JP2021013155A - Method and device for recommending and generating network setting - Google Patents

Abstract

To provide a method and a device for recommending and generating network settings.SOLUTION: A method for automatically generating a recommended network setting for a block chain network includes steps of receiving a descriptive text related to a project that a user inputs through an input device, extracting features related to predetermined characteristics from the descriptive text, calculating feature values for the features, matching the descriptive text with known network settings based on the features and the feature values, selecting the best matching known network settings as recommended network settings for the project, and showing the recommended network settings to the user by an output device.SELECTED DRAWING: Figure 3

Description

The present invention relates to a method and device for recommending and generating network settings, and more particularly to a method and device for automatically recommending or generating network settings for a blockchain network.

Blockchain technology will be realized as decentralized ledger technology. Generally speaking, blockchain networks include two types: public blockchain and authorized blockchain. For example, Bitcoin and Ethereum are public blockchain networks, and Hyperledger Fabric is a permitting blockchain network. Allowed blockchain networks are usually more complex than public blockchain networks. In the Ethereum network, each node has the same role, i.e. participates in the consensus and synchronization of ledger data. In contrast, nodes in the Hyperledger Fabric network (eg, peer-to-peer, sort node orderer, Kafka consensus algorithm, certificate issuing mechanism CA, etc.) usually have different roles, so network setup, construction, and maintenance. Is very complex and time consuming. As for beginners, they are not familiar with many of these concepts of distribution and need to spend a lot of time learning and understanding the various components of the network. Also, because there are many location points in an authorized blockchain network, and these location points are related to various attributes of the entire network, beginners often choose what network architecture for their current project. It may not be possible to decide whether to adopt it. For example, beginners may develop a "Trusted solo" with high efficiency but low fault tolerance, and a "Kafka consensus algorithm" with high fault tolerance but low efficiency. It may not be possible to determine which consensus algorithm to use in the project.

For now, allowed blockchain networks are built completely manually, so even developers familiar with blockchain network technology often have to spend a lot of time and energy. Even though Hyperledger Fabric is provided with multiple exemplary networks, developers still need to spend time understanding and modifying these exemplary networks to get the desired network.

Therefore, it is desirable to provide a method that can automatically recommend network settings to development beginners and a method that can automatically generate network settings according to the developer's request.

In view of the above problems, an object of the present invention is to provide a technique for automatically recommending and generating network settings of a blockchain network. Such technology can greatly improve the efficiency with which developers build and set up authorized blockchain networks.

According to the first aspect of the present invention, a method for automatically generating recommended network settings for a blockchain network is provided, and this method is described.
Receives descriptive text related to the project that the user inputs through the input device;
Features related to a given characteristic are extracted from the explanatory text, and feature values are calculated for the characteristics;
Matching the explanatory text with known network settings based on the features and feature values;
It involves selecting the best matching known network settings as the recommended network settings for the project; and showing the user the recommended network settings by the output device.

According to the first aspect of the present invention, a device for automatically generating recommended network settings for a blockchain network is further provided, the device including a storage device and one or more processors, said processing. The vessel is
Receives descriptive text related to the project that the user inputs through the input device;
Features related to a given characteristic are extracted from the explanatory text, and feature values are calculated for the characteristics;
Matching the explanatory text with known network settings based on the features and feature values;
A known network setting for best matching is selected as the recommended network setting for the project; and the output device is configured to show the recommended network setting to the user.

According to the second aspect of the present invention, a method for automatically generating network settings of a blockchain network is provided, and this method is described.
The user receives a system diagram related to the project input by the input device, and the system diagram contains geometric figures and characters;
By performing image recognition on the system diagram, the entities in the system diagram and the relationships between the entities are recognized;
Generate the blockchain network configuration file based on the recognized entity;
It includes generating a startup script for the blockchain network based on the relationships between the recognized entities; and providing the user with the configuration file and the startup script by an output device.

According to a second aspect of the present invention, a device for automatically generating network settings of a blockchain network is further provided, the device including a storage device and one or more processors, said processor. Is
The user receives a system diagram related to the project input by the input device, and the system diagram contains geometric figures and characters;
By performing image recognition on the system diagram, the entities in the system diagram and the relationships between the entities are recognized;
Generate the blockchain network configuration file based on the recognized entity;
The blockchain network startup script is generated based on the relationship between the recognized entities; and the output device is configured to provide the user with the configuration file and the startup script.

Further, the present invention further provides a recording medium in which a program is stored, and when the program is executed by the computer, the computer is made to realize the method as described above.

It is a figure which shows the network architecture of Hyperledger Fabric as an example of a permission type blockchain network. It is a figure which shows the relationship between a pierode, a channel and a tissue. It is an overall flowchart of the network setting recommendation method by the 1st aspect of this invention. It is a flowchart of the first embodiment of the recommended method according to the first aspect of this invention. It is a flowchart of a method of generating a classification model. It is a flowchart of the 2nd embodiment of the recommended method according to 1st aspect of this invention. It is a flowchart of the 3rd Embodiment of the recommended method by 1st aspect of this invention. It is a figure which shows the generation of the network setting based on the system diagram. It is a flowchart of the generation method of the network setting by the 2nd aspect of this invention. It is an exemplary block diagram which can realize the computer hardware which concerns on this invention.

Hereinafter, preferred embodiments for carrying out the present invention will be described in detail with reference to the attached drawings. It should be noted that such an embodiment is merely an example and does not limit the present invention.

Hereinafter, the technique according to the present invention will be described using Hyperledger Fabric as an example of a permitted blockchain network, but the present invention is not limited to Hyperledger Fabric and can be applied to other types of permitted blockchain networks. ..

Figure 1 shows the network architecture of Hyperledger Fabric, which is mainly application A1-A3, peer node P1-P3, certificate issuing mechanism CA1-CA4, sorting node Q4, channel C1-C2, contract CC1. -Includes CC2 and network settings NC4. Pianos P1-P3 are the basic elements of the network, which store ledger data and smart contracts. All transactions and smart contracts generated by the user are permanently recorded in the ledger, and the smart contract is a program stored and executed in the peer node. Users need to access ledger data by submitting transactions and interacting with smart contracts. The certificate issuing mechanism CA1-CA4 is a membership administrator for the entire network. Sorting node Q4 generates ledger data for all peer nodes P1-P3 by creating a transaction list, and ensures that all ledger data in different peer nodes match. Channels C1 and C2 allow a particular set of peer nodes and applications (smart contracts) to communicate with each other within the blockchain ledger. As shown in FIG. 1, application A1 can directly communicate with peer nodes P1 and P2 using channel C1, and application A2 can directly communicate with peer node P3 using channel C2. The channel is not a real thing, but a logical structure formed by a set of physical peer nodes.

Also, a set of peer nodes can form an organization. All peer nodes are those owned and contributed by different organizations. FIG. 2 is a diagram showing the relationship between peer nodes, channels, and organizations. As shown in FIG. 2, peer nodes P1 and P2 belong to the organization Org1, peer nodes P3-P6 belong to the organization Org2, and peer node P7 belongs to the organization Org3. Further, the peer node P1 and the peer node P7 can communicate with each other via the channel C. Similarly, peer nodes P3 and P4 can also communicate with peer nodes P1 and P7 via channel C.

Normally, to build one complete network, you need to generate two types of files: a configuration file and a startup script. Configuration files include various static configurations, such as Docker compose configuration files, Cryptogen configuration files and Genesis block configuration files. The Docke compose config file is used to generate all docker containers such as peer nodes, sort nodes, Kafka, CA and so on. The Cryptogen configuration file is used to generate encryption-related files such as peer node certificates, user certificates, and TLS certificates. The Genesis block configuration file is used to initialize network relationships and genesis blocks, such as organizations, anchor nodes, block settings (block size, block time, etc.), and consensus algorithms (Trusted solo, Kafka).

A startup script is a dynamic script that is used to launch the entire network and configure the components of the network. Startup scripts mainly include Docker container startup scripts and network related build scripts. The Docker container startup script is used to start a Docker container, i.e. initialize all physical components with a Docker container. Network relations build scripts are used to build network relations, for example, initializing peer nodes and users, creating channels, joining peer nodes to channels (each peer node all runs the associated script). You need to subscribe to one or more specific channels) and so on.

As mentioned above, the configuration file and startup script are required to build one blockchain network. Therefore, in the present invention, the recommendation or generation of the network setting of the blockchain network mainly means to automatically provide the user with the setting file and the startup script of the blockchain network.

In addition, four factors need to be considered when building a blockchain network: scalability, security, efficiency and decentralization.

Scalability requires that the network be able to support more transactions. Generally speaking, it is desirable that the network does not collapse even when the number of transactions exceeds the capacity of the entire network. The high scalability of a network means that it can accommodate more transactions. For Hyperledger Fabric networks, scalability is related to the number of channels, peer nodes and organizations, consensus algorithms, the number of peer nodes with a chaincode, the endorsement policy of the chaincode, and so on.

Security is very important for authorized blockchain networks. Security is related to the certificate issuing mechanism, the number of channels, the endorsement policy of the chain code, and so on.

Efficiency means the throughput of the blockchain network. Efficiency is related to the number of peer nodes, consensus algorithms, chaincode endorsement policies, and so on.

Descentization is related to the number of channels, peer nodes and organizations, and the number of peer nodes with chaincodes.

However, inexperienced development beginners often cannot find the best compromise because the four factors of scalability, safety, efficiency, and decentralization constrain each other. In view of this, the present invention provides a network setting recommendation method for assisting development beginners.

FIG. 3 is an overall flowchart of the recommended method according to the first aspect of the present invention. As shown in FIG. 3, the user-input project white paper is received in step S310, and the white paper is a text for explaining the project. Then, in step S320, the vocabulary related to at least one factor (characteristic) of extensibility, safety, efficiency and decentralization is extracted as a feature from the white paper, and the feature value is calculated for the extracted feature. , The feature value is a value indicating the importance of the vocabulary in the white paper.

Specifically, synonyms and antonyms of the above four factors can be extracted from the white paper as features. An exemplary vocabulary table (Table 1) is shown below. The vocabulary listed in Table 1 below can be extracted from the project white paper as a feature.

As a method for calculating the feature value, for example, a known TF (Term Frequency, word frequency) -IDF (Inverse Document Frequency) algorithm may be adopted. Further, since those skilled in the art can calculate the feature values using other known algorithms, detailed description thereof will be omitted here.

If the calculated feature value is written as W, the following feature vector can be obtained.

V _S = (W ₁ , W ₂ , ..., _Wi )-(1)
V _A = (W ₁ , W ₂ , ..., W _j )-(2)
V _W = (V _S , V _A )-(3)
Here, V _S represents a synonym feature vector, V _A represents an antonym feature vector, and V _W represents a white paper feature vector.

After obtaining the features and feature values (feature vector), the white paper is matched with the known network settings based on the features and feature values in step S330, and then the best matching known network settings are given to the user in step S340. It can be selected as the recommended network setting and the output device can provide the recommended setting to the user.

The overall flow of the network setting recommended method of the present invention has been described above, but three specific implementation methods of the recommended method will be described below with reference to FIGS. 4-Fig.

FIG. 4 is a flowchart of the first embodiment of the recommended method according to the first aspect of the present invention. As shown in FIG. 4, multiple network types may be preset in step S410, and each network type may focus on one or two factors of scalability, safety, efficiency and decentralization. Often, network settings (including configuration files and startup scripts) corresponding to each network type may be further set in advance. After that, steps S420 and S430 are executed, and since these two steps are the same as steps S310 and S320 in FIG. 3, detailed description is omitted.

After that, a classification model is generated in step S440, and the step will be described later based on FIG.

Based on the features and feature values (feature vectors) obtained in step S450, the classification model classifies the white paper into one of a plurality of preset network types, which is then obtained in step S460. We recommend users the network settings that correspond to the network types. Since the classification method based on the characteristics is a technique known to those skilled in the art, detailed description thereof will be omitted here.

FIG. 5 is a diagram showing a method of generating a classification model using machine learning. As shown in FIG. 5, multiple known project white papers are collected in step S510, and then each known white paper is manually (artificially) collected in step S520 as appropriate of a plurality of preset network types. Classify into one and then train one classification model in step S530 using multiple known white papers after manual classification. For example, the classification model can be realized by a neural network or a machine learning method. The trained classification model can be used to classify the current white paper in step S450 of FIG.

FIG. 6 is a flowchart of the second embodiment of the recommended method according to the first aspect of the present invention. First, in step S610, a plurality of known project white papers and network settings corresponding to each white paper are collected, features are extracted for each white paper, feature values (feature vectors) are calculated, and then the obtained information is used. Build the database based on. Each known white paper and its corresponding network settings are stored in this database in association with its corresponding features and feature values.

After that, steps S620 and S630 are executed, and since these two steps are the same as steps S310 and S320 in FIG. 3, detailed description is omitted here.

In step S640, the features and feature values (feature vectors) are used to calculate the similarity between the current white paper and each known white paper in the database. Here, any known similarity calculation method, for example, a cosine similarity calculation method or another text similarity calculation method may be adopted, but the present invention is not limited thereto.

Then, in step S650, the network settings of the known white paper having the highest similarity to the current white paper are confirmed, and in step S660, the confirmed network settings are provided to the user as recommended settings.

By the method according to the first and second implementation methods described above, it is possible to recommend the network setting corresponding to the known white paper to the user. This method has the advantages of being relatively easy to implement and requires less computation, but it also has the following drawbacks, that is, it is not recommended because it is not customized for the user's project. Existing network settings often fail to meet all the requirements of the user's project. Therefore, the present invention provides a third embodiment, which is a method of combining the first and second embodiments. Generally speaking, by first acquiring the closest existing network settings by the method in the second implementation method, and then making corrections based on the existing network settings by the method in the first implementation method. Make it suitable for your specific project.

FIG. 7 is a flowchart of the third embodiment of the recommended method according to the first aspect of the present invention. As shown in FIG. 7, after obtaining the network setting of the known white paper having the highest similarity by the method according to the second embodiment, it is not immediately recommended to the user, and in step S710, the same as in the first embodiment. Set up multiple network types and network settings for each network type, where each network type focuses on one or two of scalability, security, efficiency and decentralization.

Then, in step S720, the score for each of the set network types in the white paper of the current project is determined. The score reflects the degree to which the white paper should be classified into the network type. The higher the score, the more the white paper should be classified into the network type. Conversely, the lower the score, the less the white paper should be classified into the network type.

Subsequently, in step S730, the plurality of network types are sorted according to the ascending order of the determined scores, and in step S740, the network settings corresponding to the network type with the lowest score (foremost) are used. Override the network settings corresponding to the known white paper obtained in step S650 in Figure 6. Then, in step S750, according to the sorted order of the plurality of network types, the network settings corresponding to the last one network type are sequentially used to overwrite the network settings corresponding to the previous one network type. In other words, the network settings corresponding to the network types with high scores are sequentially overwritten with the network settings corresponding to the network types with low scores. In the process of overwriting each time, some or all parameters in the previous one network setting (including configuration file and startup script) are overwritten by network settings of network type that are more consistent with the current white paper. By doing so, it is possible to continuously modify the existing network settings based on the user's white paper, so that the finally obtained network settings are suitable for the user's specific project. it can. The finally obtained network settings can then be provided to the user as recommended network settings, for example, as shown in step S760.

Hereinafter, the second aspect of the present invention will be described. When building a blockchain network, unlike novice developers, experienced developers can usually design a system diagram of the network for the current project. The system diagram includes geometric figures and letters and can represent the architecture of the network in the form of figures. In the second aspect of the present invention, network settings can be automatically generated based on the system diagram provided by the developer, thereby supporting the work of the developer.

FIG. 8 is a diagram showing the generation of network settings (including configuration files and startup scripts) based on the system diagram. As shown in FIG. 8, the rectangular frame indicates an entity such as peer node P, application A, and certificate issuing mechanism CA. A configuration file is generated based on the basic information and arrangement of these entities. Lines connecting entities and channels C1 and C2 indicate relationships between entities, such as node P1 joining channel C1 and node P2 joining channels C1 and C2. Generate a startup script based on these relationships. Note that the system diagram shown in FIG. 8 is only an example, and those skilled in the art may show the substance by using other figures such as a circular frame and a triangular frame.

FIG. 9 is a flowchart of a method of generating a network setting according to the second aspect of the present invention. As shown in FIG. 9, the system diagram related to the user-input project is received in step S910, and the entity and the relationship between the entities in the system diagram are recognized by performing image recognition in step S920. For example, entities such as peer nodes P1-P3 and applications A1-A3 can be recognized based on the frame and / or the characters in the frame in the system diagram. Also, the relationship between entities can be recognized based on at least one of the following: the line connecting the frames, the letters or arrows on the line, the color of the frame and the line, the relative position of the frame. Relationships and so on.

One of ordinary skill in the art may employ an appropriate image recognition method to perform step S920, for example, a method based on machine learning. With such a method, neural networks can be trained using a large number of pre-labeled system diagrams. Trained neural networks can be used to recognize entities and relationships between entities in system diagrams. For example, in the system diagram shown in FIG. 8, the trained neural network can recognize the entities P1, P2 and the relation C1, and more specifically, the peer nodes P1 and P2 both join the channel C1. Can be recognized.

After recognition, generate a blockchain network configuration file based on the basic information and placement of the entities, and generate a network startup script based on the relationships between the entities, as shown in step S930. Can be done. Then, in step S940, the generated configuration file and startup script can be provided to the user.

The method shown in Figure 9 can automatically generate more complex configuration files and startup scripts based on a developer-designed graphical system diagram, reducing developer workload and improving development efficiency. Can be improved.

The execution of the method according to the present invention is not limited to the above-mentioned order described with reference to the drawings. As long as the effect is not affected, the method according to the present invention may be realized in an order different from the order shown in the drawings. For example, the process of FIG. 7 is after step S650 of FIG. 6, but steps S710-S730 of FIG. 7 may be executed before step S650.

Further, the above-mentioned method may be realized by software, hardware, or a combination of software and hardware. The program included in the software may be stored in a storage medium installed inside or outside the device in advance. As an example, at the time of execution, these programs are loaded into RAM and executed by a processor (for example, CPU) to realize the above-mentioned various processes.

FIG. 10 is an exemplary block diagram of computer hardware that programmatically implements the method according to the invention. This computer hardware is an example of a device that recommends or generates network settings for a blockchain network according to the present invention.

As shown in FIG. 10, the central processing unit (CPU) 1001 performs various processes based on the program stored in the ROM 1002 or the program rodged from the storage unit 1008 to the RAM 1003. The RAM 1003 can also store data required when the CPU 1001 performs various processes according to needs. CPU 1001, ROM 1002 and RAM 1003 are connected to each other via bus 1004. The input / output interface 1005 is also connected to the bus 1004.

Further, the following components are connected to the input / output interface 1005, that is, an input unit 1006 including a keyboard and the like, an output unit including a display such as a liquid crystal display (LCD), and a speaker. 1007, storage unit 1008 including hard disk, etc., communication unit 1009 including network interface card, for example, LAN card, modem, etc. The communication unit 1009 performs communication processing via a network such as the Internet or LAN.

Drive 1010 may be connected to input / output interface 1005, if desired. A removable medium 1011 such as a semiconductor memory can be set in the drive 1010 as needed, and a computer program read from the medium can be installed in the storage unit 1008.

The present invention also provides a program product that includes a machine-readable command code. When such a command code is read and executed by the machine, the method according to the embodiment of the present invention described above can be executed. Correspondingly, carry such program products, such as magnetic disks (including floppy disks (registered trademarks)), optical disks (including CD-ROMs and DVDs), magneto-optical disks (MD (registered trademarks)). ), And various storage media such as semiconductor storage devices are also included in the present invention.

In addition, each operation (process) in the above method can also be realized by a method of a computer-executable program stored in various machine-readable storage media.

In addition, in relation to the above examples and the like, it is further disclosed as an appendix as follows.

(Appendix 1)
A method for automatically generating recommended network settings for blockchain networks.
Receives descriptive text related to the project that the user inputs through the input device;
Features related to a given characteristic are extracted from the explanatory text, and feature values are calculated for the characteristics;
Matching the explanatory text with known network settings based on the features and feature values;
A method comprising selecting a known network setting for best matching as a recommended network setting for the project; and showing the user the recommended network setting by an output device.

(Appendix 2)
The method described in Appendix 1
The predetermined property comprises at least one of extensibility, safety, efficiency and decentralization, the feature is a vocabulary associated with the predetermined property in the explanatory text, and the feature value is , A method that indicates the importance of the vocabulary in the explanatory text.

(Appendix 3)
The method described in Appendix 1 or 2, and further
Set multiple network types and network settings for each network type;
Generate a classification model;
Based on the features and feature values of the explanatory text, the classification model classifies the explanatory text into one of the plurality of network types; and the network settings corresponding to the obtained network type. A method that includes showing to the user as a recommended network setting.

(Appendix 4)
The method described in Appendix 3
Generating a classification model is
A manual method is used to classify the plurality of known explanatory texts into the corresponding types of the plurality of network types, respectively; and training is performed using the plurality of known explanatory texts after the manual classification to obtain the classification model. Methods, including getting.

(Appendix 5)
The method described in Appendix 3
A method in which each network type of the plurality of network types focuses on one or two of scalability, security, efficiency and decentralization.

(Appendix 6)
The method described in Appendix 1 or 2, and further
Generate a database, said database containing network settings, features and feature values corresponding to each of several known descriptive texts;
Calculate the similarity between the description text of the project and the features and feature values of each known description text in the database. ；
A method that involves establishing network settings that correspond to the known descriptive text with the highest similarity.

(Appendix 7)
The method described in Appendix 6 and further
A method comprising showing the user a network setting corresponding to the known explanatory text having the highest similarity as the recommended network setting.

(Appendix 8)
The method described in Appendix 6 and further
Set multiple network types and network settings for each network type;
Determine the score for each of the plurality of network types in the explanatory text of the project;
Sort the multiple network types according to the ascending order of the determined scores;
Override the network configuration corresponding to the known descriptive text with the highest similarity above, using the network configuration corresponding to the network type with the lowest score;
According to the reordered order of the plurality of network types, the network settings corresponding to the last one network type are sequentially used to overwrite the network settings corresponding to the previous one network type; and finally obtained. A method comprising showing the network settings to the user as the recommended network settings.

(Appendix 9)
A method for automatically generating network settings for blockchain networks.
The user receives a system diagram related to the project input by the input device, and the system diagram contains geometric figures and characters;
By performing image recognition on the system diagram, the entities in the system diagram and the relationships between the entities are recognized;
Generate the blockchain network configuration file based on the recognized entity;
A method comprising generating a startup script for the blockchain network based on the relationships between recognized entities; and providing the user with the configuration file and the startup script by an output device.

(Appendix 10)
The method described in Appendix 9, and further
Recognize the entity based on the frame in the system diagram and at least one of the characters in the frame;
A method comprising recognizing a relationship between entities based on at least one of a line connected to the frame, a letter or arrow on the line, the color of the frame and the line, and the positional relationship of the frame. ..

(Appendix 11)
A device that automatically generates recommended network settings for blockchain networks.
Memory; and
Includes one or more processors
The processor is an apparatus configured to perform the method described in Appendix 1-8.

(Appendix 12)
A device for automatically generating network settings for blockchain networks.
Memory; and
Includes one or more processors
The processor is an apparatus configured to perform the method described in Appendix 9-10.

(Appendix 13)
A recording medium that stores programs
A recording medium that, when executed by a computer, causes the computer to implement the methods described in Appendix 1-10.

Although the preferred embodiment of the present invention has been described above, the present invention is not limited to this embodiment, and any modification to the present invention belongs to the technical scope of the present invention unless the gist of the present invention is departed.

Claims (10)

A method for automatically generating recommended network settings for blockchain networks.
Receives descriptive text related to the project that the user inputs through the input device;
Features related to a given characteristic are extracted from the explanatory text, and feature values are calculated for the characteristics;
Matching the explanatory text with known network settings based on the features and feature values;
A method comprising selecting a known network setting for best matching as a recommended network setting for the project; and showing the user the recommended network setting by an output device. The method according to claim 1.
The predetermined property comprises at least one of extensibility, safety, efficiency and decentralization, the feature is a vocabulary associated with the predetermined property in the explanatory text, and the feature value is , A method that indicates the importance of the vocabulary in the explanatory text. The method according to claim 1 or 2.
Set multiple network types and network settings corresponding to each network type;
Generate a classification model;
Based on the features and feature values of the explanatory text, the classification model classifies the explanatory text into one of the plurality of network types; and the network settings corresponding to the obtained network type. A method further comprising showing to the user as a recommended network setting. The method according to claim 3.
Generating the classification model
A manual method is used to classify the plurality of known explanatory texts into the corresponding types of the plurality of network types, respectively; and training is performed using the plurality of known explanatory texts after the manual classification to obtain the classification model. Methods, including getting. The method according to claim 3.
A method in which each of the plurality of network types focuses on one or two of scalability, security, efficiency and decentralization. The method according to claim 1 or 2.
Generate a database, said database containing network settings, features and feature values corresponding to each of several known descriptive texts;
Based on the description text of the project and the features and feature values of each known description text contained in the database, the similarity between the description text and each known description text is calculated; And a method further comprising establishing the network settings corresponding to the known descriptive text with the highest similarity. The method according to claim 6.
A method further comprising showing the user the network settings corresponding to the known descriptive text having the highest similarity as the recommended network settings. The method according to claim 6.
Set multiple network types and network settings corresponding to each network type;
Determine the score for each of the plurality of network types in the explanatory text of the project;
Sort the multiple network types according to the ascending order of the determined scores;
Override the network settings corresponding to the known descriptive text with the highest similarity above, using the network settings corresponding to the network type with the lowest score;
According to the reordered order of the plurality of network types, the network settings corresponding to the last one network type are sequentially used to overwrite the network settings corresponding to the previous one network type; and finally obtained. A method further comprising showing the network settings to the user as the recommended network settings. A method for automatically generating network settings for blockchain networks.
The user receives a system diagram related to the project input by the input device, and the system diagram contains geometric figures and characters;
Image recognition is performed on the system diagram, and the entity in the system diagram and the relationship between the entities are recognized;
Generate the blockchain network configuration file based on the recognized entity;
A method comprising generating a startup script for the blockchain network based on the relationships between recognized entities; and providing the user with the configuration file and the startup script by an output device. The method according to claim 9.
Recognize the entity based on the frame and at least one of the characters in the frame in the system diagram; and the line connected to the frame, the character or arrow on the line, the frame and the color of the line. , And the method further comprising recognizing the relationship between the entities based on at least one of the positional relationships of the frame.

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