CN114860730A

CN114860730A - Land data storage method, system and storage medium based on block chain

Info

Publication number: CN114860730A
Application number: CN202210534439.7A
Authority: CN
Inventors: 路子逵; 邓清; 张强; 于国庆; 王小娟; 何明枢
Original assignee: Beijing New Five Good Agricultural Technology Co ltd
Current assignee: Beijing New Five Good Agricultural Technology Co ltd
Priority date: 2022-05-17
Filing date: 2022-05-17
Publication date: 2022-08-05

Abstract

The invention provides a land data storage method, a land data storage system and a land data storage medium based on a block chain, wherein the method comprises the following steps: obtaining land pattern information, and determining land data to be stored based on the obtained land pattern information; carrying out Hash processing on the land data to be stored so as to extract a digital fingerprint of the land data to be stored; packaging and chaining the land data to be stored and the corresponding digital fingerprints, and acquiring corresponding transaction identification and block numbers; and storing the transaction identification and the block number in a linked database in a one-to-one correspondence manner. The method improves the safety of the land data and also improves the query speed of the land data.

Description

Land data storage method, system and storage medium based on block chain

Technical Field

The invention relates to the technical field of land data storage, in particular to a land data storage method, a land data storage system and a land data storage medium based on a block chain.

Background

The land pattern survey is an important means for comprehensively and truly surveying and clearing land resources, and is one of basic contents in land resource management, wherein a land information database is built with risks of large data volume, difficult management and easy tampering. The geographic pattern spot acquisition work is divided into three parts: firstly, remote sensing image data and a production orthophoto map are obtained, then investigation information extraction and investigation base map making are carried out, and finally, the work of on-line map drawing of the rights boundary and supplementary investigation is carried out. The investigation work comprises special land survey and evaluation, land utilization status survey and land ownership survey. And after the geographic data are collected, constructing a database, and respectively storing the geographic data in databases at different levels such as national level, provincial level, land level, county level and the like according to the type and the level. And after the geographic data is stored in the database, carrying out investigation result verification, wherein the verification is divided into automatic comparison, internal operation verification and external operation verification, and the land utilization current situation investigation result verification, the land ownership investigation result verification and the special land investigation result verification are completed. And after all the checking tasks are finished, summarizing the survey results, carrying out various statistical summary analysis, summarizing and reporting the results.

The existing land data is stored in databases of different levels and capacities, such as country level, provincial level, land level, and county level. Due to the fact that geographic data are complex in source, wide in data distribution and large in size, the situation that data are inconsistent or even slightly tampered in the database construction process is easy to occur, the situation is very difficult to perceive, and the traditional method is difficult to effectively supervise the data, so that the reliability of the data in the database is reduced. And in the later stage land checking process, the original land utilization condition is difficult to trace back, the condition that whether the land is illegally occupied can not be checked, and the workload can be increased due to the incompleteness of the tracing back information. Therefore, how to ensure the safety of land data storage and improve the query speed of land data is an urgent technical problem to be solved.

Disclosure of Invention

In view of the above, the present invention provides a land data storage method, system and storage medium based on a block chain, so as to solve one or more problems in the prior art.

According to one aspect of the invention, the invention discloses a land data storage method based on a block chain, which comprises the following steps:

obtaining land pattern information, and determining land data to be stored based on the obtained land pattern information; the land data to be stored comprises geographic data, field picture image data, pattern spot GPS data, land label data, collected personnel information data and equipment information data;

performing hash processing on the geographic data to extract a digital fingerprint of the land data to be stored;

packaging and chaining the land data to be stored and the corresponding digital fingerprints, and acquiring corresponding transaction identification and block numbers;

and storing the transaction identification and the block number in a linked database in a one-to-one correspondence manner.

In some embodiments of the invention, the geographic data is two-dimensional planar data or point cloud data.

In some embodiments of the present invention, when the geographic data is point cloud data, performing hash processing on the geographic data to extract a digital fingerprint of the land data to be stored, including:

and compressing the point cloud data, and performing hash processing on the compressed point cloud data.

In some embodiments of the invention, compressing the point cloud data comprises:

and performing singular value decomposition on each layer of two-dimensional matrix in the point cloud data based on a singular value decomposition algorithm to obtain a decomposition matrix of each layer of two-dimensional matrix, and compressing the decomposition matrix to obtain compressed point cloud data.

In some embodiments of the invention, the method further comprises:

segmenting the compressed point cloud data, and extracting the digital fingerprint of each segment;

and generating a Merck tree based on the digital fingerprints of the fragments, and determining the digital fingerprint of the land data to be stored based on the generated Merck tree.

In some embodiments of the present invention, compressing the decomposition matrix to obtain compressed point cloud data includes:

and comparing the singular values in the decomposition matrixes, discarding smaller singular values, and reserving larger singular values to obtain compressed point cloud data.

In some embodiments of the present invention, packaging and winding the land data to be stored and the corresponding digital fingerprints includes:

and verifying the transaction by each block chain link point in the block chain network according to an intelligent contract, and packaging and chaining the land data to be stored and the corresponding digital fingerprints after the verification is passed.

In some embodiments of the invention, the down-link database is a Leveldb database.

According to another aspect of the present invention, there is also disclosed a land data storage system based on a block chain, the system comprising a processor and a memory, the memory having stored therein computer instructions for executing the computer instructions stored in the memory, the system implementing the steps of the method according to any one of the above embodiments when the computer instructions are executed by the processor.

According to yet another aspect of the invention, a computer-readable storage medium is also disclosed, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any of the embodiments above.

The land data storage method based on the block chain disclosed by the embodiment of the invention realizes the distributed storage of the land data based on the block chain technology, and realizes the transparency, the non-tampering property and the traceability of the data, thereby improving the safety, the reliability, the usability and the storage efficiency of the data storage. In addition, the transaction identification and the block number are stored in the downlink database in a one-to-one correspondence manner, so that the uplink and downlink collaborative retrieval can be conveniently realized, the retrieval speed of the land data can be increased, and the checking efficiency of the land data is improved.

Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.

It will be appreciated by those skilled in the art that the objects and advantages that can be achieved with the present invention are not limited to the specific details set forth above, and that these and other objects that can be achieved with the present invention will be more clearly understood from the detailed description that follows.

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 application, illustrate embodiment(s) of the invention and together with the description serve to explain the principles of the invention. The components in the figures are not necessarily to scale, emphasis instead being placed upon illustrating the principles of the invention. For purposes of illustrating and describing some portions of the present invention, corresponding parts of the drawings may be exaggerated, i.e., may be larger, relative to other components in an exemplary apparatus actually manufactured according to the present invention. In the drawings:

fig. 1 is a schematic flow chart of a land data storage method based on a block chain according to an embodiment of the present invention.

Fig. 2 is a schematic structural diagram of a land data storage model based on a block chain according to an embodiment of the present invention.

Fig. 3 is a schematic diagram of a hash process of point cloud data according to an embodiment of the present invention.

Fig. 4 is a schematic view illustrating a land data uplink process to be stored according to an embodiment of the present invention.

Detailed Description

In order to make the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the embodiments of the present invention are further described in detail below with reference to the accompanying drawings. The exemplary embodiments and descriptions of the present invention are provided to explain the present invention, but not to limit the present invention.

It should be noted that, in order to avoid obscuring the present invention with unnecessary details, only the structures and/or processing steps closely related to the scheme according to the present invention are shown in the drawings, and other details not closely related to the present invention are omitted.

It should be emphasized that the term "comprises/comprising/comprises/having" when used herein, is taken to specify the presence of stated features, elements, steps or components, but does not preclude the presence or addition of one or more other features, elements, steps or components.

Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings. In the drawings, the same reference numerals denote the same or similar parts, or the same or similar steps.

A blockchain is a shared, non-tampered ledger, and the blockchain is stored in different servers, which are referred to as nodes in the blockchain system. If the information in the block chain is to be modified, more than half of the nodes must be authenticated and the information in all the nodes must be modified, and the nodes are usually held in different hands of different subjects, so that the information in the block chain is extremely difficult to tamper with. Compared with the traditional network, the block chain has two core characteristics: firstly, data is difficult to tamper, and secondly, decentralization is performed. Based on the two characteristics, the information recorded by the block chain is more real and reliable, and the problem that people are not trusted each other can be solved. Distributed storage is a computer data storage architecture with data stored in volumes across multiple physical servers, as opposed to a traditional centralized storage architecture. The distributed storage usually adopts a storage unit cluster form, has a mechanism for synchronizing and coordinating data among cluster nodes, and has the advantages of high expansibility, high reliability, low cost, high performance and the like.

In order to solve the problems of the existing land pattern survey system, the invention uses a block chain technology and a distributed storage technology, simultaneously designs a new block chain retrieval scheme and a point cloud data compression scheme, and constructs a land data storage model. The block chain is a distributed account book and has the characteristics of decentralization, data non-tampering, data traceability, distrust removal, multi-party coordination and the like. And a block chain technology is used for carrying out chain linking processing on the land survey task, the image data purchasing information, the professional technical team bidding information and the survey data, so that the transparency of the information, the transparency of the bidding and the traceability of the data are realized, and the possible problems in the conventional land survey system are avoided. In addition, the technology of the intelligent contract is adopted, so that the credible data verification is realized under the condition that no third party participates, and the time consumption and the complexity of the land survey process are obviously reduced. In order to solve the problems of large geographic data volume and difficult data storage, a Singular Value Decomposition (SVD) compression algorithm is used for compressing the geographic data, and under the condition that main characteristic information is not lost, the overhead required for storing the geographic information is obviously reduced.

Fig. 1 is a schematic flowchart of a land data storage method based on a block chain according to an embodiment of the present invention, and as shown in fig. 1, the land data storage method at least includes steps S10 to S40.

Step S10: obtaining land pattern information, and determining land data to be stored based on the obtained land pattern information; the land data to be stored comprises geographic data, field picture image data, pattern spot GPS data, land label data, collected personnel information data and equipment information data.

The land pattern survey is an important means for comprehensively and truly investigating and clearing land resources, and is one of the basic contents in land resource management. In this step, land data to be uploaded may be determined based on the acquired land pattern spot information. The image data of the solid picture is an image or a video acquired by image acquisition equipment; the land label data represents label information of corresponding land, such as lakes, grasslands, rivers, marshlands and the like; the collected personnel information data comprises the name, identity information and the like of a collector, and the equipment information data comprises the model of the pattern spot collecting equipment and the like. Specifically, the type of geographic data in the land data to be stored may be two-dimensional plane data or three-dimensional point cloud data; the point cloud data specifically comprises longitude and latitude, RGB color, reflection intensity, echo times, key point information and the like.

Among other things, point cloud datasets are typically in the terabyte (TByte) range, making the storage overhead of point cloud data large. Therefore, when the geographic data is point cloud data, the point cloud data is further compressed to facilitate storage of the data. Meanwhile, as the point cloud data represents the geographic space information, main geographic information characteristics are required to be reserved as far as possible for compressing the point cloud data, and unimportant information or redundant information is deleted. The geographical point cloud data is similar to raster data, and the geographical point cloud data described in the present technology is raster data of a three-dimensional matrix, and the encoding manner adopted by the raster data is exemplified by direct raster encoding, run-length encoding, block encoding, chain encoding, quadtree encoding, and the like.

Step S20: and carrying out Hash processing on the geographic data to extract the digital fingerprint of the land data to be stored.

In this step, the geographic data is encrypted. Specifically, when the geographic data is two-dimensional plane data, the digital fingerprint can be directly extracted because the data volume of the two-dimensional plane data is not large; when the geographic data is point cloud data with a huge data volume, a large amount of storage space is consumed for storing the point cloud data, so that the point cloud data is firstly compressed, and then the point cloud data after the compression is subjected to hash processing. Illustratively, compressing the point cloud data comprises: and performing singular value decomposition on each layer of two-dimensional matrix in the point cloud data based on a singular value decomposition algorithm to obtain a decomposition matrix of each layer of two-dimensional matrix, and compressing the decomposition matrix to obtain compressed point cloud data.

The Singular Value Decomposition (SVD) algorithm is to divide an m x n realThe number matrix a is decomposed into the following form: a ═ U ∑ V ^T . Wherein U and V are both unit orthogonal arrays, i.e. UU ^T I and VV ^T U is called the left singular matrix and V is called the right singular matrix. Σ has a value only on the main diagonal, called the singular value, and the other elements are all 0. The dimensionality of the decomposed matrix is respectively U epsilon R _m×m ,Σ∈R _m×m ,V∈R _n×n Generally, Σ has the following form:

since U, V, Σ cannot be directly obtained, U, V, Σ and singular values are obtained as follows:

the transposition of A and A are subjected to matrix multiplication to obtain a square matrix A of n multiplied by n ^T A. Due to the A obtained ^T A is a square matrix, and then can be paired with A ^T A, performing characteristic decomposition, wherein the obtained characteristic value lambda and the characteristic vector v satisfy the following formula: (A) ^T A)v _i ＝λ _i v _i 。

Thus, a matrix A can be obtained ^T N eigenvalues λ of a and corresponding n eigenvectors. A is to be ^T All the eigenvectors of A are expanded into an n × n matrix V, namely a V matrix in the SVD formula. Each feature vector in V is generally called the right singular vector of a.

Similarly, the transpose of A and A is subjected to matrix multiplication to obtain a m × m square matrix AA ^T . Same for the square array AA ^T And (3) carrying out characteristic decomposition, wherein the obtained characteristic value lambda and the characteristic vector v satisfy the following formula: (A) ^T A)u _i ＝λ _i u _i 。

The matrix AA can then be obtained ^T And corresponding m eigenvectors u. Mixing AA ^T All the eigenvectors of (a) are expanded into an m × m matrix U, namely a U matrix in the SVD formula. Each feature vector in U is generally called the left singular vector of a.

Then, the sigma matrix is solved, each singular value of the A matrix can be obtained according to the following formula, and then the singular value matrix sigma is obtained.

From further derivation, the following equation can be derived:

proves that A is ^T The matrix formed by the feature vectors of A is the V matrix in SVD. Furthermore, it can be obtained that the eigenvalue matrix is equal to the square of the singular value matrix, that is, the eigenvalue and the singular value satisfy the following relationship:

thus, can pass through A ^T A or AA ^T The characteristic value of the method is taken as a square root to directly obtain a singular value.

Further, after singular values corresponding to each layer of two-dimensional matrix in the point cloud data are obtained, the singular values in the decomposition matrices are compared, smaller singular values are discarded, and larger singular values are reserved, so that the compressed point cloud data are obtained. The raster data is a three-dimensional matrix, and the SVD algorithm decomposes singular values of two-dimensional matrixes of each layer of the raster data respectively to obtain U, V sigma matrixes of each layer. According to the properties of the singular matrix, the singular values often correspond to important information implied in the matrix, and the importance of the singular values and the size of the singular values are positively correlated. Therefore, the singular values obtained after the decomposition can be selectively discarded, only the singular values with larger numerical values are retained, and the singular values with smaller numerical values are discarded. And after the data is subjected to singular value decomposition and discarded, the compressed U, V sigma matrixes are stored, and compared with the original matrix storage, the storage cost can be obviously reduced.

According to the method, the point cloud data are compressed by adopting a Singular Value Decomposition (SVD) algorithm, namely, the Singular Value Decomposition (SVD) algorithm is used for performing singular value decomposition on each layer of two-dimensional matrix of the raster data, only the singular value with a larger numerical value is reserved, and finally the transformed matrix numerical value is reserved, so that the point cloud data storage space is remarkably reduced under the condition of reserving important geographic information. Therefore, the land data storage method based on the block chain not only obviously reduces the storage overhead of the point cloud data, but also retains the main data characteristics, and ensures that the main information of the geographic point cloud data is not lost due to compression.

After the point cloud data is compressed, further hash processing is carried out. Fig. 3 is a schematic diagram of a hash processing flow of point cloud data according to an embodiment of the present invention, and as shown in fig. 3, when hash processing is performed on point cloud data, firstly, segments are performed on the compressed point cloud data, and digital fingerprints of the segments are extracted; and further generating a Merck tree based on the digital fingerprints of the fragments, and determining the digital fingerprint of the land data to be stored based on the generated Merck tree. That is, in this embodiment, slice storage is performed via a distributed storage system, and distributed fingerprint extraction is performed based on the Merkel Tree (Merkel Tree) concept.

In this embodiment, because of the terabyte-sized point cloud file collected during the land spot survey, even after being compressed, the huge amount of data is extremely high for a general storage system in the face of several tens of point cloud data in one spot. Therefore, the distributed storage system is one of the innovation points of the land data storage method based on the block chain, the distributed storage system adopts an expandable system structure, a plurality of storage servers are used for sharing storage load, and the position server is used for positioning storage information, so that the reliability, the availability and the access efficiency of the system are improved, and the expansion is easy.

Step S30: and packaging and chaining the land data to be stored and the corresponding digital fingerprints, and acquiring corresponding transaction identification and block numbers.

In the step, the digital fingerprint of the geographic data, the image data of the real-time picture, the GPS data of the pattern spot, the land label data, the collected personnel information data and the equipment information data are packed and linked. It is understood that when the geographic data is two-dimensional plane data, the digital fingerprint and the real-time picture image data, the spot GPS data, the land label data, the collected personnel information data, and the equipment information data corresponding to the two-dimensional plane data are packed and stored. And when the geographic data is point cloud data, combining the digital fingerprints of each fragment of the point cloud data after compression processing to obtain a digital fingerprint chain of the compressed point cloud data, namely packaging and chain-linking the digital fingerprints of the compressed point cloud data, the image data of the real picture, the GPS data of the image spot, the land label data, the information data of the collected personnel and the information data of the equipment for storage.

Specifically, the packaging and winding of the land data to be stored and the corresponding digital fingerprints includes: and verifying the transaction by each block chain link point in the block chain network according to an intelligent contract, and packaging and chaining the land data to be stored and the corresponding digital fingerprints after the verification is passed.

A Smart contract (Smart contract) is a computer protocol intended to propagate, verify or execute contracts in an informational manner. Smart contracts allow trusted transactions to be conducted without third parties, which transactions are traceable and irreversible. The goal of smart contracts is to provide a secure method over traditional contracts and to reduce other transaction costs associated with the contracts. The invention uses the intelligent contract when checking the geographic image and the geographic point cloud data, and realizes the safe and efficient checking without the participation of a third party; as will be understood, the geographic picture and the geographic point cloud data are two-dimensional plane data and three-dimensional point cloud data, respectively, of the geographic data referred to herein.

Since smart contracts allow for trusted transactions without third party involvement, and these transactions are traceable and irreversible, smart contracts will provide security and efficiency guarantees for data verification. In the embodiment, the intelligent contract is adopted for data verification, so that the problems of long time consumption, low accuracy, susceptibility to human factors and the like in the existing data verification in a manual mode in the land pattern survey process are solved.

Step S40: and storing the transaction identification and the block number in a linked database in a one-to-one correspondence manner.

In this step, simultaneous storage of uplink and downlink data on the chain is achieved. Specifically, while the land data to be stored is stored in the uplink, the corresponding transaction ID and block number are stored in the downlink database at the same time. The arrangement enables the corresponding storage area block number to be obtained from the down-link database based on the transaction ID during verification retrieval, so that the block location is more accurate, the data retrieval process is optimized, and the retrieval speed is accelerated. The down-link database can be specifically a Leveldb database, the database is a key-value database based on an LSM-Tree, when a query request arrives, the memory needs to be checked firstly, then binary search is carried out in a disk, and the time complexity is reduced; after the data arrives, the level DB firstly stores all the data in the log file.

Fig. 4 is a schematic diagram illustrating a land data uplink process to be stored according to an embodiment of the present invention, as shown in fig. 4, during data uplink, an index is generated on a chain according to a data source, and the index is mapped with data stored under the chain, so as to implement a coordinated search under the chain, and when a user performs data verification and query, original data is obtained through the index.

In the embodiment, when the transaction is linked up, the transaction id and the block number where the transaction is located are stored in a linked database; when data query is carried out, a transaction id is input, the block number of the transaction stored in the block chain is obtained from the database under the chain, the transaction in the block is queried, and a hash value corresponding to the transaction id is output. Therefore, the whole block chain does not need to be scanned from the transaction in the created block during query, namely, the method has high query efficiency, can quickly locate the corresponding block, has small extra space consumption, and occupies much less extra storage space than the LSM-Tree.

In a land pattern investigation scene, the prior data and the current remote sensing data are often required to be compared, and when suspicious patterns are found, the data are mapped again, so that the validity of the data is guaranteed. In order to obtain real and reliable land raw data, the embodiment introduces a chain uplink and downlink coordinated search, and adds an index field in a block chain to form a mapping with a file stored under the chain, so as to realize the requirement of obtaining the real raw data.

Through the embodiment, the land data storage method based on the block chain disclosed by the invention utilizes the query and verification intelligent contract of the block chain, the intelligent contract can be used in the verification of the geographic picture and the geographic point cloud data, the manual verification is converted into the automatic verification of the block chain, the safety and high-efficiency verification under the condition of no participation of a third party is realized while the labor is saved, and the problems of low manual verification efficiency, long time consumption, low verification accuracy and the like of the conventional land data storage method are solved. In addition, the distributed storage system adopted by the invention can effectively solve the problems of low reliability, low expansibility and low fault tolerance of the existing land data storage method, because the framework of the distributed storage system determines the characteristics of high expansibility and high fault tolerance, and simultaneously, due to the characteristics of the distributed storage system, as long as all devices do not lose data at the same time, the original data can be restored through an internal mechanism, so that the distributed storage system has high reliability. The land data is stored based on the block chain technology, and the block chain is a chain type account book, so that the land data storage method has the advantages of decentralization, distrust, no tampering, data traceability and the like; therefore, the block chain is adopted for storing the data information, so that the safety, the transparency and the traceability of the data information can be ensured, convenience is provided for realizing the system function, and a plurality of defects of the existing land data storage system are improved.

Data chaining in the existing block chain technology is generally performed after digital fingerprint extraction is performed on data, the fingerprint chaining is stored to reduce occupation of storage space of a block chain, data needing to be chained under a use scene of the model often form data with a size of terabyte, and the data is too large in size, so that a lot of time is consumed when the digital fingerprint extraction is performed. Therefore, the distributed digital fingerprint extraction is one of the innovation points of the model, the distributed digital fingerprint extraction utilizes the slicing function of a distributed system to respectively extract fingerprints of all slices of one data file, and a plurality of slice files can be operated simultaneously, namely the extraction of the digital fingerprints is executed concurrently, so that the extraction speed can be greatly increased. Therefore, the land data storage method based on the block chain can effectively shorten the time spent on extracting huge digital fingerprints, saves time and improves the speed compared with a method for directly extracting the digital fingerprints commonly adopted for the huge data in the prior art.

Fig. 2 is a schematic structural diagram of a land data storage model based on a block chain according to an embodiment of the present invention, and as shown in fig. 2, data acquisition is performed based on a handheld terminal device; compressing the obtained point cloud data, slicing the compressed point cloud data, and encrypting to obtain hash values of the slices; and further performing chain linking on the obtained hash value and the corresponding land data to be stored so as to finish chain linking storage of the stored land data. In addition, when data is linked up, the transaction ID and the corresponding block number are stored in a downlink database, so that the storage of downlink and uplink data is realized, and the mapping of the downlink and uplink data relationship is realized.

Accordingly, the invention also discloses a land data storage system based on a block chain, which comprises a processor and a memory, wherein the memory stores computer instructions, the processor is used for executing the computer instructions stored in the memory, and when the computer instructions are executed by the processor, the system realizes the steps of the method according to any one of the above embodiments.

Through the above, the land data storage method and system based on the block chain disclosed by the invention realize the storage of the land data based on the block chain technology, and ensure the safety, transparency and traceability of data information. In addition, the mapping of the data relation between the uplink and the downlink of the chain is realized by means of the downlink database, in the data retrieval process, the index field is added into the block chain and the data mapping is stored under the chain by adopting the uplink and downlink collaborative retrieval technology, the authenticity of the data under the chain is ensured while the performance of the block chain is not influenced, and the retrieval efficiency is improved.

In addition to the above, the Singular Value Decomposition (SVD) algorithm is used to compress the geographical point cloud data, so that the compressed geographical point cloud data still retains the main characteristic information, and the effect of the geographical point cloud data is not affected, thereby significantly reducing the storage overhead of the geographical point cloud data. And the land information is verified in an intelligent contract form, when the transaction is linked up, the submitted task position and the task position corresponding to the task ID are verified in a matching way, if the verification is passed, the linking is allowed, and if the verification is not passed, the linking is rejected, so that the uploaded data is legal.

In addition, the invention also discloses a computer readable storage medium, on which a computer program is stored, which when executed by a processor implements the steps of the method according to any of the above embodiments.

Those of ordinary skill in the art will appreciate that the various illustrative components, systems, and methods described in connection with the embodiments disclosed herein may be implemented as hardware, software, or combinations of both. Whether this is done in 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 invention. When implemented in hardware, it may be, for example, an electronic circuit, an Application Specific Integrated Circuit (ASIC), suitable firmware, plug-in, function card, or the like. When implemented in software, the elements of the invention are the programs or code segments used to perform the required tasks. The program or code segments may be stored in a machine-readable medium or transmitted by a data signal carried in a carrier wave over a transmission medium or a communication link. A "machine-readable medium" may include any medium that can store or transfer information. Examples of a machine-readable medium include electronic circuits, semiconductor memory devices, ROM, flash memory, Erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, Radio Frequency (RF) links, and so forth. The code segments may be downloaded via computer networks such as the internet, intranet, etc.

It should also be noted that the exemplary embodiments mentioned in this patent describe some methods or systems based on a series of steps or devices. However, the present invention is not limited to the order of the above-described steps, that is, the steps may be performed in the order mentioned in the embodiments, may be performed in an order different from the order in the embodiments, or may be performed simultaneously.

Features that are described and/or illustrated with respect to one embodiment may be used in the same way or in a similar way in one or more other embodiments and/or in combination with or instead of the features of the other embodiments in the present invention.

The above description is only a preferred embodiment of the present invention, and is not intended to limit the present invention, and various modifications and changes may be made to the embodiment of the present invention by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A land data storage method based on a block chain, the method comprising:

2. The land data storage method based on block chains according to claim 1, characterized in that the geographical data is two-dimensional plane data or point cloud data.

3. The land data storage method based on the block chain as claimed in claim 2, wherein when the geographic data is point cloud data, the hashing process is performed on the geographic data to extract the digital fingerprint of the land data to be stored, and the method comprises the following steps:

4. The blockchain-based land data storage method according to claim 3, wherein compressing the point cloud data comprises:

5. The blockchain-based land data storage method according to claim 4, further comprising:

6. The land data storage method based on the block chain as claimed in claim 4, wherein compressing the decomposition matrix to obtain compressed point cloud data comprises:

7. The land data storage method based on a block chain according to claim 1, wherein packaging and winding the land data to be stored and the corresponding digital fingerprints comprises:

8. The land data storage method based on block chains according to any one of claims 1 to 7, wherein the down-chain database is a Leveldb database.

9. A land data storage system based on a blockchain, the system comprising a processor and a memory, characterized in that the memory has stored therein computer instructions for executing the computer instructions stored in the memory, the system realizing the steps of the method according to any one of claims 1 to 8 when the computer instructions are executed by the processor.

10. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 8.