frequency reaches a preset dense condition in the reading time series; Around the densely read segments in the read time series, calculate the read time difference for adjacent read records, statistically analyze the read time difference compression magnitude, construct the time difference change trajectory, and identify abnormal compression locations in the time difference change trajectory; Based on the abnormal compression location, the displacement process is calculated on the read time series. The displacement process calculation result is compared with the time range required for normal apron movement. Jump records that exceed the time range required for normal apron movement are filtered out to form abnormal jump segments. Around the time range corresponding to the abnormal jump segment, the electromagnetic fluctuation records of the apron area within the same time range are obtained. The time overlap analysis of the densely read segment and the electromagnetic fluctuation records is performed. When the degree of time overlap meets the preset conditions, the interference-triggered abnormal transfer is determined. For interference-triggered abnormal transfers, the judgment window is widened within the interference time range of the read time series. After the electromagnetic fluctuation ends, the original judgment rhythm is gradually restored according to the preset recovery rules. The impact of abnormal jump segments on the cargo flow trajectory is eliminated by rearranging the time rhythm.

[0007] Preferably, the steps for marking densely occurring read segments that appear consecutively within a short period of time in the read time series are as follows: Collect all read records generated by the same RFID tag in the apron area within a continuous time window, and attach a uniform time scale mark to the read records to generate a read time sequence according to the uniform time scale order; The reading time series is segmented according to the continuity of the time interval. Reading records with time intervals lower than a preset time interval threshold are divided into candidate segments, and the number of reading records and time coverage within the candidate segments are statistically analyzed. Based on the overall time span of the reading time series, candidate segments that meet the preset dense conditions are identified as dense reading segments, and the start and end time positions of the dense reading segments are marked in the reading time series. The reading time series is structurally rearranged around the dense reading segment, dividing the reading time series into ordinary reading segments and dense reading segments, and establishing the correspondence between reading records and their respective segments.

[0008] Preferably, candidate segments are formed based on the continuity of time intervals between adjacent read records in the read time series, and dense read segments are determined based on the distribution frequency of read records in the candidate segments within a limited time span. The start and end time positions of dense read segments are embedded into the read time series through a unified time scale to form a segment identification structure.

[0009] Preferably, the steps for determining the location of abnormal compression are as follows: Around the densely read segments in the read time series, extract the time scale values ​​of adjacent read records in a unified time scale order, calculate the read time difference between adjacent read records, form a time difference set, and bind the read time difference to the position in the read time series accordingly; Extract the change in time difference based on the continuous change relationship within the time difference set, form a change sequence, statistically analyze the continuous interval and change magnitude of the time difference decrease, and construct a segmental expression of the time difference compression magnitude. The time difference set and the segmented expression of the time difference compression range are integrated, and a time difference change trajectory is formed according to a unified time scale. The start and end time positions of the compression change segments are marked in the time difference change trajectory. Locate nodes in the compression change segment of the time difference change trajectory that reach the preset compression standard, determine the abnormal compression location, and associate the abnormal compression location with the corresponding read record in the read time series.

[0010] Preferably, the segment representation of the time difference compression amplitude is formed by recording the continuous interval and the amplitude of the continuous decrease in time difference to form a compression change segment. The abnormal compression position is the node where the time difference change occurs within the compression change segment, and is marked with the corresponding time scale in the time difference change trajectory, while maintaining a positional correspondence with the read record in the read time series.

[0011] Preferably, the abnormal jump segment formation process is as follows: By backtracking the time scale of the corresponding read record in the time series around the abnormal compression position, the time scale and source location information of adjacent read records before and after the abnormal compression position are extracted to construct a local time subsequence containing the abnormal compression position; By mapping the changes in source location information in a local time subsequence to a temporal sequence, a displacement process expression corresponding to time scale and location change is formed. The time intervals between position changes are extracted based on the displacement process, and compared segment by segment with the time range required for normal apron movement. Position changes that exceed the time range required for normal apron movement are identified as jump records. By performing temporal continuity merging on jump records, jump records that are continuously arranged under a unified time scale and whose source location information changes across regions will be formed into abnormal jump segments, and these segments will be identified in the read time series.

[0012] Preferably, the local time subsequence constructed around the abnormal compression position is extended bidirectionally along the reading time sequence with the abnormal compression position as the time anchor point to form a continuous time interval. The displacement process is expressed by connecting the changes in the source location information according to a unified time scale. The time range required for normal apron movement is defined according to the apron path movement rhythm standard. Abnormal jump segments are marked in the reading time sequence in the form of continuous time scale intervals.

[0013] Preferably, the steps for determining interference-triggered anomaly transfer are as follows: The time range corresponding to the abnormal jump segment is extracted around the start and end time scales of the abnormal jump segment, and the electromagnetic fluctuation records of the apron area within the same time range are obtained to form a time expression structure under a unified time scale. By mapping the time range of abnormal jump segments to the electromagnetic wave segments in the electromagnetic wave records, a correspondence between the time range of abnormal jump segments and the time range of electromagnetic wave segments is constructed. The time overlap intervals are represented by the intersection of the time range of the abnormal jump segment and the time range of the electromagnetic fluctuation segment, and the proportion of the time overlap intervals within the time range of the abnormal jump segment is statistically analyzed. By comparing the proportional relationship of the time overlap intervals with preset conditions, when the time overlap intervals reach the preset time coverage standard, the abnormal jump segment is identified as an interference-triggered abnormal transfer, and an interference trigger mark is made in the read time series.

[0014] Preferably, when the time overlap interval reaches the preset time coverage standard, the electromagnetic wave record shows a continuous change state within the time overlap interval, and the time range corresponding to the abnormal jump segment falls entirely within the time range of the electromagnetic wave segment. The interference-triggered abnormal transfer is determined based on the time coverage relationship and the continuous change state.

[0015] Preferably, regarding the interference-triggered abnormal transfer, the judgment window of the read time series is widened within the interference time range. After the electromagnetic fluctuation ends, the original judgment rhythm is gradually restored according to the preset recovery rule, and the time rhythm of the read time series is rearranged as follows: The interference time range is extracted based on the interference-triggered abnormal transfer, and the corresponding reading record is located in the reading time series. The judgment window is widened around the interference time range, and the reading record is re-collected according to the extended time interval. The read records re-collected around the interference time range are formed into a continuous time segment expression, and the abnormal jump segments are incorporated into the extended time interval expression structure; A recovery time interval is established around the time scale of the end of electromagnetic fluctuations. The span of the judgment window is gradually reduced according to the preset recovery rules so that the judgment window returns to the original judgment rhythm. The reading time sequence is rearranged around the original judgment beat to eliminate the impact of abnormal jump segments on the cargo flow trajectory.

[0016] The technical effects and advantages provided by the present invention in the above technical solution are as follows: This invention introduces time difference compression analysis and displacement process estimation mechanisms within the read time series, enabling the structured identification of repeated reading behaviors caused by electromagnetic fluctuations within a short period of time under a unified time scale. It also incorporates the time range required for normal apron movement to perform beat constraint processing on abnormal jump behaviors, restoring the true movement trajectory expression from the time evolution level. This avoids splitting a true displacement into multiple jump records, ensuring the continuity and integrity of the cargo flow trajectory on the time axis, and reducing the risk of triggering a freeze process due to misjudgment of abnormal transfers.

[0017] This invention correlates abnormal jump segments with electromagnetic fluctuation records in the apron area over time, and implements a widening of the judgment window and a step-by-step recovery mechanism within the interference time range. This allows the read time series to be adaptively rearranged during electromagnetic fluctuations and smoothly return to the original judgment rhythm after the electromagnetic fluctuations end. This achieves dynamic absorption and rhythm correction of interference-triggered abnormal transfers, thereby maintaining the stability of the airside cargo flow rhythm and the continuity of operations in complex electromagnetic environments. Attached Figure Description

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

[0019] Figure 1 This is a flowchart of the method for optimizing the airside cargo flow management at air ports based on RFID and big data, as described in this invention. Detailed Implementation

[0020] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that the description of this disclosure will be more complete and fully convey the concept of the exemplary embodiments to those skilled in the art.

[0021] This invention provides, for example Figure 1 The airside cargo flow optimization management method based on RFID and big data at air ports, as shown, includes the following steps: Collect reading records of the same RFID tag in the apron area within a continuous time window, sort the reading records according to a unified time scale, construct a reading time series, and mark the dense reading segments that appear continuously within a preset time span and whose reading frequency reaches a preset dense condition in the reading time series; In the airside cargo transfer scenario at air ports, to achieve a refined characterization of RFID tag reading behavior, a time-series reading behavior with temporal continuity can be constructed around the reading behavior within a continuous time window, and reading activities occurring in a concentrated period of time can be structurally identified. The specific implementation steps are as follows: A continuous time window is set within the apron area. All read records generated by the same RFID tag within the continuous time window are collected, and a unified time scale identifier is attached to each read record. The unified time scale adopts a fixed-precision time segmentation method to normalize the occurrence time of the read record, so that different reading devices form a consistent time expression under the same time standard. After the time scale normalization is completed, the read records are sorted according to the chronological order of the unified time scale to generate a read time sequence with a strict time progression relationship. The occurrence time information and source location information of each read record are retained in the read time sequence, thereby ensuring that the read time sequence can completely reflect the time evolution process of the RFID tag within the continuous time window.

[0022] After constructing the reading time series, the reading time series is segmented based on the time distribution between adjacent reading records. The reading time series is divided into several continuous reading segments according to the continuity of the time interval. A preset time interval threshold is used as the division standard. Continuous reading records with time intervals below the threshold are grouped into the same candidate segment. On this basis, the number of reading records, time coverage, and time distribution density in each candidate segment are statistically analyzed to form a segment-level time distribution description, thereby forming several continuous reading segment structures with time clustering characteristics within the reading time series.

[0023] After completing the candidate segment statistics, the time coverage length and reading record distribution frequency of each candidate segment are comprehensively analyzed in conjunction with the overall time span of the reading time series. Candidate segments that appear continuously within a limited time span and whose number of reading records reaches the preset density condition are identified as dense reading segments. The start and end time positions of the dense reading segments are marked in the reading time series. The marking method uses time scale identifiers embedded in the reading time series, so that the dense reading segments form a segment identifier structure with clear time boundaries in the reading time series. Through this marking process, the reading time series not only maintains the original time progression characteristics, but also superimposes the segment attribute information of the dense reading segments, so that subsequent processing can directly identify the time range of the dense reading segments.

[0024] After the dense reading segments are marked, the reading time series is structurally rearranged, dividing it into two types of time segments: ordinary reading segments and dense reading segments. The reading records within each dense reading segment are then finely arranged according to the time scale, maintaining consistency in the continuous expression of the time scale. Simultaneously, a segment index relationship is established in the reading time series, ensuring that each reading record has a clear correspondence with its respective segment. This enables precise location and complete representation of densely occurring reading segments within a short period, ensuring that the reading time series retains both the original temporal order of the reading records and forms a segment-level temporal structure. This lays the foundation for subsequent time difference analysis and anomaly location identification around dense reading segments.

[0025] Around the densely read segments in the read time series, calculate the read time difference for adjacent read records, statistically analyze the read time difference compression magnitude, construct the time difference change trajectory, and identify abnormal compression locations in the time difference change trajectory; After marking the densely read sections, further refined processing can be carried out around the temporal evolution relationship within the densely read sections. By continuously characterizing the changes in the time interval between adjacent read records, a change trajectory structure with time compression characteristics can be formed. The specific implementation steps are as follows: Taking the marked dense reading segments in the reading time series as the processing object, each reading record within the dense reading segment is arranged sequentially according to the progressive order of a unified time scale. For any two adjacent reading records, their corresponding time scale values ​​are extracted, and the reading time difference between the two reading records is calculated, forming a time difference set composed of multiple sequentially arranged reading time differences. After forming the time difference set, each reading time difference is bound to its corresponding position in the reading time series, so that the reading time difference not only has a numerical expression but also a clear time series position attribute. This constructs a time interval expression structure around adjacent reading records within the dense reading segment, laying a continuous time foundation for subsequent statistical analysis of the reading time difference compression magnitude.

[0026] After constructing the time difference set, each reading time difference is compared with its predecessor based on the continuous change relationship within the time difference set. The amount of change between time differences is extracted and arranged according to the time scale of the reading time series to form a change sequence reflecting the trend of time interval change. After the change sequence is formed, the continuous occurrence of time difference reduction is statistically processed, and the duration, number of times, and magnitude of the continuous decrease in time difference are recorded. This yields the overall distribution of the reading time difference compression magnitude. Based on this distribution, a segmented expression of the time difference compression magnitude is established, so that the time difference compression magnitude no longer exists in a single point form, but forms a compression trajectory structure within a continuous time range in the reading time series, providing continuous data support for constructing the time difference change trajectory.

[0027] After the time difference compression range segment expression is formed, the time difference set and the time difference compression range segment expression are integrated and processed. The time difference is mapped one-to-one with the corresponding compression range according to a unified time scale. A time difference change trajectory is formed on the time axis of the reading time series, with the time scale as the horizontal expansion and the time difference value change as the vertical expansion. In this time difference change trajectory, the intervals in which the time difference shrinks continuously and the compression range reaches the preset compression range are clustered and marked. This makes the time difference change trajectory present a trajectory structure composed of alternating stable change segments and compressed change segments. The start time position and end time position of the compressed change segment are clearly marked in the trajectory. Thus, the time difference change trajectory becomes a continuous expression form that can intuitively reflect the time compression evolution process within the dense reading segment.

[0028] After the time difference change trajectory is formed and the compressed change segment is labeled, the boundary positions of the compressed change segment within the time difference change trajectory are located. Nodes where the time difference compression amplitude reaches the preset compression standard within the continuous change segment are identified. Locations where the compression amplitude changes abruptly or where there is a break at the end of the continuous change segment are identified as abnormal compression locations. These abnormal compression locations are marked with time scales in the time difference change trajectory, establishing a positional association between the abnormal compression locations and the corresponding read records in the original read time sequence. Through this process, the time difference change trajectory not only fully records the continuous change process of the time interval within the dense read segment, but also clearly indicates the key node positions where the time difference compression behavior occurs in the trajectory structure, thus providing an accurate time positioning basis for subsequent displacement process estimation around the abnormal compression locations.

[0029] Based on the abnormal compression location, the displacement process is calculated on the read time series. The displacement process calculation result is compared with the time range required for normal apron movement. Jump records that exceed the time range required for normal apron movement are filtered out to form abnormal jump segments. After determining the location of the abnormal compression, the displacement process can be continuously calculated based on the specific time coordinates of the abnormal compression location in the reading time series. Furthermore, the jump behavior can be expressed using a beat constraint, combined with the time range required for normal apron movement. This allows for the extraction of abnormal jump segments with time mismatch characteristics from the reading time series. The specific implementation steps are as follows: Around the marked abnormal compression positions in the time difference change trajectory, the corresponding reading record time scales of the abnormal compression positions in the reading time series are traced back. Using the abnormal compression positions as time anchors, several reading records are extended forward and backward in the reading time series. The time scales and source location information of adjacent reading records before and after the abnormal compression positions are extracted. A local time subsequence containing the abnormal compression positions is constructed under a unified time scale. After the local time subsequence is formed, the reading records within the local time subsequence are continuously arranged according to the original time progression relationship of the reading time series, so that the abnormal compression positions are located in the central time interval of the local time subsequence, thus providing a continuous time basis for displacement process estimation.

[0030] After forming a local time subsequence containing the abnormal compression location, the source location changes between adjacent read records are mapped temporally based on the changes in location information within the local time subsequence. The changes in source location information are mapped one-to-one with the corresponding time scale, constructing a correspondence between time scale and location change. Based on this correspondence, the changes in location information within each time period within the local time subsequence are continuously linked together to form a displacement process expression with the time scale as the main line of development. This allows the displacement process to present a complete time progression trajectory in the read time series, thereby realizing the temporal reconstruction of the movement path of goods near the abnormal compression location.

[0031] After completing the displacement process representation, the time intervals between each position change in the displacement process representation are extracted and compared segment by segment with the predetermined time range required for normal apron movement. The time range required for normal apron movement is defined according to the established movement rhythm standard of different paths in the apron area and represented by a unified time scale. During the segment-by-segment comparison process, the time interval corresponding to each position change in the displacement process representation is matched with the time range required for normal apron movement. When the time interval corresponding to a certain position change falls outside the time range required for normal apron movement, the reading record corresponding to the position change is marked as a jump record that exceeds the time range required for normal apron movement, and the time scale and position information of the jump record are recorded in the reading time sequence, so that the jump record forms a locatable abnormal time node in the reading time sequence.

[0032] After marking jump records that exceed the time range required for normal apron movement, the jump records appearing consecutively in the reading time series are merged based on temporal continuity. Jump records that are consecutively arranged under a unified time scale and whose source location information varies across regions are grouped into the same time segment, forming abnormal jump segments with start and end time scales. These abnormal jump segments are then marked as sections in the reading time series, forming a complete time interval expression structure within the reading time series. Through this processing, jump behaviors associated with abnormal compression positions in the reading time series are grouped into abnormal jump segments with continuous time boundaries. These abnormal jump segments retain the time compression characteristics indicated by the abnormal compression positions and integrate the time mismatch information between the displacement process calculation results and the time range required for normal apron movement. This achieves a time segmentation expression of abnormal jump behaviors, providing a clear temporal positioning basis for subsequent electromagnetic wave record comparisons based on abnormal jump segments.

[0033] Around the time range corresponding to the abnormal jump segment, the electromagnetic fluctuation records of the apron area within the same time range are obtained. The time overlap analysis of the densely read segment and the electromagnetic fluctuation records is performed. When the degree of time overlap meets the preset conditions, the interference-triggered abnormal transfer is determined. After identifying the time interval of the abnormal jump segment, further environmental interference source correlation processing can be carried out around the time boundary of the abnormal jump segment in the read time series. By introducing electromagnetic wave records of the apron area and expressing the time overlap under a unified time scale, a process for determining interference-triggered abnormal transfer is formed. The specific implementation steps are as follows: Based on the start and end time scales of the marked abnormal jump segments in the read time series, the complete time range corresponding to the abnormal jump segments is extracted. Using a unified time scale as a benchmark, this time range is converted into a standard time interval expression. After the standard time interval is determined, electromagnetic wave records continuously collected in the apron area within the same time range are retrieved. The electromagnetic wave records are indexed by the time scale and contain information on electromagnetic field strength changes and their corresponding time distribution. Under the unified time scale system, the electromagnetic wave records are arranged in chronological order to form a time expression structure consistent with the read time series. This ensures that the time range of the abnormal jump segments and the time range of the electromagnetic wave records are within the same time coordinate system, thus providing a unified time basis for subsequent time overlap processing.

[0034] After extracting the electromagnetic wave records, the time range of the abnormal jump segments is mapped to the electromagnetic wave segments in the records. The continuous electromagnetic intensity change segments in the records are divided into time intervals, and the start and end times of each electromagnetic wave segment are marked on a unified time scale. After the electromagnetic wave segments are marked, the time range of each abnormal jump segment is compared with the time intervals of each electromagnetic wave segment to construct a correspondence between the time range of the abnormal jump segments and the time range of the electromagnetic wave segments. This ensures that each abnormal jump segment can be associated with a corresponding electromagnetic wave segment distribution on the time axis, thus forming an expandable mapping relationship between time intervals.

[0035] After establishing the time interval mapping relationship, the time intervals between the time range of the abnormal jump segment and the time range of the electromagnetic fluctuation segment are extracted. The time scale set that is simultaneously within the time range of the abnormal jump segment and the time range of the electromagnetic fluctuation segment on the time axis is extracted to form a time overlap interval expression. After the time overlap interval is formed, the proportion of the time overlap interval within the time range of the abnormal jump segment is statistically analyzed. Combined with the electromagnetic intensity change trend in the electromagnetic fluctuation record, the continuity of electromagnetic changes within the time overlap interval is continuously expressed. This makes the degree of time overlap not only reflected in the proportion of time length, but also in the continuous coverage state of electromagnetic fluctuations on the time axis, thereby constructing a complete time overlap degree expression structure.

[0036] After the temporal overlap expression structure is formed, the proportion of the temporal overlap interval is compared with the preset conditions. When the temporal overlap interval reaches the preset time coverage standard within the time range of the abnormal jump segment, and the electromagnetic fluctuation remains in a continuous change state within the time overlap interval, the abnormal jump segment is identified as an interference-triggered abnormal transfer. An interference triggering mark is added to the abnormal jump segment in the read time series, so that the interference-triggered abnormal transfer forms a segment expression with time boundaries and interference attributes in the read time series. Through this processing, the abnormal jump segment not only retains the time mismatch characteristics between the displacement process estimation and the time range required for normal apron movement, but also superimposes the time overlap information reflected by the electromagnetic fluctuation record, so that the abnormal jump behavior forms a direct correlation with the electromagnetic environment change at the time expression level.

[0037] For interference-triggered abnormal transfers, the judgment window is widened within the interference time range of the read time series. After the electromagnetic fluctuation ends, the original judgment rhythm is gradually restored according to the preset recovery rules. The impact of abnormal jump segments on the cargo flow trajectory is eliminated by rearranging the time rhythm. After identifying the time interval of the abnormal jump segment, further environmental interference source correlation processing can be carried out around the time boundary of the abnormal jump segment in the read time series. By introducing electromagnetic wave records of the apron area and expressing the time overlap under a unified time scale, a process for determining interference-triggered abnormal transfer is formed. The specific implementation steps are as follows: Based on the start and end time scales of the marked abnormal jump segments in the read time series, the complete time range corresponding to the abnormal jump segments is extracted. Using a unified time scale as a benchmark, this time range is converted into a standard time interval expression. After the standard time interval is determined, electromagnetic wave records continuously collected in the apron area within the same time range are retrieved. The electromagnetic wave records are indexed by the time scale and contain information on electromagnetic field strength changes and their corresponding time distribution. Under the unified time scale system, the electromagnetic wave records are arranged in chronological order to form a time expression structure consistent with the read time series. This ensures that the time range of the abnormal jump segments and the time range of the electromagnetic wave records are within the same time coordinate system, thus providing a unified time basis for subsequent time overlap processing.

[0038] After extracting the electromagnetic wave records, the time range of the abnormal jump segments is mapped to the electromagnetic wave segments in the records. The continuous electromagnetic intensity change segments in the records are divided into time intervals, and the start and end times of each electromagnetic wave segment are marked on a unified time scale. After the electromagnetic wave segments are marked, the time range of each abnormal jump segment is compared with the time intervals of each electromagnetic wave segment to construct a correspondence between the time range of the abnormal jump segments and the time range of the electromagnetic wave segments. This ensures that each abnormal jump segment can be associated with a corresponding electromagnetic wave segment distribution on the time axis, thus forming an expandable mapping relationship between time intervals.

[0039] After establishing the time interval mapping relationship, the time intervals between the time range of the abnormal jump segment and the time range of the electromagnetic fluctuation segment are extracted. The time scale set that is simultaneously within the time range of the abnormal jump segment and the time range of the electromagnetic fluctuation segment on the time axis is extracted to form a time overlap interval expression. After the time overlap interval is formed, the proportion of the time overlap interval within the time range of the abnormal jump segment is statistically analyzed. Combined with the electromagnetic intensity change trend in the electromagnetic fluctuation record, the continuity of electromagnetic changes within the time overlap interval is continuously expressed. This makes the degree of time overlap not only reflected in the proportion of time length, but also in the continuous coverage state of electromagnetic fluctuations on the time axis, thereby constructing a complete time overlap degree expression structure.

[0040] After the temporal overlap expression structure is formed, the proportion of the temporal overlap interval is compared with the preset conditions. When the temporal overlap interval reaches the preset time coverage standard within the time range of the abnormal jump segment, and the electromagnetic fluctuation remains in a continuous change state within the time overlap interval, the abnormal jump segment is identified as an interference-triggered abnormal transfer. An interference triggering mark is added to the abnormal jump segment in the read time series, so that the interference-triggered abnormal transfer forms a segment expression with time boundaries and interference attributes in the read time series. Through this processing, the abnormal jump segment not only retains the time mismatch characteristics between the displacement process estimation and the time range required for normal apron movement, but also superimposes the time overlap information reflected by the electromagnetic fluctuation record, so that the abnormal jump behavior forms a direct correlation with the electromagnetic environment change at the time expression level.

[0041] This invention introduces time difference compression analysis and displacement process estimation mechanisms within the read time series, enabling the structured identification of repeated reading behaviors caused by electromagnetic fluctuations within a short period of time under a unified time scale. It also incorporates the time range required for normal apron movement to perform beat constraint processing on abnormal jump behaviors, restoring the true movement trajectory expression from the time evolution level. This avoids splitting a true displacement into multiple jump records, ensuring the continuity and integrity of the cargo flow trajectory on the time axis, and reducing the risk of triggering a freeze process due to misjudgment of abnormal transfers.

[0042] This invention correlates abnormal jump segments with electromagnetic fluctuation records in the apron area over time, and implements a widening of the judgment window and a step-by-step recovery mechanism within the interference time range. This allows the read time series to be adaptively rearranged during electromagnetic fluctuations and smoothly return to the original judgment rhythm after the electromagnetic fluctuations end. This achieves dynamic absorption and rhythm correction of interference-triggered abnormal transfers, thereby maintaining the stability of the airside cargo flow rhythm and the continuity of operations in complex electromagnetic environments.

[0043] The foregoing has only described certain exemplary embodiments of the present invention by way of illustration. Undoubtedly, those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the foregoing drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

Claims

1. A method for optimizing the airside cargo flow management at air ports based on RFID and big data, characterized in that: Includes the following steps: Collect reading records of the same RFID tag in the apron area within a continuous time window, sort the reading records according to a unified time scale, construct a reading time series, and mark the dense reading segments that appear continuously within a preset time span and whose reading frequency reaches a preset dense condition in the reading time series; Around the densely read segments in the read time series, calculate the read time difference for adjacent read records, statistically analyze the read time difference compression magnitude, construct the time difference change trajectory, and identify abnormal compression locations in the time difference change trajectory; Based on the abnormal compression location, the displacement process is calculated on the read time series. The displacement process calculation result is compared with the time range required for normal apron movement. Jump records that exceed the time range required for normal apron movement are filtered out to form abnormal jump segments. Around the time range corresponding to the abnormal jump segment, the electromagnetic fluctuation records of the apron area within the same time range are obtained. The time overlap analysis of the densely read segment and the electromagnetic fluctuation records is performed. When the degree of time overlap meets the preset conditions, the interference-triggered abnormal transfer is determined. For interference-triggered abnormal transfers, the judgment window is widened within the interference time range of the read time series. After the electromagnetic fluctuation ends, the original judgment rhythm is gradually restored according to the preset recovery rules. The impact of abnormal jump segments on the cargo flow trajectory is eliminated by rearranging the time rhythm.

2. The method for optimizing and managing airside cargo flow at air ports based on RFID and big data as described in claim 1, characterized in that, The steps for marking densely occurring read segments that appear consecutively within a short period of time in a read time series are as follows: Collect all read records generated by the same RFID tag in the apron area within a continuous time window, and attach a uniform time scale mark to the read records to generate a read time sequence according to the uniform time scale order; The reading time series is segmented according to the continuity of the time interval. Reading records with time intervals lower than a preset time interval threshold are divided into candidate segments, and the number of reading records and time coverage within the candidate segments are statistically analyzed. Based on the overall time span of the reading time series, candidate segments that meet the preset dense conditions are identified as dense reading segments, and the start and end time positions of the dense reading segments are marked in the reading time series. The reading time series is structurally rearranged around the dense reading segment, dividing the reading time series into ordinary reading segments and dense reading segments, and establishing the correspondence between reading records and their respective segments.

3. The method for optimizing and managing airside cargo flow at air ports based on RFID and big data as described in claim 2, characterized in that, Candidate segments are formed based on the continuity of time intervals between adjacent read records in the read time series. Dense read segments are determined based on the distribution frequency of read records in the candidate segments within a limited time span. The start and end time positions of dense read segments are embedded into the read time series through a unified time scale to form a segment identification structure.

4. The method for optimizing and managing airside cargo flow at air ports based on RFID and big data as described in claim 2, characterized in that, The steps to determine the location of abnormal compression are as follows: Around the densely read segments in the read time series, extract the time scale values ​​of adjacent read records in a unified time scale order, calculate the read time difference between adjacent read records, form a time difference set, and bind the read time difference to the position in the read time series accordingly; Extract the change in time difference based on the continuous change relationship within the time difference set, form a change sequence, statistically analyze the continuous interval and change magnitude of the time difference decrease, and construct a segmental expression of the time difference compression magnitude. The time difference set and the segmented expression of the time difference compression range are integrated, and a time difference change trajectory is formed according to a unified time scale. The start and end time positions of the compression change segments are marked in the time difference change trajectory. Locate nodes in the compression change segment of the time difference change trajectory that reach the preset compression standard, determine the abnormal compression location, and associate the abnormal compression location with the corresponding read record in the read time series.

5. The method for optimizing and managing airside cargo flow at air ports based on RFID and big data as described in claim 4, characterized in that, The segment representation of the time difference compression amplitude is formed by recording the continuous interval and the amplitude of the continuous decrease in time difference to form a compression change segment. The abnormal compression position is the node where the time difference change occurs within the compression change segment, and it is marked with the corresponding time scale in the time difference change trajectory, while maintaining a positional correspondence with the read record in the read time series.

6. The method for optimizing and managing airside cargo flow at air ports based on RFID and big data as described in claim 4, characterized in that, The process of forming an abnormal jump segment is as follows: By backtracking the time scale of the corresponding read record in the time series around the abnormal compression position, the time scale and source location information of adjacent read records before and after the abnormal compression position are extracted to construct a local time subsequence containing the abnormal compression position; By mapping the changes in source location information in a local time subsequence to a temporal sequence, a displacement process expression corresponding to time scale and location change is formed. The time intervals between position changes are extracted based on the displacement process, and compared segment by segment with the time range required for normal apron movement. Position changes that exceed the time range required for normal apron movement are identified as jump records. By performing temporal continuity merging on jump records, jump records that are continuously arranged under a unified time scale and whose source location information changes across regions will be formed into abnormal jump segments, and these segments will be identified in the read time series.

7. The method for optimizing and managing airside cargo flow at air ports based on RFID and big data as described in claim 6, characterized in that, The local time subsequence constructed around the abnormal compression location is extended bidirectionally along the reading time sequence with the abnormal compression location as the time anchor point to form a continuous time interval. The displacement process is expressed by connecting the changes in the source location information according to a unified time scale. The time range required for normal apron movement is defined according to the apron path movement rhythm standard. Abnormal jump segments are marked in the reading time sequence in the form of continuous time scale intervals.

8. The method for optimizing and managing airside cargo flow at air ports based on RFID and big data as described in claim 6, characterized in that, The steps for determining interference-triggered exception transfer are as follows: The time range corresponding to the abnormal jump segment is extracted around the start and end time scales of the abnormal jump segment, and the electromagnetic fluctuation records of the apron area within the same time range are obtained to form a time expression structure under a unified time scale. By mapping the time range of abnormal jump segments to the electromagnetic wave segments in the electromagnetic wave records, a correspondence between the time range of abnormal jump segments and the time range of electromagnetic wave segments is constructed. The time overlap intervals are represented by the intersection of the time range of the abnormal jump segment and the time range of the electromagnetic fluctuation segment, and the proportion of the time overlap intervals within the time range of the abnormal jump segment is statistically analyzed. By comparing the proportional relationship of the time overlap intervals with preset conditions, when the time overlap intervals reach the preset time coverage standard, the abnormal jump segment is identified as an interference-triggered abnormal transfer, and an interference trigger mark is made in the read time series.

9. The method for optimizing and managing airside cargo flow at air ports based on RFID and big data as described in claim 8, characterized in that, When the time overlap interval reaches the preset time coverage standard, the electromagnetic wave record shows a continuous change state within the time overlap interval, and the time range corresponding to the abnormal jump segment falls entirely within the time range of the electromagnetic wave segment. The interference-triggered abnormal transfer is determined based on the time coverage relationship and the continuous change state.

10. The method for optimizing the airside cargo flow management at air ports based on RFID and big data as described in claim 8, characterized in that, Regarding interference-triggered anomaly transfer, the judgment window for the read time series is widened within the interference time range. After the electromagnetic fluctuation ends, the original judgment rhythm is gradually restored according to the preset recovery rules, and the time rhythm of the read time series is rearranged as follows: The interference time range is extracted based on the interference-triggered abnormal transfer, and the corresponding reading record is located in the reading time series. The judgment window is widened around the interference time range, and the reading record is re-collected according to the extended time interval. The read records re-collected around the interference time range are formed into a continuous time segment expression, and the abnormal jump segments are incorporated into the extended time interval expression structure; A recovery time interval expression is established around the time scale of the end of electromagnetic fluctuations. The span of the judgment window is gradually reduced according to the preset recovery rules so that the judgment window returns to the original judgment rhythm. The reading time sequence is rearranged around the original judgment beat to eliminate the impact of abnormal jump segments on the cargo flow trajectory.

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