System for constructing meta universe scene commercial co-construction

Abstract

The invention discloses a system for constructing meta universe scene commercial co-construction, and belongs to the technical field of computer programs. The method comprises the following steps: a user login permission acquisition and login verification step, a data collection step, an assembly step, a storage step, a labeling step, an IA calculation step, an asset evaluation step, a verification algorithm step, a block chain certification step, a distribution verification step, a security step, a digital asset generation and right confirmation step, and a digital asset distribution and certification step. According to the method, risks in the aspects of privacy and data security of the universe industrial chain are solved, and the problems of data security and instability of a digital currency and economic system are solved.

Description

Technical Field

[0001] The present invention belongs to the field of computer program technology and relates to a system for constructing commercial co-construction of a metaverse scenario. Background Art

[0002] The construction of the Chinese Metaverse model is the result of historical drive and the guarantee for the vigorous development of China's digital economy into a new stage and the national digital strategy. There is a huge gap in the reach of various fields, in order to solve the many barriers in China's digital transformation process.

[0003] The Metaverse is an extension and expansion of the real world, aiming to provide people with a more colorful spiritual demand scenario and enhance human ability to perceive, explore and connect with the outside world. It is a digital mapping of the real physical world, enabling humans to collaborate and innovate more effectively in the digital world, and improving fairness, equality and efficiency in the real world.

[0004] The upstream links of the Metaverse industry chain are weak in foundation and lack the underlying software links, which limits its further development and application. There are also risks in terms of privacy and data security. Users in the Metaverse may need to provide a large amount of personal information or sensitive data, as well as their creations with potential intellectual property rights, which may lead to privacy leaks or data security issues. Data privacy is one of the biggest challenges in the Metaverse. In the face of economic instability, digital currencies and economic systems in the Metaverse have data security and instability issues. Summary of the invention

[0005] In order to solve the outstanding problems of the Metaverse Big Model and the risks and problems in scene construction, a system for commercial co-construction of Metaverse scenes is constructed, which provides a comprehensive and scientific solution in terms of big model expansion mechanism, presentation of hyper-realistic virtual scenes, industrial chain connection, information security, intellectual property rights and commercialization of economic models. The present invention aims to build a big model of the Metaverse, promote the development of China's digital economy, and address the problems of insufficient technical research and development, and provides a system for commercial co-construction of Metaverse scenes.

[0006] A system for building a commercial co-construction of a metaverse scenario includes the following steps: user login authority acquisition login verification step, data collection step, compilation step, storage step, labeling step, IA calculation step, asset evaluation step, verification algorithm step, blockchain certification step, distribution verification step, security step, digital asset generation and confirmation step, digital asset distribution and certification step.

[0007] A system for building a commercial co-construction of a metaverse scenario also includes the following steps:

[0008] Data collection: Obtain real data through the Internet, construction data, etc., and collect, store, clean, redundancy, and classification.

[0009] Constructing metaverse scenes: Based on real-world scenes, complete the modeling of digital scenes, such as plots of land, plants and animals, crops, trees, streets, buildings, etc. The characteristics of scene construction include two parts: one is the scenes close to reality, and the other is the integration of animals recorded in the Chinese culture "Classic of Mountains and Seas".

[0010] Digital business model: Break through the limitations of real business, give full play to the advantages of digital economy, combine the foresight of the metaverse scene, and create foreseeable and emerging digital innovative business models such as "virtual real estate", "virtual advertising space", "virtual transaction", "virtual space", "virtual creation", etc. At the same time, the metaverse scene provides users with scenes such as "visitor", "business promotion", "tourist", "digital economy", etc., among which the digital economy not only covers the scope that has been expressed, but also includes scenes and events such as digital people, virtual animals, virtual scenes, and virtual business.

[0011] Co-construction: The system provides open source, providing a simple interface for more professional users with creative, architectural, organizational, and design skills, who can participate in the development and listing of metaverse materials. The system provides open source, providing participating users with the function of creating their own space. Users can set up virtual scenes and advertising spaces in the space, and can also open virtual restaurants, virtual companies, virtual shelves and other digital economic models, and generate income by inviting friends to enter the scene for consumption. The virtual scenes set by users are not limited to the streets, rooms, squares, and buildings that exist in the real world. Users can also create their own digital space and conduct digital operations.

[0012] Co-construction and innovation: Through the different skills of users, they can jointly participate in the creation and operation of the Metaverse. As the volume of the Metaverse expands, various materials will be greatly supplemented, providing personalized virtual digital economic services to more users, thereby enabling everyone to participate in the digital economy.

[0013] Data copyright: A system for building a commercial co-construction of the metaverse scenario, providing agricultural blockchain technology, and encrypting and proving the materials provided by users. The proof is a digital certificate that marks the work and inputs it to the uploading user. The user has ownership and use rights to the material.

[0014] Data assets: When a group user uploads more than 100 materials, the system will provide data asset services to the group through the agricultural blockchain. The working principle of data assets is: the program determines that the current group contribution has reached the standard, the system pulls up the agricultural blockchain program, and after verifying that the data volume has reached the standard, a blockchain is generated for the group. The blockchain content includes the materials currently contributed by the group. The certified materials will be recommended to the world and all users can use them.

[0015] Glossary:

[0016] World: The world refers to the entire metaverse model.

[0017] User: refers to an individual or group who obtains a personal or group digital identity through registration and is logged in to the system.

[0018] System: refers to the metaverse scene and the underlying programs, databases, security programs, agricultural blockchains, etc. that carry the metaverse scene. This scenario is one of the usage methods of the present invention.

[0019] Materials: refers to the digital models provided during system development or various digital resources uploaded and combined by users. Such digital resources are used for construction or combination, and are generally visible items. The scope of item description includes all scene content.

[0020] The advantages of the present invention are: the present invention solves the risks of privacy and data security in the metaverse industry chain, and solves the problems of data security and instability in digital currency and economic systems.

[0021] With the research direction of the construction of the metaverse model and commercial co-construction, we will study agricultural blockchain technology, data assets, digital assets, consensus mechanisms, material rights, virtual reality scenarios and other aspects to solve the resource sharing problem of large models in commercial co-construction, so that participants can actively participate while protecting their own rights and benefits. All research results will be put into practice on the "Exploration Application Platform", and the system will be continuously optimized to improve economic benefits. It can provide a practical carrier for R&D results. An excellent model development framework can be presented in all aspects such as scientific research results transformation, material rights, and management system.

[0022] It solves the data literacy mechanism and can provide digital asset services to users at the same time, which indicates that digital assets are about to enter the stage of universal digitalization.

[0023] Achieve hyper-realistic display, build a large model of the metaverse, diversify popular science, diversify scenarios, create commercial digital transformation, provide scientific research results transformation, virtual reality co-construction, innovative NPC interaction, scientific consensus mechanism, and data asset rights with shared attributes.

[0024] By building a commercial co-construction model for metaverse scenes, we can realize interactive scenes of hyper-realistic photo scenes. This solves many problems in reality, such as high cost of popular science propaganda and single propaganda scope. Through the operation of this system, users can experience digital scenes in the real world in the virtual world. It provides users with construction models of virtual scenes and materials. It opens up new ways to generate income for the commercial co-construction model and promotes the transformation of China's digital economy.

[0025] The advantages of the present invention are not limited to the following:

[0026] Hyper-realistic display: Using the most advanced animation design to achieve hyper-realistic special effects and enhance user experience.

[0027] Metaverse Large Model: Scientific breakthroughs in the fields of space, time, and quantum mechanics bring users real scene experiences and create virtual scenes.

[0028] Diversification of science popularization: Encourage users to create materials in the fields of national quintessence, celebrities, farming, science and technology, and gradually realize the digitization of real scenes. Through virtual scenes, realize the science popularization and teaching needs suitable for different groups of people.

[0029] Diversification of scenarios: The construction of real scenarios is the core of the high cost of Metaverse development. Through the way of commercial co-construction, the existing bottleneck of "each doing their own business" can be changed, and a new era of diversified scenarios can be created.

[0030] Business digital transformation: Not all data can be transformed digitally. The system cleans the data based on the characteristics of the material to ensure user data security while meeting system requirements close to hyper-realism.

[0031] Transformation of scientific research results: Providing a transformation platform for scientific research results, the system can provide simulation calculations for scientific research results or provide research and development of demand simulation methods. This research and development is initiated by initiating demands from unspecified users. When the two parties reach a consensus on cooperation, the system provides an "independent" channel for cooperation to avoid data leakage.

[0032] Co-construction of virtual reality: Layout the resource sharing of the world-class metaverse model consensus mechanism, and gather more high-end talents and elite teams to participate.

[0033] Innovative NPC interaction: (NPC, when the user approaches an object, the system will automatically display the introduction of the object). Providing NPC information for each digital object is the original intention of the system. The amount of information is immeasurable. Innovative solutions are a way of co-construction. In order to give incentives to creators, this system designs and implements data assets and digital assets to ensure the benefits of participants and form a virtuous circle.

[0034] Consensus mechanism: There are differences in the content created by different users, and the identity identification of different users is different. The solution is to establish a consensus mechanism. Building a consensus mechanism for identity consensus, material consensus, and attribute consensus for system material providers is the best way to solve collaborative creation.

[0035] Shared attributes: The advantage of data assets is their endless potential value, while the uniqueness and distributable attributes of user works are somewhat contradictory. In the Metaverse scenario commercial co-construction system, the benefit problem of shared attributes and uniqueness is solved. The solution logic is that when a user creates his own digital item, the item will be viewed or used by other users in the system. Data and assets will be generated during the viewing or use process. The data belongs to the system shared data, and the assets belong to the common assets of the system and the user. The system will perform financial attribute write-offs on the digital assets of the item obtained according to the creator's historical contribution, thereby protecting the creator's income. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. As shown in the figure:

[0037] Figure 1 This is a logic diagram for implementing the system of the present invention.

[0038] Figure 2 Method innovation for commercial co-construction of the present invention.

[0039] Figure 3 A consensus method for commercial co-construction of the present invention.

[0040] Figure 4 Co-build logic for the business environment of the present invention. DETAILED DESCRIPTION

[0041] The technical scheme in the embodiment of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiment of the present invention. Obviously, the described embodiment is only a part of the embodiment of the present invention, not all of the embodiments. Based on the embodiment of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0042] Example 1: Figure 1 , Figure 2 , Figure 3 and Figure 4As shown, a system for constructing commercial co-construction of metaverse scenarios includes the following steps: user login authority acquisition login verification step, data collection step, compilation step, storage step, labeling step, IA calculation step, asset evaluation step, verification algorithm step, blockchain proof step, distribution verification step, security step, digital asset generation and confirmation step, digital asset distribution and proof step.

[0043] The steps for obtaining user cross-domain login authority and login verification are as follows: When a user logs into the system, the WEB3.0 cross-domain login mechanism is called, and the user enters the login username and password at the login entrance of any site in the cluster or chooses a third-party quick login method to log in. When the user visits between websites in the cluster, the system automatically identifies whether the current user is logged in and determines whether the login status is valid. If valid, the system automatically records the user's access operation record. If invalid, the visitor is deemed to be an ordinary browser of the program and has no right to operate restricted content.

[0044] Data collection steps: Provide users with scene building and creation space through individual user creation, team user creation and system recommendation building models, and experience users being allowed by the system to make new creations in their own space.

[0045] Storage steps: The results created by the user are saved in the user information table by the system in case the user logs out and logs in again to save his or her own creations.

[0046] Assembly step: The system's aggregation and secondary compilation of user data and the system's large model interface is the guarantee for the interaction between elements and the large model. The assembly step includes the system receiving new element materials uploaded by users, and submitting them to the material server through users. At this time, the Metaverse Model Library confirms the established type of the element material and marks it with a label. The scope of the label includes but is not limited to "land resources", "space resources", "building resources", "agricultural resources", "knowledge resources", etc. After the label is confirmed, the system performs secondary assembly according to the interface rules of the large model. The assembly at this time includes comprehensive verification such as proofreading of material parameters, code security checks, and material model matching. The element materials that have passed the verification have completed the assembly step, and the user elements that have completed the assembly step will be recommended by the system to the large model selection scene.

[0047] Labeling step: The processing steps for the consensus label of the value of data assets are as follows:

[0048] The labeling step refers to the operation of classifying and labeling the target data.

[0049] Step 1: The default user is logged in. User data collection completes the integration of internal and external data, and performs DB distributed storage and IA deployment. The data includes database data sets, videos, pictures, documents, and audio digital data.

[0050] Step 2. The IA title confirmation compilation system provides a verification encryption algorithm for data and user operations through the blockchain system (referring to the agricultural blockchain system) (this algorithm is consistent with the encryption fingerprint algorithm), and returns the correct return value to the title confirmation compilation system. The title confirmation compilation system performs intelligent processing according to logical requirements, verifies erroneous operations, and rolls back to ensure the integrity and immutability of the system. The data that has been confirmed will be re-labeled in the form of a label, and the labeled data set information forms data element information. Data element set: refers to the collective term for data element information formed by each user and system in the system for the labeled data set information.

[0051] IA calculation steps: IA is the abbreviation of Intelligent Assistant. Through the assistant model algorithm, the data is re-verified and redundancy cleaned to ensure the classification and cleaning of data and historical data, and to ensure that users can accurately select accurate data when retrieving data in the model.

[0052] Asset evaluation step: It is divided into the digital asset evaluation step, the integrity data evaluation step, and the regulatory data evaluation step. The asset evaluation step is to read the target data and perform modular cleaning to avoid sensitive information and confidential information. Then the system calls the data evaluation module of the IA intelligent assistant system according to the data type for data evaluation. The evaluation results are divided into TRUE and FALSE. TRUE means that the data evaluation is successful and the data can be subsequently operated. FALSE means that the data evaluation fails, the data is restored, and the data evaluation step is repeated again for the next evaluation. The data evaluation will evaluate the user data asset data set, the integrity data set, and the regulatory data respectively. When all three sets of data are TRUE, the data evaluation step ends.

[0053] Business data evaluation steps: The scope of business data evaluation includes system virtual business scene data and user business data. The method of business data evaluation is that the system reads the business data and performs a "labeling step" on the group of business data. After completing the "labeling step", the group of data is subjected to secondary redundancy in the metaverse model library by the system according to the data evaluation steps. At this point, the group of data will generate a 1-bit value from 0 to 1, where 0 means that the group of business data has no value, and 0.1 means that the group of data can generate 0.1 data value for the user, and the minimum value of each value increase is 0.1. 1 means that the group of data has the highest commercial value and can generate 1 data value for the user. When the data value of the group of data is greater than 0, the system will return the business data evaluation value to the user account, and the user account data assets will increase.

[0054] The evaluation steps of data assets are as follows: users (individual users, corporate users or regulatory users) log in and make operation requests to enter the IA rights confirmation compilation system, clean the user data, and then hand over the valuable data after cleaning to the IA system, agricultural blockchain, and IA deployment for distributed storage. The processed data set is handed over to the evaluation system. The evaluation system uses the "game theory method" to perform logical operations, and the result has a completely credible blockchain proof chain content. Thus, data elements are generated, and the data is formed into a decision-making model through compiling data flow, resource integration, and value mining. The decision-making model goes through the audit and warehousing process of the system module, and the audit and warehousing are completed.

[0055] Integrity data evaluation steps: Integrity data includes user (individual or enterprise) real-name information, license information, performance data, access habits, scene contribution data and other contents. Integrity data evaluation is an important reference for comprehensively evaluating user contribution and performance capabilities. The user's integrity data evaluation value will affect their operating permissions or purchase and interaction restriction mechanisms within the scene. The evaluation steps of integrity data are that the system automatically pulls up the integrity evaluation module regularly or irregularly, and classifies and summarizes user data. The classification step is to read user data and divide it into basic information, real-name information, performance information, and interaction information. The aggregation step is to conduct a comprehensive evaluation of the user's classified information respectively, and the evaluation results are recorded in percentages. The higher the percentage score, the higher its integrity index. The evaluation rules are basic information 10% + real-name information 10% + performance information 30% + interaction information 40% + other information 10%. When there is negative information from multiple people in the interactive information, the system will deduct it, which may result in a negative number. When the integrity index is lower than a certain value, the system interaction function will make necessary restrictions to encourage users to "build" their own excellent virtual image in the big model.

[0056] Regulatory data evaluation steps: Regulatory data includes user (individual or enterprise) data and system log data. Depending on the regulatory object, the regulatory results will be fed back to the regulated user. The regulatory data evaluation steps include the sum of user assets and the sum of system assets being equal, and the system determining that there is no abnormality. When the system determines that it is abnormal, the system will automatically analyze the abnormal data and make adjustments. If it cannot be automatically adjusted by the AI ​​system, the regulatory system will give early warning information feedback to the monitoring system of the super management background, and the monitoring system will issue an early warning based on the severity of the regulatory feedback. Regulatory feedback is divided into financial early warning, data anomaly early warning, attack early warning, user operation abnormal early warning, and system data abnormal early warning. When the system cannot automatically process the early warning, the staff will intervene as soon as possible to avoid greater erroneous operations in the system.

[0057] The verification algorithm steps include data input steps and verification function writing steps.

[0058] Data input steps: clarify the input data type and automatic screening model, and enter the data that meets the requirements into the database.

[0059] Steps for writing a verification function: Perform data classification operations through different classifications of various data to ensure accurate data processing, and store the data in the database after labeling.

[0060] Build and use the model steps: write an internal test version, verify the operation of each step, analyze the results and optimize the model.

[0061] Output result step: According to the return value of the model, input the verification result to determine whether the solution is correct;

[0062] Blockchain proof step: Encrypt the correct content of the returned result.

[0063] The program verifies user permissions and data authorization. After the calculation is passed, the server responds to the user's operation request and distributes data. If the data distribution here involves sensitive information, it will also receive double confirmation from the blockchain verification; the user end confirms and verifies the data and passes the verification.

[0064] Distribution verification step: When the user save operation is triggered, the system will perform complex verification and save operations. The main verification steps include user login status, user operation permissions, metaverse model library analysis, and material server response. When all actions are passed, the system will write the user's operation into the database and conduct blockchain proof (only if digital asset content is involved will the blockchain interface be triggered). Distribution verification is for the material provision and revenue confirmation of third-party material providers. The distribution verification step is to obtain the user's submitted information application, determine the degree of material duplication, verify the user's operation permissions, confirm the material classification, and complete the save operation. At this point, the distribution verification step is completed.

[0065] Security steps: Security is divided into operational security, data security, model security, and access security. The method for judging operational security is to verify the user's login status and check whether the user's operating permissions are restricted. Data security includes system log records, user operation behavior records, and the reading, writing, and verification security of users acquiring assets (data assets, digital assets, points, and other valuable information) or financial records. Model security refers to whether the elements designed and developed by users meet the interface requirements of the Metaverse scenario when performing various operations or uploading co-constructed model elements. Those that meet the requirements are allowed to access the system, and those that do not meet the requirements are given modification suggestions. Access security includes two aspects of verification: one is the determination of user login status and operating permissions, and the other is whether the elements provided by users meet the mechanism of commercial co-construction of the Metaverse scenario.

[0066] The logical algorithm of the security step is: get the USDK value of the user login variable, check whether it is normal if it exists, if it is normal, the system returns the user login status to normal, otherwise it returns an exception and jumps to the login page. If the user login status is normal, the user enters the system to operate, and during the operation, the system will perform a security verification of the user operation. The security verification method is as described above. The generation and confirmation of digital assets: The confirmation compilation system performs intelligent processing according to logical requirements, and the confirmed data will be re-labeled in the form of tags, and the labeled data set information forms data element information. The generation and confirmation of digital assets is that the system combines redundant system data into a new data set suitable for the current user through user operations. Through multiple user operations and other users' attention to this group of data sets, the system automatically generates quantitative digital assets for distribution. The system is strictly carried out in accordance with the development plan, and AI calculations are evaluated quantitatively according to the value of user data.

[0067] The quantitative formula is:

[0068] M = SS / ((MS+Ut)×(MS / 6)).

[0069] M: represents the digital asset value.

[0070] SS: Indicates the total amount of the same type of system data.

[0071] MS: indicates the total amount of unconfirmed user data.

[0072] UT: indicates the total time contributed by the current user.

[0073] When M≥0.01, the system analyzes that if the data in this group is not confirmed, the digital asset value will be increased by 0.1, otherwise it will not be increased and will wait for the next combined evaluation.

[0074] When the data has been authenticated, no further analysis will be performed.

[0075] For verified data, the digital twin system will adopt the data content by default and will not perform secondary verification or validation operations to improve system operation efficiency.

[0076] The quantitative method of digital assets, the system provides digital asset services for users, and its quantitative method follows the minimum quantitative of 0.1, the minimum quantitative of a single increase or decrease is 0.1, and the maximum single increment is 1. That is, when the user operates, the minimum increase determined by the system is 0.1 digital assets, and the maximum increment is 1 digital asset. At this point, digital assets are generated and written into the system.

[0077] Distribution and certification steps of digital assets: digital assets will be sent to user accounts, and the user's private data will be certified by the system through redundant processing and commercial co-construction consensus methods; only certified user data may be defined as data assets or digital assets and generate income;

[0078] After the system operates on user data, it generates digital assets and confirmation information. The digital assets (including data assets) in the system are stored as system data and cannot be used by users. When digital assets that meet the starting conditions are generated in the system, the digital asset distribution and certification steps are activated. Through digital asset distribution and certification, digital assets in the system are distributed to user accounts. The digital asset rights received by users belong to the individual user.

[0079] Distribution and certification steps of digital assets: Use the logic algorithm to read the digital asset value AN1 to be distributed from the system, then retrieve the user information contained in AN1, and perform distribution retrieval according to the user's N ID. If the retrieval verification passes, the digital assets that the user deserves will be "transferred" from the system account to the user account, and the system account will be reduced and the user account will be increased. During the entire data asset distribution process, the system will use the agricultural blockchain algorithm to perform blockchain certification on the distribution records. This certification process is the distribution certification step of digital assets. At this point, the distribution and certification steps of digital assets are completed.

[0080] Example 2: Figure 1 , Figure 2 , Figure 3 and Figure 4 As shown in the figure, a system for building a commercial co-construction of a metaverse scene includes the following steps: the user determines whether to be an experience user or a creator through identity selection. The experience user directly walks into the digital reality scene to experience it. The creator completes the configuration and material design and submission according to the creation guide. The material server and the metaverse model library provide users with innovative creation methods, allowing users to realize online operations. Figure 1 Describes the implementation logic of the co-construction system, such as Figure 1 As shown, the co-construction system has at least two access modes: PC and APP. Users register / log in to the system, and after successful login, they can perform security layer verification and various system operations.

[0081] The system operation process is:

[0082] Material user:

[0083] Including individual users and group users, they can access the material application program within the system, complete the creation of materials through their own creativity or design, and submit material operations.

[0084] After the materials are reviewed and approved, they will be saved to the material server and become part of the Metaverse Model Library. The Metaverse Model Library includes not only the land resource library, space resource library, building resource library, agricultural resource library and knowledge resource library, but also the meteorological database, natural resource library, user database, configuration database, classification database and so on.

[0085] Users use materials to make innovative combinations in co-construction models or upload their own original virtual materials. This operation process will generate data assets or digital assets (digital assets for individual users and data assets for group users).

[0086] All submitted virtual digital materials will become construction elements of the metaverse scene.

[0087] Different from the common Metaverse model, this Metaverse model not only explores the application platform, but also focuses on supporting the digital transformation of the agricultural sector. The support includes helping farmers access the system, realizing visual agriculture, and helping farmers complete the publicity and promotion of contract agriculture. It can also provide virtual economic scenarios for group users to increase the group's income in the digital age.

[0088] Figure 2 Describes the method innovation of scene co-construction, such as Figure 2 As shown in the figure, the exploration application platform section on the left shows the existing content of the system scenario. The scenarios to be developed later follow Figure 2 The co-construction method shown. After the user logs in to verify the digital identity, the user creates or uploads the material. After the material enters the system integration module, the model access judgment step, material management judgment step, confidential data cleaning step, innovation consensus identification step, data asset identification step, digital asset call step of generating digital assets and blockchain certification are carried out. At this point, the process of material co-construction is completed.

[0089] When user materials become elements of the commercial co-construction of the Metaverse scenario, the system will classify the user materials through classification labels such as scenario consensus, commercial consensus, R&D model, business model, and innovation results. Once the classification is completed, the entire innovation step of the commercial co-construction material is completed.

[0090] The steps of the general model are material design, uploading, obtaining income, and user use. Within the scope of the invention, the process of data assets and digital assets is innovatively completed, and at the same time, the user's confidential data and sensitive data are cleaned to ensure the security of the data during use.

[0091] Figure 3 Describes the consensus approach for business co-construction, such as Figure 3As shown, the upload of scenes and elements is completed through commercial co-construction, which is the primary data collection stage of system elements. Next, we will enter the consensus stage of commercial co-construction. As the name implies, the consensus method of commercial co-construction solves the electronic ownership proof of user elements. This ownership will be decrypted and verified in other metaverse scenes. Other metaverses that can pass the decryption verification are called consensus metaverses. Multiple consensus metaverses generate "metaverse worlds", referred to as "worlds". The world described in the present invention is for this purpose.

[0092] The consensus method is a complete set of encryption and decryption logic, which includes not only user verification steps, but also sharing verification steps, financial verification steps, resource verification steps, and the consensus method of a certain metaverse model and the world metaverse interface. The workflow of this method is:

[0093] The user or system applies for consensus encryption or decryption steps:

[0094] The consensus mechanism responds and performs the following steps: determine the application type of the user or system, call the encryption consensus and decryption consensus programs, determine the user's login status, authorize access when in the login state, perform consensus verification if passed, release if verification is passed, otherwise prompt the user to log in.

[0095] The encryption algorithm formula is: SHA256 (N ID & blockchain private key & MD5 user real-name information). Enterprise encryption algorithm-enterprise: SHA256 (enterprise private key & N ID & MD5 (enterprise unified code & operation code)) The decryption algorithm formula is: SHA256 (NID & blockchain private key & MD5 user real-name information). Enterprise encryption algorithm-enterprise: SHA256 (enterprise private key & N ID & MD5 (enterprise unified code & operation code)) Secondary comparison, if the comparison is successful, the USDK logo is generated on the server side to prove the status of the user and avoid repeated verification of the system.

[0096] Glossary:

[0097] USDK: A variable name defined in the system, which indicates the recorded value of the user's login. Its purpose is that when the user is not logged in, the USDK value does not exist (USDK=null). When the user is logged in, USDK=TRUE. The system determines the user's login status through the value transmitted to the server by USDK.

[0098] AN1: A variable name defined in the system, indicating that the value read from the system account is securely transmitted by the variable name AN1. Avoid direct display of sensitive data. For example, AN1 = 60.

[0099] N ID: A custom name for a database field. It generates a 32-bit non-repeating code for a data row in an MsSQL data table. This is a kernel mechanism of the database.

[0100] &: A symbol in a calculation formula in a computer programming language, which is used to link the subsequent variable contents. For example, if variables A and B are combined into variable C, the regular expression is C=A&B.

[0101] SDK: The full name is Software Development Kit, which is called software development kit in Chinese. In this system, SDK is defined as a collection of system interface toolkits, which contains a lot of required interface information and security verification information.

[0102] Creator USER CODE digital signature: refers to the digital identity of the user in the Metaverse scenario commercial co-construction system, which is a string of 32-bit codes. The coding formula involves system security and is not disclosed.

[0103] At this point, the consensus method for business co-construction completes a complete workflow.

[0104] Figure 4 business environment to build a system. Figure 4 As shown, the system has gone through the method of user digital identity verification, material verification and commercial co-construction, and then the co-construction system of the commercial environment is built. The co-construction system of the commercial environment follows the consensus method of commercial co-construction. Its main work flow is:

[0105] Private domain: includes the storage and use of various resources and data, collectively referred to as private domain data.

[0106] Expansion: The scope of expansion is the world, which includes real scenes such as cities, countryside, mountains, oceans, lakes, etc., as well as material libraries, income, etc.

[0107] When the private domain and the world are in a "static" state, it means that the current user has no operation. The actions of the business environment co-construction system originate from the user's operation. Users generate new materials by registering / logging in to the system, operating scenes, or uploading materials, and the system will record and prove the operation.

[0108] The user's private data will be stored through the access system.

[0109] The user's private data will be redundantly processed by the system and proven through a consensus method jointly built by businesses.

[0110] Only proven user data may be defined as data assets or digital assets and generate revenue.

[0111] Through the above process, the system completes a workflow of the business environment co-construction system. The process is to use private domain data to prove the co-construction of the business environment and then store it in the material server. The materials in the material server will be accessed by other users. After the user selects the data that has passed the consensus, there will be a paid or free use situation. The income or points generated at this time will be returned to the original material provider through the co-construction system. The income obtained by the original material provider must be obtained through the co-construction proof. This is an effective measure to protect the data of the private domain system and an inevitable condition for protecting personal privacy and data security. Through the workflow within the business environment co-construction system, the rights and interests of all parties and data security are guaranteed to the greatest extent.

[0112] The system creates an ecosystem of original universe scenes, solves user authority consensus, resource library sharing, private domain data security, confidential data cleaning, commercial co-construction solutions, individual user creation collaboration, group data assets, and optimization of the invisible mechanism of available materials, and uses the consensus method of commercial co-construction ( Figure 4 ) provides the world-class metaverse with metaverse materials suitable for private domains.

[0113] When private domain materials produce connection errors or fatal errors, the system will lock the erroneous resources through redundant links to avoid causing peripheral system paralysis.

[0114] This co-construction method is not limited to commercial co-construction, but also provides an expansion method for multi-dimensional scene co-construction. Third parties can conduct convenient material availability verification and respond to development feedback quickly in the construction of private domain metaverse. Some users can quickly generate connectable materials through 3D scanning equipment, and participate in creation in a simple way, thereby providing more optimized volume contribution for the scale and commercial purposes of the private domain system. This provides an excellent income-generating platform for private domain metaverse managers.

[0115] A system for building commercial co-construction of metaverse scenarios also includes the following steps: user registration, real-name verification, creation of private domain data, sharing of private domain materials, data cleaning, data redundancy, data anonymization, operation verification, operation proof, blockchain encryption, and financial value assessment.

[0116] Through the entire process of user registration, real-name verification, creation of private data, sharing of private materials, data cleaning, data redundancy, data anonymization, operation verification, operation proof, blockchain encryption, and financial value assessment, users have completed the process of commercial co-construction of the metaverse scenario.

[0117] When the experience user visits the website, he can access the shared information provided by the creator. The experience user builds or creates his own private domain scene through the creator's shared information. In the process of interaction, valuable data will be generated. When the data accumulates to a certain amount, the system digital asset confirmation process will be triggered. The system will save the user's operation data result information to protect the user's rights and interests. In terms of user level promotion and income records, the system has developed a complete large model (such as Figure 4 shown).

[0118] The entire work process will be recorded on the blockchain and proven through distributed accounting.

[0119] Prevent malicious tampering or network attacks from causing loss to system data, protect system security and user data; through the content generated by users' proof of the availability of system data, the system will conduct digital asset evaluation on high-value content.

[0120] The system has not only innovated in the co-construction of metaverse scenes, but also developed a digital presentation method in the field of co-construction of business models. The creation of scenes in the system and the connection with "next door" are based on the presentation of real scenes. Users creating virtual scenes will not affect the real data of digital scenes. This is one of the core technologies of the business co-construction system.

[0121] Scene co-construction: refers to the modeling of the metaverse and the creation of elements (people, buildings, animals, plants, objects, information, etc.), which is carried out through co-construction rather than traditional independent development.

[0122] Scene creation: "Next door" means that when adjacent materials are created, the information of the currently created material (such as position, size, height, etc.) depends on the dual constraints of real scene data and real data.

[0123] Co-construction of business models: There is a subtle difference between the operating logic of the virtual world and the logic of the real scene. This difference has led to the exploration of a unique business model in the virtual world. This is a gold mine that is being gradually mined, and the "advertising companies" in the virtual world will create new business miracles.

[0124] At this point, the co-construction and implementation method of the entire system has formed a closed loop, in which data can be synchronized to the world's expansion scope, and the workflow is completed.

[0125] Example 3: Figure 1As shown, a system for building a commercial co-construction of a metaverse scenario includes the following steps: providing full access to data asset rights confirmation for the entire IA system. User login authority acquisition login verification, data collection steps, compilation steps, storage steps, labeling steps, IA calculation steps, asset evaluation steps, verification algorithm steps, blockchain certification steps, distribution verification steps, security steps, digital asset generation and rights confirmation steps, digital asset distribution and certification steps.

[0126] User cross-domain login authority acquisition login verification: When a user logs in to the system, the WEB3.0 cross-domain login mechanism will be called. The workflow is that the user enters the login username and password at the login entrance of any site in the cluster or chooses a third-party quick login method to log in to the system. When a user accesses between websites in the cluster, the system will automatically identify whether the current user is logged in and whether the login status is valid. If valid, the system automatically records the user's access operation record. If invalid, the visitor is regarded as an ordinary browser of the program and has no right to operate restricted content.

[0127] Data collection steps: Provide users with scene building and creation space through individual user creation, team user creation and system recommendation model building. Experience users can create new scenes in their own space.

[0128] Creation results: The results created by the user will be saved in the user information table by the system in case the user logs out and logs in again to save their creation results. IA is the abbreviation of Intelligent Assistant.

[0129] Asset evaluation steps: Asset evaluation is divided into five parts in this system: first, enterprise data evaluation. Second, personal user data evaluation. Third, integrity data evaluation. Fourth, regulatory data evaluation. Fifth, business data evaluation.

[0130] like Figure 2 As shown in the figure, the working steps of the digital asset rights confirmation and distribution system are described, indicating the asset evaluation steps. Users (individual users, corporate users or regulatory users) log in to the system and enter the IA rights confirmation compilation system when they make an operation request to the system. The system cleans the user data, and the valuable data after cleaning is handed over to the IA system, agricultural blockchain, and IA deployment for distributed storage. The processed data set is handed over to the evaluation system. The evaluation system uses the "game theory method" for logical operations, and the result has a completely credible blockchain proof chain content. Thus, data elements are generated. Data must be formed into a decision model through compiling data flow, resource integration, and value mining. The decision model will carry out the audit warehousing process of the system module. After the audit warehousing is completed, the system also completes the value consensus label of the group of data assets.

[0131] The specific processing steps are as follows:

[0132] Step 1, (Default user is logged in) User data collection such as Figure 1 As shown in the figure, the integration of internal and external data is completed, and data distributed storage and IA deployment are carried out. The data includes database data sets, videos, pictures, documents, audio and other digital data.

[0133] Step 2: The IA rights confirmation compilation system provides a verification encryption algorithm (which is consistent with the encryption fingerprint algorithm) for data and user operations through the blockchain system (referring to the agricultural blockchain system). At the same time, the correct return value is returned to the rights confirmation compilation system, and the rights confirmation compilation system performs intelligent processing according to logical requirements. Verification errors will be rolled back to ensure the integrity and immutability of the system. The data that has been confirmed will be re-labeled. The labeled data set information forms data element information.

[0134] Step 3, data element set: refers to the collective term for data element information formed by users and systems within the system for the labeled data set information.

[0135] The verification algorithm steps include data input steps and verification function writing steps.

[0136] Data input steps: clarify the input data type and automatic screening model, and store the qualified data into the database;

[0137] Steps for writing a verification function: Perform data classification operations through different classifications of various data to ensure accurate data processing, and store the data in the database after labeling.

[0138] IA calculation steps: Through the assistant model algorithm, the data is re-checked and redundancy cleaned to ensure the classification and cleaning of data and historical data, and to ensure that users can accurately select accurate data when retrieving data in the model.

[0139] Build and use the model steps: write an internal test version, verify the operation of each step, analyze the results and optimize the model.

[0140] Output result step: Based on the return value of the usage model, input the verification result to determine whether the solution is correct.

[0141] Blockchain proof step: Encrypt the correct content of the returned result.

[0142] The program verifies user permissions and data authorization. After the calculation is passed, the server responds to the user's operation request and distributes the data. If the data distribution here involves sensitive information, it will also receive double confirmation from blockchain verification.

[0143] The user confirms and verifies the data and passes the verification.

[0144] When the user save operation is triggered, the system will perform complex operations to verify and save. The main verification steps include user login status, user operation permissions, metaverse model library analysis, and material server response. When all actions are passed, the system will write the user's operation into the database and conduct a blockchain chain certificate (the blockchain interface will be triggered only if digital asset content is involved).

[0145] Step 4: Creation and compilation of user materials: The materials created by the user have been saved on the server, but the materials cannot be used by other users. The process to continue the operation is as follows: Submit materials: Further improve the model or materials you created. After completion, submit the materials to the system and wait for system review.

[0146] Review steps: The system receives the user's application for material submission; the intelligent system IA automatically simulates the parameters and availability of the currently submitted material and returns the result; if the result is passed, it passes the review. If the result is not passed, it will be automatically returned and suggestions for improvement will be given.

[0147] At this point, the entire workflow ends.

[0148] Embodiment 4: A system for constructing a metaverse scenario business co-construction further comprises the following steps:

[0149] like Figure 2 As shown, when users participate in business co-creation, they need to authenticate their identities. The authentication method is to use username and password for identity authentication. Users who pass the authentication can enter the system to create. User creation follows the following steps:

[0150] System Integration Module: This module specifies access specifications, management specifications, data cleaning, consensus mechanisms, asset rules, and asset interfaces. Users must obtain full module permissions to operate on it.

[0151] Access specifications: The system follows the logical structure of co-construction scenarios and commercial co-construction, including consensus mechanisms and co-construction algorithms. For operations that do not comply with access specifications or maliciously modify access information, the system will terminate the user's operation, thereby ensuring system security and maintaining the health of the metaverse scenario.

[0152] Management standards: Data received by users from malicious attacks in the integrated module will be automatically intercepted by the management module, thereby providing data security protection for users who are not familiar with computer programming languages ​​and professional development users.

[0153] Data cleaning: Clean confidential data, commercial data and sensitive data or make them invisible to protect system data security.

[0154] Consensus mechanism: The core of the commercial co-construction of the Metaverse scenario is the identity consensus and material consensus algorithms, which ensure the interaction during the transmission of encrypted data and the use of decrypted materials without any bugs or unavailability.

[0155] Asset rules: All employees reach a consensus on the generation and use (allocation) of system assets.

[0156] Asset interface: It provides the transmission and secondary verification functions of financial data for the third-party interface.

[0157] Explore application platforms such as Figure 2 shown.

[0158] Workflow: In the current system, the overall workflow is that the user logs in to the system, creates and saves, the material server and database respond, and the agricultural blockchain system is integrated into the system integration module and called when needed. The generated calculation results are used to verify user operations and encrypt creative results. The creative results are classified and saved according to the user's selection. The saved materials will form an integral part of the commercial co-construction model of the metaverse scenario.

[0159] At this point, the workflow ends.

[0160] Example 5: Figure 1 , Figure 2 , Figure 3 ,and Figure 4 As shown, a system for building a commercial co-construction of a metaverse scenario also includes the following steps:

[0161] Through the consensus mechanism, user data is created and saved. The metaverse model, scene material or business model created by the user will be presented in digital form, and the newly created model will be registered in the system to increase the issuance of the world metaverse material. For example, a user creates a lawn advertising idea, which has not been created in the world metaverse system, and the idea is accessed and used by certain users. In this case, the idea may be automatically registered by the system, and the registration information is bound to the original creator's identity and the user is asked to agree to the creation through the station letter or dialogue NPC. After creation, its income will be reasonably distributed.

[0162] The system only provides a general way of illustrating the above examples. Instances of other scenarios follow or rely on the description of this example to create logic and generate value.

[0163] There are essential differences between the business forms within the metaverse system and the real business models, which is a unique feature of the digital economy.

[0164] The system intelligently generates advertising based on the Metaverse model’s advertising needs, and allows users to create and register their own advertising business models, thereby increasing their advertising revenue.

[0165] Similar to the content of the advertising example, there are many virtual scenes in the metaverse, which are not limited to the advertising creativity of the instance itself. It can also include new things such as pet creation and pet feeding.

[0166] The logical formula for commercial co-construction is:

[0167] CY=SEARCH (traverse the database).

[0168] CY: Creates a return value to determine whether a business model exists.

[0169] SEARCH: Search the world-class database of business model tables.

[0170] Traverse the database: retrieve data with a return value greater than 0.3, and the return value is TRUE or FALSE.

[0171] When CY=True, the system determines that the idea does not exist and the system registers the idea.

[0172] Otherwise, the system determines that the idea exists and only saves the user's private data. The data is only used for private authorization and cannot be shared.

[0173] Example 6: Figure 1 , Figure 2 , Figure 3 ,and Figure 4 As shown, a system for building commercial co-construction of metaverse scenarios, Figure 1 Describes the implementation logic of the co-construction system, Figure 2 Describes the method innovation of scene co-construction, Figure 3 Meta describes the consensus method of commercial co-construction, a system for building commercial co-construction in the metaverse scenario, forming Figure 4 business environment to build a system.

[0174] A system for building commercial co-construction of metaverse scenarios, creating the ecology of original universe scenarios, solving user authority consensus, resource library sharing, private domain data security, confidential data cleaning, commercial co-construction solutions, individual user creation collaboration, group data assets, and material available invisible mechanism optimization, with a consensus method for commercial co-construction ( Figure 4 ) provides the world-class metaverse with metaverse materials suitable for private domains.

[0175] When private domain materials generate connection errors or fatal errors, the system will lock the erroneous resources through redundant links to avoid paralysis of peripheral systems. This co-construction method is not limited to commercial co-construction, but also provides an expansion method for multi-dimensional scene co-construction. In the construction of private domain metaverse, third parties can use the company's method to conveniently verify the availability of materials and respond to development feedback at a very fast speed.

[0176] Some users can quickly generate connectable materials through 3D scanning equipment, and participate in creation in a simple way, thereby providing a more optimized volume contribution to the scale and commercial purposes of the private domain system. This provides an excellent revenue-generating platform for private domain metaverse managers.

[0177] Co-construction of metaverse scenarios.

[0178] A system for building commercial co-construction of metaverse scenarios, which verifies identity by having users access a unique login interface. Verified users can choose between experience user and creative user identities.

[0179] These two identities can exist side by side for a single user.

[0180] Users can create their own metaverse materials or metaverse models to form a private data lake.

[0181] like Figure 1 As shown, users log in to the system using scientific consensus and security layer verification.

[0182] A system for building commercial co-construction of metaverse scenarios, which provides a metaverse model library. Users can easily connect their own models based on the model library.

[0183] The materials that have passed the review are stored in the material server by the system.

[0184] When a material is approved, digital assets are generated for individual users, data assets are generated for team users, and distributed accounting proof is provided to users through the blockchain system in the application.

[0185] The methods for users to achieve co-construction include but are not limited to independent development, generation through 3D scanning technology, and materials generated by scanning or generating digital models through future devices. The system is used to achieve rapid creation. After the identity of the user's materials is encrypted and data is proved, the entire material creation process is completed.

[0186] Users can quickly create scene interfaces, simple interfaces and interactive interfaces through the background, and use self-created materials to form digital reality scenes.

[0187] It provides users with an interface for building metaverse scenes. The security, intelligence and guidance functions of the interface are created based on the development of the entire system and global needs. During use, users can not only solve common problems and solutions for creation, but also ensure that when users create materials, the parameter indicators of the materials meet the direct connection with the world model.

[0188] The electronic signature of the material ensures the legal rights of the creator.

[0189] When the material successfully created by a user is used by other users, the IA system will reversely verify the creator's USER CODE digital signature, confirm the creator's identity and allocate data asset (digital asset for individuals) benefits to him or her.

[0190] Users can query and manage their own data assets (digital assets) when logging in to the backend, and can view the material income in the account. At this point, the creation process is complete.

[0191] A system for building a commercial co-construction of metaverse scenarios, providing a fully scalable metaverse model framework, including but not limited to consensus scenarios such as metaverse system, metaverse scenario, metaverse business, metaverse co-construction, metaverse experience, metaverse blockchain, metaverse data assets, and metaverse digital assets. The modeling method of each scenario uses traceable digital signature information.

[0192] There is a subordinate relationship between the material signature information of the secondary creator and the digital signature information of the system. When the system assets change, the modeling digital signature will actively update the asset change information to the associated user signature account to ensure the real-time effectiveness of the quantity, scale and user benefits of the system assets.

[0193] This process is a complex set of workflows, the main processes of which are:

[0194] Top-level architecture specifications for system model construction.

[0195] System model material connection parameter specifications.

[0196] System model material review management parameters.

[0197] System model material matching standard review.

[0198] User authentication (SSO) consensus system.

[0199] System integration module processing.

[0200] Model access specifications (classification, certification, confidentiality cleaning, consensus algorithm).

[0201] Data management system for individual and group users.

[0202] Business consensus authentication.

[0203] Business model building and material connection.

[0204] The commercial co-construction phase of the metaverse scenario has been completed.

[0205] The verification algorithm steps include data logic operation steps and verification function writing steps.

[0206] Data logic operation steps: Build a large model, define data classification into clear types and automatic screening models, and store the data that meets the requirements;

[0207] Steps for writing a verification function: Perform data classification operations through different classifications of various data to ensure accurate data processing, and store the data in the database after labeling.

[0208] IA calculation steps: Through the assistant model algorithm, the data is re-checked and redundancy cleaned to ensure the classification and cleaning of data and historical data, so that users can accurately select accurate data when retrieving data in the model.

[0209] Data watermarking step: The system embeds unique encrypted watermark information for the ownership information of each digital asset. The watermark content includes the user's N ID and public key, and the ownership information is shared on the blockchain.

[0210] Output result steps: The system pulls up the data set information, responds to user requests, and determines the operation permissions. Authorization is required to be distributed to the traceability user to apply for authorization. After authorization is passed, logical operations and verification are performed. After verification, IA calculation is performed according to the needs initiated by the user. After the data set is reorganized, data watermarking is performed to generate new encrypted information and save it to the agricultural blockchain system and user background. The encrypted information is Figure 2 The materials of various large models in the exploration application platform provide a safe and reliable traceability verification consensus. This completes the process.

[0211] Innovations in consensus mechanism systems, such as Figure 3 As shown, the consensus mechanism creates verification of the third interface through a random SDK.

[0212] The verification logic includes consensus mechanisms such as sharing mechanism, encryption consensus, decryption consensus, authorized access, financial consensus and resource sharing.

[0213] The consensus mechanism expansion in different areas of the system has a consensus standard that is different from the ordinary expansion method. This standard is in line with the system workflow, and all key operations will be recorded in the system operation log.

[0214] The working principles of the consensus mechanism are:

[0215] Sharing mechanism: The interface algorithm between the private domain and the public domain is used to connect the subject that reaches consensus with the system. The sharing mechanism is generated by the encryption consensus and the decryption consensus to perform logical operations together, and then issue an operation request instruction set to the system. The instruction set includes the data set and functional interface information required for the application, and different interface information calls the verification and authentication of different interfaces. Only operations that pass the authentication will be executed by the system, otherwise the system will record abnormal operation information.

[0216] Cryptographic consensus: refers to the result of a third-party user referencing the encryption formula in the system to perform logical operations. The result also contains the user's public key information for identity identification.

[0217] Decryption consensus: refers to the result of a third-party user referencing the encryption formula in the system to perform a logical operation. The public key information and private key information in the result are decrypted using an undisclosed algorithm to determine whether the third party has the authority to access the system.

[0218] Authorized access: When a third-party user reaches a cryptographic consensus with the system and receives a result of system verification, the system interface becomes available.

[0219] Financial consensus: Among the consensus mechanisms of the system, it is not limited to financial consensus. It also includes other necessary consensus mechanisms. This type of consensus has financial attribute operations and verification authentication. Its general process is to obtain encrypted request instructions - verify user permissions - pass authentication - generate financial attribute data - reverse proof and issue financial attribute information - users obtain financial information and record operation data - the system obtains financial information and records operation data. ("—" indicates the transition of consensus logic nodes, which is used to distinguish the expressions of different logic nodes)

[0220] Resource sharing: Resource sharing is an important part of the commercial co-construction of the Metaverse scenario, and is one of the key points that distinguish this system from other systems. User-created materials are contributed to the private domain in the form of resource sharing, and are connected to the public domain (World Metaverse). When multiple private domain platforms use a resource at the same time, superimposed benefits will be generated at the same time. This protects the legitimate rights and interests of creators to generate income.

[0221] Operation log: System log and user log are indispensable to ensure system security and program development. Operation log is written and deployed during system development according to system security requirements.

[0222] The expansion module refers to the large-scale expansion content of the Metaverse scenario business co-construction system implemented by the third-party cooperation organization based on the present invention. For example, the Metaverse scenario has built a 20,000 square meter "world" map, and its business model has N types. When the third party needs to continue to increase 2,000 square meters, the third party should comply with the present invention and follow the technical interface rules for development and design, so that the Metaverse scenario and business model designed by it can be seamlessly connected, realize data sharing, and smooth scene switching.

[0223] At this point, the consensus mechanism system workflow ends.

[0224] Consensus method for business co-construction ( Figure 4). The first consensus step is private domain consensus. Two users create their own private domain models and can repeatedly create metaverse materials. When two identical materials are used by the system at the same time, the system will perform AI matching based on the properties and appearance characteristics of the two materials, and recommend them to a more suitable commercial co-construction module to protect their respective rights and interests. The second is extended consensus. There are many personalized and overlapping materials in the world. The system will evaluate materials in the multi-dimensional data intelligent recommendation system. The evaluation dimensions include but are not limited to user views, user usage, ease of material creation, material adjustability, origin, usage scope recommendations, etc. It also includes comprehensive evaluation results of material usage prices, historical data and other dimensions for recommendation.

[0225] The self-learning ability of the consensus method. There are a huge model system and material cluster in the system. The system uses self-learning to record each consensus method and compare different logics. The results generated by the system will be automatically recommended by the system, and big data calculations will be performed regularly to optimize the consensus method to improve the intelligence of the system.

[0226] The generation and confirmation of digital assets form a "data bank" model, reshaping the authorization pattern. The steps are as follows: the generation of digital assets is an interaction with the system. When applying for authorization operations or providing valuable data to the system, the system will generate digital assets, which will be distributed to user accounts. The confirmation of user assets is mainly carried out through legality, compliance, authentication conditions and authorization letters. The workflow and record storage of the entire background system form the authorization mechanism of the "data bank". When the private domain obtains assets, the program will update the data of the entire system to ensure the real-time and validity of the data.

[0227] The logic of the open public platform has been optimized in terms of data security and operation procedures. The system service scope covers the co-construction mechanism and the authentication and confirmation mechanism. It is a platform to protect the rights and interests of personal digital assets and group data assets of all people. The system has an open participation mechanism, a harmonious ecosystem, and a secure consensus mechanism. It is a pioneer in providing metaverse co-construction and consensus standards, and an innovative platform for the development of the national digital field.

[0228] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A system for building commercial co-construction of metaverse scenarios, It is characterized in that The following steps are included: user login authority acquisition login verification step, data collection step, compilation step, storage step, labeling step, IA calculation step, asset evaluation step, verification algorithm step, blockchain certification step, distribution verification step, security step, digital asset generation and confirmation step, and digital asset distribution and certification step.

2. A system for building a commercial co-construction of a metaverse scenario according to claim 1, It is characterized in that The steps for obtaining user cross-domain login permissions and login verification include the following steps: When a user logs into the system, the WEB3.0 cross-domain login mechanism is called, and the user enters the login username and password at the login entrance of any site in the cluster or chooses a third-party quick login method to log in. When the user visits between websites in the cluster, the system automatically identifies whether the current user is logged in and determines whether the login status is valid. If valid, the system automatically records the user's access operation record. If invalid, the visitor is regarded as an ordinary browser of the program and has no right to operate restricted content. Data collection steps: Provide users with scene building and creation space through individual user creation, team user creation and system recommendation building models, and experience users being allowed by the system to create new scenes in their own space. Storage steps: The results created by the user are saved to the user information table by the system in case the user logs out and logs in again to save their own creations. Assembly step: The system aggregates and recompiles user data and the system large model interface, which is the guarantee for the interaction between elements and large models. The assembly step includes the system receiving new element materials uploaded by users, submitting them to the material server through users, Labeling step: the operation of classifying and labeling the target data. IA calculation steps: Through the assistant model algorithm, the data is re-checked and redundancy cleaned to ensure the classification and cleaning of data and historical data, and to ensure that users can accurately select accurate data when retrieving data in the model; Asset evaluation step: It is divided into digital asset evaluation step, integrity data evaluation step, and regulatory data evaluation step. The asset evaluation step is to read the target data and perform modular cleaning to avoid sensitive information and confidential information. Then the system calls the data evaluation module of the IA intelligent assistant system according to the data type for data evaluation. The data evaluation will evaluate the user data asset data set, the integrity data set, and the regulatory data respectively. When all three sets of data are TRUE, the data evaluation step ends. Business data evaluation steps: The scope of business data evaluation includes system virtual business scene data and user business data. The method of business data evaluation is that the system reads the business data and performs a "labeling step" on the group of business data. After completing the "labeling step", the group of data is subjected to secondary redundancy of the Metaverse model library by the system according to the data evaluation steps. The evaluation steps of data assets are as follows: users (individual users, corporate users or regulatory users) log in and submit operation requests to enter the IA right confirmation compilation system, clean the user data, and hand over the valuable data after cleaning to the IA system, agricultural blockchain, and IA deployment for distributed storage. The processed data set is handed over to the evaluation system to generate data elements. The data is compiled through data flow, resource integration, and value mining to form a decision model. The decision model undergoes the audit and warehousing process of the system module, and the audit and warehousing are completed. Credit data evaluation steps: The credit data evaluation step is to automatically pull up the credit evaluation module regularly or irregularly, and classify and summarize the user data. The classification step is to read the user data and divide it into basic information, real-name information, performance information, and interactive information. The summary step is to conduct a comprehensive evaluation of the user classification information. Regulatory data evaluation steps: Regulatory data includes user (individual or enterprise) data and system log data. The regulatory data evaluation steps include the sum of user assets being equal to the sum of system assets, and the system determining that there is no abnormality. When the system determines that there is an abnormality, the system will automatically analyze the abnormal data and make adjustments. If it cannot be automatically adjusted through the AI ​​system, the regulatory system will give early warning information feedback to the monitoring system of the super management background. The monitoring system will issue an early warning based on the severity of the regulatory feedback. The regulatory feedback is divided into financial early warning, data abnormality early warning, attack early warning, user operation abnormality early warning, and system data abnormality early warning. When the system cannot automatically process the early warning, the staff will intervene as soon as possible to avoid greater errors in the system. The verification algorithm steps include data input steps and verification function writing steps. Data input steps: clarify the input data type and automatic screening model, and store the qualified data into the database; Steps for writing verification functions: perform data classification operations through different classifications of various data to ensure accurate data processing, and store the data in the database after labeling; Build and use the model steps: write the internal test version, verify the operation of each step, analyze the results and optimize the model; Output result step: According to the return value of the usage model, input the verification result to determine whether the solution is correct; Blockchain proof step: Encrypt the correct content of the returned result. The program verifies user permissions and data authorization. After the calculation is passed, the server responds to the user's operation request and distributes the data. If the data distribution here involves sensitive information, it will also receive double confirmation from the blockchain verification. The user end confirms and verifies the data and passes the verification; Distribution verification steps: including user login status, user operation permissions, metaverse model library analysis, and material server response. When all actions are passed, the system will write the user's operation into the database and conduct blockchain proof (the blockchain interface will be triggered only if digital asset content is involved). Distribution verification is for the material provision and revenue confirmation of third-party material providers. Security steps: Security is divided into operation security, data security, model security, and access security. The method for judging operation security is to verify the user's login status and check whether the user's operation permissions are restricted. The logic algorithm of the security step is: get the USDK value of the user login variable, check whether it is normal if it exists, if it is normal, the system returns the user login status to normal, otherwise it returns an exception and jumps to the login page. The user login status is normal, the user enters the system to operate, and during the operation, the system will perform a security check on the user operation. The steps of digital asset generation and confirmation: The confirmation compilation system performs intelligent processing according to logical requirements. The confirmed data will be re-labeled in the form of tags, and the labeled data set information will form data element information. Distribution and certification steps of digital assets: digital assets will be sent to user accounts, and the user's private data will be certified by the system through redundant processing and commercial co-construction consensus methods; only certified user data may be defined as data assets or digital assets and generate income; Distribution and certification steps of digital assets: Use the logic algorithm to read the digital asset value AN1 to be distributed from the system, then retrieve the user information contained in AN1, and perform distribution retrieval based on the user's NID. If the retrieval is verified, the digital assets that the user deserves will be "transferred" from the system account to the user account, the system account will reduce holdings, and the user account will increase holdings. During the entire data asset distribution process, the system will use the agricultural blockchain algorithm to perform blockchain certification on the distribution records.

3. A system for building a commercial co-construction of a metaverse scenario according to claim 1, It is characterized in that The labeling step contains the following steps: The consensus labeling of the value of the data asset is processed as follows: Step 1: The default user is logged in. User data collection completes the integration of internal and external data, and performs DB distributed storage and IA deployment. The data includes database data sets, videos, pictures, documents, and audio digital data. Step 2. The IA title confirmation compilation system provides a verification encryption algorithm for data and user operations through the blockchain system (referring to the agricultural blockchain system) (this algorithm is consistent with the encryption fingerprint algorithm), and returns the correct return value to the title confirmation compilation system. The title confirmation compilation system performs intelligent processing according to logical requirements, verifies erroneous operations, and rolls back to ensure the integrity and immutability of the system. The data that has been confirmed will be re-labeled in the form of a label, and the labeled data set information forms data element information. Data element set: refers to the collective term for data element information formed by each user and system in the system for the labeled data set information.

4. A system for building a commercial co-construction of a metaverse scenario according to claim 1, It is characterized in that The compilation step includes the following steps: At this time, the Metaverse Model Library confirms the established type of the element material and marks it with a label. The scope of the label includes but is not limited to "land resources", "space resources", "building resources", "agricultural resources", and "knowledge resources". After the label is confirmed, the system performs a secondary compilation according to the interface rules of the large model. The compilation at this time includes proofreading of material parameters, code security checks, and comprehensive verification of material model matching. The element materials that pass the verification have completed the compilation step, and the user elements that have completed the compilation step will be recommended by the system to the large model selection scene.

5. According to claim 1, a system for building a commercial co-construction of a metaverse scenario, It is characterized in that The integrity data evaluation step contains the following steps: Integrity data includes user (individual or enterprise) real-name information, license information, performance data, access habits, and scene contribution data content. Integrity data evaluation is an important reference for comprehensively evaluating user contribution and performance capabilities. The user's integrity data evaluation value will affect their operating permissions or purchase and interaction restriction mechanisms within the scene. The evaluation results are recorded in percentages. The higher the percentage score, the higher the integrity index. The evaluation rules are basic information 10% + real-name information 10% + performance information 30% + interaction information 40% + other information 10%. When there is negative information from multiple feedback in the interactive information, the system will deduct it, which may result in a negative number. When the integrity index is lower than a certain value, the system interaction function will impose necessary restrictions to encourage users to "build" their own excellent virtual image in the big model.

6. A system for building a commercial co-construction of a metaverse scenario according to claim 1, It is characterized in that The asset evaluation step contains the following steps: the evaluation results are divided into TRUE and FALSE, TRUE means that the data evaluation is successful and the group of data can be used for subsequent operations, FALSE means that the data evaluation fails, the group of data is restored, and the data evaluation step is repeated again for the next evaluation.

7. A system for building a commercial co-construction of a metaverse scenario according to claim 1, It is characterized in that The commercial data evaluation step includes the following steps: the group data will generate a 1-bit value from 0 to 1, 0 means that the group of commercial data has no value, 0.1 means that the group of data can generate 0.1 data value for the user, the minimum value of each value increase is 0.1, 1 means that the group of data has the highest commercial value and can generate 1 data value for the user. When the data value of the group of data is greater than 0, the system will return the commercial data evaluation value to the user account, and the user account data assets will increase.

8. According to claim 1, a system for building a commercial co-construction of a metaverse scenario, It is characterized in that The security steps include the following steps: Data security includes system log records, user operation behavior records, and the reading, writing, and verification security when users acquire assets (data assets, digital assets, points, and other valuable information) or financial records. Model security refers to whether the designed and developed elements meet the interface requirements of the metaverse scenario when users perform various operations or upload co-construction model elements. If they meet the requirements, they are allowed to access the system. If they do not meet the requirements, modification suggestions are given. Access security includes two aspects of verification: one is the determination of user login status and operation permissions, and the other is whether the elements provided by the user meet the mechanism of commercial co-construction of the metaverse scenario.

9. A system for building a commercial co-construction of a metaverse scenario according to claim 1, It is characterized in that The generation and confirmation of digital assets includes the following steps: The generation and confirmation of digital assets is that the system combines redundant system data into a new data set suitable for the current user through user operations. Through multiple user operations and other users' attention to this data set, the system automatically generates quantitative digital assets for distribution. The system is carried out strictly in accordance with the development plan and performs quantitative AI calculations and evaluations based on the value of user data. The quantitative formula is: M=SS / ((MS+Ut)×(MS / 6)), M: represents the digital asset value, SS: indicates the total amount of the same type of system data. MS: indicates the total amount of unconfirmed user data. UT: indicates the total time contributed by the current user. When M≥0.01, the system analyzes that the data group has no confirmed rights, and the digital asset value is increased by 0.1, otherwise it will not be increased and wait for the next combined evaluation. If the data has been authenticated, no further analysis will be performed. For verified data, the digital twin system will adopt the data content by default and will not perform secondary verification or validation operations to improve system operation efficiency.

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