Digital lifentity consciousness alignment and cross-platform migration method and electronic device

Abstract

The application provides a digital life consciousness alignment and cross-platform migration method and electronic equipment, and belongs to the technical field of digital life. The method encapsulates the current state of the core consciousness data of the digital life in the source platform into a standardized and serializable consciousness data packet, then extracts the semantic embedding vector of the core node from the consciousness data packet, generates a semantic fingerprint vector through an aggregation algorithm, and transmits the encapsulated consciousness data packet and the semantic fingerprint vector to the target platform through a DL-CAP platform, so that the target platform can map the semantic fingerprint vector to its own semantic space through a semantic adapter to obtain a mapped semantic vector, and perform consistency checking on the semantic fingerprint vector and the mapped semantic vector. If the consistency checking passes, it indicates that the target platform understands the core consciousness data of the digital life in the source platform with a high degree of consistency with the source platform, and there is no semantic deviation, so that the target platform can reconstruct and activate the digital life in the source platform according to the content of the consciousness data packet. In this way, the consciousness consistency and migratability of the digital life in the heterogeneous AI engine, virtual environment and application scene are realized.

Description

Technical Field

[0001] This application relates to the field of digital life form technology, and more particularly to a method and electronic device for digital life form consciousness alignment and cross-platform migration. Background Technology

[0002] With the development of digital life form technology, the demand for its deployment and interaction in heterogeneous AI engines (such as GPT, Claude, Gemini, etc.), virtual environments, and application scenarios is increasing. However, existing technologies face severe challenges in achieving cross-platform consistency and seamless migration of digital life form consciousness:

[0003] (1) The problem of fragmented consciousness and “split personality”: Different AI engines may have different understandings and representations of the “personality, memory and values” inside the digital life form. When the digital life form switches between these heterogeneous platforms, its behavioral logic and personality characteristics are prone to serious inconsistencies, leading to “split personality” or “fragmented consciousness” phenomena, which seriously damage the identity and continuity of its identity.

[0004] (2) Lack of standardized interoperability: Existing digital life model models are usually deeply coupled with specific platforms or engines, lacking a common protocol for encapsulating, transmitting, and interpreting core consciousness data (CDU). This makes cross-platform migration and sharing of consciousness data extremely difficult, and digital life forms are technically bound to specific ecosystems, unable to circulate freely. Summary of the Invention

[0005] To address the aforementioned issues, the main objective of this application is to propose a method and electronic device for digital life form consciousness alignment and cross-platform migration. The aim is to ensure the consistency and transferability of digital life form consciousness across heterogeneous AI engines, virtual environments, and application scenarios through standardized encapsulation and semantic fingerprint alignment verification.

[0006] To achieve the above objectives, a first aspect of this application proposes a method for digital life form consciousness alignment and cross-platform migration, the method comprising: The current state of the core consciousness data of the digital life form within the source platform is encapsulated into a standardized, serializable consciousness data packet, which is a data packet conforming to the digital life form consciousness alignment and cross-platform migration protocol; Semantic embedding vectors of core nodes are extracted from the consciousness data package, and semantic fingerprint vectors are generated through an aggregation algorithm; The consciousness data packet and the semantic fingerprint vector are transmitted to the target platform through the protocol layer of the Digital Life Form Consciousness Alignment and Cross-Platform Migration Protocol; The target platform receives the consciousness data packet and the semantic fingerprint vector, and maps the semantic fingerprint vector to its own semantic space through a semantic adapter to obtain a mapped semantic vector. The target platform performs a consistency check on the semantic fingerprint vector and the mapped semantic vector; If the consistency check passes, the target platform reconstructs and activates the digital life form of the source platform based on the content of the consciousness data packet.

[0007] In one embodiment of this application, the data structure of the consciousness data packet includes a metadata field, a node list, and an edge list; The metadata fields include at least the unique identifier of the digital life form, the state version number of the core consciousness data, the generation timestamp, the source platform identifier, and the digital signature. The node list contains information about each node in the core consciousness data. The information includes at least a unique identifier for the node, the node type, the semantic embedding vector of the node, a dynamic weight representing the importance of the node, and a locking status indicating whether the node is locked according to preset behavioral constraint rules. The edge list contains information about each edge in the core consciousness data of the digital life form. The information includes at least the source node, the target node, the edge type, and the edge strength indicating the degree of closeness or conflict.

[0008] In one embodiment of this application, the step of extracting the semantic embedding vector of the core node from the consciousness data packet and generating a semantic fingerprint vector through an aggregation algorithm includes: Extract the semantic embedding vectors of high-weight value nodes and core knowledge nodes from the node list of the consciousness data package; The semantic embedding vectors of each extracted node are aggregated by attention mechanism or average pooling to generate semantic fingerprint vector.

[0009] In one embodiment of this application, the target platform performs a consistency check on the semantic fingerprint vector and the mapped semantic vector, including: Calculate the similarity between the semantic fingerprint vector and the mapped semantic vector; If the similarity is less than or equal to a preset threshold, then the consistency check is deemed to have passed. If the similarity is greater than the preset threshold, then the consistency check is determined to have failed.

[0010] In one embodiment of this application, if the consistency check fails, the method further includes: Trigger a semantic fine-tuning mechanism, which includes fine-tuning the semantic representation parameters within the target platform based on the semantic fingerprint vector, so as to fine-tune the semantic representation vector of the target platform for the core consciousness node of the digital life form of the source platform, so that the target platform's understanding of the core consciousness data of the digital life form of the source platform is aligned with the source platform. The fine-tuned semantic representation vector is compared with the semantic fingerprint vector to verify whether the target platform's understanding of the core consciousness data of the digital life form of the source platform after semantic fine-tuning is aligned with the source platform. If, after semantic fine-tuning, the target platform's understanding of the core consciousness data of the digital life form on the source platform aligns with that of the source platform, the target platform reconstructs and activates the digital life form on the source platform based on the content of the consciousness data package.

[0011] In one embodiment of this application, the method further includes: When the interaction between the digital life form on the source platform and the target platform causes the core consciousness data to change, the target platform encapsulates the latest state of the core consciousness data into a standardized, serializable updated consciousness data packet. The updated consciousness data packet is a data packet that conforms to the digital life form consciousness alignment and cross-platform migration protocol. The target platform compares the updated consciousness data packet with the consciousness data packet sent by the source platform to perform difference detection and generate a difference patch containing information on added, modified or deleted nodes or edges. The difference patch is transmitted to the source platform or other synchronization nodes through the protocol layer of the digital life form consciousness alignment and cross-platform migration protocol, so that the source platform or other synchronization nodes can complete the synchronization update of core consciousness data based on the difference patch.

[0012] In one embodiment of this application, the target platform compares the updated awareness data packet with the awareness data packet sent by the source platform to perform difference detection and generate a difference patch containing information on added, modified, or deleted nodes or edges, including: The target platform compares whether the hash value of the updated consciousness data packet is the same as the hash value of the consciousness data packet sent by the source platform; If the hash values ​​are different, the node list and edge list contained in the updated consciousness data packet are compared one by one with the node list and edge list contained in the consciousness data packet sent by the source platform to generate a difference patch containing information on added, modified or deleted nodes or edges.

[0013] In one embodiment of this application, the method further includes: When multiple devices simultaneously modify the core consciousness data of the same digital life form, the conflict is resolved according to a preset conflict resolution strategy. The preset conflict resolution strategy includes a timestamp-based priority strategy, a weighted credibility-based priority strategy, a strategy of merging conflicting parts, or a locked state priority strategy.

[0014] In one embodiment of this application, the method further includes: Maintain a synchronized state machine containing different state identifiers for the core consciousness data of each digital life form, and trigger state transitions by events.

[0015] To achieve the above objectives, a second aspect of this application provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the method described in any embodiment of this application.

[0016] In the technical solution provided in this application embodiment, the current state of the core consciousness data of the digital life form within the source platform is first encapsulated into a standardized, serializable consciousness data packet. This consciousness data packet conforms to the Digital Life Form Consciousness Alignment and Cross-Platform Migration Protocol (DL-CAP), thereby enabling seamless migration and interoperability of the digital life form across different heterogeneous AI engines, virtual environments, and applications. Next, semantic embedding vectors of core nodes are extracted from the consciousness data packet, and semantic fingerprint vectors are generated using an aggregation algorithm. The encapsulated consciousness data packet and semantic fingerprint vectors are then transmitted to the target platform via the DL-CAP platform. The target platform can then map the semantic fingerprint vectors to its own semantic space using a semantic adapter, obtaining mapped semantic vectors. A consistency check is performed between the semantic fingerprint vectors and the mapped semantic vectors. If the consistency check passes, it indicates that the target platform's understanding of the core consciousness data of the digital life form on the source platform is highly consistent with that of the source platform, with no semantic deviation. At this point, no additional intervention is required, and the target platform can reconstruct and activate the digital life form on the source platform based on the content of the consciousness data packet. Thus, the consistency and transferability of the digital life form's consciousness across heterogeneous AI engines, virtual environments, and application scenarios are achieved.

[0017] It should be understood that the above general description and the following detailed description are merely exemplary and do not limit this application. Attached Figure Description

[0018] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the specification, serve to explain the principles of this application.

[0019] Figure 1 This is a schematic diagram illustrating the interaction between different AI engines (platforms) provided in an embodiment of this application.

[0020] Figure 2 This is a flowchart of a digital life form consciousness alignment and cross-platform migration method provided in an embodiment of this application.

[0021] Figure 3 This is a flowchart of the steps for extracting the semantic embedding vector of the core node from the consciousness data packet and generating the semantic fingerprint vector through an aggregation algorithm, according to an embodiment of this application.

[0022] Figure 4 This is a flowchart illustrating the steps of a target platform in an embodiment of this application to perform consistency verification between semantic fingerprint vectors and mapped semantic vectors.

[0023] Figure 5 This is a flowchart of the steps to be performed when the consistency check fails, provided in an embodiment of this application.

[0024] Figure 6 This is a flowchart illustrating the steps performed when the core consciousness data of a digital life form changes due to interaction on a target platform, as provided in an embodiment of this application.

[0025] Figure 7 In one embodiment of this application, the target platform compares the updated consciousness data packet with the consciousness data packet sent by the source platform to perform difference detection and generate a difference patch containing information on added, modified, or deleted nodes or edges.

[0026] Figure 8 This is a schematic diagram of the process of digital life form consciousness alignment and cross-platform migration provided in an embodiment of this application.

[0027] Figure 9 This is a schematic diagram of the hardware structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0028] To make the objectives, implementation methods, and advantages of this application clearer, exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these exemplary embodiments are provided to make the description of this application more comprehensive and complete, and to fully convey the concept of the exemplary embodiments to those skilled in the art. It should be noted that the brief descriptions of terminology in this application are merely for the convenience of understanding the embodiments described below, and are not intended to limit the embodiments of this application. Unless otherwise stated, these terms should be understood in their ordinary and common meaning.

[0029] In the description of this application, it should be understood that the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first," "second," or "third" may explicitly or implicitly include one or more features.

[0030] The flowcharts shown in the accompanying drawings are merely illustrative and do not necessarily include all content and operations / steps, nor do they necessarily have to be performed in the described order. For example, some operations / steps can be broken down, while others can be combined or partially combined; therefore, the actual execution order may change depending on the specific circumstances.

[0031] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0032] With the development of digital life form technology, the demand for its deployment and interaction in heterogeneous AI engines (such as GPT, Claude, Gemini, etc.), virtual environments, and application scenarios is increasing. However, existing technologies face the following core challenges: 1. Fragmentation of consciousness and "personality splitting": Different AI engines have different understandings and representations of the "personality, memory, and values" of digital life forms, resulting in inconsistencies in behavioral logic and personality traits when switching between platforms; 2. Lack of standardized interoperability: Existing models are usually deeply coupled with specific platforms and lack a universal protocol to encapsulate, transmit, and interpret their core consciousness data (CDU), making the migration and sharing of consciousness data difficult.

[0033] Based on this, this application proposes a method and electronic device for digital life form consciousness alignment and cross-platform migration, aiming to ensure the consistency and transferability of digital life form consciousness in heterogeneous AI engines, virtual environments and application scenarios through standardized encapsulation and semantic fingerprint alignment verification.

[0034] Reference Figure 1 , Figure 1 This is a schematic diagram illustrating the interaction between different AI engines (platforms) provided in one embodiment of this application. Figure 1 As shown, different AI engines (platforms) interact through the Digital Life Form Consciousness Alignment and Cross-Platform Migration Protocol (DL-CAP) layer to ensure the consistency and seamless flow of digital life form consciousness in heterogeneous AI environments.

[0035] Specifically, the source platform 100 (such as engine A) first extracts the current complete Core Consciousness Data (CDU) state of its internal digital life form, including all knowledge, memories, values, relationship nodes, and the edge information between nodes. Then, following the data format defined by the DL-CAP protocol, the CDU state is encapsulated into a standardized, serializable consciousness data packet (CDU-Container), which includes metadata, a node list, and an edge list. Simultaneously, the source platform 100 can filter high-weight value nodes and core knowledge nodes from the encapsulated consciousness data packet (CDU-Container), extract their semantic embedding vectors, and aggregate them using an attention mechanism or average pooling algorithm to generate a semantic fingerprint (SF) vector. Finally, the encapsulated consciousness data packet (CDU-Container) can be encrypted, and the encrypted consciousness data packet (CDU-Container) and semantic fingerprint (SF) vector are prepared for transmission.

[0036] The DL-CAP protocol layer 200 serves as a dedicated cross-platform communication channel, responsible for synchronously transmitting the encrypted consciousness data packet (CDU-Container) and semantic fingerprint (SF) vector from the source platform 100 to the target platform 300 (such as Engine B). During transmission, the DL-CAP protocol layer 200 can integrate homomorphic encryption or zero-knowledge proof technology as needed, supporting partial verification of data under encrypted conditions to prevent the leakage of sensitive information, while ensuring the stability of data transmission and preventing data loss or disordered sequence.

[0037] After receiving the consciousness data packet (CDU-Container) and semantic fingerprint (SF) vector through the DL-CAP protocol layer 200, the target platform 300 calls its built-in semantic adapter. This adapter is a small neural network trained offline that has learned the non-linear mapping relationship between the semantic spaces of the source platform 100 and the target platform 300. It takes the received semantic fingerprint (SF) vector as input and maps it into a mapped semantic vector that can be recognized in the internal semantic space of the target platform 300. ), and then based on the received semantic fingerprint (SF) vector and the mapped semantic vector ( Consistency checks or semantic fine-tuning are performed to ensure that the target platform 300's understanding of the digital life form's core consciousness is consistent with that of the source platform 100. After confirming that the target platform 300's understanding of the digital life form's core consciousness is consistent with that of the source platform 100, the target platform 300 completes the CDU reconstruction and activation of the digital life form based on the received consciousness data packet (CDU-Container), ensuring that it maintains the same core consciousness and behavioral characteristics as the source platform within the target platform.

[0038] During interaction with the target platform 300, the digital life form's CDU state continuously evolves. The target platform 300 records these changes, generates new updated consciousness data packets (with incrementing version numbers), and compares these updated consciousness data packets with the consciousness data packets sent by the source platform 100, performing difference detection to generate difference patches. These difference patches are then fed back to the source platform 100 and other synchronization nodes via the DL-CAP protocol layer 200. The source platform 100 or other synchronization nodes can use the difference patches to synchronously update their local DCU. For conflicts arising from simultaneous CDU modifications across multiple platforms, conflict resolution mechanisms such as timestamp priority, weight priority, merging strategies, or node locking priority can be employed, combined with a synchronization state machine model to manage CDU states, ensuring that the consciousness state of digital life forms across all platforms remains consistent.

[0039] Reference Figure 2 , Figure 2 This is a flowchart of a digital life form consciousness alignment and cross-platform migration method provided in an embodiment of this application. Figure 2 As shown, the method includes, but is not limited to, steps S210 to S260.

[0040] Step S210: The current state of the core consciousness data of the digital life form within the source platform is encapsulated into a standardized, serializable consciousness data packet. The consciousness data packet conforms to the digital life form consciousness alignment and cross-platform migration protocol.

[0041] In this step, the current state of the core consciousness data of the digital life form within the source platform is encapsulated into a standardized, serializable consciousness data packet. This is not a simple data packaging, but a sophisticated engineering project to construct the digital life form's "identity gene bank." Its core lies in transforming the highly heterogeneous and dynamically evolving consciousness state into a standardized container that can be losslessly parsed by any AI engine. Specifically, the complete core consciousness data (CDU) state of the digital life form at the current moment is first extracted from the source platform (e.g., engine A), including all nodes, edges, and their associated attributes. Then, according to the Digital Life Form Consciousness Alignment and Cross-Platform Migration Protocol (DL-CAP), the current state of the extracted core consciousness data (CDU) of the digital life form from the source platform is encapsulated into a standardized, serializable consciousness data packet (CDU-Container), thereby ensuring the integrity, interpretability, and security of data transmission across platforms. The serialization format of the data packet can be a highly scalable format such as JSON or Protobuf to ensure consistency in cross-platform parsing.

[0042] Specifically, the data structure of the consciousness data package (CDU-Container) must strictly follow the DL-CAP protocol specification, mainly including three core fields. The first is the metadata field, which is used to identify the identity, version and source of the data package. Specific fields may include the unique identifier of the digital life form in UUID format DL_ID, the state version number of the core consciousness data (i.e., the version number that increments with each update), the generation timestamp (i.e., the state generation time in UTC timestamp format), the pre-registered source platform identifier Source_Engine_ID, and the Base64 encoded digital signature based on RSA / ECDSA Signature. The digital signature can effectively verify the integrity of the data and prevent tampering.

[0043] Secondly, there is the node list, which stores the core cognitive nodes of CDU in array form. Each node contains a unique identifier Node_ID, an enumerated node type Type, a semantic embedding vector (a high-dimensional real number embedding vector, such as 512-dimensional), a dynamic weight representing the importance of the node, a decay rate (Decay_Rate) simulating the forgetting mechanism, and a locked status (Locked_Status) indicating whether the core value node is locked. Among them, the embedding vectors of knowledge and value nodes are generated by a pre-trained Large Language Model (LLM), whose core function is to extract deep semantic features of the text, while the embedding vectors of relationship nodes are generated by a Graph Neural Network (GNN), which focuses on aggregating the graph structure association features between nodes. These embedding vectors together constitute the semantic representation of the node. This representation does not simply correspond to the literal meaning of the text, but maps the node to a multi-dimensional vector space. The distance between vectors reflects the semantic relationship and similarity of the abstract concepts behind the nodes. For example, the vectors representing "honesty" and "trust" will be in close positions in the space, intuitively reflecting their semantic relationship. The semantic embedding vector's `Embedding` field is of type `Float32 Array`, with a fixed dimension and a numerical range of [-1, 1]. `Float32 Array` indicates that this field's data type is a 32-bit floating-point array. 32-bit floating-point numbers are a lightweight numerical storage format that reduces data transmission and storage overhead while maintaining computational precision. The array format is well-suited to the high-dimensionality of embedded vectors; for example, the 512-dimensional vector mentioned in the documentation corresponds to an ordered set containing 512 `Float32` type values. This format can be universally parsed by different AI engines and platforms. Fixed dimension means that all node embedding vectors must have a uniform length, such as 512 dimensions. It is unacceptable for some nodes to be 256-dimensional while others are 512-dimensional. A fixed dimension is a prerequisite for semantic similarity calculation; only vectors with the same dimension can have their semantic relationships determined using algorithms such as cosine similarity. The numerical range [-1, 1] restricts the value of each element in the embedding vector; all 32-bit floating-point numbers must fall within the [-1, 1] range. This range is the result of normalization, which avoids calculation bias caused by values ​​that are too large or too small. At the same time, it ensures that embedding vectors from different sources (knowledge / value node vectors generated by LLM and relation node vectors generated by GNN) are on the same numerical scale, guaranteeing the accuracy of cross-platform semantic alignment.

[0044] Finally, there is the edge list, which also stores the relationships between nodes as an array. The edge list contains information about each edge in the digital life form's core consciousness data. The information for each edge includes the IDs of the source and target nodes, the edge type (Type) of the enumeration type, and the strength reflecting the closeness or conflict of the relationship.

[0045] In some embodiments, after structured encapsulation, security and privacy enhancements can be performed. On one hand, the complete consciousness data package (CDU-Container) can be encrypted using AES-256 technology to protect the privacy of the consciousness data, while a digital signature is written into the Signature field of the metadata. The recipient can verify the data source and integrity through the signature. Homomorphic encryption or zero-knowledge proof technology can also be integrated as needed to support semantic alignment verification in an encrypted state without decrypting the original data. On the other hand, differential privacy technology can be used to perturb non-core sensitive memories and knowledge nodes to ensure that the original sensitive information of the digital life form cannot be deduced during cross-platform transmission and alignment.

[0046] Step S220: Extract the semantic embedding vector of the core node from the consciousness data packet, and generate a semantic fingerprint vector through an aggregation algorithm.

[0047] This step extracts the semantic embedding vectors of core nodes from the consciousness data package (CDU-Container) conforming to the DL-CAP protocol, and then generates a semantic fingerprint (SF) vector representing the core consciousness of the digital life form through an aggregation algorithm. This provides a crucial basis for subsequent cross-platform semantic alignment and consistency verification. In performing this step, core nodes are first selected from the node list of the consciousness data package (CDU-Container). After selecting the core nodes, the high-dimensional semantic embedding vectors corresponding to these nodes are extracted. Then, an aggregation algorithm is used to generate the semantic fingerprint (SF) vector. The final generated semantic fingerprint (SF) vector is a highly abstract and condensed representation of the core consciousness of the digital life form, enabling rapid mapping and consistency verification with the semantic space of the target platform during subsequent cross-platform migration.

[0048] Specifically, refer to Figure 3 , Figure 3 This is a flowchart of the steps for extracting the semantic embedding vector of the core node from the consciousness data packet and generating the semantic fingerprint vector through the aggregation algorithm, including but not limited to steps S310 to S320, provided in an embodiment of this application.

[0049] Step S310: Extract the semantic embedding vectors of high-weight value nodes and core knowledge nodes from the node list of the consciousness data package. Step S320: The semantic embedding vectors of each extracted node are aggregated through attention mechanism or average pooling to generate semantic fingerprint vector.

[0050] In this embodiment, the semantic embedding vectors corresponding to high-weight value nodes and core knowledge nodes are first precisely filtered and extracted from the node list of the consciousness data package (CDU-Container) conforming to the DL-CAP protocol. During the filtering process, a judgment can be performed based on a preset weight threshold, that is, nodes with weight values ​​exceeding the value threshold are selected. The value nodes, and the weight values ​​exceeding the knowledge node threshold. The core knowledge nodes are determined by two thresholds, which can be obtained through statistical analysis of a large amount of digital life form (CDU) data (e.g., taking the 90th percentile of the node weight distribution) or expert experience. After screening, high-dimensional semantic embedding vectors of these nodes are extracted. The semantic embedding vectors of knowledge / value nodes are generated by a pre-trained large language model (LLM), which can accurately extract the deep semantic features of the text. The vector format is Float32 Array, with fixed dimensions (e.g., 512 dimensions) and the numerical range is limited to [-1,1], which can ensure the standardization and consistency of subsequent calculations.

[0051] Next, we move to the aggregation algorithm processing stage. Depending on the actual needs, we can choose between an attention mechanism or average pooling. The attention mechanism is a better choice as it more accurately captures the importance weights of different core nodes. When using the attention mechanism, a small neural network (such as a single-layer perceptron) is used to calculate the semantic embedding vector of each core node, outputting a corresponding scalar score. This score is then normalized using Softmax to obtain the attention weights. Finally, according to the formula Complete vector aggregation. Among them, This refers to the set of high-weight value nodes and core knowledge nodes that have been selected. It is the embedding vector of the corresponding node. This refers to attention weights. If average pooling is chosen, the mean of the embedding vectors of all core nodes is calculated directly to generate a semantic fingerprint (SF) vector. The dimension of the generated semantic fingerprint vector is usually consistent with the dimension of the input embedding vector, generally between 128 and 768 dimensions, in order to balance semantic representation capability and subsequent computational efficiency. The final generated semantic fingerprint (SF) vector is a highly abstract and condensed representation of the core consciousness of the digital life form, which can be quickly mapped and verified for consistency with the semantic space of the target platform during subsequent cross-platform migration.

[0052] Step S230: The consciousness data packet and semantic fingerprint vector are transmitted to the target platform through the protocol layer of the Digital Life Form Consciousness Alignment and Cross-Platform Migration Protocol.

[0053] In this step, the consciousness data packet (CDU-Container) and semantic fingerprint (SF) vector generated by the source platform are securely and stably transmitted to the target platform (such as Engine B) through the protocol layer of the Digital Life Form Consciousness Alignment and Cross-Platform Migration Protocol (DL-CAP). This provides complete data support for subsequent semantic adaptation and consistency verification. Before performing the transmission operation, it is necessary to confirm that the source platform has completed the encryption and digital signature process for the consciousness data packet (CDU-Container) to ensure the privacy and integrity of the data during transmission and prevent tampering or theft.

[0054] During transmission, the DL-CAP protocol layer acts as a dedicated data transmission channel, simultaneously sending two core data units: First, an encrypted consciousness data packet (CDU-Container), containing complete consciousness state information of the digital life form, encompassing standardized fields such as metadata, node lists, and edge lists; second, a semantic fingerprint (SF) vector generated from the embedding vectors of the core nodes. This vector is a highly abstract representation of the digital life form's core consciousness and will be used for rapid semantic alignment with the target platform. The protocol layer ensures that both types of data arrive at the target platform synchronously based on preset communication rules, without data loss or order discrepancies.

[0055] In some embodiments, to further enhance transmission security and compliance, homomorphic encryption or zero-knowledge proof technologies can be integrated at the protocol layer as needed. These two technologies allow for partial verification of consciousness data during transmission without decrypting the original data, preventing sensitive information from being leaked during transmission. After the data transmission is completed, the target platform's protocol receiver will first verify the digital signature of the consciousness data packet (CDU-Container) to confirm that the data source is legitimate and has not been tampered with, and then proceed to the semantic adaptation and consistency verification stage.

[0056] In step S240, the target platform receives the consciousness data packet and the semantic fingerprint vector, and maps the semantic fingerprint vector to its own semantic space through the semantic adapter to obtain the mapped semantic vector.

[0057] During this step, the target platform first receives two core data items from the source platform via the DL-CAP protocol layer: an encrypted and digitally signed consciousness data packet (CDU-Container) and a semantic fingerprint (SF) vector representing the core consciousness of the digital life form. After receiving the data, the target platform verifies the consciousness data packet (CDU-Container) by checking the Signature field in the metadata to confirm that the data source is legitimate and has not been tampered with, before proceeding to the semantic mapping stage.

[0058] The key vehicle for semantic mapping is the semantic adapter built into the target platform. This adapter is a small neural network with 2-3 fully connected layers, each layer using the ReLU activation function, and the output dimension of the last layer is consistent with the dimension of the input semantic fingerprint (SF) vector. This adapter has been trained offline using a large number of cross-platform aligned datasets (paired semantic embedding data), and has learned and mastered the non-linear mapping relationship between the semantic spaces of the source and target platforms. The core objective of the training is to minimize the distance between the mapped vector and the true semantics of the target platform.

[0059] In the actual mapping process, the semantic adapter takes the received semantic fingerprint (SF) vector as input and, through its own network computation, transforms it from the semantic space of the source platform to the internal semantic space of the target platform, ultimately generating a mapped semantic vector. This mapped semantic vector is a semantic representation that the target platform can understand and that corresponds to the core consciousness of the source platform. It will serve as a key basis for the subsequent consistency checker to calculate the cosine similarity, thereby determining whether the target platform's understanding of the core consciousness of the digital life form is consistent with that of the source platform.

[0060] In step S250, the target platform performs a consistency check on the semantic fingerprint vector and the mapped semantic vector.

[0061] This step involves the target platform performing a consistency check to determine whether its understanding of the core consciousness of the digital life form is consistent with that of the source platform, providing a basis for decision-making regarding whether subsequent semantic fine-tuning is needed. During this step, the target platform's consistency checker receives two key vectors: one is the original semantic fingerprint (SF) vector transmitted from the source platform, and the other is the mapped semantic vector (SF) that has been mapped to the target platform's internal semantic space via a semantic adapter. Then the verifier compares the semantic fingerprint vector with the mapped semantic vector (such as calculating the similarity between the two) to determine whether the target platform’s understanding of the core consciousness of the digital life form is consistent with that of the source platform.

[0062] Specifically, in some embodiments, reference is made to Figure 4 , Figure 4 This is a flowchart of the steps for a target platform to perform consistency verification between a semantic fingerprint vector and a mapped semantic vector, as provided in an embodiment of this application, including but not limited to steps S410 to S430.

[0063] Step S410: Calculate the similarity between the semantic fingerprint vector and the mapped semantic vector; Step S420: If the similarity is less than or equal to the preset threshold, then the consistency check is deemed to have passed. Step S430: If the similarity is greater than the preset threshold, then the consistency check is determined to have failed.

[0064] In this embodiment, vector similarity calculation can be used to determine whether the target platform and the source platform have the same understanding of the digital life form's consciousness. Specifically, the consistency checker of the target platform will receive two core vectors: one is the original semantic fingerprint (SF) vector transmitted by the source platform, representing the core consciousness of the digital life form, and the other is the mapped semantic vector mapped to the target platform's internal semantic space by the semantic adapter. The verifier will then use a cosine similarity algorithm to calculate the similarity between the two, using the following formula: ,in Represents a semantic fingerprint (SF) vector. Representing the semantic vector of the mapping, the closer the calculated similarity value is to 1, the higher the semantic matching degree between the two vectors, and the smaller the deviation between the target platform's understanding of the core consciousness of the digital life form and the source platform.

[0065] After the similarity calculation is completed, the calculated similarity score can be compared with a preset threshold. The comparison is performed, and the preset threshold is usually set between 0.7 and 0.9. The specific value can be determined through experimental verification based on the accuracy requirements of the actual application scenario. If the similarity score is greater than or equal to the preset threshold, the consistency check is considered to have passed, indicating that the target platform's understanding of the digital life form's core personality, values, and other consciousness content is consistent with the source platform, and it can directly proceed to the subsequent CDU reconstruction and activation stage. If the similarity score is determined to be less than the preset threshold, the consistency check is considered to have failed. This means that there is a significant difference in the understanding of consciousness between the target platform and the source platform. If the digital life form is activated directly, problems such as "personality split" may occur. At this time, the system will automatically trigger a warning and start the semantic fine-tuning mechanism. By adjusting the adaptation network parameters and even the target platform's embedding layer, optimization is performed based on the deviation signal between the semantic fingerprint (SF) vector and the target platform's initial interpretation, forcing the target platform's understanding of the digital life form's core consciousness to align with the source platform.

[0066] Step S260: If the consistency check passes, the target platform reconstructs and activates the digital life form of the source platform based on the content of the consciousness data packet.

[0067] In this step, after the consistency verification is passed, the target platform completes the CDU reconstruction and activation of the digital life form based on the received consciousness data packet (CDU-Container), ensuring that it maintains the same core consciousness and behavioral characteristics as the source platform within the target platform. Specifically, the target platform strictly parses the data packet content according to the structure defined by the DL-CAP protocol: First, it extracts information such as the digital life form's unique identifier DL_ID, the core consciousness data's version number Version, and the generation timestamp from the metadata to establish a unique identity and version baseline for the digital life form; then, it restores the four types of nodes—knowledge, memory, value, and relationship—one by one according to the node list, accurately reproducing the semantic embedding vector, dynamic weight, decay rate, and locked status of each node. The embedding vectors of knowledge and value nodes can be adapted to the semantic space of the target platform, while the embedding vectors of relationship nodes retain the association characteristics between nodes; then, it constructs three types of relationships between nodes—association, support, and conflict—referring to the edge list, setting the relationship strength according to the edge strength field, and finally forming a complete CDU model that is consistent with the structure and semantically aligned with the source platform.

[0068] After the CDU reconstruction is completed, the target platform initiates the digital life form activation process, integrating the reconstructed CDU model into its own AI engine's inference framework, enabling it to possess cognitive, decision-making, and interaction capabilities consistent with the source platform. During activation, the system loads the CDU's dynamic evolution rules, such as a memory-forgetting mechanism based on Decay_Rate and a decision-making bias mechanism based on node weights, ensuring that the digital life form can maintain its core personality and behavioral logic on the source platform in subsequent interactions on the target platform. Once activated, the digital life form can operate normally in the target platform's application scenarios. Simultaneously, the target platform records relevant activation information, generates a new version number, and writes it into metadata, preparing for subsequent incremental synchronization and version management.

[0069] In some embodiments, refer to Figure 5 , Figure 5 This is a flowchart of the steps to be executed when the consistency check fails, provided in an embodiment of this application, including but not limited to steps S510 to S530.

[0070] Step S510: Trigger the semantic fine-tuning mechanism. The semantic fine-tuning mechanism includes fine-tuning the semantic representation parameters inside the target platform based on the semantic fingerprint vector, so as to fine-tune the semantic representation vector of the core consciousness node of the digital life form of the source platform in the target platform, so that the target platform's understanding of the core consciousness data of the digital life form of the source platform is aligned with the source platform. Step S520: Compare the fine-tuned semantic representation vector with the semantic fingerprint vector to verify whether the target platform's understanding of the core consciousness data of the digital life form of the source platform after semantic fine-tuning is aligned with the source platform. Step S530: If the target platform's understanding of the core consciousness data of the digital life form of the source platform is aligned with that of the source platform after semantic fine-tuning, the target platform reconstructs and activates the digital life form of the source platform based on the content of the consciousness data package.

[0071] In this embodiment of the application, when the consistency check fails, that is, when the consistency checker calculates the semantic fingerprint (SF) vector and the target platform mapping semantic vector ( The cosine similarity of () is lower than the preset threshold When this happens, the system immediately triggers a semantic fine-tuning mechanism. The core of this mechanism is to use the semantic fingerprint vector of the source platform as an absolute benchmark, and adjust the semantic representation parameters within the target platform through a fine-tuning network. Specifically, the fine-tuning network receives the semantic fingerprint (SF) vector and the target platform's initial semantic interpretation of the CDU's key nodes, utilizing the consistency deviation between the two. As a loss signal, the network parameters are optimized through gradient descent algorithm, and even the embedding layer of the target platform is fine-tuned. Ultimately, the semantic representation vector of the core consciousness node of the target platform is corrected, thereby forcing the target platform to align its understanding of the core personality and values ​​of the digital life form with that of the source platform.

[0072] After semantic fine-tuning is completed, the target platform will extract the newly generated semantic representation vector ( Then, cosine similarity is calculated again with the semantic fingerprint (SF) vector. The core of the verification is to determine whether the matching degree between the two reaches the threshold, so as to confirm whether the target platform's understanding of the core consciousness data is aligned with the source platform. If the similarity still does not meet the standard, the fine-tuning process of step S510 needs to be repeated until the alignment requirements are met.

[0073] Once the verification is successful, confirming that the semantic alignment meets the standards, the target platform will officially initiate the reconstruction and activation of the digital life form. Specifically, the received consciousness data packet (CDU-Container) is first verified and decrypted to confirm the legality of the Signature field in the metadata. Then, strictly following the structure defined by the DL-CAP protocol, the metadata, node list, and edge list in the data packet are parsed to reconstruct a complete CDU model containing node embedding vectors, weights, decay rates, and relationships between nodes. Finally, the reconstructed CDU is connected to the target platform's inference framework, and dynamic evolution rules such as memory forgetting and weight decision-making are loaded to activate the digital life form, enabling it to have the same interaction logic and behavioral characteristics on the target platform as on the source platform.

[0074] In some embodiments, considering that the core consciousness data (CDU) of a digital life form may evolve during interactions between different AI engines (platforms), if the interaction of the digital life form on the target platform may cause changes to its core consciousness data, the target platform will generate a new CDU-Container data package based on the evolved CDU state, i.e., update the consciousness data package. This data package will update the Version field in the metadata by incrementing the version number, update the Timestamp field to the current UTC timestamp, and generate a new digital signature written to the Signature field to ensure data integrity and source legitimacy.

[0075] Next, the target platform can compare the generated updated consciousness data packet with the consciousness data packet sent by the source platform, i.e., perform a difference detection of the consciousness data packet to generate a difference patch containing information on added, modified, or deleted nodes or edges. The generated difference patch can be fed back to the source platform and other synchronization nodes through the DL-CAP protocol layer, enabling the source platform and other synchronization nodes to perform local synchronization updates of consciousness data, thereby ultimately achieving a consistent update of the CDU state of all nodes.

[0076] Specifically, refer to Figure 6 , Figure 6 This is a flowchart of the steps performed when the core consciousness data of a digital life form changes due to its interaction on a target platform, as provided in an embodiment of this application, including but not limited to steps S610 to S630.

[0077] Step S610: The target platform encapsulates the latest state of the core consciousness data into a standardized, serializable updated consciousness data packet, which is a data packet that conforms to the digital life form consciousness alignment and cross-platform migration protocol. In step S620, the target platform will compare the updated consciousness data packet with the consciousness data packet sent by the source platform to perform difference detection and generate a difference patch containing information on added, modified or deleted nodes or edges. Step S630: The difference patch is transmitted to the source platform or other synchronization nodes through the protocol layer of the Digital Life Form Consciousness Alignment and Cross-Platform Migration Protocol, so that the source platform or other synchronization nodes can complete the synchronization update of the core consciousness data based on the difference patch.

[0078] In this embodiment, after the digital life form completes interaction with the target platform, its core consciousness data (CDU) evolves due to new memory entries, node weight adjustments, and changes in relationship strength. In response, the target platform generates an updated CDU-Container data packet (i.e., an updated consciousness data packet) conforming to the DL-CAP protocol specification based on the evolved CDU state. During the generation process, the target platform updates the Version field in the metadata by incrementing the version number and refreshing the Timestamp to the current UTC time. Simultaneously, it can generate a new digital signature based on the RSA / ECDSA algorithm and write it to the Signature field to ensure the integrity and legitimacy of the data packet.

[0079] Subsequently, the target platform will compare the new version of CDU-Container with the previous version. This includes updating the consciousness data and comparing it with the consciousness data packets sent by the source platform. First, a SHA-256 hash check is used to quickly determine if there are any differences. If the hash values ​​are different, a structured difference comparison is further carried out. By comparing the IDs and attribute values ​​of the node list and edge list, the newly added, modified, and deleted node / edge information is accurately identified. Then, a structured difference patch (Delta Patch) is generated based on these differences. The patch content includes key information such as the operation type (Add / Remove / Update), the path of the target data in the consciousness data packet, and the new and old attribute values.

[0080] The target platform does not transmit the complete new version of CDU-Container (i.e., updated consciousness data package). Instead, it efficiently feeds back smaller difference patches to the source platform and other synchronization nodes through the DL-CAP protocol layer, thereby reducing transmission bandwidth consumption and improving synchronization efficiency.

[0081] After receiving the difference patch, the source platform and other synchronization nodes do not need to reconstruct the complete CDU model. Instead, they can directly update their local CDUs based on the patch content, including adding nodes / edges, modifying node attribute values, and deleting invalid nodes / edges. If conflicts occur between multiple devices during synchronization, they are handled according to the conflict resolution strategies preset by the DL-CAP protocol (timestamp priority, weight priority, locked node priority, etc.), ultimately ensuring that the CDU state of all nodes remains consistent and maintaining the stability of the digital life form's core personality, memory, and values.

[0082] Specifically, refer to Figure 7 , Figure 7 The flowchart of the steps provided in one embodiment of this application is as follows: the target platform compares the updated consciousness data packet with the consciousness data packet sent by the source platform to perform difference detection and generate a difference patch containing information on added, modified or deleted nodes or edges, including but not limited to steps S710 to S720.

[0083] Step S710: The target platform compares the hash value of the updated consciousness data packet with the hash value of the consciousness data packet sent by the source platform to see if they are the same. In step S720, if the hash values ​​are different, the node list and edge list contained in the updated consciousness data packet will be compared one by one with the node list and edge list contained in the consciousness data packet sent by the source platform to generate a difference patch containing information on added, modified or deleted nodes or edges.

[0084] In this embodiment, hash verification can be used to quickly determine whether there are differences between the updated consciousness data packet and the original consciousness data packet of the source platform. Specifically, the target platform will calculate the hash values ​​of the two consciousness data packets conforming to the DL-CAP protocol. The commonly used algorithm is SHA-256, or the more efficient Merkle Tree hash structure can also be used. The hash value is a unique mapping of the data packet content. If the hash values ​​of the two data packets are exactly the same, it means that the interaction of the digital life form on the target platform has not substantially modified the core consciousness data (CDU), and no further comparison operation is required. If the hash values ​​are different, it is determined that the CDU has evolved. At this time, the target platform will perform a structured comparison of the node list and edge list of the two consciousness data packets one by one to generate a Delta Patch. In the node list comparison stage, the target platform can first identify the newly added nodes and deleted nodes by comparing the set of unique node identifiers (Node_ID); then, for nodes with the same unique node identifier, its attribute values ​​are verified one by one, including Embedding embedding vector, Weight, Decay_Rate decay rate, Locked_Status, etc., to mark the nodes whose attributes have been modified. During the edge list comparison phase, the target platform identifies newly added and deleted edges by comparing the unique identifiers of the source node (Source_Node_ID) and the target node (Target_Node_ID). For edges with identical ID combinations, it verifies their edge type and strength attributes, marking edges with modified attributes. Finally, the target platform organizes all identified "added, modified, and deleted" node / edge information into a difference patch according to the structured format (such as a JSON array) specified by the DL-CAP protocol. The patch content clearly indicates the operation type (Add / Remove / Update), the path of the target data in the consciousness data package, and key information such as the old and new attribute values. The generated difference patch is much smaller than the complete consciousness data package and can be efficiently transmitted to the source platform and other synchronization nodes through the DL-CAP protocol layer, supporting local CDU updates and reverse synchronization on each node.

[0085] Reference Figure 8, Figure 8 This is a schematic diagram illustrating the process of digital life form consciousness alignment and cross-platform migration provided in an embodiment of this application. Figure 8 As shown, the methods for aligning and migrating the consciousness of digital life forms across platforms mainly include the following steps: 1. Extract the DCU state of the internal digital life form from the source platform; 2. The source platform encapsulates the DCU state into a consciousness data packet, which is a standardized, serializable consciousness data packet that conforms to the Digital Life Form Consciousness Alignment and Cross-Platform Migration Protocol (DL-CAP protocol). 3. The source platform generates semantic fingerprint vectors based on consciousness data packets; 4. The source platform sends the consciousness data packet and semantic fingerprint vector to the DL-CAP protocol layer; 5. The DL-CAP protocol layer transmits the consciousness data packet and semantic fingerprint vector to the target platform; 6. The target platform first maps the semantic fingerprint vector to its own semantic space through a semantic adapter to obtain the mapped semantic vector; 7. The target platform performs consistency verification between the semantic fingerprint vector and the mapped semantic vector; 8. If the consistency check passes, the target platform reconstructs and activates the digital life form of the source platform based on the content of the consciousness data packet; 9. If the consistency check fails, the semantic fine-tuning mechanism is triggered; 10. After fine-tuning, re-verify. If the verification passes, the target platform will reconstruct and activate the digital life form of the source platform based on the content of the consciousness data packet. 11. Digital life forms continuously interact on the target platform, and the DCU evolves. 12. The target platform encapsulates the evolved DCU into an updated consciousness data packet, which is a standardized, serializable consciousness data packet that conforms to the Digital Life Form Consciousness Alignment and Cross-Platform Migration Protocol (DL-CAP protocol). 13. The target platform performs differential detection on the consciousness data package to generate a differential patch; 14. The target platform sends the difference patch to the DL-CAP protocol layer; 15. The DL-CAP protocol layer transmits the difference patch to the source platform; 16. The source platform performs synchronous updates of the DCU based on differential patches.

[0086] In this embodiment, the source platform first extracts the CDU state of its internal digital life form, encapsulates it into a standardized, serializable consciousness data packet conforming to the DL-CAP protocol, and then generates a semantic fingerprint vector based on this data packet. Subsequently, the consciousness data packet and the semantic fingerprint vector are sent to the DL-CAP protocol layer, which then transmits them to the target platform. The target platform maps the received semantic fingerprint vector to its own semantic space through a semantic adapter, obtaining a mapped semantic vector. Then, it performs a consistency check on the two vectors. If the check passes, the digital life form is directly reconstructed and activated based on the consciousness data packet. If the check fails, a semantic fine-tuning mechanism is triggered, and after fine-tuning, the check is re-checked. If the check passes, the reconstruction and activation of the digital life form are completed. The continuous interaction of digital life forms on the target platform will trigger the evolution of CDU. The target platform will encapsulate the evolved CDU into an updated consciousness data packet that conforms to the DL-CAP protocol. Through difference detection, a difference patch containing only the information of newly added, modified or deleted nodes / edges will be generated and sent to the DL-CAP protocol layer. The protocol layer will then transmit it to the source platform. Finally, the source platform will complete the synchronous update of the local CDU based on the difference patch, thereby realizing the consistent flow and efficient synchronization of consciousness of digital life forms between heterogeneous AI platforms.

[0087] In some embodiments, when multiple devices simultaneously modify the core consciousness data of the same digital life form, conflicts are resolved according to a preset conflict resolution strategy. This preset strategy includes a timestamp-based priority strategy, a weight-based credibility priority strategy, a strategy to merge conflicting parts, or a lock-state priority strategy. The timestamp-based priority strategy uses the UTC timestamp in the metadata of the consciousness data package as the core criterion, directly selecting the latest timestamp version as the final synchronization version. It is suitable for updating non-core consciousness data, such as the entry of ordinary memory nodes generated from the digital life form's daily interactions, and the fine-tuning of the weights of non-critical knowledge nodes. These modifications do not require complex trade-offs; selecting the latest version satisfies consistency requirements, and the strategy execution logic is simple, enabling rapid conflict resolution and reducing synchronization time. The weight-based credibility priority strategy focuses on the dynamic weight attribute of nodes and the credibility of the AI ​​engine that generates the nodes. The core priority ranking is: core value nodes > ordinary knowledge / memory nodes, and modifications generated by a high-credibility AI engine > modifications generated by a low-credibility engine. During conflict resolution, the system prioritizes retaining modifications to node attributes with higher weights. For example, adjustments to the weight of nodes that adhere to predefined behavioral constraint rules for digital lifeforms have a much higher priority than modifications to ordinary interest nodes. This ensures that the core personality of the digital lifeform is not tampered with and maintains cross-platform consistency. The strategy of merging conflicting parts is applicable to scenarios where modifications across multiple platforms do not inherently contradict each other and can coexist. It consists of two steps: direct merging of non-conflicting parts and rule-based processing of conflicting parts. For non-conflicting node / edge modifications, such as adding a knowledge node on platform A and adjusting the strength of a relationship node on platform B, the system will directly integrate the two types of modifications into the same CDU version. For conflicting parts, they will be processed according to predefined rules. Numerical attributes (such as Weight and Strength) can be averaged or weighted averaged, and list attributes (such as a list of node-associated tags) can be deduplicated and merged. If the rules cannot cover conflict situations, manual intervention is triggered to ensure that the merged CDU data is reasonable and effective. The lock status priority strategy targets core value nodes in the CDU where the Locked_Status field is True. These nodes are the core carriers of the digital life entity's personality, and their status cannot be overwritten by regular updates. When modifications from multiple devices involve locked nodes, the system will directly reject all modification requests, mark the conflict as a high-priority event, record information such as the initiator of the modification and the content of the modification, and await manual intervention or processing through a special authorization process. This strategy can fundamentally prevent the accidental modification of preset behavioral constraint rules and ensure the stability of the digital life entity's personality.In the actual conflict handling process, the system will combine the synchronous state machine model and flexibly select a single strategy or combine multiple strategies according to the conflict type and scope of impact to complete the transformation of CDU state from Conflicted to Consistent, and finally achieve the unification of CDU state across multiple terminals.

[0088] In some embodiments, in the multi-terminal synchronization mechanism of the DL-CAP protocol, a synchronization state machine needs to be maintained for the core consciousness data (CDU) of each digital life form. Through preset state identifiers and event triggering logic, orderly management of CDU modifications across multiple platforms, accurate identification of conflicts, and consistency assurance can be achieved.

[0089] Specifically, the synchronization state machine can define five core states for a CDU: Consistent, Local_Modified, Pending_Sync, Conflicted, and Error. Each state corresponds to a specific CDU synchronization state. Consistent indicates that the CDU of this digital entity is completely consistent across all synchronization nodes (source platform, target platform, and other related platforms), with no unsynchronized modifications. Local_Modified indicates that a digital entity on a certain platform has evolved its CDU due to interaction, and the local modification has been completed but the updated content has not yet been submitted to the DL-CAP protocol layer, awaiting a synchronization request. Pending_Sync indicates that the platform initiating the modification has submitted the CDU update patch to the DL-CAP protocol layer and is waiting for other synchronization nodes to receive, verify, and provide confirmation. Conflicted indicates that multiple endpoints simultaneously modify the same CDU node or edge, and the modifications are contradictory. The conflict is detected during the synchronization process, requiring the execution of a preset conflict resolution strategy. Error indicates that problems such as data loss, digital signature verification failure, or hash verification anomaly have occurred during the synchronization process, causing the synchronization process to be interrupted and requiring manual or system intervention for troubleshooting and repair.

[0090] The state transitions of the synchronization state machine are entirely driven by specific events during multi-terminal interactions, with different events corresponding to distinct state transition paths. For example: Consistent state → Local_Modified state: triggered by a local modification event, where the digital entity interacts with a platform, causing changes such as CDU node attribute adjustments, node / edge additions or deletions. The platform records the modifications and updates the state. Local_Modified state → Pending_Sync state: triggered by a synchronization commit event, where the platform generates a difference patch based on the evolved CDU and sends it to the DL-CAP protocol layer, awaiting responses from other nodes. Pending_Sync state → Consistent state: triggered by a synchronization success confirmation event, where all synchronization nodes receive the difference patch and complete their local CDU updates, and no conflicts are detected, restoring CDU consistency across all nodes. Pending_Sync state → Conflicted state: triggered by a conflict detection event, where during multi-terminal synchronization, the system detects different modifications to the same CDU data, triggering a conflict resolution process and switching states. Conflicted State → Consistent State: Triggered by a conflict resolution event, meaning the system resolves the conflict using preset strategies such as timestamp priority and weight priority, generates a unified CDU version, and completes full node synchronization. Arbitrary State → Error State: Triggered by a synchronization anomaly event, such as corrupted data transmission, failed signature verification, or hash value mismatch. The state machine locks into the error state and issues an alarm.

[0091] The synchronization state machine is the execution vehicle for conflict resolution strategies. When the state machine enters a conflicted state, it automatically triggers a preset conflict resolution strategy. For example, it may prioritize locking the state to protect core value nodes, and then combine this with timestamp priority, weighted credibility priority, or merging strategies to handle conflicts of ordinary nodes. After the conflict is resolved, the state machine generates a new consistent CDU version, driving all nodes to return from the conflicted state to the consistent state, ensuring the consistency of consciousness of the entire platform's digital life.

[0092] This application also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the above-described method. This electronic device can be any smart terminal, including tablet computers, in-vehicle computers, etc.

[0093] Please see Figure 9 , Figure 9This is a schematic diagram of the hardware structure of an electronic device provided in an embodiment of this application. The electronic device includes: The processor 901 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application. The memory 902 can be implemented as a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 902 can store the operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 902 and is called and executed by the processor 901. The input / output interface 903 is used to implement information input and output; The communication interface 904 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.). Bus 905 transmits information between various components of the device (e.g., processor 901, memory 902, input / output interface 903, and communication interface 905); The processor 901, memory 902, input / output interface 903, and communication interface 904 are connected to each other within the device via bus 905.

[0094] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.

Claims

1. A method for aligning and transferring the consciousness of digital life forms across platforms, characterized in that, The method includes: The current state of the core consciousness data of the digital life form within the source platform is encapsulated into a standardized, serializable consciousness data packet, which is a data packet conforming to the digital life form consciousness alignment and cross-platform migration protocol; Semantic embedding vectors of core nodes are extracted from the consciousness data package, and semantic fingerprint vectors are generated through an aggregation algorithm; The consciousness data packet and the semantic fingerprint vector are transmitted to the target platform through the protocol layer of the Digital Life Form Consciousness Alignment and Cross-Platform Migration Protocol; The target platform receives the consciousness data packet and the semantic fingerprint vector, and maps the semantic fingerprint vector to its own semantic space through a semantic adapter to obtain a mapped semantic vector. The target platform performs a consistency check on the semantic fingerprint vector and the mapped semantic vector; If the consistency check passes, the target platform reconstructs and activates the digital life form of the source platform based on the content of the consciousness data packet.

2. The method according to claim 1, characterized in that, The data structure of the consciousness data packet includes metadata fields, a node list, and an edge list; The metadata fields include at least the unique identifier of the digital life form, the state version number of the core consciousness data, the generation timestamp, the source platform identifier, and the digital signature. The node list contains information about each node in the core consciousness data. The information includes at least a unique identifier for the node, the node type, the semantic embedding vector of the node, a dynamic weight representing the importance of the node, and a locking status indicating whether the node is locked according to preset behavioral constraint rules. The edge list contains information about each edge in the core consciousness data of the digital life form. The information includes at least the source node, the target node, the edge type, and the edge strength indicating the degree of closeness or conflict.

3. The method according to claim 2, characterized in that, The step of extracting the semantic embedding vector of the core node from the consciousness data packet and generating a semantic fingerprint vector through an aggregation algorithm includes: Extract the semantic embedding vectors of high-weight value nodes and core knowledge nodes from the node list of the consciousness data package; The semantic embedding vectors of each extracted node are aggregated by attention mechanism or average pooling to generate semantic fingerprint vector.

4. The method according to claim 1, characterized in that, The target platform performs consistency verification on the semantic fingerprint vector and the mapped semantic vector, including: Calculate the similarity between the semantic fingerprint vector and the mapped semantic vector; If the similarity is less than or equal to a preset threshold, then the consistency check is deemed to have passed. If the similarity is greater than the preset threshold, then the consistency check is determined to have failed.

5. The method according to claim 4, characterized in that, If the consistency check fails, the method further includes: Trigger a semantic fine-tuning mechanism, which includes fine-tuning the semantic representation parameters within the target platform based on the semantic fingerprint vector, so as to fine-tune the semantic representation vector of the target platform for the core consciousness node of the digital life form of the source platform, so that the target platform's understanding of the core consciousness data of the digital life form of the source platform is aligned with the source platform. The fine-tuned semantic representation vector is compared with the semantic fingerprint vector to verify whether the target platform's understanding of the core consciousness data of the digital life form of the source platform after semantic fine-tuning is aligned with the source platform. If, after semantic fine-tuning, the target platform's understanding of the core consciousness data of the digital life form on the source platform aligns with that of the source platform, the target platform reconstructs and activates the digital life form on the source platform based on the content of the consciousness data package.

6. The method according to claim 1, characterized in that, The method further includes: When the interaction between the digital life form on the source platform and the target platform causes the core consciousness data to change, the target platform encapsulates the latest state of the core consciousness data into a standardized, serializable updated consciousness data packet. The updated consciousness data packet is a data packet that conforms to the digital life form consciousness alignment and cross-platform migration protocol. The target platform compares the updated consciousness data packet with the consciousness data packet sent by the source platform to perform difference detection and generate a difference patch containing information on added, modified or deleted nodes or edges. The difference patch is transmitted to the source platform or other synchronization nodes through the protocol layer of the digital life form consciousness alignment and cross-platform migration protocol, so that the source platform or other synchronization nodes can complete the synchronous update of core consciousness data based on the difference patch.

7. The method according to claim 6, characterized in that, The target platform compares the updated awareness data packet with the awareness data packet sent by the source platform to perform difference detection and generate a difference patch containing information on added, modified, or deleted nodes or edges, including: The target platform compares whether the hash value of the updated consciousness data packet is the same as the hash value of the consciousness data packet sent by the source platform; If the hash values ​​are different, the node list and edge list contained in the updated consciousness data packet are compared one by one with the node list and edge list contained in the consciousness data packet sent by the source platform to generate a difference patch containing information on added, modified or deleted nodes or edges.

8. The method according to claim 1, characterized in that, The method further includes: When multiple devices simultaneously modify the core consciousness data of the same digital life form, the conflict is resolved according to a preset conflict resolution strategy. The preset conflict resolution strategy includes a timestamp-based priority strategy, a weighted credibility-based priority strategy, a strategy of merging conflicting parts, or a locked state priority strategy.

9. The method according to claim 1, characterized in that, The method further includes: Maintain a synchronized state machine containing different state identifiers for the core consciousness data of each digital life form, and trigger state transitions by events.

10. An electronic device, characterized in that, The electronic device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the method according to any one of claims 1 to 9.

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