A blockchain-based audio data archiving method and system

Abstract

The present application relates to the technical field of blockchain storage, in particular to a recording data storage method and system based on blockchain, comprising the following steps: obtaining fingerprint feature encoding and XORing with audio, extracting phase and trajectory to align and compress azimuth angle, fusing identification and amplitude, splicing to generate time data stream and writing into block, and analyzing time path to update abstract. In the present application, interval mapping encoding is formed by fingerprint grayscale map and feature point displacement difference, and participates in audio segment-by-segment XORing, so that acoustic data carries individual feature constraint to form a coupling relationship, and the phase change and trajectory azimuth angle are synchronously aligned and direction consistency is discriminated, so that time evolution and space change form a synergistic association, the internal correlation degree of data is enhanced, the source information and content features form a composite structure through the cross fusion of device identification embedding and amplitude sampling, and the time sequence and path sequence jointly participate in abstract updating and chain association, so as to improve the anti-interference ability and the evidence credibility.

Description

Technical Field

[0001] This invention relates to the field of blockchain evidence storage technology, and in particular to a blockchain-based method and system for storing audio recording data. Background Technology

[0002] Blockchain-based evidence storage technology refers to a technical system that uses a distributed ledger structure to record and manage data. Its core lies in the synchronous storage and verification of data across multiple nodes. Combined with timestamp generation mechanisms, chained hash structures, and consensus rules, it achieves continuous data recording and consistency maintenance. This technology mainly includes core aspects such as data on-chain processing, block generation, hash calculation, time-sequential recording, and data synchronization between nodes. It is widely used in scenarios such as electronic data ownership confirmation, evidence fixation, and digital content traceability to achieve traceability and consistency maintenance during the data recording process. One traditional blockchain-based method for storing audio recording data involves segmenting the recording file, extracting audio feature values, calculating the corresponding hash digest, appending time information, and then writing the hash digests into the block data structure in chronological order. A chained structure is used to associate the hash value of the previous block with the current block. Simultaneously, block data is stored synchronously across multiple nodes to form a complete ledger record. During data retrieval or verification, the hash value of the recording file is recalculated and compared with the hash digest recorded in the ledger to complete the recording and verification process.

[0003] Existing technologies focus on single-summary representation of audio content, lacking stable feature constraints in complex acquisition environments. Noise interference or acquisition differences can easily cause feature fluctuations, resulting in insufficient ability to distinguish between similar content. At the same time, the lack of a mechanism to associate with individual features or device sources makes it difficult to establish a clear mapping relationship between multi-source data. In situations where multiple devices are used alternately or acquisition is carried out across scenarios, there is a risk of ambiguity in source determination. Furthermore, the recording structure does not reflect the synergistic relationship between temporal evolution and spatial changes. In scenarios with intermittent acquisition or significant path changes, the correlation between data segments is loose. For example, segmented recordings during movement cannot reflect the actual trajectory changes, thereby weakening the continuous expression and credible support capabilities of the overall recording. Summary of the Invention

[0004] To address the technical problems existing in the prior art, this invention provides a blockchain-based method for storing audio recording data. To achieve the above objectives, the present invention adopts the following technical solution: a method for storing audio recording data based on blockchain, comprising the following steps: S1: Obtain the fingerprint grayscale image and feature point coordinates, calculate the adjacent displacement difference in sequence and divide the interval mapping code, splice the code sequence and XOR it with the audio sample segment by segment, extract the distance distribution range and interval number and write it into the digest to obtain the fingerprint-driven encrypted audio data block. S2: Based on the fingerprint-driven encrypted audio data block, the audio segments are divided by frame and the phase change sequence is extracted. The azimuth sequence is calculated by combining the position trajectory and aligned by time. The consistency of direction is judged and the result is compressed to obtain the phase path coupling feature data block. S3: Based on the phase path coupling feature data block, obtain the device identifier and divide it into segments, divide the audio sub-segments, extract the amplitude splicing sampling number and cross-fuse it to obtain the identifier embedded mixed data block; S4: Based on the identifier, embed the mixed data block and concatenate them sequentially. At the same time, combine the phase path features and time stamps to generate a data stream, summarize the data stream and write it into the block field to obtain the audio recording evidence block data body. S5: Based on the recorded evidence block data, analyze the time interval and trajectory changes to obtain the time series and path series, splice them together, update the summary and associate them with the previous block to obtain the data blockchain evidence record.

[0005] As a further embodiment of the present invention, the fingerprint-driven encrypted audio data block includes an encoded sequence structure, an XOR encrypted segment, a distance distribution identifier, and an interval quantity parameter; the phase path coupling feature data block includes a phase change sequence, an azimuth change sequence, a direction consistency marker, and a compression discrimination segment; the identifier embedded hybrid data block includes a device identifier segment, an amplitude feature set, a sampling quantity label, and a cross-embedding structure; the audio recording evidence block data body includes a sequentially concatenated data stream, a time stamp field, a digest block field, and a coupling feature index; and the data blockchain evidence record includes a time series chain segment, a path change sequence, a block header digest link, and an updated data digest.

[0006] As a further aspect of the present invention, the segment-by-segment XOR processing refers to corresponding the encoded sequence with the audio samples in fixed segments, and performing bitwise XOR operations on each segment of data for feature embedding and encryption. The phase change sequence refers to a sequence formed by recording the direction and magnitude of phase changes between adjacent sampling points of an audio signal in chronological order.

[0007] As a further embodiment of the present invention, the compression result refers to merging consecutive identical discriminant values ​​and retaining only the feature data sequence after the changing node; The sequential splicing refers to the process of connecting multiple data segments sequentially according to a predetermined index and time order to form a continuous data stream.

[0008] As a further aspect of the present invention, the specific steps of S1 are as follows: S101: Obtain the grayscale image matrix of the fingerprint acquisition interface, detect pixel gradient changes to extract the feature point coordinate list, read the horizontal and vertical coordinates and arrange them sequentially according to the scanning path, extract the displacement of adjacent coordinates and divide the interval according to the preset distance range, map the displacement to the corresponding code number and concatenate them sequentially to obtain the displacement code sequence. S102: Based on the displacement encoding sequence, extract the displacement distribution range and count the number of intervals, divide the distance distribution range boundary according to the extreme value relationship of displacement, and write the distance distribution range boundary and interval number parameters into the summary field to obtain the distance distribution summary field; S103: Call the displacement encoding sequence and obtain the audio sampling sequence of the recording interface. Align the corresponding segments according to the fixed length segmentation rule. Perform bitwise XOR processing on each segment's encoding value and audio sampling value and concatenate them in order. At the same time, call the distance distribution summary field and append it to the end of the sequence to obtain the fingerprint-driven encrypted audio data block.

[0009] As a further aspect of the present invention, the specific steps of S2 are as follows: S201: Based on the fingerprint-driven encrypted audio data block, segments are divided according to frame order. Phase extraction is performed on the audio segments and the continuous phase change is tracked. The change trajectory is extracted according to the phase change direction of adjacent sampling points and connected sequentially to obtain the phase change sequence. S202: Based on the phase change sequence, obtain the satellite positioning receiver interface position trajectory sequence, extract the latitude and longitude coordinates of adjacent positions and derive the azimuth angle, extract the angle change according to the direction of the line connecting adjacent coordinates and connect them in sequence to obtain the azimuth angle sequence; S203: Based on the phase change sequence and the azimuth sequence, align the time, extract the discrimination value sequence based on the consistency between the phase change direction and the azimuth change discrimination direction at the time point, compress consecutive identical discrimination values ​​and retain the change node sequence to obtain the phase path coupling feature data block.

[0010] As a further aspect of the present invention, the specific steps of S3 are as follows: S301: Based on the phase path coupling feature data block, detect the internal sequence structure of the data block and divide it into segments of fixed length, extract continuous segments and process them by sequential numbering to obtain a coupling feature segment sequence; S302: Based on the coupling feature segment sequence, call the device unique identifier code, and perform equal-length segmentation to obtain the identifier segment sequence. Call the fingerprint driver to encrypt the audio data block to divide the audio segments in order, extract the amplitude of the audio segments and count the number of sampling points, and splice the amplitude value and the number of samples to obtain the amplitude sampling splicing sequence. S303: Based on the amplitude sampling splicing sequence and the identifier fragment sequence, perform cross splicing, perform synchronous insertion on the coupled feature fragment sequence and maintain the order consistency to obtain the identifier embedded hybrid data block; As a further aspect of the present invention, the specific steps of S4 are as follows: S401: Based on the identifier, embed the mixed data block, detect the internal sequence structure and splice it sequentially, connect the segments according to their index positions and keep the order consistent to obtain the mixed splicing sequence; S402: Based on the hybrid splicing sequence, call the phase path coupling feature data block and the time stamp field, align them according to the time order, and splice the three types of sequences according to the corresponding time index to obtain a multi-source spliced ​​data stream; S403: Based on the multi-source spliced ​​data stream, extract key segments and compress the summary, write the compression result into the block data field and complete the field encapsulation to obtain the audio recording evidence block data body; As a further aspect of the present invention, the specific steps of S5 are as follows: S501: Based on the audio recording evidence block data body, parse the internal time stamps and location trajectories, extract the time stamp intervals of adjacent blocks and expand them in time order to obtain the time sequence, and at the same time extract the changes in adjacent coordinate paths for the location trajectory and connect them in order to obtain the path change sequence. S502: Based on the time series and the path change sequence, determine the splicing order, write the splicing result into the specified field of the audio recording and evidence storage block data body and update the content to obtain the spatiotemporal splicing sequence; S503: Based on the spatiotemporal splicing sequence, extract the summary and write it into the block data body summary area, and at the same time call the previous block summary splicing order and write it into the block header field to obtain the data blockchain storage record. A blockchain-based audio recording data storage system includes: The fingerprint acquisition module obtains the fingerprint grayscale image and feature point coordinates, calculates the adjacent displacement difference in sequence and divides the interval mapping code, splices the code sequence and performs XOR processing with the audio sample segment by segment, extracts the distance distribution range and the number of intervals and writes it into the digest to obtain the fingerprint-driven encrypted audio data block. The phase trajectory module, based on the fingerprint-driven encrypted audio data block, divides the audio segments by frame and extracts the phase change sequence, calculates the azimuth sequence by combining the position trajectory and aligns it by time, judges the direction consistency and compresses the result to obtain the phase path coupling feature data block; The identifier embedding module obtains the device identifier and divides it into segments based on the phase path coupling feature data block. It then divides the audio sub-segments, extracts the amplitude, splices the sampling number, and cross-merges them to obtain the identifier embedding hybrid data block. The block construction module embeds a hybrid data block based on the identifier and splices it sequentially. At the same time, it combines phase path features and time stamps to generate a data stream and a summary data stream and writes them into the block field to obtain the audio recording evidence block data body. The chain recording module, based on the audio recording evidence block data body, parses the time interval and trajectory changes to obtain the time series and path series, splices them together, updates the summary and associates them with the previous block to obtain the data blockchain evidence record.

[0011] Compared with the prior art, the advantages and positive effects of the present invention are as follows: In this invention, the fingerprint grayscale image and the feature point displacement difference are used to form an interval mapping code and participate in the segment-by-segment XOR of the audio. This enables the acoustic data to carry individual feature constraints and form a coupling relationship. In conjunction with the phase change and trajectory azimuth angle synchronous alignment and direction consistency discrimination, the temporal evolution and spatial change form a synergistic correlation, enhancing the intrinsic correlation of the data. Combined with the device identifier embedding and amplitude sampling cross-fusion, the source information and content features form a composite structure. Furthermore, the time series and path series jointly participate in the summary update and chain association, improving the anti-interference ability and the credibility of evidence collection. Attached Figure Description

[0012] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0013] Figure 1 This is a schematic diagram of the steps of the present invention; Figure 2 This is a detailed schematic diagram of S1 of the present invention; Figure 3 This is a detailed schematic diagram of S2 of the present invention; Figure 4 This is a detailed schematic diagram of S3 of the present invention; Figure 5 This is a detailed schematic diagram of S4 of the present invention; Figure 6 This is a detailed schematic diagram of S5 of the present invention; Figure 7 This is a system module diagram of the present invention. Detailed Implementation

[0014] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0015] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0016] Please see Figure 1 This invention provides a blockchain-based method for storing audio recording data, comprising the following steps: S1: Obtain the grayscale image matrix of the fingerprint acquisition interface, call the feature point coordinate list, read the horizontal and vertical coordinates and arrange them in the scanning order, perform displacement difference processing on adjacent coordinates and divide the distance interval and mapping encoding, concatenate the encoding sequence in sequence and perform segment-by-segment XOR processing with the audio sampling sequence of the recording interface, extract the distance distribution range and interval number and write them into the summary field to obtain the fingerprint-driven encrypted audio data block. S2: Based on fingerprint-driven encrypted audio data blocks, audio segments are divided according to frame order. Phase information of each audio segment is extracted and phase change sequence is obtained. At the same time, the position trajectory sequence of the satellite positioning receiver interface is obtained. The azimuth angle of adjacent positions is calculated to obtain the azimuth angle sequence. The phase change sequence and the direction change sequence are aligned in time order and direction consistency discrimination is performed to obtain the discrimination sequence. The continuous values ​​of the discrimination sequence are compressed to obtain the phase path coupling feature data block. S3: Based on the phase path coupling feature data block, obtain the unique device identifier code and divide the segment sequence. At the same time, call the fingerprint driver to encrypt the audio data block to divide the audio segments in order. Extract the amplitude features of each audio segment and concatenate them with the sampling number. Cross-concatenate the result with the device identifier segment to obtain the identifier embedded hybrid data block. S4: Based on the identifier-embedded hybrid data block, perform sequential splicing, and simultaneously call the phase path coupling feature data block and time stamp field to splice the data stream and summary data stream and write them into the block data field to obtain the audio recording evidence block data body; S5: Based on the audio recording evidence block data body, read the time stamp and location trajectory, parse the time stamp interval of adjacent blocks and extract the time sequence, analyze the path changes of adjacent location trajectories to obtain the path sequence, concatenate the time sequence and path sequence and write them into the audio recording evidence block data body, generate the updated data digest and concatenate it with the previous block digest and write it into the block header field to obtain the data blockchain evidence record.

[0017] The fingerprint-driven encrypted audio data block includes an encoded sequence structure, an XOR encrypted segment, a distance distribution identifier, and an interval quantity parameter. The phase path coupling feature data block includes a phase change sequence, an azimuth change sequence, a direction consistency marker, and a compression discrimination segment. The identifier embedded hybrid data block includes a device identifier segment, an amplitude feature set, a sample quantity label, and a cross-embedding structure. The audio recording evidence block data body includes a sequentially spliced ​​data stream, a time stamp field, a digest block field, and a coupling feature index. The data blockchain evidence record includes a time series chain segment, a path change sequence, a block header digest link, and an updated data digest.

[0018] Please see Figure 2 The specific steps of S1 are as follows: S101: Obtain the grayscale image matrix of the fingerprint acquisition interface, detect pixel gradient changes to extract the feature point coordinate list, read the horizontal and vertical coordinates and arrange them sequentially according to the scanning path, extract the displacement of adjacent coordinates and divide the interval according to the preset distance range, map the displacement to the corresponding code number and concatenate them sequentially to obtain the displacement code sequence. First, the raw, weak current signal generated by finger pressure is acquired using the capacitive sensor array of the fingerprint acquisition device and converted into a grayscale image matrix reflecting the distribution of fingerprint ridges and valleys. This matrix is ​​composed of pixels, with each pixel's brightness value ranging from 0 to 255. After obtaining the grayscale image matrix, the image gradient operator is used to perform local partial derivative calculations on each pixel position in the matrix. At pixel coordinates 120 and 180, the horizontal and vertical brightness differences are extracted. If the horizontal difference is 30 and the vertical difference is 40, the pixel gradient amplitude at that point is calculated using the square root of the sum of squares, specifically the square root of the sum of the squares of 30 and 40, resulting in 50. This gradient amplitude of 50 is compared with a preset feature point determination threshold. This threshold was determined experimentally using 500 fingerprint samples under different lighting conditions, with the top 15% of abrupt changes in the gradient distribution curve selected as the benchmark and set to 45. Since 50 is greater than 45, this point is determined to be a fingerprint feature point. The process iterates through all matching pixels in the image, extracting their x and y coordinates in a matrix to form a list of feature point coordinates. Following a scan path from left to right and top to bottom, the extracted coordinates are linearly arranged. For example, taking two adjacent feature points: the first point has an x-coordinate of 120 and a y-coordinate of 180, and the second point has an x-coordinate of 125 and a y-coordinate of 184. Subtraction is performed, yielding a horizontal displacement of 5 and a vertical displacement of 4. Then, by summing the squares of these two displacements and taking the square root, the displacement of adjacent coordinates is calculated to be approximately 6.403. Based on a preset distance interval division rule, the displacement space is divided into multiple levels. For example, 0 to 3 is defined as interval 1, 3 to 6 as interval 2, and 6 to 9 as interval 3. Since 6.403 falls within interval 3, this displacement is mapped to code number 3. This process continues, mapping and sequentially concatenating the displacements along the entire path to obtain a displacement coding sequence.

[0019] S102: Based on the displacement coding sequence, extract the displacement distribution range and count the number of intervals. Divide the distance distribution range boundary according to the extreme value relationship of displacement. Write the distance distribution range boundary and interval number parameters into the summary field to obtain the distance distribution summary field. First, after calling the displacement encoding sequence, extreme value filtering is performed on all values ​​in the sequence to determine the fluctuation boundaries of the displacement. For example, in a sequence containing 1000 encoded values, the minimum displacement is found to be 1.2 and the maximum displacement is 15.8. Interval statistics are performed, dividing the overall span from 1.2 to 15.8 into several sub-intervals with fixed step sizes, and counting the encoding frequency falling into each interval. During this process, based on the extreme value relationship of the displacement, the arithmetic mean of the maximum value 15.8 and the minimum value 1.2 is calculated; that is, the sum of the two and divided by 2 equals 8.5, which is used as the initial boundary of the distance distribution range. The interval quantity parameter is statistically determined to be 4. Subsequently, the distance distribution range boundary value 8.5 and the interval quantity parameter 4 are converted into binary strings and written into a specific starting position of the summary field. This summary field uses a fixed 128-bit structure. By analyzing the dispersion of the displacement distribution range, the distribution coefficient is calculated. For example, the ratio of the standard deviation to the mean of the quantity in each interval is calculated. If the number of intervals distributed are 240, 310, 280, and 170, with an average of 250 and a standard deviation of approximately 53.38, then the distribution coefficient is calculated as 53.38 divided by 250, resulting in approximately 0.213. This value reflects the uniformity of the fingerprint feature distribution. The advantage of this operational logic is that by introducing extreme value relationships to dynamically define boundaries, the summary information can accurately characterize the density of fingerprint features among different individuals. Finally, the above parameters are encapsulated to obtain the distance distribution summary field.

[0020] S103: Call the displacement encoding sequence and obtain the audio sampling sequence of the recording interface. Align the corresponding segments according to the fixed length segmentation rules. Perform bitwise XOR processing on each segment's encoded value and audio sampling value and concatenate them in order. At the same time, call the distance distribution summary field and append it to the end of the sequence to obtain the fingerprint-driven encrypted audio data block. First, each bit of the shift-coded sequence is extracted, and a real-time audio sampling sequence is acquired through the mobile terminal's recording interface. Audio sampling uses a frequency of 44.1 kHz, with each sampling point having a 16-bit depth. Following a fixed-length segmentation rule, the coded sequence and audio sampling sequence are aligned. One shift-coded value is assigned for every 100 audio sampling points. If the current coded value is 3, it is converted to an 8-bit binary code 00000011. The corresponding audio sampling segment is selected, and its quantization value, such as 1250, is extracted. After converting 1250 to binary 10011100010, a bitwise XOR operation is performed with the coded value. This operation logic conceals fingerprint feature information within the audio waveform data. After processing, the XOR results of each segment are reassembled and concatenated according to the original audio sampling order. After completing the XOR concatenation of all segments, the distance distribution summary field generated in S102 is called. This summary field is directly appended to the end of the audio data stream. For example, a 128-bit digest is appended to the end of the encrypted audio sequence to form a complete data packet structure. The resulting data block not only contains the encrypted recording information but also carries fingerprint distribution features for decryption verification, thus producing a fingerprint-driven encrypted audio data block.

[0021] Please see Figure 3 The specific steps of S2 are as follows: S201: Based on fingerprint-driven encrypted audio data blocks, segments are divided according to frame order. Phase extraction is performed on the audio segments and the continuous phase change is tracked. The change trajectory is extracted according to the phase change direction of adjacent sampling points and connected sequentially to obtain the phase change sequence. First, the fingerprint-driven encrypted audio data block is processed by framing, with each frame lasting 20 milliseconds. Within each frame, the discrete audio sampling points are analyzed using Hilbert transform to extract the instantaneous phase angle. At sampling point index 500, the phase value is extracted to be 1.5 radians. A phase tracking algorithm is used to monitor the continuous evolution of the phase between adjacent sampling points. If the previous sampling point has a phase of 1.5 radians and the next sampling point has a phase of 1.7 radians, a subtraction operation is performed to obtain a phase difference of 0.2 radians. According to the phase change direction determination rule, if the phase difference is positive, it is marked as an ascending trajectory; if the phase difference is negative, it is marked as a descending trajectory. In the above example, the difference is 0.2, recorded as ascending. The phase change directions within the entire frame are connected in chronological order to form a phase change trajectory. For example, a frame contains 10 sampling intervals, and its change direction sequence is recorded as 1, 1, -1, 1, etc. The advantage of this operation logic is that by extracting subtle fluctuations at the phase level, it can effectively capture the minute perturbation features generated in the audio signal during encryption. By sequentially concatenating the processing results of all frames, a phase change sequence is obtained.

[0022] S202: Based on the phase change sequence, obtain the position trajectory sequence of the satellite positioning receiver interface, extract the latitude and longitude coordinates of adjacent positions and derive the azimuth angle, extract the angle change according to the direction of the line connecting adjacent coordinates and connect them in sequence to obtain the azimuth angle sequence; First, the location trajectory sequence synchronized with the recording time is retrieved through the satellite positioning receiver interface. This sequence contains a series of latitude and longitude coordinates with timestamps. The coordinate information of two adjacent location points is extracted; for example, the longitude of the first point is 116.39742 and the latitude is 39.90812; the longitude of the second point is 116.39815 and the latitude is 39.90885. Based on the logic of spherical trigonometry, the angle between the line connecting the two coordinate points and due north is calculated. By calculating the latitude difference (0.00073) and the longitude difference (0.00073), and taking the arctangent of the longitude difference divided by the latitude difference, the azimuth angle is found to be 45 degrees. Subsequently, the difference between adjacent azimuth angles is calculated to extract the change in angle. For example, if the current azimuth angle is 45 degrees and the previous azimuth angle was 40 degrees, the change is 5 degrees. These changes are arranged in chronological order. If the change is greater than 0, it indicates a clockwise shift in direction; if it is less than 0, it indicates a counterclockwise shift. By sequentially connecting these directional features, we obtain the azimuth sequence.

[0023] S203: Based on the phase change sequence and azimuth sequence, align the time, determine the consistency of the phase change direction and azimuth change direction at the time point, extract the discriminant value sequence, compress consecutive identical discriminant values ​​and retain the change node sequence to obtain the phase path coupling feature data block; First, the phase change sequence and azimuth sequence are placed under a unified time base. The phase change direction and azimuth change direction at the same time point are extracted. At time 10 seconds, the phase change direction is upward, corresponding to a value of 1; the azimuth change direction is clockwise, corresponding to a value of 1. A consistency discrimination operation is performed, multiplying the two values. If the product is 1, the directions are considered consistent, and the discrimination value is recorded as 1; if the product is -1, the directions are considered inconsistent, and the discrimination value is recorded as -1. By traversing all time points, the original discrimination value sequence is obtained. Data compression is performed on this sequence. Consecutive identical discrimination values ​​are detected in the sequence. For example, if 50 consecutive values ​​of 1 appear in the sequence, this process only retains the starting value of the consecutive segment and the position of the changing node. If the value changes to -1 at the 51st point, the 1st and 51st positions are recorded as the changing node. The advantage of this compression logic is that it greatly reduces the redundancy of feature data while retaining the key turning information of phase and path coupling. By arranging these node data sequentially, a phase path coupling feature data block is obtained.

[0024] Please see Figure 4 The specific steps of S3 are as follows: S302: Based on the coupled feature segment sequence, call the device unique identifier encoding, and perform equal-length segmentation to obtain the identifier segment sequence. Call the fingerprint driver to encrypt the audio data block and divide the audio segments in order. Extract the amplitude of the audio segments and count the number of sampling points. Concatenate the amplitude value and the number of samples to obtain the amplitude sampling concatenation sequence. First, the device's unique identifier code, a 64-bit globally unique serial number, is retrieved from the mobile terminal's security chip. An equal-length segmentation operation is performed, dividing the 64-bit code into four 16-bit identifier segments, resulting in an identifier segment sequence. Simultaneously, the fingerprint driver is invoked to encrypt the audio data block. This data block is divided into segments according to the physical length of the audio frame. Each 1024 bytes is defined as an audio segment. For each segment, the absolute value of its audio amplitude is extracted. After extracting the amplitude, the number of sampling points involved in the calculation within that segment is counted. The amplitude value and the number of sampling points are concatenated. For example, if a segment has a peak amplitude of 3270 and 1024 samples, the concatenation result is 32701024. All segments are processed in this way to obtain the amplitude sampling concatenation sequence.

[0025] S303: Based on the amplitude sampling splicing sequence and the identifier fragment sequence, perform cross splicing, perform synchronous insertion on the coupled feature fragment sequence and maintain the order consistency to obtain the identifier embedded hybrid data block; First, the amplitude sampling splicing sequence and the identifier fragment sequence are invoked. A cross-sponging operation is performed, embedding the identifier fragment into the amplitude sequence according to a specific insertion ratio. An identifier fragment is inserted after every two amplitude splicing values. If the amplitude splicing values ​​are 32701024 and 31551024, and the identifier fragment is 1A2B, the splicing result is 32701024315510241A2B. Next, the coupling feature fragment sequence in S301 is invoked. While maintaining the original splicing sequence order, synchronous insertion is performed. A synchronous insertion point is set, inserting the coupling feature fragment into a fixed index position in the cross-sponging sequence. A coupling feature is inserted every 512 bits. Through this process, device identity, audio features, and phase path coupling features are deeply fused. During insertion, the current fill rate is calculated. If the fill rate reaches 100%, a complete hybrid encapsulation is completed. The advantage of this operational logic is that, through the cross-embedding of multi-source features, the anti-spoofing capability of the data block is enhanced. Finally, all the information is merged sequentially to obtain the identifier embedded in the mixed data block.

[0026] Please see Figure 5 The specific steps of S4 are as follows: S401: Based on the identifier-embedded mixed data block, the internal sequence structure is detected and sequentially spliced ​​together. The segments are connected sequentially according to their index positions while maintaining the same order to obtain a mixed spliced ​​sequence. First, the internal sequence structure of the embedded data block is detected to identify the boundary markers of different feature segments. Then, all discrete segments are concatenated sequentially from the first to the last. This process is achieved by sequentially reading from the memory buffer. For example, the embedded segment at index 1 is read first, followed by the segment at index 2, and they are concatenated end-to-end. During the concatenation process, smoothness is calculated at the connection points of adjacent segments. If the difference in feature values ​​between two segments at the connection point is less than 0.05, the sequence consistency is considered good. Then, by traversing all index positions, it is ensured that no segments are missed or misplaced. After the last segment is concatenated, the entire mixed sequence forms a closed-loop bitstream. This process does not change the original bit values; it only maintains the temporal logic of the data through logical connections. Finally, the segments are concatenated sequentially to obtain the mixed concatenated sequence.

[0027] S402: Based on the hybrid splicing sequence, call the phase path coupling feature data block and the time stamp field, align them according to the time order, and splice the three types of sequences according to the corresponding time index to obtain a multi-source splicing data stream; First, the hybrid spliced ​​sequence is retrieved, and phase path coupling feature data blocks and real-time generated timestamp fields are simultaneously acquired. The timestamps use Unix timestamp format with millisecond precision. Based on the chronological order of the timestamps, the hybrid spliced ​​sequence, coupling feature data blocks, and timestamps are aligned and analyzed in a unified manner. The three types of sequences are then horizontally spliced ​​according to their corresponding time indices. At the same millisecond point 1713755437001, the corresponding segment of the hybrid sequence is used as a prefix, the coupling feature as an infix, and the timestamp as a suffix for merging. The advantage of this operational logic is that by introducing a high-precision time axis, it provides an absolute coordinate reference for subsequent evidence tracing. Through cyclic processing of the data across the entire time period, a multi-source spliced ​​data stream is obtained.

[0028] S403: Based on multi-source spliced ​​data stream, extract key segments and compress the summary, write the compression result into the block data field and complete the field encapsulation to obtain the audio recording evidence block data body; First, a deep scan is performed on the multi-source spliced ​​data stream to extract key segments representing the crucial characteristics of the recording. 1024 bits of data are extracted from the start, middle, and end positions of each minute's data stream. The extracted segments are then input into a digest function to perform a compressed digest operation. This digest function uses multiple rounds of iterative logical XOR and bitwise operations to compress the large-scale data into a fixed-length 256-bit digest value. This compressed result is written as the core credential into the block's data field. Subsequently, metadata such as the version number and block height are added according to the blockchain standard field encapsulation protocol. The version number is set to 2.0, and the block height to 1500. The encapsulated data fields and digest value are then encapsulated one last time to obtain the recording evidence block data body.

[0029] Please see Figure 6 The specific steps of S5 are as follows: S501: Based on the audio recording evidence block data body, parse the internal time stamps and location trajectories, extract the time stamp intervals of adjacent blocks and expand them in chronological order to obtain the time series, and at the same time extract the changes in adjacent coordinate paths for the location trajectory and connect them in order to obtain the path change sequence. First, the audio recording evidence block data is parsed to extract embedded timestamps and location trajectory coordinates. For the timestamps, the time difference between adjacent blocks is extracted. If the timestamp of block A is 1713755437000 and the timestamp of block B is 1713755447000, subtraction yields an interval of 10000 milliseconds. These time intervals are linearly expanded in the order of their generation to form a time series reflecting the frequency of evidence storage. For the location trajectory, path change features between adjacent coordinate points are extracted. If moving from coordinate point 1 to coordinate point 2, with a longitude difference of 0.00073 and a latitude difference of 0.00073, the distance traveled is calculated to be approximately 0.00103 units using the Pythagorean theorem. The direction vector of the movement is also recorded. These path displacements and direction vectors are then concatenated sequentially. If the path change exceeds a preset movement threshold of 0.0001, it is recorded as a valid path change. Finally, combining the time and spatial dimensions, a path change sequence is obtained.

[0030] S502: Based on the time series and path change sequence, determine the splicing order, write the splicing result into the specified field of the audio recording and evidence storage block data body and update the content to obtain the spatiotemporal splicing sequence; First, the time series and path change sequence are invoked. A splicing order operation is performed, mapping the time interval data as the preceding dimension and the path change data as the correlation dimension. At the node with a time interval of 10000 milliseconds, a corresponding path displacement of 0.00103 is attached. This splicing result, possessing spatiotemporal correlation, is written into a specified extended field of the audio recording evidence block data body. By updating the original data body content, the spatiotemporal features are solidified within the block. The advantage of this operational logic is that by deeply coupling the time interval and the physical path, it provides a dual verification dimension for the authenticity of the recording process. After completing the field update, the block data body possesses richer multidimensional feature information, resulting in a spatiotemporal splicing sequence.

[0031] S503: Based on the spatiotemporal splicing sequence, extract the summary and write it into the block data body summary area. At the same time, call the previous block summary splicing order and write it into the block header field to obtain the data blockchain storage record. First, the overall features of the spatiotemporal concatenation sequence are extracted, and a unique digest of the current block is generated through hash operations. This digest uses SHA256 logic to map the spatiotemporal data stream into a string of 64 hexadecimal characters. This digest is written into the digest area of ​​the block data body. Next, the digest information of the previous block is retrieved. If the digest of the previous block is 7D8E9F, it is concatenated with the header field of the current block. This process anchors the fingerprint of the previous block into the current block, forming a chain structure. If the connection check value between the previous digest and the current block header conforms to the preset consensus rules, the evidence record is deemed valid. The hash value after concatenation is calculated; if its difficulty coefficient satisfies the characteristic of the first digit being 0, it meets the admission requirements. The advantage of this operation logic is that it ensures the integrity and untraceability of the entire data chain. Finally, all fields are encapsulated and recorded to obtain the data blockchain evidence record.

[0032] Please see Figure 7 A blockchain-based audio recording data storage system includes: The fingerprint acquisition module obtains the fingerprint grayscale image and feature point coordinates, calculates the adjacent displacement difference in sequence and divides the interval mapping code, splices the code sequence and performs XOR processing with the audio sample segment by segment, extracts the distance distribution range and the number of intervals and writes it into the digest to obtain the fingerprint-driven encrypted audio data block. The phase trajectory module, based on fingerprint-driven encrypted audio data blocks, divides audio segments by frame and extracts phase change sequences, calculates azimuth sequences by combining position trajectories and aligns them by time, determines directional consistency and compresses the results to obtain phase path coupling feature data blocks; The identifier embedding module obtains the device identifier and divides it into segments based on the phase path coupling feature data block. It then divides the audio sub-segments, extracts the amplitude, splices the sampling number, and cross-fuses them to obtain the identifier embedding hybrid data block. The block construction module embeds hybrid data blocks based on identifiers and concatenates them sequentially. It also combines phase path features and time stamps to generate a data stream and a summary data stream, which are then written into the block field to obtain the audio recording evidence block data body. The chained recording module, based on the audio recording evidence block data, parses the time interval and trajectory changes to obtain the time series and path series, splices them together, updates the summary and associates them with the previous block to obtain the data blockchain evidence record.

[0033] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for storing audio recording data based on blockchain, characterized in that, include: S1: Obtain the fingerprint grayscale image and feature point coordinates, calculate the adjacent displacement difference in sequence and divide the interval mapping code, splice the code sequence and XOR it with the audio sample segment by segment, extract the distance distribution range and interval number and write it into the digest to obtain the fingerprint-driven encrypted audio data block. S2: Based on the fingerprint-driven encrypted audio data block, the audio segments are divided by frame and the phase change sequence is extracted. The azimuth sequence is calculated by combining the position trajectory and aligned by time. The consistency of direction is judged and the result is compressed to obtain the phase path coupling feature data block. S3: Based on the phase path coupling feature data block, obtain the device identifier and divide it into segments, divide the audio sub-segments, extract the amplitude splicing sampling number and cross-fuse it to obtain the identifier embedded mixed data block; S4: Based on the identifier, embed the mixed data block and concatenate them sequentially. At the same time, combine the phase path features and time stamps to generate a data stream, summarize the data stream and write it into the block field to obtain the audio recording evidence block data body. S5: Based on the recorded evidence block data, analyze the time interval and trajectory changes to obtain the time series and path series, splice them together, update the summary and associate them with the previous block to obtain the data blockchain evidence record.

2. The method for storing audio recording data based on blockchain according to claim 1, characterized in that, The fingerprint-driven encrypted audio data block includes an encoded sequence structure, an XOR encrypted segment, a distance distribution identifier, and an interval quantity parameter. The phase path coupling feature data block includes a phase change sequence, an azimuth change sequence, a direction consistency marker, and a compression discrimination segment. The identifier embedded hybrid data block includes a device identifier segment, an amplitude feature set, a sampling quantity label, and a cross-embedding structure. The audio recording evidence block data body includes a sequentially concatenated data stream, a time stamp field, a digest block field, and a coupling feature index. The data blockchain evidence record includes a time series chain segment, a path change sequence, a block header digest link, and an updated data digest.

3. The method for storing audio recording data based on blockchain according to claim 1, characterized in that: The segment-by-segment XOR processing refers to mapping the encoded sequence to audio samples in fixed segments, and performing bitwise XOR operations on each segment of data for feature embedding and encryption. The phase change sequence refers to a sequence formed by recording the direction and magnitude of phase changes between adjacent sampling points of an audio signal in chronological order.

4. The method for storing audio recording data based on blockchain according to claim 1, characterized in that: The compression result refers to merging consecutive identical discriminant values ​​and retaining only the feature data sequence after the changing node; The sequential splicing refers to the process of connecting multiple data segments sequentially according to a predetermined index and time order to form a continuous data stream.

5. The method for storing audio recording data based on blockchain according to claim 1, characterized in that, The specific steps of S1 are as follows: S101: Obtain the grayscale image matrix of the fingerprint acquisition interface, detect pixel gradient changes to extract the feature point coordinate list, read the horizontal and vertical coordinates and arrange them sequentially according to the scanning path, extract the displacement of adjacent coordinates and divide the interval according to the preset distance range, map the displacement to the corresponding code number and concatenate them sequentially to obtain the displacement code sequence. S102: Based on the displacement encoding sequence, extract the displacement distribution range and count the number of intervals, divide the distance distribution range boundary according to the extreme value relationship of displacement, and write the distance distribution range boundary and interval number parameters into the summary field to obtain the distance distribution summary field; S103: Call the displacement encoding sequence and obtain the audio sampling sequence of the recording interface. Align the corresponding segments according to the fixed length segmentation rule. Perform bitwise XOR processing on each segment's encoding value and audio sampling value and concatenate them in order. At the same time, call the distance distribution summary field and append it to the end of the sequence to obtain the fingerprint-driven encrypted audio data block.

6. The method for storing audio recording data based on blockchain according to claim 1, characterized in that, The specific steps of S2 are as follows: S201: Based on the fingerprint-driven encrypted audio data block, segments are divided according to frame order. Phase extraction is performed on the audio segments and the continuous phase change is tracked. The change trajectory is extracted according to the phase change direction of adjacent sampling points and connected sequentially to obtain the phase change sequence. S202: Based on the phase change sequence, obtain the satellite positioning receiver interface position trajectory sequence, extract the latitude and longitude coordinates of adjacent positions and derive the azimuth angle, extract the angle change according to the direction of the line connecting adjacent coordinates and connect them in sequence to obtain the azimuth angle sequence; S203: Based on the phase change sequence and the azimuth sequence, align the time, extract the discrimination value sequence based on the consistency between the phase change direction and the azimuth change discrimination direction at the time point, compress consecutive identical discrimination values ​​and retain the change node sequence to obtain the phase path coupling feature data block.

7. The method for storing audio recording data based on blockchain according to claim 1, characterized in that, The specific steps for S3 are as follows: S301: Based on the phase path coupling feature data block, detect the internal sequence structure of the data block and divide it into segments of fixed length, extract continuous segments and process them by sequential numbering to obtain a coupling feature segment sequence; S302: Based on the coupling feature segment sequence, call the device unique identifier code, and perform equal-length segmentation to obtain the identifier segment sequence. Call the fingerprint driver to encrypt the audio data block to divide the audio segments in order, extract the amplitude of the audio segments and count the number of sampling points, and splice the amplitude value and the number of samples to obtain the amplitude sampling splicing sequence. S303: Based on the amplitude sampling splicing sequence and the identifier fragment sequence, perform cross splicing, perform synchronous insertion on the coupled feature fragment sequence and maintain the order consistency to obtain the identifier embedded hybrid data block.

8. The method for storing audio recording data based on blockchain according to claim 1, characterized in that, The specific steps of S4 are as follows: S401: Based on the identifier, embed the mixed data block, detect the internal sequence structure and splice it sequentially, connect the segments according to their index positions and keep the order consistent to obtain the mixed splicing sequence; S402: Based on the hybrid splicing sequence, call the phase path coupling feature data block and the time stamp field, align them according to the time order, and splice the three types of sequences according to the corresponding time index to obtain a multi-source spliced ​​data stream; S403: Based on the multi-source spliced ​​data stream, extract key segments and compress the summary, write the compression result into the block data field and complete the field encapsulation to obtain the audio recording evidence block data body.

9. The method for storing audio recording data based on blockchain according to claim 1, characterized in that, The specific steps of S5 are as follows: S501: Based on the audio recording evidence block data body, parse the internal time stamps and location trajectories, extract the time stamp intervals of adjacent blocks and expand them in time order to obtain the time sequence, and at the same time extract the changes in adjacent coordinate paths for the location trajectory and connect them in order to obtain the path change sequence. S502: Based on the time series and the path change sequence, determine the splicing order, write the splicing result into the specified field of the audio recording and evidence storage block data body and update the content to obtain the spatiotemporal splicing sequence; S503: Based on the spatiotemporal splicing sequence, extract the summary and write it into the block data body summary area. At the same time, call the previous block summary splicing order and write it into the block header field to obtain the data blockchain storage record.

10. A blockchain-based audio recording data storage system, characterized in that, The system is used to implement the blockchain-based method for storing audio recording data as described in any one of claims 1-9, and the system comprises: The fingerprint acquisition module obtains the fingerprint grayscale image and feature point coordinates, calculates the adjacent displacement difference in sequence and divides the interval mapping code, splices the code sequence and performs XOR processing with the audio sample segment by segment, extracts the distance distribution range and the number of intervals and writes it into the digest to obtain the fingerprint-driven encrypted audio data block. The phase trajectory module, based on the fingerprint-driven encrypted audio data block, divides the audio segments by frame and extracts the phase change sequence, calculates the azimuth sequence by combining the position trajectory and aligns it by time, judges the direction consistency and compresses the result to obtain the phase path coupling feature data block; The identifier embedding module obtains the device identifier and divides it into segments based on the phase path coupling feature data block. It then divides the audio sub-segments, extracts the amplitude, splices the sampling number, and cross-merges them to obtain the identifier embedding hybrid data block. The block construction module embeds a hybrid data block based on the identifier and splices it sequentially. At the same time, it combines phase path features and time stamps to generate a data stream and a summary data stream and writes them into the block field to obtain the audio recording evidence block data body. The chain recording module, based on the audio recording evidence block data body, parses the time interval and trajectory changes to obtain the time series and path series, splices them together, updates the summary and associates them with the previous block to obtain the data blockchain evidence record.

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