A spatiotemporal constraint-based blockchain digital collectible dynamic generation method and system

Abstract

The application discloses a kind of spatiotemporal constraint's blockchain digital collectible dynamic generation method and system, it is related to blockchain technology field, including, digital collectible technical service provider carries out digitization collection and modeling based on multi-view three-dimensional reconstruction technology to physical entity, generates high-precision initial three-dimensional model;The initial three-dimensional model is bound with the environmental data of the physical entity that is collected in real time by sensor network, and the digital twin model of the physical entity is constructed;Intelligent contract is deployed on blockchain, and the preset spatiotemporal constraint condition is defined as the verifiable rule in the intelligent contract;When user client meets the spatiotemporal constraint condition, it submits its spatiotemporal information to the intelligent contract Zero-knowledge proof that the verifiable rule is met.The application evolves initial three-dimensional model, generates final digital collectible file, finally the hash value and metadata of the file are notarized in blockchain, complete casting.

Description

Technical Field

[0001] This invention relates to the field of blockchain technology, and in particular to a method and system for dynamically generating blockchain digital collectibles under spatiotemporal constraints. Background Technology

[0002] The technology for dynamically generating digital artifacts is evolving from static to dynamic interactive models. Existing solutions generate high-precision models through multi-view 3D reconstruction and integrate digital twins and smart contracts to achieve basic dynamism. For example, sensors monitor the state of the entity and map it to the model, or AR interaction is triggered based on preset spatiotemporal conditions. These technologies provide support for scenarios such as the digitization of cultural heritage.

[0003] Existing technologies suffer from insufficient integration of dynamic generation and spatiotemporal constraints. Their verification mechanisms largely rely on user-submitted plaintext spatiotemporal data, posing privacy risks and being difficult to prevent tampering. Furthermore, the dynamic generation process is disconnected from the continuous state of the physical world, failing to achieve irreversible evolution based on the cumulative effect of environmental data, thus limiting the depth of application of digital collections in high-end cultural preservation and other scenarios. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides a method for dynamically generating blockchain digital collectibles with spatiotemporal constraints, which solves the problems of poor privacy in spatiotemporal constraint verification and lack of correlation with the continuous state of the physical world in the existing technology.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: In a first aspect, the present invention provides a method for dynamically generating blockchain digital collectibles under spatiotemporal constraints, which includes a digital collectibles technology service provider digitally collecting and modeling physical entities based on multi-view 3D reconstruction technology to generate a high-precision initial 3D model. The initial 3D model is bound to the environmental data of the physical entity collected in real time through a sensor network to construct a digital twin model of the physical entity; Deploy smart contracts on the blockchain and define preset spatiotemporal constraints as verifiable rules in the smart contracts; When the spatiotemporal constraints are met, the user client submits a zero-knowledge proof to the smart contract that its spatiotemporal information satisfies the verifiable rules. After verifying the zero-knowledge proof, the smart contract triggers an AI-driven dynamic generation algorithm based on real-time environmental data in the digital twin model to evolve the initial 3D model and generate the final digital collection file. The hash value and metadata of the final digital collection file are stored on the blockchain, thus completing the creation of the digital collection.

[0007] As a preferred embodiment of the spatiotemporally constrained blockchain digital collectible dynamic generation method described in this invention, the digital collectible technology service provider performs digital acquisition and modeling of physical entities based on multi-view 3D reconstruction technology to generate a high-precision initial 3D model, including the following steps: Digital collection technology service providers perform multi-view image acquisition to obtain multi-perspective image data of physical entities; The multi-view image data is preprocessed and calibrated to eliminate lens distortion and achieve feature point matching, generating registered multi-view image data; Registered multi-view image data are used to generate an initial 3D model with normal maps and high-precision textures through surface reconstruction and material estimation techniques; The initial 3D model simulates the surface properties of materials using physical rendering technology, generating a high-precision initial 3D model.

[0008] As a preferred embodiment of the spatiotemporally constrained blockchain digital collectible dynamic generation method of the present invention, the method involves binding the initial 3D model with the environmental data of the physical entity collected in real time through a sensor network to construct a digital twin model of the physical entity, including the following steps: Sensor networks collect environmental data from physical entities. The environmental data is aggregated and standardized in format through an IoT gateway to generate a standard format environmental data stream. The standard format environmental data stream and the initial 3D model are mapped and associated in the digital twin engine; The digital twin engine dynamically adjusts the material or shape parameters of the initial 3D model based on the mapping relationship to generate a digital twin model of the physical entity.

[0009] As a preferred embodiment of the method for dynamically generating blockchain digital collectibles with spatiotemporal constraints as described in this invention, the method includes: deploying a smart contract on the blockchain and defining the preset spatiotemporal constraints as verifiable rules in the smart contract, comprising the following steps: In a blockchain development environment, smart contract code containing pre-defined time and space constraints is written. The smart contract code is then compiled and optimized by a compiler to generate smart contract bytecode. The smart contract bytecode is sent to the blockchain network through the transaction interface of the blockchain node. The consensus nodes in the blockchain network verify the smart contract bytecode and execute the deployment operation to generate the deployed smart contract. The addresses and preset spatiotemporal constraints of deployed smart contracts are recorded on the blockchain, thus completing the definition of verifiable rules.

[0010] As a preferred embodiment of the spatiotemporal constraint-based blockchain digital collectible dynamic generation method described in this invention, the user client submits a zero-knowledge proof to the smart contract that its spatiotemporal information satisfies the verifiable rules when the spatiotemporal constraint conditions are met, including the following steps: The user client calls the device positioning and timing service to obtain the user's current geographic coordinates and standard timestamp. The user's current geographic coordinates and standard timestamp, together with the preset spatiotemporal constraints read from the smart contract, are input into the local zero-knowledge proof generation circuit. The local zero-knowledge proof generation circuit performs a logical compliance evaluation on the user's current geographic coordinates and standard timestamp based on preset spatiotemporal constraints, and generates a zero-knowledge proof. The user client sends the generated zero-knowledge proof as a transaction parameter to the smart contract address on the blockchain network.

[0011] As a preferred embodiment of the spatiotemporally constrained blockchain digital collectible dynamic generation method of the present invention, the smart contract, after verifying the zero-knowledge proof, triggers an AI-driven dynamic generation algorithm based on real-time environmental data in the digital twin model to evolve the initial three-dimensional model and generate the final digital collectible file, including the following steps: Once a deployed smart contract successfully verifies the zero-knowledge proof submitted by the user client on the blockchain, it generates a smart contract event indicating that the verification has passed. The smart contract event is listened to and captured by the digital twin engine, triggering the execution process of the AI-driven dynamic generation algorithm. The AI-driven dynamic generation algorithm reads the environmental data of the physical entity collected in real time by the sensor network in the digital twin model as input parameters. AI-driven dynamic generation algorithms calculate and adjust the material or shape properties of the initial 3D model based on the input environmental data parameters. The adjusted initial 3D model is then used by the rendering engine to generate the final digital collection file.

[0012] As a preferred embodiment of the spatiotemporally constrained blockchain digital collectible dynamic generation method of the present invention, the method includes: storing the hash value and metadata of the final digital collectible file on the blockchain to complete the creation of the digital collectible, comprising the following steps: A cryptographic hash function is applied to the final digital collection file to generate the hash value of the final digital collection file. The hash value of the final digital collection file and the associated metadata are combined and formatted into a metadata record to be stored. The metadata record to be notified is digitally signed by the user client's private key to generate a signed metadata record transaction, which is then broadcast to the blockchain network. The consensus nodes of the blockchain network verify the validity of the signed metadata record transaction. Once the verification is successful, the metadata record is written into the distributed ledger of the blockchain, completing the creation of the digital collectible.

[0013] Secondly, the present invention provides a spatiotemporally constrained blockchain digital collectible dynamic generation system, including a data acquisition module, wherein a digital collectible technology service provider performs digital acquisition and modeling of physical entities based on multi-view 3D reconstruction technology to generate a high-precision initial 3D model. The module binds the initial 3D model with the environmental data of the physical entity collected in real time through a sensor network to construct a digital twin model of the physical entity. The deployment module deploys smart contracts on the blockchain and defines preset spatiotemporal constraints as verifiable rules in the smart contracts. The zero-knowledge proof module allows the user client to submit a zero-knowledge proof to the smart contract that its spatiotemporal information satisfies the verifiable rules when the spatiotemporal constraints are met. The evolution module, after the smart contract verifies the zero-knowledge proof, triggers an AI-driven dynamic generation algorithm based on real-time environmental data in the digital twin model to evolve the initial 3D model and generate the final digital collection file; The notarization module notifies the final digital collection file of its hash value and metadata on the blockchain, thus completing the creation of the digital collection.

[0014] Thirdly, the present invention provides a computer device including a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, it implements any step of the spatiotemporally constrained blockchain digital collectible dynamic generation method as described in the first aspect of the present invention.

[0015] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the spatiotemporally constrained blockchain digital collectible dynamic generation method as described in the first aspect of the present invention.

[0016] The beneficial effects of this invention are as follows: A high-precision initial 3D model is generated by digitally acquiring and modeling a physical entity using multi-view 3D reconstruction technology. This model is then bound to environmental data collected in real-time by a sensor network to construct a digital twin model. Subsequently, a smart contract defining spatiotemporal constraints is deployed on the blockchain. When a user client meets the spatiotemporal constraints, it submits a zero-knowledge proof to the smart contract to verify its spatiotemporal information. After successful verification, an AI-driven dynamic generation algorithm based on real-time environmental data from the digital twin model is triggered to evolve the initial 3D model, generating the final digital collection file. Finally, the hash value and metadata of this file are stored on the blockchain, completing the minting process. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 A flowchart of a method for dynamically generating blockchain digital collectibles under spatiotemporal constraints.

[0019] Figure 2 A schematic diagram of a blockchain-based digital collectibles dynamic generation system constrained by time and space. Detailed Implementation

[0020] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0021] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0022] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0023] Reference Figures 1-2 As one embodiment of the present invention, this embodiment provides a method for dynamically generating blockchain digital collectibles under spatiotemporal constraints, comprising the following steps: S1. Digital collection technology service providers use multi-view 3D reconstruction technology to digitally collect and model physical entities, generating high-precision initial 3D models.

[0024] S1.1 The digital collection technology service provider performs multi-view image acquisition to obtain multi-view image data of physical entities.

[0025] Furthermore, multiple cameras are set up around the physical entity or a single camera is controlled to move along a predetermined path to capture images of the physical entity from different angles and distances, ensuring sufficient overlap between adjacent images. The acquired multi-view image data contains the color information and two-dimensional structural information of the physical entity, thus completing the acquisition of multi-view image data.

[0026] S1.2 The multi-view image data is preprocessed and calibrated to eliminate lens distortion and achieve feature point matching, generating registered multi-view image data.

[0027] Furthermore, a correction transformation based on the camera intrinsic matrix and distortion coefficients is applied to each image in the multi-view image data to eliminate radial and tangential distortion. Then, a feature detection algorithm is used to extract scale-invariant feature transformation feature points from the multi-view image data. The correspondence between feature points in different images of the multi-view image data is established through nearest neighbor matching of feature descriptors. The matching pairs are optimized using a random sampling consensus algorithm. Finally, all camera pose parameters are optimized through bundle adjustment to make the three-dimensional coordinates of all feature points in the multi-view image data globally consistent with the two-dimensional projection. The output is a registered multi-view image data with distortion eliminated and consistent spatial relationships of feature points.

[0028] S1.3 The registered multi-view image data is used to generate an initial 3D model with normal maps and high-precision textures through surface reconstruction and material estimation techniques.

[0029] Furthermore, based on the registered multi-view image data and their corresponding camera poses, a dense 3D point cloud is calculated using a multi-view stereo vision algorithm. The Poisson surface reconstruction algorithm is then used to transform the 3D point cloud into a closed triangular mesh surface, which forms the basic geometry of the initial 3D model. Simultaneously, from the registered multi-view image data, the diffuse reflectance, specular coefficient, and other material parameters of each surface point are estimated using a photometric consistency optimization method. These parameters are then fused with color information to generate a high-precision texture map that seamlessly fits the triangular mesh surface. Surface normal information is also calculated to generate a normal map, resulting in an initial 3D model with complete geometry, texture, and material properties, complete with a normal map and high-precision texture.

[0030] S1.4 The initial 3D model simulates the surface properties of materials using physical rendering technology to generate a high-precision initial 3D model.

[0031] Furthermore, the initial 3D model with normal maps and high-precision textures is placed in a virtual lighting environment. Based on the triangular mesh surface, high-precision texture maps, normal maps, and estimated material parameters of the initial 3D model, a physically based rendering shading model is used to calculate the reflection, refraction, and scattering effects of light interacting with the model surface. The rendering equations are solved using global lighting methods such as path tracing or radiosity algorithms to generate a high-precision initial 3D model with realistic material representation, accurate shadows, and details under set lighting conditions.

[0032] S2. Bind the initial 3D model with the environmental data of the physical entity collected in real time through a sensor network to construct a digital twin model of the physical entity.

[0033] S2.1 The sensor network collects environmental data of physical entities. The environmental data is aggregated and standardized in format through the Internet of Things gateway to generate a standard format environmental data stream.

[0034] Furthermore, temperature and humidity sensors and light sensors deployed around physical entities continuously monitor environmental conditions and output analog or digital signals. The IoT gateway periodically polls or receives environmental data pushed by these sensors, converts environmental data with different vendor protocols and data formats into a unified JSON or XML format, and synchronizes and calibrates the timestamps to eliminate jitter in data transmission. Finally, it outputs an environmental data stream with a unified timestamp, clearly defined data fields, and a continuous time series standard format.

[0035] S2.2 The standard format environmental data stream and the initial 3D model are mapped and associated in the digital twin engine.

[0036] Furthermore, the digital twin engine loads a high-precision initial 3D model and instantiates it in the virtual scene. At the same time, it establishes a correspondence between specific data fields in the standard format environmental data stream and adjustable parameters of the initial 3D model. For example, it maps the illuminance data stream to the intensity attribute of parallel light sources in the initial 3D model scene, and maps the temperature and humidity data stream to a certain control variable in the material parameters of the initial 3D model. This mapping relationship is achieved through a predefined configuration table or mapping function, so that changes in the data stream can directly point to the adjustment target of the model parameters.

[0037] S2.3 The digital twin engine dynamically adjusts the material or shape parameters of the initial 3D model based on the mapping relationship to generate a digital twin model of the physical entity.

[0038] Furthermore, the digital twin engine reads the latest values ​​of the standard format environmental data stream in real time. Based on the established mapping relationship, it calculates the target values ​​of the material parameters or morphological parameters corresponding to the initial 3D model. For example, when the illuminance increases, the roughness of the model material is increased linearly to simulate the diffuse reflection effect under strong light. Or, when the temperature continues to exceed the threshold, the vertex shader parameters are gradually adjusted to make the model surface present a thermal deformation visual effect. Through real-time rendering loop, these parameter adjustments are continuously applied to keep the visual state of the initial 3D model synchronized with the real environmental state of the physical entity. Finally, a digital twin model of the physical entity that can dynamically respond to changes in the physical entity's environment is generated.

[0039] S3. Deploy smart contracts on the blockchain and define the preset spatiotemporal constraints as verifiable rules in the smart contracts.

[0040] S3.1 Write smart contract code containing preset time and space constraints in the blockchain development environment. The smart contract code is compiled and optimized by the compiler to generate smart contract bytecode.

[0041] Furthermore, the Solidity language is used to define the smart contract structure in the Remix integrated development environment. The preset spatiotemporal constraints are manifested as specific function decorators and conditional statements. For example, a decorator named duringEvent is defined to verify whether the block timestamp is within the preset start and end time range, and a function named inLocation is defined to calculate the spatial location by taking the input coordinate parameters and the preset geofence center point coordinates and radius. After the code is written, the Solidity compiler is called to perform syntax checking and static analysis on the smart contract code, converting the high-level language code into low-level smart contract bytecode that can run in the Ethereum Virtual Machine or similar blockchain environments.

[0042] S3.2 The smart contract bytecode is sent to the blockchain network through the transaction interface of the blockchain node. The consensus nodes in the blockchain network verify the smart contract bytecode and execute the deployment operation to generate the deployed smart contract.

[0043] Furthermore, the deployment transaction containing the smart contract bytecode is sent to a connected blockchain node via a JSON-RPC interface. This node broadcasts the transaction to the peer-to-peer network. After receiving the transaction, the consensus node in the network verifies its digital signature and gas fee, then executes the initialization code in the transaction to store the smart contract bytecode in the blockchain's state database, and returns a deployed smart contract address representing the contract instance after successful deployment.

[0044] S3.3 The addresses and preset spatiotemporal constraints of deployed smart contracts are recorded on the blockchain, completing the definition of verifiable rules.

[0045] Furthermore, the address of the deployed smart contract is recorded as a permanent identifier in the receipt of the transaction that generated the contract, while the preset spatiotemporal constraints, such as specific time window values ​​and geofence parameters, are directly encoded as constants or immutable variables within the bytecode of the deployed smart contract. This allows any user to obtain the address of the deployed smart contract by querying the blockchain and calling its public functions to verify these preset spatiotemporal constraints, thereby completing the definition of a transparent and tamper-proof verifiable set of rules.

[0046] S4. When the spatiotemporal constraints are met, the user client submits a zero-knowledge proof to the smart contract that its spatiotemporal information satisfies the verifiable rules.

[0047] S4.1 The user client calls the device positioning and timing service to obtain the user's current geographic coordinates and standard timestamp. The user's current geographic coordinates and standard timestamp, together with the preset spatiotemporal constraints read from the smart contract, are input into the local zero-knowledge proof generation circuit.

[0048] Furthermore, the user client obtains the user's current geographic coordinates in latitude and longitude format through the operating system's location service interface on the mobile device, and obtains a standard timestamp in Coordinated Universal Time format through the Network Time Protocol service. At the same time, the user client calls the public read-only function of the deployed smart contract through the query interface of the blockchain node to read the preset spatiotemporal constraints encoded therein, including the coordinates of the geofence center point, the effective radius, and the start and end timestamps of the time window. Subsequently, the user client passes this data as private and public inputs to the zero-knowledge proof generation circuit running locally. This circuit usually exists in the form of a pre-compiled WebAssembly library or a local library.

[0049] S4.2 The local zero-knowledge proof generation circuit performs a logical compliance evaluation on the user's current geographic coordinates and standard timestamp based on preset spatiotemporal constraints, and generates a zero-knowledge proof.

[0050] Furthermore, after receiving the input, the local zero-knowledge proof generation circuit performs arithmetic operations internally to calculate the square of the Euclidean distance between the user's current geographic coordinates and the coordinates of the preset geofence center point. It then verifies whether this squared distance is less than or equal to the square of the preset effective radius. Simultaneously, it verifies whether the user's current standard timestamp is greater than or equal to the preset start timestamp and less than or equal to the preset end timestamp. Only when all these conditions are met simultaneously will the circuit successfully execute and generate a cryptographic proof, which is the zero-knowledge proof. It can prove that the user's current geographic coordinates and standard timestamp satisfy all preset spatiotemporal constraints, but it will not reveal the specific values ​​of the user's current geographic coordinates and standard timestamp in the proof.

[0051] S4.3 The user client sends the generated zero-knowledge proof as a transaction parameter to the smart contract address on the blockchain network.

[0052] Furthermore, the user client constructs a transaction that calls the verification function in the deployed smart contract, uses zero-knowledge proof as the core call parameter of the transaction, and digitally signs the transaction using the user client's private key. Then, the signed transaction is sent out through the broadcast interface of the blockchain network, and finally the transaction carrying zero-knowledge proof parameters is transmitted to the target smart contract address.

[0053] S5. After the smart contract verifies the zero-knowledge proof, it triggers an AI-driven dynamic generation algorithm based on real-time environmental data in the digital twin model to evolve the initial 3D model and generate the final digital collection file.

[0054] S5.1 After a deployed smart contract successfully verifies the zero-knowledge proof submitted by the user client on the blockchain, it generates a smart contract event indicating that the verification has passed.

[0055] Furthermore, the verification function in the deployed smart contract receives the transaction submitted by the user client. This function calls the pre-built zero-knowledge proof verification library associated with the smart contract to perform on-chain cryptographic verification of the zero-knowledge proof in the transaction parameters. The verification process confirms the validity of the zero-knowledge proof and the correctness of its corresponding public input. Once the verification is successful, the smart contract executes an emit statement, issuing a log containing a specific event identifier and the initiating user address. This log constitutes a smart contract event of a verification successful instruction and is recorded in the transaction receipt on the blockchain.

[0056] S5.2 The smart contract event is listened to and captured by the digital twin engine, triggering the execution process of the AI-driven dynamic generation algorithm. The AI-driven dynamic generation algorithm reads the environmental data of the physical entity collected in real time by the sensor network in the digital twin model as input parameters.

[0057] Furthermore, a continuously running digital twin engine connected to the blockchain node subscribes to the blockchain event log and scans for new blocks in real time. When it scans a smart contract event containing a verification pass instruction with a specific event identifier, the digital twin engine immediately parses the event to obtain the relevant user context and launches an AI-driven dynamic generation algorithm. This algorithm then requests the data interface of the digital twin model to obtain the latest values ​​of environmental data of the physical entity collected in real time by the sensor network, such as the latest illuminance and temperature and humidity readings. These environmental data values ​​are passed as input parameters to the core processing logic of the AI-driven dynamic generation algorithm.

[0058] S5.3, the AI-driven dynamic generation algorithm calculates and adjusts the material or shape attributes of the initial 3D model based on the input environmental data parameters. The adjusted initial 3D model is then used by the rendering engine to generate the final digital collection file.

[0059] Furthermore, the AI-driven dynamic generation algorithm inputs the received environmental data parameters into a pre-trained generative adversarial network. This network outputs a set of adjustment coefficients corresponding to the material properties of the initial 3D model, such as base color, metallicity, and roughness, or a set of displacement vectors to drive subtle deformations at the vertex positions of the initial 3D model's mesh. Subsequently, the AI-driven dynamic generation algorithm applies these adjustment coefficients or displacement vectors to the initial 3D model, modifying its material properties or geometry to obtain an adjusted initial 3D model. Finally, the adjusted initial 3D model is sent to a physically based rendering engine. Based on the modified model data and preset lighting conditions, the rendering engine performs complete ray tracing rendering calculations and outputs a digital image with highly realistic visual effects or a 3D model file containing complete material and geometric information. This file is the final digital collectible file.

[0060] S6. Store the hash value and metadata of the final digital collection file on the blockchain to complete the creation of the digital collection.

[0061] S6.1 Apply a cryptographic hash function to the final digital collection file to generate the hash value of the final digital collection file. The hash value of the final digital collection file and the associated metadata are combined and formatted into a metadata record to be stored.

[0062] Furthermore, the SHA256 algorithm is used to perform a one-way hash operation on the binary content of the final digital collection file, generating a fixed-length hexadecimal string as the hash value of the final digital collection file. At the same time, descriptive information such as collection name, creator, generation timestamp, smart contract event identifier, and associated physical entity identifier are organized into metadata in JSON format. The hash value of the final digital collection file is merged with the metadata as a specific field in the JSON object to form a structured metadata record to be stored.

[0063] S6.2 The metadata record to be stored is digitally signed by the user client's private key to generate a signed metadata record transaction, which is then broadcast to the blockchain network.

[0064] Furthermore, the user client uses the elliptic curve digital signature algorithm private key stored in a secure environment to calculate a digital signature on the complete content of the metadata record to be notified. The signature, the metadata record to be notified itself, and the user's blockchain address are then encapsulated together to construct a standard transaction that conforms to the blockchain network transaction format. This transaction is the signed metadata record transaction. Subsequently, the user client broadcasts this signed metadata record transaction to the peer-to-peer network through the network interface connected to the blockchain node.

[0065] S6.3 The consensus nodes of the blockchain network verify the validity of the signed metadata record transaction. After successful verification, the metadata record is written into the distributed ledger of the blockchain, completing the creation of the digital collectible.

[0066] Furthermore, after receiving the signed metadata record transaction, the consensus node responsible for producing blocks in the blockchain network first verifies that the format and size of the transaction conform to the network rules. Then, it uses the public key corresponding to the sender's address in the transaction to verify the correctness of the digital signature to ensure that the transaction was indeed authorized by the private key holder. Next, it verifies that the account balance of the transaction sender's address is sufficient to pay the network transaction fee required for this transaction. After all verifications are passed, the consensus node packages the signed metadata record transaction into a new block. After the block reaches network consensus and is confirmed, the transaction and the metadata record to be notified contained therein are permanently recorded in the distributed ledger of the blockchain. This process marks the final completion of the digital collectible's creation.

[0067] This embodiment also provides a spatiotemporally constrained blockchain digital collectible dynamic generation system, including: a data acquisition module, in which a digital collectible technology service provider digitally acquires and models physical entities based on multi-view 3D reconstruction technology to generate a high-precision initial 3D model; The module binds the initial 3D model with the environmental data of the physical entity collected in real time through a sensor network to build a digital twin model of the physical entity. The deployment module deploys smart contracts on the blockchain and defines the preset spatiotemporal constraints as verifiable rules in the smart contracts. The zero-knowledge proof module allows user clients to submit zero-knowledge proofs to smart contracts that their spatiotemporal information satisfies verifiable rules when spatiotemporal constraints are met. The evolution module, after the smart contract verifies the zero-knowledge proof, triggers an AI-driven dynamic generation algorithm based on real-time environmental data in the digital twin model to evolve the initial 3D model and generate the final digital collection file. The notarization module stores the hash value and metadata of the final digital collectible file on the blockchain, thus completing the creation of the digital collectible.

[0068] This embodiment also provides a computer device applicable to the dynamic generation method of blockchain digital collectibles with spatiotemporal constraints, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to realize the dynamic generation method of blockchain digital collectibles with spatiotemporal constraints as proposed in the above embodiment.

[0069] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0070] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements the method for dynamically generating blockchain digital collectibles with spatiotemporal constraints as proposed in the above embodiments. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0071] In summary, this invention uses multi-view 3D reconstruction technology to digitally acquire and model physical entities, generating a high-precision initial 3D model. This model is then bound to environmental data collected in real-time by a sensor network to construct a digital twin model. Subsequently, a smart contract defining spatiotemporal constraints is deployed on the blockchain. When a user client meets the spatiotemporal constraints, it submits a zero-knowledge proof to the smart contract to verify its spatiotemporal information. After successful verification, an AI-driven dynamic generation algorithm based on real-time environmental data from the digital twin model is triggered to evolve the initial 3D model, generating the final digital collection file. Finally, the hash value and metadata of this file are stored on the blockchain, completing the creation process.

[0072] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for dynamically generating blockchain digital collectibles under spatiotemporal constraints, characterized in that: This includes digital collection technology service providers using multi-view 3D reconstruction technology to digitally collect and model physical entities, generating high-precision initial 3D models; The initial 3D model is bound to the environmental data of the physical entity collected in real time through a sensor network to construct a digital twin model of the physical entity; Deploy smart contracts on the blockchain and define preset spatiotemporal constraints as verifiable rules in the smart contracts; When the spatiotemporal constraints are met, the user client submits a zero-knowledge proof to the smart contract that its spatiotemporal information satisfies the verifiable rules. After verifying the zero-knowledge proof, the smart contract triggers an AI-driven dynamic generation algorithm based on real-time environmental data in the digital twin model to evolve the initial 3D model and generate the final digital collection file. The hash value and metadata of the final digital collection file are stored on the blockchain, thus completing the creation of the digital collection.

2. The method for dynamically generating blockchain digital collectibles with spatiotemporal constraints as described in claim 1, characterized in that: Digital artifact technology service providers use multi-view 3D reconstruction technology to digitally acquire and model physical entities, generating high-precision initial 3D models, including the following steps: Digital collection technology service providers perform multi-view image acquisition to obtain multi-perspective image data of physical entities; The multi-view image data is preprocessed and calibrated to eliminate lens distortion and achieve feature point matching, generating registered multi-view image data; Registered multi-view image data are used to generate an initial 3D model with normal maps and high-precision textures through surface reconstruction and material estimation techniques; The initial 3D model simulates the surface properties of materials using physical rendering technology, generating a high-precision initial 3D model.

3. The method for dynamically generating blockchain digital collectibles with spatiotemporal constraints as described in claim 2, characterized in that: The initial 3D model is bound to the environmental data of the physical entity collected in real time through a sensor network to construct a digital twin model of the physical entity, including the following steps: Sensor networks collect environmental data from physical entities. The environmental data is aggregated and standardized in format through an IoT gateway to generate a standard format environmental data stream. The standard format environmental data stream and the initial 3D model are mapped and associated in the digital twin engine; The digital twin engine dynamically adjusts the material or shape parameters of the initial 3D model based on the mapping relationship to generate a digital twin model of the physical entity.

4. The method for dynamically generating blockchain digital collectibles with spatiotemporal constraints as described in claim 3, characterized in that: Deploying smart contracts on a blockchain and defining preset spatiotemporal constraints as verifiable rules within the smart contracts includes the following steps: In a blockchain development environment, smart contract code containing pre-defined time and space constraints is written. The smart contract code is then compiled and optimized by a compiler to generate smart contract bytecode. The smart contract bytecode is sent to the blockchain network through the transaction interface of the blockchain node. The consensus nodes in the blockchain network verify the smart contract bytecode and execute the deployment operation to generate the deployed smart contract. The addresses of deployed smart contracts and their preset spatiotemporal constraints are recorded on the blockchain, thus completing the definition of verifiable rules.

5. The method for dynamically generating blockchain digital collectibles with spatiotemporal constraints as described in claim 4, characterized in that: When the spatiotemporal constraints are met, the user client submits a zero-knowledge proof to the smart contract that its spatiotemporal information satisfies the verifiable rules, including the following steps: The user client calls the device positioning and timing service to obtain the user's current geographic coordinates and standard timestamp. The user's current geographic coordinates and standard timestamp, together with the preset spatiotemporal constraints read from the smart contract, are input into the local zero-knowledge proof generation circuit. The local zero-knowledge proof generation circuit performs a logical compliance evaluation on the user's current geographic coordinates and standard timestamp based on preset spatiotemporal constraints, and generates a zero-knowledge proof. The user client sends the generated zero-knowledge proof as a transaction parameter to the smart contract address on the blockchain network.

6. The method for dynamically generating blockchain digital collectibles with spatiotemporal constraints as described in claim 5, characterized in that: After verifying the zero-knowledge proof, the smart contract triggers an AI-driven dynamic generation algorithm based on real-time environmental data in the digital twin model to evolve the initial 3D model and generate the final digital collection file, including the following steps: Once a deployed smart contract successfully verifies the zero-knowledge proof submitted by the user client on the blockchain, it generates a smart contract event indicating that the verification has passed. The smart contract event is listened to and captured by the digital twin engine, triggering the execution process of the AI-driven dynamic generation algorithm. The AI-driven dynamic generation algorithm reads the environmental data of the physical entity collected in real time by the sensor network in the digital twin model as input parameters. AI-driven dynamic generation algorithms calculate and adjust the material or shape properties of the initial 3D model based on the input environmental data parameters. The adjusted initial 3D model is then used by the rendering engine to generate the final digital collection file.

7. The method for dynamically generating blockchain digital collectibles with spatiotemporal constraints as described in claim 6, characterized in that: The hash value and metadata of the final digital collectible file are stored on the blockchain to complete the creation of the digital collectible, including the following steps: A cryptographic hash function is applied to the final digital collection file to generate the hash value of the final digital collection file. The hash value of the final digital collection file and the associated metadata are combined and formatted into a metadata record to be stored. The metadata record to be notified is digitally signed by the user client's private key to generate a signed metadata record transaction, which is then broadcast to the blockchain network. The consensus nodes of the blockchain network verify the validity of the signed metadata record transaction. Once the verification is successful, the metadata record is written into the distributed ledger of the blockchain, thus completing the creation of the digital collectible.

8. A spatiotemporally constrained blockchain digital collectible dynamic generation system, based on the spatiotemporally constrained blockchain digital collectible dynamic generation method according to any one of claims 1 to 7, characterized in that: This includes a data acquisition module, where digital collection technology service providers use multi-view 3D reconstruction technology to digitally acquire and model physical entities, generating high-precision initial 3D models; The construction module binds the initial 3D model with the environmental data of the physical entity collected in real time through a sensor network to construct a digital twin model of the physical entity; The deployment module deploys smart contracts on the blockchain and defines preset spatiotemporal constraints as verifiable rules in the smart contracts. The zero-knowledge proof module allows the user client to submit a zero-knowledge proof to the smart contract that its spatiotemporal information satisfies the verifiable rules when the spatiotemporal constraints are met. The evolution module, after the smart contract verifies the zero-knowledge proof, triggers an AI-driven dynamic generation algorithm based on real-time environmental data in the digital twin model to evolve the initial 3D model and generate the final digital collection file; The notarization module notifies the final digital collection file of its hash value and metadata on the blockchain, thus completing the creation of the digital collection.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the method for dynamically generating blockchain digital collectibles with spatiotemporal constraints as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the method for dynamically generating blockchain digital collectibles with spatiotemporal constraints as described in any one of claims 1 to 7.

Images