WO2022147923A1 - 一种面向空间数据的区块网安全组织存储映射方法 - Google Patents

一种面向空间数据的区块网安全组织存储映射方法 Download PDF

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WO2022147923A1
WO2022147923A1 PCT/CN2021/085952 CN2021085952W WO2022147923A1 WO 2022147923 A1 WO2022147923 A1 WO 2022147923A1 CN 2021085952 W CN2021085952 W CN 2021085952W WO 2022147923 A1 WO2022147923 A1 WO 2022147923A1
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data
block
spatial
block network
spatial data
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French (fr)
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吕智涵
乔亮
李劲华
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青岛大学
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/27—Replication, distribution or synchronisation of data between databases or within a distributed database system; Distributed database system architectures therefor
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/12—Computing arrangements based on biological models using genetic models
    • G06N3/126—Evolutionary algorithms, e.g. genetic algorithms or genetic programming
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L9/00—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols
    • H04L9/50—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols using hash chains, e.g. blockchains or hash trees
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/22—Indexing; Data structures therefor; Storage structures
    • G06F16/2228—Indexing structures
    • G06F16/2255—Hash tables
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/23—Updating
    • G06F16/2358—Change logging, detection, and notification
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/29—Geographical information databases
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
    • G06F21/60—Protecting data
    • G06F21/62—Protecting access to data via a platform, e.g. using keys or access control rules
    • G06F21/6218—Protecting access to data via a platform, e.g. using keys or access control rules to a system of files or objects, e.g. local or distributed file system or database
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
    • G06F21/60—Protecting data
    • G06F21/64—Protecting data integrity, e.g. using checksums, certificates or signatures
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T17/00—Three dimensional [3D] modelling, e.g. data description of 3D objects
    • G06T17/05—Geographic models
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T19/00—Manipulating 3D models or images for computer graphics
    • G06T19/003—Navigation within 3D models or images
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T19/00—Manipulating 3D models or images for computer graphics
    • G06T19/006—Mixed reality
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/20—Image preprocessing
    • G06V10/22—Image preprocessing by selection of a specific region containing or referencing a pattern; Locating or processing of specific regions to guide the detection or recognition
    • G06V10/23—Image preprocessing by selection of a specific region containing or referencing a pattern; Locating or processing of specific regions to guide the detection or recognition based on positionally close patterns or neighbourhood relationships
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
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    • G06V10/761—Proximity, similarity or dissimilarity measures
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L9/00—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols
    • H04L9/32—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols including means for verifying the identity or authority of a user of the system or for message authentication, e.g. authorization, entity authentication, data integrity or data verification, non-repudiation, key authentication or verification of credentials
    • H04L9/3236—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols including means for verifying the identity or authority of a user of the system or for message authentication, e.g. authorization, entity authentication, data integrity or data verification, non-repudiation, key authentication or verification of credentials using cryptographic hash functions

Definitions

  • the invention relates to the technical field of block networks, in particular to a block network security organization storage mapping method oriented to spatial data.
  • Geographical location is generally used to describe the temporal and spatial relationship of geographical things. From the universe to the cell, there is a geographical location attribute. How to effectively divide the geographical location and build a model for the reality-virtual mapping is a difficult problem.
  • 5G mobile communication and Internet of Things technology in order to adapt to the mainstream storage methods of next-generation cloud computing and distributed integration, the data collection work will tend to be decentralized. At that time, the point-to-point data storage and consensus mechanism will be To break the traditional data oligopoly, it is necessary to ensure the security and accuracy of multi-dimensional data in the process of mapping to virtual space, and to build a reliable data organization and mapping model for multi-dimensional data.
  • the urgent problem to be solved is: how to organize and store the complex and large amount of 3D geographic data including latitude, longitude and height, and realize the division of geographic data with different granularities from macro to micro, so as to build a unique multi-level and multi-granularity data structure, realize data mapping from real space to virtual space, and strictly guarantee data security.
  • the present invention provides a block network security organization storage mapping method oriented to spatial data, and solves the defects existing in the prior art.
  • a block network security organization storage mapping method for spatial data comprising:
  • the block network gene transmission mechanism Firstly, build the block network gene transmission mechanism according to the characteristics of the block network storage space data, and then design the multi-source gene transmission mechanism for the multi-spatial data center scenario.
  • the control mechanism of the number of transmission rounds and the in-exit degree control mechanism is designed for the possible radiation crossover and radiation shock problems.
  • the block network information update scheme is designed according to the data modification and update requirements in the spatial data storage scenario.
  • the data retrieval method is provided from the perspectives of chain search and row and column index.
  • the elevation data is added on the basis of the original 2D Hash geocoding, and the data index conversion suitable for multi-level division is carried out, and the 3D logical distance calculation algorithm is used to judge the 3D logical distance.
  • the longitude and latitude encoding and the elevation encoding of the target block are subjected to XOR operation, and then the result of the two XOR operations is subjected to Euclidean distance calculation to obtain the three-dimensional logical distance.
  • the spherical multi-source translation projection method is used to first map spherical data to three-dimensional space with random starting points, and find the spatial geometric center of multiple target points.
  • the translation transformation makes the three-dimensional spatial data center align with the spatial geometric center.
  • the block network gene transmission mechanism is constructed, as follows:
  • Step 1.1 Select the starting node of the block network construction according to the importance of the spatial data
  • Step 1.2 Perform Hash encoding and propagation on the nodes in the four-connected area, that is, the next node performs SHA256 calculation on the set of nodes pointed to it to obtain the hash value of the node pointed to it.
  • Step 2.1 Multi-scale division and coding of scene data based on 3D Hash geocoding
  • Step 2.2 Select the center point according to the key area in the spatial data
  • Step 2.3 Use spherical translation projection to translate the spatial position, and start from the selected center point to construct the block network storage.
  • the present invention avoids the repeated update paradox caused by the two-way Hash transmission mechanism, proves the principle of one-way transmission, and establishes a gene transmission mechanism based on this.
  • This paper proposes the concept of selecting source blocks and conducts radiation propagation tests, so as to explore the propagation rules in different scenarios and establish propagation standards applied to the combination of 3D Hash geocoding. Analyze the out-degree and in-degree of blocks, study the relationship between security and logical distance of source blocks, and explore solutions for edge block security. Since the block needs to calculate the hash value of the block that points to it, in the process of building the block network, two blocks cannot be propagated in both directions, so each block is propagated in the opposite direction of the central block. Thereby realizing the construction of the block network. After the block network is constructed, the safety factor is calculated according to the number of propagation rounds of each node, so as to evaluate the overall safety of the model.
  • the number of transmission rounds control and access control mechanism are designed for the possible radiation cross and radiation shock problems, as follows:
  • a multi-source block solution is proposed for the multi-center block network, and the risk of radiation propagation of the multi-center block is predicted and carried out. Effectively avoid it, study and solve the problem of radiation flow intersection in multi-source blocks, and establish multi-source block radiation propagation rules to effectively prevent the paradox of repeated updates.
  • the radiation propagation speed will also be different, and the risk of radiation shock may occur, which may cause the propagation of a certain source block to be affected. Therefore, the concept of the number of propagation rounds is proposed to limit the propagation speed to ensure that each central area.
  • the safety factor is stable, and a safety factor evaluation mechanism is established accordingly to achieve an objective index of the security of the block network.
  • each source block needs to be selected for propagation, and the propagation speed of each source block is controlled by the number of propagation rounds to keep synchronization, that is, during the construction of the block network
  • the global variables are compared during each round of propagation, and the block number belongs to this round before continuing to propagate.
  • the global security factor of the block network is calculated by counting the number of propagation rounds to which each block belongs.
  • the in-out-degree state marker is designed for the four-connected neighborhood of each block, indicating the node in the corresponding direction and its inheritance relationship.
  • the workflow of the in-out degree control mechanism is as follows:
  • Step 3.1 All blocks initialize the in-degree status flags of the quad-connected neighborhood as in-degree;
  • Step 3.2 When the block triggers the propagation operation, check the in- and out-degree status of the quadruple-connected neighborhood block, if it is out-degree, go to step 3.3, otherwise go to step 3.4;
  • Step 3.3 Stop propagating in this direction, and set the in-degree status flag of the corresponding direction to in-degree;
  • Step 3.4 Mark the in-out-degree status of the corresponding direction as out-degree, put the corresponding direction node into the next round of propagation list, and perform step 3.2.
  • the retrieval process is performed in two ways: chain search and row-column indexing.
  • chain search For spatial data utilization scenarios, the retrieval process is performed in two ways: row search and row-column indexing.
  • row-column indexing When sharing and preloading adjacent scene resources, the following steps are required:
  • Step 4.1 The data demand node broadcasts the row and column index corresponding to its current scene position to the adjacent nodes;
  • Step 4.1 The adjacent node finds the location of the block network where the current scene resource is located through the row and column index;
  • Step 4.2 Traverse the four Unicom direction nodes in the block information of the current location, obtain the data of these nodes and load them, so as to realize the chain search;
  • Step 4.3 Send the corresponding scene resource to the data demand node.
  • Hash geocoding technology in multi-dimensional geographic information organization, starting from the needs of spatial data collection and sorting, the division characteristics of spatial data are deeply analyzed.
  • the three-dimensional space including longitude, latitude and height is divided and a data model is constructed to realize spatial dataization.
  • the spatial data is divided into multi-layer hexadecimal trees according to the longitude and latitude observation plane.
  • the division unit also stores different degrees of elevation information in the vertical direction, and then generates a three-dimensional Hash geocoding by performing three-digit hexadecimal encoding on the elevation information to generate vertical encoding and longitude and latitude encoding.
  • the spatial division method can realize the unrestricted gradient division from macro to micro.
  • the prefix longitude and latitude codes of the two 3D Hash geocoding are first differentiated. Or calculate the plane distance, and then perform the XOR calculation on the suffix elevation code to obtain the vertical distance, and finally obtain the space Euclidean distance by calculating the plane distance and the vertical distance.
  • block network data modification and update method is as follows:
  • the propagation method mentioned above is used to construct the network for the multi-center area scenario of the block network, so as to ensure the high restoration of the real space data and realize the efficient update of the block network data at the same time. Specific steps are as follows:
  • Step 5.1 When the data modification operation is triggered on the block network, change the data field of the block to be modified;
  • Step 5.2 Traverse the blocks pointed to by the block, and recalculate the Hash values corresponding to these blocks;
  • Step 5.3 Repeat step 5.2 until an edge is reached to end the recursive process.
  • the spherical multi-source translation projection method is as follows:
  • Geo-geographic data is a typical application scenario of spatial data mapping model, and its application in virtual reality is also the most extensive.
  • the block network stores data on a two-dimensional plane.
  • the earth is a sphere, which will inevitably be divided. Since data is farther away from the source block, the security factor is lower, and even if we can select multiple source blocks, there will be cases where the source block appears on the edge.
  • the spherical multi-source translation projection technology is designed. Based on this technology, the spherical surface is re-mapped in two dimensions to ensure that the security factor of the block network reaches the optimal value. The specific steps are as follows:
  • Step 6.1 Select a random space position as the center point for geometric mapping from sphere to space;
  • Step 6.2 Select several central areas according to the importance of spatial data
  • Step 6.3 Calculate the spatial geometric centers of these centers
  • Step 6.4 Take the geometric center position obtained in step 6.3 as the center point to perform a geometric mapping from the sphere to the space again.
  • the present invention Compared with the traditional latitude and longitude geocoding and two-dimensional Hash geocoding, the present invention not only realizes the unique primary key block coding for spatial data to speed up the indexing speed, but also preserves the elevation information through multi-dimensional coding, expands the amount of information, and improves the space. Data utilization efficiency.
  • the block network proposed by the present invention breaks through the bottleneck that the traditional block chain technology can only store linear data, improves the dimension of stored data, and broadens the application scenarios of the block chain technology.
  • the proposed block network information modification and update scheme while ensuring data security, it makes it possible to dynamically update data on the blockchain, and its application in spatial data greatly improves the security of data mapping and storage processes.
  • this model is applied to the division and indexing of geographic three-dimensional space, data utilization scene space and data storage network space to provide a multi-dimensional and reliable experimental environment that is highly fitted to the real layer for theoretical research based on the virtual layer.
  • FIG. 2 is a block chain storage two-dimensional data structure diagram according to an embodiment of the present invention.
  • FIG. 3 is a schematic diagram of the result of horizontal stretching of the blockchain according to the embodiment of the present invention.
  • FIG. 4 is a schematic diagram of repeated updates caused by a two-way chain according to an embodiment of the present invention.
  • FIG. 5 is a schematic diagram of a block network gene transmission mechanism according to an embodiment of the present invention.
  • FIG. 6 is a diagram of a multi-source block network gene propagation model according to an embodiment of the present invention.
  • FIG. 7 is a schematic diagram of the number of rounds of gene transmission and the safety factor of the block network according to the embodiment of the present invention.
  • FIG. 8 is a schematic diagram of radiation impact according to an embodiment of the present invention.
  • FIG. 9 is a multi-source block network model diagram of a synchronous propagation mechanism according to an embodiment of the present invention.
  • FIG. 10 is a schematic diagram of modifying block online data according to an embodiment of the present invention.
  • FIG. 11 is a schematic diagram of a Hash geocoding division method according to an embodiment of the present invention.
  • FIG. 13 is a result diagram of spherical multi-source translation projection according to an embodiment of the present invention.
  • the invention is based on theoretical methods such as Hash geocoding, block chain and virtual reality, and faces the application requirements of full-spatial data security organization, storage and mapping. Research and explore the block network security organization storage mapping model for spatial data. Finally, a research method combining theoretical analysis and application requirements, algorithm design and system implementation is used to design and develop a prototype system, and the validity of the security data storage mapping based on 3D Hash geocoding at the city level and molecular scale is verified. .
  • Figure 1 shows the overall architecture flow of the research content of the present invention.
  • the research on the block network technology carried out is to organize and store the spatial data with high security.
  • Hash geocoding is carried out on a two-dimensional plane.
  • the principle is to divide the plane into 16 blocks, and each block can be further divided to achieve multi-level division;
  • the longitude and latitude are converted into binary encoding and then cross-combined, and then each four-bit group is converted into the corresponding hexadecimal; but its disadvantage is that it can only perform a single primary key index on the information of the two-dimensional plane, and cannot represent height or index. high-dimensional information.
  • the index of multi-dimensional information is extremely necessary, so height is also a main parameter.
  • To add height to Hash geocoding there are two schemes:
  • Another implementation method is to cross only n-bit longitude and latitude to form a 2n-bit binary code combination when performing the cross-combination of codes, and perform hexadecimal coding.
  • the height parameter is converted into hexadecimal and spliced at the back, as a suffix code and two-dimensional hash geocoding to form a fixed-length code.
  • Table 1 takes Laoshan as an example of three-dimensional hash geocoding. :
  • the longitude and latitude are all taken as 8-bit binary codes, which are combined to form 16-bit binary codes after cross-combination, and 4-bit two-dimensional Hash geocoding after hexadecimal conversion.
  • the 5-digit hexadecimal is used as the fixed-length height code.
  • the logical distance judgment is divided into two cases: when the plane logical distance judgment is performed, the plane Hash geocoding of the two nodes is converted into binary and XOR is performed to obtain the logical distance.
  • the plane distance is calculated by XOR through latitude and longitude coding, and then the height distance is calculated by XOR through height encoding, and the three-dimensional logical distance between the two regions is obtained by calculating the spatial geometric function.
  • the realization of spatial data security organization mapping based on traditional blockchain technology is to use the blockchain to organize and store the spatial geographic data divided by Hash geocoding.
  • Each geographic data unit is represented as a block connected before and after in the blockchain.
  • the data structure for storing geographic information is not one-dimensional linear, but mostly two-dimensional table-like structures with four or eight connections. If the geocoding is connected through the blockchain, the chain structure is used to express the tabular data. At present, most of the solutions are to select a certain corner as the creation block, and follow a "zigzag" in a certain direction to connect the data.
  • Figure 2 shows Current solutions for storing two-dimensional data using blockchain.
  • the blockchain is a one-way linear storage structure similar to a linked list. Its characteristic is that in addition to the genesis block and the tail block, any other block only saves the hash value of its previous node, and its own The Hash value is stored in the next block, that is, the blockchain only records the sequence relationship of two adjacent nodes, regardless of the coordinate position of a block in a two-dimensional plane. Because of this nature, using linear data structure to store multi-dimensional data will inevitably face the problem of relative position failure caused by chain structure stretching.
  • Figure 3 is the result of horizontally stretching the blockchain.
  • the sequence relationship of each block remains unchanged, but the relative positions of most blocks in the two-dimensional plane have changed.
  • the location information is not recorded in the blockchain, so the blockchain mechanism cannot detect this change.
  • the spatial location is the most important data and the only primary key for information retrieval. This phenomenon is undoubtedly fatal. Therefore, the traditional blockchain is only suitable for secure and decentralized storage of one-dimensional linear data, but faced with the requirement of two-dimensional data security storage mapping, another data storage model needs to be designed.
  • each block saves the hash value of the data of the previous block. If the data of a certain block changes, its adjacent blocks also need to be modified accordingly, then modify a block.
  • the price of data is to update all on-chain data.
  • each block saves the hash value of its four-connected block data, but this method has the problem of repeated updates, such as: block A saves the data of block B on its right side Hash value b, block B saves the data Hash value a of block A, but after this operation is completed, the data of block B has changed, and b in block A needs to be updated, which will cause block B to need to be updated. a, leading to the emergence of an infinite loop.
  • Figure 4 shows the repeated data update between adjacent blocks caused by the two-way chain mechanism.
  • the chain between blocks should be one-way, which requires us to find a block as the starting block for diffusion gene propagation (because the next block stores the current block data Hash value, we liken this to genetic inheritance).
  • the first block that starts to propagate is called the source block.
  • the central block was tested the central block as the source block, and finally designed the first-generation propagation rules of the block network, that is, each block propagates in the opposite direction relative to the source block. , according to this rule, the block network spread can realize the spread spread of the two-dimensional data on the whole network.
  • Figure 5 shows the gene transmission mechanism of the block network.
  • the average in-degree and out-degree of each block are both 2. Because the data of each block is Hash-encoded and added to its subsequent blocks, the block with higher out-degree is The less easy it is to be tampered with, the lower the cost of tampering with the data of the blocks farther from the central block.
  • the source block is the genesis block, with no in-degree and out-degree of 4. It is the safest block in the entire network; the edge block has only in-degree but no out-degree, and is the most easily tampered block. almost 0.
  • edge block data In order to ensure the security of edge block data, we pack the data hash values of edge blocks together and save them in a separate block for supervision by other blocks.
  • the block network is designed for the secure storage of multi-dimensional data. Taking plane data as an example, it is essentially a two-dimensional tabular database realized by pointers, so we design based on the tabular data structure. Each block is mapped to a pair of row and column indexes in the block object table.
  • the block In addition to saving necessary data information, the block must also contain four boolean status flags, upStatus, downStatus, leftStatus, and rightStatus, which are used to indicate the current
  • the in-out degree status of the quad-connected area of the block 0 means in-degree, 1 means out-degree; and there are four pointers upNode, downNode, leftNode, rightNode respectively pointing to the block location of its quad-connected neighborhood; in addition,
  • each block maintains a hash list with a maximum of 4, and the hash value of the parent node is stored in the list in the order of up, down, left and right. Therefore, the data structure of each block is shown in Table 2:
  • the source block is likely to cause a more important location to appear on the edge block. For example, Beijing and Washington are far away on the earth but have high requirements for data security.
  • Figure 6 is a multi-source block network gene transmission model.
  • the model selects two blocks as source blocks.
  • the numbers in the blocks indicate the number of transmission rounds and can also be regarded as the security level. It can be seen that the distance from the center block is more The lower the security level of the far block, the situation does exist in the virtual reality application scenario. It can be seen that it is feasible to realize the security storage with priority for two-dimensional data according to this technology.
  • the node has not pointed to itself and can be regarded as a propagation target, and the xStatus of the corresponding direction is set to 1; if it is 1, it means that the node has pointed to itself, that is, the node is its own parent node, stop going to Propagating in this direction, avoiding radiation intersections.
  • the safety factor can be calculated by counting the number of propagation rounds to evaluate the security of the block network.
  • Figure 7 shows the relationship between the number of rounds of gene transmission and the safety factor in the block network.
  • the numbers in the blocks indicate the number of propagation rounds.
  • the source A travels too fast and hits the source B, causing the source B to propagate only two rounds.
  • the radiation flow shock We mentioned earlier that the higher the number of propagation rounds, the lower the safety factor. Due to the impact of radiation flow, the overall safety factor in this case is lower.
  • a synchronous propagation mechanism based on message broadcasting, that is, after ensuring that all nodes have completed one round of propagation, the next round of propagation is carried out.
  • Figure 9 is a multi-source block network model that follows the synchronous propagation mechanism.
  • the blockchain network model we propose supports both centralized network construction and decentralized network construction.
  • Centralized construction means that the block network data construction process is directly carried out by network administrators.
  • Decentralized network construction is to build a point-to-point decentralized network, and users can independently complete information collection and reach a consensus to build a block network database. These two construction methods are suitable for different application scenarios and need to be selected according to specific applications.
  • the number of propagation rounds needs to be controlled, and the number of propagation rounds control quantity Term is added to the program that controls the propagation, which indicates the current ongoing propagation. Number of rounds; Waves[][] is defined as a dynamically growing multidimensional array.
  • Waves[i] stores several block information, indicating the target blocks that need to be propagated in the i-th round. For example, Waves[0] stores several source blocks, and Waves[1] stores four source blocks. Connect the neighborhood blocks, when the number of propagation rounds Term is 1, schedule the blocks in Waves[1] for propagation, and put the out-degree node information of these blocks into Waves[2], and repeat the above process until the network is constructed complete.
  • the data of a block is modified in the lower left corner, only the block it has propagated needs to update the data.
  • the data of the entire network may need to be refreshed (depending on whether there are other source block).
  • the centralized network administrator or the data modification initiator in the decentralized network will call the DataChange method, modify the Data field on the target block, and then traverse the xStatus of the four-link area to find the out-degree For blocks, the Update method is called for the xNode corresponding to the out-degree block. These blocks will recalculate the parent node Hash value and immediately modify the corresponding data in the ParentHash, and then repeat the above process until the edge block EdgeBlock is reached, which is completed. A block network data update was made.
  • Waves represents the set of blocks that need to be propagated in each round of propagation
  • Term represents the number of propagation rounds
  • I and J represent its row and column index in the two-dimensional table
  • map represents the location information of the block under the row-column index structure
  • DIRECTIONS represents the direction constant of the four-connected area
  • xNode and xStatus represent the pointing node and pointing status of the corresponding direction
  • ParentHash represents the result set after SHA256 settlement of the parent node:
  • Data represents the spatial data information stored in each block
  • count represents the parent block sequence number of the current block's occurrence information update
  • Hash geocoding the encoding of geographic information is multi-level, such as: we can use a 1-digit hexadecimal number Divide geographic information into 16 blocks, and use 2-digit hexadecimal numbers to divide geographic information into 256 blocks.
  • Figure 11 shows the division method of Hash geocoding.
  • Hash geocoding increases by 16 times for each additional digit, and the area of the earth is about 510 million square kilometers.
  • Hash geocoding to divide 10 times (that is, 10-bit Hash geocoding) precision It can reach 0.463m, which means that we have 10 layers of blocks from coarse to fine. If the block network technology is applied to each layer, it will take a lot of time to build the block network, and if only in the lowest application area The block network can ensure data security while saving resources.
  • Table 3 shows the relationship between the number of Hash geocoding digits and the number and precision of blocks.
  • Earth geographic data is a typical application scenario of Hash geocoding combined with virtual reality technology.
  • the block network is a technology to store data on a two-dimensional plane.
  • the earth is spherical, which will inevitably be divided.
  • the data security factor far from the source block is lower, and even if we can select multiple source blocks, there may be cases where the source block appears on the edge.
  • Figure 12 is a block of the earth before using the spherical multi-source translation projection technology. Web multi-source data organization mapping.
  • Figure 12 we selected three source blocks: Guangzhou, Los Angeles, and Paris, but found that Los Angeles appeared on the right edge, resulting in extremely low safety factors in areas such as Washington and New York.
  • spherical multi-source translation projection we designed spherical multi-source translation projection, and selected the relative center points of all sources as the center point for translation projection.
  • Figure 13 is the result of spherical multi-source translation projection. The coefficient increased significantly.

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Abstract

一种面向空间数据的区块网安全组织存储映射方法，包括：先根据区块网存储空间数据特性构建区块网基因传播机制，再针对多空间数据中心场景设计多源基因传播机制；在多源基因传播机制中针对可能出现的辐射交叉问题和辐射冲击问题设计传播轮数控制和出入度控制机制；针对空间数据存储场景中数据修改更新需求设计区块网信息更新方案。该方法实现对空间数据进行唯一主键块状编码加快索引速度外，还通过多维度编码保留了高程信息，扩大了信息量，提升了空间数据的利用效率，大大提升了数据映射、存储过程的安全性。

Description

一种面向空间数据的区块网安全组织存储映射方法 技术领域
本发明涉及区块网技术领域，特别涉及一种面向空间数据的区块网安全组织存储映射方法。
背景技术
[根据细则9.2更正 28.09.2021]　
近年来随着计算机虚拟现实技术的发展和数据提取与分析能力的提升，人们对于虚拟现实模拟真实场景的研究也越来越重视，而在按地理区块提取现实世界信息、并映射到虚拟世界的过程是跨域的，在此过程中可能会出现数据解释错误和数据被篡改的情况。区块链技术因其去中心化、不可修改、可溯源等特性常常应用于数据安全存储场景，目前已经在征信、金融和溯源等方面取得了良好的应用，并改变了人民群众的生活，而对空间三维结构数据进行划分和索引是区块链技术的一个关键应用场景。
地理位置一般是用来描述地理事物时间和空间关系，从宇宙至细胞皆存在地理位置属性，如何有效地针对现实-虚拟映射进行地理位置划分并为其构建模型是一大难点问题。其次，随着5G移动通信、物联网技术的发展，为适应下一代云计算、分布式结合的主流存储方式，数据采集工作将会偏向于去中心化，届时，点对点的数据存储及共识机制将打破传统数据寡头垄断，需要在向虚拟空间映射的过程中保证多维数据的安全性、准确性，并构建一个面向多维数据的可靠数据组织映射模型。而在此过程中，亟待解决的问题是：如何将复杂且大量的包含经纬度及高度的三维地理数据进行组织存储，并实现从宏观到微观不同粒度的地理数据划分，为此构建独特的多层次和多粒度数据结构，实现现实空间到虚拟 空间的数据映射，并严格保障数据安全性。
发明内容
本发明针对现有技术的缺陷，提供了一种面向空间数据的区块网安全组织存储映射方法，解决了现有技术中存在的缺陷。
为了实现以上发明目的，本发明采取的技术方案如下：
一种面向空间数据的区块网安全组织存储映射方法，包括：
先根据区块网存储空间数据特性构建区块网基因传播机制，再针对多空间数据中心场景设计多源基因传播机制。在多源基因传播机制中针对可能出现的辐射交叉问题和辐射冲击问题设计传播轮数控制和出入度控制机制。针对空间数据存储场景中数据修改更新需求设计区块网信息更新方案,针对空间数据利用场景，从链式搜索和行列索引两个角度提供数据检索方式。
对于三维空间数据的划分索引，在原有二维Hash地理编码的基础上加入高程数据，并进行适应多层次划分的数据索引转换，运用三维逻辑距离计算算法在进行三维逻辑距离判断时，先后对两目标块的经纬度编码和高程编码进行异或运算，再将两次异或运算的结果进行欧氏距离计算得到三维逻辑距离。
针对球面数据映射到三维数据过程中可能出现的多中心情况，运用球面多源平移投影方法先将球面数据以随机起始点映射到三维空间，找到多目标点的空间几何中心，对三维空间数据进行平移转换，使得三维空间数据中心与空间几何中心对准。
进一步地，根据区块网存储空间数据特性构建区块网基因传播机制，具体如下：
通过对比传统区块链技术应用于三维Hash地理编码中存在的问题，分析空 间数据组织映射过程的特殊性。不同于区块链的线性存储方案，提出不可拉伸、变形的面向二维数据的区块存储方案：
步骤1.1：根据空间数据的重要程度选取区块网构建的起始节点；
步骤1.2：对其四联通区域节点进行Hash编码传播，即下一个节点对指向其的节点集合进行SHA256计算得到指向其的节点的Hash值。
从而实现由中心到边缘的全网蔓延，最终构建区块网数据存储模型。具体到虚拟现实的应用场景：
步骤2.1：基于三维Hash地理编码对场景数据进行多尺度划分编码；
步骤2.2：根据空间数据中关键区域选取中心点；
步骤2.3：利用球面平移投影对空间位置进行平移映射，从选取的中心点开始进行区块网存储构建。
不同于区块链传递同步机制，本发明避免双向Hash传播机制导致的重复更新悖论，证明了单向传播原则并以此建立基因传播机制。提出选取源区块概念并以此进行辐射传播测试，从而探索不同场景下的传播规则并建立应用于三维Hash地理编码结合的传播标准。分析区块的出度和入度，研究安全性与源区块逻辑距离的关系，探索边缘区块安全性的解决方案。由于区块需要对指向其的区块进行Hash值的计算，在区块网构建过程中，两个区块间不能进行双向的传播，所以每个区块往中心区块的相反方向进行传播，从而实现区块网的构建。在区块网构建完成后，根据每个节点的传播轮数计算安全系数，从而评估该模型的整体安全性。
进一步地，在多源基因传播机制中针对可能出现的辐射交叉问题和辐射冲击问题设计传播轮数控制和出入度控制机制，具体如下：
在单源区块网络构建中若两关键区域距离较远会导致某一区域安全指标不足，针对多中心区块网络提出多源区块解决方案，预测多中心区块进行辐射传播的风险并进行有效规避，研究解决多源区块辐射流交叉问题，建立多源区块辐射传播规则有效预防重复更新悖论。在实际网络构建中因网络传输、硬件限制，辐射传播速度也将有差异，则可能会产生辐射冲击风险导致某一源区块传播受到冲击，从而提出传播轮数概念限制传播速度保证各中心区域安全系数稳定，并依此建立安全系数评估机制，实现对区块网安全性的客观指标化。
当空间场景中存在多个需要重要描述的中心点时，则需要选取多个源区块进行传播，利用传播轮数控制每个源区块的传播速度保持同步，即在区块网构建过程中有一个控制传播轮数的全局变量，在进行每一轮传播时对全局变量进行比对，区块编号属于该轮次才继续进行传播。同样的，通过统计每个区块所属的传播轮数，计算该区块网的全局安全系数。
对每个区块的四联通邻域设计出入度状态标记量，表示其对应方向的节点与其的继承关系，在区块网的传播构建阶段，出入度控制机制工作流程如下：
步骤3.1：所有区块将四联通邻域的出入度状态标记量初始化为入度；
步骤3.2：区块触发传播操作时，检查四联通邻域区块对其的出入度状态，如果是出度，则执行步骤3.3，否则执行步骤3.4；
步骤3.3：停止往该方向传播，并将对应方向的出入度状态标记设置为入度；
步骤3.4：将对应方向的出入度状态标记为出度，将对应方向节点放入下一轮传播列表，执行步骤3.2。
进一步地，实现多数据节点间相互查找与数据检索。在此过程中，需要模型 能够同时对三维球面模型、全球地理区域空间数据节点网络进行位置映射和统一划分与索引，将三个领域的空间划分方法进行优势整合，
针对空间数据利用场景，检索过程通过链式查找和行列索引两种方式进行，当进行临近场景资源共享预加载时，需经过以下步骤：
步骤4.1：数据需求节点向临近节点广播自己当前场景位置对应的行列索引；
步骤4.1：临近节点通过行列索引找到当前场景资源所在区块网位置；
步骤4.2：遍历当前位置的区块信息中四联通方向节点，获取这些节点数据并加载，从而实现链式查找；
步骤4.3：将对应的场景资源发送给数据需求节点。
进一步地，所述三维Hash地理编码设计三维逻辑距离计算算法具体如下：
针对现有Hash地理编码技术在多维度地理信息组织中的不足，从空间数据的采集和整理需求出发，深度分析空间数据的划分特点。通过比较分析空间信息数据与Hash地理编码优缺点，对包含经度、纬度和高度的三维空间进行划分并构建数据模型，实现空间数据化，将空间数据按照经纬度观测平面进行多层十六叉树划分，并在每一层对每个节点附加对应编码，直到叶子节点划分粒度表示空间数据的最小单元，即构建对空间数据的满十六叉索引树，每个叶子节点表示经纬度观测平面上的最小划分单元，其在垂直方向还存储着不同程度的高程信息，再通过将这些高程信息进行三位十六进制编码生成垂直编码与经纬度编码组成三维Hash地理编码。其中空间划分方法可实现从宏观到微观的无限制梯度划分。使其更加适应于多维度多层次空间数据分块存储，并探索基于三维Hash地理编码的逻辑距离判断方法，在进行三维空间距离判断时，先对两个三维Hash地理 编码的前缀经纬度编码进行异或计算得到平面距离，再对后缀高程编码进行异或计算得到垂直距离，最后通过将平面距离和垂直距离进行计算得到其空间欧式距离。
进一步地，区块网数据修改更新方法具体如下：
在虚拟现实数据映射等应用场景中，利用前文所提到的传播方法，针对区块网的多中心区域场景进行网络构建，保证对现实空间数据的高度还原地同时实现高效更新区块网上数据，具体步骤如下：
步骤5.1：当区块网上触发了数据修改操作时，将要修改的区块的数据字段更改；
步骤5.2：遍历该区块指向的区块，将这些区块对应的Hash值进行重新计算；
步骤5.3：重复步骤5.2直到到达边缘结束递归过程。
进一步地，球面多源平移投影方法具体如下：
地球地理数据是空间数据映射模型的一个典型应用场景，其在虚拟现实中的应用也最为广泛。目前，区块网是在一个二维平面上存储数据，实际上地球是一个球形，这会不可避免地进行分割。由于远离源区块的数据安全系数更低，并且即使我们可以选择多个源区块也会出现源区块出现在边缘的情况。针对此问题设计球面多源平移投影技术，基于此技术重新对球面进行二维映射，保证区块网安全系数达到最优值，具体步骤如下：
步骤6.1：选取随机空间位置作为中心点进行球面到空间的几何映射；
步骤6.2：根据空间数据的重要性选取若干中心区域；
步骤6.3：计算这些中心的空间几何中心；
步骤6.4：以步骤6.3中得到的几何中心位置作为中心点重新进行一次球面到空间的几何映射。
与现有技术相比，本发明的优点在于：
相较于传统经纬度地理编码和二维Hash地理编码，本发明除实现对空间数据进行唯一主键块状编码加快索引速度外，还通过多维度编码保留了高程信息，扩大了信息量，提升了空间数据的利用效率。并且本发明所提出的区块网突破了传统区块链技术仅能保存线性数据的瓶颈，提升了存储数据的维度，拓宽了区块链技术的应用场景，提出的区块网信息修改更新方案，在保证数据安全性的同时使得区块链上数据动态更新成为了可能，并且其在空间数据的应用大大提升了数据映射、存储过程的安全性。同时，将此模型应用于地理三维空间、数据利用场景空间和数据存储网络空间的划分索引，为基于虚拟层的理论研究提供与现实层高度拟合的多维度可靠实验环境。
附图说明
图1是本发明实施例技术路线图；
图2是本发明实施例区块链存储二维数据结构图；
图3是本发明实施例区块链进行横向拉伸后的结果示意图；
图4是本发明实施例双向链条导致反复更新示意图；
图5是本发明实施例区块网基因传播机制示意图；
图6是本发明实施例多源区块网基因传播模型图；
图7是本发明实施例区块网基因传播轮数与安全系数示意图；
图8是本发明实施例辐射冲击示意图；
图9是本发明实施例同步传播机制的多源区块网模型图；
图10是本发明实施例修改区块网上数据示意图；
图11是本发明实施例Hash地理编码划分方式示意图；
图12是本发明实施例经球面多源平移投影前的结果图；
图13是本发明实施例经球面多源平移投影后的结果图。
具体实施方式
为使本发明的目的、技术方案及优点更加清楚明白，以下根据附图并列举实施例，对本发明做进一步详细说明。
本发明以Hash地理编码、区块链和虚拟现实等理论方法为基础，面向全空间数据安全组织存储映射应用需求，通过三维Hash地理编码、区块网基因传播构建方法和辐射交叉冲击等关键技术研究，探索面向空间数据的区块网安全组织存储映射模型。最后采用理论分析与应用需求相结合、算法设计与系统实现相结合的研究方法，设计和开发原型系统，并对城市级别与分子尺度下基于三维Hash地理编码的安全性数据存储映射进行有效性验证。
图1展示了本发明所研究内容的总体架构流程。所开展的区块网技术研究是为了对空间数据进行高安全性组织存储映射。
(1)多维度多层次三维Hash地理编码的设计
主流的Hash地理编码都是在二维平面上进行，其原理是将平面划分成16个区块，对每一个区块可再进行划分，从而实现多层次划分；其编码过程是将地理现实空间的经度纬度转换成二进制编码后交叉组合，然后每四位为一组转换成对应的十六进制；但其缺点是仅能对二维平面的信息进行单一主键索引，并不能表示高度或索引高维度信息。而在空间数据组织映射过程尤其是虚拟空间构建过程中，对于多维度信息的索引极具必要性，所以高度也是一个主要参数，要把高度 加入Hash地理编码，有两种方案：
1.在交叉组合时，同时交叉经度、纬度、高度，也就是用三段二进制编码代替原来的两段二进制编码进行交叉组合，即将经度、纬度高度各取n位二进制有效数字，按先后顺序拼接经度、纬度、高度编码的第一项，随后是第二项，以此类推，从而组成3n位的组合编码，再以4位为一组转换为十六进制组成三维Hash地理编码。但这种方法由于高度的插入，导致在进行平面逻辑距离计算过程中，嵌在其中的高度信息成为了不可避免的噪声数据，经纬度相近的点逻辑距离反而很远；
2.除直接将经度、纬度、高度编码交叉组合外，另一种实现方式是，在进行编码的交叉组合时，先仅交叉n位经度、纬度，组成2n位二进制编码组合，进行十六进制转换组合成二维Hash地理编码后，将高度参数转换为十六进制拼接在后面，作为后缀码与二维Hash地理编码组成定长码，表1是以崂山为例的三维Hash地理编码：
表1.三维Hash地理编码
Figure PCTCN2021085952-appb-000001
在上表中，经度纬度均取8位二进制，进行交叉组合后形成16位二进制编码，进行十六进制转换后是4位二维Hash地理编码。以地球为例，由于大气层厚度在1000千米以上，而5位十六进制能表示的最大十进制数字恰好为1048575，则将5位十六进制作为定长高度编码。
逻辑距离判断分为两种情况：在进行平面逻辑距离判断时，将两个节点的平 面Hash地理编码转换为二进制进行异或得到逻辑距离。在进行三维逻辑距离判断时，先通过经纬度编码进行异或计算平面距离，再通过高度编码进行异或计算高度距离，通过空间几何函数计算得到两个区域间的三维逻辑距离。
(2)多维度数据安全存储技术——区块网
基于传统区块链技术实现空间数据安全组织映射是将Hash地理编码划分后的空间地理数据利用区块链进行组织存储，每个地理数据单元在区块链中表现为前后连接的区块，而存放地理信息的数据结构并非是一维线性，大多是四联通或八联通的二维表状结构。如果通过区块链将地理编码连接起来即用链式结构表达表状数据，目前大部分方案是选取某一角为创世区块，沿某一方向走“之”字型进行串联，图2是目前利用区块链存储二维数据的解决方案。
然而，区块链是类似链表的单向线性存储结构，其特性是链上区块中除创世区块和尾区块，其它任意区块仅保存其前一节点的Hash值，它本身的Hash值保存在下一个区块中，即区块链仅记录两个相邻节点的先后关系，不考虑某一区块在二维平面中的坐标位置。因其这一性质，利用线性数据结构存储多维数据，就会不可避免地面临链式结构拉伸导致相对位置失效问题。
图3是对区块链进行横向拉伸后的结果，每一个区块的先后顺序关系不变，但大部分区块在二维平面中的相对位置发生了变化。区块链中并不记录位置信息，所以区块链机制不能检测这一变化，而在空间数据组织映射过程中，空间位置是最主要的数据，也是信息检索的唯一主键，这种现象无疑是致命的。所以，传统区块链仅适合针对一维线性数据进行安全性、去中心化存储，而面临二维数据安全性存储映射需求，需要设计另一种数据存储模型。
针对这个问题我们提出了一个面向二维数据的区块存储方案——区块网：在区块网存储模型中，区块之间不仅有先后连接的关系，不同于传统区块链每个节 点仅有一个入度和一个出度，区块网中每个节点有多个入度、多个出度，经过全网蔓延式基因传播后，生成的区块网模型将具有很强的健壮性和抗拉伸性，从而实现区块网不可拉伸和变形等特性。接下来将对区块网的基因传播及其他问题进行详细说明。
(3)区块网构建基因传播机制
区块链技术实现数据不可篡改的核心方法是：每个区块保存其上一个区块数据的Hash值，若某一个区块数据变更，其相邻区块也需要进行相应修改，那么修改一条数据的代价是更新全部链上数据。
在此模型中，起初我们设计的构建方案是每个区块保存其四连通区块数据的Hash值，但该方法存在重复更新的问题，如：A区块保存其右侧B区块的数据Hash值b，B区块保存A区块的数据Hash值a，但此操作完成后，B区块的数据发生了变化，A区块中的b需要更新，而又会导致B区块需要更新a，导致死循环的出现。图4是双向链条机制导致的相邻区块间反复更新数据。
所以我们认为在该模型中，区块之间的链条应该是单向的，这就要求我们需要找一个区块作为起始区块进行扩散式基因传播(因下一个区块存储当前区块数据的Hash值，我们将此比作基因遗传)。第一个开始传播的区块称为源区块，我们以中心区块为源区块进行测试，最终设计了区块网的初代传播规则，即每一个区块向相对源区块相反方向传播，依此规则进行区块网传播即可对二维数据实现全网蔓延式传播。图5是区块网的基因传播机制。
通过观察构造的模型我们发现，平均每个区块的入度和出度都为2，因每个区块的数据都被Hash编码后添加至其后继区块中，出度越高的区块越不容易被篡改，距离中心区块越远的区块篡改数据的代价越低，我们可以观察到有两种区块比较特殊：区块网正中间的源区块和四个顶角的边缘区块。源区块是创世区 块，没有入度、出度为4，是整个网络中最安全的区块；边缘区块仅有入度没有出度，是最容易被篡改的区块，篡改难度几乎为0。
为保证边缘区块数据的安全性，我们将边缘区块的数据Hash值打包在一起保存到一个单独的区块中供其它区块进行监督。
区块网被设计用于多维数据的安全性存储，以平面数据为例，其本质上还是通过指针实现的二维表格状数据库，所以我们基于表格数据结构进行设计。每个区块被映射到区块对象表格中的一对行列索引，区块中除保存必要的数据信息外，还必须包含upStatus、downStatus、leftStatus、rightStatus四个布尔类状态标记，用于表示当前区块四联通区域的出入度状态，0表示入度、1表示出度；并且还有upNode、downNode、leftNode、rightNode四个指针分别指向其四联通邻域的区块位置；除此之外，为实现上文提到的基因遗传过程，每个区块维护一个最大为4的Hash列表，列表中按照上下左右顺序存储父节点的Hash值。所以每个区块的数据结构如表2所示：
表2.区块数据结构
Figure PCTCN2021085952-appb-000002
在进行区块网基因传播时，先根据数据重要性通过主观判断或者权重计算的方式选取一个或多个源区块，利用Hash地理编码将该区块映射的空间数据存储值Data字段；源区块的四个方向均为出度，所以xStaus均为1，并且ParentHash列表的大小为0，然后根据该区块在二维数组中的行列索引计算四联通方向的四个子节点位置，本别存入xNode中。在构建后续节点时，先将父节点对应方向的xStatus状态设置为0，并将其他方向置为1；然后按照顺序根据副 节点中存储的区块信息利用SHA256算法计算Hash编码存入ParentHash，此时ParentHash列表的大小为该区块的父节点数量；最后同样的计算四联通区域的行列位置分别存入xNode中。重复以上过程直到区块网络构建完毕。
(4)区块网基因传播辐射交叉问题
在该模型中距离源区块越远的区块安全性越低，而在现实世界到虚拟世界的数据组织映射过程中不止有一个需要重点描述的热区，若在区块网中仅选取一个源区块，则大概率会使得一个比较重要的位置出现在边缘区块上，如地球上北京和华盛顿距离较远但对于数据安全性要求都很高。
针对此问题，我们选择多个区块作为源区块同时进行传播，而在多个源区块的共同边缘区块处还会出现重复更新的问题，我们称此为辐射交叉。为防止两个源区块的辐射流交叉，在传播过程中，若目标传播区块已有对当前区块的出度时，停止传播，传播结束后将出度为0的节点信息存入边缘节点信息区块。
图6是多源区块网基因传播模型，该模型选取了两个区块作为源区块，区块内的数字表示传播轮数同时也可视作安全等级，可以看出距离中心区块越远的区块安全等级越低，在虚拟现实应用场景中确实也存在这种情况，可见依此技术面向二维数据实现带有优先级的安全存储是可行的。
为实现以上过程，我们只需要在原有传播过程中加入一个条件判断，即区块的出入度状态标记xStatus缺省值均为0，即先将所有节点的四个方向设置为向内收敛的入度，在某个节点进行构建前，先检查四联通区域的邻近节点对应自己方向的出入度标记。若为0则认为该节点尚未指向自己并且可以将其视为传播目标，将对应方向的xStatus置为1；若为1则表明该节点已经指向自己，即该节点是自己的父节点，停止往该方向传播，避免辐射交叉。
由于在该模型中，基因传播是从源区块开始一层一层地向外扩散，我们可以观察到传播轮数越高的区块出度越少，而在之前我们讨论过出度越少的区块越容易被篡改，即传播轮数越高安全系数越低。当通过基因传播算法构建完区块网后，可以通过统计传播轮数计算安全系数，从而评估该区块网的安全性。图7表示了区块网基因传播轮数和安全系数的关系。
(5)区块网基因传播辐射冲击问题
在区块网多源传播场景中，为防止两个源的辐射流交叉，在目标传播区块已有对当前区块的出度时，停止传播，而因网络时延或计算机算力等问题，当用户对某一源区块的共识速度较快导致其传播速度过快，其蔓延速度大于其他区块，从而导致区块网中其它节点传播的区域较小，造成区块网整体安全系数偏低，从而冲击到区块网中其它源区块，图8表示了在区块网多源传播场景中，传播速度较快的源区块A冲击传播速度较慢的源区块B的情况。
在图8中，区块中数字表示传播轮数。我们可以观察到由于源A传播速度过快，冲击到了源B，导致源B仅传播了两轮，我们称此现象为辐射流冲击。在前面我们提到传播轮数越高安全系数越低，由于辐射流冲击，该情况下的总体安全系数较低。为防止这种情况，我们提出基于消息广播的同步传播机制，即在确保所有节点传播完一轮后，再进行下一轮的传播，图9是遵循同步传播机制的多源区块网模型。
我们提出的区块网模型支持中心化网络构建和去中心化网络构建两种方式。中心化构建是指区块网数据构建过程直接由网络管理员进行，去中心化网络构建是搭建一个点对点的去中心化网络，由用户自主完成信息采集并达成共识，从而构建区块网数据库。这两种构建方式适应不同的应用场景，需要根据具体应用进行选型。然而正如上文提到，不管是中心化网络构建还是去中心化网络构建，都 需要对传播轮数进行控制，在控制传播的程序中加入传播轮数控制量Term，其表示当前正在进行的传播轮数；Waves[][]被定义为一个动态增长的多维数组。Waves[i]中存储着若干区块信息，表示在第i轮需要进行传播的目标区块，如Waves[0]中存储着若干源区块，Waves[1]中存储着源区块的四联通邻域区块，当传播轮数Term为1时，调度Waves[1]中的区块进行传播，并将这些区块的出度节点信息放入Waves[2]，重复以上过程直到网络构建完毕。
(6)区块网数据修改更新方案
为实现保证安全性的前提下虚拟现实空间高度还原现实空间数据，在区块网上进行信息更新是不可避免的。由于区块链的去中心化特性，目前区块链修改链上数据的方法仅有一个，即掌握网络上51％的算力，这种情况在某种意义上是不可能实现的。而在区块网模型中，修改某一区块的数据是被允许的，并且修改某一区块的数据并不会导致全网数据刷新，图10是区块网上某一节点的数据修改后，部分区块网节点进行信息更新的过程。
在左下角有一个区块的数据被修改，那么只有被它传播过的区块需要更新数据，特别的，如果是源区块被修改，那么可能需要刷新全网数据(这取决于是否有其它源区块)。
当有区块的信息需要修改时，中心化网络管理员或去中心化网络中的数据修改发起者会调用DataChange方法，修改目标区块上的Data字段，然后遍历四联通区域的xStatus寻找出度区块，对于出度区块对应的xNode调用Update方法，这些区块会重新计算父节点Hash值并立即修改ParentHash中对应的数据，然后再重复上述过程，直到抵达边缘区块EdgeBlock，这就完成了一次区块网数据更新。
综上所述，区块网数据库构建过程算法描述如下，其中Waves表示每一轮传 播需要进行传播的区块集合、Term表示传播轮数、I和J表示其在二维表中的行列索引、map表示在行列索引结构下区块的位置信息、DIRECTIONS表示四联通区域的方向常量、xNode和xStatus分别表示对应方向的指向节点和指向状态、ParentHash表示对父节点进行SHA256结算后的结果集合：
Figure PCTCN2021085952-appb-000003
数据修改及更新过程算法描述如下，其中Data表示每个区块中存储的空间数据信息、count表示当前区块的发生信息更新的父区块序号：
Figure PCTCN2021085952-appb-000004
Figure PCTCN2021085952-appb-000005
Figure PCTCN2021085952-appb-000006
(7)多层次空间数据组织方式
为实现全空间数据组织映射，需要区块网结合Hash地理编码进行细粒度数据组织存储，在Hash地理编码中，对于地理信息的编码是多层次的，如：我们可以用1位16进制数将地理信息划分成16个区块，而用2位16进制数可以将地理信息划分成256个区块，图11是Hash地理编码划分方式。
由图11我们可以看出Hash地理编码每多一位区块数量就增加16倍，地球的面积大约为5.1亿平方千米，利用Hash地理编码划分10次(也就是10位Hash地理编码)精度可以达到0.463m，而这意味着我们精度从粗到细有10层区 块，如果每一层都应用区块网技术，那么构建区块网需要耗费大量时间，而如果仅在最底层应用区块网，在节省资源的同时也可以保证数据的安全性。表3是Hash地理编码位数与区块数量、精度的关系。
表3.编码位数、区块数量、精度
Figure PCTCN2021085952-appb-000007
(8)球面多源平移投影技术
地球地理数据是Hash地理编码结合虚拟现实技术的一个典型应用场景，而目前区块网是在一个二维平面上存储数据的技术，而实际上地球是球形，这会不可避免地进行分割，由于远离源区块的数据安全系数更低，并且即使我们可以选择多个源区块也可能有源区块出现在边缘的情况，图12是使用球面多源平移投影技术前，对地球进行区块网多源数据组织映射。
在图12中我们选取了三个源区块：广州、洛杉矶、巴黎，但发现洛杉矶出现在右边缘，导致华盛顿、纽约等地区的安全系数极低。针对此问题，我们设计了球面多源平移投影，选取所有源的相对中心点作为中心点进行平移投影，图13是经球面多源平移投影后的结果，在此基础上构建的区块网安全系数明显上升。
本领域的普通技术人员将会意识到，这里所述的实施例是为了帮助读者理解 本发明的实施方法，应被理解为本发明的保护范围并不局限于这样的特别陈述和实施例。本领域的普通技术人员可以根据本发明公开的这些技术启示做出各种不脱离本发明实质的其它各种具体变形和组合，这些变形和组合仍然在本发明的保护范围内。

Claims (7)

  1. 一种面向空间数据的区块网安全组织存储映射方法，其特征在于，包括：
    先根据区块网存储空间数据特性构建区块网基因传播机制，再针对多空间数据中心场景设计多源基因传播机制。在多源基因传播机制中针对可能出现的辐射交叉问题和辐射冲击问题设计传播轮数控制和出入度控制机制。针对空间数据存储场景中数据修改更新需求设计区块网信息更新方案，针对空间数据利用场景，从链式搜索和行列索引两个角度提供数据检索方式；
    对于三维空间数据的划分索引，在原有二维Hash地理编码的基础上加入高程数据，并进行适应多层次划分的数据索引转换，运用三维逻辑距离计算算法在进行三维逻辑距离判断时，先后对两目标块的经纬度编码和高程编码进行异或运算，再将两次异或运算的结果进行欧氏距离计算得到三维逻辑距离。
    针对球面数据映射到三维数据过程中可能出现的多中心情况，运用球面多源平移投影方法先将球面数据以随机起始点映射到三维空间，找到多目标点的空间几何中心，对三维空间数据进行平移转换，使得三维空间数据中心与空间几何中心对准。
  2. 根据权利要求1所述的一种面向空间数据的区块网安全组织存储映射方法，其特征在于：根据区块网存储空间数据特性构建区块网基因传播机制，具体如下：
    步骤1.1：根据空间数据的重要程度选取区块网构建的起始节点；
    步骤1.2：对其四联通区域节点进行Hash编码传播，即下一个节点对指向其的节点集合进行SHA256计算得到指向其的节点的Hash值。
    具体到虚拟现实的应用场景：
    步骤2.1：基于三维Hash地理编码对场景数据进行多尺度划分编码；
    步骤2.2：根据空间数据中关键区域选取中心点；
    步骤2.3：利用球面平移投影对空间位置进行平移映射，从选取的中心点开始进行区块网存储构建。
    在区块网构建完成后，根据每个节点的传播轮数计算安全系数，从而评估该模型的整体安全性。
  3. 根据权利要求1所述的一种面向空间数据的区块网安全组织存储映射方法，其特征在于：在多源基因传播机制中针对可能出现的辐射交叉问题和辐射冲击问题设计传播轮数控制和出入度控制机制，具体如下：
    传播轮数控制：当空间场景中存在多个需要重要描述的中心点时，则需要选取多个源区块进行传播，利用传播轮数控制每个源区块的传播速度保持同步，即在区块网构建过程中有一个控制传播轮数的全局变量，在进行每一轮传播时对全局变量进行比对，区块编号属于该轮次才继续进行传播。同样的，通过统计每个区块所属的传播轮数，计算该区块网的全局安全系数。
    出入度控制机制流程如下：
    步骤3.1：所有区块将四联通邻域的出入度状态标记量初始化为入度；
    步骤3.2：区块触发传播操作时，检查四联通邻域区块对其的出入度状态，如果是出度，则执行步骤3.3，否则执行步骤3.4；
    步骤3.3：停止往该方向传播，并将对应方向的出入度状态标记设置为入度；
    步骤3.4：将对应方向的出入度状态标记为出度，将对应方向节点放入下一轮传播列表，执行步骤3.2。
  4. 根据权利要求1所述的一种面向空间数据的区块网安全组织存储映射方法，其特征在于：针对空间数据利用场景，从链式搜索和行列索引两个角度提供数据检索方式，具体步骤如下：
    步骤4.1：数据需求节点向临近节点广播自己当前场景位置对应的行列索引；
    步骤4.1：临近节点通过行列索引找到当前场景资源所在区块网位置；
    步骤4.2：遍历当前位置的区块信息中四联通方向节点，获取这些节点数据并加载，从而实现链式查找；
    步骤4.3：将对应的场景资源发送给数据需求节点。
  5. 根据权利要求1所述的一种面向空间数据的区块网安全组织存储映射方法，其特征在于：所述三维Hash地理编码设计三维逻辑距离计算算法具体如下：
    针对现有Hash地理编码技术在多维度地理信息组织中的不足，从空间数据的采集和整理需求出发，深度分析空间数据的划分特点。通过比较分析空间信息数据与Hash地理编码优缺点，对包含经度、纬度和高度的三维空间进行划分并构建数据模型，实现空间数据化，将空间数据按照经纬度观测平面进行多层十六叉树划分，并在每一层对每个节点附加对应编码，直到叶子节点划分粒度表示空间数据的最小单元，即构建对空间数据的满十六叉索引树，每个叶子节点表示经纬度观测平面上的最小划分单元，其在垂直方向还存储着不同程度的高程信息，再通过将这些高程信息进行三位十六进制编码生成垂直编码与经纬度编码组成三维Hash地理编码。其中空间划分方法可实现从宏观到微观的无限制梯度划分。使其更加适应于多维度多层次空间数据分块存储，并探索基于三维Hash地理编码的逻辑距离判断方法，在进行三维空间距离判断时，先对两个三维Hash地理编码的前缀经纬度编码进行异或计算得到平面距离，再对后缀高程编码进行异或计算得到垂直距离，最后通过将平面距离和垂直距离进行计算得到其空间欧式距离。
  6. 根据权利要求1所述的一种面向空间数据的区块网安全组织存储映射方法，其特征在于：区块网数据修改更新方法具体如下：
    步骤5.1：当区块网上触发了数据修改操作时，将要修改的区块的数据字段更改；
    步骤5.2：遍历该区块指向的区块，将这些区块对应的Hash值进行重新计算；
    步骤5.3：重复步骤5.2直到到达边缘结束递归过程。
  7. 根据权利要求1所述的一种面向空间数据的区块网安全组织存储映射方法，其特征在于：球面多源平移投影方法具体如下：
    步骤6.1：选取随机空间位置作为中心点进行球面到空间的几何映射；
    步骤6.2：根据空间数据的重要性选取若干中心区域；
    步骤6.3：计算这些中心的空间几何中心；
    步骤6.4：以步骤6.3中得到的几何中心位置作为中心点重新进行一次球面到空间的几何映射。
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