Vehicle information authentication and tracing method and system based on block chain

Abstract

The invention provides a block chain-based vehicle information authentication and tracing method and system, and relates to the technical field of block chains, and the method comprises the steps: firstly obtaining an initial vehicle data set of a target vehicle containing multi-dimensional vehicle state data and environment interaction data, carrying out the dynamic authentication identifier generation processing of the initial vehicle data set, and obtaining a real-time authentication identifier set; generating a target vehicle authentication result set through multi-node collaborative verification according to a preset block chain consensus verification strategy, packaging the target vehicle authentication result set and the initial data set into a target vehicle data block, synchronizing the target vehicle data block to a block chain network multi-distributed node, and finally, when a vehicle information tracing request is received, sending the target vehicle data block to the block chain network. And the stored target vehicle data block is subjected to tracing verification, and a tracing feedback result is generated, so that the real-time performance of vehicle information authentication and the accuracy and reliability of tracing are ensured by means of block chain characteristics.

Description

Technical Field

[0001] The present invention relates to the field of blockchain technology, and more specifically, to a method and system for vehicle information authentication and traceability based on blockchain. Background Art

[0002] In the current field of vehicle information management, accurate vehicle information authentication and efficient information traceability are crucial for ensuring vehicle use safety, maintaining market order, and meeting users' needs for understanding vehicle full-life cycle information.

[0003] Currently, traditional vehicle information authentication methods mainly rely on centralized authentication agencies. These agencies usually adopt static authentication mechanisms and only authenticate based on some fixed information of the vehicle, such as vehicle identification numbers, driving license information, etc. This method has many limitations. On the one hand, due to the static nature of the information, it cannot timely reflect the dynamic changes during vehicle use, such as the real-time running state of the vehicle, the wear condition of parts, etc.; on the other hand, there is a risk of single-point failure in centralized authentication agencies. Once data leakage, system failures, or malicious attacks occur in these agencies, the security and reliability of vehicle information will be seriously threatened.

[0004] In terms of vehicle information traceability, most existing traceability systems are built based on centralized databases. Data is centrally stored in a single server or a small number of server clusters, and the integrity and authenticity of the data depend on the operation and maintenance of database administrators. This makes the data easily tampered with or lost, and it is difficult to ensure the accuracy and credibility of traceability information. In addition, different vehicle-related enterprises and institutions may have their own independent information systems, and data sharing is difficult. As a result, when conducting vehicle information traceability, it is often necessary to perform cumbersome queries and coordination among multiple systems, resulting in low efficiency. Summary of the Invention

[0005] In view of the above-mentioned problems, in combination with the first aspect of the present invention, embodiments of the present invention provide a method for vehicle information authentication and traceability based on blockchain, and the method includes: Obtain an initial vehicle data set corresponding to a target vehicle, where the initial vehicle data set includes vehicle status data in multiple dimensions and associated environmental interaction data; Perform dynamic authentication identifier generation processing on the initial vehicle data set to obtain a real-time authentication identifier set corresponding to the target vehicle; Perform multi-node collaborative verification processing on the real-time authentication identifier set according to a preset blockchain consensus verification strategy to generate a target vehicle authentication result set; Encapsulate the target vehicle authentication result set and the initial vehicle data set in a chained storage structure to generate a target vehicle data block, and synchronize the target vehicle data block to multiple distributed nodes in the blockchain network; In response to a vehicle information traceability request, perform traceability verification processing on the target vehicle data block stored in the blockchain network to generate a vehicle information traceability feedback result.

[0006] In a possible implementation manner of the first aspect, the dynamic authentication identifier generation processing of the initial vehicle data set to obtain the real-time authentication identifier set corresponding to the target vehicle includes: Perform hash digest calculation processing on each vehicle status data in the initial vehicle data set to generate a first hash digest value corresponding to each vehicle status data; Obtain the data acquisition timestamp of each vehicle status data, splice the data acquisition timestamp with the corresponding first hash digest value to generate a plurality of initial authentication identifier units; Perform spatio-temporal correlation feature extraction processing on the environment interaction data to obtain a spatio-temporal correlation coding sequence corresponding to the environment interaction data; Perform dynamic weight assignment processing on the plurality of initial authentication identifier units based on the spatio-temporal correlation coding sequence to generate a dynamic weight coefficient corresponding to each initial authentication identifier unit; Perform weighted aggregation processing on the plurality of initial authentication identifier units according to the dynamic weight coefficient to generate the real-time authentication identifier set corresponding to the target vehicle.

[0007] In a possible implementation manner of the first aspect, the spatio-temporal correlation feature extraction processing of the environment interaction data to obtain the spatio-temporal correlation coding sequence corresponding to the environment interaction data includes: Identify the geographical location identifier and time interval identifier included in the environment interaction data, and convert the geographical location identifier into a geographical coordinate coding vector; Split the time interval identifier into a start timestamp and an end timestamp, calculate the time span value between the start timestamp and the end timestamp, and convert the time span value into a time span coding vector; Perform multi-dimensional space mapping processing on the geographical coordinate coding vector and the time span coding vector to generate a spatio-temporal correlation mapping matrix; Perform serialization coding processing on the spatio-temporal correlation mapping matrix to obtain the spatio-temporal correlation coding sequence corresponding to the environment interaction data.

[0008] In a possible implementation manner of the first aspect, the dynamic weight allocation process for the multiple initial authentication identification units based on the spatio-temporal correlation coding sequence to generate a dynamic weight coefficient corresponding to each initial authentication identification unit includes: Dividing the spatio-temporal correlation coding sequence into multiple coding segments, each coding segment corresponding to an initial authentication identification unit; Performing feature parsing processing on each coding segment to generate a segment weight parameter corresponding to each coding segment; Generating a dynamic weight coefficient for each initial authentication identification unit through calculation processing based on the correlation degree between the segment weight parameter and the corresponding initial authentication identification unit, where the correlation degree calculation processing includes: Obtaining whether the data collection timestamp included in the initial authentication identification unit is within the start timestamp and end timestamp interval corresponding to the spatio-temporal correlation coding sequence to obtain a time matching degree, where the time matching degree is 1 or 0; Verifying whether the preset geographical area corresponding to the data type of the vehicle status data corresponding to the initial authentication identification unit contains the position represented by the geographical coordinate coding vector to obtain a space matching degree, where the space matching degree is 1 or 0; Performing weighted summation on the time matching degree and the space matching degree to obtain the dynamic weight coefficient.

[0009] In a possible implementation manner of the first aspect, the multi-node collaborative verification process for the real-time authentication identification set according to the preset blockchain consensus verification strategy to generate a target vehicle authentication result set includes: Selecting multiple verification nodes from the blockchain network and distributing a hash digest copy of the real-time authentication identification set and the corresponding initial vehicle data set to each verification node; Performing a verification operation in each verification node: Regenerating a verification hash digest value for each vehicle status data based on the hash digest copy of the initial vehicle data set, and performing item-by-item comparison processing on the verification hash digest value and the first hash digest value in the real-time authentication identification set to generate a hash matching degree index; Extracting the data collection timestamps of each initial authentication identification unit in the real-time authentication identification set and arranging them in chronological order to generate a timestamp verification sequence; Verifying whether the continuity of the timestamp verification sequence conforms to a preset time increasing rule to generate a time continuity index; When the hash matching degree index exceeds a first verification threshold and the time continuity index exceeds a second verification threshold, generating a local authentication pass result for this verification node; Generate the target vehicle authentication result set according to the comparison relationship between the number of local authentication passing results of all verification nodes and the preset consensus node number threshold.

[0010] In a possible implementation manner of the first aspect, the step of comparing the verification hash digest value with the first hash digest values in the real-time authentication identifier set item by item to generate a hash matching degree metric includes: Traverse each initial authentication identifier unit in the real-time authentication identifier set, and extract the corresponding first hash digest value; Locate the original data unit corresponding to the initial authentication identifier unit in the hash digest copy, and recalculate the verification hash digest value of the original data unit; Calculate the character difference degree between the first hash digest value and the verification hash digest value, and determine that the hash match is successful when the character difference degree is zero; Count the number of successful hash matches of all initial authentication identifier units, and calculate the ratio of the number of successful hash matches to the total number of initial authentication identifier units to generate the hash matching degree metric; When the hash matching degree metric reaches the first verification threshold, trigger a data integrity verification pass event.

[0011] In a possible implementation manner of the first aspect, the step of verifying whether the continuity of the timestamp verification sequence conforms to a preset time increasing rule to generate a time continuity metric includes: Convert the timestamp verification sequence into a timestamp numerical sequence and arrange it in ascending order to generate a reference time sequence; Calculate the time interval values between adjacent timestamps in the timestamp verification sequence to generate an actual time interval distribution; Generate a theoretical time interval value according to the preset acquisition frequency of the vehicle data acquisition device; Perform a difference calculation between the actual time interval distribution and the theoretical time interval value to generate a time deviation value set; Count the number of abnormal deviations exceeding the preset time tolerance threshold in the time deviation value set; Calculate the ratio of the number of abnormal deviations to the total number of timestamps to generate the time continuity metric, and trigger a time validity verification pass event when the time continuity metric is lower than the second verification threshold.

[0012] In a possible implementation manner of the first aspect, the step of encapsulating the target vehicle authentication result set and the initial vehicle data set into a chained storage structure to generate a target vehicle data block includes: Perform a hash tree construction process on the set of authentication results of the target vehicle to generate an authentication result Merkle tree, and obtain the root hash value of the authentication result Merkle tree; Perform data chunking on the initial vehicle data set to generate multiple data chunk units; Generate a hash value for each data chunk unit and construct a Merkle tree to obtain a data chunk Merkle tree and a root hash value; Perform encryption processing on the data chunk units to generate encrypted data chunks, and perform associated storage processing on the encrypted data chunks and the leaf node hash values of the corresponding data chunk Merkle trees to generate a data chunk index table; Construct a block header structure, which includes the root hash value, the hash value of the previous block, timestamp information, geographical coordinate coding vector, time span coding vector, and consensus verification parameters; Perform combined encapsulation processing on the block header structure, the data chunk index table, and the encrypted data chunks to generate the target vehicle data block.

[0013] In a possible implementation manner of the first aspect, the response to the vehicle information traceability request is to perform traceability verification processing on the target vehicle data block stored in the blockchain network to generate a vehicle information traceability feedback result, including: Parse the target time range and target geographical area in the vehicle information traceability request, and locate a set of target data blocks in the blockchain network that meet the target time range and target geographical area; Perform integrity verification processing on each target data block in the set of target data blocks. The integrity verification processing includes: Extract the root hash value in the block header structure of the target data block; Recalculate the current Merkle tree root hash value of the encrypted data chunk based on the data chunk index table; Verify whether the current Merkle tree root hash value is consistent with the root hash value in the block header structure; When the integrity verification of all target data blocks passes, send a decryption key application to the key management service of the blockchain network according to the authorization credential carried in the vehicle information traceability request; after verifying the legality of the authorization credential, the key management service generates a decryption key matching the target data block based on the dynamic sharding technology, and decrypts the encrypted data chunk according to the decryption key to obtain the original vehicle data chunk; Reorganize the original vehicle data chunks according to the data chunk index table to generate a complete traceable vehicle data set; Perform an association analysis process on the traced vehicle data set and the target vehicle authentication result set to generate the vehicle information traceability feedback result.

[0014] For example, in a possible implementation manner of the first aspect, the performing an association analysis process on the traced vehicle data set and the target vehicle authentication result set to generate the vehicle information traceability feedback result includes Identify the authentication identification unit corresponding to each data unit in the traced vehicle data set; Search for the authentication result status corresponding to the authentication identification unit in the target vehicle authentication result set; Statistically analyze the proportional relationship between the number of authentication identification units in the passed state and the number of authentication identification units in the failed state; Generate a data credibility score based on the proportional relationship, and determine the overall verification level of the traced vehicle data set based on the data credibility score; Perform a combined encapsulation process on the overall verification level and the traced vehicle data set to generate a vehicle information traceability feedback result including a verification level identifier.

[0015] In another aspect, an embodiment of the present invention further provides a blockchain-based vehicle information authentication and traceability system, including a processor and a machine-readable storage medium. The machine-readable storage medium is connected to the processor. The machine-readable storage medium is used to store programs, instructions, or codes, and the processor is used to execute the programs, instructions, or codes in the machine-readable storage medium to implement the above method.

[0016] Based on the above aspects, the embodiment of the present invention can comprehensively and dynamically reflect the vehicle state and environmental interaction situation by obtaining a multi-dimensional initial vehicle data set. Perform a dynamic authentication identification generation process on the initial vehicle data set and perform collaborative verification by multiple nodes to ensure the real-time nature and authenticity of vehicle information, effectively resist the risks of data tampering and forgery, and enhance the credibility of authentication. Package the authentication result and the initial data in a chained storage structure and synchronize them to multiple distributed nodes of the blockchain network to construct a decentralized storage system. Utilize the immutable characteristic of the blockchain to ensure the integrity and permanence of vehicle data, and avoid data loss or damage problems. When responding to a vehicle information traceability request, it is possible to perform traceability verification based on the complete and reliable data stored in the blockchain, quickly and accurately generate a traceability feedback result, providing an efficient, transparent, and trustworthy solution for information query and verification throughout the vehicle life cycle, and greatly improving the overall efficiency and user experience of vehicle information management. Description of the Drawings

[0017] Figure 1It is a schematic execution flow diagram of a vehicle information authentication and traceability method based on blockchain provided by an embodiment of the present invention.

[0018] Figure 2 It is a schematic diagram of exemplary hardware and software components of a vehicle information authentication and traceability system based on blockchain provided by an embodiment of the present invention. Detailed implementation manners

[0019] The present invention will be specifically described below in conjunction with the accompanying drawings of the specification. Figure 1 It is a schematic flow diagram of a vehicle information authentication and traceability method based on blockchain provided by an embodiment of the present invention. The vehicle information authentication and traceability method based on blockchain will be introduced in detail below.

[0020] Step S110: Obtain an initial vehicle data set corresponding to a target vehicle, where the initial vehicle data set includes vehicle status data in multiple dimensions and associated environmental interaction data.

[0021] In this embodiment, an ordinary family car is used as the target vehicle. This car is equipped with a variety of sensors to collect vehicle status data. For example, its vehicle speed sensor collects speed information at fixed time intervals to form a sequence S1, S2, S3... Sn, where Si represents the vehicle speed collected at time ti, and the unit is kilometers per hour. The engine speed sensor of the vehicle collects the engine speed and records it in a sequence R1, R2, R3... Rn, where Ri represents the engine speed at time ti, and the unit is revolutions per minute. The brake system sensor of the vehicle records the brake state and identifies it with B1, B2, B3... Bn, where Bi represents different states such as normal, slightly worn, or severely worn of the brake at time ti.

[0022] In terms of environmental interaction data, the global positioning system (GPS) of the car continuously obtains the vehicle position to generate geographical location identifiers P1, P2, P3... Pn, and each identifier corresponds to a specific geographical location code. The clock system of the vehicle records the data collection time each time to form time interval identifiers T1, T2, T3... Tn, and each time interval identifier contains the start and end time points of data collection.

[0023] After that, these vehicle status data in different dimensions and environmental interaction data are integrated together to form an initial vehicle data set I, and this initial vehicle data set comprehensively covers various information of the car during driving.

[0024] Step S120: Perform dynamic authentication identifier generation processing on the initial vehicle data set to obtain a real-time authentication identifier set corresponding to the target vehicle.

[0025] In the scenario of the above-mentioned ordinary household car, dynamic authentication identifier generation processing is performed on the initial vehicle data set I.

[0026] Step S121: Perform hash digest calculation processing on each vehicle status data in the initial vehicle data set to generate a first hash digest value corresponding to each vehicle status data.

[0027] For the vehicle status data in the initial vehicle data set I, taking the vehicle speed sequences S1, S2, S3... Sn as an example. A common hash algorithm is used, such as the SHA-256 algorithm. Perform a hash calculation on S1. According to the rules of the SHA-256 algorithm, take the value of S1 and related information as input, and through a series of bit operations and transformations, generate the corresponding first hash digest value H1. Similarly, perform the same hash calculation on S2 to generate H2, and so on, perform a hash calculation on Sn to generate Hn. For the engine speed sequences R1, R2, R3...Rn, the same SHA-256 algorithm is also used. Calculate the first hash digest value Hn+1 for R1, calculate Hn+2 for R2, until calculate H2n for Rn. For the brake state identifiers B1, B2, B3...Bn, the SHA-256 algorithm is also used. Calculate the first hash digest value H2n+1 for B1, calculate H2n+2 for B2, until calculate H3n for Bn.

[0028] Step S122: Obtain the data collection timestamps of each vehicle status data, and splice the data collection timestamps with the corresponding first hash digest values to generate a plurality of initial authentication identifier units.

[0029] For the vehicle speed sequences S1, S2, S3... Sn, the corresponding data collection timestamps are t1, t2, t3... tn. Splice t1 with H1 to form the initial authentication identifier unit U1, and its form is to connect the time value of t1 with the character sequence of H1 according to the set format. Similarly, splice t2 with H2 to form U2, and splice tn with Hn to form Un. For the engine speed sequences R1, R2, R3...Rn, the corresponding timestamps are also t1, t2, t3... tn. Splice t1 with Hn+1 to form Un+1, t2 with Hn+2 to form Un+2, and tn with H2n to form U2n. For the brake state identifiers B1, B2, B3...Bn, splice t1 with H2n+1 to form U2n+1, t2 with H2n+2 to form U2n+2, and tn with H3n to form U3n. In this way, a series of initial authentication identifier units are generated.

[0030] Step S123: Perform spatio-temporal correlation feature extraction processing on the environmental interaction data to obtain the spatio-temporal correlation coding sequence corresponding to the environmental interaction data.

[0031] In the scenario of this household car, environmental interaction data is processed.

[0032] Step S1231: Identify the geographical location identifier and time interval identifier included in the environmental interaction data, and convert the geographical location identifier into a geographical coordinate coding vector.

[0033] Extract geographical location identifiers P1, P2, P3... Pn and time interval identifiers T1, T2, T3... Tn from the environmental interaction data. Taking the geographical location identifier P1 as an example, assume that P1 corresponds to a specific geographical location code. Through the conventional geographical coding conversion rules of the prior art, it is converted into a geographical coordinate coding vector G1. G1 is a multi-dimensional vector containing information such as longitude and latitude. For example, G1 = [g11, g12], where g11 represents the longitude-related value and g12 represents the latitude-related value. Similarly, P2 is converted into G2 = [g21, g22], and Pn is converted into Gn = [gn1, gn2].

[0034] Step S1232: Split the time interval identifier into a start timestamp and an end timestamp, calculate the time span value between the start timestamp and the end timestamp, and convert the time span value into a time span coding vector.

[0035] For the time interval identifier T1, assume that the time interval it represents is from t1s to t1e. Split it into the start timestamp t1s and the end timestamp t1e. Calculate the time span value Δt1 = t1e - t1s, with the unit of seconds. Through the conventional time span coding rules of the prior art, convert Δt1 into a time span coding vector Tq1. Tq1 may be a one-dimensional vector. For example, Tq1 = [tq11], where tq11 is a value obtained by converting Δt1 according to the coding rules. Similarly, for T2, split to obtain the start timestamp t2s and the end timestamp t2e, calculate Δt2 = t2e - t2s, and convert it into a time span coding vector Tq2 = [tq21]. For Tn, obtain Δtn = tne - tns and convert it into a time span coding vector Tqn = [tqn1].

[0036] Step S1233: Perform multi-dimensional space mapping processing on the geographical coordinate coding vector and the time span coding vector to generate a spatio-temporal correlation mapping matrix.

[0037] Taking the geographical coordinate encoding vector G1 = [g11, g12] and the time span encoding vector Tq1 = [tq11] as an example, through the conventional multi-dimensional space mapping rules of the prior art, G1 and Tq1 are subjected to mapping processing. Suppose the mapping rule is to combine g11, g12, and tq11 into a matrix element according to a set mathematical relationship. Generate a spatio-temporal correlation mapping matrix M1, for example, M1 = [[m111, m112], [m121, m122]], where m111, m112, m121, and m122 are numerical values calculated according to g11, g12, and tq11 through the mapping rule. Similarly, M2 is generated for G2 and Tq2, and Mn is generated for Gn and Tqn.

[0038] Step S1234: Perform serialization encoding processing on the spatio-temporal correlation mapping matrix to obtain the spatio-temporal correlation encoding sequence corresponding to the environmental interaction data.

[0039] Perform serialization encoding processing on the spatio-temporal correlation mapping matrix M1. Suppose a conventional serialization encoding rule of the prior art is adopted, and the matrix elements in M1 are arranged in a set order and converted into a character sequence. For example, convert M1 = [[m111, m112], [m121, m122]] into the character sequence Sq1 = "m111, m112, m121, m122", and after further encoding processing, form the final spatio-temporal correlation encoding sequence C1. Similarly, C2 is obtained by processing M2, and Cn is obtained by processing Mn. Combine C1, C2, Cn, etc. together to form the complete spatio-temporal correlation encoding sequence C corresponding to the environmental interaction data.

[0040] Step S124: Perform dynamic weight assignment processing on the multiple initial authentication identification units based on the spatio-temporal correlation encoding sequence to generate a dynamic weight coefficient corresponding to each initial authentication identification unit.

[0041] In the above-mentioned household car scenario, perform dynamic weight assignment on multiple initial authentication identification units based on the generated spatio-temporal correlation encoding sequence C.

[0042] Step S1241: Divide the spatio-temporal correlation encoding sequence into multiple encoding segments, and each encoding segment corresponds to an initial authentication identification unit.

[0043] Suppose the spatio-temporal correlation encoding sequence C is composed of the character sequence "c1, c2, c3... cn". According to the set division rule, divide it into multiple encoding segments F1, F2, F3... Fn, and each encoding segment Fi corresponds to an initial authentication identification unit Ui. For example, the encoding segment F1 may be "c1, c2, c3", corresponding to the initial authentication identification unit U1.

[0044] Step S1242: Perform feature parsing processing on each encoded segment to generate segment weight parameters corresponding to each encoded segment.

[0045] Taking the encoded segment F1 = "c1, c2, c3" as an example, analyze the characters in F1 according to the conventional feature parsing rules of the prior art. Assume that the parsing rule is to calculate based on information such as the numerical value and position of the characters, and obtain the segment weight parameter Wp1. For example, Wp1 may be a numerical value obtained by performing weighted calculation on c1, c2, and c3. Similarly, parse F2 to obtain Wp2, and parse Fn to obtain Wpn.

[0046] Step S1243: Calculate and process according to the correlation degree between the segment weight parameter and the corresponding initial authentication identification unit to generate a dynamic weight coefficient for each initial authentication identification unit, where the correlation degree calculation process includes: Step S12431: Obtain whether the data acquisition timestamp included in the initial authentication identification unit is within the start timestamp and end timestamp interval corresponding to the spatio-temporal correlation coding sequence, and obtain a time matching degree, where the time matching degree is 1 or 0.

[0047] Taking the initial authentication identification unit U1 and the corresponding encoded segment F1 as an example, assume that the time interval represented by the spatio-temporal correlation coding sequence part corresponding to F1 is from t1s to t1e, and the data acquisition timestamp included in U1 is t1. If t1s <= t1 <= t1e, then the time matching degree Tm1 = 1; otherwise Tm1 = 0. Similarly, for U2 and the corresponding F2, if the time interval corresponding to F2 is from t2s to t2e, and the timestamp in U2 is t2, if t2s <= t2 <= t2e, then Tm2 = 1, otherwise Tm2 = 0.

[0048] Step S12432: Verify whether the preset geographical area corresponding to the data type of the vehicle state data corresponding to the initial authentication identification unit includes the position represented by the geographical coordinate coding vector, and obtain a space matching degree, where the space matching degree is 1 or 0.

[0049] Assume that the vehicle status data corresponding to the initial authentication identification unit U1 is the vehicle speed S1, and the geographical area preset for the vehicle speed data type is several specific urban areas. The geographical coordinate coding vector G1 = [g11, g12] corresponding to the coding segment F1. If the position represented by [g11, g12] is within the geographical area preset for the vehicle speed data type, then the spatial matching degree Sm1 = 1; otherwise, Sm1 = 0. Similarly, for U2 and the corresponding F2, if the vehicle status data corresponding to U2 is the engine speed R2, the geographical area preset for the R2 data type is compared with the position represented by the geographical coordinate coding vector G2 corresponding to F2. If it is within the preset area, then Sm2 = 1; otherwise, Sm2 = 0.

[0050] Step S12433: Perform weighted summation on the time matching degree and the spatial matching degree to obtain the dynamic weight coefficient.

[0051] For the initial authentication identification unit U1, assume that the time matching degree Tm1 = 1 and the spatial matching degree Sm1 = 0. According to the weighted summation rule, assume that the weight of the time matching degree is 0.6 and the weight of the spatial matching degree is 0.4. Then the dynamic weight coefficient W1 = 0.6 * Tm1 + 0.4 * Sm1 = 0.6 * 1 + 0.4 * 0 = 0.6. Similarly, for U2, if Tm2 = 0 and Sm2 = 1, then W2 = 0.6 * Tm2 + 0.4 * Sm2 = 0.6 * 0 + 0.4 * 1 = 0.4. And so on, the corresponding dynamic weight coefficient Wn is calculated for each initial authentication identification unit.

[0052] Step S125: Perform weighted aggregation processing on the multiple initial authentication identification units according to the dynamic weight coefficient to generate the real-time authentication identification set corresponding to the target vehicle.

[0053] In the above-mentioned family car scenario, there are multiple initial authentication identification units U1, U2, U3... Un, and the corresponding dynamic weight coefficients W1, W2, W3... Wn. Adopt the weighted aggregation rule. For example, for U1 and W1, U2 and W2, assume that the weighted aggregation is to combine the initial authentication identification unit with the weight coefficient in a set format. Combine U1 with W1 to form the aggregation unit Au1. For example, the form of Au1 may be to connect the character sequence of U1 and the value of W1 in the conventional format of the existing technology. Similarly, combine U2 with W2 to form Au2, and combine Un with Wn to form Aun. Combine Au1, Au2, Aun, etc. together to generate the real-time authentication identification set R corresponding to the target vehicle.

[0054] Step S130: Perform multi-node collaborative verification processing on the real-time authentication identification set according to the preset blockchain consensus verification strategy to generate the target vehicle authentication result set.

[0055] Based on the set R of real-time authentication identifiers generated in the above-mentioned household car scenario, multi-node collaborative verification is carried out according to a preset blockchain consensus verification strategy.

[0056] Step S131: Screen multiple verification nodes from the blockchain network, and distribute a copy of the hash digest of the set of real-time authentication identifiers and the corresponding initial vehicle data set to each verification node.

[0057] Suppose there are many nodes in the blockchain network. According to the conventional screening rules of the existing technology, N nodes are selected from these nodes as verification nodes, denoted as V1, V2, V3... VN. For the set R of real-time authentication identifiers, its complete content is distributed to each verification node Vi. At the same time, the initial vehicle data set I is subjected to hash calculation. Using the same hash algorithm as before, such as the SHA-256 algorithm, a copy of the hash digest Hc of the initial vehicle data set I is generated, and Hc is distributed to each verification node Vi.

[0058] Step S132: Perform verification operations in each verification node: Step S1321: Regenerate the verification hash digest value of each vehicle status data based on the copy of the hash digest of the initial vehicle data set, and compare the verification hash digest value item by item with the first hash digest value in the set of real-time authentication identifiers to generate a hash matching degree index.

[0059] In the verification node V1, take the copy Hc of the hash digest of the initial vehicle data set I as an example. Extract the information related to the vehicle status data from Hc, and recalculate the hash value of the vehicle status data according to the same hash algorithm as the one used to generate the first hash digest value before, such as the SHA-256 algorithm. Take the vehicle speed sequence S1, S2, S3... Sn as an example, and recalculate to obtain the verification hash digest values Vh1, Vh2, Vh3... Vhn. Compare Vh1 with the first hash digest value H1 corresponding to the vehicle speed S1 in the set R of real-time authentication identifiers. Through the character comparison rule, calculate the character difference degree between the two. For example, compare the character sequences of Vh1 and H1 bit by bit, and count the number of different characters. Suppose Vh1 = "abcdef" and H1 = "abcdeg", then the character difference degree is 1. When the character difference degree is zero, it is determined that the hash match is successful. Perform the same comparison operation on the verification hash digest values and the first hash digest values corresponding to all vehicle status data, and count the number of successful hash matches. Suppose there are n data in the vehicle speed sequence, and among them, the verification hash digest values of m data have a character difference degree of zero when compared with the first hash digest value, that is, the number of successful hash matches is m. Calculate the hash matching degree index Hm = m / n. Similarly, perform the same operation in the verification nodes V2, V3... VN to obtain their respective hash matching degree indexes Hm2, Hm3... HmN.

[0060] Step S1322: Extract the data collection timestamps of each initial authentication identifier unit in the real-time authentication identifier set, and arrange them in chronological order to generate a timestamp verification sequence.

[0061] In verification node V1, extract the data collection timestamp ti of each initial authentication identifier unit Ui from the real-time authentication identifier set R. Arrange these timestamps in ascending chronological order to generate a timestamp verification sequence Ts = [t1, t2, t3... tn]. Similarly, perform the same operation in verification nodes V2, V3... VN to obtain their respective timestamp verification sequences Ts2, Ts3... TsN.

[0062] Step S1323: Verify whether the continuity of the timestamp verification sequence conforms to a preset time increment rule to generate a time continuity index.

[0063] In verification node V1, for the timestamp verification sequence Ts = [t1, t2, t3... tn], convert it into a timestamp numerical sequence and arrange it in ascending order to generate a reference time sequence Rs = [r1, r2, r3... rn], where r1 <= r2 <= r3 <=... <= rn. Calculate the time interval values between adjacent timestamps, such as Δr1 = r2 - r1, Δr2 = r3 - r2, to generate an actual time interval distribution ΔRs = [Δr1, Δr2, Δr3... Δrn-1]. According to the preset collection frequency of the vehicle data collection device, assuming the preset collection frequency is to collect data every fixed time interval T0, then the theoretical time interval value is T0. Calculate the difference between the actual time interval distribution ΔRs and the theoretical time interval value T0 to obtain a time deviation value set Db = [db1, db2, db3... dbn-1], where db1 = Δr1 - T0, db2 = Δr2 - T0, and so on.

[0064] Then count the number of abnormal deviations in the time deviation value set Db that exceed the preset time tolerance threshold Td. For example, for db1, if its absolute value is greater than Td, it is recorded as an abnormal deviation; for db2, similarly judge whether its absolute value is greater than Td, and if so, it is recorded as an abnormal deviation, and so on to count the number of abnormal deviations as An. Then calculate the ratio of the number of abnormal deviations An to the total number of timestamps n to generate a time continuity index Tc, that is, Tc = An / n. When the time continuity index Tc is lower than the preset second verification threshold T2, trigger the time validity verification passed event. Similarly, perform the same operation in verification nodes V2, V3... VN to obtain their respective time continuity indices Tc2, Tc3... TcN.

[0065] Step S1324: When the hash matching degree index exceeds the first verification threshold and the time continuity index exceeds the second verification threshold, generate a local authentication pass result for this verification node.

[0066] In verification node V1, compare the hash matching degree index Hm with a preset first verification threshold T1, and at the same time compare the time continuity index Tc with a preset second verification threshold T2. If Hm is greater than T1 and Tc is greater than T2, it is determined that the local authentication of this verification node V1 passes, and a local authentication pass result Pass1 is generated; if these two conditions are not met, that is, Hm is less than or equal to T1 or Tc is less than or equal to T2, a local authentication fail result Fail1 is generated. Similarly, in verification nodes V2, V3... VN, perform the same comparison operation to generate their respective local authentication results Pass2 or Fail2, Pass3 or Fail3... PassN or FailN.

[0067] Step S1325: Generate the target vehicle authentication result set according to the comparison relationship between the number of local authentication pass results of all verification nodes and a preset consensus node number threshold.

[0068] Count the number of local authentication pass results Pass generated in all verification nodes V1, V2, V3... VN, denoted as Pn. Compare Pn with a preset consensus node number threshold Cn. If Pn is greater than or equal to Cn, generate the authentication result in the target vehicle authentication result set as overall authentication passed, denoted as OverallPass; if Pn is less than Cn, generate the authentication result in the target vehicle authentication result set as overall authentication failed, denoted as OverallFail. Thus, generate the target vehicle authentication result set O, which contains the results of overall authentication passed or overall authentication failed for subsequent further processing of the target vehicle data.

[0069] Step S140: Perform a chained storage structure encapsulation process on the target vehicle authentication result set and the initial vehicle data set to generate a target vehicle data block.

[0070] In the above family car scenario, perform a chained storage structure encapsulation process based on the generated target vehicle authentication result set O and the initial vehicle data set I.

[0071] Step S141: Perform a hash tree construction process on the target vehicle authentication result set to generate an authentication result Merkle tree, and obtain the root hash value of the authentication result Merkle tree.

[0072] For the set O of target vehicle authentication results, assume it contains the authentication results OverallPass or OverallFail, as well as some possibly attached relevant authentication information, such as the local authentication results of each verification node, etc. Use the conventional hash tree construction algorithm in the prior art to process this information. First, take each information unit as a leaf node, calculate the hash value for each leaf node using an algorithm such as SHA-256. For example, for the authentication result OverallPass, the calculated hash value is HOP, and for the local authentication pass result Pass1 of a certain verification node, the calculated hash value is HP1. Then, combine the hash values of adjacent leaf nodes in pairs and calculate the hash value again to generate a new hash value as the hash value of the upper-layer node. For example, combine HOP and HP1 to calculate the new hash value H1. And so on, continuously repeat this process until the entire authentication result Merkle tree is constructed. Finally, obtain the root hash value RootHash_O of this authentication result Merkle tree.

[0073] Step S142: Perform data chunking on the initial vehicle data set to generate multiple data chunk units.

[0074] For the initial vehicle data set I, this set contains vehicle status data and environmental interaction data in multiple dimensions, such as vehicle speed sequences S1, S2, S3... Sn, engine speed sequences R1, R2, R3... Rn, geographical location identifiers P1, P2, P3... Pn, etc. According to the set data chunking rules, chunk this data. Assume chunking is based on a fixed data volume size standard. For example, every X data elements are divided into one chunk. For the vehicle speed sequences S1, S2, S3... Sn, it may be divided into data chunk units B1 = [S1, S2,..., SX], B2 = [SX + 1, SX + 2,..., S2X], etc. For the engine speed sequences R1, R2, R3... Rn, similar chunking is also performed to obtain data chunk units Bn1 = [R1, R2,..., RX], Bn2 = [RX + 1, RX + 2,..., R2X], etc. Similarly, chunking processing is also performed on geographical location identifiers and other environmental interaction data, and finally multiple data chunk units B1, B2, B3... Bm are generated.

[0075] Step S143: Generate hash values for each data chunk unit and construct a Merkle tree to obtain the data chunk Merkle tree and the root hash value.

[0076] For each data chunk unit B1, B2, B3... Bm, use the same hashing algorithm as that for constructing the authentication result Merkle tree, such as the SHA-256 algorithm, to calculate the hash value for each data chunk unit, generating their respective hash values. For example, calculate the hash value Hb1 for data chunk unit B1 and calculate the hash value Hb2 for B2. Then, take these hash values as leaf nodes and, in the same way as constructing the authentication result Merkle tree, combine them in pairs for hash calculation to construct the data chunk Merkle tree. For example, combine Hb1 and Hb2 to calculate a new hash value H1b, and continue to build upwards until obtaining the root hash values RootHash_B1, RootHash_B2, RootHash_B3... RootHash_Bm of the data chunk Merkle tree.

[0077] Step S144: Encrypt the data chunk unit to generate an encrypted data chunk, and perform an associated storage process on the encrypted data chunk and the leaf node hash value of the corresponding data chunk Merkle tree to generate a data chunk index table.

[0078] For each data chunk unit B1, B2, B3... Bm, use a conventional encryption algorithm in the prior art, such as the AES encryption algorithm, to encrypt it. Taking the data chunk unit B1 as an example, use B1 as the input and generate the encrypted data chunk Eb1 according to the rules of the AES encryption algorithm. Similarly, encrypt B2 to generate Eb2 and encrypt Bm to generate Ebm. Then, associate each encrypted data chunk with the leaf node hash value of the corresponding data chunk Merkle tree. For example, associate Eb1 with Hb1 and associate Eb2 with Hb2. Through a conventional storage structure in the prior art, such as in the form of key-value pairs, use the encrypted data chunk as the value and the corresponding leaf node hash value as the key to generate a data chunk index table IndexTable, which is used for subsequent search and verification of the encrypted data chunk.

[0079] Step S145: Construct a block header structure, and the block header structure includes the root hash value, the hash value of the previous block, timestamp information, geographical coordinate encoding vector, time span encoding vector, and consensus verification parameters.

[0080] Obtain relevant information from the previous processing results to construct the block header structure. Among them, the root hash value uses the root hash value RootHash_O of the authenticated result Merkle tree. Assume that the hash value of the previous block is PrevHash, which is obtained from the previous generated block according to the chain structure of the blockchain. The timestamp information uses the timestamp Tnow at the current processing moment, which can be obtained from the clock of the vehicle system or the time synchronization mechanism of the blockchain network. The geographical coordinate encoding vector and the time span encoding vector can be obtained from the environmental interaction data processing process. For example, select the parts related to the current data from the geographical coordinate encoding vector Gn and the time span encoding vector Tqn generated in steps S1231 and S1232. The consensus verification parameters include preset first verification threshold T1, second verification threshold T2, consensus node number threshold Cn and other parameters related to consensus verification. Combine these information together to construct the block header structure BlockHeader, which contains the key information of the current block and is used to identify and verify the legality of the block.

[0081] Step S146: Combine and encapsulate the block header structure, the data block index table, and the encrypted data blocks to generate the target vehicle data block.

[0082] Combine and encapsulate the constructed block header structure BlockHeader, the data block index table IndexTable, and the encrypted data blocks Eb1, Eb2, Ebm, etc. Use the conventional encapsulation format of the existing technology. For example, place the block header structure at the front, followed by the data block index table, and finally the encrypted data blocks. Combine them in this order to generate the target vehicle data block Block, which will be synchronized to multiple distributed nodes in the blockchain network for storage and management, completing the structured storage of vehicle data on the blockchain.

[0083] Step S150: In response to a vehicle information traceability request, perform a traceability verification process on the target vehicle data block stored in the blockchain network to generate a vehicle information traceability feedback result.

[0084] In the above-mentioned family car scenario, when a vehicle information traceability request is received, perform corresponding processing on the target vehicle data block stored in the blockchain network.

[0085] Step S151: Analyze the target time range and target geographical area in the vehicle information traceability request, and locate a set of target data blocks in the blockchain network that meet the target time range and target geographical area.

[0086] Suppose the received vehicle information traceability request contains a target time range from Tstart to Tend, and the target geographical area is defined by the geographical coordinate range Gmin = [gmin1, gmin2] to Gmax = [gmax1, gmax2]. In the blockchain network, traverse each stored target vehicle data block. For each block, extract the timestamp information Tnow and the geographical coordinate encoding vector Gn from its block header structure. For example, for the geographical coordinate encoding vector Gn = [gn1, gn2] of a certain block, determine whether it satisfies gmin1 <= gn1 <= gmax1 and gmin2 <= gn2 <= gmax2, and at the same time determine whether the timestamp Tnow satisfies Tstart <= Tnow <= Tend. If both conditions are met, include this block in the target data block set TargetBlocks, and in this way, locate all target data block sets that meet the target time range and the target geographical area.

[0087] Step S152: Perform integrity verification processing on each target data block in the target data block set, and the integrity verification processing includes: Step S1521: Extract the root hash value from the block header structure of the target data block.

[0088] For each target data block Block in the target data block set TargetBlocks, extract the root hash value RootHash_O from its block header structure BlockHeader. This root hash value is the root hash value of the Merkle tree of the authentication results obtained after the previous hash tree construction processing on the target vehicle authentication result set, and it is used to verify the consistency between the data in the block and the authentication results.

[0089] Step S1522: Recalculate the current Merkle root hash value of the encrypted data chunks based on the data chunk index table.

[0090] According to the data chunk index table IndexTable in the target data block, obtain the leaf node hash values Hb1, Hb2, Hbm corresponding to each encrypted data chunk Eb1, Eb2, Ebm. Then, following the method of constructing the data chunk Merkle tree before, start from these leaf node hash values, combine them in pairs for hash calculation, and reconstruct the data chunk Merkle tree. For example, combine Hb1 and Hb2 to calculate a new hash value H1b, and continue to calculate upward until the current Merkle root hash value CurrentRootHash_B is obtained.

[0091] Step S1523: Verify whether the current Merkle root hash value is consistent with the root hash value in the block header structure.

[0092] Compare the currently recalculated Merkle root hash value CurrentRootHash_B with the root hash value RootHash_O extracted from the block header structure. If CurrentRootHash_B is exactly the same as RootHash_O, it indicates that the data in the target data block has not been tampered with during storage, and the integrity verification passes; if the two are inconsistent, it indicates that there may be problems with the data and the integrity verification fails. Perform such verification operations on all target data blocks in the target data block set TargetBlocks.

[0093] Step S153: When the integrity verification of all target data blocks passes, initiate a decryption key application to the key management service of the blockchain network according to the authorization credential carried in the vehicle information traceability request; after verifying the legality of the authorization credential, the key management service generates a decryption key matching the target data block based on the dynamic sharding technology, and decrypts the encrypted data chunks according to the decryption key to obtain the original vehicle data chunks.

[0094] After all target data blocks in the target data block set TargetBlocks pass the integrity verification, extract the authorization credential Credential from the vehicle information traceability request. Send this authorization credential to the key management service KeyManagementService of the blockchain network. After receiving the authorization credential, the key management service verifies its legality according to the preset verification rules. For example, check whether the format of the authorization credential is correct, whether it contains the necessary information fields, and whether it is within the validity period, etc. If the authorization credential is legal, the key management service generates a decryption key DecryptionKey matching the target data block based on the dynamic sharding technology according to the characteristics of the target data block, such as block number, timestamp, data type, etc. Then, use the decryption algorithm corresponding to the encryption algorithm, for example, for the previously used AES encryption algorithm, use the corresponding AES decryption algorithm, take the decryption key DecryptionKey as the input, and decrypt the encrypted data chunks Eb1, Eb2, Ebm to obtain the original vehicle data chunks B1, B2, Bm.

[0095] Step S154: Reorganize the original vehicle data chunks according to the data chunk index table to generate a complete traceable vehicle data set.

[0096] According to the association relationships recorded in the data block index table IndexTable, reorganize the original vehicle data blocks B1, B2, ..., Bm obtained by decryption. For example, the data block index table records the original data position information corresponding to each data block unit. According to this information, reorganize the data blocks B1, B2, ..., Bm into a complete vehicle data sequence. For the vehicle speed data blocks, splice them in the original order, and perform the same operation for the engine speed data blocks, etc. Finally, generate a complete trace vehicle data set TraceData, which contains the complete data of the vehicle that meets the target time range and target geographical area at that time.

[0097] Step S155: Perform an association analysis process on the trace vehicle data set and the target vehicle authentication result set to generate the vehicle information trace feedback result.

[0098] In the above-mentioned family car scenario, perform an association analysis process on the generated trace vehicle data set TraceData and the target vehicle authentication result set O.

[0099] For example, step S1551: Identify the authentication identification unit corresponding to each data unit in the trace vehicle data set.

[0100] In the trace vehicle data set TraceData, each data unit has its corresponding authentication identification unit. For example, for the vehicle speed data unit S1, during the process of generating the real-time authentication identification set before, it corresponds to an initial authentication identification unit U1. Through the conventional identification association rules in the prior art, identify the corresponding relationship between each data unit and the authentication identification unit. Such association rules may be based on information such as data acquisition timestamps and data types. For example, if two data have the same data acquisition timestamp and belong to the same data type, it is considered that they have a corresponding relationship.

[0101] Step S1552: Search for the authentication result status corresponding to the authentication identification unit in the target vehicle authentication result set.

[0102] For each identified authentication identification unit, search for its corresponding authentication result status in the target vehicle authentication result set O. For example, the authentication result corresponding to the authentication identification unit U1 may be Pass or Fail. By searching in the target vehicle authentication result set according to the set search rules, such as searching according to the unique identification information of the authentication identification unit, determine its authentication result status.

[0103] Step S1553: Statistically analyze the proportional relationship between the number of authentication identification units in the passed state and the number of authentication identification units in the failed state.

[0104] The number of all authentication identification units in the traceable vehicle data set TraceData that are in the pass state is PassCount, and the number of those that are not in the pass state is FailCount. Then the ratio between them is calculated as Ratio = PassCount / FailCount, which reflects the overall authentication pass status of the data in the traceable vehicle data set.

[0105] Step S1554: Generate a data credibility score according to the proportional relationship, and determine the overall verification level of the retrospective vehicle data set based on the data credibility score.

[0106] According to the ratio relationship Ratio, the data credibility score Score is generated by setting the scoring rules. For example, if the Ratio is greater than a certain threshold R1, the Score is high; if the Ratio is less than a certain threshold R2 (R2 <R1),则Score为低分;在两者之间则为中等分数。然后根据数据可信度评分Score,按照预设的等级划分规则确定追溯车辆数据集合的整体验证等级Level。例如,高分对应的整体验证等级为高等级,中等分数对应的为中等级,低分对应的为低等级。

[0107] Step S1555: The overall verification level and the traceable vehicle data set are combined and packaged to generate a vehicle information traceability feedback result including a verification level identifier.

[0108] The determined overall verification level Level is combined and encapsulated with the traceable vehicle data set TraceData. The conventional encapsulation format of the prior art is adopted, such as adding the identification information of the overall verification level at the beginning or end of the traceable vehicle data set, and generating the vehicle information traceability feedback result FeedbackResult containing the verification level identification. The result will be returned to the entity that initiated the vehicle information traceability request, such as an insurance company, a vehicle owner or a relevant regulatory department.

[0109] To explain this process more clearly, assume that the traceable vehicle data set TraceData contains multiple different types of data units, such as the vehicle speed data unit sequence VS=[V1, V2, ..., Vx], the engine speed data unit sequence ER=[E1, E2, ..., Ey] and other related data units. The authentication identification units correspond to these data units one by one, namely VU=[U1, U2, ..., Ux] for vehicle speed data, EU=[Ux+1, Ux+2, ..., Ux+y] for engine speed data, etc.

[0110] When identifying the authentication identification unit corresponding to each data unit, through the conventional mapping rules of the prior art, for example, the corresponding relationship is jointly determined according to the data acquisition timestamp and the data type. Taking the vehicle speed data unit V1 as an example, its data acquisition timestamp is t1 and the data type is vehicle speed. By looking up the information recorded during the generation of the real-time authentication identification set before, the authentication identification unit U1 with the same timestamp t1 and corresponding to the vehicle speed data type is found.

[0111] When looking up the authentication result status corresponding to the authentication identification unit in the target vehicle authentication result set O, if the authentication result of the authentication identification unit U1 in O is Pass, then record this result. The same operation is performed on all authentication identification units, and the number of authentication identification units in the passed state PassCount and the number of authentication identification units in the failed state FailCount are counted.

[0112] When calculating the proportional relationship Ratio = PassCount / FailCount and generating the data credibility score Score based on this proportional relationship, a set of preset score mapping rules can be defined. For example, there are multiple threshold intervals, and when Ratio is in different intervals, it corresponds to different score ranges. If Ratio falls within the interval R1, the value of Score is between S1 - S2; if it falls within the interval R2, the value of Score is between S3 - S4, etc.

[0113] Determine the overall verification level Level according to the data credibility score Score. For example, when Score is in a certain higher score segment, Level is a high level; when it is in the middle score segment, Level is a medium level; when it is in a lower score segment, Level is a low level.

[0114] Finally, combine and encapsulate to generate the vehicle information traceability feedback result FeedbackResult, which can adopt a conventional format of the prior art. For example, add a field to the header of FeedbackResult to identify the overall verification level Level, followed by the complete traceable vehicle data set TraceData. In this way, after the recipient obtains FeedbackResult, they can first see the overall verification level and quickly understand the credibility of the data, and then view the specific traceable vehicle data set to obtain detailed vehicle operation information.

[0115] In this embodiment, the vehicle owner initiates a tracing request to understand the detailed situation of the vehicle during a specific journey. By obtaining the vehicle information tracing feedback result FeedbackResult, the vehicle owner can not only know the credibility of the vehicle data during this journey, but also understand the operating conditions of each component of the vehicle based on the traced vehicle data set TraceData, such as whether the engine speed is stable and whether there are abnormal fluctuations, so as to determine whether the vehicle needs maintenance.

[0116] For relevant regulatory authorities, the obtained vehicle information tracing feedback result FeedbackResult can be used to monitor whether the vehicle complies with traffic rules, environmental protection requirements, etc. For example, by tracing the emission-related data in the vehicle data set TraceData (assuming such data has been collected and stored previously), and determining the reliability of the data by combining the overall verification level, it can be determined whether the vehicle meets the emission standards and whether further regulatory measures need to be taken.

[0117] Through the description of the above embodiments, those skilled in the art can clearly understand the specific implementation process of the vehicle information authentication and tracing method based on blockchain, so as to accurately carry out system development and deployment in actual application scenarios, meet the needs of different entities for vehicle information authentication and tracing, and ensure that the entire process is carried out within the framework of law, morality and public interest.

[0118] For example, in terms of data collection, it is necessary to ensure that the collected privacy-sensitive data (such as the personal information of the vehicle owner, high-precision information of the vehicle location, etc.) follows technical means for privacy protection and anti-disclosure. Assuming the use of data encryption technology, when collecting data, encrypt the privacy-sensitive data, use conventional encryption algorithms in the existing technology, such as the AES encryption algorithm, to convert the data into ciphertext form for storage and transmission. When these data need to be used, through a legal decryption process, such as obtaining the decryption key by virtue of an authorization certificate for decryption, to ensure the security and privacy of the data.

[0119] Another example is that in terms of rule setting, all the threshold settings involved (such as the first verification threshold, the second verification threshold, the consensus node number threshold, etc.), scoring rules, level division rules, etc. need to follow the principles of fairness, justice and reasonableness, and there is no situation of discrimination and prejudice that violates fairness and justice. These rules are clearly set at the stage of system design and development and remain consistent and stable during the entire system operation process to ensure the reliability and effectiveness of the vehicle information authentication and tracing method.

[0120] Figure 2FIG. 0 shows a schematic diagram of exemplary hardware and software components of a blockchain-based vehicle information authentication and traceability system 100 provided by some embodiments of the present application that can implement the idea of the present application. For example, the processor 120 can be used on the blockchain-based vehicle information authentication and traceability system 100 and is used to execute the functions in the present application.

[0121] The blockchain-based vehicle information authentication and traceability system 100 can be a general-purpose server or a special-purpose server, both of which can be used to implement the blockchain-based vehicle information authentication and traceability method of the present application. Although only one server is shown in the present application, for convenience, the functions described in the present application can be implemented in a distributed manner on multiple similar platforms to balance the processing load.

[0122] For example, the blockchain-based vehicle information authentication and traceability system 100 can include a network port 110 connected to a network, one or more processors 120 for executing program instructions, a communication bus 130, and different forms of storage media 140, such as disks, ROM, or RAM, or any combination thereof. Exemplarily, the blockchain-based vehicle information authentication and traceability system 100 can also include program instructions stored in ROM, RAM, or other types of non-transitory storage media, or any combination thereof. The method of the present application can be implemented according to these program instructions. The blockchain-based vehicle information authentication and traceability system 100 also includes an I / O interface 150 between the computer and other input / output devices.

[0123] For ease of explanation, only one processor is described in the blockchain-based vehicle information authentication and traceability system 100. However, it should be noted that the blockchain-based vehicle information authentication and traceability system 100 in the present application can also include multiple processors. Therefore, the steps executed by one processor described in the present application can also be jointly executed or separately executed by multiple processors. For example, if the processor of the blockchain-based vehicle information authentication and traceability system 100 executes step A and step B, it should be understood that step A and step B can also be jointly executed by two different processors or separately executed in one processor. For example, the first processor executes step A, the second processor executes step B, or the first processor and the second processor jointly execute steps A and B.

[0124] In addition, an embodiment of the present invention also provides a readable storage medium, in which computer-executable instructions are preset. When the processor executes the computer-executable instructions, the above-mentioned blockchain-based vehicle information authentication and traceability method is implemented.

[0125] It should be noted that, in order to simplify the description of the present invention disclosed, so as to help the understanding of one or more embodiments of the invention, in the foregoing description of the embodiments of the present invention, sometimes a plurality of features are merged into one embodiment, drawing or description thereof.

Claims

1. A method for vehicle information authentication and traceability based on blockchain, characterized in that, The method includes: Obtaining an initial vehicle data set corresponding to a target vehicle, where the initial vehicle data set includes vehicle status data in multiple dimensions and associated environment interaction data; Performing dynamic authentication identifier generation processing on the initial vehicle data set to obtain a real-time authentication identifier set corresponding to the target vehicle; Performing multi-node collaborative verification processing on the real-time authentication identifier set according to a preset blockchain consensus verification policy to generate a target vehicle authentication result set; Performing chained storage structure encapsulation processing on the target vehicle authentication result set and the initial vehicle data set to generate a target vehicle data block, and synchronizing the target vehicle data block to multiple distributed nodes of a blockchain network; In response to a vehicle information traceability request, performing traceability verification processing on the target vehicle data block stored in the blockchain network to generate a vehicle information traceability feedback result.

2. The method for vehicle information authentication and traceability based on blockchain according to claim 1, characterized in that, The performing dynamic authentication identifier generation processing on the initial vehicle data set to obtain a real-time authentication identifier set corresponding to the target vehicle includes: Performing hash digest calculation processing on each vehicle status data in the initial vehicle data set to generate a first hash digest value corresponding to each vehicle status data; Obtaining the data collection timestamp of each vehicle status data, and splicing the data collection timestamp with the corresponding first hash digest value to generate a plurality of initial authentication identifier units; Performing spatio-temporal correlation feature extraction processing on the environment interaction data to obtain a spatio-temporal correlation coding sequence corresponding to the environment interaction data; Performing dynamic weight allocation processing on the plurality of initial authentication identifier units based on the spatio-temporal correlation coding sequence to generate a dynamic weight coefficient corresponding to each initial authentication identifier unit; Performing weighted aggregation processing on the plurality of initial authentication identifier units according to the dynamic weight coefficient to generate a real-time authentication identifier set corresponding to the target vehicle.

3. The method for vehicle information authentication and traceability based on blockchain according to claim 2, characterized in that, The performing spatio-temporal correlation feature extraction processing on the environment interaction data to obtain a spatio-temporal correlation coding sequence corresponding to the environment interaction data includes: Identifying a geographical location identifier and a time interval identifier included in the environment interaction data, and converting the geographical location identifier into a geographical coordinate coding vector; Splitting the time interval identifier into a start timestamp and an end timestamp, calculating a time span value between the start timestamp and the end timestamp, and converting the time span value into a time span coding vector; Performing multi-dimensional space mapping processing on the geographical coordinate coding vector and the time span coding vector to generate a spatio-temporal correlation mapping matrix; Performing serialization coding processing on the spatio-temporal correlation mapping matrix to obtain a spatio-temporal correlation coding sequence corresponding to the environment interaction data.

4. The method for vehicle information authentication and traceability based on blockchain according to claim 2, characterized in that, The performing dynamic weight allocation processing on the plurality of initial authentication identifier units based on the spatio-temporal correlation coding sequence to generate a dynamic weight coefficient corresponding to each initial authentication identifier unit includes: Dividing the spatio-temporal correlation coding sequence into multiple coding segments, and each coding segment corresponds to an initial authentication identifier unit; Perform feature parsing processing on each encoded segment to generate segment weight parameters corresponding to each encoded segment; Calculate and process based on the correlation between the segment weight parameters and the corresponding initial authentication identification units to generate dynamic weight coefficients for each initial authentication identification unit, where the correlation calculation processing includes: Obtain whether the data acquisition timestamp included in the initial authentication identification unit is within the start timestamp and end timestamp range corresponding to the spatio-temporal correlation encoding sequence to obtain a time matching degree, where the time matching degree is 1 or 0; Verify whether the preset geographical area corresponding to the data type of the vehicle status data corresponding to the initial authentication identification unit contains the position represented by the geographical coordinate encoding vector to obtain a spatial matching degree, where the spatial matching degree is 1 or 0; Perform weighted summation of the time matching degree and the spatial matching degree to obtain the dynamic weight coefficient.

5. The method for vehicle information authentication and traceability based on blockchain according to claim 1, characterized in that, The multi-node collaborative verification processing of the real-time authentication identification set according to the preset blockchain consensus verification strategy to generate a target vehicle authentication result set includes: Screen multiple verification nodes from the blockchain network and distribute hash digest copies of the real-time authentication identification set and the corresponding initial vehicle data set to each verification node; Perform verification operations in each verification node: Based on the hash digest copy of the initial vehicle data set, regenerate the verification hash digest value of each vehicle status data, and perform item-by-item comparison processing of the verification hash digest value with the first hash digest value in the real-time authentication identification set to generate a hash matching degree index; Extract the data acquisition timestamps of each initial authentication identification unit in the real-time authentication identification set and arrange them in chronological order to generate a timestamp verification sequence; Verify whether the continuity of the timestamp verification sequence conforms to the preset time increment rule to generate a time continuity index; When the hash matching degree index exceeds the first verification threshold and the time continuity index exceeds the second verification threshold, generate a local authentication pass result for this verification node; Generate the target vehicle authentication result set according to the comparison relationship between the number of local authentication pass results of all verification nodes and the preset consensus node number threshold.

6. The method for vehicle information authentication and traceability based on blockchain according to claim 5, wherein, The item-by-item comparison processing of the verification hash digest value with the first hash digest value in the real-time authentication identification set to generate a hash matching degree index includes: Traverse each initial authentication identification unit in the real-time authentication identification set and extract the corresponding first hash digest value; Locate the original data unit corresponding to the initial authentication identification unit in the hash digest copy and recalculate the verification hash digest value of the original data unit; Calculate the character difference degree between the first hash digest value and the verification hash digest value, and determine that the hash match is successful when the character difference degree is zero; Count the number of successful hash matches of all initial authentication identification units, and calculate the ratio of the number of successful hash matches to the total number of initial authentication identification units to generate the hash matching degree index; When the hash matching degree index reaches the first verification threshold, trigger an event indicating that the data integrity verification has passed.

7. The method for vehicle information authentication and traceability based on blockchain according to claim 5, wherein, Verify whether the continuity of the timestamp verification sequence conforms to a preset time increment rule, and generate a time continuity index, including: Convert the timestamp verification sequence into a timestamp numerical sequence and sort it in ascending order to generate a reference time sequence; Calculate the time interval values between adjacent timestamps in the timestamp verification sequence to generate an actual time interval distribution; Generate a theoretical time interval value according to the preset acquisition frequency of the vehicle data acquisition device; Perform a difference calculation between the actual time interval distribution and the theoretical time interval value to generate a set of time deviation values; Count the number of abnormal deviations exceeding the preset time tolerance threshold in the set of time deviation values; Calculate the ratio of the number of abnormal deviations to the total number of timestamps to generate the time continuity index. When the time continuity index is lower than the second verification threshold, trigger an event indicating that the time validity verification has passed.

8. The method for vehicle information authentication and traceability based on blockchain according to claim 1, wherein, Perform a chained storage structure encapsulation process on the target vehicle authentication result set and the initial vehicle data set to generate a target vehicle data block, including: Perform a hash tree construction process on the target vehicle authentication result set to generate an authentication result Merkle tree, and obtain the root hash value of the authentication result Merkle tree; Perform a data chunking process on the initial vehicle data set to generate multiple data chunk units; Generate a hash value for each data chunk unit and construct a Merkle tree to obtain a data chunk Merkle tree and its root hash value; Perform an encryption process on the data chunk unit to generate an encrypted data chunk, and perform an associated storage process on the encrypted data chunk and the leaf node hash value of the corresponding data chunk Merkle tree to generate a data chunk index table; Construct a block header structure, which includes the root hash value, the hash value of the previous block, timestamp information, geographical coordinate coding vector, time span coding vector, and consensus verification parameters; Perform a combined encapsulation process on the block header structure, the data chunk index table, and the encrypted data chunk to generate the target vehicle data block.

9. The method for vehicle information authentication and traceability based on blockchain according to claim 8, wherein, In response to a vehicle information traceability request, perform a traceability verification process on the target vehicle data block stored in the blockchain network to generate a vehicle information traceability feedback result, including: Parse the target time range and target geographical area in the vehicle information traceability request, and locate a set of target data blocks in the blockchain network that meet the target time range and target geographical area; Perform an integrity verification process on each target data block in the set of target data blocks. The integrity verification process includes: Extract the root hash value in the block header structure of the target data block; Recalculate the current Merkle root hash value of the encrypted data chunk based on the data chunk index table; Verify whether the current Merkle root hash value is consistent with the root hash value in the block header structure; When the integrity verification of all target data blocks passes, initiate a decryption key application to the key management service of the blockchain network according to the authorization certificate carried in the vehicle information traceability request; after verifying the legality of the authorization certificate, the key management service generates a decryption key matching the target data block based on the dynamic sharding technology, and decrypts the encrypted data chunks according to the decryption key to obtain the original vehicle data chunks; Reorganize the original vehicle data chunks according to the data chunk index table to generate a complete traceable vehicle data set; Perform an association analysis process on the traceable vehicle data set and the target vehicle authentication result set to generate the vehicle information traceability feedback result.

10. A vehicle information authentication and traceability system based on blockchain, wherein, It includes a processor and a memory. The memory is connected to the processor. The memory is used to store programs, instructions or codes. The processor is used to execute the programs, instructions or codes in the memory to implement the blockchain-based vehicle information authentication and traceability method according to any one of claims 1-9 above.

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