Unmanned aerial vehicle flight data secure storage system based on block chain

Abstract

The invention discloses an unmanned aerial vehicle flight data security storage system based on a block chain. The system comprises a data identification module, a consensus verification module, an authority management module, a hierarchical encryption module, an audit tracing module, an information processing station and the like. According to the method and the system, the specific security requirement of each stage can be accurately evaluated, the data security management strategy can be optimized from the perspective of the whole life cycle, and the security storage system with dynamic evaluation, hierarchical encryption and cross-chain verification is realized.

Description

Technical Field

[0001] This application relates to the technical field of data security storage, and particularly to a blockchain-based secure storage system for unmanned aerial vehicle (UAV) flight data. Background Art

[0002] With the wide application of UAVs in fields such as aerial photography, logistics transportation, environmental monitoring, and public safety, the need for secure storage and management of the massive data generated during UAV flights (such as flight parameters, images and videos, location information, etc.) has become increasingly prominent. Existing technologies mostly use single hash verification or centralized database storage, lacking an efficient distributed verification mechanism, and it is difficult to detect data tampering in real time and trace the source. Although the immutable feature of blockchain provides a new idea for data deposit, existing solutions do not fully combine the heterogeneity of UAV data to design a hierarchical storage architecture.

[0003] Existing storage methods usually lack the ability to dynamically track data flow and the evolution of security status, making it difficult to discover potential security risks in a timely manner. In addition, single hash verification is difficult to adapt to heterogeneous data, with low verification efficiency, insufficient coordination of indexes and keys between on-chain metadata and off-chain storage, and poor data trust correlation. Summary of the Invention

[0004] This application provides a blockchain-based secure storage system for UAV flight data, which can not only accurately evaluate the specific security requirements at each stage, but also optimize the data security management strategy from the perspective of the entire life cycle, realizing a secure storage system with dynamic evaluation, hierarchical encryption, and cross-chain verification.

[0005] This application provides a blockchain-based secure storage system for UAV flight data. The blockchain-based secure storage system for UAV flight data includes:

[0006] A data identification module, at least used to filter abnormal UAV flight data, classify the filtered data, and establish metadata for different classifications;

[0007] A consensus verification module, at least used to verify different classified data according to the metadata;

[0008] A permission management module, at least used to calculate the sensitivity values of different classifications, and perform permission management classification on different classifications according to the sensitivity values to obtain permission classification data;

[0009] A hierarchical encryption module, at least used to divide each permission classification data into on-chain and off-chain parts, encrypt the on-chain and off-chain parts respectively, and store the encrypted data;

[0010] An audit traceability module, at least used to record data operation logs, generate an audit trail based on the operation logs, trace the audit trail, and determine whether the traceability result is unauthorized through a preset permission range;

[0011] An information processing station, at least used to receive data sent by other modules and issue an alarm based on the received data.

[0012] Further, the "filtering abnormal data of UAV flight data, classifying the filtered data, and establishing metadata for different classifications" includes:

[0013] Obtain UAV flight data and record the data source, where the data source is set with device information and blockchain identity information, and the UAV flight data is set with a timestamp and location data;

[0014] Divide the UAV flight data into image / video data and non-image / video data according to the device information of the data source;

[0015] Perform field detection on the non-image / video data to determine whether each field of the non-image / video data is complete;

[0016] Perform file header information detection on the image / video evidence to determine whether the file header information of the image / video data is complete;

[0017] If the image / video data is determined to be incomplete, send the specific data content of the image / video data and the corresponding data source of the image / video data to the information processing station;

[0018] If the non-image / video data is determined to be incomplete, send the specific data content of the non-image / video data and the corresponding data source of the non-image / video data to the information processing station;

[0019] If both the image / video data and the non-image / video data are determined to be complete, classify the UAV flight data according to the device information of the data source to obtain different classified data types, and establish metadata for different classifications.

[0020] Further, the "establishing metadata for different classifications" includes:

[0021] If the data type belongs to non-image / video data, identify the timestamps of all data in the data type, and slice the non-image / video data according to the timestamps using a preset time series, and mark each slice with a serial number;

[0022] Bind and mark the slices of the corresponding non-image / video data using the timestamp, slice serial number, and data type to obtain the basic fields of the non-image / video data;

[0023] The image / video data includes video data and image data;

[0024] If the data type belongs to image and video data, key frames of the video data are extracted at a preset frequency, and each key frame is used as a shard of the video data;

[0025] Each picture in the image data is used as a shard of the image data;

[0026] Serial numbers are marked for the shards of the video data and the shards of the image data;

[0027] Using the time stamp, shard serial number, and data type, binding marks are made for the corresponding shards of the image and video data to obtain the basic fields of the image and video data;

[0028] The hash value data of each shard is calculated respectively;

[0029] The position data corresponding to each shard is encoded to a preset encoding length to obtain position encoding data;

[0030] For each shard, the basic fields, hash value data, blockchain identity information, and position encoding data are concatenated to form a metadata.

[0031] Further, the "data verification of different classified data according to the marks" includes:

[0032] A data verification tree is constructed through the metadata, and integrity data verification is performed on data of different data types by using the data verification tree;

[0033] The speed and acceleration of the drone during flight are calculated by using the position encoding data and time series corresponding to the metadata;

[0034] The position encoding data of the next time period is calculated through the speed, acceleration, and position encoding data of the current time period;

[0035] It is judged whether the position encoding data of the next time period belongs to a preset coordinate library;

[0036] If the position encoding data of the next time period belongs to the preset coordinate library, no further processing is required;

[0037] If the position encoding data of the next time period does not belong to the preset coordinate library, an out-of-bounds warning is sent to the information processing station.

[0038] Further, the "constructing a data verification tree through the metadata and performing integrity data verification on data of different data types by using the data verification tree" includes:

[0039] The hash value data in the metadata is used as the leaf nodes;

[0040] Concatenate the hash values of adjacent leaf nodes to obtain a parent node, and calculate the hash value of the parent node;

[0041] Concatenate layer by layer and calculate the hash value of the next layer of parent nodes, and finally obtain the root hash value;

[0042] Convert the root hash value and its corresponding metadata into a preset blockchain format according to the blockchain, and encapsulate it into a structured data unit that can be transmitted, verified and finally written into the block in the blockchain network, and write the structured data unit into the blockchain through the consensus algorithm;

[0043] Upload the drone flight data to the InterPlanetary File System (IPFS), and obtain the hash value data corresponding to the metadata;

[0044] Attach the hash value data corresponding to the metadata to the structured data unit, and establish a cross-chain index between the blockchain and the InterPlanetary File System using the hash value data;

[0045] By downloading the target shard from the InterPlanetary File System, obtain the hash value data corresponding to the target shard, and generate the path hash value from the target shard to the root node;

[0046] Verify the integrity of the data by comparing the path hash value and the root hash value for data matching.

[0047] Further, the "calculate the sensitive values of different classifications, and perform permission management classification on different classifications according to the sensitive values to obtain permission classification data" includes:

[0048] Preset different initial sensitive value for different data types;

[0049] Calculate the sensitive value of each classification through preset sensitive indicators;

[0050] Calculate the total sensitive value of each classification according to the initial sensitive value and the sensitive value of each classification respectively;

[0051] Use the total sensitive value to map to the preset sensitive level standard to obtain the sensitive level of each classification;

[0052] Re-classify the classified data according to the sensitive level to obtain permission classification data.

[0053] Further, the "divide each permission classification data into on-chain and off-chain, and encrypt the on-chain and off-chain respectively" includes:

[0054] Divide the permission classification data into on-chain data and off-chain data;

[0055] Among them, on-chain includes metadata, root hash value and sensitive level, and off-chain includes the drone flight data corresponding to the metadata;

[0056] Match the encryption length according to the sensitivity level of the upper chain;

[0057] Use a preset random number generator to randomly generate a key with the same length as the encryption length;

[0058] Use a preset key derivation function to convert the key into a 32-byte key K1;

[0059] Use the preset random number generator again to randomly generate a key with a length that is half of the encryption length, and use the preset key derivation function to convert the regenerated key into a 16-byte key IV1;

[0060] Bind the key IV1 to the blockchain identity information of the metadata;

[0061] Encrypt the key K1 to obtain the key EK1, and encapsulate the key EK1 as an independent field in the blockchain network;

[0062] Generate the ciphertext C1 using the metadata key IV1;

[0063] Convert the encrypted key EK1 and the current group ciphertext C1 into a preset blockchain format, and encapsulate them into a structured data unit that can be transmitted, verified, and finally written into the block in the blockchain network. Write the structured data unit into the blockchain for storage through the consensus algorithm;

[0064] Match the encryption length according to the sensitivity level of the lower chain;

[0065] Use the key generation method of the upper chain to obtain the key EK2 and the key IV2, and encapsulate the key EK2 as an independent field in the blockchain network and associate it with the upper chain metadata;

[0066] Use the ciphertext generation method of the upper chain to obtain the ciphertext C2, bind the key IV2 and the ciphertext C2 to the InterPlanetary File System, associate them with the metadata on the blockchain through the hash value data cross-chain index, and store them.

[0067] Further, the "generating the ciphertext C1 using the metadata key IV1" includes:

[0068] Convert the metadata into a byte format, and divide the byte format of the metadata according to a standard length of 16 bytes to obtain multiple plaintext groups P;

[0069] Perform a bitwise exclusive OR operation on the first group P and the key IV1 to obtain a new 16-byte sequence

[0070] Use a preset algorithm to encrypt the bytes to obtain the first ciphertext c1;

[0071] For each plaintext block p i Repeat the above process to obtain multiple ciphertexts c i , where i represents the sequence number of the block;

[0072] XOR the current plaintext block p i with the previous block ciphertext C i-1 bit by bit to obtain bytes

[0073] where i - 1 represents the previous block of the i-th block;

[0074] Encrypt the bytes to obtain the current block ciphertext C1.

[0075] Further, the "recording the data operation log, generating an audit trail based on the operation log, tracing the audit trail, and determining whether the tracing result is unauthorized through a preset permission scope" includes:

[0076] When an active operation occurs, record the specific operation content and match the timestamp of the current operation;

[0077] Convert the specific operation content and timestamp into a preset record format and record them to obtain the recorded data;

[0078] Integrate the recorded data based on the timestamp sequence to form an audit trail;

[0079] Identify the specific operation types in the audit trail and assign initial sensitivity values to different operation types;

[0080] Establish a permission template through a preset permission scope and calculate the permission sensitivity values of each audit trail through the permission template respectively;

[0081] Calculate the total sensitivity value of each segment of the audit trail based on the initial sensitivity value and the permission sensitivity value respectively;

[0082] Trace the sensitivity value of each segment of the audit trail and determine whether the sensitivity value is higher than a preset threshold;

[0083] If the sensitivity value is higher than the preset threshold, the judgment result is unauthorized, and an unauthorized warning is sent to the information processing station;

[0084] If the sensitivity value is lower than the preset threshold, the judgment result is not unauthorized.

[0085] Further, the "receiving data sent by other modules and alarming according to the received data" includes:

[0086] An information processing station is established based on distributed node technology, and the elliptic curve encryption algorithm is used to encrypt the information processing station;

[0087] If the information processing station successfully receives the specific data content of the image and video data or the data source corresponding to the image and video data, an alarm for abnormal data is issued, and the received data is sent to the information processing station;

[0088] If the information processing station successfully receives an out-of-bounds warning, an out-of-bounds warning is issued, and the position coding data for the next time period, the speed, acceleration, and position coding data for the current time period are sent to the information processing station;

[0089] If the information processing station successfully receives an unauthorized warning, an unauthorized alarm is issued, and the audit trail corresponding to the unauthorized warning and the sensitive value of the audit trail are sent to the information processing station.

[0090] Compared with the prior art, the technical solution provided by this application has at least the following advantages:

[0091] First, the data identification module avoids storage failure caused by data damage or tampering through the dual identification of device information and blockchain identity. By binding the three-dimensional identification of time, type, and space with hash value data and blockchain identity, metadata including shard basic attributes, content summary, source identification, and location data is constructed, effectively solving the problems of unified management and secure storage of heterogeneous data, and improving the integrity verification of UAV flight data.

[0092] Second, the consensus verification module predicts the theoretical flight coordinates for the next time period by parsing the position coding information and its corresponding time series in the metadata, automatically triggering the intelligent warning mechanism. By effectively combining the spatio-temporal trust characteristics of blockchain metadata with the dynamic model prediction ability, a forward-looking UAV flight safety monitoring system is constructed, and an efficient data integrity verification mechanism in a distributed environment is constructed through hierarchical hash calculation of the data verification tree, blockchain deposit evidence, and cross-chain indexing technology.

[0093] Third, the permission management module realizes the adaptive adjustment of the data security protection intensity through quantitative evaluation and level mapping, ensures that high-sensitive data matches strong encryption and strict permission policies, and low-sensitive data takes into account access efficiency, constructing a multi-level security protection system.

[0094] Fourth, the hierarchical encryption module realizes efficient distributed storage and fine-grained permission control while ensuring data privacy and integrity through key independent encapsulation, hierarchical encryption, and cross-chain indexing, constructing a blockchain-IPFS collaborative UAV data security storage system; at the same time, maintaining the reversibility of the encryption process, providing a chain-on storage solution that takes into account both efficiency and security for metadata.

[0095] Fifthly, the audit traceability module realizes the automatic identification and traceability of operation risks through the blockchain immutable log and the dynamic quantification model, ensuring the timeliness of drone data access.

[0096] Sixthly, the information processing station ensures high availability through a distributed architecture, combines ECC encryption and blockchain evidence storage, realizes the trusted aggregation, real-time response and full-link traceability of multi-source alarm data, and constructs a drone security control system. Description of the Drawings

[0097] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0098] Figure 1 Schematic diagram of a blockchain-based drone flight data secure storage system in an embodiment of the present application;

[0099] Figure 2 Flowchart of a blockchain-based drone flight data secure storage system in an embodiment of the present application;

[0100] Figure 3 Schematic diagram of the information processing station of a blockchain-based drone flight data secure storage system in an embodiment of the present application. Detailed Embodiments

[0101] The embodiments of the present application provide a blockchain-based drone flight data secure storage system. The terms "first", "second", "third", "fourth", etc. (if any) in the specification, claims and drawings of the present application are used to distinguish similar objects, and do not have to be used to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments described here can be implemented in an order different from that shown or described here. In addition, the terms "comprising" or "having" and any variation thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device comprising a series of steps or units does not have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0102] For ease of understanding, the specific processes of the embodiments of the present application will be described below. Please refer to Figure 1 and Figure 2, a blockchain-based secure storage system for UAV flight data in an embodiment of the present application includes a data identification module, a consensus verification module, a permission management module, a hierarchical encryption module, an audit and traceability module, and an information processing station, etc.

[0103] Among them, the data identification module is at least used to filter abnormal UAV flight data, classify the filtered data, and establish metadata for different classifications.

[0104] In an embodiment of the present invention, the metadata is a structured data unit used to uniquely identify UAV flight data shards and carry key index information; as the core carrier for data to be uploaded to the chain, this metadata contains both the basic attributes of the data shards (timestamp, data type, location data), and establishes a cross-chain index with the original UAV flight data in the InterPlanetary File System through hash value data. At the same time, it carries blockchain identity information to achieve permission management binding, becoming the key hub connecting data storage, verification, encryption, and traceability, ensuring the coordinated operation of each module of the system.

[0105] Furthermore, the process of "filtering abnormal UAV flight data, classifying the filtered data, and establishing metadata for different classifications" specifically includes:

[0106] Obtain UAV flight data and record the data source. The data source is set with device information and blockchain identity information, and the UAV flight data is set with a timestamp and location data;

[0107] Divide the UAV flight data into image / video data and non-image / video data according to the device information of the data source;

[0108] Perform field detection on the non-image / video data to determine whether each field of the non-image / video data is complete;

[0109] Perform file header information detection on the image / video evidence to determine whether the file header information of the image / video data is complete;

[0110] If the image / video data is determined to be incomplete, send the specific data content of the image / video data and the corresponding data source of the image / video data to the information processing station;

[0111] If the non-image / video data is determined to be incomplete, send the specific data content of the non-image / video data and the corresponding data source of the non-image / video data to the information processing station;

[0112] If both the image / video data and the non-image / video data are determined to be complete, classify the UAV flight data according to the device information of the data source to obtain different classification data types, and establish metadata for different classifications.

[0113] In an embodiment of the present invention, the flight data of the drone can be obtained through an external terminal. The device information refers to the device name, device model, drone name, etc., such as a wind speed sensor, a temperature sensor, and a camera for taking pictures, etc., which can be set according to the actual usage requirements; the blockchain identity information refers to the unique identification address of the device in the blockchain network; the timestamp refers to the time when the device collects data, and the location data refers to the longitude and latitude coordinates and altitude data; the image and video data refers to the image data and video data collected by the vision device; the non-image and video data refers to all data other than the image data and video data.

[0114] In an embodiment of the present invention, classifying the drone flight data according to the device information of the data source means classifying the data into different data types according to the device name of the device information. For example, the data collected by the temperature sensor is classified into the temperature data type, and the data collected by the wind speed sensor is classified into the wind speed data type, etc.; detecting the fields of the non-image and video data means judging whether the fields of the non-image and video data are empty. For example, the fields such as the timestamp, latitude, and longitude specified in the MAVLink protocol (Micro Air Vehicle Link, a communication protocol for micro air vehicles) cannot be empty. If they are missing, the data is determined to be incomplete; detecting the file header information of the image and video evidence means verifying whether the file header mark of the image data and the parsing container format header of the video data are complete. For example, the header file of the image data includes meta-information such as the format mark, resolution, and shooting time, and the header file of the video data includes the container format, encoding parameters, key frame timestamp, etc.

[0115] Specifically, the drone flight data is obtained through the terminal, and the device information of the data source (such as camera model, sensor type), blockchain identity information (the unique identifier of the device in the blockchain network), as well as the timestamp and location data (latitude and longitude coordinates) carried by the data are synchronously recorded to form the basic identification system of the data. Subsequently, according to the device function positioning and data output form, the data is divided into image and video data (such as aerial videos, real-time images) and non-image and video data (such as flight attitude parameters, control instructions). Differentiated integrity detection is implemented for the heterogeneous characteristics of the two types of data. For non-image and video data, field-level verification is performed (verifying the existence of fields, format compliance, and logical consistency), and for image and video data, file header parsing is performed (verifying format markers, metadata integrity, and timestamp synchronization). If incomplete data is detected (such as the missing location field in non-image and video data, or the damaged file header in image and video data), the system will package the data content and data source information (device blockchain identity, spatio-temporal data) and send it to the information processing station to trigger an exception alarm. If the data is complete, it is further divided into specific data types (such as control instructions, visible light images, etc.) according to the device information, and metadata is established by combining information such as timestamps, shard numbers, and data types, providing structured input for subsequent consensus verification, permission management, and other modules.

[0116] In the embodiment of the present invention, the data identification module establishes a trusted association of "data-device-identity" through the dual identification of device information and blockchain identity, solving the problem of unclear traceability of data sources in traditional systems. Through a differentiated integrity detection mechanism, abnormal data is filtered to avoid storage failures caused by data damage or tampering, providing effective and trusted data input for subsequent consensus verification, permission management, and other modules, and ensuring the availability and security of drone flight data.

[0117] If the data type belongs to non-image and video data, the timestamps of all data in the data type are identified, and the non-image and video data is sharded according to the timestamps using a preset time series, and each shard is marked with a serial number.

[0118] Using timestamps, shard numbers, and data types, the shards of the corresponding non-image and video data are bound and marked to obtain the basic fields of the non-image and video data.

[0119] Image and video data includes video data and image data.

[0120] If the data type belongs to image and video data, key frames of the video data are extracted at a preset frequency, and each key frame is used as a shard of the video data.

[0121] Each picture in the image data is used as a shard of the image data.

[0122] Number the shards of video data and the shards of image data;

[0123] Use timestamps, shard numbers, and data types to bind and mark the corresponding shards of image and video data to obtain the basic fields of the image and video data;

[0124] Calculate the hash value data for each shard respectively;

[0125] Encode the position data corresponding to each shard to a preset encoding length to obtain position encoding data;

[0126] For each shard, concatenate the basic field, hash value data, blockchain identity information, and position encoding data to form a metadata.

[0127] In the embodiments of the present invention, a shard is to divide continuous original data into independent and traceable minimum data units and assign a unique identifier; for shards of non-image and video data, based on the continuity of timestamps, the continuous parameter stream (such as flight altitude, speed, control instructions) is divided into shards at equal time intervals according to a preset time series (such as 10 times per second, 1 time per millisecond); for image data, a single image (such as an aerial photo in JPEG format) is directly used as the minimum shard, and for video data, key frames are extracted from the continuous video stream through a preset frequency (such as extracting 1 frame per second), and the dynamic video is converted into a sequence of static key frames (such as I frames and P frames extracted from a 1080p video stream), and each key frame is used as an independent shard. In the embodiments of the present invention, a mature hash algorithm can be used to calculate the hash value data for each shard. For example, the SHA-256 algorithm.

[0128] Specifically, for non-image video data (such as flight attitude data), by identifying timestamps and segmenting them into equally spaced shards at a preset time series (such as 10 times per second), each shard is assigned a unique serial number label (such as 001, 002), and then the timestamp, shard serial number, and data type (such as "flight attitude data") are bound to form a basic field (such as 20250415143000001 attitude data); for image video data, key frames of the video data are extracted at a preset frequency (such as extracting 1 frame per second), and each key frame is used as a shard. For image data, a single image is directly used as a shard. Both types of shards are numbered (such as video shard V001, image shard 1001), and combined with the timestamp and data type (such as "visible light image") to generate a basic field (such as 20250415143005V001 visible light image). Subsequently, a common processing step is performed on all shards: calculating the hash value data of the shard content (such as SHA-256 hash), encoding the location data (such as longitude and latitude) into a binary string of a preset length, and finally splicing the basic field, hash value data, blockchain identity information of the data source, and location encoding in a fixed format to form independent metadata for each shard (such as [basic field]#[hash value]#[device ID]#[location encoding]).

[0129] In the embodiment of the present invention, through the binding of three-dimensional identifiers of time, type, and space with hash value data and blockchain identity, metadata containing shard basic attributes, content summary, source identifier, and location data is constructed, effectively solving the problems of unified management and secure storage of heterogeneous data, and improving the processing efficiency and reliability of UAV flight data in aspects such as integrity verification, permission management, and cross-chain collaboration.

[0130] The consensus verification module is at least used to perform data verification on different types of data according to the metadata.

[0131] In the embodiment of the present invention, performing data verification on different types of data according to the metadata means constructing a Merkle tree by extracting the hash value in the metadata to verify data integrity, and calculating the flight state of the UAV using the location encoding data and time series, and determining whether it exceeds the preset coordinate library to achieve out-of-bounds warning, providing distributed verification support for the credibility and security of the data.

[0132] The process of "performing data verification on different types of data according to the label" specifically includes:

[0133] Constructing a data verification tree through the metadata and using the data verification tree to perform integrity data verification on data of different data types;

[0134] Calculating the speed and acceleration of the UAV during flight using the location encoding data and time series corresponding to the metadata;

[0135] Calculate the position encoding data for the next time period based on the speed, acceleration, and position encoding data of the current time period;

[0136] Determine whether the position encoding data for the next time period belongs to a preset coordinate library;

[0137] If the position encoding data for the next time period belongs to the preset coordinate library, no further processing is required;

[0138] If the position encoding data for the next time period does not belong to the preset coordinate library, send an out-of-bounds warning to the information processing station.

[0139] In the embodiment of the present invention, by parsing the position encoding information and its corresponding time series in the metadata, the kinematic parameters (including flight speed and acceleration) of the drone in flight are calculated in real time. Based on the dynamic parameters at the current moment, the kinematic model is used to predict the theoretical flight coordinates for the next time period, and the predicted coordinates are automatically compared with the preset legal flight area coordinate library. When it is detected that the predicted coordinates exceed the permitted flight boundary, the system automatically triggers an intelligent warning mechanism and sends an out-of-bounds warning to the information processing station to achieve compliance prediction and proactive safety control of the flight path. Through this solution, the spatio-temporal trust characteristics of blockchain metadata and the dynamic model prediction ability are effectively combined to construct a forward-looking drone flight safety monitoring system.

[0140] The process of "constructing a data verification tree through metadata and using the data verification tree to perform integrity data verification on data of different data types" specifically includes:

[0141] Use the hash value data in the metadata as leaf nodes;

[0142] Concatenate the hash values of adjacent leaf nodes to obtain a parent node, and calculate the hash value of the parent node;

[0143] Layer by layer, concatenate and calculate the hash value of the next layer of parent nodes, and finally obtain the root hash value;

[0144] Convert the root hash value and its corresponding metadata in the blockchain into a preset blockchain format, and encapsulate it into a structured data unit that can be transmitted, verified, and finally written into the block in the blockchain network. Write the structured data unit into the blockchain through the consensus algorithm;

[0145] Upload the drone flight data to the InterPlanetary File System and obtain the hash value data corresponding to the metadata;

[0146] Attach the hash value data corresponding to the metadata to the structured data unit, and establish a cross-chain index between the blockchain and the InterPlanetary File System using the hash value data;

[0147] By downloading the target shard from the InterPlanetary File System, obtaining the hash value data corresponding to the target shard, and generating the path hash value from the target shard to the root node;

[0148] Data integrity verification is performed by comparing the path hash value and the root hash value for data matching.

[0149] In the embodiment of the present invention, a leaf node is an end node in the data verification tree that has no child nodes and is the bottommost element of the tree hierarchy; a parent node is a hierarchical node above the leaf node (or child node) that is responsible for aggregating the data of the child nodes and constructing the upper-layer verification logic; the InterPlanetary File System refers to the InterPlanetary File System (IPFS), which is a decentralized peer-to-peer distributed file storage protocol. In the blockchain-based drone flight data secure storage system, cross-chain indexing refers to a mechanism that, through technical means, makes a trusted association between the metadata (such as hash values, timestamps, permission information) stored on the blockchain and the actual data shards (such as videos, images, sensor logs) stored in the InterPlanetary File System (IPFS). The path hash value refers to a hash sequence that starts from the leaf node hash of the data shard, calculates the parent node hash layer by layer upward, and finally forms a complete verification path hash from the underlying data to the root hash.

[0150] In the embodiment of the present invention, the hash value of each shard can be generated by the SHA-256 algorithm to ensure uniqueness.

[0151] In the embodiment of the present invention, data integrity verification by comparing the path hash value and the root hash value for data matching means that the root hash is compared with the original root hash solidified by the consensus algorithm on the blockchain. If they are the same, the data has not been tampered with and the integrity verification passes.

[0152] Specifically, the hash value data in the metadata is used as the leaf node to construct a data verification tree. By splicing the hash values of adjacent leaf nodes layer by layer and calculating the parent node hash value, the root hash value is finally obtained; then the root hash value and its corresponding metadata are converted into a preset blockchain format and encapsulated into a structured data unit that can be transmitted, verified, and written into the block in the blockchain network, and written into the blockchain through the consensus algorithm; at the same time, the drone flight data is uploaded to the InterPlanetary File System, the hash value data corresponding to the metadata is appended to the structured data unit, and a cross-chain index between the blockchain and the InterPlanetary File System is established using the hash value data; during integrity verification, the target shard is downloaded from the InterPlanetary File System, its hash value data is obtained and the path hash value from the target shard to the root node is generated, and data matching is performed by comparing the path hash value and the root hash value to achieve verification of data integrity.

[0153] In the embodiments of the present invention, an efficient data integrity verification mechanism in a distributed environment is constructed through hierarchical hashing calculation of a data verification tree, blockchain evidence storage, and cross-chain indexing technology, ensuring the immutability and credibility of UAV flight data during storage and transmission, and providing a secure and reliable verification basis for data interaction among system modules.

[0154] The permission management module is at least used to calculate sensitive values of different classifications, and perform permission management classification on different classifications according to the sensitive values to obtain permission classification data.

[0155] In the embodiments of the present invention, the sensitive value is a parameter used to quantify the security risk level and access permission requirements of data classifications (such as videos, images, sensor logs, etc.).

[0156] The process of "calculating sensitive values of different classifications, performing permission management classification on different classifications according to the sensitive values, and obtaining permission classification data" specifically includes:

[0157] Presetting different initial sensitive values for different data types;

[0158] Calculating the sensitive value of each classification respectively through preset sensitive indicators;

[0159] Calculating the total sensitive value of each classification respectively according to the initial sensitive value and the sensitive value of each classification;

[0160] Mapping the total sensitive value to a preset sensitive level standard to obtain the sensitive level of each classification;

[0161] Reclassifying the classified data according to the sensitive level to obtain permission classification data.

[0162] In the embodiments of the present invention, calculating the sensitive value of each classification respectively through preset sensitive indicators means that, according to dynamic indicators such as data content characteristics (such as whether it contains geographical coordinates), source device security level, and compliance requirements, different weight coefficients are assigned to each indicator, and the different weight coefficients are used as the sensitive value of the current classification.

[0163] Specifically, initial sensitive values are preset according to data types (such as images, logs). Subsequently, based on preset sensitive indicators such as data content, source device, and compliance requirements, for example, setting the credibility weight of the data source device to 0.3 or other appropriate values, the dynamic sensitive values of each classification are calculated through a weighted fusion algorithm; the dynamic sensitive value is superimposed with the initial value to generate the total sensitive value, and it is mapped to a preset sensitive level standard (such as low / medium / high). Finally, the data is reclassified according to the sensitive level to generate permission management classification data.

[0164] In the embodiments of the present invention, through the dynamic quantization evaluation and level mapping of the permission management module, the adaptive adjustment of the data security protection intensity is realized, ensuring that high-sensitive data matches strong encryption and strict permission policies, and the access efficiency of low-sensitive data is taken into account, and a multi-level security protection system is constructed.

[0165] The hierarchical encryption module is at least used to divide each permission classification data into an upper chain and a lower chain, encrypt the upper chain and the lower chain respectively, and store the encrypted data.

[0166] In the embodiments of the present invention, the upper chain and the lower chain refer to the hierarchical design for data storage location, encryption method and function, which can balance the storage efficiency and data security of the blockchain.

[0167] The process of "dividing each permission classification data into an upper chain and a lower chain, and encrypting the upper chain and the lower chain respectively" specifically includes:

[0168] Dividing the permission classification data into upper chain data and lower chain data;

[0169] Among them, the upper chain includes metadata, root hash value and sensitive level, and the lower chain includes the UAV flight data corresponding to the metadata;

[0170] Matching the encryption length according to the sensitive level of the upper chain;

[0171] Using a preset random number generator to randomly generate a key with the same length as the encryption length;

[0172] Using a preset key derivation function to convert the key into a 32-byte key K1;

[0173] Using the preset random number generator to randomly generate a key with a length half of the encryption length again, and using the preset key derivation function to convert the regenerated key into a 16-byte key IV1;

[0174] Binding the key IV1 to the blockchain identity information of the metadata;

[0175] [[ID=:33]]Encrypting the key K1 to obtain the key EK1, and encapsulating the key EK1 as an independent field in the blockchain network;

[0176] Generating the ciphertext C1 using the metadata key IV1;

[0177] Converting the encrypted key EK1 and the current group ciphertext C1 into a preset blockchain format, and encapsulating them into a structured data unit that can be transmitted, verified and finally written into the block in the blockchain network, and writing the structured data unit into the blockchain for storage through the consensus algorithm;

[0178] Matching the encryption length according to the sensitive level of the lower chain;

[0179] Use the key generation method for on-chain data to obtain the key EK2 and the key IV2, and encapsulate the key EK2 as an independent field in the blockchain network, associating it with the on-chain metadata;

[0180] Use the ciphertext generation method for on-chain data to obtain the ciphertext C2, bind the key IV2 and the ciphertext C2 to the InterPlanetary File System, associate them with the metadata on the blockchain through the cross-chain index of hash value data, and store them.

[0181] In this embodiment, when binding the key IV1 to the blockchain identity information of the metadata, an asymmetric encryption algorithm or the like can be used to sign IV1 to ensure that the binding process is tamper-proof.

[0182] In the embodiment of the present invention, matching the encryption length according to the on-chain sensitivity level means that according to the sensitivity level (such as high, medium, low) marked in the on-chain data metadata, different lengths of encryption keys are respectively associated (for example, high sensitivity corresponds to a 256-bit key, medium sensitivity corresponds to 192 bits, and low sensitivity corresponds to 128 bits). The random number generator can use a pseudo-random number generation algorithm to generate a random number sequence with high entropy value, and according to the encryption length requirement (such as 256 bits for high sensitivity level) matched by the on-chain data sensitivity level, dynamically output the original random number with the same length as the target key length; the generated random number is converted into a standardized encryption key (such as the 32-byte key K1) through a key derivation function (such as HKDF). The encryption of the key K1 can be implemented by a symmetric encryption algorithm: first, use the preset master key as the key encryption key, and the original key K1 can be encrypted by the AES-GCM or AES-CBC algorithm to generate the ciphertext EK1; the initialization vector IV1 and the blockchain identity identifier are bound during the encryption process to ensure the uniqueness of encryption; the encrypted EK1 and the metadata ciphertext C1 are jointly encapsulated into a blockchain structured data unit and written into the on-chain storage through the consensus algorithm.

[0183] In the embodiment of the present invention, the encryption cooperation between on-chain and off-chain is realized through the cross-chain binding of keys and data: based on the sensitivity level of the on-chain metadata, match the encryption length, and use the on-chain consistent random number generation and key derivation method to generate the symmetric key EK2 and the initialization vector IV2 required for off-chain data encryption; among them, EK2 is encrypted and written into the blockchain as an independent field to ensure the trusted storage and permission control of the key, while IV2 and the off-chain ciphertext C2 are jointly stored in the InterPlanetary File System (IPFS), and a cross-chain index is constructed through the shard hash value, so that the off-chain ciphertext and the on-chain metadata form a two-way verification relationship. By separating the on-chain key escrow and the off-chain encrypted data storage, it not only prevents the cracking of all data caused by key leakage, but also utilizes the anti-tampering feature of the blockchain and the distributed storage advantage of IPFS to realize the integrity guarantee and efficient access control of the entire life cycle of encrypted data.

[0184] Specifically, the hierarchical encryption module is implemented through an on-chain and off-chain collaborative mechanism. The data is divided into on-chain metadata (including sensitivity level and root hash value) and off-chain data through permission classification. For the on-chain data, the encryption length is matched based on the sensitivity level, and the original key is dynamically generated by a cryptographically secure random number generator. After being converted into a standard key K1 and an initialization vector IV1 through a key derivation function, the IV1 is bound to the blockchain identity information, and the metadata ciphertext C1 is generated by chained encryption. The key K1 is encrypted into EK1 and then jointly encapsulated with C1 into a blockchain structured data unit and stored on the chain; for the off-chain data, the same method is reused to generate the key EK2 and IV2, the EK2 is associated with the on-chain metadata, the ciphertext C2 is bound to the IV2 and stored in the InterPlanetary File System (such as IPFS), and a cross-chain index is constructed through the shard hash value to achieve a trusted mapping between the on-chain metadata and the off-chain ciphertext.

[0185] In the embodiment of the present invention, the hierarchical encryption module realizes efficient distributed storage and fine-grained permission control while ensuring data privacy and integrity through key independent encapsulation, hierarchical encryption, and cross-chain indexing, and constructs a blockchain-IPFS collaborative drone data security storage system.

[0186] The process of "generating the ciphertext C1 using the metadata key IV1" specifically includes:

[0187] Convert the metadata into a byte format, and divide the byte format of the metadata according to a standard length of 16 bytes to obtain multiple plaintext groups P;

[0188] Perform a bitwise exclusive OR operation on the first group P and the key IV1 to obtain a new 16-byte sequence

[0189] Use a preset algorithm to encrypt the byte to obtain the first ciphertext c1;

[0190] For each plaintext group p i Repeat the above process to obtain multiple ciphertexts c i , where i represents the serial number of the group;

[0191] Perform a bitwise exclusive OR operation on the current plaintext group p i and the previous group ciphertext C i-1 to obtain the byte where i - 1 represents the previous group of the i-th group;

[0192] Encrypt the byte to obtain the current group ciphertext C1.

[0193] In the embodiments of the present invention, metadata encryption is implemented in a chained encryption mode: First, the metadata is converted into a byte stream in a specific format and divided into plaintext groups (P1, P2,..., P n ) according to the standard length of 16 bytes; The first group P1 and the initialization vector IV1 are bitwise XORed (XOR) to generate an intermediate value which is then processed by a preset encryption algorithm (such as AES) to obtain the first ciphertext segment C1; Each subsequent plaintext group p i is XORed with the previous ciphertext c i-1 and then encrypted to generate the current ciphertext c i to form a chained dependency structure. This step introduces randomness and the correlation with the previous ciphertext through the XOR operation, effectively resisting replay attacks and pattern analysis, ensuring that the same plaintext generates different ciphertexts in different contexts, while maintaining the reversibility of the encryption process, providing an on-chain storage solution that takes into account both efficiency and security for metadata.

[0194] The audit and traceability module is at least used to record data operation logs, generate an audit trail based on the operation logs, trace the audit trail, and determine whether the trace result is unauthorized through a preset permission range.

[0195] Among them, the process of "recording data operation logs, generating an audit trail based on the operation logs, tracing the audit trail, and determining whether the trace result is unauthorized through a preset permission range" specifically includes:

[0196] When an active operation occurs, record the specific operation content and match the timestamp of the current operation;

[0197] Convert the specific operation content and timestamp into a preset record format and record them to obtain record data;

[0198] Integrate the record data in chronological order of timestamps to form an audit trail;

[0199] Identify the specific operation types in the audit trail and assign initial sensitivity values to different operation types;

[0200] Establish a permission template through a preset permission range and calculate the permission sensitivity values of each audit trail through the permission template respectively;

[0201] Calculate the total sensitivity values of each segment of the audit trail according to the initial sensitivity values and the permission sensitivity values respectively;

[0202] Trace the sensitivity values of each segment of the audit trail and determine whether the sensitivity values are higher than a preset threshold;

[0203] If the sensitivity value is higher than the preset threshold, the judgment result is unauthorized, and an unauthorized warning is sent to the information processing station; ​

[0204] If the sensitive value is lower than the preset threshold, the judgment result is that there is no over - authorization.

[0205] In the embodiments of the present invention, a permission template is established through a preset permission range, and the permission sensitive value of each audit track is calculated through the permission template. It means that according to data classification (such as high - sensitive video, low - sensitive log) and operation type (such as read, modify, delete), permission levels are pre - divided (such as administrator, auditor, ordinary user); and a basic sensitive value is assigned to each permission level (such as administrator = 100, auditor = 50, ordinary user = 20), and a weight is attached to the operation type (such as delete operation weight = 1.5, read operation weight = 1.0). Finally, the permission sensitive value of each audit track is calculated according to the specific content of the audit track.

[0206] In the embodiments of the present invention, the audit traceability module is implemented through dynamic sensitive value evaluation. When a data operation is triggered, the operation content, timestamp, and executor identity are recorded, and they are standardized into structured log entries; after aggregating the logs in timestamp order to generate an audit track chain, the operation type is identified and an initial sensitive value is assigned (such as data download = high - sensitive, metadata read = low - sensitive), and the permission sensitive value of the audit track is calculated in combination with the preset permission template; the total sensitive value is generated by superimposing the initial value and the permission value, and it is compared with the preset threshold in real - time. If it exceeds the threshold, an over - authorization behavior is determined, and an alarm is automatically triggered and pushed to the information processing station.

[0207] In the embodiments of the present invention, the audit traceability module realizes the automatic identification and traceability of operation risks through the blockchain immutable log and the dynamic quantification model, ensuring the timeliness of UAV data access.

[0208] The information processing station is at least used to receive data sent by other modules and alarm according to the received data.

[0209] In the embodiments of the present invention, the alarm can be broadcast to authorized terminals (such as supervision platforms, UAV operators) through distributed nodes.

[0210] The process of "receiving data sent by other modules and alarming according to the received data" specifically includes:

[0211] An information processing station is established based on distributed node technology, and the information processing station is encrypted using the elliptic curve encryption algorithm;

[0212] If the information processing station successfully receives the specific data content of the image and video data or the data source corresponding to the image and video data, an alarm for abnormal data is made, and the received data is sent to the information processing station;

[0213] If the information processing station successfully receives an out-of-bounds warning, it issues an out-of-bounds warning and sends the position coding data for the next time period of the out-of-bounds warning, the speed, acceleration, and position coding data for the current time period to the information processing station;

[0214] If the information processing station successfully receives an unauthorized warning, it issues an unauthorized alarm and sends the audit trail corresponding to the unauthorized warning and the sensitive values of the audit trail to the information processing station.

[0215] In the embodiments of the present invention, as Figure 3 shown, establishing an information processing station based on distributed node technology means adopting the distributed node technology in the blockchain network, deploying the information processing station as a decentralized node cluster, configuring an edge computing module for each node to achieve lightweight data processing, and defining the communication rules and data collaboration mechanisms (such as data reception priority, alarm trigger conditions) between nodes through smart contracts, ensuring that when an abnormal alarm occurs in the UAV flight data, each node can synchronize the alarm information through the consensus algorithm to avoid single-point failures, where the receiving end is used to receive data and the processing end is used to process data. The information processing station is encrypted using the elliptic curve encryption algorithm, and the device-level public and private key pairs can be generated using ECC (the public key is uploaded to the chain and made public, and the private key is securely stored by the authorized node), implementing double encryption protection for the communication channels and stored data of the information processing station.

[0216] Specifically, the information processing station is built based on a distributed node network. Each node realizes data synchronization through the consensus protocol and uses the elliptic curve encryption algorithm to encrypt and authenticate the communication and stored data between nodes; when a node receives abnormal image and video data, it triggers an abnormal alarm and uploads the data source and content to the chain for encrypted storage after encryption; if it receives an out-of-bounds warning, it automatically associates the predicted position coding for the next time period and the current spatio-temporal dynamic parameters (speed, acceleration) to generate an out-of-bounds event package and pushes it to the information processing station; for unauthorized alarms, the system extracts the associated audit trail chain and its sensitive value analysis results, triggers permission freezing through smart contracts, and encrypts and reports them.

[0217] In the embodiments of the present invention, the information processing station ensures high availability through a distributed architecture, combines ECC encryption and blockchain storage evidence, realizes the trusted aggregation, real-time response, and full-link traceability of multi-source alarm data, and constructs a UAV security control system.

[0218] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A blockchain-based secure storage system for drone flight data, characterized in that Including: A data recognition module, at least used to filter abnormal data of UAV flight data, classify the filtered data, and establish metadata of different classifications; A consensus verification module, at least used to verify data of different classifications according to the metadata; A permission management module, at least used to calculate sensitive values of different classifications, and perform permission management classification on different classifications according to the sensitive values to obtain permission classification data; A hierarchical encryption module, at least used to divide each permission classification data into on-chain and off-chain, encrypt the on-chain and off-chain respectively, and store the encrypted data; An audit traceability module, at least used to record data operation logs, generate an audit trail according to the operation logs, trace the audit trail, and judge whether the traceability result exceeds the authority through a preset permission range; An information processing station, at least used to receive data sent by other modules and alarm according to the received data.

2. The blockchain-based drone flight data secure storage system according to claim 1, wherein The "filtering abnormal data of UAV flight data, classifying the filtered data, and establishing metadata of different classifications" includes: Obtaining UAV flight data and recording the data source, where the data source is set with device information and blockchain identity information, and the UAV flight data is set with a timestamp and location data; Dividing the UAV flight data into image / video data and non-image / video data according to the device information of the data source; Performing field detection on the non-image / video data to judge whether each field of the non-image / video data is complete; Performing file header information detection on the image / video evidence to judge whether the file header information of the image / video data is complete; If the image / video data is judged to be incomplete, send the specific data content of the image / video data and the corresponding data source of the image / video data to the information processing station; If the non-image / video data is judged to be incomplete, send the specific data content of the non-image / video data and the corresponding data source of the non-image / video data to the information processing station; If both the image / video data and the non-image / video data are judged to be complete, classify the UAV flight data according to the device information of the data source to obtain different classification data types, and establish metadata of different classifications.

3. The blockchain-based drone flight data secure storage system according to claim 2, characterized in that, The "establishing metadata of different classifications" includes: If the data type belongs to non-image / video data, identify the timestamps of all data in the data type, and slice the non-image / video data according to the timestamps using a preset time series, and mark each slice with a serial number; Bind and mark the slices of the corresponding non-image / video data using the timestamp, slice serial number, and data type to obtain the basic fields of the non-image / video data; The image / video data includes video data and image data; If the data type belongs to image / video data, extract key frames from the video data at a preset frequency, and use each key frame as a slice of the video data; Use each picture in the image data as a slice of the image data; Mark the slices of the video data and the slices of the image data with serial numbers; Bind and mark the slices of the corresponding image / video data using the timestamp, slice serial number, and data type to obtain the basic fields of the image / video data; Calculate the hash value data of each slice respectively; Encode the position data corresponding to each shard into a preset encoding length to obtain position encoding data; For each shard, concatenate the basic field, hash value data, blockchain identity information, and position encoding data to form a metadata.

4. The blockchain-based drone flight data secure storage system according to claim 3, wherein, The "data verification for different classifications of data according to tags" includes: Construct a data verification tree through the metadata, and use the data verification tree to perform integrity data verification on data of different data types; Calculate the speed and acceleration of the drone during flight using the position encoding data and time series corresponding to the metadata; Calculate the position encoding data for the next time period based on the speed, acceleration, and position encoding data of the current time period; Determine whether the position encoding data for the next time period belongs to a preset coordinate library; If the position encoding data for the next time period belongs to the preset coordinate library, no further processing is required; If the position encoding data for the next time period does not belong to the preset coordinate library, send an out-of-bounds warning to the information processing station.

5. The blockchain-based secure storage system for drone flight data according to claim 4, wherein The "construct a data verification tree through the metadata, and use the data verification tree to perform integrity data verification on data of different data types" includes: Use the hash value data in the metadata as leaf nodes; Concatenate the hash values of adjacent leaf nodes to obtain a parent node, and calculate the hash value of the parent node; Layer by layer, concatenate and calculate the hash values of the next layer of parent nodes, and finally obtain the root hash value; Convert the root hash value and its corresponding metadata according to the blockchain into a preset blockchain format, and encapsulate it into a structured data unit that can be transmitted, verified, and finally written into the block in the blockchain network, and write the structured data unit into the blockchain through the consensus algorithm; Upload the drone flight data to the InterPlanetary File System and obtain the hash value data corresponding to the metadata; Attach the hash value data corresponding to the metadata to the structured data unit, and establish a cross-chain index between the blockchain and the InterPlanetary File System using the hash value data; By downloading the target shard from the InterPlanetary File System, obtain the hash value data corresponding to the target shard, and generate the path hash value from the target shard to the root node; Perform integrity data verification on the data by comparing the path hash value and the root hash value.

6. The blockchain-based drone flight data secure storage system according to claim 5, characterized in that, The "calculate the sensitive values of different classifications, and perform permission management classification on different classifications according to the sensitive values to obtain permission classification data" includes: Preset different initial sensitive value for different data types; Calculate the sensitive value of each classification through preset sensitive indicators; Calculate the total sensitive value of each classification according to the initial sensitive value and the sensitive value of each classification; Map the total sensitive value to a preset sensitive level standard to obtain the sensitive level of each classification; Re-classify the classified data according to the sensitive level to obtain permission classification data.

7. The blockchain-based drone flight data secure storage system according to claim 6, wherein, The "divide each permission classification data into on-chain and off-chain, and encrypt the on-chain and off-chain respectively" includes: Divide the permission classification data into on-chain data and off-chain data; Among them, on-chain includes metadata, root hash value, and sensitive level, and off-chain includes the drone flight data corresponding to the metadata; Match the encryption length according to the sensitive level of the on-chain; Use a preset random number generator to randomly generate a key with the same length as the encryption length; Convert the key into a 32-byte key K1 using a preset key derivation function; Use the preset random number generator to randomly generate a key with a length that is half of the encryption length, and convert the regenerated key into a 16-byte key IV1 using the preset key derivation function; Bind the key IV1 to the blockchain identity information of the metadata; Encrypt the key K1 to obtain the key EK1, and encapsulate the key EK1 as an independent field in the blockchain network; Generate the ciphertext C1 using the metadata key IV1; Convert the encrypted key EK1 and the current group of ciphertext C1 into a preset blockchain format, and encapsulate them into a structured data unit that can be transmitted, verified, and finally written into a block in the blockchain network. Write the structured data unit into the blockchain for storage through a consensus algorithm; Match the encryption length according to the off-chain sensitivity level; Obtain the key EK2 and the key IV2 using the on-chain key generation method, and encapsulate the key EK2 as an independent field in the blockchain network and associate it with the on-chain metadata; Obtain the ciphertext C2 using the on-chain ciphertext generation method, bind the key IV2 and the ciphertext C2 to the InterPlanetary File System, associate them with the metadata on the blockchain through hash value data cross-chain indexing, and store them; 8. The blockchain-based secure storage system for drone flight data according to claim 7, wherein The "generating the ciphertext C1 using the metadata key IV1" includes: Convert the metadata into a byte format, and divide the byte format of the metadata according to a standard length of 16 bytes to obtain multiple plaintext groups P; Perform a bitwise exclusive OR operation on the first group P and the key IV1 to obtain a new 16-byte sequence Encrypt the bytes using a preset algorithm to obtain the first ciphertext c1; For each plaintext block p i Repeat the above process to obtain multiple ciphertexts c i , where i represents the block number; Group the current plaintext p i and the previous ciphertext c i-1 for bitwise XOR processing to obtain a byte where i - 1 represents the previous group of the i-th group; Encrypt the byte to obtain the current group ciphertext C1.

9. The blockchain-based UAV flight data secure storage system according to claim 8, wherein The recording of the data operation log, generating an audit trail based on the operation log, tracing the audit trail, and judging whether the tracing result is unauthorized through a preset permission range includes: When an active operation occurs, record the specific operation content and match the timestamp of the current operation; Convert the specific operation content and the timestamp into a preset record format and record them to obtain recorded data; Integrate the recorded data in chronological order of timestamps to form an audit trail; Identify the specific operation types in the audit trail and assign initial sensitivity values to different operation types; Establish a permission template through a preset permission range, and calculate the permission sensitivity values of each audit trail through the permission template; Calculate the total sensitivity value of each segment of the audit trail according to the initial sensitivity value and the permission sensitivity value respectively; Trace the sensitivity value of each segment of the audit trail and judge whether the sensitivity value is higher than a preset threshold; If the sensitivity value is higher than the preset threshold, the judgment result is unauthorized, and an unauthorized warning is sent to the information processing station; If the sensitivity value is lower than the preset threshold, the judgment result is not unauthorized.

10. The blockchain-based drone flight data secure storage system according to claim 9, characterized in that, The "receiving data sent by other modules and alarming according to the received data" includes: Establish an information processing station based on distributed node technology, and encrypt the information processing station using the elliptic curve encryption algorithm; If the information processing station successfully receives the specific data content of the image and video data or the data source corresponding to the image and video data, an alarm for abnormal data is issued, and the received data is sent to the information processing station; If the information processing station successfully receives an out-of-bounds warning, it issues an out-of-bounds warning and sends the position coding data for the next time period of the out-of-bounds warning, the speed, acceleration, and position coding data for the current time period to the information processing station; If the information processing station successfully receives an unauthorized warning, it issues an unauthorized alarm and sends the audit trail corresponding to the unauthorized warning and the sensitive values of the audit trail to the information processing station.

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