A distributed RID receiving system based on edge multi-source fusion

Abstract

The application discloses a kind of distributed RID receiving systems based on edge multi-source fusion, it is related to low-altitude supervision technical field, including: the grid deployment scheme of fixed node and mobile node cooperation is used in distributed RID receiving node cluster, real-time capture and preliminary analysis low-altitude aircraft RID signal, export node data after processing by first-level fusion algorithm;Edge multi-source fusion processing unit is deployed in each control partition, gather node data in area and complete calibration, deduplication and noise reduction by second-level fusion algorithm, output structured RID data;Cloud control platform realizes global data overall planning, situation visualization, intelligent decision and instruction issuing by third-level fusion algorithm, linkage city low-altitude scheduling system forms closed-loop control;Clock synchronization module provides nanosecond level time reference for the whole system.The application solves the problem of incomplete coverage and insufficient data processing precision of traditional RID receiving scheme, realizes global perception, accurate data processing and closed-loop control to low-altitude aircraft.

Description

Technical Field

[0001] This invention relates to the field of low-altitude surveillance technology, and in particular to a distributed RID receiving system based on edge multi-source fusion. Background Technology

[0002] With the advancement of the low-altitude economy industrialization, applications such as urban low-altitude logistics and manned eVTOL (electric vertical takeoff and landing) commuting are rapidly being implemented. The number of low-altitude aircraft is surging, and flight paths are becoming increasingly complex, creating a rigid demand for comprehensive, high-precision, and highly reliable airspace perception. RID (Real-Time Information) technology, as a core means of identifying and tracing low-altitude aircraft, requires aircraft to actively broadcast standardized information such as their identity, location, and status. This information, combined with ground-based receiving equipment to establish a control link, is crucial for ensuring the orderly operation of low-altitude traffic.

[0003] Existing RID receiving solutions suffer from two major technical bottlenecks: First, insufficient adaptability to deployment modes. Traditional single-point or simple network receiving devices are easily affected by urban building obstruction and multipath effects, resulting in perception blind spots and failing to achieve full coverage without dead angles. Second, lack of data processing accuracy and reliability. Data from a single receiving node is easily affected by electromagnetic interference and GNSS signal drift, and there is a lack of effective multi-source data fusion mechanisms, leading to large deviations in the analysis of core parameters such as position and speed, making it difficult to meet the accuracy requirements for low-altitude aircraft flight path planning and conflict early warning.

[0004] Therefore, there is an urgent need to design an RID receiving system based on distributed deployment and multi-source data fusion technology to solve the coverage and accuracy problems in urban low-altitude scenarios and provide technical support for the safe operation of the low-altitude economy. Summary of the Invention

[0005] In view of this, this application provides a distributed RID receiving system based on edge multi-source fusion to address the shortcomings of existing technologies.

[0006] The first aspect of this application provides a distributed RID receiving system based on edge multi-source fusion, including a distributed RID receiving node cluster, an edge multi-source fusion processing unit, a cloud management and control platform, and a clock synchronization module. The various parts within the system achieve data interaction and collaborative work through redundant communication links.

[0007] The distributed RID receiving node cluster adopts a grid-based deployment scheme that coordinates fixed nodes and mobile nodes to capture and initially analyze the RID signals of low-altitude aircraft in real time, thereby obtaining the corresponding raw analytical data. The distributed RID receiving node cluster contains multiple distributed RID receiving nodes, and each distributed RID receiving node performs a first-level fusion algorithm on the raw analytical data to output node data that meets the requirements of second-level fusion.

[0008] The edge multi-source fusion processing unit is deployed at a set location in each control zone to aggregate node data from all nodes within the control zone, and completes data calibration, deduplication, and noise reduction through a two-level fusion algorithm to output structured RID data; all grids are divided into several control zones according to set rules;

[0009] The cloud-based management and control platform uses a three-level fusion algorithm to achieve comprehensive structured RID data coordination, situation visualization, intelligent decision-making, and command issuance across the entire domain, and links with the city's low-temperature air conditioning system to form a closed-loop management and control system.

[0010] The clock synchronization module provides a time reference for the entire system.

[0011] In one possible implementation of the first aspect, the first-level fusion algorithm includes:

[0012] Each distributed RID receiving node uses historical parsing data cached within a preset time period to correct errors caused by GNSS positioning data drift through a Kalman filter algorithm, while simultaneously performing data noise reduction and removal of anomalous jump data.

[0013] In one possible implementation of the first aspect, the two-level fusion algorithm includes:

[0014] Data from multiple nodes of the same low-altitude aircraft is aggregated, and the timestamps are aligned with the reference time provided by the clock synchronization module. A weighted average fusion algorithm is used in conjunction with a digital city map to correct positioning deviations caused by obstructions. The weights in the weighted average fusion algorithm are dynamically allocated based on the node signal strength and the distance from the low-altitude aircraft.

[0015] In one possible implementation of the first aspect, the edge multi-source fusion processing unit is further configured to:

[0016] Within the corresponding control zone, the flight path of the low-altitude aircraft after positioning deviation correction is obtained, and based on the preset low-altitude flight path rules and aircraft safety distance threshold, it is determined whether the corresponding low-altitude aircraft is in violation. If so, a local warning is triggered; otherwise, no action is taken.

[0017] In one possible implementation of the first aspect, the three-level fusion algorithm includes:

[0018] The system aggregates structured RID data output from various edge multi-source fusion processing units. For cross-regional multi-node data of the same low-altitude aircraft, a multi-source heterogeneous RID data fusion algorithm is adopted to remove corresponding false signals and duplicate data, thereby achieving accurate tracking of the entire flight path of the low-altitude aircraft.

[0019] In one possible implementation of the first aspect, the cloud management platform is further used for:

[0020] For the same low-altitude aircraft, the full flight path data of the low-altitude aircraft is obtained based on cross-regional multi-node data that eliminates false signals and duplicate data;

[0021] Based on the full flight path data, the status of the corresponding low-altitude aircraft is displayed in real time on the city's electronic map. The system analyzes whether the low-altitude aircraft exhibits abnormal behavior or potential conflicts. If so, a tiered warning is triggered, and warning information and flight path adjustment suggestions are sent to the low-altitude aircraft operator and the ground control center.

[0022] In one possible implementation of the first aspect, each distributed RID receiving node is an independent hardware unit that integrates a radio frequency receiving module, a protocol decoding module, a local preprocessing module, an anti-interference module, and a communication module.

[0023] One possible implementation of the first aspect also includes:

[0024] The radio frequency receiving module specifically supports 2.4GHz / 5.2GHz / 5.8GHz three-band wideband reception, adopts a high-gain combined antenna design, and incorporates adaptive gain adjustment technology;

[0025] The protocol decoding module specifically includes: a built-in national standard protocol library and ASTM F3411 protocol library, supporting multi-protocol adaptive parsing, extracting the corresponding core fields of low-altitude aircraft and completing data legality verification;

[0026] The local preprocessing module is specifically used to execute the first-level fusion algorithm;

[0027] The anti-interference module specifically integrates spectrum sensing and frequency hopping avoidance technology, detects interference sources in real time and automatically switches the operating frequency band, and uses the SM4 encryption algorithm to ensure data transmission security.

[0028] The communication module specifically supports dual-link transmission of fiber optic and 5G private networks. The fixed node uses a fiber optic link for data transmission, while the mobile node uses a dedicated 5G link for data transmission.

[0029] In one possible implementation of the first aspect, the cloud-based management and control platform is deployed in the urban low-altitude traffic command center and interfaces with the urban intelligent transportation system, logistics dispatch platform and emergency management system to achieve data sharing and collaborative dispatch.

[0030] In one possible implementation of the first aspect, the clock synchronization module adopts a combination of BeiDou time synchronization and local calibration to provide a nanosecond-level time reference for the entire system.

[0031] Its beneficial effects are as follows: This invention provides a distributed RID receiving system based on edge multi-source fusion, comprising: a distributed RID receiving node cluster adopting a grid-based deployment scheme of fixed nodes and mobile nodes to capture and initially analyze low-altitude aircraft RID signals in real time, and output node data that meets the requirements of secondary fusion after processing by a first-level fusion algorithm; an edge multi-source fusion processing unit deployed in each control zone to aggregate node data within the area and complete calibration, deduplication, and noise reduction through a secondary fusion algorithm to output structured RID data; a cloud-based control platform to achieve overall data coordination, situational awareness, intelligent decision-making, and command issuance through a third-level fusion algorithm, linking with the urban low-altitude air conditioning system to form a closed-loop control; and a clock synchronization module to provide a nanosecond-level time reference for the entire system. This invention, through its distributed collaborative deployment and three-level fusion processing architecture, solves the problems of incomplete coverage and insufficient data processing accuracy in traditional RID receiving schemes, achieving full-domain perception, accurate data processing, and closed-loop control of low-altitude aircraft, adapting to the airspace supervision needs of urban low-altitude logistics, passenger commuting, and other scenarios. Attached Figure Description

[0032] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of this application. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0033] Figure 1 This is a schematic diagram of the overall architecture of a distributed RID receiving system based on edge multi-source fusion provided in an embodiment of this application;

[0034] Figure 2 This is a schematic diagram of a distributed RID receiving node hardware structure provided in an embodiment of this application. Detailed Implementation

[0035] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0036] In this application, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes the element.

[0037] Example

[0038] In existing technologies, RID receiving schemes suffer from two major technical bottlenecks: First, insufficient adaptability to deployment modes. Traditional single-point or simple network receiving devices are easily affected by urban building obstruction and multipath effects, resulting in perception blind spots and failing to achieve full coverage without dead angles. Second, lack of data processing accuracy and reliability. Data from a single receiving node is easily affected by electromagnetic interference and GNSS signal drift, and there is a lack of effective multi-source data fusion mechanisms, leading to large deviations in the analysis of core parameters such as position and speed, making it difficult to meet the accuracy requirements for low-altitude aircraft flight path planning and conflict early warning.

[0039] Therefore, this application provides a distributed RID receiving system based on edge multi-source fusion, such as... Figure 1 As shown, it includes a distributed RID receiving node cluster, an edge multi-source fusion processing unit, a cloud management and control platform, and a clock synchronization module. The various parts within the system achieve data interaction and collaborative work through redundant communication links.

[0040] The distributed RID receiving node cluster adopts a grid-based deployment scheme that coordinates fixed nodes and mobile nodes to capture and initially analyze the RID signals of low-altitude aircraft in real time, thereby obtaining the corresponding raw analytical data. The distributed RID receiving node cluster contains multiple distributed RID receiving nodes, and each distributed RID receiving node performs a first-level fusion algorithm on the raw analytical data to output node data that meets the requirements of second-level fusion.

[0041] The edge multi-source fusion processing unit is deployed at a set location in each control zone to aggregate node data from all nodes within the control zone, and completes data calibration, deduplication, and noise reduction through a two-level fusion algorithm to output structured RID data; all grids are divided into several control zones according to set rules;

[0042] The cloud-based management and control platform uses a three-level fusion algorithm to achieve comprehensive structured RID data coordination, situation visualization, intelligent decision-making, and command issuance across the entire domain, and links with the city's low-temperature air conditioning system to form a closed-loop management and control system.

[0043] The clock synchronization module provides a time reference for the entire system.

[0044] This embodiment provides a distributed RFID receiving system based on edge multi-source fusion, aiming to solve the problems of incomplete coverage, low data accuracy, and weak anti-interference capability of existing RFID receiving systems. Through grid-based deployment of distributed RFID receiving devices and combined with multi-source data fusion technology, it achieves all-time, all-round, and high-precision capture and analysis of RFID information for low-altitude aircraft in urban areas, meeting the airspace perception and control needs of low-altitude logistics and passenger commuting scenarios. The system includes a distributed RFID receiving node cluster, an edge multi-source fusion processing unit, a cloud-based management platform, and a clock synchronization module. Data interaction and collaborative work are achieved through redundant communication links, forming a complete closed-loop management system. Specifically:

[0045] Distributed RID receiver node cluster:

[0046] As a signal acquisition terminal, it adopts a grid-based deployment scheme of fixed nodes and mobile nodes, and is responsible for the real-time acquisition and preliminary analysis of RID signals of low-altitude aircraft, providing raw data support for subsequent data fusion.

[0047] Edge multi-source fusion processing unit:

[0048] Deployed in the core locations of each control zone, it aggregates node data within the region and completes data calibration, deduplication, and noise reduction through multi-source data fusion algorithms, outputting high-precision structured data and reducing cloud transmission pressure.

[0049] Cloud-based management platform:

[0050] As the system's central hub, it enables comprehensive RID data coordination, situational awareness visualization, intelligent decision-making, and command issuance across the entire domain, linking with the city's low-temperature air conditioning system to form a closed loop of perception, fusion, decision-making, and control.

[0051] Clock synchronization module:

[0052] By adopting a combination of BeiDou time synchronization and local calibration, a nanosecond-level time reference is provided for the entire system, ensuring the time consistency of data across multiple nodes and laying the foundation for data fusion.

[0053] The core of the distributed RID receiving system based on edge multi-source fusion in this embodiment lies in utilizing a distributed RID receiving node cluster, an edge multi-source fusion processing unit, and a cloud management platform, combined with a three-level fusion algorithm, to construct a three-level fusion data processing mechanism. This mechanism improves parsing accuracy and reliability by fusing multi-node data from different RID receiving devices, avoiding compatibility issues caused by cross-device data fusion. Specifically:

[0054] First-level fusion algorithm (multi-node collaborative calibration and deviation correction within the region):

[0055] To address the pain points of single receiving nodes being susceptible to GNSS drift and electromagnetic interference, resulting in random errors and abnormal jumps, preliminary data purification is performed locally on each distributed RID receiving node, laying the foundation for subsequent multi-node fusion.

[0056] Using the raw data parsed in real time by the nodes (including the latitude, longitude, speed, etc. of the aircraft) as the core, and combining it with the historical parsed data cached within 10 seconds, a sample set of real-time data and historical time-series data is constructed to ensure the continuity of the correction process.

[0057] Core logic:

[0058] Specifically, the Kalman filter algorithm is adopted, which is essentially a prediction-correction mode based on time-series data. The algorithm first predicts the current state of the aircraft (position and speed) based on historical data, and then weights and fuses the real-time parsed data with the predicted value, dynamically adjusting the weights to reduce random errors (deviations caused by GNSS positioning data drift). At the same time, through a preset threshold filtering mechanism, abnormal jump data that exceeds the reasonable range (such as instantaneous error values ​​caused by electromagnetic interference) are removed.

[0059] Data noise reduction, preliminary error correction, and historical data caching were implemented, and the positional error of the single-node output data was initially controlled within 3m, ensuring that the output node data has basic reliability and consistency, and meeting the quality requirements of the input data for secondary fusion.

[0060] Two-level fusion algorithm (multi-node collaborative calibration and deviation correction within the region):

[0061] To address the pain points of positioning deviation and data redundancy caused by differences in deployment location and building obstruction when multiple nodes capture data from the same aircraft, collaborative optimization of multi-node data within a region is achieved at the edge processing unit level.

[0062] The data is aggregated from all nodes within the same control zone, and all data is timestamped based on the nanosecond-level reference time of the clock synchronization module. This is the premise of multi-node data fusion, ensuring that all data corresponds to the state of the aircraft at the same moment.

[0063] Core logic:

[0064] The first step is dynamic weight allocation, which adopts a weighted average fusion algorithm. The weights are not fixed values, but are dynamically calculated based on two key indicators: the strength of the signal received by the node (the stronger the signal, the higher the data reliability, and the greater the weight); and the distance between the node and the aircraft (the closer the distance, the less the obstruction, the higher the data accuracy, and the greater the weight). This design highlights the contribution of high-reliability data and reduces the interference of low-quality data.

[0065] The second step is scenario-based deviation correction. By combining the geographic information of the city's digital map (such as building location and height), the positioning deviation caused by building obstruction (such as position shift caused by signal refraction) is identified. The data is then corrected through map matching algorithms to ensure that the positioning result is consistent with the actual geographic scene.

[0066] The third step is data deduplication and noise reduction. Data from the same aircraft captured repeatedly by multiple nodes is merged, redundant information is removed, and residual random interference noise is further filtered out.

[0067] Eliminate inconsistencies in multi-node data within the region, optimize positional errors to within 1m and velocity errors to ≤0.1m / s, output structured, high-precision regional-level data, while reducing the amount of data transmitted to the cloud and improving system response efficiency.

[0068] Three-level fusion algorithm (full-domain, cross-regional, multi-source heterogeneous data purification and trajectory tracing):

[0069] To address the pain points of data output discrepancies between edge units in different regions when aircraft fly across control zones, as well as the potential presence of false signals (such as RID signals simulated by interference sources) and duplicate data, the system performs final purification of all data and restoration of the entire flight path at the cloud level.

[0070] It aggregates the secondary fusion structured data output from all control zone edge units. The data type is multi-node fusion data of the same aircraft across regions and time periods, which belongs to multi-source heterogeneous data (due to differences in the deployment environment and signal conditions of nodes in different regions).

[0071] Core logic:

[0072] The algorithm employs a multi-source heterogeneous RID data fusion approach, with core functions including consistency verification, anomaly data removal, and trajectory continuity correction. Consistency verification compares the positioning and velocity data of the same aircraft in different regions, judging data continuity based on physical laws (such as maximum speed and turning angle limits). If data from one region differs from data from adjacent regions beyond a reasonable range, it is marked as suspicious data. False signal removal uses a dual mechanism of identity verification and trajectory rationality verification to identify and remove false signals, ensuring data authenticity. Duplicate data merging removes duplicate data caused by communication delays during cross-regional transmission and corrects cross-regional data connection deviations, ensuring smooth and continuous trajectory throughout the entire flight path.

[0073] It achieves false signal elimination, duplicate data removal, cross-regional deviation benchmarking, and accurate tracking of the entire flight path trajectory, keeping the false alarm rate of target identification within 0.5%, and providing high-precision and highly reliable full-domain data support for cloud-based full-domain situational awareness and intelligent decision-making.

[0074] In summary, Level 1 fusion is local refinement, addressing the local error problem of single-node data; Level 2 fusion is regional collaboration, resolving the consistency problem of multi-node data; and Level 3 fusion is global coordination, addressing the connectivity and accuracy issues of cross-regional data. These three levels are progressively advanced, with each algorithm building upon the output data of the previous level, addressing pain points in specific scenarios through targeted technical solutions, ultimately achieving a complete optimization chain from single-node data to accurate trajectory data across the entire domain.

[0075] Each distributed RID receiver node is an independent hardware unit (e.g., Figure 2 As shown, it integrates an RF receiving module, a protocol decoding module, a local preprocessing module, an anti-interference module, and a communication module. Its compact hardware structure makes it suitable for deployment in various urban scenarios.

[0076] The radio frequency receiving module supports wideband reception in three frequency bands: 2.4GHz / 5.2GHz / 5.8GHz. It adopts a high-gain combined antenna design and adaptive gain adjustment technology to capture weak RID signals in environments with building obstruction and electromagnetic interference, with a receiving distance of ≥8km.

[0077] The protocol decoding module has a built-in national standard GB / T 38948-2020 protocol library and ASTM F3411 protocol library. It supports multi-protocol adaptive parsing and can extract core fields such as aircraft identification, latitude and longitude, altitude, speed, heading, timestamp, and flight plan, and complete data legality verification.

[0078] The local preprocessing module uses the Kalman filter algorithm to perform preliminary noise reduction on the parsed data, remove abnormal jump data, and cache historical data within 10 seconds to provide local data support for multi-source fusion.

[0079] The anti-interference module integrates spectrum sensing and frequency hopping avoidance technology, which can detect interference sources such as 5G and WiFi in real time and automatically switch the working frequency band. At the same time, it adopts the SM4 encryption algorithm to ensure data transmission security.

[0080] The communication module supports dual-link transmission of fiber optic and 5G private networks. Fixed nodes prioritize fiber optic links (latency ≤ 50ms), while mobile nodes use 5G private networks to ensure stable data upload.

[0081] The node hardware adopts an industrial-grade design, with an operating temperature range of -40℃ to 80℃. It is waterproof, dustproof, and shock-resistant. Fixed nodes are suitable for deployment on rooftops, communication towers, utility poles, and other locations, while mobile nodes are integrated into vehicle-mounted / portable platforms, enabling rapid response to temporary control needs.

[0082] This embodiment presents a distributed RID receiving system based on edge multi-source fusion. Its system workflow consists of four main steps: signal acquisition, local preprocessing, edge fusion, and cloud management. The entire process is automated, as detailed below:

[0083] Signal acquisition phase: The distributed receiving node cluster continuously scans the target frequency band to capture the RID signal broadcast by the low-altitude aircraft. The radio frequency receiving module adaptively adjusts its gain to resist electromagnetic interference and multipath effects, ensuring stable signal reception.

[0084] Local preprocessing stage: The protocol decoding module parses the RID signal, extracts core data fields and verifies their legality. The local preprocessing module reduces noise through filtering algorithms, caches historical data and uploads it to the edge multi-source fusion processing unit.

[0085] Edge fusion stage: The edge multi-source fusion processing unit aggregates data from multiple nodes, completes timestamp alignment based on clock synchronization, calibrates data deviations through a two-level fusion algorithm, generates high-precision structured data after deduplication, and at the same time judges whether the aircraft violates the control rules and triggers local early warnings.

[0086] In the cloud-based control phase: The cloud platform receives data from various edge multi-source fusion processing units, performs full-domain fusion of RID data from multiple regions and nodes through a three-level fusion algorithm, further optimizes data accuracy and consistency, displays the aircraft status in real time on the electronic map, intelligently analyzes abnormal behavior and potential conflicts, issues scheduling instructions or early warning information, and coordinates with the low-temperature air conditioning system to complete closed-loop control.

[0087] For urban scenarios, the deployment solution adopts a grid-based and key coverage deployment strategy, specifically targeting the integration of urban low-altitude logistics and passenger commuting. The details are as follows:

[0088] Fixed node deployment: In the core urban area, low-altitude air route turning points, eVTOL take-off and landing sites, and around logistics parks, fixed receiving nodes are deployed at intervals of 3-5km. They are installed on the tops of high-rise buildings, communication towers, utility poles, etc., and high-gain antennas are used to cover low-altitude air routes, forming a full-area grid coverage.

[0089] Mobile node deployment: Deploy 6-8 vehicle-mounted mobile nodes in large business districts, major event sites, and temporary take-off and landing points. The location can be flexibly adjusted according to logistics peaks and event control needs to make up for the insufficient dynamic coverage of fixed nodes.

[0090] Edge multi-source fusion processing unit deployment: Divide the city into 3-4 control zones according to the city's administrative districts, and deploy one edge multi-source fusion processing unit in each zone. Connect fixed nodes through fiber optic links and mobile nodes through 5G private networks to realize localized data processing within the region.

[0091] Cloud platform deployment: Deployed in the city's low-altitude traffic command center, it seamlessly connects with the city's intelligent transportation system, logistics dispatch platform, and emergency management system to achieve data sharing and collaborative dispatch.

[0092] This embodiment makes at least the following technical contributions compared to the prior art:

[0093] Comprehensive coverage: Through a distributed fixed and mobile node grid deployment, combined with high-gain antennas and anti-blocking design, it effectively overcomes the urban "canyon effect" and achieves low-altitude full-area coverage without dead zones, with a coverage radius that can be extended to more than 50km;

[0094] Excellent data accuracy: Relying on three-level multi-source data fusion technology and high-precision clock synchronization, the error caused by electromagnetic interference and GNSS drift is greatly reduced. The position and velocity resolution accuracy far exceeds that of traditional systems, meeting the needs of low-altitude aircraft scheduling and conflict early warning.

[0095] Strong anti-interference and reliability: The system integrates frequency hopping avoidance, dual-link communication, and multi-node backup designs, which significantly improves the anti-interference capability and fault tolerance of the system, with a reliability of over 99.9%, making it suitable for complex urban electromagnetic environments and high-density flight scenarios.

[0096] Wide adaptability to various scenarios: It can be flexibly adapted to multiple scenarios such as low-altitude logistics, passenger commuting, and major event control. It supports seamless integration with existing urban infrastructure, reuses resources, and has low deployment costs, helping the low-altitude economy to take off on a large scale.

[0097] To further illustrate the technical contributions of this embodiment to the prior art, specific scenarios will be used as examples, as follows:

[0098] System Deployment:

[0099] Taking a scenario of integrated management and control of low-altitude logistics and passenger commuting in a certain city as an example, the deployment scheme of this system is as follows: Fixed receiving nodes are deployed at intervals of 3-5 kilometers in the city's core area, suburban logistics parks, and along commuter routes, forming a grid-like coverage with a total of 20 nodes. These nodes are installed on the tops of high-rise buildings, communication towers, utility poles, etc., using high-gain combined antennas to avoid building obstruction. Six mobile receiving nodes are deployed around logistics distribution centers, eVTOL take-off and landing sites, and urban commercial districts, integrated into a new energy vehicle platform, allowing for flexible adjustment of their positions according to logistics peaks and large-scale event needs. Three control zones are divided according to administrative districts, with one edge multi-source fusion processing unit deployed in each zone. Fixed nodes are connected via urban fiber optic links, and mobile nodes are connected via a 5G private network. The cloud-based management and control platform is deployed in the city's low-altitude traffic command center, linking with the city's intelligent transportation system, logistics dispatch platform, and emergency management system. A clock synchronization module provides all nodes and processing units with dual services of BeiDou time synchronization and fiber optic synchronization, ensuring time consistency in complex urban environments.

[0100] System engineering process:

[0101] Signal Acquisition and Analysis: Each receiving node continuously scans the 2.4GHz / 5.2GHz / 5.8GHz frequency bands to capture the RID broadcast signals of logistics drones, eVTOL and other aircraft entering the controlled area. The radio frequency processing module adaptively adjusts the gain and suppresses multipath interference. The protocol decoding module parses out the aircraft's identification, latitude and longitude, speed, heading, flight plan, operator information and other content. The local filtering module performs noise reduction processing and removes abnormal data caused by GNSS drift.

[0102] Data Collaboration and Calibration: The edge multi-source fusion processing unit aggregates data from various nodes, aligns timestamps based on clock synchronization benchmarks, and uses a weighted average algorithm to fuse multi-node data. It also combines urban digital maps to correct positional deviations and deduplicates to form structured data. Simultaneously, it compares the data with low-altitude flight path rules and aircraft spacing thresholds to determine whether there are any violations such as deviation from the flight path or close-range flight.

[0103] Global situational awareness: The edge multi-source fusion processing unit uploads the processed data to the cloud-based management platform. The platform integrates radar and optoelectronic equipment data and displays the real-time situation of various aircraft on the city's electronic map. The intelligent analysis module identifies abnormal behavior and potential conflicts. If an aircraft is found to be deviating from its flight path or about to collide, a graded warning is immediately triggered, and warning information and flight path adjustment suggestions are sent to the aircraft operator and the ground dispatch center.

[0104] Adaptive adjustment and maintenance: The cloud platform monitors the working status and communication link quality of each receiving node in real time. If interference sources such as 5G and Wi-Fi are detected, the affected nodes are automatically instructed to switch their working frequency bands. If a node experiences signal interruption due to equipment failure or obstruction, the edge multi-source fusion processing unit automatically activates surrounding nodes to fill the gap, ensuring uninterrupted control of high-density aircraft and guaranteeing continuous operation of low-altitude traffic.

[0105] Performance metrics:

[0106] The key performance indicators of this system during actual operation are as follows: single-node RID signal receiving distance ≥ 8 km; position error after multi-node data fusion ≤ 1 m, speed error ≤ 0.1 m / s; time synchronization accuracy ≤ 10 nanoseconds; number of drones tracked simultaneously ≥ 100; system response delay ≤ 100 milliseconds; anti-interference capability: can withstand electromagnetic interference in the range of -80dBm to 0dBm; system reliability ≥ 99.9%; operating environment temperature: -40℃ to 80℃, adaptable to extreme climatic conditions.

[0107] In some embodiments, the first-level fusion algorithm includes:

[0108] Each distributed RID receiving node uses historical parsing data cached within a preset time period to correct errors caused by GNSS positioning data drift through a Kalman filter algorithm, while simultaneously performing data noise reduction and removal of anomalous jump data.

[0109] In some embodiments, the secondary fusion algorithm includes:

[0110] Data from multiple nodes of the same low-altitude aircraft is aggregated, and the timestamps are aligned with the reference time provided by the clock synchronization module. A weighted average fusion algorithm is used in conjunction with a digital city map to correct positioning deviations caused by obstructions. The weights in the weighted average fusion algorithm are dynamically allocated based on the node signal strength and the distance from the low-altitude aircraft.

[0111] In some embodiments, the edge multi-source fusion processing unit is further configured to:

[0112] Within the corresponding control zone, the flight path of the low-altitude aircraft after positioning deviation correction is obtained, and based on the preset low-altitude flight path rules and aircraft safety distance threshold, it is determined whether the corresponding low-altitude aircraft is in violation. If so, a local warning is triggered; otherwise, no action is taken.

[0113] In some embodiments, the three-level fusion algorithm includes:

[0114] The system aggregates structured RID data output from various edge multi-source fusion processing units. For cross-regional multi-node data of the same low-altitude aircraft, a multi-source heterogeneous RID data fusion algorithm is adopted to remove corresponding false signals and duplicate data, thereby achieving accurate tracking of the entire flight path of the low-altitude aircraft.

[0115] In some embodiments, the cloud management platform is further configured to:

[0116] For the same low-altitude aircraft, the full flight path data of the low-altitude aircraft is obtained based on cross-regional multi-node data that eliminates false signals and duplicate data;

[0117] Based on the full flight path data, the status of the corresponding low-altitude aircraft is displayed in real time on the city's electronic map. The system analyzes whether the low-altitude aircraft exhibits abnormal behavior or potential conflicts. If so, a tiered warning is triggered, and warning information and flight path adjustment suggestions are sent to the low-altitude aircraft operator and the ground control center.

[0118] In some embodiments, each distributed RID receiving node is an independent hardware unit that integrates a radio frequency receiving module, a protocol decoding module, a local preprocessing module, an anti-interference module, and a communication module.

[0119] In some embodiments, it also includes:

[0120] The radio frequency receiving module specifically supports 2.4GHz / 5.2GHz / 5.8GHz three-band wideband reception, adopts a high-gain combined antenna design, and incorporates adaptive gain adjustment technology;

[0121] The protocol decoding module specifically includes: a built-in national standard protocol library and ASTM F3411 protocol library, supporting multi-protocol adaptive parsing, extracting the corresponding core fields of low-altitude aircraft and completing data legality verification;

[0122] The local preprocessing module is specifically used to execute the first-level fusion algorithm;

[0123] The anti-interference module specifically integrates spectrum sensing and frequency hopping avoidance technology, detects interference sources in real time and automatically switches the operating frequency band, and uses the SM4 encryption algorithm to ensure data transmission security.

[0124] The communication module specifically supports dual-link transmission of fiber optic and 5G private networks. The fixed node uses a fiber optic link for data transmission, while the mobile node uses a dedicated 5G link for data transmission.

[0125] In some embodiments, the cloud-based management and control platform is deployed in the urban low-altitude traffic command center and interfaces with the urban intelligent transportation system, logistics dispatch platform and emergency management system to achieve data sharing and collaborative dispatch.

[0126] In some embodiments, the clock synchronization module adopts a combination of BeiDou time synchronization and local calibration to provide a nanosecond-level time reference for the entire system.

[0127] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computing software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0128] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0129] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A distributed RID receiving system based on edge multi-source fusion, characterized in that, It includes a distributed RID receiving node cluster, an edge multi-source fusion processing unit, a cloud management and control platform, and a clock synchronization module. The various parts within the system achieve data interaction and collaborative work through redundant communication links. The distributed RID receiving node cluster adopts a grid-based deployment scheme that coordinates fixed nodes and mobile nodes to capture and initially analyze the RID signals of low-altitude aircraft in real time, thereby obtaining the corresponding raw analytical data. The distributed RID receiving node cluster contains multiple distributed RID receiving nodes, and each distributed RID receiving node performs a first-level fusion algorithm on the raw analytical data to output node data that meets the requirements of second-level fusion. The edge multi-source fusion processing unit is deployed at a set location in each control zone to aggregate node data from all nodes within the control zone, and completes data calibration, deduplication, and noise reduction through a two-level fusion algorithm to output structured RID data; all grids are divided into several control zones according to set rules; The cloud-based management and control platform uses a three-level fusion algorithm to achieve comprehensive structured RID data coordination, situation visualization, intelligent decision-making, and command issuance across the entire domain, and links with the city's low-temperature air conditioning system to form a closed-loop management and control system. The clock synchronization module adopts a combination of BeiDou time synchronization and local calibration to provide a nanosecond-level time reference for the entire system; The first-level fusion algorithm includes: Each distributed RID receiving node uses historical parsing data cached within a preset time period to correct errors caused by GNSS positioning data drift through a Kalman filter algorithm, while simultaneously performing data noise reduction and removal of anomalous jump data. The two-level fusion algorithm includes: Data from multiple nodes of the same low-altitude aircraft is aggregated, and the timestamps are aligned with the reference time provided by the clock synchronization module. A weighted average fusion algorithm is used in conjunction with a city digital map to correct positioning deviations caused by obstructions. The weights in the weighted average fusion algorithm are dynamically allocated based on the node signal strength and the distance from the low-altitude aircraft. The three-level fusion algorithm includes: The system aggregates structured RID data output from various edge multi-source fusion processing units. For cross-regional multi-node data of the same low-altitude aircraft, a multi-source heterogeneous RID data fusion algorithm is adopted to remove corresponding false signals and duplicate data, thereby achieving accurate tracking of the entire flight path of the low-altitude aircraft.

2. The distributed RID receiving system based on edge multi-source fusion according to claim 1, characterized in that, The edge multi-source fusion processing unit is also used for: Within the corresponding control zone, the flight path of the low-altitude aircraft after positioning deviation correction is obtained, and based on the preset low-altitude flight path rules and aircraft safety distance threshold, it is determined whether the corresponding low-altitude aircraft is in violation. If so, a local warning is triggered; otherwise, no action is taken.

3. The distributed RID receiving system based on edge multi-source fusion according to claim 1, characterized in that, The cloud-based management platform is also used for: For the same low-altitude aircraft, the full flight path data of the low-altitude aircraft is obtained based on cross-regional multi-node data that eliminates false signals and duplicate data; Based on the full flight path data, the status of the corresponding low-altitude aircraft is displayed in real time on the city's electronic map. The system analyzes whether the low-altitude aircraft exhibits abnormal behavior or potential conflicts. If so, a tiered warning is triggered, and warning information and flight path adjustment suggestions are sent to the low-altitude aircraft operator and the ground control center.

4. A distributed RID receiving system based on edge multi-source fusion according to claim 1, characterized in that, Each distributed RID receiving node is an independent hardware unit, integrating an RF receiving module, a protocol decoding module, a local preprocessing module, an anti-interference module, and a communication module.

5. A distributed RID receiving system based on edge multi-source fusion according to claim 4, characterized in that, Also includes: The radio frequency receiving module specifically supports 2.4GHz / 5.2GHz / 5.8GHz three-band wideband reception, adopts a high-gain combined antenna design, and incorporates adaptive gain adjustment technology; The protocol decoding module specifically includes: a built-in national standard protocol library and ASTM F3411 protocol library, supporting multi-protocol adaptive parsing, extracting the corresponding core fields of low-altitude aircraft and completing data legality verification; The local preprocessing module is specifically used to execute the first-level fusion algorithm; The anti-interference module specifically integrates spectrum sensing and frequency hopping avoidance technology, detects interference sources in real time and automatically switches the operating frequency band, and uses the SM4 encryption algorithm to ensure data transmission security. The communication module specifically supports dual-link transmission of fiber optic and 5G private networks. The fixed node uses a fiber optic link for data transmission, while the mobile node uses a dedicated 5G link for data transmission.

6. A distributed RID receiving system based on edge multi-source fusion according to claim 1, characterized in that, The cloud-based management and control platform is deployed in the city's low-altitude traffic command center and interfaces with the city's intelligent transportation system, logistics dispatch platform, and emergency management system to achieve data sharing and collaborative dispatch.

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