CN120179650B

CN120179650B - A trusted query configuration method and system for blockchain petabyte-scale multimodal data

Info

Publication number: CN120179650B
Application number: CN202510310798.8A
Authority: CN
Inventors: 王赛; 商建伟; 周君平
Original assignee: Shengan Information Technology Co ltd
Current assignee: Shengan Information Technology Co ltd
Priority date: 2025-03-17
Filing date: 2025-03-17
Publication date: 2025-11-28
Anticipated expiration: 2045-03-17
Also published as: CN120179650A

Abstract

The invention belongs to the technical field of blockchains, and discloses a trusted query configuration method and a trusted query configuration system for PB-level multi-modal data of a blockchain, wherein the trusted query configuration method comprises the steps of obtaining multi-modal uplink data, and extracting metadata building block indexes of the multi-modal data; constructing a block index table based on the block ID, carrying out logic organization on the block index table, adaptively and dynamically adjusting the block index table, constructing an index partition table for storing the block index table identification and the address, constructing a coarse granularity index model, quickly positioning the block index table in the index partition table according to the query condition, searching the inverted index in the block index table to obtain the block ID and the storage position, searching the index in the block, querying a specific data record, verifying the hash path and realizing the trusted query of the data. The invention adopts a decentralization self-adaptive multi-level index mechanism to optimize decentralization query performance, supports complex query and traceable query operations such as connection and aggregation, and ensures effective reliability of query results through a trusted query algorithm.

Description

Trusted query configuration method and system for PB-level multi-mode data of block chain

Technical Field

The invention relates to the technical field of blockchains, in particular to a trusted query configuration method and a trusted query configuration system for PB-level multi-mode data of a blockchain.

Background

The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.

The block chain is a novel calculation paradigm for preventing data tampering by an encryption mode, organizing a plurality of data by block granularity, storing blocks by a linked list additional mode, ensuring agreement of multiparty block copies, and realizing block integrity consensus by transaction processing of each party, is mainly suitable for an unreliable multiparty value exchange scene characterized by opening and sharing, and mainly goes through three development stages of digital currency, intelligent contracts and intelligent society according to the difference of a block chain storage model, inquiry capability and transaction processing mechanisms.

As the focus of blockchain application gradually shifts from digital currency to digital society, blockchain starts to play a positive role in digital government, medical, financial, etc. areas, and will become an important enabling technology for improving socioeconomic collaboration patterns in the future. But as the basic core of the blockchain application system, the problems of insufficient multi-mode data expression capability, poor expandability of a storage model, missing complex query capability and low transaction processing performance still exist in blockchain storage and data management, and the problems still have a great gap with PB (Petabytes) level multi-mode data storage capability, million/second complex query and trusted traceability query and tens of thousands TPS (Transaction per second) transaction processing capability required by digital society.

This means that blockchain data is no longer limited to numerical values, but theoretically supports more modalities of text, sequence, video, etc. The multi-mode data such as video is introduced, so that the storage requirement of a single block is rapidly increased, and the full-node bottleneck with the weakest storage capacity is easier to trigger. To address this challenge, blockchain systems mainly employ a storage architecture model under the chain, move data in blocks on the chain to a local storage under the chain or cloud storage, reconstruct indexes on the chain in a "pointer form" and synchronize to a relational database, and convert the query problem of the blockchain to the index and retrieval problem of the database, such as FabricSQL, bigChainDB, chainSQL, trustSQL. Furthermore, to support efficient consensus among parties for query results, FISCO BCOS employs a deterministic transaction concurrency processing method based on DAG, and the long security chain employs an optimistic transaction concurrency mechanism. Nevertheless, the blockchain 2.0 transaction capability is only 20-1000 TPS, and the total amount of storage rarely breaks through PB level.

In summary, the current blockchain storage architecture model, data querying mechanism, transaction processing mechanism still face a number of fundamental challenges in supporting PB-level multi-modal data storage, millions of trusted queries per second, 70,000TPS transactions in their infancy. The following problems are mainly present:

1. The method cannot be suitable for index construction of PB-level multi-mode data, and is a construction method for lack of indexes of unstructured data such as video, audio and pictures.

2. The lack of an efficient index construction method, limited expression capability of the K-V model, causes insufficient semantic description of the blockchain data, is difficult to support complex query, and cannot provide linkage query of data query in the blockchain block and cloud storage data.

3. The method for realizing the quick query is lacking, and complex query, traceable query and the like cannot be performed.

Disclosure of Invention

In order to solve the problems, the invention provides a trusted query configuration method and a system for PB-level multi-mode data of a blockchain, which are oriented to PB-level multi-mode data, adopt a decentralization self-adaptive multi-level index mechanism, optimize decentralization query performance, support complex query and traceable query operations such as connection and aggregation, and ensure effective and trusted query results through a trusted query algorithm.

In order to achieve the above purpose, the present invention adopts the following technical scheme:

in a first aspect, the present invention provides a trusted query configuration method for blockchain PB-level multimodal data, including the steps of:

Acquiring multi-mode uplink data, extracting metadata of the multi-mode data, constructing an intra-block index by MERKLE TREE based on the metadata, and optimizing the constructed index by using a B+ tree;

Constructing a block index table based on the block ID, logically organizing the block index table in an incremental view mode, and adaptively and dynamically adjusting the block index table according to the change of the block chain data;

Constructing an index partition table according to the type of the metadata, and storing the block index table identification and the address;

Constructing a coarse-granularity index model, quickly positioning a block index table to be queried in an index partition table according to query conditions, searching an inverted index in the block index table according to metadata to be queried to obtain a block ID and a storage position, searching the index in the block, querying a specific data record, verifying a hash path, and realizing the trusted query of data.

As an alternative embodiment, the intra-block index is built based on metadata, specifically:

Metadata of the multimodal data is used as a leaf node intra-block index of MERKLE TREE, and the constructed index is optimized by using a B+ tree.

As an alternative embodiment, the compressed feature code is processed in a data truncation manner when metadata of the multi-mode data is extracted.

As an alternative implementation mode, synchronous updating rules are preset for the indexes in the block, various basic framework data, intelligent perception data and association relation data cached in the memory are processed in batches, and frequent writing and updating operations of the global index and the local index are avoided.

Alternatively, a coarse-granularity index model is built using bloom filters or encrypted multiset accumulators.

As an alternative embodiment, the block index table is an inverted sequence index, supporting a single column index, a unique index, a primary key index, and an aggregate index.

In a second aspect, the present invention provides a trusted query configuration system for blockchain PB-level multimodal data, comprising:

The intra-block index constructing module is configured to acquire multi-mode uplink data, extract metadata of the multi-mode data, construct an intra-block index by MERKLE TREE based on the metadata, and optimize the constructed index by using a B+ tree;

The block index construction module is configured to construct a block index table based on the block ID, logically organize the block index table in an incremental view mode and adaptively and dynamically adjust the block index table according to the change of the block chain data;

The partition index construction module is configured to construct an index partition table according to the type of the metadata and is used for storing the block index table identification and the address;

The trusted query module is configured to construct a coarse granularity index model, quickly locate a block index table to be queried in the index partition table according to query conditions, search an inverted index in the block index table according to metadata to be queried to obtain a block ID and a storage position, search the index in the block, query specific data records, verify a hash path and realize trusted query of data.

In a third aspect, the invention provides an electronic device comprising a memory and a processor and computer instructions stored on the memory and running on the processor, which when executed by the processor, perform the method of the first aspect.

In a fourth aspect, the present invention provides a computer readable storage medium storing computer instructions which, when executed by a processor, perform the method of the first aspect.

In a fifth aspect, the invention provides a computer program product comprising a computer program which, when executed by a processor, implements the method of the first aspect.

Compared with the prior art, the invention has the beneficial effects that:

1. The invention provides a trusted query configuration method and a system for block chain PB-level multi-modal data, which creatively provides a multi-level index structure, adopts a verification chain to ensure the credibility of the data under the chain, adopts a hash self-learning algorithm to continuously optimize the index structure for the multi-modal data, and simultaneously provides a new idea for complex query and traceable query. In the specific implementation, the techniques of distributed storage, encryption multi-set accumulator, multi-mode hash method and the like are adopted to support the indexing of multi-mode data.

2. The invention provides a trusted query configuration method and a trusted query configuration system for PB-level multi-mode data of a blockchain, which adopt improved MERKLE TREE to provide quick query and trusted verification of the data in the blockchain, use a verification chain to store hash values of the data under the chain to ensure the authenticity and the integrity of the data under the chain, and provide good technical support for collaborative query and trusted verification of the data on the chain and the data under the chain, and solve the problem of high-efficiency trusted query in the blockchain system.

Additional aspects of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.

Drawings

The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the invention.

FIG. 1 is a schematic diagram of a trusted query configuration method for PB-level multi-modal data in a blockchain according to embodiment 1 of the present invention;

FIG. 2 is a flowchart of an adaptive index construction provided in embodiment 1 of the present invention;

FIG. 3 is a diagram of a multi-level index structure according to embodiment 1 of the present invention;

fig. 4 is a trusted query mechanism for blockchain PB-level multi-modal data provided in embodiment 1 of the present invention.

Detailed Description

The invention is further described below with reference to the drawings and examples.

It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the invention. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular forms also are intended to include the plural forms, and furthermore, it is to be understood that the terms "comprises" and "comprising" and any variations thereof are intended to cover a non-exclusive inclusion, e.g., a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements explicitly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.

Embodiments of the invention and features of the embodiments may be combined with each other without conflict.

Example 1

As shown in fig. 1, the embodiment provides a trusted query configuration method for blockchain PB-level multi-modal data, which includes the following steps:

S1, acquiring multi-mode uplink data, extracting metadata of the multi-mode data, constructing an intra-block index by MERKLE TREE based on the metadata, and optimizing the constructed index by using a B+ tree;

s2, constructing a block index table based on the block ID, logically organizing the block index table in an incremental view mode, and adaptively and dynamically adjusting the block index table according to the change of the block chain data;

s3, constructing an index partition table according to the type of the metadata, and storing the block index table identification and the address;

S4, constructing a coarse-granularity index model, rapidly positioning a block index table to be queried in an index partition table according to query conditions, searching an inverted index in the block index table according to metadata to be queried to obtain a block ID and a storage position, searching the index in the block, querying a specific data record, verifying a hash path, and realizing the trusted query of data.

The invention provides an efficient index construction method and a quick query mechanism for PB-level multi-mode data. The method comprises the steps of providing a decentralizing self-adaptive multilevel index construction model, including a slice index, a block index and an intra-block index, meeting the requirements of index self-adaptive construction and verifiable, designing metadata modeling of large-scale multi-mode data, logically organizing an index table in an incremental view mode, self-adaptively and dynamically adjusting the index table according to changes of transaction data to realize efficient trusted indexes of on-chain and off-chain data, designing a trusted traceability query and optimization algorithm by adopting a query expression based on the multi-mode data, improving query execution capacity, realizing millions/second query efficiency, and supporting time window query, range query, keyword query and subscription query.

The distributed index server is adopted, namely, a block, a multi-level verifiable index in a block and a single index, a multi-index and a partition index in a chain are established for the uplink of the block chain data, a bloom filter is optimized, the decentralization query delay can be greatly reduced, a verification block chain for storing hash values of transaction data under the chain is established for the trusted query problem of the data under the chain, and the authenticity and the integrity of the data under the chain are ensured through verification of the hash values.

The method comprises the steps of carrying out dimension reduction and quantization on multi-mode data, mapping the data to a binary coding space through hashing, generating specific mode hash codes and multi-mode collaborative hash codes for different retrieval tasks, supporting multi-keyword query, fuzzy query, top-k query and the like, and carrying out trusted verification on hierarchical indexes, wherein the trusted verification mainly comprises hash result verification, link-up and link-down data collaborative verification, encryption multi-set accumulator and the like.

The basic idea of the incremental index organization is that in order to more fully take care of small, medium, large and extra-large jobs, various organization methods can be adopted to construct the physical structure of the file. If the size of the disk block is 1KB or 4KB, for small files, only 10 disk blocks are occupied at most, and in order to increase the access speed to numerous small jobs, it is preferable to directly put each of their disk block addresses into the file control block FCB (or index node), so that the disk block address of the file can be directly obtained from the FCB. This manner of addressing is commonly referred to as direct addressing. For medium files, a single level index organization approach may be employed. In order to obtain the disk block address of the file, the index table of the file is only needed to be found from the FCB, and the index table can be obtained from the FCB, and can be called as a primary address, and for large and extra-large files, a two-level and three-level index organization mode or secondary addresses and tertiary addresses can be adopted. The incremental index organization is organized based on the basic idea described above, and adopts both the direct addressing mode and the single-level and multi-level index organization (indirect addressing) mode.

The index table is dynamically adjusted in a self-adaptive manner according to the change of transaction data, specifically, a hash function is dynamically adjusted according to the characteristics of the data, so that hash collision is reduced, the index efficiency is improved, and the size of the index is dynamically adjusted according to the increase and change of the data, so that the newly added data is adapted, and the hash collision is reduced.

The method for constructing the intra-block index based on the metadata of the multi-modal data comprises the following steps:

the block chain has limited storage space, PB-level multi-mode data needs to be cooperatively processed with the data on and off the chain when being stored, which brings great difficulty to the quick query of the block chain data. The adoption of an indexing mechanism is an effective solution for improving query performance, metadata is firstly required to be extracted for multi-modal data, and the metadata is used for describing multi-modal data such as contract data, transaction data, pictures, videos and the like and is the basis for constructing indexes. Metadata is extracted by monitoring uplink data, the metadata is used as indexes in leaf node building blocks of MERKLE TREE, and a model is built by optimizing trusted data indexes by adopting optimization methods such as B+, multi-way tree and the like, so that multi-mode data query support and trusted verification in a block are realized. The specific steps are shown in figure 2 of the drawings,

(1) Metadata extraction, monitoring of multi-mode uplink data, mapping of the multi-mode data to a binary coding space by adopting a hash method, data truncation mode of compressed feature codes, such as SM3 hash algorithm, is adopted to output 16-byte feature codes, the first 12-byte feature codes can be adopted, the intercepted feature codes have the same effect of hash verification, meanwhile, similarity among data points is also saved, abstract expression is carried out on the multi-mode data by adopting LPG (Labeled Property Graphs, label attribute graph) through a reduction and quantization expression method, and the multi-mode data mainly comprises four expression modes, namely nodes (data entities), relations (relations among the nodes), attributes (key value forms exist among the nodes and the relations), labels (representing roles and types of the nodes) and the like.

(2) And (3) data is uplink, metadata is used as indexes in leaf node building blocks of MERKLE TREE, and a B+, multi-way tree and other optimization methods are adopted to optimize the trusted data indexes to build a model, so that multi-mode data query support and trusted verification in the block are realized.

(3) And synchronizing and updating indexes in the block chain block, presetting a synchronous updating rule, and processing various basic frame data, intelligent perception data and association relation data cached in a memory in batches to avoid frequent writing and updating operations on the global index and the local index.

(4) The deep learning model is combined, a query mode of multi-mode data is supported, a hash code and a multi-mode collaborative hash code which are converted into specific modes aiming at different query tasks are utilized, query conditions are converted into complex query of metadata, and multi-keyword query, fuzzy query, top-k query and the like are supported.

(5) For the under-chain data, the link address of the under-chain data and the hash value of the under-chain data are stored in the block chain in a verification chain mode, the verification chain is stored in the position of the verification chain through an index table, the decentralised verification chain can ensure the authenticity of the hash value of the under-chain data, and effective verification support is provided for trusted inquiry.

The construction method of the multi-level index comprises the following steps:

To improve retrieval performance, a model of a multi-level index is constructed according to the type of metadata within a block for indexing of block data, as shown in fig. 3. Firstly, constructing an index partition table according to metadata types, which is used for storing specific index table identifications and addresses, constructing a coarse-granularity index model by adopting methods such as a bloom filter and an encryption multi-set accumulator, and rapidly positioning the index table to be queried according to query conditions, secondly, dynamically constructing according to block changes, constructing an inverted sequence index in an incremental index mode, constructing the index based on metadata such as keywords and feature codes, supporting single-column index, unique index, main key index and aggregation index, and optimizing by using a B+tree as a third-level index.

The method comprises the steps of adopting a decentralizing index server, storing an index table by a distributed database independently of a blockchain, establishing a multi-level verifiable index in a partition, a block or a block, monitoring the change of blockchain data, dynamically updating and maintaining the multi-level index table, and providing a complex query function based on SQL language by utilizing the query capability of the database. The specific steps are as follows:

(1) The distributed index database is constructed, index data are stored on a plurality of computers in a scattered mode, collaborative processing is carried out through a network, and the efficiency of index construction and query can be remarkably improved by utilizing the parallel processing capacity of the plurality of computers. An appropriate data source is selected and the index is dynamically written to a distributed storage system, such as Hadoop's HDFS (Hadoop Distributed FILE SYSTEM).

(2) Master index construction, in a distributed storage system, constructing a master index for master data, creating a table or view on the index data, and defining an index field. Taking APACHE HIVE as an example, a Hive table may be created to store data and create a master index based on a field (e.g., an ID).

(3) An index partition table fast check model is constructed, a coarse granularity index model is designed by adopting a bloom filter, and whether index partition data are in a set is judged by utilizing a binary vector (bit array) and a series of random mapping functions (SM 3). The bloom filter is designed into a coarse granularity index model, and efficient searching and filtering functions are provided for index partition data.

Initializing, namely creating a bit array, initializing all bits to 0, selecting a group of independent keys K1, K2 and K3 as inputs of a random function to perform hash calculation, and selecting an SM3 algorithm as the hash function.

The partition index type is inserted, namely the index type on the distributed index server is input, and hash values of the index type are calculated by using all hash functions. The bit in the bit array corresponding to the hash value position is set to 1.

Index type is queried-for each data type to be queried, its hash value is calculated using all hash functions. It is checked whether the bits in the bit array corresponding to the hash value positions are all 1. If any bit is 0, it is determined that the element is not present, and by this method, it can be quickly determined whether there is data to be retrieved on the distributed index server.

(4) An index partition table is constructed, dynamic construction is carried out according to block change, an increment index mode is adopted, an inverted sequence index is constructed, the index can be constructed based on metadata such as keywords and feature codes, and single-column index, unique index, primary key index and aggregation index are supported.

(5) MERKLE TREE optimizing, namely optimizing the construction of intra-block indexes in a blockchain by adopting a B+ tree, wherein the B+ tree is a self-balancing and ordered tree data structure, and searching, sequential accessing, inserting and deleting operations are performed within 0 (log n) time complexity. B+ trees are widely used as index structures in databases and file systems, and can efficiently support range query and ordering operations. The following is the construction process of the b+ tree.

Selecting an index column, namely selecting a column with high selectivity as an index column according to the query requirement.

The root node is created by taking the first key value of the index column as the key of the root node and creating the corresponding pointer (if there are child nodes).

Inserting key values-for each key value to be inserted, searching for its insertion location starting from the root node. If the root node is not full, the key is inserted directly. If the root node is full, node splitting is performed, the key is assigned to two new nodes, and a new parent node is created to store the split node's intermediate key.

Balance is maintained-if a node splits during the insertion, the split result needs to be propagated upward, which may lead to splitting of the parent node. Through this splitting and propagation process, the b+ tree maintains its balance.

Delete key value delete operation is similar to insert operation, but the merging of nodes needs to be considered. If the node becomes unsatisfied after deletion (below the minimum filling level), an attempt is made to borrow or merge nodes from siblings to maintain balance.

MERKLE TREE is optimized with the b+ tree to accommodate large-scale data storage and querying, with each layer of MERKLE TREE stored as an independent b+ tree node. For example, the hash value for each layer may be used as a node, and the nodes may be stored in a b+ tree. This can take advantage of the efficient query and range query capabilities of the b+ tree. In each leaf node of the b+ tree, a pointer to the corresponding node in MERKLE TREE is stored or a partial hash value is directly stored. Thus, the method can quickly locate a specific hash value and further verify the integrity of the data. The hash value of MERKLE TREE is dynamically updated using the b+ tree. When the data block changes, only the hash value of the corresponding node in the B+ tree is updated, and then the hash value of the upper node is recalculated until the root node. This may reduce unnecessary full tree reconstruction. The method comprises the following specific steps:

(1) Constructing a basic data structure, namely firstly, constructing a B+ tree structure.

(2) Initialization MERKLE TREE in each node of the b+ tree, the corresponding layer of MERKLE TREE is initialized.

(3) And (3) data insertion and updating, namely, when a new data block is inserted or an existing data block is updated, calculating a new hash value and updating a corresponding node in the B+ tree.

(4) And (3) inquiring and verifying, namely quickly positioning to a specific MERKLE TREE node through the inquiring function of the B+ tree, and verifying the integrity of the data.

And carrying out quick trusted traceability query by using the constructed multi-level index:

as shown in fig. 4, hash result checking, link up and link down data cooperative checking, encryption multiset accumulator checking, etc. are used. The method comprises the steps of firstly, quickly positioning a block index table to be queried in an index partition table according to query conditions, searching an inverted index in the block index table according to metadata to be queried, positioning the inverted index to a block ID and a storage position, searching the index in the block according to the block ID and the storage position, querying specific data records, verifying a hash path through an improved B+Tree, realizing trusted query on data in the block, acquiring the data under a chain according to an under-chain data address of the block index table, querying a hash value on a verification chain, checking the integrity of the data under the chain according to the hash value on the verification chain, and performing cooperative trusted query on the data under the chain and the chain. The specific query steps are as follows:

(1) Accessing a distributed index server, inputting keys k1, k2 and k3 according to the type of the search data, distributing and calculating the hash result of SM3, inquiring a bloom filter according to the hash result, and if all corresponding positions are 1, indicating that the index server stores a relevant index partition table.

(2) And querying the index partition table, rapidly positioning the blocks meeting the conditions according to the query data, and verifying the integrity of the data on the chain and the data under the chain according to the hash value of the integrity verification.

(3) Traversing multi-mode data in the block chain block, verifying the integrity from leaf nodes to tree root nodes in the B+ tree, and returning a query result.

Example 2

The embodiment provides a trusted query configuration system for PB-level multi-modal data of a blockchain, which comprises the following steps:

the intra-block index constructing module is configured to acquire multi-mode uplink data, extract metadata of the multi-mode data and construct an intra-block index based on the metadata;

It should be noted that the above modules correspond to the steps described in embodiment 1, and the above modules are the same as examples and application scenarios implemented by the corresponding steps, but are not limited to those disclosed in embodiment 1. It should be noted that the modules described above may be implemented as part of a system in a computer system, such as a set of computer-executable instructions.

In further embodiments, there is also provided:

an electronic device comprising a memory and a processor and computer instructions stored on the memory and running on the processor, which when executed by the processor, perform the method described in embodiment 1. For brevity, the description is omitted here.

It should be understood that in this embodiment, the processor may be a central processing unit CPU, and the processor may also be other general purpose processors, digital signal processors DSP, application specific integrated circuits ASIC, off-the-shelf programmable gate array FPGA or other programmable logic device, discrete gate or transistor logic devices, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.

The memory may include read only memory and random access memory and provide instructions and data to the processor, and a portion of the memory may also include non-volatile random access memory. For example, the memory may also store information of the device type.

A computer readable storage medium storing computer instructions which, when executed by a processor, perform the method described in embodiment 1.

The method in embodiment 1 may be directly embodied as a hardware processor executing or executed with a combination of hardware and software modules in the processor. The software modules may be located in a random access memory, flash memory, read only memory, programmable read only memory, or electrically erasable programmable memory, registers, etc. as well known in the art. The storage medium is located in a memory, and the processor reads the information in the memory and, in combination with its hardware, performs the steps of the above method. To avoid repetition, a detailed description is not provided herein.

A computer program product comprising a computer program which, when executed by a processor, implements the method described in embodiment 1.

The present invention also provides at least one computer program product tangibly stored on a non-transitory computer-readable storage medium. The computer program product comprises computer executable instructions, such as instructions comprised in program modules, being executed in a device on a real or virtual processor of a target to perform the processes/methods as described above. Generally, program modules include routines, programs, libraries, objects, classes, components, data structures, etc. that perform particular tasks or implement particular abstract data types. In various embodiments, the functionality of the program modules may be combined or split between program modules as desired. Machine-executable instructions for program modules may be executed within local or distributed devices. In distributed devices, program modules may be located in both local and remote memory storage media.

Computer program code for carrying out methods of the present invention may be written in one or more programming languages. These computer program code may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus such that the program code, when executed by the computer or other programmable data processing apparatus, causes the functions/operations specified in the flowchart and/or block diagram to be implemented. The program code may execute entirely on the computer, partly on the computer, as a stand-alone software package, partly on the computer and partly on a remote computer or entirely on the remote computer or server.

In the context of the present invention, computer program code or related data may be carried by any suitable carrier to enable an apparatus, device or processor to perform the various processes and operations described above. Examples of carriers include signals, computer readable media, and the like. Examples of signals may include electrical, optical, radio, acoustical or other form of propagated signals, such as carrier waves, infrared signals, etc.

Those of ordinary skill in the art will appreciate that the elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.

While the foregoing description of the embodiments of the present invention has been presented in conjunction with the drawings, it should be understood that it is not intended to limit the scope of the invention, but rather, it is intended to cover all modifications or variations within the scope of the invention as defined by the claims of the present invention.

Claims

1. A method for configuring trusted queries for petabyte-scale multimodal data in blockchain, characterized by comprising the following steps:

Acquire multimodal on-chain data and extract metadata from the multimodal data. Based on the metadata, construct an intra-block index using a Merkle Tree and optimize the constructed index using a B+ tree.

Pre-set synchronous update rules for intra-block indexes to batch process various basic framework data, intelligent sensing data and relational data cached in memory, avoiding frequent write and update operations on global and local indexes;

A block index table is constructed based on the block ID, and the block index table is logically organized through incremental views, and the block index table is dynamically adjusted adaptively according to changes in blockchain data.

An index partition table is constructed based on the type of metadata to store the block index table identifier and address;

A coarse-grained index model is constructed using a Bloom filter or a cryptographic multiset accumulator. Based on the query conditions, the required block index table is quickly located in the index partition table. Within the block index table, the inverted index is searched based on the metadata to be queried to obtain the block ID and storage location. The intra-block index is retrieved to find the specific data record and verify the hash path, thus achieving reliable data query.

2. The trusted query configuration method for blockchain petabyte-level multimodal data as described in claim 1, characterized in that, an intra-block index is constructed based on metadata, specifically as follows:

Metadata from multimodal data is used as leaf nodes of a Merkle Tree to build an intra-block index, and the constructed index is optimized using a B+ tree.

3. The trusted query configuration method for blockchain petabyte-level multimodal data as described in claim 1, characterized in that the compressed feature code is processed by data truncation when extracting the metadata of the multimodal data.

4. The trusted query configuration method for blockchain petabyte-level multimodal data as described in claim 1, wherein the block index table is an inverted index that supports single-column index, unique index, primary key index and clustered index.

5. A trusted query configuration system for blockchain petabyte-scale multimodal data, employing the trusted query configuration method for blockchain petabyte-scale multimodal data as described in any one of claims 1-4, characterized in that it comprises:

The intra-block index building module is configured to: obtain multimodal on-chain data, extract metadata of the multimodal data, build an intra-block index based on the metadata using a Merkle Tree, and optimize the built index using a B+ tree;

The block index construction module is configured to: build a block index table based on the block ID, logically organize the block index table through incremental views, and dynamically adjust the block index table adaptively according to changes in blockchain data;

The partitioned index building module is configured to: build an index partition table based on the type of metadata, which is used to store the block index table identifier and address;

The trusted query module is configured to: build a coarse-grained index model, quickly locate the block index table to be queried in the index partition table according to the query conditions, search the inverted index in the block index table according to the metadata to be queried, obtain the block ID and storage location, retrieve the index within the block, query the specific data record, and verify the hash path to achieve trusted data query.

6. An electronic device, characterized in that it comprises a memory and a processor, and computer instructions stored in the memory and running on the processor, wherein the computer instructions, when executed by the processor, perform the method according to any one of claims 1-4.

7. A computer-readable storage medium, characterized in that it is used to store computer instructions, which, when executed by a processor, perform the method described in any one of claims 1-4.

8. A computer program product, characterized in that it comprises a computer program, which, when executed by a processor, implements the method described in any one of claims 1-4.