Digital asset information management method and management system based on block chain technology

Abstract

The invention discloses a digital asset information management method and management system based on a block chain technology. According to the digital asset information management method and management system based on the block chain technology, block chain planning of a block chain can be realized by using different connection modes based on a topological structure of a block chain distribution network. A coupling difference branch diagram is formed based on a distribution topological diagram and a processing equipment distribution characteristic diagram, and application and importance of equipment distribution in different fields can be realized. According to the method, the texture of the coupling difference branch diagram is extracted, and selection and combination can be performed according to specific application scenes and requirements, so that efficient and accurate equipment data acquisition is realized, and effective analysis of data in the block chain distribution network is further realized. The problem of current digital asset information management difficulty is solved. Based on the perturbation function, a digital asset information management distribution topology is formed, and the security and transparency of block chain data are improved.

Description

Technical Field

[0001] This application relates to the field of blockchain technology. Specifically, it relates to a method and system for managing digital asset information based on blockchain technology. Background Art

[0002] Blockchain is a block-chain storage, secure and trustworthy decentralized distributed ledger that combines technologies such as distributed storage, peer-to-peer transmission, consensus mechanism, and cryptography. It records transactions and information through a continuously growing data block chain to ensure the security and transparency of data.

[0003] Traditional blockchains can be used for digital assets and manage digital asset information. However, traditional blockchains are difficult to handle complex blockchain path parsing. In fact, based on distributed storage, peer-to-peer transmission, consensus mechanism, and cryptography, how to implement blockchain path planning, application, and parsing of blockchains is a current difficulty in digital asset information management. Therefore, it is necessary to propose a method and system for managing digital asset information based on blockchain technology to address the deficiencies of low security and transparency of traditional blockchain data. Summary of the Invention

[0004] The content part of this application is used to briefly introduce concepts, which will be described in detail in the subsequent specific implementation part. The content part of this application is not intended to identify the key features or essential features of the claimed technical solution, nor is it intended to limit the scope of the claimed technical solution.

[0005] To solve the technical problems mentioned in the above background art part, some embodiments of this application provide a method for managing digital asset information based on blockchain technology, including:

[0006] Establish a distribution topology map of the blockchain distribution network;

[0007] Based on the distribution topology map and the distribution feature map of processing devices, form a coupled difference branch map;

[0008] Extract the texture of the coupled difference branch map;

[0009] Obtain a perturbation noise map and a trunk vein map;

[0010] Based on a perturbation function, analyze the perturbation noise map;

[0011] Incorporate the analysis result into the trunk vein map;

[0012] Obtain a link map of the blockchain distribution network;

[0013] Based on the link map of the blockchain distribution network, form a distribution topology for digital asset information management.

[0014] Further, based on the distribution type of the blockchain distributed network, determine whether the blockchain distributed network is a star topology structure;

[0015] If the blockchain distributed network is a star topology structure, determine the distribution topology graph of the star topology structure;

[0016] If the blockchain distributed network is not a star topology structure, determine whether the blockchain distributed network is a mesh topology structure;

[0017] If the blockchain distributed network is a mesh topology structure, determine the distribution topology graph of the mesh topology structure.

[0018] Further, based on the type of digital asset information of the blockchain, determine that the processing device is one or more of an Internet of Things platform device, a network cabinet device, and an Android device system.

[0019] Further, based on the data collected by the device controller, determine the first distribution feature map of the processing device;

[0020] Using the data acquisition software of the device, determine the second distribution feature map of the processing device;

[0021] Based on the distributed data acquisition, determine the third distribution feature map of the processing device;

[0022] Incorporate the first distribution feature map of the processing device, the second distribution feature map of the processing device, and the third distribution feature map of the processing device into the distribution feature map of the processing device.

[0023] Further, extract the features of the distribution topology graph;

[0024] Obtain the main line features of the distribution topology graph;

[0025] Extract the features of the distribution feature map of the processing device;

[0026] Obtain the main line features of the distribution feature map of the processing device;

[0027] Perform feature sorting on the main line features of the distribution topology graph and the main line features of the distribution feature map of the processing device;

[0028] Calculate the affine matrix of the sorted features;

[0029] Based on the affine matrix, form an interpolation projection transformation graph.

[0030] Further, perform image fusion on the interpolation projection transformation graph to generate a coupled difference branch graph;

[0031] Obtain the gray-level co-occurrence matrix of the coupled difference branch graph;

[0032] Based on the gray-level co-occurrence matrix, determine the information features of the texture of the coupled difference branch diagram;

[0033] Utilize the information features of the texture of the coupled difference branch diagram to obtain the texture of the coupled difference branch diagram.

[0034] Furthermore, reduce the dimension of the texture of the coupled difference branch diagram;

[0035] Obtain the longitudinal dimension based on the pixel size and the transverse dimension based on the pixel size;

[0036] Select the longitudinal dimension based on the pixel size;

[0037] Based on the perturbation function, analyze each longitudinal row of the longitudinal dimension;

[0038] Obtain the first analytical expression regarding the longitudinal dimension;

[0039] Select the transverse dimension based on the pixel size;

[0040] Based on the perturbation function, analyze each transverse column of the transverse dimension;

[0041] Obtain the second analytical expression regarding the transverse dimension;

[0042] Incorporate the first analytical expression and the second analytical expression into the perturbation noise analysis equation.

[0043] Furthermore, utilize the perturbation function to parameterize the equation of the coupled difference branch diagram;

[0044] Obtain the equation of the coupled difference branch diagram;

[0045] Sum up the perturbation noise analysis equations in the equation of the coupled difference branch diagram;

[0046] Obtain the expression equation of the branch and trunk texture diagram;

[0047] Based on the expression equation of the branch and trunk texture diagram, obtain the branch and trunk texture diagram.

[0048] Furthermore, the perturbation function is:

[0049]

[0050] Where x is the position coordinate of the pixel, and i to k are random constants.

[0051] Some embodiments of the present application also provide a digital asset information management system based on blockchain technology, including:

[0052] A processor for executing the digital asset information management system based on blockchain technology;

[0053] A memory, communicatively connected to the processor.

[0054] In summary:

[0055] Establish a distribution topology map of the blockchain distributed network. Based on the topological structure of the blockchain distributed network, the organizational form of the connection method between each node in the blockchain distributed network has different impacts on the performance, reliability, and scalability of the network. Different connection methods can be used to implement the blockchain path planning of the blockchain. Based on the distribution topology map and the distribution feature map of the processing device, a coupling difference branch map is formed, which can realize the application and importance of device distribution in different fields. Extracting the texture of the coupling difference branch map can be selected and combined according to specific application scenarios and requirements to achieve efficient and accurate device data acquisition, and then realize the effective parsing of data in the blockchain distributed network. This solves the current difficulties in digital asset information management. Based on the perturbation function, parse the perturbation noise map, incorporate the parsing result into the branch and trunk texture map, obtain the link map of the blockchain distributed network, and based on the link map of the blockchain distributed network, form the distribution topology of digital asset information management, improving the security and transparency of blockchain data. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] The drawings forming a part of this application are used to provide a further understanding of this application, making other features, objects, and advantages of this application more obvious. The schematic embodiments and descriptions thereof of this application are used to explain this application and do not constitute an improper limitation of this application.

[0057] In addition, throughout the drawings, the same or similar reference numerals denote the same or similar elements. It should be understood that the drawings are schematic and the elements and elements are not necessarily drawn to scale.

[0058] In the drawings:

[0059] Figure 1 is a method flowchart of a digital asset information management method based on blockchain technology in an embodiment of this application.

[0060] Figure 2 is a structural connection diagram of a digital asset information management system based on blockchain technology in an embodiment of this application.

[0061] REFERENCE NUMERALS:

[0062] 100 - Processor; 200 - Memory. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0063] Embodiments of the present application will be described in more detail below with reference to the accompanying drawings. Although some embodiments of the present application are shown in the drawings, it should be understood that the present application can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. On the contrary, these embodiments are provided to more thoroughly and completely understand the present application. It should be understood that the drawings and embodiments of the present application are only for exemplary purposes and are not used to limit the protection scope of the present application.

[0064] In addition, it should be noted that for ease of description, only parts related to the relevant invention are shown in the drawings. Without conflict, the embodiments in the present application and the features in the embodiments can be combined with each other.

[0065] The present application will be described in detail below with reference to the drawings and in combination with embodiments.

[0066] As Figure 1 In one embodiment of the present application, a digital asset information management method based on blockchain technology includes:

[0067] S100, establishing a distribution topology diagram of the blockchain distribution network.

[0068] Specifically, the distributed network structure is a mesh structure formed by computer systems distributed at different locations, and each node has at least two links connected to other nodes. When any one link fails, data packets can pass through other links, so the network has high reliability.

[0069] S200, forming a coupling difference branch diagram based on the distribution topology diagram and the processing device distribution feature diagram.

[0070] Specifically, the topology structure of the network is the organizational form of the connection method between each node in the network. Different topology structures have different impacts on the performance, reliability, and scalability of the network. The network topology structure in this embodiment includes star structure, ring structure, bus structure, distributed structure, tree structure, mesh structure, and honeycomb structure, etc.

[0071] More specifically, the distribution characteristics of the processing device, such as the network cabinet device layout diagram, are used to show the installation and layout methods of network devices inside the cabinet, including standard layout diagram, intensive layout diagram, high-availability layout diagram, centralized layout diagram, and distributed layout diagram, etc.

[0072] S300, extracting the texture of the coupling difference branch diagram.

[0073] S400, obtaining the perturbation noise diagram and the branch and trunk texture diagram.

[0074] Specifically, after determining the fusion position, methods such as fade-in and fade-out are used to fuse the images. Two images in the splicing part are each multiplied by a coefficient that changes with the distance to reduce the splicing traces of the branch and trunk texture map.

[0075] S500, Based on the perturbation function, analyze the perturbation noise map.

[0076] S600, Incorporate the analysis result into the branch and trunk texture map.

[0077] Specifically, there are other algorithms for the analysis algorithm, such as the Multiresolution Spline algorithm. It performs hierarchical decomposition of the image by using a Gaussian operator and fuses the images of each layer on a given curve (generally referring to the overlapping trace), and finally obtains a smooth splicing effect.

[0078] By removing the perturbation noise from the branch and trunk texture map, the link diagram of the blockchain distribution network can be obtained more accurately and transparently.

[0079] S700, Obtain the link diagram of the blockchain distribution network.

[0080] S800, Based on the link diagram of the blockchain distribution network, form the distribution topology of digital asset information management.

[0081] In this embodiment, by establishing the distribution topology diagram of the blockchain distribution network, based on the topological structure of the blockchain distribution network, the organizational form of the connection method between each node in the blockchain distribution network has different impacts on the performance, reliability, and scalability of the network. Different connection methods can be used to implement the blockchain path planning of the blockchain. Based on the distribution topology diagram and the distribution feature diagram of the processing device, a coupled difference branch diagram is formed, which can realize the application and importance of device distribution in different fields. Extracting the texture of the coupled difference branch diagram can be selected and combined according to specific application scenarios and requirements to achieve efficient and accurate device data collection, and then realize the effective analysis of the data in the blockchain distribution network. This solves the current difficulties in digital asset information management. Based on the perturbation function, analyze the perturbation noise map, incorporate the analysis result into the branch and trunk texture map, obtain the link diagram of the blockchain distribution network, and based on the link diagram of the blockchain distribution network, form the distribution topology of digital asset information management, improving the security and transparency of blockchain data.

[0082] In an embodiment of the present application, S100 includes:

[0083] S111, Based on the distribution type of the blockchain distribution network, determine whether the blockchain distribution network is a star topology structure.

[0084] S112, If the blockchain distribution network is a star topology structure, determine the distribution topology diagram of the star topology structure.

[0085] S113. If the blockchain distributed network is not a star topology structure, then determine whether the blockchain distributed network is a mesh topology structure.

[0086] S114. If the blockchain distributed network is a mesh topology structure, then determine the distribution topology map of the mesh topology structure.

[0087] Specifically, the star structure is a common network topology structure, in which all nodes are directly connected to a central node, forming a star shape. The central node acts as the hub of the network and is responsible for forwarding and routing data. The star structure has the advantages of simplicity, easy management and expansion.

[0088] The ring structure is a topology structure that connects nodes in a ring. Each node is directly connected to its adjacent front and rear nodes, forming a closed loop. The ring structure has the advantages of low cost and small transmission delay.

[0089] The distributed structure is a topology structure that disperses network nodes at different locations. Each node can communicate directly with other nodes without passing through a central node. The distributed structure has the advantages of high reliability, flexibility and scalability.

[0090] Based on the distribution types of different blockchain distributed networks, a blockchain distribution topology type with higher working efficiency can be obtained.

[0091] In an embodiment of the present application, S100 further includes:

[0092] S121. Based on the type of digital asset information of the blockchain, determine that the processing device is one or more of an Internet of Things platform device, a network cabinet device, and an Android device system.

[0093] Specifically, device distribution is a broad concept, which can refer to the arrangement and allocation of various different types of devices in different scenarios.

[0094] Since the blockchain can accommodate different device clusters and manage the digital asset information of different device clusters.

[0095] For example, laboratory equipment distribution involves the arrangement of professional equipment, analytical equipment, support equipment and general equipment, which are widely used in clinical and diagnostic laboratories, pharmaceutical and biotechnology fields, and academic institutions.

[0096] The device distribution in the Internet of Things platform is displayed in the form of a map so that users can clearly understand the geographical location and distribution of the devices.

[0097] The distribution of road traffic technical monitoring equipment, these equipment are distributed in Area A, Area XA and Area TA for traffic monitoring.

[0098] Android device system distribution. Android 11 and Android 10 are currently the top two Android system versions in terms of proportion.

[0099] Network cabinet equipment layout. The network cabinet equipment layout diagram is used to show the installation and layout methods of network equipment inside the cabinet, including standard layout diagrams, dense layout diagrams, high-availability layout diagrams, centralized layout diagrams, and distributed layout diagrams, etc.

[0100] In an embodiment of the present application, S100 further includes:

[0101] S131, based on the data collected by the device controller, determine the first distribution feature map of the processing device.

[0102] Specifically, in industrial and Internet of Things applications, sensors are widely used to monitor and collect various physical quantities, such as temperature, pressure, flow, etc. The sensor converts the physical quantity into an electrical signal or other processable signals, which are then collected and analyzed by the system.

[0103] S132, use the data acquisition software of the device to determine the second distribution feature map of the processing device.

[0104] Specifically, device controllers such as programmable logic controllers (PLCs) are widely used in industrial automation control. They can not only monitor the operating status of the device but also record and store this data. By reading the data in the PLC, the operating data of the device can be obtained.

[0105] S133, based on the distributed collected data, determine the third distribution feature map of the processing device.

[0106] Specifically, in a distributed system, data acquisition usually involves collecting data from multiple nodes or devices. This method can effectively process a large amount of data and allows adding a layer of recording interface between the members and the RTI to record more information and reduce the impact on network bandwidth.

[0107] S134, incorporate the first distribution feature map of the processing device, the second distribution feature map of the processing device, and the third distribution feature map of the processing device into the distribution feature map of the processing device.

[0108] These methods can be selected and combined according to specific application scenarios and requirements to achieve effective and accurate device data acquisition, and then achieve the acquisition of digital asset information.

[0109] In an embodiment of the present application, S200 includes:

[0110] S210, extract the features of the distribution topology map.

[0111] S220. Obtain the main features of the distribution topology map.

[0112] Specifically, use an algorithm such as SIFT (Scale-Invariant Feature Transform) to extract feature points in the image. The SIFT algorithm determines the position and scale of feature points by establishing a multi-scale space and extracts rotation-invariant feature descriptors.

[0113] The feature points should be obvious and easy to extract points in the image and be widely distributed in the image to be matched.

[0114] S230. Extract the features of the processing device distribution feature map.

[0115] S240. Obtain the main features of the processing device distribution feature map.

[0116] Specifically, the distribution topology map and the processing device distribution feature map respectively correspond to the server distribution features and the terminal device distribution features.

[0117] S250. Sort the main features of the distribution topology map and the main features of the processing device distribution feature map.

[0118] Specifically, determine the sorting of the images by calculating the matching degree of the feature points.

[0119] This step helps to automatically find the correct order for the images for subsequent stitching.

[0120] S260. Calculate the affine matrix of the sorted features.

[0121] Specifically, calculate the affine transformation matrix between the images according to the corresponding relationship of the feature points between the two images. The affine matrix describes the stretching, rotation, and translation relationships between the images.

[0122] S270. Based on the affine matrix, form an interpolation projection transformation map.

[0123] Specifically, in the processing of projection transformation and subsequent image fusion, interpolation calculation needs to be performed on the coordinate transformation. Bilinear interpolation is a commonly used method, which can effectively weaken the sawtooth edges and the phenomenon of discontinuous gray values in the image.

[0124] In an embodiment of the present application, S300 includes:

[0125] S310. Perform image fusion on the interpolation projection transformation map to generate a coupled difference branch map.

[0126] Specifically, after determining the fusion position, use methods such as fade-in and fade-out to fuse the images. The two images at the splicing part are respectively multiplied by a coefficient, and the coefficient changes with the distance to reduce the splicing trace.

[0127] S320. Obtain the gray-level co-occurrence matrix of the coupled difference branch diagram.

[0128] Specifically, based on the statistical method, the texture information is captured by calculating the frequencies of pixel pairs appearing in different directions and distances in the image. The GLCM can reflect the spatial relationship between pixels and extract features such as the directionality, periodicity, and randomness of the texture.

[0129] S330. Based on the gray-level co-occurrence matrix, determine the information features of the texture of the coupled difference branch diagram.

[0130] Specifically, the information features of the texture of the coupled difference branch diagram include features such as the directionality, periodicity, and randomness of the texture.

[0131] S340. Utilize the information features of the texture of the coupled difference branch diagram to obtain the texture of the coupled difference branch diagram.

[0132] It is worth mentioning that the Local Binary Pattern (LBP). LBP is a simple and effective texture feature extraction method. It generates a binary code by comparing the gray value of a pixel with those of its neighboring pixels, reflecting the texture structure around the pixel point. LBP has rotational invariance and gray-scale invariance and is applicable to various texture analysis tasks.

[0133] Convolutional Neural Network (CNN). The CNN automatically extracts texture information by learning complex features in the image and is applicable to various image processing tasks.

[0134] The Local Binary Pattern and the Convolutional Neural Network can be used to obtain the texture of the coupled difference branch diagram.

[0135] In an embodiment of the present application, S400 includes:

[0136] S411. Reduce the dimension of the texture of the coupled difference branch diagram.

[0137] S412. Obtain the longitudinal dimension based on the pixel size and the transverse dimension based on the pixel size.

[0138] S413. Select the longitudinal dimension based on the pixel size.

[0139] S414. Based on the perturbation function, analyze each longitudinal row of the longitudinal dimension.

[0140] S415. Obtain the first analytical formula regarding the longitudinal dimension.

[0141] S416. Select the transverse dimension based on the pixel size.

[0142] S417. Based on the perturbation function, analyze each transverse column of the transverse dimension.

[0143] S418, obtain the second analytical expression regarding the horizontal dimension.

[0144] S419, incorporate the first analytical expression and the second analytical expression into the perturbation noise analytical equation.

[0145] S421, use the perturbation function to parameterize the equation of the coupled difference branch diagram.

[0146] S422, obtain the equation of the coupled difference branch diagram.

[0147] S423, sum up the perturbation noise analytical equations in the equation of the coupled difference branch diagram.

[0148] S424, obtain the expression equation of the branch and trunk texture diagram.

[0149] S425, obtain the branch and trunk texture diagram based on the expression equation of the branch and trunk texture diagram.

[0150] The said perturbation function is:

[0151]

[0152] where x is the position coordinate of the pixel, and i to k are random constants.

[0153] A noise can be used to simulate a large number of effects. So far, the most varied form in its application is to apply it to the so-called perturbation function, as defined by Perlin, which takes a position x and returns a perturbed scalar value. It is written in series form, and its one-dimensional form can be defined as:

[0154] The summation of the perturbation function is truncated at k, and the truncated range limits the function, ensuring proper anti-aliasing. Consider the difference between the first two terms in the series, namely noise(x) and noise(2x) / 2. The number of noise in the subsequent terms will change twice as fast as the first term, that is, it has twice the frequency and it also has the property that it is half the size of the first term. In addition, its contribution to the final value of the perturbation is also half. The amount of noise added to the series at each level of detail is proportional to the level of detail of the noise and inversely proportional to the frequency of the noise. This is self-similar and similar to the self-similarity obtained through the subdivision of irregular patches. Only at this time, the subdivision does not drive the displacement, but drives the octave variation of the noise, and functions that exhibit the same noise behavior within a certain level range.

[0155] The perturbation function in isolation only shows half the effect. However, plotting the perturbation function directly leads to a homogeneous pattern that cannot be described in a natural form. This is due to the fact that most textures that appear in natural forms contain some non-homogeneous structural features, so they cannot be simulated by perturbations alone.

[0156] The use of perturbation functions does not have to be strictly limited to adjusting the color of an object. Any parameter that affects the appearance of an object can be perturbed, and perturbations can also drive transparency like blockchain.

[0157] like Figure 2 In one embodiment of the present application, a digital asset information management system based on blockchain technology includes:

[0158] The processor 100 is used to execute the digital asset information management method based on blockchain technology.

[0159] The memory 200 is communicatively connected to the processor 100 .

[0160] The processor 100 establishes a distribution topology map of the blockchain distribution network. Based on the topological structure of the blockchain distribution network, the organizational form of the connection mode between each node in the blockchain distribution network has different effects on the performance, reliability and scalability of the network. Different connection methods can be used to realize the blockchain road planning of the blockchain. Based on the distribution topology map and the processing equipment distribution feature map, a coupling difference branch map is formed, which can realize the application and importance of equipment distribution in different fields. The processor 100 extracts the coupling difference branch map texture from the image data of the memory 200, and can select and combine it according to the specific application scenario and requirements to achieve efficient and accurate equipment data collection, thereby realizing the effective analysis of data in the blockchain distribution network. Based on the link map of the blockchain distribution network, a digital asset information management distribution topology is formed, which improves the security and transparency of blockchain data.

[0161] The above description is only some preferred embodiments of the present application and an explanation of the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the embodiments of the present application is not limited to the technical solutions formed by the specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above invention concept. For example, the above features are replaced with (but not limited to) the technical features with similar functions disclosed in the embodiments of the present application to form a technical solution.

Claims

1. A digital asset information management method based on blockchain technology, comprising: Establish a distribution topology map of the blockchain distribution network; Based on the distribution topology map and the processing equipment distribution characteristic map, a coupling difference branch map is formed; Extract coupling difference branch map texture; Obtain disturbance noise map and branch texture map; Analyze the disturbance noise graph based on the disturbance function; Incorporate the analytical results into the branch and trunk pattern diagram; Obtain the link graph of the blockchain distribution network; Based on the link diagram of the blockchain distribution network, a digital asset information management distribution topology is formed.

2. The digital asset information management method based on blockchain technology according to claim 1 is characterized in that: The distribution topology diagram of the blockchain distribution network is established, including: Based on the distribution type of the blockchain distribution network, determine whether the blockchain distribution network is a star topology structure; If the blockchain distribution network is a star topology, determine the distribution topology diagram of the star topology; If the blockchain distribution network is not a star topology, determine whether the blockchain distribution network is a mesh topology; If the blockchain distribution network is a mesh topology structure, then determine the distribution topology diagram of the mesh topology structure.

3. The digital asset information management method based on blockchain technology according to claim 2 is characterized in that: After establishing the distribution topology diagram of the blockchain distribution network, the method includes: Based on the type of digital asset information on the blockchain, it is determined that the processing device is one or more of an Internet of Things platform device, a network cabinet device, and an Android device system.

4. The digital asset information management method based on blockchain technology according to claim 3 is characterized in that: After establishing the distribution topology diagram of the blockchain distribution network, the method further includes: Determine a first distribution characteristic diagram of the processing equipment based on the data collected by the equipment controller; Determine a second distribution characteristic diagram of the processing device using data acquisition software of the device; Determine a third distribution characteristic graph of the processing equipment based on the distributed collected data; The first distribution characteristic graph of processing equipment, the second distribution characteristic graph of processing equipment and the third distribution characteristic graph of processing equipment are incorporated into the processing equipment distribution characteristic graph.

5. The digital asset information management method based on blockchain technology according to claim 4 is characterized in that: The forming of a coupling difference branch graph based on the distribution topology graph and the processing equipment distribution characteristic graph includes: Extract the features of the distribution topology map; Obtain the main line features of the distribution topology map; Extracting features of a processing equipment distribution feature map; Obtaining main line characteristics of a processing equipment distribution characteristic diagram; Perform feature sorting on the main line features of the distribution topology diagram and the main line features of the processing equipment distribution feature diagram; Calculate the affine matrix of the sorted features; Based on the affine matrix, an interpolation projection transformation graph is formed.

6. The digital asset information management method based on blockchain technology according to claim 5 is characterized in that: The step of extracting the coupled difference branch graph texture comprises: Perform image fusion on the interpolation projection transformation image to generate a coupling difference branch graph; Obtain the gray-level co-occurrence matrix of the coupled difference cladogram; Based on the gray-level co-occurrence matrix, the information characteristics of the coupling difference branch map texture are determined; The information feature of the coupled difference branch map texture is used to obtain the coupled difference branch map texture.

7. The digital asset information management method based on blockchain technology according to claim 6 is characterized in that: The obtaining of the disturbance noise map and the branch and trunk texture map comprises: Reduce the dimension of the coupled difference branch graph texture; Obtaining a vertical dimension based on pixel size and a horizontal dimension based on pixel size; Select the vertical dimension based on pixel size; Based on the perturbation function, each vertical row in the vertical dimension is analyzed; Obtain the first analytical expression about the longitudinal dimension; Select the horizontal dimension based on pixel size; Based on the perturbation function, each horizontal column in the horizontal dimension is analyzed; Obtain the second analytical expression about the horizontal dimension; The first analytical expression and the second analytical expression are incorporated into the disturbance noise analytical equation.

8. The digital asset information management method based on blockchain technology according to claim 7 is characterized in that: The obtaining of the disturbance noise map and the branch and trunk texture map also includes: The coupling difference branch diagram is parameterized by equation using perturbation function; Obtain equations for coupled difference branch diagrams; Summing the perturbation noise analytical equations in the equations of the coupled difference branch diagram; Obtain the expression equation of the branch and trunk texture map; Based on the expression equation of the branch-stem grain map, the branch-stem grain map is obtained.

9. The digital asset information management method based on blockchain technology according to claim 8 is characterized in that: The perturbation function is: Among them, x is the position coordinate of the pixel, and i to k are random constants.

10. A digital asset information management system based on blockchain technology, comprising: A processor, configured to execute a digital asset information management method based on blockchain technology as described in any one of claims 1 to 9; A memory is communicatively connected to the processor.

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