Blockchain-based low-altitude economy data management method and system

Abstract

The application relates to the field of data management and discloses a low-altitude economy data management method and system based on a block chain, which is used for solving problems such as static identification, consensus vulnerability and contract rule rigidity in a low-altitude data scene. The method comprises the following steps: standardizing original data of equipment and extracting a hash, combining time information with a chaotic mapping model to generate a dynamic fingerprint for on-chain storage, and enhancing data anti-fake and anti-replay capabilities; in the consensus verification stage, a chaotic disturbance sequence is derived from a hash of a previous block to dynamically rearrange candidate verification nodes, and network attack resistance is improved; meanwhile, a smart contract generates chaotic parameters based on historical storage features to realize dynamic self-adaptive correction of a business rule threshold, and the system performs on-chain and off-chain real-time consistency verification and decentralized abnormality processing based on the dynamic fingerprint. The application improves the safety, consensus reliability and business management flexibility of the whole life cycle of low-altitude economy data.

Description

Technical Field

[0001] This invention relates to the field of data management, and in particular to a blockchain-based method and system for managing low-altitude economic data. Background Technology

[0002] With the rapid development of the low-altitude economy, the amount of data generated by various devices in the low-altitude domain is exploding. This data covers key information such as flight trajectories, equipment status, and environmental data, and is of immense value for low-altitude traffic management, safety monitoring, and resource optimization. However, low-altitude economic data management faces numerous challenges: Low-altitude economic data is highly vulnerable to malicious attacks such as tampering and forgery during transmission and storage, making it difficult to guarantee the authenticity and integrity of the data. Once the data is tampered with, it may cause serious safety incidents, such as flight conflicts or misjudgments due to equipment malfunctions.

[0003] Due to the lack of effective data identification and evidence preservation mechanisms, it is difficult to quickly and accurately trace the source and flow of data when data problems occur, which brings great difficulties to accident investigation and liability determination.

[0004] In scenarios where blockchain technology is applied to low-altitude economic data management, existing consensus mechanisms suffer from inefficiencies and insufficient fairness in determining the selection of verification nodes and the order of block generation. Some nodes may monopolize verification rights by leveraging their resource advantages, impacting the performance and decentralized nature of the entire blockchain network.

[0005] The thresholds for business rules in smart contracts are usually fixed and cannot be adjusted in real time according to dynamic changes in low-altitude economic data. This results in a lack of flexibility in practical applications, making it difficult to meet the complex and ever-changing business needs of low-altitude economic operations.

[0006] Therefore, we propose a blockchain-based method and system for managing low-altitude economic data to address the aforementioned issues. Summary of the Invention

[0007] This invention provides a blockchain-based method and system for managing low-altitude economic data, which addresses issues such as static identifiers, vulnerability to consensus attacks, and rigid contract rules in low-altitude data scenarios.

[0008] The first aspect of this invention provides a blockchain-based method for managing low-altitude economic data. The method includes: processing raw data from low-altitude economic equipment to form data packets; calculating the encrypted hash value of the data packets; generating an initial seed based on the encrypted hash value; obtaining a chaotic sequence value based on the initial seed; and generating a dynamic fingerprint associated with the data packets by fusing current time information; constructing notarized transaction data using the encrypted hash value, dynamic fingerprint, timestamp, and submitter's digital signature, and sending it to a blockchain network for storage; during consensus verification on the blockchain network, calculating a chaotic perturbation sequence based on the hash information of the previous block; rearranging the order of candidate verification nodes in the current round using the chaotic perturbation sequence; and executing block verification and generation according to the rearranged order; and in the smart contract of the blockchain network, generating chaotic parameter seeds based on the characteristics of historical notarized data; calculating dynamic adjustment parameters using the chaotic parameter seeds; and managing preset business rule thresholds in the smart contract using the dynamic adjustment parameters.

[0009] Optionally, in a first implementation of the first aspect of the present invention, the method includes: extracting a byte segment of a specified length from the encrypted hash value; processing the byte segment to obtain an initial seed; inputting the initial seed into a preset chaotic mapping model for iterative calculation to obtain a chaotic state value; obtaining the current time information of the system and determining a corresponding time window factor based on the time information; fusing the chaotic state value with the time window factor to generate fingerprint parameters; and generating a fixed-length dynamic fingerprint according to the fingerprint parameters and a predetermined encoding rule.

[0010] Optionally, in a second implementation of the first aspect of the present invention, the method includes: generating a transaction random number based on the dynamic fingerprint using chaotic mapping; combining the transaction random number, the encrypted hash value, the dynamic fingerprint, the timestamp, and the submitter's digital signature to construct a transaction data object; performing a hash calculation on the transaction data object to obtain a transaction hash value; and merging the transaction hash value with the transaction data object to form evidence-based transaction data.

[0011] Optionally, in a third implementation of the first aspect of the present invention, the method includes: obtaining the hash information of the previous block from the blockchain network, combining it with the private key of the current round master node, generating a random number and a corresponding zero-knowledge proof through a verifiable random function; performing numerical transformation processing on the random number to obtain initial parameters for the chaotic mapping; inputting the initial parameters into the chaotic mapping model for iterative calculation to generate a chaotic numerical sequence; converting the chaotic numerical sequence into a perturbation factor list; and performing a rearrangement operation on the candidate verification node list of the current round according to the perturbation factor list to generate a new verification node order.

[0012] Optionally, in the fourth implementation of the first aspect of the present invention, each perturbation factor in the perturbation factor list is bound to the corresponding node identity identifier in the candidate verification node list to form a node-perturbation factor pair; all node-perturbation factor pairs are sorted according to the magnitude of the perturbation factor values ​​in the node-perturbation factor pairs to obtain a sorted node sequence; node identity identifiers are extracted from the node sequence to generate a preliminary node order list in sequence; the preliminary node order list is correlated with the hash information of the previous block to obtain an order verification value; the preliminary node order list is adjusted using the order verification value to output a new verification node order.

[0013] Optionally, in the fifth implementation of the first aspect of the present invention, the random number generated by the verifiable random function is subjected to a SHA-256 hash operation, and the decimal integer representation of its hash value is extracted as follows: According to the formula Calculate the sequence check value; Where N is the total number of candidate verification nodes.

[0014] Optionally, in a sixth implementation of the first aspect of the present invention, the method includes: acquiring historical evidence data stored in a blockchain network within a specified time period; extracting multiple feature values ​​related to business rules from the historical evidence data to form a feature set; generating a chaotic parameter seed based on the feature set; using the chaotic parameter seed as input, calling a chaotic mapping model for iterative computation to obtain a chaotic parameter sequence; calculating a dynamic adjustment parameter based on the elements in the chaotic parameter sequence according to a preset rule; reading the currently stored business rule threshold in the smart contract, and using the dynamic adjustment parameter to correct the business rule threshold in real time.

[0015] Optionally, in the seventh implementation of the first aspect of the present invention, the method further includes real-time consistency verification of the data packet based on the dynamic fingerprint and the on-chain evidence record: According to the dynamic fingerprint, the corresponding original data or encrypted original data is retrieved from the off-chain storage system; the on-chain evidence record associated with the dynamic fingerprint is obtained, and the stored encrypted hash value is extracted from it; the encrypted hash value of the retrieved original data or decrypted original data is recalculated to obtain a verification hash value; the verification hash value is compared with the encrypted hash value extracted from the chain; when the comparison result is consistent, a data consistency confirmation signal is generated; when the comparison result is inconsistent, a data anomaly alarm signal is generated, and a preset anomaly handling process is triggered.

[0016] Optionally, in the eighth implementation of the first aspect of the present invention, the current state information of the blockchain network is obtained, and multiple network node parameters are extracted from it; based on the network node parameters, a dynamic node selection sequence is generated through a preset chaotic mapping; according to the node selection sequence, multiple nodes are selected from the currently online verification nodes to form an anomaly handling node set; the original data or encrypted original data, as well as the encrypted hash value extracted from the chain, are assigned to each node in the anomaly handling node set, requiring parallel independent consistency verification; the independent verification results from each node in the anomaly handling node set are collected, and these results are summarized and analyzed based on preset consistency judgment rules; when the summary analysis result confirms that the data is abnormal, an anomaly report including an abnormal data identifier, relevant node information, and verification evidence is generated.

[0017] A second aspect of this invention provides a blockchain-based low-altitude economic data management system, comprising: a fingerprint module for processing raw data from low-altitude economic equipment to form data packets and calculating the encrypted hash value of the data packets; a sending module for generating an initial seed based on the encrypted hash value, obtaining a chaotic sequence value based on the initial seed, and generating a dynamic fingerprint associated with the data packets by fusing current time information; a sorting module for constructing notarized transaction data from the encrypted hash value, dynamic fingerprint, timestamp, and submitter's digital signature, and sending it to the blockchain network for storage; a seed module for calculating a chaotic perturbation sequence based on the hash information of the previous block during consensus verification on the blockchain network, rearranging the order of candidate verification nodes in the current round using the chaotic perturbation sequence, and executing block verification and generation according to the rearranged order; and a management module for generating chaotic parameter seeds based on the characteristics of historical notarized data in the smart contract of the blockchain network, calculating dynamic adjustment parameters using the chaotic parameter seeds, and managing the preset business rule thresholds in the smart contract using the dynamic adjustment parameters.

[0018] The mechanism of this invention is as follows: it transforms the traditional static and rigid evidence storage system into an active defense network with strong resilience, inherent random security and business flexibility, which substantially solves the contradiction between high dynamics, high concurrency and high security requirements in the complex low-altitude economic scenario; Beneficial effects: By processing the raw data of low-altitude economic equipment into data packets, calculating encrypted hash values, and generating dynamic fingerprints based on these hash values, the encrypted hash values, dynamic fingerprints, timestamps, and submitter's digital signatures are combined to construct evidence-based transaction data, which is then sent to the blockchain network for storage. This unique identification and evidence-based method ensures the integrity and immutability of the data, while providing a reliable basis for data traceability and auditing. During consensus verification in a blockchain network, a chaotic perturbation sequence is calculated based on the hash information of the previous block. This sequence is then used to rearrange the order of candidate verification nodes for the current round, and block verification and generation are performed according to the rearranged order. This method of dynamically adjusting the order of verification nodes improves the efficiency and fairness of the consensus mechanism and enhances the security of the blockchain network. In smart contracts on blockchain networks, chaotic parameter seeds are generated based on the characteristics of historical evidence data. These seeds are then used to calculate dynamically adjusted parameters, which are used to manage preset business rule thresholds within the smart contract. This allows the smart contract to adjust business rule thresholds in real time according to dynamic changes in low-altitude economic data, improving its flexibility and adaptability and better meeting the needs of low-altitude economic operations. Attached Figure Description

[0019] Figure 1 This is a schematic diagram of one embodiment of the blockchain-based low-altitude economic data management method of the present invention; Figure 2 This is a schematic diagram of another embodiment of the blockchain-based low-altitude economic data management method of the present invention; Figure 3 A schematic diagram illustrating the process of generating a dynamic fingerprint associated with a data packet by extracting an initial seed from a cryptographic hash value to produce a chaotic sequence value. Figure 4 This is a schematic diagram of an embodiment of the blockchain-based low-altitude economic data management system of the present invention. Figure 5 This is a schematic diagram of one embodiment of a blockchain-based low-altitude economic data management device according to the present invention. Detailed Implementation

[0020] This invention provides a blockchain-based method and system for managing low-altitude economic data, addressing issues such as static identifiers, vulnerability to consensus attacks, and rigid contract rules in low-altitude data scenarios. The terms "first," "second," "third," "fourth," etc. (if present)," in the specification, claims, and accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" or "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that includes a series of steps or units is not necessarily limited to those explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0021] For ease of understanding, the specific process of the embodiments of the present invention is described below. Please refer to [link / reference]. Figure 1 One embodiment of the blockchain-based low-altitude economic data management method of the present invention includes: 101. Standardize the raw data of low-altitude economic equipment to form data packets, and calculate the encrypted hash value of the data packets.

[0022] It is understood that the executing entity of this invention can be a blockchain-based low-altitude economic data management system, or it can be a terminal or a server; no specific limitation is made here. This embodiment of the invention will be described using a server as an example.

[0023] It should be noted that a detailed implementation example is given using a drone (a typical low-altitude economical device) that performs urban logistics delivery tasks.

[0024] At some point during flight, the sensors of the drone, designated as UAV-XA99, transmitted a complex set of raw operational data back to the server. The drone has a built-in Trusted Execution Environment (TEE) that uses a factory-hardcoded private key to perform an initial digital signature on the raw data at its source, generating a source anti-counterfeiting package. The server, acting as the execution entity, verifies the signature legitimacy of the source anti-counterfeiting package. After confirming that the data has not been tampered with by a middleman, it standardizes and maps these heterogeneous data within the server's trusted isolation zone, unifying the format and physical units: converting the scattered local times of various sensors into standard Coordinated Universal Time (UTC) March 14, 2026, 10:00:00; parsing the original GPS message and converting it into standard decimal latitude and longitude (31.2304°N, 121.4737°E); calibrating the relative altitude using a geographic information system to an absolute altitude of 120.5 meters; and extracting the core flight data in standardized units, with flight speed standardized to 15.5 meters per second, remaining battery power at 85%, and current cargo weight at 2.5 kilograms.

[0025] After standardization, the above data are concatenated and combined in a strict, pre-defined order, and all redundant characters are removed to form a logically clear data packet. This data packet includes the device identifier, standard time, location coordinates, and operational status, and is the fundamental digital asset that accurately reflects the current flight path of the UAV.

[0026] Using the data packet content as input, the secure SHA-256 algorithm is invoked for processing. After processing, the drone's data packet at this moment generates a unique hash digest, resulting in the string "a2c4e67798fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b8dc". This fixed-length string constitutes a unique digital fingerprint for the data packet at this moment. Even the slightest change in the data will result in a completely different recalculated hash value.

[0027] 102. Generate an initial seed based on the encrypted hash value, input the initial seed into the chaotic mapping model for iterative calculation to obtain a chaotic sequence value, and integrate the current time information to generate a dynamic fingerprint associated with the data packet.

[0028] It should be noted that in step 101, the server generates a fixed-length hash string starting with a2c4e677. To trigger chaotic computation, the server extracts the first 8 hexadecimal characters (a2c4e677) of this hash value according to preset rules, converts it to a decimal number, and performs a normalization operation to ensure it falls within the range greater than 0 and less than 1. After mapping, the initial seed value is set to 0.8894. This initial seed is firmly bound to the drone's flight state at this moment.

[0029] An embedded logistic chaotic mapping model, highly sensitive to initial values, is implemented. The server inputs an initial seed of 0.8894 into the model and sets control parameters for a chaotic state. To eliminate initial transient effects, the control model performs 50 consecutive iterative calculations, outputting a series of chaotic sequence values ​​with extremely high pseudo-randomness. The final value of this effective chaotic sequence is set to 371592. This number is difficult to reverse and highly nonlinear.

[0030] To ensure the timeliness of data storage and prevent replay attacks, the current absolute time information is extracted and set to UTC March 14, 2026, 10:00:00:150, converting it to a millisecond-level timestamp value of 1773482400150. The system extracts the time window factor according to preset time slicing rules (e.g., dividing by a specific base and rounding to obtain 177348240). A fusion operation is performed, concatenating the business identifier (e.g., AERO representing aviation), the time window factor (177348240), and the chaotic sequence truncation value (3715) according to a predetermined unified rule to generate a standardized dynamic fingerprint DF-AERO-177348240-3715.

[0031] 103. Construct the notarized transaction data by combining the encrypted hash value, dynamic fingerprint, timestamp, and submitter's digital signature, and send it to the blockchain network for storage.

[0032] It should be noted that at this stage, all the key digital identifiers generated in the previous steps are collected. First, there's the encrypted hash value representing the drone's standard flight data at that moment (i.e., the fixed-length string starting with a2c4e677 mentioned earlier); second, there's the dynamic fingerprint DF-AERO-177348240-3715, which incorporates complex perturbation features; and third, there's the corresponding high-precision timestamp value 1773482400150. The server extracts these three core pieces of basic data into the memory area to be processed, preparing for subsequent encapsulation.

[0033] To prove the legitimate origin of this stored data to the blockchain network and ensure that the data is absolutely not tampered with during transmission, the server (registered as East China Logistics Dispatch Node A in the consortium blockchain) needs to endorse its identity. The server invokes the underlying SM2 asymmetric encryption algorithm, using the private key securely held by Node A, to perform a signature operation on the aggregated hash value, fingerprint, and timestamp, generating an extremely difficult-to-forge ciphertext signature string: Sign-MEQCIA8x9...3fE. All participants in the network possessing the node's public key can use this to verify the true origin of the data.

[0034] After signing, the encrypted hash value, dynamic fingerprint, timestamp, and digital signature are assembled into a complete notarized transaction data message according to the standard format of blockchain smart contracts. The message header includes basic transaction metadata, declaring the transaction type and identifying the business entity, drone-XA99. It is important to emphasize that the massive original details, such as specific latitude and longitude coordinates, are stored locally, and only a lightweight digest message is used for on-chain storage. The message is broadcast to the entire low-altitude economy blockchain network via a P2P network, received by other participants, and temporarily stored in the transaction pool to be packaged, thus formally entering the distributed verification process.

[0035] 104. When performing consensus verification in the blockchain network, calculate the chaotic perturbation sequence based on the hash information of the previous block, use the chaotic perturbation sequence to rearrange the order of the candidate verification nodes in the current round, and perform block verification and generation according to the rearranged order.

[0036] It should be noted that when the network prepares to package the notarized transaction into the latest block height 45100, it extracts the block header hash value of the block height 45099, which has just completed consensus. The private key of the current master validator node is used, combined with this hash value as input, to generate unpredictable random numbers and zero-knowledge proofs using a Verifiable Random Function (VRF). The first few characteristics of this random number are converted into a basic decimal seed of 0.4782. Subsequently, the system inputs 0.4782 into the network's built-in chaotic mapping algorithm for iteration, outputting a set of irregular numerical sequences with a length equal to the number of candidate nodes. Assuming the current network selects 5 candidate values, the calculated chaotic perturbation sequence is: 0.83, 0.15, 0.62, 0.91, 0.34.

[0037] The system currently has five candidate nodes qualified to produce blocks, initially in the following order: Node 1 (Air Traffic Control Bureau), Node 2 (East China Logistics), Node 3 (Meteorology), Node 4 (Aviation Insurance), and Node 5 (Public Security Supervision). To prevent malicious collusion by nodes using a fixed order, the system maps and binds a chaotic sequence to each of the five nodes: Node 1 corresponds to 0.83, Node 2 to 0.15, Node 3 to 0.62, Node 4 to 0.91, and Node 5 to 0.34. The nodes are then reordered from largest to smallest based on their chaotic sequence values ​​(0.91, 0.83, 0.62, 0.34, 0.15), completely shuffling the verification order. The new order is: Node 4, Node 1, Node 3, Node 5, Node 2.

[0038] Node 4, ranked first, automatically becomes the block producer for this round. Node 4 extracts the evidence data of the drone-XA99 from the pending transaction pool to construct a candidate block, and broadcasts it to subsequent nodes in the new order. Each slave node performs a validity check one by one. When a majority of nodes pass the verification and confirm the signature, the block is officially generated and appended to the distributed ledger.

[0039] 105. In the smart contract of the blockchain network, a chaotic parameter seed is generated based on the characteristics of historical evidence data. The chaotic parameter seed is used to calculate the dynamic adjustment parameter, and the dynamic adjustment parameter is used to correct the preset business rule threshold in the smart contract in real time.

[0040] It should be noted that the smart contract on the consortium blockchain automatically triggers environmental awareness logic every 100 blocks generated. This logic proactively reads operational characteristics from recent historical data and summarizes the concurrent number of drones, average relative distance, and yaw warning frequency for the airspace over the past hour. Based on this, the contract calculates the current airspace's overall congestion characteristic value to be 0.72. Subsequently, the contract combines this characteristic value with the current on-chain state root to generate the chaos parameter seed for this period, setting the initial seed value to 0.758.

[0041] The contract inputs a seed value of 0.758 into its built-in lightweight chaotic mapping logic to perform nonlinear iterations. Because chaotic algorithms are extremely sensitive to input states, they can amplify linear historical feature changes into nonlinear system feedback. The contract outputs a set of dynamically adjusting parameters to regulate the safety margin. As the system determines risk increases, a positive safety margin expansion coefficient of 1.15 is generated, while a negative safety margin contraction coefficient of 0.87 is generated based on rigorous reciprocal derivation logic.

[0042] The contract uses these dynamic parameters to overwrite the static daily flight safety thresholds for drones within the storage area. The revised rules are immediately broadcast to devices such as the XA99 drone via on-chain events, mandating compliance with the new standards.

[0043] The specific business rule thresholds before and after correction are shown in Table 1 below: Table 1 Minimum safe distance between equipment 50.0 meters 1.15 (Positive Expansion) 57.5 meters Enhance the depth of collision avoidance safety Maximum speed limit in congested airspace 20.0 m / s 0.87 (reverse contraction) 17.4 m / s Forced deceleration to reduce kinetic energy risk Abnormal status retry reporting interval 100 milliseconds 1.15 (Positive Expansion) 115 milliseconds Relieve the concurrent pressure on communication networks Through this step, adaptive adjustment of low-altitude flight rules was successfully achieved using chaotic parameters.

[0044] Please see Figure 2 and Figure 3 Another embodiment of the blockchain-based low-altitude economic data management method of the present invention includes: 201. Standardize the raw data of low-altitude economic equipment to form data packets, and calculate the encrypted hash value of the data packets.

[0045] Specifically, the system receives raw data with device hardware signatures through trusted edge computing nodes and verifies the signatures; it cleans the raw data that has passed the signature verification to remove invalid and redundant information, thus obtaining a valid dataset; it segments the valid dataset according to preset rules to form multiple data segments; it extracts feature information from each data segment and combines it to generate data packet metadata; it performs standardized encapsulation processing on the data segments and data packet metadata together to obtain a standard format data packet; and it calculates the cryptographic hash value of the data packet.

[0046] It should be noted that during the monitoring period when the drone flew to 10:05 UTC, its onboard sensors wrote 1000 raw operational data entries to the local memory. Of these, 15 entries were invalid and contained only zeros due to communication jitter, while the high-frequency GPS module generated 30 redundant location entries with completely overlapping timestamps. After cleaning and precisely removing invalid and redundant data, 955 accurate and valid data entries were selected.

[0047] According to the preset data attribute classification rules, these 955 valid datasets are divided into three logically independent data slices: Data slice A (spatial navigation) includes 400 latitude, longitude and altitude information; Data slice B (environmental monitoring) includes 300 PM2.5 concentration and temperature and humidity readings; Data slice C (body status) includes 255 motor speed and battery voltage data.

[0048] The system independently scanned and extracted features from the above data segments: the maximum height of 125.4 meters was extracted from data segment A; the peak PM2.5 concentration of 45 micrograms per cubic meter was extracted from data segment B; and the minimum battery level of 78% was extracted from data segment C. The system combined Aero-Env-01, 10:05, 125.4 meters, PM2.5 peak of 45 micrograms per cubic meter, and minimum battery level of 78% to construct the data packet metadata.

[0049] Metadata is placed at the beginning of the file, followed by the three main data chunks in strict order. During this process, all times are standardized to Coordinated Universal Time (UTC), and distances are measured in meters, completing the standardized encapsulation process and generating a clearly structured standard data packet.

[0050] Using the entire standardized data packet as the input source, the SHA-256 algorithm is called to calculate the encrypted hash value. A unique 64-bit hexadecimal character set, 7f83b1657ff1fc53b92dc18148a1d65dfc2d4b1fa3d677284addd200126d9069, is generated. This hash value firmly locks down all the details of the data packet.

[0051] 202. Generate an initial seed based on the encrypted hash value, input the initial seed into the chaotic mapping model for iterative calculation to obtain a chaotic sequence value, and integrate the current time information to generate a dynamic fingerprint associated with the data packet.

[0052] Specifically, a specified length of byte segment is extracted from the encrypted hash value; the byte segment is numerically normalized to obtain an initial seed located within a specific numerical range; the initial seed is input into a preset chaotic mapping model for iterative calculation to obtain a chaotic state value; the current time information of the system is obtained, and the corresponding time window factor is determined based on the time information; the chaotic state value and the time window factor are fused and calculated to generate a fingerprint parameter; based on the fingerprint parameter, a fixed-length dynamic fingerprint is generated according to a predetermined encoding rule.

[0053] It should be noted that the first 8 hexadecimal characters, 7f83b165, are extracted from the header of the hash value generated in step 201. This string is converted to a decimal value and then normalized by dividing it by the largest possible range of values ​​within that length. After this mapping and conversion, the byte segment is safely compressed into a decimal range greater than 0 and less than 1, and the resulting initial seed value is set to 0.4981. This seed is now strongly associated with the drone's flight data at that moment.

[0054] An initial seed of 0.4981 is input into a preset chaotic mapping model. To eliminate the short-term regularity of the initial state, the system control model continuously performs 100 complex nonlinear iterative calculations. After 100 evolutions, the model outputs a highly pseudo-random and difficult-to-reverse chaotic state value, which is set to 0.8352 for this output.

[0055] The system obtains the current time information with high precision. The exact Coordinated Universal Time (UTC) is 10:05:02:500, corresponding to an absolute timestamp in milliseconds of 1773482702500. The system extracts its absolute value in seconds, 1773482702, and performs a rigorous conversion according to the system's preset slicing rule of 5 seconds as a verification window. That is, 1773482702 is divided by 5 and rounded to determine the corresponding time window factor as 354696540.

[0056] A fusion operation is performed, cross-concatenating and fusing the chaotic state value (0.8352) representing data characteristics with the time window factor (354696540) representing external timeliness characteristics to generate fingerprint parameters. According to the encoding rules, these parameters are formatted as a fixed-length dynamic fingerprint, output as DF-AERO-354696540-8352. This fingerprint perfectly integrates perturbation randomness with timeliness.

[0057] 203. Construct the notarized transaction data by combining the encrypted hash value, dynamic fingerprint, timestamp, and submitter's digital signature, and send it to the blockchain network for storage.

[0058] Specifically, based on the dynamic fingerprint, a transaction random number is generated through chaotic mapping; the transaction random number, encrypted hash value, dynamic fingerprint, timestamp, and submitter's digital signature are combined to construct a transaction data object; the transaction data object is hashed to obtain a transaction hash value; the transaction hash value is merged with the transaction data object to form complete evidence-based transaction data. Further, based on the transaction random number, a chaotically modulated random padding sequence is generated; the chaotically modulated random padding sequence, encrypted hash value, dynamic fingerprint, and timestamp are assembled according to a preset structure to form a structured data block; the structured data block is serialized to obtain data to be signed; the submitter's private key is used to perform a digital signature operation on the data to be signed to generate signature data; the signature data is associated and encapsulated with the structured data block to form a transaction data object.

[0059] It should be noted that the pseudo-random number generator is driven by the random number 883192, generating a chaotically modulated random padding sequence PAD-994A-B7C2. This padding code ensures that the underlying byte stream is completely different, improving resistance to cryptographic analysis.

[0060] The system concatenates a randomized sequence, a data packet cryptographic hash (starting with 7f83b165), a dynamic fingerprint (DF-AERO-354696540-8352), and a timestamp accurate to milliseconds (1773482702500) according to the preset structure of the blockchain protocol to form a structured data block. This multi-level data block is then serialized and flattened into a unidirectional character stream to obtain the data to be signed.

[0061] The municipal environmental monitoring node responsible for management intervenes, calling upon a secure encryption machine to perform a national cryptographic standard signature operation on the data to be signed, generating non-repudiable signature data Sig-EnvNode-AB89...3F. Subsequently, the system physically merges and encapsulates the signature data with the structured data block, forming a transaction data object containing a complete chain of evidence.

[0062] A hash calculation is performed on the entire transaction data object to obtain a transaction hash value Tx-55b2c9a1...d4e, representing the unique identity of the transaction. This hash value is placed in the header and merged with the transaction data object to form complete evidence-based transaction data. At this point, the evidence-based data is officially pushed to the Low Altitude Economy Blockchain Network and broadcast to all consensus nodes awaiting packaging.

[0063] 204. When performing consensus verification in the blockchain network, calculate the chaotic perturbation sequence based on the hash information of the previous block, use the chaotic perturbation sequence to rearrange the order of the candidate verification nodes in the current round, and perform block verification and generation according to the rearranged order.

[0064] Specifically, the hash information of the previous block is obtained from the blockchain network. Combined with the private key of the current round's master node, a random number and corresponding zero-knowledge proof are generated using a Verifiable Random Function (VRF). The random number undergoes numerical transformation to obtain the initial parameters of the chaotic mapping. The initial parameters are input into the chaotic mapping model for iterative calculation to generate a chaotic numerical sequence. The chaotic numerical sequence is converted into a perturbation factor list. The candidate verification node list for the current round is rearranged according to the perturbation factor list to generate a new verification node order. Further, each perturbation factor in the perturbation factor list is bound to the corresponding node identity in the candidate verification node list to form a node-perturbation factor pair. All node-perturbation factor pairs are sorted according to the magnitude of the perturbation factor values ​​in the node-perturbation factor pairs to obtain a sorted node sequence. Node identity identifiers are extracted from the sorted node sequence to generate a preliminary node order list. The preliminary node order list is correlated with the hash information of the previous block to obtain a sequence check value. The preliminary node order list is adjusted using the sequence check value to output a new verification node order.

[0065] It should be noted that when the network is preparing to package the transaction into block height 8520, it obtains the hash information of the previous block (height 8519). Combining this with a network-wide verifiable random number generated by VRF, the feature values ​​are extracted and processed through numerical transformation to obtain the initial parameter 0.6434 for the chaotic mapping. The system inputs 0.6434 into the Logistic chaotic mapping model to generate a chaotic numerical sequence for the four currently online candidate verification nodes: 0.8921, 0.3845, 0.9412, and 0.2103. This sequence is then converted into a list of perturbation factors.

[0066] The initial order of candidate nodes participating in consensus is: Node A, Node B, Node C, Node D. The system binds perturbation factors to corresponding nodes, forming pairs: (A, 0.8921), (B, 0.3845), (C, 0.9412), (D, 0.2103). Based on descending order of perturbation factors, a preliminary node order list is obtained: Node C, Node A, Node B, Node D. The node position index P... k In order: P C =1,P A =2,P B =3,P D =4.

[0067] The initial list is associated with random numbers generated by VRF, and an offset formula based on modulo of verifiable random numbers is introduced to calculate the order check value: ; Where N=4; The decimal large integer feature of the VRF random number generated in the previous block. Assume the extracted and truncated feature large integer is 235871.

[0068] Calculation results: Using a checksum of 3, the system performs a rightward cyclic shift of 3 times on the initial sequence (C, A, B, D). The new verification node sequence is: Node A, Node B, Node D, Node C. By introducing a Virtual Random Field (VRF), the offset is guaranteed. It cannot be predicted in advance by one party and can be rigorously verified across the entire network, thus eliminating the manipulation risks that may arise from simple chaotic sequences.

[0069] Node A, ranked first, automatically becomes the master validator for this round. Node A extracts the transactions with dynamic fingerprints from the drone, packages them into candidate blocks, and broadcasts them to nodes B, D, and C. Subsequent nodes strictly verify the transactions in the new order, and once verified, officially append them to the ledger.

[0070] 205. In the smart contract of the blockchain network, a chaotic parameter seed is generated based on the characteristics of historical evidence data. The chaotic parameter seed is used to calculate the dynamic adjustment parameter, and the dynamic adjustment parameter is used to correct the preset business rule threshold in the smart contract in real time.

[0071] Specifically, the process involves: acquiring historical evidence data stored in the blockchain network within a specified time period; extracting multiple feature values ​​related to business rules from the historical evidence data to form a feature set; generating a chaotic parameter seed based on the feature set using a preset aggregation algorithm; using the chaotic parameter seed as input and calling a chaotic mapping model for iterative computation to obtain a chaotic parameter sequence; calculating a dynamic adjustment parameter based on the elements in the chaotic parameter sequence according to preset rules; reading the currently stored business rule threshold in the smart contract and using the dynamic adjustment parameter to correct the business rule threshold in real time.

[0072] It should be noted that the smart contract automatically retrieves historical data stored over the past hour on a periodic basis, accurately extracting three key features: average flight altitude deviation (2.5 meters), average environmental data reporting network latency (120 milliseconds), and online device concurrency density (15 aircraft / square kilometer). The contract organizes these features into a feature set and generates a chaotic parameter seed between 0 and 1 using a preset weighted aggregation algorithm, with an initial seed value of 0.685.

[0073] The contract inputs a chaotic parameter seed of 0.685 into a lightweight chaotic mapping model for iterative computation. The model outputs a sequence of chaotic parameters that is highly sensitive to the initial state. Based on the spatial congestion situation reflected by the features, the system generates a positive expansion coefficient of 1.25 for increasing physical distance and a negative contraction coefficient of 0.80, which are reciprocals of each other, according to preset safety risk control rules and rigorous reciprocal logic derivation (the product of the two is perfectly equal to 1, ensuring the mathematical rigor of the model).

[0074] The smart contract reads the static business rule thresholds stored in the state machine and directly performs real-time multiplication correction using dynamically adjusted parameters. The corrected rule immediately triggers an on-chain event, which is broadcast to the governing device for execution.

[0075] The specific business rule thresholds before and after correction are shown in Table 2 below: Table 2 Minimum safe distance between equipment 30.0 meters 1.25 (positive expansion) 37.5 meters Increase collision avoidance safety depth in congested airspace Inspection data reporting interval 5000 milliseconds 0.80 (reverse contraction) 4000 milliseconds Increase the sampling and monitoring frequency of environmental pollution data Maximum speed limit in congested airspace 15.0 m / s 0.80 (reverse contraction) 12.0 m / s Forced drones to slow down to reduce kinetic energy risks This step transforms static regulations into adaptive criteria that fluctuate with the environment, significantly improving the level of intelligent supervision.

[0076] 206. It also includes real-time consistency verification of data packets based on dynamic fingerprints and on-chain evidence records; real-time consistency verification includes: Based on the dynamic fingerprint, retrieve the corresponding original data or encrypted original data from the off-chain storage system; obtain the on-chain evidence record associated with the dynamic fingerprint and extract the stored encrypted hash value from it; recalculate the encrypted hash value of the retrieved original data or decrypted original data to obtain the verification hash value; compare the verification hash value with the encrypted hash value extracted from the chain; when the comparison result is consistent, generate a data consistency confirmation signal; when the comparison result is inconsistent, generate a data anomaly alarm signal and trigger the preset anomaly handling process.

[0077] Furthermore, the system acquires the current state information of the blockchain network and extracts multiple network node parameters from it. Based on the network node parameters, a dynamic node selection sequence is generated through a pre-defined chaotic mapping. According to the dynamic node selection sequence, multiple nodes are selected from the currently online verification nodes to form an anomaly handling node set. The system assigns raw data or encrypted raw data, as well as encrypted hash values ​​extracted from the chain, to each node in the anomaly handling node set, requiring parallel and independent consistency verification. The system collects the independent verification results from each node in the anomaly handling node set and summarizes and analyzes these results based on pre-defined consistency judgment rules. When the summary analysis results confirm that the data is abnormal, an anomaly report is generated, including an anomaly data identifier, relevant node information, and verification evidence, and the anomaly report is submitted to the blockchain network for storage.

[0078] It should be noted that when business personnel retrieved the inspection results at 10:05 AM, the system used the dynamic fingerprint DF-AERO-354696540-8352 to retrieve the original environmental data packet from off-chain and extracted the authoritative encrypted hash value (starting with 7f83b165) from on-chain. The system re-executed the SHA-256 operation on the local original data packet to obtain the verification hash value. Assuming the local database was compromised and the core PM2.5 data was altered from 45 to 20, the system's recalculated verification hash value drastically changed to start with d8e9f2a4. Comparing the two, due to their complete inconsistency, the system blocked the output, generated an anomaly alarm signal, and triggered the anomaly handling process.

[0079] The system obtains the real-time status of the current consortium blockchain network and extracts the CPU load rate and network latency of each node as parameters. The system inputs the parameters into a chaotic mapping model to evolve a dynamic node selection sequence. Based on this sequence, three low-load and unrelated nodes are selected from the online nodes to form an anomaly handling node set: node E (municipal meteorological bureau), node F (urban management bureau), and node G (research institute).

[0080] The modified data packet and the authoritative hash value on the chain are sent to the three anomaly handling nodes, requiring them to perform independent consistency verification in parallel. Each node independently recalculates and compares the results after receiving the task.

[0081] The specific independent verification results are summarized in Table 3 below: Table 3 Node E Municipal Meteorological Bureau d8e9f2a4... (Does not match the chain) Completely inconsistent The data content has been determined to have been illegally tampered with. Node F Urban Management Bureau d8e9f2a4... (Does not match the chain) Completely inconsistent The data content has been determined to have been illegally tampered with. Node G Research institutes d8e9f2a4... (Does not match the chain) Completely inconsistent The data content has been determined to have been illegally tampered with. After collecting feedback, the data is summarized and analyzed based on the unanimous approval rule. If the data is confirmed to be forged, an anomaly report is generated, including an anomaly data identifier, a corrupted dynamic fingerprint (DF-AERO-354696540-8352), and logs from each node. This report is then submitted to the blockchain for evidence storage, thus blocking the business flow channel.

[0082] The blockchain-based low-altitude economic data management method in the embodiments of the present invention has been described above. The blockchain-based low-altitude economic data management system in the embodiments of the present invention will be described below. Please refer to [link / reference]. Figure 4An embodiment of the blockchain-based low-altitude economic data management system of the present invention includes: a fingerprint module 301, used to process the raw data of low-altitude economic equipment to form a data packet and calculate the encrypted hash value of the data packet; a sending module 302, used to generate an initial seed based on the encrypted hash value, obtain a chaotic sequence value based on the initial seed, and generate a dynamic fingerprint associated with the data packet by fusing current time information; a sorting module 303, used to construct a notarized transaction data by the encrypted hash value, dynamic fingerprint, timestamp, and submitter's digital signature, and send it to the blockchain network for storage; a seed module 304, used to calculate a chaotic perturbation sequence based on the hash information of the previous block when performing consensus verification on the blockchain network, use the chaotic perturbation sequence to rearrange the order of candidate verification nodes in the current round, and execute block verification and generation according to the rearranged order; and a management module 305, used to generate a chaotic parameter seed based on the characteristics of historical notarized data in the smart contract of the blockchain network, calculate a dynamic adjustment parameter using the chaotic parameter seed, and use the dynamic adjustment parameter to manage the preset business rule thresholds in the smart contract.

[0083] above Figure 4 The blockchain-based low-altitude economic data management system in this embodiment of the invention will be described in detail from the perspective of modular functional entities. The blockchain-based low-altitude economic data management device in this embodiment of the invention will be described in detail from the perspective of hardware processing.

[0084] Figure 5 This is a schematic diagram of the structure of a blockchain-based low-altitude economic data management device 400 provided in an embodiment of the present invention. The blockchain-based low-altitude economic data management device 400 can vary significantly due to different configurations or performance. It may include one or more central processing units (CPUs) 410 (e.g., one or more processors) and a memory 420, and one or more storage media 430 (e.g., one or more mass storage devices) for storing applications 433 or data 432. The memory 420 and storage media 430 can be temporary or persistent storage. The program stored in the storage media 430 may include one or more modules (not shown in the diagram), each module may include a series of instruction operations on the blockchain-based low-altitude economic data management device 400. Furthermore, the processor 410 may be configured to communicate with the storage media 430 and execute the series of instruction operations in the storage media 430 on the blockchain-based low-altitude economic data management device 400.

[0085] The blockchain-based low-altitude economic data management device 400 may also include one or more power supplies 440, one or more wired or wireless network interfaces 450, one or more input / output interfaces 460, and / or one or more operating systems 431, such as Windows Server, MacOSX, Unix, Linux, FreeBSD, etc. Those skilled in the art will understand that... Figure 5 The illustrated structure of the blockchain-based low-altitude economic data management device does not constitute a limitation on the blockchain-based low-altitude economic data management device. It may include more or fewer components than illustrated, or combine certain components, or have different component arrangements.

[0086] The present invention also provides a blockchain-based low-altitude economic data management device, which includes a memory and a processor. The memory stores computer-readable instructions. When the computer-readable instructions are executed by the processor, the processor performs the steps of the blockchain-based low-altitude economic data management method described in the above embodiments.

[0087] The present invention also provides a computer-readable storage medium, which can be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium, wherein the computer-readable storage medium stores instructions that, when the instructions are executed on a computer, cause the computer to perform the steps of the blockchain-based low-altitude economic data management method.

[0088] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0089] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0090] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A blockchain-based method for managing low-altitude economic data, characterized in that: include: The raw data of the low-altitude economic equipment is processed to form a data packet, and the encrypted hash value of the data packet is calculated. An initial seed is generated based on the encrypted hash value, a chaotic sequence value is obtained based on the initial seed, and a dynamic fingerprint associated with the data packet is generated by fusing the current time information. The encrypted hash value, dynamic fingerprint, timestamp, and submitter's digital signature are used to construct the notarized transaction data, which is then sent to the blockchain network for storage. During consensus verification in the blockchain network, a chaotic perturbation sequence is calculated based on the hash information of the previous block. This chaotic perturbation sequence is then used to rearrange the order of candidate verification nodes for the current round. Block verification and generation are then performed according to the rearranged order, including: The hash information of the previous block is obtained from the blockchain network. Combined with the private key of the current round's master node, a random number and corresponding zero-knowledge proof are generated using a verifiable random function. The random number is then subjected to numerical transformation to obtain the initial parameters of the chaotic mapping. The initial parameters are input into the chaotic mapping model for iterative calculation to generate a chaotic numerical sequence. The chaotic numerical sequence is converted into a perturbation factor list. Based on the perturbation factor list, the candidate verification node list for the current round is rearranged to generate a new verification node order. In a smart contract on a blockchain network, a chaotic parameter seed is generated based on the features of historical evidence data. The chaotic parameter seed is then used to calculate a dynamic adjustment parameter, which is used to manage preset business rule thresholds within the smart contract. This process includes: acquiring historical evidence data stored in the blockchain network within a specified time period; extracting multiple feature values ​​related to business rules from the historical evidence data to form a feature set; generating a chaotic parameter seed based on the feature set; using the chaotic parameter seed as input to iteratively calculate a chaotic mapping model to obtain a chaotic parameter sequence; calculating the dynamic adjustment parameter based on the elements in the chaotic parameter sequence according to preset rules; and reading the currently stored business rule thresholds in the smart contract and using the dynamic adjustment parameter to correct the business rule thresholds in real time.

2. The blockchain-based low-altitude economic data management method according to claim 1, characterized in that, include: Extract a byte segment of a specified length from the cryptographic hash value; The byte segment is processed to obtain the initial seed; The initial seed is input into a preset chaotic mapping model for iterative calculation to obtain chaotic state values; Obtain the current system time information and determine the corresponding time window factor based on the time information; The chaotic state value is fused with the time window factor to generate fingerprint parameters; Based on the fingerprint parameters, a fixed-length dynamic fingerprint is generated according to a predetermined encoding rule.

3. The blockchain-based low-altitude economic data management method according to claim 2, characterized in that, include: Based on the dynamic fingerprint, transaction random numbers are generated through chaotic mapping; The transaction random number, encrypted hash value, dynamic fingerprint, timestamp, and submitter's digital signature are combined to construct a transaction data object; Perform a hash calculation on the transaction data object to obtain the transaction hash value; The transaction hash value is merged with the transaction data object to form the evidence-based transaction data.

4. The blockchain-based low-altitude economic data management method according to claim 1, characterized in that, Each perturbation factor in the perturbation factor list is bound to the corresponding node identity in the candidate verification node list to form a node-perturbation factor pair; Based on the magnitude of the perturbation factor in the node-perturbation factor pair, all node-perturbation factor pairs are sorted to obtain the sorted node sequence; Extract node identifiers from the node sequence and generate a preliminary node order list in sequence; The preliminary node order list is correlated with the hash information of the previous block to obtain the order verification value; The initial node order list is adjusted using the sequence check value, and a new verification node order is output.

5. The blockchain-based low-altitude economic data management method according to claim 4, characterized in that, The random number generated by the verifiable random function is hashed using SHA-256, and its decimal integer representation is extracted as follows: According to the formula Calculate the sequence check value; Where N is the total number of candidate verification nodes.

6. The blockchain-based low-altitude economic data management method according to claim 1, characterized in that, It also includes real-time consistency verification of the data packet based on the dynamic fingerprint and on-chain evidence records: Based on the dynamic fingerprint, the corresponding original data or encrypted original data is retrieved from the off-chain storage system. Obtain the on-chain evidence record associated with the dynamic fingerprint, and extract the stored cryptographic hash value from it; The encrypted hash value of the retrieved original data or the decrypted original data is recalculated to obtain the verification hash value; The verification hash value is compared with the cryptographic hash value extracted from the chain; When the comparison results are consistent, a data consistency confirmation signal is generated; When the comparison results are inconsistent, a data anomaly alarm signal is generated, and a preset anomaly handling process is triggered.

7. The blockchain-based low-altitude economic data management method according to claim 6, characterized in that, Obtain the current state information of the blockchain network and extract multiple network node parameters from it; Based on the network node parameters, a dynamic node selection sequence is generated through a preset chaotic mapping; Based on the node selection sequence, multiple nodes are selected from the currently online verification nodes to form an anomaly handling node set; The original data or encrypted original data, as well as the encrypted hash value extracted from the chain, are assigned to each node in the set of exception handling nodes, and independent consistency verification is required to be performed in parallel. Collect independent verification results from each node in the anomaly handling node set, and summarize and analyze these results based on preset consistency judgment rules; When the summary analysis results confirm that the data is abnormal, an anomaly report is generated, which includes an anomaly data identifier, relevant node information, and verification evidence.

8. A blockchain-based low-altitude economic data management system, characterized in that, include: The fingerprint module is used to process the raw data of low-altitude economic equipment, form data packets, and calculate the encrypted hash value of the data packets; The sending module is used to generate an initial seed based on the encrypted hash value, obtain a chaotic sequence value based on the initial seed, and generate a dynamic fingerprint associated with the data packet by fusing the current time information. The sorting module is used to construct the encrypted hash value, dynamic fingerprint, timestamp, and submitter's digital signature into notarized transaction data and send it to the blockchain network for storage. The seed module is used to calculate a chaotic perturbation sequence based on the hash information of the previous block during consensus verification in the blockchain network. This chaotic perturbation sequence is then used to rearrange the order of candidate verification nodes in the current round, and block verification and generation are performed according to the rearranged order. This includes: The hash information of the previous block is obtained from the blockchain network. Combined with the private key of the current round's master node, a random number and corresponding zero-knowledge proof are generated using a verifiable random function. The random number is then subjected to numerical transformation to obtain the initial parameters of the chaotic mapping. The initial parameters are input into the chaotic mapping model for iterative calculation to generate a chaotic numerical sequence. The chaotic numerical sequence is converted into a perturbation factor list. Based on the perturbation factor list, the candidate verification node list for the current round is rearranged to generate a new verification node order. The management module is used to generate chaotic parameter seeds based on the features of historical evidence data in smart contracts of a blockchain network, calculate dynamic adjustment parameters using the chaotic parameter seeds, and manage preset business rule thresholds in the smart contract using the dynamic adjustment parameters. The module includes: acquiring historical evidence data stored in the blockchain network within a specified time period; extracting multiple feature values ​​related to business rules from the historical evidence data to form a feature set; generating chaotic parameter seeds based on the feature set; using the chaotic parameter seeds as input to iteratively calculate a chaotic mapping model to obtain a chaotic parameter sequence; calculating dynamic adjustment parameters according to preset rules based on the elements in the chaotic parameter sequence; reading the currently stored business rule thresholds in the smart contract and using the dynamic adjustment parameters to correct the business rule thresholds in real time.

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