Blockchain-based natural resource information system distributed storage virtual node construction method

Abstract

The application discloses a natural resource information system distributed storage virtual node construction method based on a blockchain, relates to the technical field of the blockchain, constructs a virtual resource node to extract a key resource, obtains a node keyword, extracts features of the node keyword, and obtains a key node coefficient; performs symbol conversion and in-segment grouping on the obtained key node coefficient to obtain a key node code group, performs dynamic combination and exchange variation on the key node code group to obtain a front-end node matrix, statistically combines the front-end node matrix to obtain a node matrix set; transforms comprehensive resource data to obtain a resource code group set, matches and extracts the resource code group set according to the node matrix set to obtain an extraction state code, and performs matching storage and multi-edge matching through the extraction state code to obtain matched node resources and multi-state matching node resources; the utilization rate of the natural resources is improved, hardware resources are better managed and distributed, management is simplified, and costs are reduced.

Description

Technical Field

[0001] This invention relates to the field of blockchain technology, and more specifically to a method for constructing distributed storage virtual nodes for a natural resource information system based on blockchain. Background Technology

[0002] Blockchain is a decentralized, public, and transparent distributed ledger technology. It consists of multiple self-proclaimed nodes that reach consensus through a consistent algorithm and record data in a chain-like structure of blocks. Each block contains transaction data and links to other blocks, forming a chain-like data structure that follows specific rules and protocols.

[0003] A natural resources information system is a data system used to manage and monitor natural resources. Distributed storage virtual node construction is a technology for creating and managing virtual nodes in a distributed storage system. By simulating multiple independent virtual nodes on a physical server, each virtual node runs its own operating system and applications. Therefore, in the process of researching and building the underlying infrastructure of the Ningxia natural resources blockchain, managing data by distributing it across virtual nodes in multiple physical locations through distributed storage has important theoretical and practical significance.

[0004] The problem we need to solve is how to utilize blockchain technology to construct virtual resource nodes for key resource extraction, obtain node keywords, extract features from node keywords to obtain key node coefficients, perform code transformation and matrix combination on key node coefficients to obtain a node matrix set, transform comprehensive resource data to obtain a resource code set, match and extract the resource code set according to the node matrix set to obtain extraction status codes, and perform matching storage and multilateral matching through the extracted status codes to obtain matched node resources and polymorphic matched node resources. To this end, we now provide a method for constructing distributed storage virtual nodes for natural resource information systems based on blockchain. Summary of the Invention

[0005] The purpose of this invention is to provide a method for constructing distributed storage virtual nodes for a natural resource information system based on blockchain, so as to solve the problems mentioned in the background art.

[0006] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows:

[0007] The method for constructing distributed storage virtual nodes for a natural resource information system based on blockchain includes the following steps:

[0008] Step S1: Construct a natural area plan and collect comprehensive resource data;

[0009] Step S2: Construct virtual resource nodes to extract key resources, obtain node keywords, perform state transitions on node keywords to obtain key node signals, set adjustment modal coefficients for dynamic adjustment and parameter extraction to obtain dynamic intervals, perform coefficient division and hierarchical verification on adjustment modal coefficients according to dynamic intervals to obtain polymorphic adjustment coefficients, and use polymorphic adjustment coefficients to adjust and extract key node signals to obtain key node coefficients.

[0010] Step S3: Perform symbol conversion and intra-segment grouping on the obtained key node coefficients to obtain key node code groups. Dynamically combine and exchange the key node code groups to obtain the front-end node matrix. Statistically combine the front-end node matrix to obtain the node matrix set.

[0011] Step S4: Perform state transition and adjustment extraction on the obtained comprehensive resource data to obtain comprehensive resource coefficients. Perform symbol conversion and intra-segment grouping on the comprehensive resource coefficients to obtain resource code sets. Perform virtual combination and virtual positioning on the virtual resource nodes according to the natural area plan map to obtain a virtual resource node map. Perform matching extraction on the resource code sets according to the node matrix set to obtain extracted status codes. Perform matching storage and multilateral matching through the extracted status codes to obtain matched node resources and polymorphic matched node resources.

[0012] A further improvement to the technical solution of this invention lies in the fact that the process of collecting comprehensive resource data includes:

[0013] The natural resource area is divided into blocks to obtain natural monitoring sites, and a natural area plan is constructed based on the natural resource area.

[0014] Based on the obtained natural monitoring sites, a capture and extraction terminal is set up to collect data from the natural monitoring sites and obtain comprehensive resource data.

[0015] A further improvement to the technical solution of this invention lies in that the process of obtaining key node signals includes:

[0016] Virtual resource nodes are constructed based on the obtained natural area plan;

[0017] Set up an inquiry terminal based on the obtained virtual resource nodes, and issue resource survey commands through the inquiry terminal;

[0018] Based on the obtained resource survey instructions, key resources are extracted from virtual resource nodes to obtain node keywords;

[0019] The obtained node keywords are state-transformed using digital signal encoding rules. The node keywords are input in text form and encoded using ASCII code. Each character is converted into its corresponding digital code, resulting in a digital sequence that represents the digital signal of the node keyword.

[0020] The digital sequence is normalized so that its value ranges between 0 and 1, and the normalized digital signal is output. The digital signal is then subjected to a Discrete Fourier Transform to convert it from the time domain to the frequency domain, and the frequency domain signal is output. The amplitude spectrum and phase spectrum of the frequency domain signal are then extracted to obtain the node key signal in the frequency domain form. The amplitude spectrum represents the amplitude of the signal at different frequencies, and the phase spectrum represents the phase of the signal at different frequencies.

[0021] A further improvement to the technical solution of this invention lies in the fact that the process of obtaining the key node coefficients includes:

[0022] The signal characteristics of key node signals in frequency domain form are analyzed, the amplitude spectrum of the signal is calculated, its frequency range is determined, the cutoff frequency of the signal (i.e. the boundary of the frequency range in which the signal energy is mainly concentrated) is determined based on the amplitude spectrum, the bandwidth of the signal is calculated, i.e. the frequency range between the cutoff frequencies, and the obtained frequency range, cutoff frequency and bandwidth are output. Then, a bandpass filter is selected and the filter parameters are adjusted to output the modal coefficients.

[0023] Choose the variational mode decomposition method, adjust the number of modes and center frequency parameters to optimize the decomposition effect, control and adjust the mode coefficients to perform scaling and translation transformations, and record the distance of scaling and translation transformations, which are the dynamic parameters;

[0024] Based on the obtained dynamic parameters, the adjustment mode coefficients are extracted to obtain the dynamic interval, and the obtained dynamic interval is marked as b;

[0025] The modal coefficients are divided into multiple levels based on the dynamic interval. Weights are assigned based on the size of the dynamic interval, and the coefficient weight θ of each level is calculated to obtain the modal level.

[0026] Each modal level is calibrated to ensure that its corresponding modal coefficients are uniformly distributed within the frequency range and bandwidth, and the parameters of each modal level are adjusted to meet the signal characteristic requirements to obtain the multimodal modal coefficients.

[0027] By combining the polymorphic adjustment coefficients and the key node signals, the polymorphic adjustment coefficients and the key node signals are convolved to obtain the interval convolution coefficients. The interval convolution coefficients are then summed to obtain the node adjustment coefficients. Finally, the node adjustment coefficients are filtered by the response adjustment window to extract the key node coefficients.

[0028] A further improvement to the technical solution of this invention lies in the fact that the process of obtaining the node matrix set includes:

[0029] Obtain the key node coefficients in continuous numerical form, determine the quantization interval based on the numerical range of the key node coefficients, select the quantization bit depth, map the key node coefficients to the quantization level, and obtain the quantized discrete values.

[0030] Each quantized value is converted into an n-bit binary code, and all binary code are concatenated into a continuous data stream to obtain a data stream consisting of several binary code segments, which is the key node stream segment.

[0031] The critical node stream segments are grouped into groups of 7 binary code elements to obtain critical node code groups. If the last group has less than 7 code elements, the groups are padded forward until each group has 7 binary code elements, thus obtaining critical node code groups.

[0032] The key node code group is cyclically split to obtain normal code elements and key code elements. Specifically, the first 4 binary code elements of each key node code group are split into normal code elements, and the last 3 binary code elements of each key node code group are split into key code elements.

[0033] The key node code groups are dynamically combined in groups of three to form a key node matrix. The normal code elements in each key node matrix are then combined into a matrix of three rows and four columns (normal sub-matrix), and the key code elements are combined into a matrix of three rows and three columns (key sub-matrix).

[0034] Perform modulo-2 addition on the key node matrix, monitor the key submatrix until the key submatrix is ​​transformed into an identity matrix, and mark the transformed key node matrix as the front-end node matrix;

[0035] Upload all front-end node matrices to the node matrix set, number the node matrix set, and associate the obtained node matrix set with the corresponding virtual resource node.

[0036] A further improvement to the technical solution of this invention lies in that the process of obtaining the resource code set includes:

[0037] The acquired comprehensive resource data is subjected to state transition to obtain comprehensive resource signals;

[0038] The comprehensive resource signal is adjusted and extracted based on the obtained polymorphic adjustment coefficients to obtain the comprehensive resource coefficients;

[0039] The obtained comprehensive resource coefficients are converted into symbols to obtain comprehensive resource stream segments;

[0040] The obtained integrated resource flow segments are grouped within segments to obtain integrated resource code groups;

[0041] Based on the obtained integrated resource stream segments, the obtained integrated resource code groups are statistically combined to obtain a resource code group set.

[0042] A further improvement to the technical solution of this invention lies in that the process of obtaining the virtual resource node graph includes:

[0043] Virtual resource nodes are virtually combined based on the natural region plan map to obtain a virtual resource space map;

[0044] The virtual monitoring nodes are stored and matched based on the node keywords of the virtual resource nodes to obtain the matching location nodes;

[0045] The virtual resource nodes are virtually located based on the obtained matching location nodes to obtain a virtual resource node graph.

[0046] A further improvement to the technical solution of this invention lies in that the process of obtaining matching node resources and polymorphic matching node resources includes:

[0047] Based on the obtained node matrix set, the resource code set is matched and extracted to obtain the extraction status code. The obtained extraction status code is then grouped by status to obtain the matching status code and the non-matching status code.

[0048] The virtual resource nodes are matched and stored according to the obtained matching status codes to obtain the matching node resources. Multi-sided matching is performed on the obtained non-matching status codes to obtain polymorphic matching node resources.

[0049] A further improvement to the technical solution of this invention lies in that the process of grouping the extracted status codes into states includes:

[0050] The obtained node matrix set and resource code group set are integrated, and an initial status code is assigned to each resource code group. The status code is used to record the matching status (matching success or failure). A matching result table is initialized to record the matching node number and status of each resource code group. The node matrix set contains multiple front-end node matrices, each matrix corresponding to a virtual resource node. The resource code group set contains multiple resource code groups, and each code group is a discrete data unit.

[0051] Features including code group length, binary mode and frequency are extracted for each resource code group, and features including matrix dimension and key code element mode are extracted for each node matrix. The matching degree between each resource code group and each node matrix is ​​calculated based on feature similarity.

[0052] Based on the calculated matching degree, the node matrix with the highest matching degree is selected for each resource code group. If the matching degree is higher than the preset threshold, the matching is considered successful, the resource code group is assigned to the node matrix, and the status code is marked as "match successful". If the matching degree is lower than the preset threshold, the status code is marked as "mismatch". Then, the extracted status code and the matching result table are obtained. The extracted status code contains the matching status of each resource code group, and the matching result table records the node matrix number assigned to each resource code group.

[0053] The system iterates through and extracts status codes, filters out resource code groups with a status of "successful match", and assigns resource code groups to corresponding virtual resource nodes according to the matching result table. For each successfully matched resource code group, it stores it in the corresponding virtual resource node, updates the status of the virtual resource node, records the stored resource code group information, and thus obtains the matching node resource, which is the resource code group stored on the virtual resource node.

[0054] A further improvement to the technical solution of this invention lies in that the process of performing multilateral matching on mismatched status codes includes:

[0055] Iterate through and extract status codes, filter out resource code groups with a status of "mismatch", and mark the resource code groups as resource code groups to be matched. For each mismatched resource code group, recalculate its matching degree with all unloaded virtual resource nodes. Unloaded virtual resource nodes refer to nodes whose storage capacity has not reached the upper limit. For each mismatched resource code group, try to allocate it to unloaded virtual resource nodes in descending order of matching degree. If a node with a matching degree higher than the preset threshold is found, the resource code group is allocated to that node and the status code is updated to "match successful". If all unloaded nodes cannot match, the resource code group is marked as "unmatchable".

[0056] For resource code groups that have been successfully matched on multiple sides, they are stored in the corresponding virtual resource nodes, the status of the virtual resource nodes is updated, the information of the stored resource code groups is recorded, and thus the polymorphic matching node resources are obtained, which are the resource code groups stored on the virtual resource nodes through multiple matching.

[0057] Due to the adoption of the above technical solution, the technical progress achieved by this invention compared to the prior art is as follows:

[0058] 1. This invention provides a method for constructing distributed storage virtual nodes for a natural resource information system based on blockchain. By constructing virtual resource nodes, natural resource data is stored in these virtual nodes, achieving effective data isolation and preventing unauthorized access or tampering of data, thereby greatly improving data security and privacy. At the same time, based on the distributed storage characteristics of blockchain, even if some nodes fail, the data can be recovered from other nodes, ensuring data integrity and reliability.

[0059] 2. This invention provides a method for constructing distributed storage virtual nodes for a blockchain-based natural resource information system. By constructing a natural area plan map, dividing the natural resource area into blocks, and setting up a capture and extraction terminal to collect comprehensive resource data, it helps to achieve comprehensive monitoring and precise management of natural resources, optimize resource allocation and utilization, and at the same time, the introduction of virtual nodes makes data storage more flexible and efficient, and reduces the management cost of hardware resources.

[0060] 3. This invention provides a method for constructing distributed storage virtual nodes for a natural resource information system based on blockchain. By extracting features and converting code elements from node keywords, key node code groups can be generated. These code groups can then be dynamically combined and exchanged to form a front-end node matrix. This processing method can significantly improve the efficiency and accuracy of data processing, enabling the natural resource information system to respond to query and update requests more quickly. Attached Figure Description

[0061] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0062] Figure 1 This is a schematic diagram of the present invention. Detailed Implementation

[0063] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0064] like Figure 1 As shown, this invention provides a method for constructing distributed storage virtual nodes for a blockchain-based natural resource information system, comprising the following steps:

[0065] Step S1: Construct a natural area plan and collect comprehensive resource data;

[0066] Step S2: Construct virtual resource nodes to extract key resources, obtain node keywords, perform state transitions on node keywords to obtain key node signals, set adjustment modal coefficients for dynamic adjustment and parameter extraction to obtain dynamic intervals, perform coefficient division and hierarchical verification on adjustment modal coefficients according to dynamic intervals to obtain polymorphic adjustment coefficients, and use polymorphic adjustment coefficients to adjust and extract key node signals to obtain key node coefficients.

[0067] Step S3: Perform symbol conversion and intra-segment grouping on the obtained key node coefficients to obtain key node code groups. Dynamically combine and exchange the key node code groups to obtain the front-end node matrix. Statistically combine the front-end node matrix to obtain the node matrix set.

[0068] Step S4: Perform state transition and adjustment extraction on the obtained comprehensive resource data to obtain comprehensive resource coefficients. Perform symbol conversion and intra-segment grouping on the comprehensive resource coefficients to obtain resource code sets. Perform virtual combination and virtual positioning on the virtual resource nodes according to the natural area plan map to obtain a virtual resource node map. Perform matching extraction on the resource code sets according to the node matrix set to obtain extracted status codes. Perform matching storage and multilateral matching through the extracted status codes to obtain matched node resources and polymorphic matched node resources.

[0069] It should be further explained that, in the specific implementation process, the process of collecting comprehensive resource data includes:

[0070] The natural resource area is divided into blocks to obtain natural monitoring sites. The obtained natural monitoring sites are marked and denoted as i, where i = 1, 2, 3, ..., v1, and v1 is a positive integer.

[0071] Construct a natural area plan based on the natural resource areas, and mark the obtained natural monitoring sites at the corresponding locations on the natural area plan;

[0072] Based on the obtained natural monitoring sites, a capture and extraction terminal is set up, and data is collected from the natural monitoring sites through the capture and extraction terminal to obtain comprehensive resource data;

[0073] The comprehensive resource data includes spatial location data, natural resource attribute data, resource dynamic change information, and natural resource correlation data;

[0074] Furthermore, the spatial location data includes geographic coordinates, administrative divisions, and topography; the natural resource attribute data includes the type, quantity, quality, utilization status, and ownership information of natural resources; the resource dynamic change information includes resource growth, reduction, and migration; and the natural resource correlation data includes the correlation data between natural resources and their environmental, economic, and social factors.

[0075] Virtual resource nodes are constructed based on the obtained natural area plan, and transmission links between virtual resource nodes are established.

[0076] Furthermore, the virtual resource node is used to store comprehensive resource data;

[0077] Set up an inquiry terminal based on the obtained virtual resource nodes, and issue resource survey commands through the inquiry terminal;

[0078] Based on the obtained resource survey instructions, key resources are extracted from virtual resource nodes to obtain node keywords;

[0079] It should be further explained that, in the specific implementation process, the resource survey instruction indicates the scope of storage of comprehensive resource data on the virtual resource node, including but not limited to the type, type, and storage volume of comprehensive resource data that the virtual resource node can store, comprehensive resource data for a specific time, and comprehensive resource data for a designated natural monitoring location; for example, the resource survey instruction is to store natural resource data of type A; in particular, the issued resource survey instruction limits the scope of comprehensive resource data that the virtual resource node can store, and each virtual resource node includes an query terminal;

[0080] Furthermore, the key resource extraction refers to extracting keywords based on the obtained resource survey instructions to obtain the keywords corresponding to the virtual resource nodes; for example, if the resource survey instructions are to store natural resource data of type A, then the node keywords are natural resource data of type A.

[0081] The obtained node keywords are state-transformed using digital signal encoding rules. The node keywords are input in text form and encoded using ASCII code. Each character is converted into its corresponding digital code, resulting in a digital sequence that represents the digital signal of the node keyword.

[0082] The digital sequence is normalized so that its value ranges between 0 and 1, and the normalized digital signal is output. The digital signal is then subjected to a Discrete Fourier Transform to convert it from the time domain to the frequency domain, and the frequency domain signal is output. The amplitude spectrum and phase spectrum of the frequency domain signal are then extracted to obtain the node key signal in the frequency domain form. The amplitude spectrum represents the amplitude of the signal at different frequencies, and the phase spectrum represents the phase of the signal at different frequencies.

[0083] Adjust the modal coefficients based on the obtained key node signals;

[0084] The signal characteristics of key node signals in frequency domain form are analyzed, the amplitude spectrum of the signal is calculated, its frequency range is determined, the cutoff frequency of the signal (i.e. the boundary of the frequency range in which the signal energy is mainly concentrated) is determined based on the amplitude spectrum, the bandwidth of the signal is calculated, i.e. the frequency range between the cutoff frequencies, and the obtained frequency range, cutoff frequency and bandwidth are output. Then, a bandpass filter is selected and the filter parameters are adjusted to output the modal coefficients.

[0085] Choose the variational mode decomposition method, adjust the number of modes and center frequency parameters to optimize the decomposition effect, control and adjust the mode coefficients to perform scaling and translation transformations, and record the distance of scaling and translation transformations, which are the dynamic parameters;

[0086] Based on the obtained dynamic parameters, the adjustment mode coefficients are extracted to obtain the dynamic interval, and the obtained dynamic interval is marked as b;

[0087] The modal coefficients are divided into multiple levels based on the dynamic interval. Weights are assigned based on the size of the dynamic interval, and the coefficient weight θ of each level is calculated to obtain the modal levels. The obtained modal levels are labeled as MT, where... θ represents the coefficient weight, E represents the dynamic parameter, and ΔF represents the frequency range of the node's key signal, that is, the range between the minimum and maximum frequency values ​​of the node's key signal.

[0088] The coefficient division is based on the obtained calculation formula, i.e. To obtain the modal hierarchy for adjusting the modal coefficients;

[0089] Each modal level is calibrated to ensure that its corresponding modal coefficients are uniformly distributed within the frequency range and bandwidth, and the parameters of each modal level are adjusted to meet the signal characteristic requirements to obtain multimodal modal coefficients. Level calibration means dividing the modal coefficients equally according to the number of obtained modal levels to obtain multimodal modal coefficients of equal length.

[0090] By combining the polymorphic adjustment coefficients and the key node signals, the polymorphic adjustment coefficients and the key node signals are convolved to obtain the interval convolution coefficients. The interval convolution coefficients are summed to obtain the node adjustment coefficients. Then, the node adjustment coefficients are filtered by the response adjustment window to extract the key node coefficients.

[0091] The obtained polymorphic adjustment coefficients are combined at nodes to obtain combined adjustment coefficients. The combined adjustment coefficients are obtained by combining three polymorphic adjustment coefficients and then performing intra-group transformations.

[0092] Set the response adjustment window based on the obtained combined adjustment coefficients, and label the obtained response adjustment window as P, where, Z 多 Let Y1, Y2, and Y3 represent the three polymorphic regulation coefficients within the combined regulation coefficient, and Y1 = β1 * Z. 多 Y2=β2*Z 多 Y3 = β3 * Z 多 β1, β2, and β3 are transformation factors, and β1≠β2≠β3;

[0093] Based on the obtained response adjustment window, feature filtering is performed on the node adjustment coefficients to obtain the key node coefficients;

[0094] Furthermore, the feature filtering means extracting the node adjustment coefficients through the response adjustment window, that is, convolving the corresponding positions of the node adjustment coefficients through the response adjustment window to obtain the window node coefficients, until all node adjustment coefficients are convolved with the response adjustment window, taking the logarithmic value of the obtained window node coefficients to obtain the logarithmic window coefficients, and performing an inverse discrete cosine transform on the obtained logarithmic window coefficients to obtain the key node coefficients.

[0095] Perform node matching on the obtained key node coefficients and virtual resource nodes, and associate the successfully matched virtual resource nodes with the corresponding key node coefficients.

[0096] The process of obtaining the node matrix set includes:

[0097] Obtain the key node coefficients in continuous numerical form, determine the quantization interval based on the numerical range of the key node coefficients, select the quantization bit depth, map the key node coefficients to the quantization level, and obtain the quantized discrete values.

[0098] Each quantized value is converted into an n-bit binary code, and all binary code are concatenated into a continuous data stream to obtain a data stream consisting of several binary code segments, which is the key node stream segment.

[0099] The critical node stream segments are grouped into groups of 7 binary code elements to obtain critical node code groups. If the last group has less than 7 code elements, the groups are padded forward until each group has 7 binary code elements, thus obtaining critical node code groups.

[0100] The key node code group is cyclically split to obtain normal code elements and key code elements. Specifically, the first 4 binary code elements of each key node code group are split into normal code elements, and the last 3 binary code elements of each key node code group are split into key code elements.

[0101] The key node code groups are dynamically combined in groups of three to form a key node matrix. The normal code elements in each key node matrix are then combined into a matrix of three rows and four columns (normal sub-matrix), and the key code elements are combined into a matrix of three rows and three columns (key sub-matrix).

[0102] Perform modulo-2 addition on the key node matrix, monitor the key submatrices until the key submatrices are transformed into identity matrices, and mark the transformed key node matrix as the front-end node matrix. Let R be the obtained key node matrix. Then R can be expressed as... Where Q represents the normal submatrix and S represents the key submatrix;

[0103] Upload all front-end node matrices to the node matrix set, number the node matrix set, associate the obtained node matrix set with the corresponding virtual resource node, and label the obtained front-end node matrix as R1. j j represents the number of the front node matrix in the node matrix set, j = 1, 2, 3, ..., v2, where v2 is a positive integer.

[0104] The process of obtaining a resource code set includes:

[0105] The obtained comprehensive resource data is preprocessed, including normalization and denoising, to unify the numerical range, remove noise or outliers, and ensure the accuracy of the data. The preprocessed data is then subjected to discrete Fourier transform to convert it from the time domain to the frequency domain. The amplitude spectrum and phase spectrum of the frequency domain signal are calculated to extract frequency features. The extracted features are then combined into a feature vector, which serves as the comprehensive resource signal after state transformation.

[0106] The modal coefficients are convolved with the comprehensive resource signal to extract specific frequency features of the signal. The convolved signal is then used to extract features, calculate its energy, mean and variance, and finally combine the extracted features into comprehensive resource coefficients.

[0107] Based on the numerical range of the comprehensive resource coefficient, the quantization interval is determined, the quantization bit number is selected, the comprehensive resource coefficient is mapped to the quantization level, the quantized discrete value of the comprehensive resource coefficient is obtained, and then the quantized discrete value of each comprehensive resource coefficient is converted into n-bit binary code. At the same time, all binary code are concatenated into a continuous data stream to obtain a data stream composed of several binary code, which is the comprehensive resource stream segment.

[0108] Select an appropriate grouping length according to system requirements, and group the integrated resource stream segments according to the selected length. If the last group has less than 7 binary code elements, add more to the previous group until each group has 7 binary code elements. Combine all resource code groups statistically and number the resource code group sets to form resource code group sets.

[0109] The process of obtaining a virtual resource node graph includes:

[0110] Based on the number of blocks in the natural area plan, initialize the same number of virtual resource nodes. Each virtual resource node corresponds to a natural monitoring site, which is used to store the natural resource data of the block. Assign a unique identifier to each virtual resource node, set the storage capacity and data type attributes of the virtual resource nodes to ensure that they can meet the data storage requirements of the corresponding block, and obtain the initialized set of virtual resource nodes.

[0111] Based on the positional relationship of blocks in the natural area plan, virtual resource nodes are mapped to virtual space to ensure that the position of virtual resource nodes in virtual space corresponds one-to-one with the position of natural monitoring sites in the natural area plan. All virtual resource nodes are combined according to the geographical distribution of their corresponding blocks to form a virtual resource space map. The structure of the virtual resource space map should be consistent with the geographical structure of the natural area plan.

[0112] Based on the node keywords (data type, storage range, etc.) of the virtual resource nodes, the virtual resource nodes are classified. The keywords of each virtual resource node are matched with the resource type of the natural monitoring site to determine its storage location. For each virtual resource node, based on the keyword matching results, its corresponding virtual monitoring site is found, and then the virtual resource node is marked as a node that matches the virtual monitoring site.

[0113] In the virtual resource space map, virtual resource nodes are precisely located based on the positional relationship of virtual monitoring sites. Each virtual resource node is placed at its corresponding virtual monitoring site location, and all virtual resource nodes are integrated according to their positions in the virtual space to form a virtual resource node map. The virtual resource node map should clearly show the location, attributes, and relationships with other nodes of each node.

[0114] The process of obtaining matching node resources and polymorphic matching node resources includes:

[0115] The obtained node matrix set and resource code group set are integrated, and an initial status code is assigned to each resource code group. The status code is used to record the matching status (matching success or failure). A matching result table is initialized to record the matching node number and status of each resource code group. The node matrix set contains multiple front-end node matrices, each matrix corresponding to a virtual resource node. The resource code group set contains multiple resource code groups, and each code group is a discrete data unit.

[0116] Features including code group length, binary mode and frequency are extracted for each resource code group, and features including matrix dimension and key code element mode are extracted for each node matrix. The matching degree between each resource code group and each node matrix is ​​calculated based on feature similarity.

[0117] Based on the calculated matching degree, the node matrix with the highest matching degree is selected for each resource code group. If the matching degree is higher than the preset threshold, the matching is considered successful, the resource code group is assigned to the node matrix, and the status code is marked as "match successful". If the matching degree is lower than the preset threshold, the status code is marked as "mismatch". Then, the extracted status code and the matching result table are obtained. The extracted status code contains the matching status of each resource code group, and the matching result table records the node matrix number assigned to each resource code group.

[0118] Iterate through and extract status codes, filter out resource code groups with a status of "match successful", allocate resource code groups to the corresponding virtual resource nodes according to the matching result table, store each successfully matched resource code group in the corresponding virtual resource node, update the status of the virtual resource node, record the stored resource code group information, and thus obtain the matching node resources, which are the resource code groups stored on the virtual resource nodes.

[0119] Iterate through and extract status codes, filter out resource code groups with a status of "mismatch", and mark the resource code groups as resource code groups to be matched. For each mismatched resource code group, recalculate its matching degree with all unloaded virtual resource nodes. Unloaded virtual resource nodes refer to nodes whose storage capacity has not reached the upper limit. For each mismatched resource code group, try to allocate it to unloaded virtual resource nodes in descending order of matching degree. If a node with a matching degree higher than the preset threshold is found, the resource code group is allocated to that node and the status code is updated to "match successful". If all unloaded nodes cannot be matched, the resource code group is marked as "unmatchable".

[0120] For resource code groups that have been successfully matched on multiple sides, they are stored in the corresponding virtual resource nodes, the status of the virtual resource nodes is updated, the information of the stored resource code groups is recorded, and thus the polymorphic matching node resources are obtained, which are the resource code groups stored on the virtual resource nodes through multiple matching.

[0121] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to any specific implementation. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A method for constructing distributed storage virtual nodes for a blockchain-based natural resource information system, characterized in that: Includes the following steps: Step S1: Construct a natural area plan and collect comprehensive resource data; Step S2: Construct virtual resource nodes to extract key resources, obtain node keywords, perform state transitions on node keywords to obtain key node signals, set adjustment modal coefficients for dynamic adjustment and parameter extraction to obtain dynamic intervals, perform coefficient division and hierarchical verification on adjustment modal coefficients according to dynamic intervals to obtain polymorphic adjustment coefficients, and use polymorphic adjustment coefficients to adjust and extract key node signals to obtain key node coefficients. Step S3: Perform symbol conversion and intra-segment grouping on the obtained key node coefficients to obtain key node code groups. Dynamically combine and exchange the key node code groups to obtain the front-end node matrix. Statistically combine the front-end node matrix to obtain the node matrix set. Step S4: Perform state transition and adjustment extraction on the obtained comprehensive resource data to obtain comprehensive resource coefficients. Perform symbol conversion and intra-segment grouping on the comprehensive resource coefficients to obtain resource code sets. Perform virtual combination and virtual positioning on the virtual resource nodes according to the natural area plan map to obtain a virtual resource node map. Perform matching extraction on the resource code sets according to the node matrix set to obtain extracted status codes. Perform matching storage and multilateral matching through the extracted status codes to obtain matched node resources and polymorphic matched node resources.

2. The method for constructing distributed storage virtual nodes for a blockchain-based natural resource information system according to claim 1, characterized in that: The process of collecting comprehensive resource data includes: The natural resource area is divided into blocks to obtain natural monitoring sites, and a natural area plan is constructed based on the natural resource area. Based on the obtained natural monitoring sites, a capture and extraction terminal is set up to collect data from the natural monitoring sites and obtain comprehensive resource data.

3. The method for constructing distributed storage virtual nodes for a blockchain-based natural resource information system according to claim 2, characterized in that: The process of obtaining key signals from nodes includes: Virtual resource nodes are constructed based on the obtained natural area plan; Set up an inquiry terminal based on the obtained virtual resource nodes, and issue resource survey commands through the inquiry terminal; Based on the obtained resource survey instructions, key resources are extracted from virtual resource nodes to obtain node keywords; The obtained node keywords are state-transformed using digital signal encoding rules. The node keywords are input in text form and encoded using ASCII code. Each character is converted into its corresponding digital code, resulting in a digital sequence that represents the digital signal of the node keyword. The digital sequence is normalized so that its value ranges between 0 and 1, and the normalized digital signal is output. The digital signal is then subjected to a discrete Fourier transform to convert it from the time domain to the frequency domain, and the frequency domain signal is output. The amplitude spectrum and phase spectrum of the frequency domain signal are then extracted to obtain the node key signal in frequency domain form.

4. The method for constructing distributed storage virtual nodes for a blockchain-based natural resource information system according to claim 3, characterized in that: The process of obtaining the key node coefficients includes: The signal characteristics of key node signals in frequency domain form are analyzed, the amplitude spectrum of the signal is calculated, its frequency range is determined, the cutoff frequency of the signal is determined based on the amplitude spectrum, the bandwidth of the signal is calculated, that is, the frequency range between the cutoff frequencies, and the obtained frequency range, cutoff frequency and bandwidth are output. Then, a bandpass filter is selected and the filter parameters are adjusted to output the modal coefficients. Choose the variational mode decomposition method, adjust the number of modes and center frequency parameters to optimize the decomposition effect, control and adjust the mode coefficients to perform scaling and translation transformations, and record the distance of scaling and translation transformations, which are the dynamic parameters; Based on the obtained dynamic parameters, the adjustment mode coefficients are extracted to obtain the dynamic interval, and the obtained dynamic interval is marked as b; The modal coefficients are divided into multiple levels based on the dynamic interval. Weights are assigned based on the size of the dynamic interval, and the coefficient weight θ of each level is calculated to obtain the modal level. Each modal level is calibrated to ensure that its corresponding modal coefficients are uniformly distributed within the frequency range and bandwidth, and the parameters of each modal level are adjusted to meet the signal characteristic requirements to obtain the multimodal modal coefficients. By combining the polymorphic adjustment coefficients and the key node signals, the polymorphic adjustment coefficients and the key node signals are convolved to obtain the interval convolution coefficients. The interval convolution coefficients are then summed to obtain the node adjustment coefficients. Finally, the node adjustment coefficients are filtered by the response adjustment window to extract the key node coefficients.

5. The method for constructing distributed storage virtual nodes for a blockchain-based natural resource information system according to claim 4, characterized in that: The process of obtaining the node matrix set includes: Obtain the key node coefficients in continuous numerical form, determine the quantization interval based on the numerical range of the key node coefficients, select the quantization bit depth, map the key node coefficients to the quantization level, and obtain the quantized discrete values. Each quantized value is converted into an n-bit binary code, and all binary code are concatenated into a continuous data stream to obtain a data stream consisting of several binary code segments, which is the key node stream segment. The critical node stream segments are grouped into groups of 7 binary code elements to obtain critical node code groups. If the last group has less than 7 code elements, the groups are padded forward until each group has 7 binary code elements, thus obtaining critical node code groups. The key node code group is cyclically split to obtain normal code elements and key code elements. Specifically, the first 4 binary code elements of each key node code group are split into normal code elements, and the last 3 binary code elements of each key node code group are split into key code elements. The key node code groups are dynamically combined in groups of three to form a key node matrix. The normal code elements in each key node matrix are arranged into a matrix of three rows and four columns, and the key code elements are arranged into a matrix of three rows and three columns. Perform modulo-2 addition on the key node matrix, monitor the key submatrix until the key submatrix is ​​transformed into an identity matrix, and mark the transformed key node matrix as the front-end node matrix; Upload all front-end node matrices to the node matrix set, number the node matrix set, and associate the obtained node matrix set with the corresponding virtual resource node.

6. The method for constructing distributed storage virtual nodes for a blockchain-based natural resource information system according to claim 5, characterized in that: The process of obtaining a resource code set includes: The acquired comprehensive resource data is subjected to state transitions to obtain comprehensive resource signals; The comprehensive resource signal is adjusted and extracted based on the obtained polymorphic adjustment coefficients to obtain the comprehensive resource coefficients; The obtained comprehensive resource coefficients are converted into symbols to obtain comprehensive resource stream segments; The obtained integrated resource flow segments are grouped within segments to obtain integrated resource code groups; Based on the obtained integrated resource stream segments, the obtained integrated resource code groups are statistically combined to obtain a resource code group set.

7. The method for constructing distributed storage virtual nodes for a blockchain-based natural resource information system according to claim 6, characterized in that: The process of obtaining a virtual resource node graph includes: Virtual resource nodes are virtually combined based on the natural region plan map to obtain a virtual resource space map; The virtual monitoring nodes are stored and matched based on the node keywords of the virtual resource nodes to obtain the matching location nodes; The virtual resource nodes are virtually located based on the obtained matching location nodes to obtain a virtual resource node graph.

8. The method for constructing distributed storage virtual nodes for a blockchain-based natural resource information system according to claim 7, characterized in that: The process of obtaining matching node resources and polymorphic matching node resources includes: Based on the obtained node matrix set, the resource code set is matched and extracted to obtain the extraction status code. The obtained extraction status code is then grouped by status to obtain the matching status code and the non-matching status code. The virtual resource nodes are matched and stored according to the obtained matching status codes to obtain the matching node resources. Multi-sided matching is performed on the obtained non-matching status codes to obtain polymorphic matching node resources.

9. The method for constructing distributed storage virtual nodes for a blockchain-based natural resource information system according to claim 8, characterized in that: The process of grouping extracted status codes into states includes: Integrate the obtained node matrix set and resource code group set, assign an initial status code to each resource code group, the status code is used to record the matching status, and initialize a matching result table to record the matching node number and status of each resource code group; Features including code group length, binary mode and frequency are extracted for each resource code group, and features including matrix dimension and key code element mode are extracted for each node matrix. The matching degree between each resource code group and each node matrix is ​​calculated based on feature similarity. Based on the calculated matching degree, the node matrix with the highest matching degree is selected for each resource code group. If the matching degree is higher than the preset threshold, the matching is considered successful, the resource code group is assigned to the node matrix, and the status code is marked as "match successful". If the matching degree is lower than the preset threshold, the status code is marked as "mismatch". Then, the extracted status code and the matching result table are obtained. The extracted status code contains the matching status of each resource code group, and the matching result table records the node matrix number assigned to each resource code group. Iterate through and extract status codes, filter out resource code groups with a status of "match successful", allocate resource code groups to the corresponding virtual resource nodes according to the matching result table, store each successfully matched resource code group in the corresponding virtual resource node, update the status of the virtual resource node, record the stored resource code group information, and thus obtain the matching node resource, which is the resource code group stored on the virtual resource node.

10. The method for constructing distributed storage virtual nodes for a blockchain-based natural resource information system according to claim 9, characterized in that: The process of performing a multilateral match on a mismatched status code includes: Iterate through and extract status codes, filter out resource code groups with a status of "mismatch", and mark the resource code groups as resource code groups to be matched. For each mismatched resource code group, recalculate its matching degree with all unloaded virtual resource nodes. Unloaded virtual resource nodes refer to nodes whose storage capacity has not reached the upper limit. For each mismatched resource code group, try to allocate it to unloaded virtual resource nodes in descending order of matching degree. If a node with a matching degree higher than the preset threshold is found, the resource code group is allocated to that node and the status code is updated to "match successful". If all unloaded nodes cannot be matched, the resource code group is marked as "unmatchable". For resource code groups that have been successfully matched in a multilateral manner, they are stored in the corresponding virtual resource nodes, the status of the virtual resource nodes is updated, the stored resource code group information is recorded, and thus the polymorphic matching node resources are obtained, which are the resource code groups stored on the virtual resource nodes through multilateral matching.

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