Smart traffic data storage method, system and device based on Internet of Things, and medium

Abstract

The invention discloses an intelligent traffic data storage method, system and device based on the Internet of Things and a medium, and relates to the technical field of electric digital data processing, and the method comprises the steps that an edge node packages received data and inserts the data into a local directed acyclic graph account book; dividing the verified data into Level-0 fragments according to a fixed duration window, and performing variable-length fragmentation based on a Rabin fingerprint algorithm; calculating Hash values of the variable-length fragments, and performing duplicate removal through a Bloom filter and a Hash index table in sequence; generating a local erasure block and a global erasure block for the deduplicated fragments, and storing complete copies of the key fragments on a plurality of storage nodes; and periodically checking a local erasure block and a complete copy, and triggering self-healing reconstruction when detecting that the fragment is lost. According to the method, the technical problems of high delay of distributed write-in consistency, high storage redundancy, complex cross-node time sequence correction, slow data recovery, poor system expansibility and the like are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of electrical digital data processing, and particularly to a method, system, device and medium for storing intelligent transportation data based on the Internet of Things. Background Art

[0002] With the large-scale deployment of Internet of Things devices in traffic monitoring, vehicle-mounted terminals and roadside sensing nodes, the image, video, trajectory and environmental monitoring data generated by the intelligent transportation system have increased exponentially. The existing centralized or simple distributed storage architectures face problems such as high concurrent write latency, serious data redundancy, difficulty in ensuring cross-node consistency, inaccurate time series alignment, and low efficiency of device failure recovery, and are difficult to meet the requirements of real-time, reliability and scalability. Summary of the Invention

[0003] In view of the above existing problems, the present invention is proposed.

[0004] Therefore, the present invention provides a distributed intelligent transportation data storage method based on the Internet of Things. By introducing a directed acyclic graph (DAG) consistency verification protocol at the edge node, combining multi-level semantic sharding with variable-length content-addressable deduplication algorithm, hardware-level nanosecond time stamps and skip list indexes, and local / global erasure codes and multi-copy self-healing mechanisms, the technical problems of high latency of distributed write consistency, high storage redundancy, complex cross-node time series correction, slow data recovery and poor system scalability are solved.

[0005] To solve the above technical problems, the present invention provides the following technical solutions. A method for storing intelligent transportation data based on the Internet of Things includes:

[0006] The Internet of Things terminal collects traffic data and sends it to the edge node. The edge node encapsulates the received data into a transaction node containing a transaction identifier, a list of parent transaction identifiers, a load summary and a hardware-level time stamp, and inserts it into the local directed acyclic graph ledger for consistency verification; executes a multi-level sharding strategy, divides the verified data into Level-0 shards according to a fixed-duration window, extracts key frame shards, moving area shards and static background shards in each Level-0 shard, and then performs variable-length sharding based on the Rabin fingerprint algorithm; calculates the hash value of the variable-length shards, and performs deduplication through a Bloom filter and a hash index table in sequence; generates local erasure blocks and global erasure blocks for the deduplicated shards, and stores complete copies of the key shards on multiple storage nodes; periodically checks the local erasure blocks and complete copies, and triggers self-healing reconstruction when a shard loss is detected.

[0007] As a preferred solution of the Internet of Things-based intelligent transportation data storage method described in the present invention, where: the edge node collects the hardware-level timestamp generated by the PTP module and calibrated by the GNSS module at the data packet egress, then performs SHA-256 operation on the original traffic data block to obtain the payload digest, retrieves the transaction identifiers of two confirmed transactions from the local DAG ledger, inputs the hardware-level timestamp, payload digest, and parent transaction identifiers into the SHA-256 operation to generate a unique transaction identifier, and serializes the hardware-level timestamp field, payload digest field, parent transaction identifier list field, and transaction identifier field into a transaction node structure according to a predefined format, and inserts the transaction node structure into the local DAG ledger;

[0008] The hardware-level timestamp field is obtained by calling the PTP module or GNSS module at the network interface for sampling and writing into the metadata area; the payload digest field is obtained by performing SHA-256 hash operation on the received traffic data; the parent transaction identifier list field is obtained by retrieving the transaction identifiers of confirmed transactions from the local DAG ledger; the transaction identifier field is obtained by performing SHA-256 hash operation on the foregoing hardware-level timestamp field, payload digest field, and parent transaction identifier list field; then the hardware-level timestamp field, payload digest field, parent transaction identifier list field, and transaction identifier field are concatenated in a predefined order and reversibly encoded to form a serialized transaction node structure.

[0009] As a preferred solution of the Internet of Things-based intelligent transportation data storage method described in the present invention, where: the multi-level sharding strategy includes three levels. The first level divides the Level-0 shards according to a fixed-duration window; the second level extracts key frame shards, moving area shards, and static background shards respectively within the Level-0 shards; the third level performs variable-length division on the second-level shards using the Rabin fingerprint algorithm.

[0010] As a preferred solution of the Internet of Things-based intelligent transportation data storage method described in the present invention, where: the key frame shards are generated by the key frame extraction algorithm, the moving area shards are generated by the frame difference detection algorithm, and the static background shards are generated by the image boundary extraction algorithm.

[0011] As a preferred solution of the Internet of Things-based intelligent transportation data storage method described in the present invention, where: the size of the third-level shards is dynamically adjusted according to data characteristics and system requirements, and a hash value is calculated for each shard.

[0012] As a preferred solution of the Internet of Things-based intelligent transportation data storage method described in the present invention, where: the deduplication through the Bloom filter and hash index table in sequence includes calculating the SHA-256 digest for each shard and then using A hash function maps the digest to a pre-filtering of the bit Bloom bit array, where is the total number of hash functions, is the total number of Bloom bit arrays;

[0013] If the pre-filtering result is negative, the digest is added to the Bloom filter and written to the distributed hash index bucket of the corresponding node according to the consistent hashing algorithm; if the pre-filtering result is positive, the index bucket is located through the consistent hashing algorithm and the digest list in the bucket is queried, and when the digest is not hit, the digest is written to the index bucket and inserted into the Bloom filter, and when the digest is hit, the current shard is marked as repeated and the storage is skipped.

[0014] As a preferred solution of the Internet of Things-based intelligent transportation data storage method described in the present invention, wherein: the generation of the local erasure block and the global erasure block includes generating local check blocks for each original shard using a locally recoverable erasure code algorithm, then generating global check blocks using a cross-cluster erasure code algorithm, writing the original shards and the local check blocks to the same storage node group, writing the global check blocks to remote storage nodes through consistent hashing mapping, and writing complete copies of the original shards marked as critical to two different physical storage nodes respectively.

[0015] As a preferred solution of the Internet of Things-based intelligent transportation data storage system described in the present invention, wherein: it includes a data collection module, an algorithm module, and a verification and reconstruction module; the data collection module is used to collect and transmit data information; the algorithm module is used to carry the algorithms for the system to run; the verification and reconstruction module is used to perform integrity verification, and when a shard loss is detected, use the local erasure block for reconstruction or complete the reconstruction using the global erasure block or the complete copy.

[0016] A computer device includes a memory and a processor, the memory stores a computer program, and is characterized in that when the processor executes the computer program, the steps of the Internet of Things-based intelligent transportation data storage method are implemented.

[0017] A computer-readable storage medium stores a computer program, and is characterized in that when the computer program is executed by a processor, the steps of the Internet of Things-based intelligent transportation data storage method are implemented.

[0018] Advantages of the present invention: The overall solution of the present invention realizes lightweight local consistency verification to significantly reduce write confirmation latency, effectively introduces semantic-based multi-level sharding and content-addressable deduplication to greatly reduce storage redundancy and network bandwidth consumption, uses hardware-level nanosecond timestamps combined with skiplist indexes to ensure accurate cross-node timing alignment and efficient range retrieval, and improves the fault recovery speed and data availability through a combined local and global erasure code multi-copy self-healing reconstruction mechanism. Thus, while ensuring real-time performance, reliability, and security, it has excellent scalability. Description of the Drawings

[0019] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0020] Figure 1 It is a schematic flowchart of a method for storing intelligent transportation data based on the Internet of Things provided by an embodiment of the present invention. Detailed Embodiments

[0021] To make the above objects, features, and advantages of the present invention more obvious and understandable, the following will provide a detailed description of the specific embodiments of the present invention with reference to the accompanying drawings of the specification. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of them. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the scope of protection of the present invention.

[0022] Many specific details are set forth in the following description to facilitate a thorough understanding of the present invention. However, the present invention can also be implemented in other ways different from those described herein. Those skilled in the art can make similar extensions without departing from the spirit of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0023] Secondly, the so-called "one embodiment" or "embodiment" herein refers to specific features, structures, or characteristics that can be included in at least one implementation manner of the present invention. The appearances of "in one embodiment" in different places in this specification do not all refer to the same embodiment, nor are they separate or alternative embodiments that exclude each other from other embodiments.

[0024] The present invention will be described in detail with reference to the schematic diagrams. When describing the embodiments of the present invention, for the convenience of explanation, the cross-sectional views showing the device structure will be enlarged locally in a non-general proportion, and the schematic diagrams are only examples and should not limit the scope of protection of the present invention herein. In addition, in actual production, three-dimensional spatial dimensions including length, width, and depth should be included.

[0025] At the same time, in the description of the present invention, it should be noted that the orientation or positional relationship indicated by terms such as "upper, lower, inner, and outer" is based on the orientation or positional relationship shown in the drawings. It is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation to the present invention. In addition, the terms "first, second, or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0026] Unless otherwise clearly defined and limited in the present invention, the terms "installed, connected, and coupled" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can also be a mechanical connection, an electrical connection, or a direct connection, and can also be indirectly connected through an intermediate medium, or can be the communication inside two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific situations.

[0027] Example 1, referring to Figure 1 , is the first embodiment of the present invention. This embodiment provides a method for storing intelligent transportation data based on the Internet of Things, including:

[0028] S1: The Internet of Things terminal collects traffic data and sends it to the edge node. The edge node encapsulates the received data into a transaction node containing a transaction identifier, a list of parent transaction identifiers, a payload digest, and a hardware-level timestamp, and inserts it into the local directed acyclic graph ledger for consistency verification.

[0029] The edge node collects the hardware-level timestamp generated by the PTP module and calibrated by the GNSS module at the packet egress, performs the SHA-256 operation on the original traffic data block to obtain the payload digest, retrieves the transaction identifiers of two confirmed transactions from the local DAG ledger, inputs the hardware-level timestamp, payload digest, and parent transaction identifier into the SHA-256 operation to generate a unique transaction identifier, and serializes the hardware-level timestamp field, payload digest field, parent transaction identifier list field, and transaction identifier field into a transaction node structure according to a predefined format, and inserts the transaction node structure into the local DAG ledger.

[0030] The hardware-level timestamp field is obtained by calling the PTP module or GNSS module at the network interface for sampling and writing into the metadata area; the payload digest field is obtained by performing a SHA-256 hash operation on the received traffic data; the parent transaction identifier list field is obtained by retrieving the transaction identifiers of the confirmed transactions from the local DAG ledger; the transaction identifier field is obtained by performing a SHA-256 hash operation on the aforementioned hardware-level timestamp field, payload digest field, and parent transaction identifier list field; then the hardware-level timestamp field, payload digest field, parent transaction identifier list field, and transaction identifier field are concatenated in a predefined order and reversibly encoded to form a serialized transaction node structure.

[0031] S2: Execute a multi-level sharding strategy. Divide the verified data into Level-0 shards according to a fixed-duration window. After extracting key-frame shards, motion-region shards, and static-background shards within each Level-0 shard, perform variable-length sharding based on the Rabin fingerprint algorithm.

[0032] The multi-level sharding strategy includes three levels. The first level divides the data into Level-0 shards according to a fixed-duration window; the second level extracts key-frame shards, motion-region shards, and static-background shards within the Level-0 shards respectively; the third level performs variable-length division on the shards of the second level using the Rabin fingerprint algorithm.

[0033] The key-frame shards are generated by a key-frame extraction algorithm, the motion-region shards are generated by a frame-difference detection algorithm, and the static-background shards are generated by an image boundary extraction algorithm.

[0034] The size of the shards at the third level is dynamically adjusted according to data characteristics and system requirements, and a hash value is calculated for each shard.

[0035] Specifically, first divide the original data sequence into first-level shards according to a fixed-duration window:

[0036]

[0037] where, is the original data sequence, is the start time of the th fixed-duration window, is the fixed-duration window length, is the first-level shard. Call three algorithms respectively for each first-level shard to generate non-overlapping second-level shards:

[0038]

[0039] and satisfy

[0040]

[0041] Among them, is the key frame extraction, frame difference detection, and image boundary extraction function. respectively call three algorithms to generate non-overlapping secondary shards. is an empty set.

[0042] Complexity metric and parameter dynamic determination is

[0043] Among them, is the metric weight coefficient; is the complexity metric of the secondary shard. is the fragment pixel entropy function. is the motion intensity variance function; is the secondary shard, and i is the index; is the complexity metric of the i-th secondary shard; are the upper and lower limits of the sliding window length. is the target shard size. is the dynamic sliding window length. is the maximum complexity value. is the dynamic boundary detection modulus;

[0044] Calculate the fingerprint in each secondary shard with a sliding window of length :

[0045]

[0046] Generate a shard boundary set according to the remainder matching condition of the fingerprint and the modulus, and supplement the start and end offsets:

[0047]

[0048] Among them, is the length of this secondary shard.

[0049] Finally, sort the boundary set in ascending order as and cut out all the tertiary shards:

[0050]

[0051] Among them, represents the original data sequence in the th position starting from the th unit and offsetting is the rolling fingerprint value at the position ; is the rolling hash base, is the modulo polynomial, is the remainder trigger constant, is the set of shard boundary offsets, is the length of the secondary shard, is the th offset in the boundary set, is the th offset in the boundary set, is the third-level variable-length shard, n is the offset index within the sliding window, and m is the starting offset position of the current sliding window within the entire secondary shard, is the last index value in the shard boundary set. The boundary set contains offset positions in total, so segments of variable-length third-level shards can be generated.

[0052] S3: Calculate the hash value for the variable-length shards and perform deduplication through the Bloom filter and the hash index table in sequence.

[0053] The performing deduplication through the Bloom filter and the hash index table in sequence includes, after calculating the SHA-256 digest for each shard, using hash functions to map the digest to the -bit Bloom bit array for pre-filtering, where is the total number of hash functions, is the total number of Bloom bit arrays;

[0054] If the pre-filtering result is negative, add the digest to the Bloom filter and write the digest to the distributed hash index bucket corresponding to the node according to the consistent hashing algorithm; if the pre-filtering result is positive, locate the index bucket through the consistent hashing algorithm and query the digest list in the bucket, and when the digest is not found, write the digest to the index bucket and insert the digest into the Bloom filter, and when the digest is found, mark the current shard as duplicate and skip storage.

[0055] S4: Generate local erasure blocks and global erasure blocks for the deduplicated shards and store the complete copies of the key shards on multiple storage nodes.

[0056] The generating local erasure blocks and global erasure blocks includes, for each original shard, generating local parity blocks using the locally recoverable erasure coding algorithm, then generating global parity blocks using the cross-cluster erasure coding algorithm, writing the original shard and the local parity blocks to the same storage node group, writing the global parity blocks to the remote storage node through consistent hashing mapping, and writing the complete copies of the original shards marked as key to two different physical storage nodes respectively.

[0057] S5: Periodically verify the local erasure blocks and complete copies, and trigger self-healing reconstruction when a shard loss is detected.

[0058] To ensure the executability of the first verification, set the initial value of the loss set to an empty set and initialize the local threshold to the base value , the system triggers the verification task at fixed intervals:

[0059]

[0060] At time The scheduling module sequentially reads the content of the th original shard and compares it with the stored reference hash to generate a consistency indication. The set of lost shard indices is:

[0061]

[0062] Dynamically adjust the local recoverable threshold based on the previous loss scale:

[0063]

[0064] Select the reconstruction method according to the current number of lost shards:

[0065]

[0066] After completing the reconstruction, regenerate the verification block and update the mapping:

[0067]

[0068] Among them, is the initial value of the loss set, is the initial value of the local threshold, is the initial verification trigger time, is the verification period interval, is the th original shard's data content at time , is the th shard's reference hash value when it was initially written, Hash is the SHA-256 hash function, is the th shard's consistency indication in the th verification. 1 indicates consistency, and 0 indicates loss; is the th verification's set of lost shard indices, is the th verification's set of lost shard indices, is the base local erasure block recoverable threshold, is the threshold adaptation coefficient, used to adjust the local threshold according to the last loss scale, is the local erasure recoverable threshold for the th check, is the number of available global erasure blocks, are the generator matrices representing local and global erasure codes respectively; is the sub - matrix extracted from the generator matrix corresponding to the lost index rows, are the local and global erasure block column vectors corresponding to the lost indices, is the lost shard data column vector reconstructed for the th check, is the complete copy data of the th shard, is the overall local erasure block and global erasure block matrix generated after reconstruction; D is the vector of the original shard data; is the size of the lost set, that is, the number of lost shards, is the serial number of the check task.

[0069] Embodiment 2

[0070] The second embodiment of the present invention provides an Internet - of - Things - based intelligent transportation data storage system, specifically including:

[0071] The system includes a data acquisition module, an algorithm module, and a check and reconstruction module; the data acquisition module is used to collect and transmit data information; the algorithm module is used to carry the algorithms for the system operation; the check and reconstruction module is used to perform integrity checks, and when a shard loss is detected, use local erasure blocks for reconstruction or use global erasure blocks or complete copies to complete the reconstruction.

[0072] Embodiment 3

[0073] The third embodiment of the present invention is different from the previous two embodiments in that:

[0074] If the above-mentioned functions are implemented in the form of software function units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs.

[0075] This application is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram, as well as the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0076] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in this computer-readable memory generate a manufactured article including instruction means, and the instruction means implements the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0077] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process. Thus, the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0078] Although the preferred embodiments of the present application have been described, additional changes and modifications can be made to these embodiments by those skilled in the art once they learn of the basic creative concept. Therefore, the appended claims are intended to be construed to include the preferred embodiments as well as all changes and modifications falling within the scope of the present application.

[0079] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalent technologies, the present application is also intended to include these modifications and variations.

Claims

1. A method for storing intelligent transportation data based on the Internet of Things, characterized in that: including, The IoT terminal collects traffic data and sends it to the edge node. The edge node encapsulates the received data into a transaction node containing a transaction identifier, a list of parent transaction identifiers, a payload digest, and a hardware-level timestamp, and inserts it into the local directed acyclic graph ledger for consistency verification; Execute a multi-level sharding strategy, divide the verified data into Level-0 shards according to a fixed-duration window, extract key-frame shards, motion-region shards, and static-background shards within each Level-0 shard, and then perform variable-length sharding based on the Rabin fingerprint algorithm; Calculate the hash value of the variable-length shards, and perform deduplication through the Bloom filter and the hash index table in sequence; Generate local erasure blocks and global erasure blocks for the deduplicated shards, and store complete copies of the key shards on multiple storage nodes; Periodically verify the local erasure blocks and the complete copies, and trigger self-healing reconstruction when shard loss is detected.

2. The intelligent transportation data storage method based on the Internet of Things according to claim 1, wherein: The edge node collects the hardware-level timestamp generated by the PTP module and calibrated by the GNSS module at the packet egress, then performs the SHA-256 operation on the original traffic data block to obtain the payload digest, retrieves the transaction identifiers of two confirmed transactions from the local DAG ledger, inputs the hardware-level timestamp, payload digest, and parent transaction identifier into the SHA-256 operation to generate a unique transaction identifier, and serializes the hardware-level timestamp field, payload digest field, parent transaction identifier list field, and transaction identifier field into a transaction node structure according to a predefined format, and inserts the transaction node structure into the local DAG ledger; The hardware-level timestamp field is obtained by calling the PTP module or GNSS module at the network interface for sampling and writing into the metadata area; The payload digest field is obtained by performing the SHA-256 hash operation on the received traffic data; the parent transaction identifier list field is obtained by retrieving the transaction identifiers of the confirmed transactions from the local DAG ledger; the transaction identifier field is obtained by performing the SHA-256 hash operation on the aforementioned hardware-level timestamp field, payload digest field, and parent transaction identifier list field; then the hardware-level timestamp field, payload digest field, parent transaction identifier list field, and transaction identifier field are concatenated in a predefined order and reversibly encoded to form a serialized transaction node structure.

3. The intelligent transportation data storage method based on the Internet of Things according to claim 2, wherein: The multi-level sharding strategy includes three levels. The first level divides the Level-0 shards according to a fixed-duration window; The second level extracts key-frame shards, motion-region shards, and static-background shards within the Level-0 shards respectively; the third level performs variable-length division on the shards of the second level using the Rabin fingerprint algorithm.

4. The method for storing intelligent transportation data based on the Internet of Things according to claim 3, characterized in that: The key-frame shards are generated by the key-frame extraction algorithm, the motion-region shards are generated by the frame difference detection algorithm, and the static-background shards are generated by the image boundary extraction algorithm.

5. The method for storing intelligent transportation data based on the Internet of Things according to claim 4, characterized in that: The size of the shards in the third level is dynamically adjusted according to data characteristics and system requirements, and the hash value is calculated for each shard.

6. The method for storing intelligent transportation data based on the Internet of Things according to claim 5, characterized in that: The duplicate removal by sequentially passing through the Bloom filter and the hash index table includes calculating the SHA-256 digest for each shard and then using hash functions to map the digest to -bit Bloom bit arrays for pre-filtering, where is the total number of hash functions, and is the total number of Bloom bit arrays; If the pre-filtering result is negative, add the digest to the Bloom filter and write the digest to the distributed hash index bucket of the corresponding node according to the consistent hashing algorithm; if the pre-filtering result is positive, locate the index bucket through the consistent hashing algorithm and query the list of digests in the bucket, and when the digest is not found, write the digest to the index bucket and insert the digest into the Bloom filter, and when the digest is found, mark the current shard as duplicate and skip storage.

7. The method for storing intelligent transportation data based on the Internet of Things according to claim 6, wherein: The generation of local erasure blocks and global erasure blocks includes generating local parity blocks for each original shard using a locally recoverable erasure code algorithm, then generating global parity blocks using a cross-cluster erasure code algorithm, writing the original shards and local parity blocks to the same storage node group, writing the global parity blocks to remote storage nodes through consistent hashing mapping, and writing complete copies of the original shards marked as critical to two different physical storage nodes respectively.

8. A system adopting a method for storing intelligent transportation data based on the Internet of Things as described in any one of claims 1 to 7, characterized in that: It includes a data collection module, an algorithm module, and a verification and reconstruction module; The data collection module is used to collect and transmit data information; The algorithm module is used to carry the algorithms for the system to run; The verification and reconstruction module is used to perform integrity verification, and when a shard loss is detected, use local erasure blocks for reconstruction or use global erasure blocks or complete copies to complete the reconstruction.

9. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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