Low-altitude unmanned aerial vehicle cluster scheduling and storage sharing system based on task aggregation degree

Abstract

This invention discloses a low-altitude UAV swarm scheduling, evidence storage, and sharing system based on task aggregation degree, relating to the field of low-altitude flight. It solves the problem of poor sharing effect in existing low-altitude UAV swarm scheduling, evidence storage, and sharing systems. The system includes a data acquisition module: creating a scheduling and evidence storage monitoring period, analyzing the record volume of historical scheduling and evidence storage records generated by the target UAV swarm within the monitoring period, and obtaining a comprehensive index of time-series evidence storage volume based on the analysis results; a data transmission module: analyzing the record transmission of historical scheduling and evidence storage records generated by the target UAV swarm within the monitoring period, and obtaining a comprehensive index of time-series evidence storage transmission based on the analysis results; and an evidence sharing module: sharing evidence of the target UAV swarm based on the comprehensive index of time-series evidence storage volume and the comprehensive index of time-series evidence storage transmission. This invention can improve the efficiency of evidence information sharing.

Description

Technical Field

[0001] This invention belongs to the field of low-altitude flight and involves data sharing technology, specifically a low-altitude UAV swarm scheduling, storage, and sharing system based on task aggregation degree. Background Technology

[0002] The existing low-altitude UAV swarm scheduling and evidence sharing system has the following shortcomings when sharing evidence information: 1. Existing low-altitude UAV cluster scheduling and evidence sharing systems cannot effectively combine the dynamic changes in the data volume of evidence records and the scale of sharing entities under the time-series dimension to conduct comprehensive quantitative evaluation during the process of sharing evidence information. Most of them rely on a single static parameter to select the sharing transmission mode. In low-altitude business scenarios with multiple concurrent tasks and dynamic fluctuations in task aggregation, the static judgment method is difficult to adapt to the real-time changes in the sharing interaction load at different times, which easily leads to insufficient matching between the selection of the sharing mode and the actual business scenario. 2. The existing low-altitude UAV cluster scheduling and evidence sharing system lacks differentiated protection design for different transmission scenarios such as full sharing and lightweight sharing. It is difficult to ensure the traceability of data while taking into account the availability of original detailed data and the data interaction efficiency of the multi-subject sharing process.

[0003] To address this, we propose a low-altitude UAV swarm scheduling, evidence storage, and sharing system based on task aggregation degree. Summary of the Invention

[0004] To address the shortcomings of existing technologies, the purpose of this invention is to provide a low-altitude UAV swarm scheduling and evidence sharing system based on task aggregation degree. This invention can improve the sharing efficiency of low-altitude UAV swarm scheduling and evidence sharing information.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: a low-altitude unmanned aerial vehicle (UAV) swarm scheduling, evidence storage, and sharing system, the specific working process of each module of which is as follows: Data acquisition module: Collects data on low-altitude UAV clusters that require scheduling, evidence storage and sharing, obtains the target UAV cluster, creates a scheduling, evidence storage and monitoring period, analyzes the volume of historical scheduling, evidence storage records generated by the target UAV cluster within the scheduling, evidence storage and monitoring period, and obtains a comprehensive index of time-series evidence storage volume based on the analysis results. Data transmission module: Records and analyzes the historical scheduling and evidence storage records generated by the target UAV cluster during the scheduling and evidence storage monitoring period, and obtains the time-series evidence storage transmission comprehensive index based on the analysis results; Evidence sharing module: Based on the comprehensive index of time-series evidence volume and the comprehensive index of time-series evidence transmission, evidence sharing is performed on the target drone cluster.

[0006] Furthermore, a volume analysis of the target drone swarm is performed, as detailed below. Data is collected from low-altitude UAV clusters that require scheduling, evidence storage, and sharing to obtain the target UAV cluster. During the data collection for scheduling and evidence preservation of the target drone cluster, the time point when the target drone cluster fully takes off is marked as the first evidence preservation feature time point, the time point corresponding to the current moment is marked as the second evidence preservation feature time point, and the time period between the first evidence preservation feature time point and the second evidence preservation feature time point is marked as the scheduling and evidence preservation monitoring cycle.

[0007] The scheduling and evidence storage data generated at different times during the scheduling and evidence storage monitoring period of the target drone cluster are collected to obtain multiple historical scheduling and evidence storage records.

[0008] Furthermore, a volume analysis of the target drone swarm was performed, as detailed below: Select any sample record from the acquired historical scheduling and evidence records, perform data volume analysis on the sample record, and obtain the shared volume coordinate points and the comprehensive measurement value of the shared volume corresponding to the sample record based on the analysis results. Obtain the shared volume coordinates and the comprehensive measurement value of the shared volume corresponding to each historical scheduling record; The average of the multiple evidence preservation measurement values ​​obtained is calculated to obtain the average evidence preservation measurement. A dispersion analysis is performed on the shared volume coordinate points in the Cartesian coordinate system of the data volume, and the shared task reverse aggregation index is obtained based on the analysis results; The product of the mean of the comprehensive metric for evidence storage and the reversal aggregation index of shared tasks is used to obtain the comprehensive index of the time-series evidence storage volume.

[0009] Furthermore, a data volume analysis was conducted on the sample evidence records, as detailed below: The amount of record data corresponding to the sample evidence storage record is collected to obtain the amount of evidence storage record data. The number of data sharing entities corresponding to the sample evidence storage record is counted to obtain the number of evidence storage record sharing entities. In the Cartesian coordinate system of data volume, the coordinate point with the number of shared entities of the evidence records as the horizontal axis and the data volume of the evidence records as the vertical axis is marked as the shared volume coordinate point corresponding to the sample evidence records.

[0010] Furthermore, a data volume analysis was conducted on the sample evidence records, as detailed below: The distance between the shared volume coordinate point and the origin is obtained by using the Cartesian coordinate system of the data volume plane. The time interval between the time point of sample evidence collection and the start time point of the scheduling evidence collection monitoring cycle is calculated to obtain the evidence collection sample duration. The duration of the scheduling evidence collection monitoring cycle is obtained to obtain the total duration of the evidence collection cycle. The ratio of the evidence collection sample duration to the total duration of the evidence collection cycle is calculated to obtain the evidence sampling period proportion. The product of the volume origin coordinate distance and the evidence sampling period proportion is calculated to obtain the comprehensive metric value of the shared volume corresponding to the sample evidence collection record.

[0011] Furthermore, a dispersion analysis was performed on the shared volume coordinate points, as detailed below: Create a timeline for evidence storage measurement and mark the collection time points corresponding to different historical scheduling evidence storage records on the timeline for evidence storage measurement. Group the shared volume coordinate points corresponding to adjacent collection time points in the evidence storage measurement time axis into a group to obtain multiple shared volume coordinate point groups; Two shared volume coordinate points in the shared volume coordinate point group are marked as the first shared volume coordinate point and the second shared volume coordinate point according to the order of acquisition time; The coordinate distance between the first shared volume coordinate point and the second shared volume coordinate point is obtained to obtain the first volume coordinate distance. The distance between the first shared volume coordinate point and the origin is obtained to obtain the second volume coordinate distance. The distance between the second shared volume coordinate point and the origin is obtained to obtain the third volume coordinate distance.

[0012] Furthermore, a dispersion analysis was performed on the shared volume coordinate points, as detailed below: If the distance between the second volume coordinates is greater than or equal to the distance between the third volume coordinates, then the ratio of the distance between the first volume coordinates to the distance between the second volume coordinates is calculated to obtain the grouped volume dispersion corresponding to the shared volume coordinate point group. If the distance between the second volume coordinates is less than the distance between the third volume coordinates, then the ratio of the distance between the first volume coordinates to the distance between the third volume coordinates is calculated to obtain the grouped volume dispersion corresponding to the shared volume coordinate point group. The average of the grouped volume dispersion corresponding to each shared volume coordinate point group is calculated to obtain the shared task reverse aggregation index.

[0013] Furthermore, the historical scheduling records are analyzed during transmission, as follows: Obtain historical scheduling and evidence storage records generated by the target UAV cluster during the scheduling and evidence storage monitoring period to obtain multiple historical scheduling and evidence storage records. Select any feature record from the acquired historical scheduling and evidence records, perform transmission rate analysis on the feature record, and obtain the shared transmission coordinates and the comprehensive shared transmission metric value corresponding to the feature record based on the analysis results. The number of data sharing entities corresponding to the feature-based evidence storage records is statistically analyzed to obtain the number of evidence storage record sharing entities; The average value of the data transmission rate of the evidence storage records between different data sharing entities is taken to obtain the evidence storage record transmission rate.

[0014] Furthermore, the historical scheduling records are analyzed during transmission, as follows: In the Cartesian coordinate system of data transmission plane, the coordinate point with the transmission rate of the evidence record as the horizontal axis and the number of sharing subjects of the evidence record as the vertical axis is marked as the shared transmission coordinate point corresponding to the feature evidence record. The line connecting the shared transmission coordinate point and the origin is marked as the shared transmission line. The slope value of the shared transmission line is collected to obtain the comprehensive shared transmission metric value corresponding to the feature storage record. Obtain the shared transmission comprehensive metric value corresponding to each historical scheduling and evidence storage record, and calculate the average of the multiple evidence storage comprehensive metric values ​​to obtain the time-series evidence storage transmission comprehensive index.

[0015] Furthermore, evidence sharing is performed on the target drone swarm, as detailed below: Obtain the comprehensive index of time-series evidence storage volume and the comprehensive index of time-series evidence storage transmission, and create benchmark intervals for the comprehensive index of storage volume and the comprehensive index of transmission, respectively. If the comprehensive index of time-series evidence storage volume is within the benchmark range of the comprehensive index of storage volume, and the comprehensive index of time-series evidence storage transmission is within the benchmark range of the comprehensive index of transmission, then the evidence storage records generated by the target drone cluster will be shared in full transmission mode. If the comprehensive index of time-series evidence storage volume is not within the benchmark range of the comprehensive index of storage volume, or the comprehensive index of time-series evidence storage transmission is within the benchmark range of the comprehensive index of transmission, then the evidence storage records generated by the target UAV cluster will be shared using a lightweight transmission method.

[0016] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are: 1. This invention combines the data volume of the evidence record, the number of sharing subjects, and the time sequence change characteristics to comprehensively determine the sharing transmission mode. It no longer relies on a single static parameter for mode selection. It can dynamically adapt the full transmission or lightweight transmission mode according to the real-time load of low-altitude services, reducing the unnecessary bandwidth occupation. 2. This invention can ensure the availability of complete scheduling and evidence storage information in task review scenarios, and can effectively reduce the scale of transmitted data in compliance verification scenarios. Thus, while meeting different business needs, it can significantly improve the overall resource utilization efficiency of evidence storage information in the sharing process, and achieve accurate adaptation and efficient collaboration. Attached Figure Description

[0017] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings.

[0018] Figure 1 This is an overall system block diagram of the present invention; Figure 2 This is a schematic diagram of the timeline for evidence preservation measurement in this invention; Figure 3 This is a schematic diagram of the shared transmission coordinate points of the present invention. Detailed Implementation

[0019] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. 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 of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0020] Example 1 Please see Figure 1 This invention provides a technical solution: a low-altitude UAV swarm scheduling, evidence storage and sharing system based on task aggregation degree. The specific working process of each module is as follows: The data acquisition module collects data from the low-altitude UAV clusters that need to be scheduled, stored, and shared, obtains the target UAV cluster, creates a scheduling and evidence storage monitoring period, analyzes the volume of historical scheduling and evidence storage records generated by the target UAV cluster within the scheduling and evidence storage monitoring period, and obtains a comprehensive index of time-series evidence storage volume based on the analysis results.

[0021] Specifically as follows: Data is collected from low-altitude UAV clusters that require scheduling, evidence storage, and sharing to obtain the target UAV cluster. During the data collection for scheduling and evidence preservation of the target drone cluster, the time point when the target drone cluster fully takes off is marked as the first evidence preservation feature time point, the time point corresponding to the current moment is marked as the second evidence preservation feature time point, and the time period between the first evidence preservation feature time point and the second evidence preservation feature time point is marked as the scheduling and evidence preservation monitoring cycle.

[0022] The scheduling and evidence storage data generated at different times during the scheduling and evidence storage monitoring period of the target drone cluster are collected to obtain multiple historical scheduling and evidence storage records.

[0023] It should be noted here that: In this application, the scheduling and evidence storage records involved here are specifically the records continuously generated by the low-altitude intelligent sharing platform during the entire process of UAV cluster task planning, resource allocation, real-time command issuance, cluster status feedback, and anomaly identification and handling. The records involved here include, but are not limited to, task priority parameters, resource allocation schemes, and scheduling control commands.

[0024] Select any sample record from the acquired historical scheduling and evidence records, perform data volume analysis on the sample record, and obtain the shared volume coordinate points and the comprehensive measurement value of the shared volume corresponding to the sample record based on the analysis results. The amount of record data corresponding to the sample evidence storage record is collected to obtain the amount of evidence storage record data. The number of data sharing entities corresponding to the sample evidence storage record is counted to obtain the number of evidence storage record sharing entities.

[0025] It should be noted here that: In this application, the amount of evidence storage record data involved here specifically refers to the data volume contained when a single historical scheduling evidence storage record is fully shared; In this application, the number of entities sharing the evidence storage records refers to the total number of independent participating entities authorized to obtain and access the evidence storage records of this sample. The independent participating entities involved here include, but are not limited to, operating units, regulatory agencies, partner companies, and platform operators.

[0026] In the Cartesian coordinate system of the data volume, the coordinate point with the number of shared entities of the evidence record as the x-axis and the data volume of the evidence record as the y-axis is marked as the shared volume coordinate point corresponding to the sample evidence record; The distance between the shared volume coordinate point and the origin is obtained by using the Cartesian coordinate system of the data volume plane. The time interval between the sample evidence record and the start time of the scheduling evidence monitoring cycle is obtained to obtain the evidence collection sample duration. The duration of the scheduling evidence monitoring cycle is obtained to obtain the total duration of the evidence period. The ratio of the evidence collection sample duration to the total duration of the evidence period is calculated to obtain the evidence sampling period proportion. The product of the volume origin coordinate distance and the evidence sampling period proportion is calculated to obtain the comprehensive metric value of the shared volume corresponding to the sample evidence record.

[0027] It should be noted here that: In this application, a Cartesian coordinate system for data volume is constructed. The coordinate point with the number of sharing entities of the evidence record as the horizontal axis and the data volume of the evidence record as the vertical axis is marked as the shared volume coordinate point corresponding to the sample evidence record. The Euclidean distance of the shared volume coordinate point relative to the origin is calculated to obtain the volume origin coordinate distance. The volume origin coordinate distance is multiplied by the evidence sampling time ratio to obtain the comprehensive metric value of the shared volume. This index integrates the data volume of the evidence record, the number of sharing access entities, and the time sampling coverage of the sample to comprehensively reflect the interactive load characteristics when the evidence record carries out data sharing. It can be used as a quantitative criterion for scheduling evidence records to select the full data sharing or lightweight credential sharing mode. The larger the index value, the higher the sharing interactive load, and the more likely to choose lightweight credential sharing. The smaller the index value, the lower the sharing interactive load, and the more likely to choose full data sharing.

[0028] Repeat the process of obtaining the shared volume coordinates and the comprehensive measurement value of the shared volume corresponding to the sample storage record, and obtain the shared volume coordinates and the comprehensive measurement value of the shared volume corresponding to each historical scheduling storage record. The average of the multiple evidence preservation measurement values ​​obtained is calculated to obtain the average evidence preservation measurement. A dispersion analysis is performed on the shared volume coordinate points in the Cartesian coordinate system of the data volume, and the shared task reverse aggregation index is obtained based on the analysis results.

[0029] Specifically as follows: Please see Figure 2 Create a timeline for evidence storage measurement and mark the collection time points corresponding to different historical scheduling evidence storage records in the timeline for evidence storage measurement. Group the shared volume coordinate points corresponding to adjacent collection time points in the evidence storage measurement time axis into a group to obtain multiple shared volume coordinate point groups; Two shared volume coordinate points in the shared volume coordinate point group are marked as the first shared volume coordinate point and the second shared volume coordinate point according to the order of acquisition time; The coordinate distance between the first shared volume coordinate point and the second shared volume coordinate point is obtained to obtain the first volume coordinate distance. The distance between the first shared volume coordinate point and the origin is obtained to obtain the second volume coordinate distance. The distance between the second shared volume coordinate point and the origin is obtained to obtain the third volume coordinate distance. If the distance between the second volume coordinates is greater than or equal to the distance between the third volume coordinates, then the ratio of the distance between the first volume coordinates to the distance between the second volume coordinates is calculated to obtain the grouped volume dispersion corresponding to the shared volume coordinate point group. If the distance between the second volume coordinates is less than the distance between the third volume coordinates, then the ratio of the distance between the first volume coordinates to the distance between the third volume coordinates is calculated to obtain the grouped volume dispersion corresponding to the shared volume coordinate point group. The average of the grouped volume dispersion corresponding to each shared volume coordinate point group is calculated to obtain the shared task reverse aggregation index.

[0030] It should be noted here that: In this application, the shared task retrograde aggregation index is a quantitative evaluation index constructed based on the dynamic offset characteristics of shared volume coordinate points in the time-series dimension. It is used to characterize the degree of volume retrograde change and aggregation change of UAV swarm scheduling evidence storage tasks over time. The specific calculation logic is as follows: On the evidence storage measurement time axis, the shared volume coordinate points corresponding to adjacent collection time points are divided into multiple independent coordinate point groups. The coordinate points within the group are marked sequentially as the first shared volume coordinate point and the second shared volume coordinate point according to the time sequence. The coordinate distance between two points within the group is calculated to obtain the first volume coordinate distance. The distance of the first coordinate point relative to the origin is calculated to obtain the second volume coordinate distance. The distance of the second coordinate point relative to the origin is calculated to obtain the third volume coordinate distance. By adaptively selecting the benchmark distance through size comparison, the relative distance ratio within the group is calculated to obtain the grouped volume dispersion of each group. Finally, the average value of all grouped volume dispersions is calculated to obtain the shared task retrograde aggregation index. This indicator can accurately depict the reverse fluctuation amplitude of the data volume of evidence storage and the scale of sharing entities, as well as the characteristics of task aggregation changes during the time-series evolution. The larger the index value, the more significant the reverse shift of the data volume characteristics of evidence storage and sharing in different time-series stages, the more drastic the fluctuation of the task aggregation trend, and the higher the degree of difference in cross-entity sharing interaction load. It is suitable for lightweight certificate sharing mode and avoids the transmission pressure and adaptation risks brought about by large-scale differentiated data interaction.

[0031] The product of the mean of the comprehensive metric for evidence storage and the reversal aggregation index of shared tasks is used to obtain the comprehensive index of the time-series evidence storage volume.

[0032] It should be noted here that: In this application, the time-series evidence volume comprehensive index characterizes the overall average shared interaction load of the sample and the degree of discrete fluctuation of the shared volume characteristics at each collection time. It can comprehensively reflect the changing trend of the shared volume of evidence records in the time-series dimension, and serve as the basis for selecting full data sharing or lightweight credential sharing for scheduling evidence records. The larger the time-series evidence volume comprehensive index, the higher the overall shared interaction load of the sample, and the greater the difference in the shared volume changes in different time segments, indicating a preference for lightweight credential sharing. The smaller the time-series evidence volume comprehensive index, the lower the overall shared interaction load, and the more stable the shared volume characteristics in each time period, indicating a preference for full data sharing.

[0033] The data transmission module collects data from the low-altitude UAV cluster that needs to be scheduled and stored for sharing, creates a scheduling and storage monitoring period, records and analyzes the historical scheduling and storage records generated by the target UAV cluster within the scheduling and storage monitoring period, and obtains a comprehensive index of time-series storage transmission based on the analysis results. Specifically as follows: Select any feature record from the acquired historical scheduling and evidence records, perform transmission rate analysis on the feature record, and obtain the shared transmission coordinates and the comprehensive shared transmission metric value corresponding to the feature record based on the analysis results. The number of data sharing entities corresponding to the feature-based evidence storage records is statistically analyzed to obtain the number of evidence storage record sharing entities; The average value of the data transmission rate of the evidence storage records between different data sharing entities is taken to obtain the evidence storage record transmission rate.

[0034] It should be noted here that: In this application, the transmission rate of the evidence record specifically refers to the amount of data that the evidence record can transmit per unit time during the data sharing process, characterizing the data transmission and reception speed of the evidence record when it performs sharing interaction.

[0035] Please see Figure 3 In the Cartesian coordinate system of data transmission plane, the coordinate point with the transmission rate of the evidence record as the horizontal axis and the number of sharing subjects of the evidence record as the vertical axis is marked as the shared transmission coordinate point corresponding to the feature evidence record. The line connecting the shared transmission coordinate point and the origin is marked as the shared transmission line. The slope value of the shared transmission line is collected to obtain the comprehensive shared transmission metric value corresponding to the feature storage record. It should be noted here that: In this application, the comprehensive metric for shared transmission is the slope of the line connecting the shared transmission coordinate point and the origin. The shared transmission coordinate point is set in a Cartesian coordinate system on the data transmission plane, with the transmission rate of the evidence record as the abscissa and the number of sharing entities of the evidence record as the ordinate. The comprehensive metric for shared transmission represents the scale of sharing entities corresponding to a unit transmission rate and is used to help determine the sharing mode of the evidence record. The larger the comprehensive metric for shared transmission, the more sharing entities need to be served at the same transmission rate, and the higher the transmission interaction pressure, so lightweight credential sharing is preferred. The smaller the comprehensive metric for shared transmission, the fewer sharing entities are at the same transmission rate, and the more abundant the transmission resources are, so full data sharing is preferred.

[0036] Repeat the process of obtaining the shared transmission coordinates and the comprehensive shared transmission metric value corresponding to the feature evidence record, obtain the comprehensive shared transmission metric value corresponding to each historical scheduling evidence record, and calculate the average of the multiple evidence comprehensive metric values ​​to obtain the time-series evidence transmission comprehensive index. The evidence sharing module performs evidence sharing on the target drone cluster based on the comprehensive index of time-series evidence volume and the comprehensive index of time-series evidence transmission. Specifically as follows: Obtain the comprehensive index of time-series evidence storage volume and the comprehensive index of time-series evidence storage transmission, and create benchmark intervals for the comprehensive index of storage volume and the comprehensive index of transmission, respectively. It should be noted here that: In this application, historical evidence records that have been shared using the full transmission method are obtained, and the comprehensive index of time-series evidence transmission and the comprehensive index of time-series evidence volume corresponding to each record of evidence storage are obtained respectively.

[0037] The maximum time-series evidence volume comprehensive index is set as the upper limit of the benchmark range of the stock comprehensive index, the minimum time-series evidence volume comprehensive index is set as the lower limit of the benchmark range of the stock comprehensive index, the maximum time-series evidence transmission comprehensive index is set as the upper limit of the benchmark range of the transmission comprehensive index, and the minimum time-series evidence transmission comprehensive index is set as the lower limit of the benchmark range of the transmission comprehensive index.

[0038] If the comprehensive index of time-series evidence storage volume is within the benchmark range of the comprehensive index of storage volume, and the comprehensive index of time-series evidence storage transmission is within the benchmark range of the comprehensive index of transmission, then the evidence storage records generated by the target drone cluster will be shared in full transmission mode. If the comprehensive index of time-series evidence storage volume is not within the benchmark range of the comprehensive index of storage volume, or the comprehensive index of time-series evidence storage transmission is within the benchmark range of the comprehensive index of transmission, then the evidence storage records generated by the target UAV cluster will be shared using a lightweight transmission method. It should be noted here that: The UAV scheduling and evidence storage data transmission methods involved in this invention include two types: full transmission and lightweight transmission. Both transmission methods rely on a unified data standardization and cross-platform sharing architecture, layered encryption and dynamic access control mechanisms, UAV digital identity authentication system and blockchain trusted simulation system to achieve standardized, secure and traceable transmission of multi-source heterogeneous evidence storage data in cross-departmental and cross-system scenarios.

[0039] The full-volume transmission method refers to a transmission mode that completely encapsulates and transmits all original evidence data of UAV cluster scheduling in a trusted, closed, and shared scenario. The transmitted content includes complete business details such as task resource configuration parameters, multi-task orchestration strategies, real-time scheduling instructions, UAV operational status feedback, task execution sequence records, and anomaly handling logs. This transmission method is based on the cross-platform data interoperability technology framework constructed in this invention. It unifies and normalizes the format of multi-source heterogeneous scheduling evidence data and performs interface adaptation processing, solving problems such as inconsistent data formats across multiple systems, poor protocol compatibility, and high interoperability barriers. This ensures the integrity and consistency of the original evidence data during cross-platform transmission and can fully meet the high-precision business optimization needs of task strategy review, scheduling algorithm iteration, SLA performance evaluation, and full-link fault tracing. Simultaneously, the full-volume transmission process relies on a transport layer encryption mechanism to complete encrypted transmission, combined with dynamic access control policies to achieve targeted data distribution to authorized entities, and a UAV digital identity credential verification mechanism to ensure that all original data flows only between authorized and trusted entities, achieving traceable transmission behavior and auditable data access.

[0040] The lightweight transmission method refers to a transmission mode in multi-entity open and shared scenarios that abandons the transmission of original business detail data and only encapsulates and transmits lightweight verification data such as evidence hash digests, task index identifiers, data verification credentials, and access traceability logs. This transmission method is suitable for routine data interoperability scenarios across departments and systems. It relies on a unified cross-platform sharing architecture to achieve standardized adaptation and rapid interoperability of lightweight credential data, significantly reducing the data interaction overhead of multi-source heterogeneous systems. At the same time, the lightweight transmission method is deeply adapted to the dual protection mechanism of "transmission layer encryption + business layer desensitization" of this invention. Sensitive business information such as core scheduling strategies, route data, and resource configuration parameters are desensitized and shielded at the business layer, retaining only the lightweight trusted credentials required for compliance verification. Encrypted transmission is completed at the transmission layer, preventing the leakage of core business data from the data source. By combining the unique and tamper-proof digital identity credential of drones, the entire lifecycle traceability of aircraft registration verification, operator qualification verification, and data access behavior recording can be realized. Relying on the blockchain multi-source heterogeneous data sharing simulation system, the lightweight credential is solidified on the chain and stored in a reliable manner, ensuring that the data exchange process between multiple entities and cross platforms is authentic, reliable, traceable, and tamper-proof, effectively adapting to multi-entity, high-security, and normalized compliance verification and sharing scenarios.

[0041] In summary, this invention distinguishes between full-data transmission and lightweight transmission modes, and combines a standardized cross-platform interoperability framework, a two-layer security protection mechanism, a digital identity authentication system, and blockchain trusted storage technology to achieve adaptive and differentiated transmission of UAV scheduling and storage data based on the sharing scenario, sharing entity, and transmission load. This ensures the complete availability of full data in internal technology iteration scenarios and the secure and controllable data in external cross-platform sharing scenarios, comprehensively solving the technical pain points of difficult interoperability of multi-source heterogeneous data in low-altitude intelligent connectivity, weak security protection, and insufficient traceability capabilities.

[0042] Compared to the problems described in the background technology, the present invention combines the data volume of the evidence record, the number of sharing subjects, and the time sequence change characteristics to comprehensively determine the sharing transmission mode. It no longer relies on a single static parameter for mode selection and can dynamically adapt the full transmission or lightweight transmission mode according to the real-time load of low-altitude services, thereby reducing the unnecessary bandwidth occupation. Furthermore, this invention can ensure the availability of complete scheduling and evidence storage information in task review scenarios, and effectively reduce the scale of transmitted data in compliance verification scenarios. Thus, while meeting different business needs, it can significantly improve the overall resource utilization efficiency of evidence storage information in the sharing process, and achieve accurate adaptation and efficient collaboration.

[0043] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to specific implementation methods. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The present invention is limited only to the technical solutions and their full scope and equivalents.

Claims

1. A low-altitude UAV swarm scheduling, evidence storage, and sharing system based on task aggregation degree, characterized in that, include: Data acquisition module: acquires the target UAV cluster, creates a scheduling and evidence storage monitoring period, analyzes the volume of historical scheduling and evidence storage records generated by the target UAV cluster within the scheduling and evidence storage monitoring period, and obtains a comprehensive index of time-series evidence storage volume; Data transmission module: Records and analyzes the historical scheduling and evidence storage records generated by the target UAV cluster during the scheduling and evidence storage monitoring period to obtain a comprehensive index of time-series evidence storage transmission; Evidence sharing module: Based on the comprehensive index of time-series evidence volume and the comprehensive index of time-series evidence transmission, evidence sharing is performed on the target drone cluster.

2. The low-altitude UAV swarm scheduling, evidence storage, and sharing system based on task aggregation degree as described in claim 1, characterized in that, The target drone swarm was recorded and its size analyzed, as follows: Data is collected from low-altitude UAV clusters that require scheduling, evidence storage, and sharing to obtain the target UAV cluster. During the process of collecting scheduling and evidence storage data for the target drone cluster, a scheduling and evidence storage monitoring cycle is created. The scheduling and evidence storage data generated at different times during the scheduling and evidence storage monitoring period of the target drone cluster are collected to obtain multiple historical scheduling and evidence storage records.

3. The low-altitude UAV swarm scheduling, evidence storage, and sharing system based on task aggregation degree according to claim 2, characterized in that, The target drone swarm was recorded and its size analyzed, as follows: Select any sample record from the acquired historical scheduling and evidence records, perform data volume analysis on the sample record, and obtain the shared volume coordinate points and the comprehensive measurement value of the shared volume corresponding to the sample record based on the analysis results. Obtain the shared volume coordinates and the comprehensive measurement value of the shared volume corresponding to each historical scheduling record; The average of the multiple evidence preservation measurement values ​​obtained is calculated to obtain the average evidence preservation measurement. A dispersion analysis was performed on the shared volume coordinate points, and the shared task reverse aggregation index was obtained based on the analysis results; The time-series evidence volume comprehensive index is obtained by calculating the mean of the comprehensive measurement of evidence storage and the aggregation index of the reverse flow of shared tasks.

4. The low-altitude UAV swarm scheduling, storage, and sharing system based on task aggregation degree as described in claim 3, characterized in that, A data volume analysis of the sample evidence records was conducted, as follows: The amount of record data corresponding to the sample evidence storage record is collected to obtain the amount of evidence storage record data. The number of data sharing entities corresponding to the sample evidence storage record is counted to obtain the number of evidence storage record sharing entities. In the created Cartesian coordinate system of data volume, the coordinate point with the number of shared entities of the evidence record as the x-axis and the data volume of the evidence record as the y-axis is marked as the shared volume coordinate point.

5. The low-altitude UAV swarm scheduling, storage, and sharing system based on task aggregation degree according to claim 4, characterized in that, A data volume analysis of the sample evidence records was conducted, as follows: The distance between the shared volume coordinate point and the origin is obtained by using the Cartesian coordinate system of the data volume plane. The time interval between the sample evidence record and the start time of the scheduling evidence monitoring cycle is obtained to obtain the evidence collection sample duration. The duration of the scheduling evidence monitoring cycle is obtained to obtain the total duration of the evidence storage cycle. Based on the evidence collection sample duration and the total duration of the evidence storage cycle, the proportion of the evidence sampling period is obtained. The distance of the volume origin coordinates and the proportion of the evidence sampling period are calculated to obtain the comprehensive metric value of the shared volume corresponding to the sample evidence record.

6. The low-altitude UAV swarm scheduling, storage, and sharing system based on task aggregation degree according to claim 3, characterized in that, A dispersion analysis was performed on the shared volume coordinate points, as detailed below: Create a timeline for evidence preservation measurement, and group the shared volume coordinate points corresponding to adjacent collection time points in the timeline for evidence preservation measurement into a group to obtain multiple groups of shared volume coordinate points. Two shared volume coordinate points in the shared volume coordinate point group are marked as the first shared volume coordinate point and the second shared volume coordinate point according to the order of acquisition time; The coordinate distance between the first shared volume coordinate point and the second shared volume coordinate point is obtained to obtain the first volume coordinate distance. The distance between the first shared volume coordinate point and the origin is obtained to obtain the second volume coordinate distance. The distance between the second shared volume coordinate point and the origin is obtained to obtain the third volume coordinate distance.

7. The low-altitude UAV swarm scheduling, storage, and sharing system based on task aggregation degree as described in claim 6, characterized in that, A dispersion analysis was performed on the shared volume coordinate points, as detailed below: If the distance between the second volume coordinates is greater than or equal to the distance between the third volume coordinates, then the ratio of the distance between the first volume coordinates to the distance between the second volume coordinates is calculated to obtain the grouped volume dispersion corresponding to the shared volume coordinate point group. If the distance between the second volume coordinates is less than the distance between the third volume coordinates, then the ratio of the distance between the first volume coordinates to the distance between the third volume coordinates is calculated to obtain the grouped volume dispersion corresponding to the shared volume coordinate point group. The dispersion of the grouped volumes corresponding to each shared volume coordinate point group is calculated to obtain the shared task reverse aggregation index.

8. The low-altitude UAV swarm scheduling, evidence storage, and sharing system based on task aggregation degree according to claim 1, characterized in that, The historical scheduling records were analyzed during transmission, as follows: Obtain historical scheduling and evidence storage records generated by the target UAV cluster during the scheduling and evidence storage monitoring period to obtain multiple historical scheduling and evidence storage records. Select any feature record from the acquired historical scheduling and evidence records, perform transmission rate analysis on the feature record, and obtain the shared transmission coordinates and the comprehensive shared transmission metric. The number of entities sharing data is statistically analyzed to obtain the number of entities sharing evidence records. The average value of the data transmission rate of the evidence storage records between different data sharing entities is taken to obtain the evidence storage record transmission rate.

9. The low-altitude UAV swarm scheduling, storage, and sharing system based on task aggregation degree according to claim 8, characterized in that, The historical scheduling records were analyzed during transmission, as follows: The coordinate point with the transmission rate of the evidence record as the horizontal axis and the number of sharing subjects of the evidence record as the vertical axis is marked as the shared transmission coordinate point corresponding to the feature evidence record. The line connecting the shared transmission coordinate point and the origin is marked as the shared transmission line. The slope value of the shared transmission line is collected to obtain the comprehensive metric value of shared transmission. Obtain the shared transmission comprehensive metric value corresponding to each historical scheduling and evidence storage record, and calculate the time-series evidence storage and transmission comprehensive index by combining the obtained comprehensive evidence storage metric values.

10. The low-altitude UAV swarm scheduling, evidence storage, and sharing system based on task aggregation degree according to claim 1, characterized in that, The target drone swarm will be stored and shared as evidence, as follows: Obtain the comprehensive index of time-series evidence storage volume and the comprehensive index of time-series evidence storage transmission, and create benchmark intervals for the comprehensive index of storage volume and the comprehensive index of transmission, respectively. If the comprehensive index of time-series evidence storage volume is within the benchmark range of the comprehensive index of storage volume, and the comprehensive index of time-series evidence storage transmission is within the benchmark range of the comprehensive index of transmission, then the evidence storage records generated by the target drone cluster will be shared in full transmission mode. If the comprehensive index of time-series evidence storage volume is not within the benchmark range of the comprehensive index of storage volume, or the comprehensive index of time-series evidence storage transmission is within the benchmark range of the comprehensive index of transmission, then the evidence storage records generated by the target UAV cluster will be shared using a lightweight transmission method.

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