Blockchain unmanned aerial vehicle telemetry processing data method
By using telemetry data value assessment and anomaly detection models, consensus nodes are dynamically selected for hierarchical on-chain processing, solving the problem of resource waste in UAV telemetry data processing, achieving efficient data transmission and anomaly detection, and ensuring flight safety.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- BEIJING ZIYI TECHNOLOGY CO LTD
- Filing Date
- 2026-04-15
- Publication Date
- 2026-07-10
AI Technical Summary
Existing technologies fail to effectively distinguish between high-value and low-value data in UAV telemetry data processing, resulting in a waste of computing resources, bandwidth, and storage space, making it difficult to achieve full-process control of flight safety.
A telemetry data value assessment model is used for type identification and semantic parsing. Consensus nodes are dynamically selected for hierarchical on-chaining. Lightweight consensus algorithms and anomaly detection models are combined to monitor telemetry data in real time, identify flight anomalies, and trigger alarms.
It enables efficient transmission and storage of high-value data, reduces the consumption of computing and storage resources, improves data processing efficiency and resource utilization, and ensures timely response to flight safety and anomaly detection.
Smart Images
Figure CN122365367A_ABST
Abstract
Description
Technical Field
[0001] This application belongs to the field of unmanned aerial vehicle (UAV) control technology, and more specifically, relates to a blockchain-based UAV telemetry data processing method. Background Technology
[0002] As the low-altitude economy and unmanned aerial vehicle (UAV) systems develop towards clustering, intelligence, and autonomy, the massive amounts of telemetry data generated by UAVs during flight are not only the core basis for flight safety monitoring but also key evidence for post-accident tracing and liability determination. Blockchain technology, due to its decentralized, tamper-proof, and fully traceable characteristics, is gradually being applied to the field of UAV telemetry data storage. By writing the hash value or data packet of telemetry data into the blockchain, the integrity and credibility of the data can be effectively guaranteed.
[0003] While some progress has been made in applying blockchain technology to UAV telemetry data processing, existing technologies typically employ a one-size-fits-all encryption, compression, and on-chain strategy for all telemetry data frames in practical applications. During actual flight, a large amount of repetitive low-value data generated during stable cruise phases and high-value abnormal data generated during abnormal states are processed indiscriminately without considering the value differences of telemetry data for hierarchical processing. This results in either insufficient processing of core data or excessive processing of auxiliary data, leading to significant redundancy and waste of UAV-side computing resources, bandwidth, and blockchain storage space. Consequently, high-value critical data does not receive high-priority on-chain responses, making it impossible to promptly identify cluster anomalies in multi-UAV collaborative operations and hindering the achievement of full-process control over flight safety. Summary of the Invention
[0004] To address the aforementioned problems and technical deficiencies, this application adopts the following technical solution: a blockchain-based method for processing remote sensing data from unmanned aerial vehicles, comprising the following steps:
[0005] Collect raw telemetry data streams, perform type identification and semantic parsing based on a preset telemetry data value assessment model, realize value classification of each frame of telemetry data, and generate telemetry data uplink requests;
[0006] Conduct drone node mobility assessment, dynamically select consensus nodes, and select matching on-chain strategies and priorities based on the value level of telemetry data to perform tiered on-chaining of consensus nodes;
[0007] An anomaly detection model is established based on a pre-defined anomaly rule base. The model monitors newly added telemetry data on the sidechain in real time, identifies flight anomaly events, performs flight control, and triggers alarms.
[0008] Preferably, the raw telemetry data stream includes: flight attitude parameters, navigation and positioning parameters, link status parameters, and mission payload parameters, which are acquired in real time through the UAV flight control system, sensor array, and communication link.
[0009] Furthermore, the telemetry data value assessment model extracts parameter deviation features, parameter change rate features, state transition features, and event correlation features from each frame of telemetry data;
[0010] The four types of features are then fused to obtain the telemetry data value score for the current frame. The value score is then mapped to three value levels. The fusion calculation formula is as follows:
[0011]
[0012] in, Score the value of the telemetry data in the current frame. R represents the deviation feature value of the flight attitude parameters in the current frame, and R represents the rate of change feature value in the current frame. Assign a score to the current state. For event-related feature values, Scoring the deviation. Score the rate of change. State transition score, , , and These are the weighting coefficients. ;
[0013] The value classification consists of three levels: high, medium, and low.
[0014] Preferably, the telemetry data uplink request includes:
[0015] Based on the value level of telemetry data, a corresponding hierarchical encryption strategy is adopted, followed by compression and encoding, and then the data packet structure is used for blockchain storage.
[0016] Calculate the integrity check code of the telemetry data packet, bind it with the UAV's identity and GPS timestamp, and generate a telemetry data upload request.
[0017] Furthermore, the binding of the identity identifier and GPS timestamp is achieved by first designing a dual time anchoring mechanism that combines GPS timing and blockchain timestamps, while simultaneously recording the GPS timestamp and blockchain timestamp of each frame of telemetry data;
[0018] When multiple drone telemetry data are uploaded to the blockchain, the time consistency of each drone's data is verified through an on-chain time synchronization contract. The time-series characteristics of the telemetry data are used to construct a time-series index structure, and the time-series index structure is used to mark and eliminate errors for nodes with time deviations exceeding the limit.
[0019] Preferably, the consensus node first predicts the flight trajectory of the UAV, then assesses the mobility of the UAV node, dynamically evaluates the network dwell time and communication stability of each UAV node, and selects nodes with long dwell time and stable links as consensus nodes.
[0020] Furthermore, the hierarchical on-chain process includes:
[0021] A lightweight consensus algorithm is used to compress the block header information to generate a simplified block that includes telemetry data;
[0022] At the same time, the time-series characteristics that can be used for fast retrieval within time windows are used as block indexes, and the blocks are added and embedded into the telemetry data processing sidechain.
[0023] The sidechain periodically submits state-anchored transactions to the main chain to determine the sidechain data.
[0024] Furthermore, the identification of the aforementioned abnormal flight events includes:
[0025] It receives telemetry data from multiple drones under the same task, performs spatiotemporal alignment and situational coordination, generates cluster-level telemetry situational data, and stores it on the blockchain for evidence.
[0026] The anomaly detection model performs anomaly detection on cluster-level telemetry situational data. When a preset trigger condition is detected, a matching preset flight control scheme is triggered to control the UAV to switch flight modes, replan the mission, or return to home. At the same time, the decision-making process is fully recorded on the chain.
[0027] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the content of a blockchain-based drone telemetry data processing method as described above.
[0028] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the content of a blockchain-based drone telemetry data processing method as described above.
[0029] Compared to existing technologies, the beneficial effects of this application are as follows:
[0030] (1) This application constructs a telemetry data value assessment model with multi-dimensional feature fusion, calculates the comprehensive value score of each frame of data and maps it to three levels: high, medium and low. While ensuring the efficient and secure transmission of high-value data, it reduces the occupation of computing, bandwidth and storage resources by low-value data, realizes fine allocation under limited communication resources, reduces the power consumption of the UAV terminal, and improves data processing efficiency and resource utilization.
[0031] (2) This application predicts flight trajectories, assesses the network dwell time and communication link stability of each UAV node, dynamically selects the preferred node as the consensus node, reduces the probability of consensus switching due to node disconnection, and ensures on-chain data throughput and stability in high-speed mobile cluster scenarios.
[0032] (3) This application adopts a lightweight consensus compression block header and embeds the time range of telemetry data as a block index into the side chain. Through the time index structure, the ground station can directly locate the specific block height when querying telemetry historical data for a specific time period, thereby reducing the retrieval time complexity and shortening the data retrieval and reproduction cycle in accident investigation. Attached Figure Description
[0033] In the attached diagram:
[0034] Figure 1 This is a schematic diagram of the method steps in an embodiment of this application. Detailed Implementation
[0035] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, 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 some embodiments of this application, but not all embodiments. Generally, the components of the embodiments of this application described and shown in the accompanying drawings can be arranged and designed in various different configurations.
[0036] Example 1, such as Figure 1 As shown, a blockchain-based method for processing drone telemetry data includes the following steps:
[0037] Collect raw telemetry data streams, perform type identification and semantic parsing based on a preset telemetry data value assessment model, realize value classification of each frame of telemetry data, and generate telemetry data uplink requests;
[0038] The raw telemetry data stream includes flight attitude parameters, navigation and positioning parameters, link status parameters, and mission payload parameters, which are acquired in real time through the UAV flight control system, sensor array, and communication link.
[0039] Flight attitude parameters include: roll angle, pitch angle, yaw angle, three-axis angular velocity, and three-axis acceleration;
[0040] Navigation and positioning parameters include: GPS / BeiDou positioning latitude and longitude, altitude, ground velocity, heading angle, number of satellites, and positioning accuracy factor;
[0041] Link status parameters include: uplink and downlink signal strength, bit error rate, signal-to-noise ratio, link delay, and packet loss rate;
[0042] The mission payload parameters include: payload working status code, payload pointing angle, data acquisition quality index, and payload health status word.
[0043] The telemetry data value assessment model extracts parameter deviation features, parameter change rate features, state transition features, and event correlation features from each frame of telemetry data.
[0044] The four types of features are then fused to obtain the telemetry data value score for the current frame. The value score is then mapped to three value levels. The fusion calculation formula is as follows:
[0045]
[0046] in, Score the value of the telemetry data in the current frame. R represents the deviation feature value of the flight attitude parameters in the current frame, and R represents the rate of change feature value in the current frame. Assign a score to the current state. For event-related feature values, Scoring the deviation. Score the rate of change. State transition score, , , and These are the weighting coefficients. ;
[0047] The value classification consists of three levels: high, medium, and low.
[0048] The parameter deviation feature calculates the degree of deviation of the current frame's flight attitude parameters from the historical sliding window baseline values, as shown in the following formula:
[0049]
[0050] in, , and This represents the average values of the roll, pitch, and yaw angles over the past N frames. , and The deviation thresholds for roll angle, pitch angle, and yaw angle. , and These are the weighting coefficients. .
[0051] The parameter change rate characteristic calculates the rate of change of the target parameter across consecutive frames. It is calculated using a combination of first-order difference and moving standard deviation, as shown in the following formula:
[0052]
[0053] in, The difference in parameters between adjacent frames. This is the inter-frame time interval. The standard deviation of the historical sliding window for the target parameter.
[0054] State transition features are generated by combining the state transition matrix of telemetry data frames based on flight mode and link status, identifying telemetry data frames at state boundaries, and then assigning scores to the states corresponding to different state boundaries according to preset state transition scoring rules, representing the state transition feature values.
[0055] The event association feature detects whether the current telemetry data frame is associated with a preset high-value event. If the current frame is associated with a high-value event, the event association flag is triggered, and the event association feature value E=1. Otherwise, E is a preset fixed value, which is less than 1.
[0056] Telemetry data upload requests include:
[0057] Based on the value level of telemetry data, a corresponding hierarchical encryption strategy is adopted, followed by compression and encoding, and then the data packet structure is used for blockchain storage.
[0058] Calculate the integrity check code of the telemetry data packet, bind it with the UAV's identity and GPS timestamp, and generate a telemetry data upload request.
[0059] The frame structure of each frame of telemetry data includes: frame header, UAV identification, GPS timestamp, telemetry parameter set, and frame checksum.
[0060] The binding of identity and GPS timestamp is achieved by first designing a dual time anchoring mechanism that combines GPS timing and blockchain timestamps, while simultaneously recording the GPS timestamp and blockchain timestamp of each frame of telemetry data;
[0061] When multiple drone telemetry data are uploaded to the blockchain, the time consistency of each drone's data is verified through an on-chain time synchronization contract. The time-series characteristics of the telemetry data are used to construct a time-series index structure, and the time-series index structure is used to mark and eliminate errors for nodes with time deviations exceeding the limit.
[0062] The time series index structure adopts a time-layered B+ tree structure. The top layer uses 1-hour time buckets for coarse-grained indexing, the middle layer uses 1-minute time slices, and the bottom layer uses 10-second time blocks. Each time block stores the block height and transaction hash mapping of all on-chain telemetry data packets in the current time period. The index key value is composed of UAV ID and time period identifier.
[0063] Hierarchical encryption strategies include:
[0064] High-value data is encrypted across all fields.
[0065] Encryption of characteristic fields in high-value data;
[0066] Perform hash digest calculations on low-value data.
[0067] Conduct drone node mobility assessment, dynamically select consensus nodes, and select matching on-chain strategies and priorities based on the value level of telemetry data to perform tiered on-chaining of consensus nodes;
[0068] The consensus node first predicts the drone's flight trajectory, then assesses the drone node's mobility, dynamically evaluates the network dwell time and communication stability of each drone node, and selects nodes with long dwell time and stable links as consensus nodes.
[0069] Hierarchical on-chain implementation includes:
[0070] A lightweight consensus algorithm is used to compress the block header information to generate a simplified block that includes telemetry data;
[0071] At the same time, the time-series characteristics that can be used for fast retrieval within time windows are used as block indexes, and the blocks are added and embedded into the telemetry data processing sidechain.
[0072] The sidechain periodically submits state-anchored transactions to the main chain to determine the sidechain data.
[0073] The on-chain strategy is to have high-priority data enter the fast consensus channel and medium- and low-priority data enter the batch consensus queue.
[0074] An anomaly detection model is established based on a pre-defined anomaly rule base. The model monitors newly added telemetry data on the sidechain in real time, identifies flight anomaly events, performs flight control, and triggers alarms.
[0075] The exception rules in the exception rule library include: single-machine exception rules, multi-machine collaborative exception rules, and environment / task exception rules;
[0076] Single-unit anomaly rules: For a single drone, the rules include: attitude angle exceeding the limit, sudden altitude drop, GPS signal loss, link interruption, and sudden drop in battery voltage. Each rule contains a trigger condition logical expression, anomaly level, and matching action type.
[0077] Multi-machine collaboration anomaly rules: Anomalies in collaboration between multiple machines are defined as follows: the safe distance between multiple machines is less than the minimum safe interval, the formation configuration is severely deformed, the task coverage area overlaps and conflicts, and the time synchronization deviation exceeds the limit.
[0078] Environmental mission anomaly rules: mission execution-related anomaly patterns, such as deviating from the preset flight path beyond the allowable range, entering a no-fly zone or geofence area, or failing to reach the mission point within the specified time.
[0079] The identification of abnormal flight events includes:
[0080] It receives telemetry data from multiple drones under the same task, performs spatiotemporal alignment and situational coordination, generates cluster-level telemetry situational data, and stores it on the blockchain for evidence.
[0081] The anomaly detection model performs anomaly detection on cluster-level telemetry situational data. When a preset trigger condition is detected, a matching preset flight control scheme is triggered to control the UAV to switch flight modes, replan the mission, or return to home. At the same time, the decision-making process is fully recorded on the chain.
[0082] Spatiotemporal alignment uses the master node's time as a reference and employs a time deviation correction value to align the telemetry data of each UAV. Let the reference time be T, and the time deviation of UAV i be δ. Then, the timestamp T' of its telemetry data after alignment is:
[0083] T' = T_GPS + δ_i
[0084] Spatial alignment employs coordinate system transformation to unify the position coordinates of each UAV to the same world coordinate system.
[0085] Situational coordination and fusion involves fusing telemetry data from multiple UAVs in aligned space-time to generate cluster-level situational data, including: formation geometry matrix, cluster coverage area, cluster energy state vector, cluster communication topology diagram, and cluster mission progress vector.
[0086] On-chain cluster-level situational data involves submitting the generated cluster-level telemetry situational data as an aggregated transaction to the sidechain for on-chain storage and evidence preservation, which is then analyzed by the ground station.
[0087] Example 2, from a hardware perspective, this application provides an embodiment of an electronic device containing all or part of a blockchain drone telemetry data processing method. The electronic device includes a service processor and a distributed memory. The service processor is connected to the memory. The distributed memory stores a service self-management program configured to store machine-readable instructions. The service processor executes the service self-management program. When the instructions are executed by the processor, a blockchain drone telemetry data processing method as described above can be implemented.
[0088] Example 3: This application also provides a computer-readable storage medium capable of implementing a blockchain drone telemetry data processing method with a server or client as the execution subject in the above embodiments. The computer-readable storage medium stores a computer program, which, when executed by a processor, implements all the contents of the blockchain drone telemetry data processing method with a server or client as the execution subject in the above embodiments.
[0089] The embodiments described above are merely preferred embodiments of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this application. It should be noted that those skilled in the art can make various modifications, improvements, and substitutions without departing from the concept of this application, and these all fall within the protection scope of this application.
Claims
1. A blockchain-based method for processing remote sensing data from unmanned aerial vehicles (UAVs), characterized in that, Includes the following steps: Collect raw telemetry data streams, perform type identification and semantic parsing based on a preset telemetry data value assessment model, realize value classification of each frame of telemetry data, and generate telemetry data uplink requests; Conduct drone node mobility assessment, dynamically select consensus nodes, and select matching on-chain strategies and priorities based on the value level of telemetry data to perform tiered on-chaining of consensus nodes; An anomaly detection model is established based on a pre-defined anomaly rule base. The model monitors newly added telemetry data on the sidechain in real time, identifies flight anomaly events, performs flight control, and triggers alarms.
2. The blockchain-based UAV telemetry data processing method according to claim 1, characterized in that, The raw telemetry data stream includes flight attitude parameters, navigation and positioning parameters, link status parameters, and mission payload parameters, which are acquired in real time through the UAV flight control system, sensor array, and communication link.
3. The blockchain-based UAV telemetry data processing method according to claim 2, characterized in that, The telemetry data value assessment model extracts parameter deviation features, parameter change rate features, state transition features, and event correlation features from each frame of telemetry data. The four types of features are then fused to obtain the telemetry data value score for the current frame. The value score is then mapped to three value levels. The fusion calculation formula is as follows: in, Score the value of the telemetry data in the current frame. R represents the deviation feature value of the flight attitude parameters in the current frame, and R represents the rate of change feature value in the current frame. Assign a score to the current state. For event-related feature values, Scoring the deviation. Score the rate of change. State transition score, , , and These are the weighting coefficients. ; The value classification consists of three levels: high, medium, and low.
4. The blockchain-based drone telemetry data processing method according to claim 1, characterized in that, The telemetry data upload request includes: Based on the value level of telemetry data, a corresponding hierarchical encryption strategy is adopted, followed by compression and encoding, and then the data packet structure is used for blockchain storage. Calculate the integrity check code of the telemetry data packet, bind it with the UAV's identity and GPS timestamp, and generate a telemetry data upload request.
5. A blockchain-based method for processing remote sensing data from unmanned aerial vehicles according to claim 4, characterized in that, The binding of the identity identifier and GPS timestamp is achieved by first designing a dual time anchoring mechanism that combines GPS timing and blockchain timestamps, while simultaneously recording the GPS timestamp and blockchain timestamp of each frame of telemetry data. When multiple drone telemetry data are uploaded to the blockchain, the time consistency of each drone's data is verified through an on-chain time synchronization contract. The time-series characteristics of the telemetry data are used to construct a time-series index structure, and the time-series index structure is used to mark and eliminate errors for nodes with time deviations exceeding the limit.
6. A blockchain-based method for processing remote sensing data from unmanned aerial vehicles according to claim 1, characterized in that, The consensus node first predicts the flight trajectory of the UAV, then assesses the mobility of the UAV node, dynamically evaluates the network dwell time and communication stability of each UAV node, and selects the node with the longest dwell time and the most stable link as the consensus node.
7. A blockchain-based method for processing remote sensing data from unmanned aerial vehicles according to claim 6, characterized in that, The hierarchical on-chain process includes: A lightweight consensus algorithm is used to compress the block header information to generate a simplified block that includes telemetry data; At the same time, the time-series characteristics that can be used for fast retrieval within time windows are used as block indexes, and the blocks are added and embedded into the telemetry data processing sidechain. The sidechain periodically submits state-anchored transactions to the main chain to determine the sidechain data.
8. A blockchain-based method for processing remote sensing data from unmanned aerial vehicles according to claim 7, characterized in that, The identification of the abnormal flight events includes: It receives telemetry data from multiple drones under the same task, performs spatiotemporal alignment and situational coordination, generates cluster-level telemetry situational data, and stores it on the blockchain for evidence. The anomaly detection model performs anomaly detection on cluster-level telemetry situational data. When a preset trigger condition is detected, a matching preset flight control scheme is triggered to control the UAV to switch flight modes, replan the mission, or return to home. At the same time, the decision-making process is fully recorded on the chain.
9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the content of the blockchain drone telemetry data processing method as described in claim 1.
10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the content of the blockchain drone telemetry data processing method as described in claim 1.