Compliance management method, device, equipment and readable storage medium

Abstract

The application discloses a compliance management method, device and equipment and a readable storage medium. The method comprises the following steps: constructing a carbon offset multidimensional compliance consensus substrate; generating a scenario-based compliance identification vector for each carbon offset scenario based on the carbon offset multidimensional compliance consensus substrate; performing aggregation processing on the compliance identification vectors corresponding to different types of carbon offset scenarios to obtain a global compliance characterization vector; obtaining a compliance causal chain based on the global compliance characterization vector, wherein the compliance causal chain is formed by splicing a plurality of causal coupling segments; performing multi-structure submodule collaborative embedding on the basis of the compliance causal chain to obtain a fusion result of a multi-structure symbiotic network; and performing hierarchical traceability interpretation on carbon offset nodes according to the fusion result of the multi-structure symbiotic network, and performing compliance determination on each carbon offset node. The application can enhance the compatibility of complex business scenarios, and the system can respond to dynamic supervision requirements across regions with high accuracy and flexibility.

Description

Technical Field

[0001] This application belongs to the field of carbon emission management technology, specifically relating to a compliance management method, apparatus, equipment, and readable storage medium. Background Technology

[0002] Existing technical solutions typically employ a single structured modeling approach to handle compliance elements in carbon offsetting operations, using blockchain or distributed ledgers as the primary means of storing and verifying transaction data. These solutions are mostly based on fixed rule engines or predefined policy sets, managing offsetting processes and transaction information in a centralized or semi-decentralized manner. For example, some systems perform batch verification of basic data for carbon offsetting projects during the preprocessing stage, matching it with information stored on the blockchain, and then using a set of rule scripts to conduct compliance checks on the accounting logic and transaction behavior. The entire process relies on simplified numerical models or constraints at the implementation level to determine whether predetermined compliance standards are met. This approach results in a lack of flexible scalability when facing diverse, frequently updated compliance requirements across regions, and makes it difficult to simultaneously address the multi-dimensional representation of transaction flow topology, regulatory changes, and regional differences. Summary of the Invention

[0003] This application provides a compliance management method, apparatus, device, and readable storage medium, which solves the problem that existing compliance calculation and interpretation processes are based on coarse granularity or a single dimension, resulting in poor flexibility in compliance management.

[0004] Firstly, a compliance management method is provided, comprising: constructing a multi-dimensional compliance consensus base for carbon offsetting; generating a scenario-based compliance identifier vector for each carbon offsetting scenario based on the multi-dimensional compliance consensus base for carbon offsetting; aggregating the compliance identifier vectors corresponding to different types of carbon offsetting scenarios to obtain a global compliance representation vector; obtaining a compliance causal chain based on the global compliance representation vector, wherein the compliance causal chain is formed by splicing together multiple causal coupling segments; performing multi-structure sub-module collaborative embedding on the compliance causal chain to obtain a fusion result of a multi-structure symbiotic network; and performing hierarchical source tracing interpretation of carbon offsetting nodes and compliance determination for each carbon offsetting node according to the fusion result of the multi-structure symbiotic network.

[0005] Secondly, a compliance management device is provided, comprising: a first processing module for constructing a multi-dimensional compliance consensus base for carbon offsetting; a second processing module for generating a scenario-based compliance identifier vector for each carbon offsetting scenario based on the multi-dimensional compliance consensus base for carbon offsetting; a third processing module for aggregating the compliance identifier vectors corresponding to different types of carbon offsetting scenarios to obtain a global compliance representation vector; a fourth processing module for obtaining a compliance causal chain based on the global compliance representation vector, wherein the compliance causal chain is formed by splicing together multiple causal coupling segments; a fifth processing module for performing multi-structure sub-module collaborative embedding on the compliance causal chain to obtain a fusion result of a multi-structure symbiotic network; and a compliance management module for performing hierarchical source tracing interpretation of carbon offsetting nodes and making compliance determination for each carbon offsetting node according to the fusion result of the multi-structure symbiotic network.

[0006] Thirdly, an electronic device is provided, comprising: a processor, a memory, and a program stored in the memory and executable on the processor, wherein the program, when executed by the processor, implements the steps of the above-described compliance management method.

[0007] Fourthly, a computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, implements the steps of the above-described compliance management method.

[0008] The beneficial effects of this application are as follows: In the embodiments of this application, by parsing cross-scenario causal chains and weighted fusion of different compliance segments, the contribution of each causal coupling segment can be displayed when scoring carbon offset nodes, thereby maintaining high transparency in multi-dimensional compliance scenarios. With the support of multi-structured symbiotic networks and causal coupling segment reorganization, cross-regional differences and dynamic regulatory updates are synchronously incorporated into the scoring and reasoning process along with core compliance logic, enhancing compatibility with complex business scenarios. By combining multi-structured symbiotic networks with interpretable large models, the system can respond to cross-regional dynamic regulatory requirements with high accuracy and flexibility. Attached Figure Description

[0009] Figure 1 This is one of the flowcharts illustrating the compliance management method of this application.

[0010] Figure 2 This is the second flowchart illustrating the compliance management method of this application.

[0011] Figure 3 This is the third flowchart illustrating the compliance management method of this application.

[0012] Figure 4 This is the fourth flowchart illustrating the compliance management method of this application.

[0013] Figure 5 This is the fifth flowchart illustrating the compliance management method of this application.

[0014] Figure 6 This is the sixth flowchart illustrating the compliance management approach of this application.

[0015] Figure 7 This is a structural schematic diagram of the compliance management device of this application.

[0016] Figure 8 This is a schematic diagram of the electronic device of this application. Detailed Implementation

[0017] The technical solutions of the embodiments of this application will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. 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.

[0018] The terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and are not used to describe a specified order or sequence. It should be understood that such use of data can be interchanged where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first" and "second" are generally of the same class, not limited in number; for example, a first object can be one or more. Furthermore, in the specification and claims, "and" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0019] It is worth noting that the technologies described in this application are not limited to Long Term Evolution (LTE) / LTE-Advanced (LTE-A) systems, but can also be used in other wireless communication systems, such as Code Division Multiple Access (CDMA), Time Division Multiple Access (TDMA), Frequency Division Multiple Access (FDMA), Orthogonal Frequency Division Multiple Access (OFDMA), Single-carrier Frequency-Division Multiple Access (SC-FDMA), and other systems. The terms "system" and "network" in this application are often used interchangeably, and the described technologies can be used with the systems and radio technologies mentioned above, as well as with other systems and radio technologies. However, the following description describes New Radio (NR) systems for illustrative purposes, and NR terminology is used in most of the following description. These technologies can also be applied to applications beyond NR systems, such as 6th Generation (6G) communication systems.

[0020] It should be understood that the phrase "one embodiment" or "an embodiment" throughout the specification means that a specific feature, structure, or characteristic related to the embodiment is included in at least one embodiment of this application. Therefore, "in one embodiment" or "in an embodiment" appearing throughout the specification does not necessarily refer to the same embodiment. Furthermore, these specific features, structures, or characteristics can be combined in any suitable manner in one or more embodiments.

[0021] In the various embodiments of this application, it should be understood that the sequence number of each process described below does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0022] In addition, the terms "system" and "network" are often used interchangeably in this article.

[0023] In the embodiments provided in this application, it should be understood that "B corresponding to A" means that B is associated with A, and B can be determined based on A. However, it should also be understood that determining B based on A does not mean determining B solely based on A; B can also be determined based on A and / or other information.

[0024] like Figure 1 As shown, this application embodiment provides a compliance management method, the method including: step 101, constructing a carbon offset multidimensional compliance consensus base.

[0025] Step 102: Based on the carbon offset multidimensional compliance consensus base, generate a scenario-based compliance identifier vector for each carbon offset scenario.

[0026] Step 103: Aggregate the compliance identifier vectors corresponding to different types of carbon offset scenarios to obtain a global compliance representation vector.

[0027] Step 104: Obtain the compliance causal chain based on the global compliance representation vector. The compliance causal chain is formed by splicing together multiple causal coupling segments.

[0028] Step 105: Based on the compliant causal chain, perform multi-structure sub-module collaborative embedding to obtain the fusion result of the multi-structure symbiotic network.

[0029] Step 106: Based on the fusion results of the multi-structure symbiotic network, perform hierarchical source tracing interpretation of the carbon offset nodes and make compliance determination for each carbon offset node.

[0030] In this embodiment, the system first constructs a multi-dimensional compliance consensus base for carbon offsetting. This base is compatible with regulatory requirements in different regions and enables rapid abstraction and dynamic updating of cross-industry compliance. Furthermore, based on this multi-dimensional compliance consensus base, the system performs hierarchical segmentation according to different types of carbon offsetting scenarios and establishes semantic compliance mapping rules. Through semantic parsing of specific business scenarios, these scenarios are matched with the multi-dimensional compliance feature parameters in the aforementioned multi-dimensional compliance consensus base to achieve accurate identification of dominant compliance elements, compliance nodes, and scenario differences, generating a scenario-based compliance identifier vector for each carbon offsetting scenario.

[0031] The system iteratively aggregates compliance identifier vectors from different scenarios through multiple iterations, detecting compliance conflicts and changes in each iteration. It adaptively weights the base parameters based on the compliance evolution rate, ultimately producing a global compliance representation vector, providing crucial input for the interpretable large-scale model. Building upon this global compliance representation vector, the system breaks down the inherent causal relationships of carbon offset certificates into several causal coupling segments. Using the interpretable large-scale model, it calculates the structural similarity and regulatory coupling degree of these causal coupling segments and concatenates them in a progressive manner, forming a compliance causal chain that is both applicable across scenarios and portable.

[0032] Based on the aforementioned cross-scenario compliance causal chain, through the collaboration of multiple structural sub-modules, the transaction topology, compliance standard changes, and cross-regional differences of offset certificates are embedded at multiple levels, ultimately forming a compliance learning system with high interpretability and flexibility, outputting the fusion result of a multi-structure symbiotic network. This fusion result of the multi-structure symbiotic network can be provided to an interpretable large model for compliance judgment and inference tasks. Based on the multi-structure symbiotic network, by performing hierarchical tracing and explanation of the model's inference process, compliance judgment is performed on each carbon offset node, clarifying which elements and constraints in the causal chain shape the compliance score of each carbon offset node. This allows for the construction of a compliance adaptive optimization mechanism that can dynamically adjust offset amounts and transaction strategies, ensuring the refinement and flexibility of compliance management. In this application embodiment, "compliance" can be understood as norms or standards; "compliance management" can be understood as the management of whether carbon offset nodes comply with relevant norms or standards.

[0033] In the embodiments of this application, by parsing cross-scenario causal chains and weighted fusion of different compliance segments, the contribution of each causal coupling segment can be displayed when scoring carbon offset nodes, thereby maintaining high transparency in multi-dimensional compliance scenarios. Supported by multi-structured symbiotic networks and the recombination of causal coupling segments, cross-regional differences and dynamic regulatory updates are synchronously incorporated into the scoring and reasoning process along with core compliance logic, enhancing compatibility with complex business scenarios. By combining multi-structured symbiotic networks with interpretable large models, the system can respond to dynamic regulatory requirements across regions with high accuracy and flexibility.

[0034] In some embodiments, constructing a multi-dimensional compliance consensus base for carbon offsetting includes: obtaining compliance feature parameters of multiple dimensions based on the carbon offsetting business system; the compliance feature parameters include: compliance constraint information extracted from different types of carbon offsetting scenarios, transaction and regulatory correlation parameters extracted from historical transaction behavior and corresponding review status, and cross-regional regulatory difference information; integrating and calculating the compliance feature parameters using a global function, the calculation result of the global function being a global reference value of the multi-dimensional compliance consensus base for carbon offsetting.

[0035] In this embodiment, an initial database is first established to reflect the compliance and trading behavior of carbon offset certificates in different regions and of different types. In the carbon offset business system, regulatory regulations of various regions, verification processes of various types of offset projects, and historical transaction records can be queried to generate compliance feature parameters of several dimensions. The compliance feature parameters may include the following items.

[0036] (1) Compliance constraint information extracted from different types of carbon offset scenarios; different types of carbon offset scenarios, such as forestry carbon sinks, green electricity offsets, and other carbon offset project types. Compliance constraint information is extracted from multiple scenarios such as forestry carbon sinks and green electricity offsets to form a cross-domain constraint set. Unified compliance modeling is achieved for different regions and different types of carbon offset certificates.

[0037] (2) Transaction and regulatory correlation parameters extracted from historical transaction behavior and corresponding review status; the system can obtain historical transaction behavior and corresponding review status from the trading platform, thereby extracting transaction and regulatory correlation parameters.

[0038] (3) Cross-regional regulatory difference information; can be understood as obtaining carbon offset-related regulatory information in different regions, and further obtaining regulatory difference information in each region.

[0039] The aforementioned multi-dimensional compliance feature parameters are placed in a unified data structure to form a set of compliance feature parameters that meet the needs of multi-dimensional compliance modeling. This embodiment, based on a distributed data acquisition component, reads compliance information from blockchain nodes and industry regulatory databases to form a multi-dimensional compliance feature parameter vector. Furthermore, a global function is used to integrate and calculate the collected compliance feature parameters; the result of the global function is the global reference value of the carbon offset multi-dimensional compliance consensus base. Addressing the shortcomings of current compliance identification methods based on single weighting or single attention mechanisms, this application constructs a multi-dimensional element fusion and dynamic parameter registration mechanism based on the collected multi-dimensional compliance feature parameters to improve the adaptability to compliance elements in different scenarios.

[0040] In some embodiments, the integration calculation of the compliance feature parameters using global functions includes: integrating the compliance feature parameters using the following global functions.

[0041] .

[0042] in, This represents the result of a global function calculation. The vector represents the compliance feature parameters, and each component in this set corresponds to a specific compliance dimension, extracted from different regulatory regions and project types; The importance weights of compliance feature parameters in different dimensions can be assigned based on regulatory intensity and transaction risk level; These are coefficients representing the sensitivity to changes in compliance feature parameters across different dimensions, used to characterize the weight adjustments of a particular dimension during compliance changes. This represents the nonlinear transformation exponent, used to enhance the characteristic response amplitude under extreme or emergency regulatory conditions; Representing a scene or dimension , .

[0043] For the calculation formula of the global function mentioned above, based on the heterogeneity of multidimensional feature parameters, a logarithmic operation is introduced to smooth the differences between different scales, and then... Enhance the measurement of local extrema; based on To measure the importance of this dimension's compliance characteristics to the overall compliance assessment, and to adopt... Its scope of influence is dynamically adjusted; ultimately, a global function is formed by summing all dimensions. The higher the value of this global function, the more consistent the multidimensional compliance features are at the current moment.

[0044] In this embodiment, The calculation result is a global reference value used to build a multidimensional compliance consensus base for carbon offsetting. It can also be understood as the core quantitative embodiment of the multidimensional compliance consensus base for carbon offsetting. By continuously updating this value, dynamic unification of compliance elements in different scenarios can be achieved.

[0045] By integrating multi-dimensional compliance features through the above formula, cross-regional regulatory differences and various types of carbon offset projects can be evaluated under a unified metric, providing a quantifiable baseline indicator for subsequent scenario segmentation and on-chain registration. The carbon offset multi-dimensional compliance consensus basis in this step is a unified compliance basis calculated using a global function based on multi-dimensional compliance feature parameters. The calculation result serves as the global reference value for this consensus base. By constructing a multi-dimensional compliance consensus base, it is compatible with regulatory requirements in different regions and enables rapid abstraction and dynamic updating of cross-industry compliance.

[0046] In some embodiments, the method further includes: hashing the calculation result of the global function, the compliance feature parameters, and the weight information of the compliance feature parameters and then storing them in the blockchain.

[0047] In this embodiment, trusted registration is performed based on a blockchain environment, and multi-dimensional compliance feature parameters are stored securely at the underlying layer, providing a secure foundation for subsequent dynamic adjustments and access to compliance policies. The encrypted uploading of multi-dimensional compliance feature parameters to the blockchain network is completed, and the formula calculation results are then... Synchronous registration to an immutable distributed ledger ensures traceability and data integrity for future compliance verification. Specifically, the transaction node invokes a smart contract to transmit the compliance feature parameter vector. After hashing its weight information, it is added to the blockchain and stored. value.

[0048] Optionally, when regulatory authorities or the scenario adaptation layer issue new compliance instructions, comparative analysis can be performed using historical parameters from trusted registration to quickly update multi-dimensional compliance feature parameters dynamically.

[0049] In this embodiment, if a regulatory authority or the scenario adaptation layer issues a new compliance instruction, the previously obtained multi-dimensional compliance feature parameters will be dynamically updated based on the historical parameters of trusted registration; because It is a global reference value obtained by integrating multi-dimensional compliance feature parameters. Therefore, after the multi-dimensional compliance feature parameters are updated, The results should be recalculated and recorded on the blockchain as an updated version to maintain consistency between the compliance basis and current regulatory requirements. Based on the consensus algorithm in the blockchain network, the trust of all parties in the compliance basis calculation results can be increased, ensuring that any node can unanimously accept the conclusion.

[0050] The following example illustrates the process of constructing a multi-dimensional compliance consensus base for carbon offsetting in this application embodiment, such as... Figure 2 As shown, the process includes: retrieving regulatory regulations for various regions, verification processes for multiple types of offset projects, and historical transaction records; transmitting the acquired data to the corresponding carbon offset business system, aggregating the data and reading compliance information through distributed data acquisition components (such as blockchain nodes and regulatory databases); standardizing and organizing the compliance information, extracting compliance constraint information from cross-regional and multi-type offset scenarios, and then integrating transaction review status and cross-regional regulatory differences information to generate multi-dimensional compliance feature parameters. The compliance feature parameters are hashed, and the value of the global function Q is calculated; the compliance feature parameters, Q value, and other parameters are synchronized onto the blockchain to form the initial parameter registration; subsequently, changes in regulatory information are monitored, and when a new compliance instruction is issued by the regulatory department or scenario adaptation layer, i.e., when a new compliance instruction is received, the parameters are dynamically updated by comparing them with the recorded historical parameters.

[0051] In some embodiments, generating a scenario-based compliance identifier vector for each carbon offset scenario based on the carbon offset multidimensional compliance consensus base includes: extracting scenario semantic vectors corresponding to different types of carbon offset scenarios; selecting compliance feature parameters associated with different types of carbon offset scenarios from the carbon offset multidimensional compliance consensus base; mapping the scenario semantic vectors to the compliance feature parameters to obtain a matching result between the scenario semantic vectors and the compliance feature parameters under the mapping; and generating a scenario-based compliance identifier vector for each carbon offset scenario based on the matching result.

[0052] In this embodiment, based on the established multi-dimensional compliance consensus foundation, hierarchical segmentation is performed according to different types of carbon offsetting scenarios, and semantic compliance mapping rules are established. By semantically parsing specific business scenarios and matching them with multi-dimensional compliance feature parameters, accurate identification of dominant compliance elements, compliance nodes, and scenario differences can be achieved.

[0053] Specifically, firstly, scenario-specific documents and trading agreements are retrieved from the independent business systems of forestry carbon sinks, green electricity certificates, and international voluntary emission reduction projects to obtain key elements describing offset calculation methods, delivery cycles, and offset proof types. Then, scenario semantic vectors are constructed. This allows for the extraction of core semantic information reflecting business logic and compliance requirements from the application systems of different carbon offset scenarios. Since current scenario matching processes rely solely on keyword comparison and lack consideration for regulatory differences across regions, inaccurate mapping can occur. This application's embodiment overcomes the problem of insufficient compliance identification accuracy by constructing an adjustable feature parameter through multi-layered comparison of cross-regional semantic elements and a multi-dimensional compliance consensus base.

[0054] In some embodiments, mapping the scene semantic vector to the compliance feature parameters to obtain a matching result between the scene semantic vector and the compliance feature parameters under the mapping includes: mapping the scene semantic vector to the compliance feature parameters using the following formula to obtain a matching result between the scene semantic vector and the compliance feature parameters under the mapping.

[0055] .

[0056] in, This represents the matching result between the scenario semantic vector and the compliance feature parameters, which is used to subsequently generate a scenario-based compliance identifier vector. Indicates the first Business semantic vectors for various carbon offsetting scenarios This indicates that the selection of scenarios is based on the multidimensional compliance consensus base of carbon offsetting. The set of associated compliance feature parameters, This indicates the number of compliance feature parameters selected. ; This indicates the importance of compliance feature parameters in the current scenario. Indicates compliance feature parameters Its importance in the current context stems from the inductive analysis of cross-regional regulatory rules; This represents the amplification factor that adjusts the degree of compliance matching for different features. This indicates adjusting different compliance feature parameters. The amplification factor for compliance matching reflects the emphasis on key elements; This represents the adaptive factor affecting the scenario, which is the adaptive factor affecting the matching curve of multi-dimensional elements. Based on this, we can determine the dimensions or scenarios in business semantics that have a greater impact on compliance.

[0057] In the above formula, logarithmic operations and the square root term in the denominator are used to enhance the dynamic characterization of convergence speed and local feature differences; based on The format involves superimposing different dimensions using weighted exponents to amplify the degree of feature difference; through By controlling the coupling strength of multidimensional factors, adaptive adjustment of the matching degree between scene features and basis features can be achieved.

[0058] Based on this formula, a more accurate matching degree between quantitative scenario semantics and multi-dimensional compliance feature parameters can be obtained, which can overcome the shortcomings of low accuracy of simple keyword comparison in traditional solutions and maintain high scalability in a changing regulatory environment.

[0059] After calculating the matching result between the scenario semantic vector and the compliance feature parameters based on the above formula, a scenario-based compliance identifier vector is generated for each carbon offset scenario based on the matching result. The formula calculation results are then used to... By performing normalization and threshold judgment, we can determine the identifier values ​​of the scenario in terms of dominant compliance elements, key transaction nodes and differentiated characteristics. These identifier values ​​together constitute a scenario-based compliance identifier vector.

[0060] Specifically, the calculation results of the above formula are divided into multiple indicator intervals to automatically identify highly sensitive and high-priority compliance features, thereby obtaining the identification values ​​of the scenario in the dimensions of dominant compliance elements, key transaction nodes, and differentiated features, and thus obtaining a scenario-based compliance identification vector. After generating the scenario-based compliance identification vector, it is output to the subsequent compliance fusion engine to form a customized tag set that can be used for multiple rounds of iterative fusion operations. When regulatory changes occur, the system recalculates and updates the scenario-based compliance identification vector according to the updated parameters to ensure the sustainable execution of the dynamic compliance management function.

[0061] The scenario-based compliance identifier vector in this application can also be understood as a structured identifier of the compliance status of a specific carbon offsetting scenario. It is used to characterize the focus and identification results of the scenario in terms of dominant compliance elements, key transaction nodes, and differentiated feature dimensions. After generating this compliance identifier vector, it will be output to the subsequent compliance fusion engine to form a customized tag set that can be used for multiple rounds of iterative fusion operations. Therefore, this compliance identifier vector is both an expression of the compliance features of the current scenario and the input basis for the construction of the subsequent global compliance representation vector.

[0062] The following example illustrates the process of generating scenario-specific compliance identifier vectors for each carbon offset scenario in this application. For instance... Figure 3 As shown, the process includes: retrieving business documents based on business systems (such as forestry carbon sinks, green electricity, and international resource emission reduction projects); performing scenario semantic parsing based on the business documents, extracting core elements of business logic, and constructing scenario semantic vectors to achieve cross-regional semantic difference labeling; performing regulatory rule change detection and updating semantic rules when dynamic update events occur. Semantic compliance mapping is performed based on compliance feature parameters in the constructed carbon offset multidimensional compliance consensus base and scenario semantic vectors. Specifically, a set of compliance feature parameters associated with different types of carbon offset scenarios is selected from the carbon offset multidimensional compliance consensus base, and the matching degree between the scenario semantic vector and the compliance feature parameters is calculated; the matching degree is adjusted by dynamically adjusting the amplification coefficient. A scenario-based compliance identifier vector is generated based on the obtained matching degree results. Specifically, the obtained matching degree results are normalized and thresholded to determine the identifier values ​​of the scenario in the dominant compliance elements, key transaction nodes, and differentiated feature dimensions, thereby generating a scenario-based compliance identifier vector and labeling the priority of compliance features. The generated scenario-based compliance identifier vector is stored in an identifier vector library, forming a set of scenario-based tags that can be used for multi-round iterative fusion operations, facilitating version management. When regulatory rules change, the system recalculates the scenario-based compliance identifier vector based on the updated parameters, thus dynamically updating the parameters.

[0063] In some embodiments, the process of aggregating compliance identifier vectors corresponding to different types of carbon offset scenarios to obtain a global compliance representation vector includes: recording the compliance identifier vectors corresponding to different types of carbon offset scenarios into the iteration management module; aggregating the compliance identifier vectors corresponding to different types of carbon offset scenarios with compliance conflict information and change point information during multiple iterations; and obtaining the global compliance representation vector after the iteration is completed.

[0064] In this embodiment, compliance identifier vectors for different scenarios are aggregated step by step through multiple iterations. In each iteration, compliance conflicts and changes are detected, and the basis parameters are adaptively weighted according to the compliance evolution rate. Finally, a global compliance representation vector is generated, providing key input for an interpretable large model.

[0065] First, compliance identifier vectors for different types of carbon offset business scenarios are recorded in the iteration management module. An indexing mechanism is then used for rapid retrieval and aggregation to achieve unified collection and preprocessing of scenario-based compliance identifier vectors, forming an initial state that can participate in multiple iterations. Specifically, a scenario index table is established to synchronously store compliance identifier vectors and scenario attribute information; an iteration round counter and initial fusion weight are set for dynamic adjustment in subsequent rounds; and the system is linked with the constructed multi-dimensional compliance consensus base to ensure the accuracy and completeness of the collected compliance identifier vectors.

[0066] Subsequently, through multi-round fusion, the compliance identifier vectors, compliance conflict information, and change point information of each scenario are aggregated during multiple iterations to obtain a gradually stable global compliance representation vector. Specifically, an improved formula for single-round iterative fusion is introduced, enabling the system to dynamically adjust parameter weights based on detected compliance conflicts and compliance evolution rates, achieving layer-by-layer weighting and priority correction.

[0067] In some embodiments, the aggregation of compliance identification vectors, compliance conflict information, and change point information corresponding to different types of carbon offset scenarios during multiple iterations includes: aggregating compliance identification vectors, compliance conflict information, and change point information corresponding to different types of carbon offset scenarios using the following formula.

[0068] .

[0069] in, Indicates the first The global compliance representation vector obtained from each iteration is used as the comprehensive output of multi-scenario compliance aggregation; Indicates the first Round iteration for scenarios The calculated base weights are derived from the compliance identifier vector and the initial fusion weights; This represents the compliance evolution rate coefficient, which is assessed based on the recent frequency of standard updates and the magnitude of changes in the amount of offsetting. Denotes the adaptive correction function, where Representative at the Scenes detected in round iteration Compliance conflict level Represents the current round (number) The standard changes identified by the wheel; This is the magnification factor, used to characterize the scene. Impact on the integration process; Representing a scene In the Core components of the compliance identifier vector generated in each iteration; It is a power exponent, used to non-linearly amplify the quantification results of the identifier to improve the sensitivity to compliance conflict nodes.

[0070] The formula outputs a new value after each iteration. Through multiple iterations, the global compliance representation vector gradually converges while taking into account the latest regulatory standards. After the final iteration, the result is obtained from the above formula. As the final characterization result.

[0071] In the above formula, conflict information and change point information in the compliance identifier vector of the scenario are used as correction terms and introduced into the correction function. And by compliance evolution rate coefficient Weighting is applied; a combination of logarithmic and exponential operations is used on the compliance identifier vector to accelerate conflict convergence and highlight key elements during the fusion process; the basic weights for multiple scenarios are also applied. With correction function After addition, the compliance differences between scenarios and the impact of regulatory evolution are integrated step by step. This formula can give higher weight and higher sensitivity to relevant scenarios when there are significant compliance conflicts or frequent changes, overcoming the shortcomings of conventional aggregation methods that cannot efficiently keep up with new standards and ignore historical compliance information, and improving the precision of carbon offset compliance management.

[0072] In some embodiments, the method further includes: recording change information and convergence index during each iteration; wherein the change information includes standard change information identified during the iteration and adjustment information caused by the standard change to the compliance identifier vector fusion process corresponding to different types of carbon offset scenarios; the convergence index is used to represent the stability of the global compliance representation vector in continuous iterations.

[0073] In this embodiment, during each iteration, the changes and convergence indices are recorded for subsequent scenario analysis and compliance strategy evaluation. The changes refer to new standard changes identified during multiple iterations, and the adjustments these changes cause to the compliance identifier vector fusion process for each scenario. The convergence indices reflect whether the global compliance representation vector gradually stabilizes after multiple iterations. These indices, in conjunction with the changes in each iteration, determine whether the current fusion result has reached a relatively stable state, thus serving as a basis for subsequent scenario analysis and compliance strategy evaluation. In other words, the convergence indices correspond to the stability of the global compliance representation vector in continuous iterations.

[0074] Optionally, the method further includes: storing the global compliance representation vector in a blockchain and associating it with parameters contained in the carbon offset multidimensional compliance consensus base.

[0075] In this embodiment, the final global compliance representation vector is written into the blockchain trusted storage and associated with the constructed carbon offset multidimensional compliance consensus base. When regulatory standards are switched or new standards are released, conflict detection and multi-round iterative aggregation are triggered again to ensure the real-time update of the compliance representation vector.

[0076] The following example illustrates the process of aggregating compliance identifier vectors corresponding to different types of carbon offset scenarios and obtaining a global compliance representation vector in this application embodiment.

[0077] like Figure 4 As shown, the execution process includes: constructing an initial iteration state based on scenario-based compliance identifier vectors and invoking data contained in the carbon offset multidimensional compliance consensus base, such as the global function Q-value and historical parameters. Specifically, a scenario index table is established, and an iteration counter and initial fusion weights are set. The initial state is loaded and input into the iteration management module to perform compliance conflict detection, compliance evolution rate assessment, and change point marking. Based on information such as conflict level and change status, multiple rounds of iterative fusion calculations are performed on the compliance identifier vectors, compliance conflict information, and change point information for different scenarios. Specifically, an adaptive correction function is applied, and the compliance evolution rate coefficient is used. Weighted calculations are performed on the compliance identifier vector using a combination of logarithmic and exponential operations to obtain the final result. As a global compliance representation vector, it contains convergence indicators and records of changes. This global compliance representation vector is stored on the blockchain and associated with parameters included in the carbon offset multi-dimensional compliance consensus base, with version snapshots retained. Regulatory standard switch detection is performed; when regulatory standards change or new standards are released, conflict detection and multi-round iterative aggregation are triggered again to ensure the real-time updating of the compliance representation vector.

[0078] In some embodiments, obtaining the compliance causal chain based on the global compliance representation vector includes: splitting the inherent causal relationship of carbon offset certificates into causal coupling segments based on the global compliance representation vector; calculating the comprehensive score of the causal coupling segments in terms of structural similarity and regulatory coupling degree; sorting the causal coupling segments according to the comprehensive score; splicing and combining the causal coupling segments with high comprehensive scores according to the sorting; and performing compliance annotation on each spliced ​​and combined causal coupling segment to obtain the compliance causal chain.

[0079] In this embodiment, based on the global compliance representation vector, the inherent causal relationship of carbon offset certificates is broken down into several causal coupling segments. An interpretable large model is used to calculate the structural similarity and regulatory coupling degree of these causal coupling segments, and these segments are then progressively assembled to form a compliance causal chain that is both applicable across scenarios and portable. First, with the data-driven support of the global compliance representation vector, the causal relationship of carbon offset certificates is analyzed from multiple perspectives, forming several causal coupling segments that cover offset amount calculation logic, historical transaction traceability, and regulatory judgment criteria.

[0080] Optionally, based on the global compliance representation vector, the inherent causal relationship of carbon offset certificates is split into causal coupling segments, including: extracting primary causal coupling segments from the offset amount accounting log; extracting intermediate causal coupling segments from blockchain historical transactions; and marking the primary causal coupling segments and the intermediate causal coupling segments with regulatory conflict causes and regulatory characteristics based on the global compliance representation vector.

[0081] In this embodiment, data records for each stage are extracted from the offset amount calculation log, and the calculation formula, input parameters, and output results are combined to form a primary causal coupling segment. Transaction entities, settlement nodes, and secondary transaction flow elements are extracted from historical blockchain transactions and matched with corresponding regulatory judgments to obtain an intermediate causal coupling segment. These primary and intermediate causal coupling segments are two layers of pre-processing segments obtained from different sources. The primary causal coupling segment, derived from the offset amount calculation log, is formed by combining the calculation formula, input parameters, and output results, focusing on characterizing the calculation process itself. The intermediate causal coupling segment, derived from historical blockchain transactions and combined with corresponding regulatory judgments, focuses on characterizing the correspondence between transaction flow and regulatory constraints. These primary and intermediate causal coupling segments serve as the foundation for subsequent causal coupling analysis. This step involves interactive mapping between the global compliance representation vector and the causal coupling segments, marking potential conflict causes and regulatory characteristics within the causal coupling segments, and completing the final splitting and archiving of the causal coupling segments.

[0082] Optionally, the method further includes storing the primary causal coupling fragment and the intermediate causal coupling fragment in a causal coupling fragment library. By uniformly storing causal association parameters from different sources in the causal coupling fragment library, it facilitates subsequent multi-level similarity and coupling degree calculations. The global compliance representation vector clarifies the compliance aggregation results between different scenarios, providing data support for identifying fragment conflict elements and compliance attributes during this step of segmentation.

[0083] Traditional causal chain extraction methods rely on a single rule tree and fixed attention weights, failing to break down offset accounting, transaction tracing, and regulatory judgment into recombinable causal segments, resulting in insufficient cross-scenario reusability. In situations with high regulatory coupling, traditional causal modeling methods lack decoupling and splicing mechanisms, making rapid adaptation to new scenarios difficult. Therefore, this application proposes multi-dimensional analysis and sorting of causal coupling segments to achieve the portability of compliant causal chains across different carbon offset scenarios. Based on this, by quantifying the structural similarity and regulatory coupling of causal coupling segments, the splicing order is determined, progressively combining multiple causal coupling segments into an interpretable, large-scale, identifiable compliant causal chain, thus improving the explanatory power of the entire causal chain.

[0084] In some embodiments, calculating the comprehensive score of the causal coupling segment in terms of structural similarity and regulatory coupling includes: calculating the comprehensive score of the causal coupling segment in terms of structural similarity and regulatory coupling using the following formula.

[0085] .

[0086] .

[0087] in, This represents the combined score of the set of causal coupling segments in terms of both structural similarity and regulatory coupling. The higher the score, the better the compliant causal chain is in terms of overall coherence and regulatory relevance. This is a set of causal coupling fragments to be spliced ​​together. The structural similarity reference information corresponding to each causal coupling segment comes from the structural feature evaluation of similar segments in the causal coupling segment library; This indicates the regulatory coupling degree reference information corresponding to each causal coupling segment, including the scope of application of regulatory rules and sensitivity level; This is a structural similarity coefficient used to reflect the consistency impact of causal coupling segments at the architectural and logical levels. This is a coefficient measuring regulatory coupling, used to reflect the degree to which causal segments are dependent on regulatory rules; A represents the number of causal coupling segments to be spliced. ; For causal coupling segments With structural similarity reference information The similarity function is calculated by combining the number of nodes, the distribution of directed edges, and the coupling strength of data flow. For causal coupling segments Coupling with regulation reference information The coupling degree function involves the number of compliance clauses, the matching degree of accounting requirements, and the historical transaction judgment results; This is a weighting term for the position of causal coupling segments. It is a power exponent, which uses a combination of logarithmic and exponential operations to nonlinearly amplify the influence of causal coupling segments when they appear later in the splicing order; , These are weighting coefficients, used to control the contribution ratio of different similarity measures in the final result; and They respectively represent: the causal coupling segment in the k-th (k=1,2,…K) structural feature dimension. With structural similarity reference information Each metric is expressed using a vector or embedding. Represents causal coupling segments With structural similarity reference information The distance or mismatch between them is used to reflect the magnitude of the difference in structural topology or logical layout between the two. , These are weighting coefficients, used to balance the contribution and importance of causal coupling segments in various regulatory metrics; These represent the causal coupling segments at the b-th (b=1,2,…B) regulatory feature dimension. Coupling with regulation reference information The measurement value; Demonstrating causal coupling fragments Coupling with regulation reference information Constraint deviations or stage differences between them; It is a dynamic adjustment term based on sine function correction; It is a constant. Used to Projected to [0, The range forms a fluctuation cycle.

[0088] In this formula, the structural similarity function and the regulatory coupling function are first preprocessed to map the similarity or coupling between the causal coupling segment and the benchmark to... Interval; further and Different linear weights are assigned to each segment to allow for dynamic adjustments in situations that emphasize structural explanation or regulatory compliance; when progressively splicing multiple causal coupling segments, [the following is used]. For index The ascending causal coupling segments are amplified to varying degrees. This process can also be understood as a nonlinear amplification process, which enables the later spliced ​​causal coupling segments to obtain higher weights in the entire causal chain, facilitating sensitivity correction to newly introduced regulatory requirements.

[0089] Based on this formula, it is possible to achieve fine sorting and splicing of causal coupling fragments across scenarios, ensuring that the final causal chain formed by splicing has both high structural similarity and high regulatory adaptability, providing data basis for the prediction and compliance determination of interpretable large models in carbon offset scenarios.

[0090] Based on the calculation results of the above formula, the causal coupling fragments are sorted, and the causal chains are spliced ​​according to the sorting results to form a compliant causal chain with cross-scenario applicability, providing a portable template for inclusion in new scenarios; specifically, according to The scores are used to prioritize all causal coupling segments, and segments with higher scores are combined first. In the interpretable large model, cross-scenario causal chains are constructed according to the splicing order, and each causal coupling segment is annotated with compliance information to clarify its position in accounting logic, transaction traceability, and regulatory judgment.

[0091] Optionally, the assembled compliant causal chain can be associated with and stored in a causal coupling fragment library to provide a foundation for rapid replication and fine-tuning when compliantly accessing new scenarios.

[0092] The following example illustrates the execution process of obtaining the compliance causal chain based on the global compliance representation vector in the embodiments of this application.

[0093] like Figure 5 As shown, the execution process includes: inputting compliance features based on the global compliance representation vector, and referring to historical segments in the causal coupling segment library (containing historical splitting records) to split the causal coupling segments. Specifically, the offset amount calculation log is parsed to obtain intermediate and primary causal coupling segments, and intermediate causal coupling segments are obtained by tracing the blockchain historical transactions. Regulatory and judgment element matching is performed, and interactive mapping is performed between the global compliance representation vector and the obtained causal coupling segments to mark potential conflict causes and regulatory features in the causal coupling segments, resulting in the final splitting and archiving of the causal coupling segments. Multi-dimensional calculations are performed on the split causal coupling segments to obtain a comprehensive score for the causal coupling segments in terms of structural similarity and regulatory coupling degree. Specifically, structural similarity calculation and regulatory coupling degree calculation are performed, and dynamic weight adjustment is executed. Optionally, regulatory change detection is also performed based on the rule observation period, updating parameters when regulatory standards switch. Causal chain splicing is performed on the causal coupling segments in terms of structural similarity and regulatory coupling degree to obtain a compliance causal chain containing a consistent template across scenarios. Specifically, priority sorting, non-linearity optimization, and interpretable annotation embedding processes are performed on the causal coupling segments. Optionally, new scenario access can be detected based on model update triggers, thereby optimizing the splicing strategy of causal coupling fragments. After obtaining the compliant causal chain, it is stored in the causal chain template library. Specifically, scenario tag indexes can be established for the compliant causal chain based on various scenarios. Digital fingerprint evidence is stored in the blockchain's positive anchor point based on the compliant causal chain, realizing zero-knowledge proof.

[0094] In some embodiments, based on the compliance causal chain, multi-structure sub-module collaborative embedding is performed to obtain the fusion result of the multi-structure symbiotic network, including: based on the compliance causal chain, determining the embedding vector set corresponding to each sub-module, wherein each vector in the embedding vector set corresponds to a vectorized representation of the compliance element in the compliance causal chain by the sub-module; and performing weighted transformation, activation, and norm operation on the vectors in the embedding vector set to obtain the fusion result of the multi-structure symbiotic network.

[0095] In this embodiment, based on the aforementioned cross-scenario compliance causal chain, the transaction topology, compliance standard changes, and cross-regional differences of offset certificates are embedded in multiple layers through the collaboration of multiple structural sub-modules, ultimately forming a compliance learning system with high interpretability and flexibility. The multiple structural sub-modules include, for example, one or more of the following.

[0096] 1) Transaction Topology Submodule: This module receives network graph data based on blockchain transaction records and causal chain output, performs graph convolution and path parsing on the flow path of offset certificates, and outputs topology embedding vectors.

[0097] 2) Compliance Standard Change Submodule: This module collects regulatory update logs and a semi-supervised standard set, uses a temporal neural network to extract the differences between the old and new standards, and outputs a compliance change embedding vector.

[0098] 3) Cross-regional traceability correction submodule: It is used to match the regulatory preferences of different regions with the corresponding nodes in the causal chain, enhance the ability to express cross-regional differences with the help of attention gating mechanism, and output the regional correction embedding vector.

[0099] Traditional single-module learning networks lack the ability to integrate multi-dimensional scenarios and also lack real-time cross-module linkage and portability. This application achieves interoperability and collaboration between different-dimensional learning processes through a combination of multi-structured sub-modules and internal causal connection hubs, improving the network's ability to track and adapt to real-time compliant environments. This application completes the fusion of multi-structured deep networks by performing weighted transformations, activations, and joint norm operations on multi-path embedding vectors.

[0100] In some embodiments, the step of performing weighted transformation, activation, and norm operation on the vectors in the embedded vector set to obtain the fusion result of the multi-structure co-occurrence network includes: performing weighted transformation, activation, and norm operation on the vectors in the embedded vector set using the following formula to obtain the fusion result of the multi-structure co-occurrence network.

[0101] .

[0102] in, This represents the fusion result of multi-structured symbiotic networks, which can be used as a global compliant representation or further reasoned through an interpretable large model. This represents the set of embedding vectors output from D sub-modules, where each vector corresponds to a sub-module's deep representation of the compliance element; This represents the embedding vector generated by the d-th (d=1,2,…D) submodule; Let represent the set of learnable weight matrices, where For the first submodule d ( Transformation parameters for (1, 2, ..., L) activation units; For the set of bias terms, in conjunction with Perform a linear transformation to capture fine-grained feature shifts. For the first submodule d Bias terms for each activation unit; This represents a non-linear activation function, used to improve the diversity of representations and network capacity; This is a weight vector used for cross-submodule fusion, designed to enhance the importance of certain submodules in the current scenario. It is the weight vector of submodule d; The power exponent is used to nonlinearly amplify and suppress the norm of the embedded vector, thereby highlighting the focus on high-intensity compliance features.

[0103] In this embodiment, the output embedding vector of each submodule is combined with linear transformation and nonlinear activation to ensure that compliance-related elements and transaction characteristics are adequately expressed. Applying vector norms amplifies high-intensity or critical scenario embeddings, providing higher priority for subsequent compliance assessments. Furthermore, it weights and sums the activation results of multiple sub-modules, utilizing... The relative contribution of each sub-module is dynamically adjusted under different time periods and regulatory environments.

[0104] Through the above formula, the output vectors of the multi-structured sub-modules can complement each other at the aggregation level, making the compliance analysis of various factors such as carbon offset calculation, transaction flow, and regulatory changes more harmonious and unified. Finally, the network output processed by the multi-structured symbiotic aggregation formula is provided to the interpretable large model for compliance determination and inference tasks.

[0105] In some embodiments, the step of performing hierarchical source tracing interpretation of carbon offset nodes based on the fusion results of the multi-structure symbiotic network and making compliance determination for each carbon offset node includes: retrieving accounting logic, transaction source tracing path, and regulatory determination information related to each carbon offset node from a causal coupling fragment library; performing hierarchical source tracing interpretation of the carbon offset nodes based on the fusion results of the multi-structure symbiotic network and the accounting logic, transaction source tracing path, and regulatory determination information related to the carbon offset nodes, and making compliance determination for each carbon offset node.

[0106] In this embodiment, a hierarchical source tracing interpretation is performed based on an interpretable large model. Building upon a multi-structured symbiotic network, the hierarchical source tracing interpretation of the model's reasoning process clarifies which elements and constraints in the causal chain shape the compliance score of each carbon offset node. Based on this, a compliance adaptive optimization mechanism is constructed to dynamically adjust offset amounts and trading strategies, ensuring both precision and flexibility in compliance management.

[0107] First, the accounting logic, transaction traceability path, and regulatory judgment information directly related to the carbon offset node are retrieved from the causal coupling fragment library. Then, the representation vector of the carbon offset node in the network is read from the multi-structure symbiotic network to identify the upstream and downstream nodes or transaction links that interact most frequently with the carbon offset node. This information is then aggregated into a preliminary compliance description list for the carbon offset node, providing basic data for the next step of scoring calculation. The fusion result of the multi-structure symbiotic network can serve as the representation vector of the carbon offset node within the multi-structure symbiotic network.

[0108] Since traditional source attribution interpretation methods are difficult to reflect the layer-by-layer impact of causal coupling segments on the compliance score of carbon offset nodes, this application provides a two-layer structure of hierarchical source attribution interpretation and adaptive optimization, and constructs an improved hierarchical source attribution interpretation formula to analyze the contribution of nodes in each coupling segment of the causal chain, summarize them to form a compliance score, and then generate a traceable contribution decomposition.

[0109] In some embodiments, based on the fusion result of the multi-structure symbiotic network and the accounting logic, transaction traceability path, and regulatory judgment information related to the carbon offsetting node, a hierarchical traceability interpretation is performed on the carbon offsetting node, and a compliance judgment is made for each carbon offsetting node. This includes: using the fusion result of the multi-structure symbiotic network as the representation vector of the carbon offsetting node in the multi-structure symbiotic network; reading the representation vector using the multi-structure symbiotic network; and combining the accounting logic, transaction traceability path, and regulatory judgment information related to the carbon offsetting node to output a characterization vector of the feature output of the multi-structure symbiotic network for the carbon offsetting node; the characterization vector is used to represent the comprehensive attributes of the carbon offsetting node in accounting logic, transaction traceability, and regulatory judgment; calculating the compliance score of the carbon offsetting node based on the characterization vector of the feature output of the multi-structure symbiotic network for the carbon offsetting node; and making a compliance judgment on the carbon offsetting node based on the compliance score.

[0110] Specifically, calculating the compliance score of the carbon offset node based on the characterization vector of the feature output of the multi-structure symbiotic network for the carbon offset node includes: calculating the compliance score of the carbon offset node using the following formula based on the characterization vector of the feature output of the multi-structure symbiotic network for the carbon offset node.

[0111] .

[0112] .

[0113] in, Indicates the first The compliance score of each carbon offset node is provided, and the contribution coefficient of each causal coupling segment is analyzed in the hierarchical traceability process. For the first One carbon offset node, For this carbon offset node The compliance impact coefficients associated with each causal coupling segment in the compliance causal chain; For multi-structure symbiotic networks targeting carbon offset nodes The feature output vector is used to reflect the comprehensive attributes of carbon offset nodes in terms of accounting logic, transaction traceability and regulatory judgment. For hierarchical traceability, the first The weights of the contributions of each causal coupling segment; It is a power exponent; This function represents a hybrid mapping between the coefficients of the causal coupling segment and the characterization vector of the network output, which can perform a nonlinear transformation on the degree of compliance coupling between the carbon offset node and the causal coupling segment; Used to dynamically amplify and suppress the deviation between carbon offset nodes and coupling segments when quantifying compliance risks; This is an exponentiation operation on the embedded vector norm, used to highlight the scoring of carbon offsetting nodes in scenarios with high compliance risk and high impact; No. The carbon offsetting node and the first The compliance impact coefficient between causal coupling segments can be understood as a comprehensive measure of the correlation between the causal chain and the carbon offsetting node and the coupling segment. For multi-structured symbiotic networks to the first The carbon offsetting node at the _th ... Embedded values ​​on each feature dimension are used to reflect the specific characterization of the carbon offset node in terms of accounting logic and regulatory coupling. This represents a nonlinear control factor used to control the slope and saturation value of the piecewise response. When it is large, for The function is more sensitive to increases in , and its value tends to 1 more quickly; when . When the input is small, the function output changes relatively smoothly with the input. It is an exponential operation used to construct a nonlinear mapping of logistic regression. The range of values ​​for the output result is: , represents the local coupling strength of the carbon offset node within the context of this causal coupling segment, used for further weighting and statistical summarization in the comprehensive scoring and hierarchical explanation models. Among these, The sum-of-squares form is used to simultaneously consider the influence of causal coupling segments and the magnitude of network embedding values ​​during the mixing process, thereby enabling the combination of carbon offset nodes with large compliance impact values ​​with causal coupling segments, while suppressing small values ​​deviating from zero.

[0114] In the above formula, compliance constraints for each causal coupling segment are mapped to the network output. This enables a comprehensible fusion of influence coefficients and eigenvectors, allowing for the construction of adjustable factors. Used to adjust the degree of non-linearity, making score changes more pronounced when compliance risks surge or regulatory standards are temporarily tightened; power operation The ability to nonlinearly amplify the characteristic norm of carbon offset nodes allows for higher judgment weights at critical or compliance-sensitive nodes. Using the above formula, the contribution of each causal coupling segment to the source tracing explanation can be output simultaneously when calculating the final compliance score for a carbon offset node, enabling regulators to more clearly understand the origin and impact details of the node score.

[0115] In some embodiments, determining the compliance of the carbon offset node based on the compliance score includes: if the compliance score of the carbon offset node is within a preset range, switching the carbon offset accounting method of the carbon offset node based on the carbon offset node's level in the federal governance system, risk level, and impact on the overall carbon offset target.

[0116] In this embodiment, after calculating the compliance score of a carbon offset node, a compliance determination is made based on this score. Specifically, if the compliance score of a carbon offset node falls within a preset range, it is considered necessary to adaptively optimize the node. The compliance adaptive optimization mechanism proactively triggers a dynamic correction process. At this point, the system switches the calculation method for the carbon offset node from a general estimation mode to a refined monitoring mode based on the node's level in the federal governance system, its risk level, and its impact on the overall carbon offset target. Optionally, the system can restrict the trading permissions of the carbon offset node, requiring it to undergo a more stringent third-party audit before engaging in further offset transactions. Ultimately, based on hierarchical traceability interpretation combined with compliance adaptive optimization, the system achieves full-process tracking of compliance management for carbon offset nodes and outputs traceable and correctable management decisions.

[0117] The following example illustrates the process of hierarchically tracing carbon offset nodes based on the fusion results of the multi-structure symbiotic network in this application embodiment, and making compliance determinations for each carbon offset node.

[0118] In the stage of obtaining the fusion results of the multi-structure symbiotic network, it is assumed that there is a forestry carbon sink project node A in the offset certificate management platform. The multi-structure symbiotic network first parses the blockchain transaction paths between project node A and multiple downstream carbon purchasers in the transaction topology submodule, and outputs a set of topology embedding vectors (eA1, eA2, eA3).

[0119] Subsequently, the compliance standard change submodule discovered two supplementary clauses related to deforestation restrictions in the recent regulatory update log, and generated a set of compliance change embedded parameters (zA1, zA2) for project node A.

[0120] Simultaneously, the cross-regional traceability correction submodule assesses the accounting requirements of the project in different regulatory zones and outputs a set of cross-regional correction vectors (rA1, rA2). After integrating the above output embedding vector results, the multidimensional embedding vector E=(eA1,eA2,eA3,zA1,zA2,rA1,rA2) of project node A is obtained.

[0121] During the tiered source tracing interpretation phase, the system performs causal chain analysis and source tracing score calculation for node A of the forestry carbon sink project.

[0122] Retrieve the set of causal coupling fragments of project node A in terms of offsetting accounting and transaction history from the causal coupling fragment library, and extract several compliance impact coefficients cA. j Then, the multidimensional embedding vector E of project node A and the coefficients of each causal coupling segment are fed into the formula one by one. The system obtains the local contribution value of the node in each causal coupling segment. These local contribution values ​​are then weighted, summed, and nonlinearly amplified to output the compliance score for project node A.

[0123] The scoring clearly defines whether node A of the forestry carbon sink project meets the latest supplementary forest logging restriction clauses, and analyzes the implementation of the offset accounting specifications layer by layer.

[0124] If project node A's compliance score falls within a preset critical range, the compliance adaptive optimization mechanism proactively triggers a dynamic correction process. At this point, the system switches project node A's accounting method from a general estimation mode to a refined monitoring mode, based on the node's hierarchy within the federal governance system, its risk level, and its impact on the overall carbon offset target. Simultaneously, the system restricts project node A's trading permissions, requiring it to undergo more stringent third-party audits before engaging in further offset transactions. Ultimately, based on tiered traceability and interpretation combined with compliance adaptive optimization, the system achieves full-process tracking of project node A's compliance management and outputs traceable and correctable management decisions.

[0125] The execution flow of the compliance management method provided in the embodiments of this application is illustrated below.

[0126] like Figure 6 As shown, the execution process includes: Step 1, constructing a multi-dimensional compliance consensus base for carbon offsetting. Specifically, this involves collecting compliance feature parameters from across regions and of various types, hashing them, and storing them on the blockchain; generating a global reference value based on dynamic weights and sensitivity coefficients; and outputting the constructed multi-dimensional compliance consensus base for carbon offsetting.

[0127] Step 2: Scenario Segmentation and Semantic Matching. Specifically, core semantic information is extracted from business documents, transaction agreements, etc., to generate scenario semantic vectors; these scenario semantic vectors are then matched with compliance feature parameters to obtain scenario-based compliance identifier vectors.

[0128] Step 3: Aggregate the compliance identifier vectors from different scenarios to obtain a global compliance representation vector. Specifically, summarize the compliance identifier vectors corresponding to each scenario, monitor compliance conflict points, determine if there are new compliance conflicts or changes, and if there are new compliance conflicts or changes, perform multiple rounds of iterative fusion based on compliance conflicts and evolution rates; after completing the iteration, output the global compliance representation vector and register it on the blockchain.

[0129] Step 4: Assemble causal coupling fragments to obtain a compliant causal chain. Specifically, causal coupling fragments are obtained based on split offset accounting, transaction tracing, and regulatory judgment, and stored in a causal coupling fragment library; the structural similarity and regulatory coupling degree are evaluated, and the fragments are submitted for assembly to obtain a compliant causal chain.

[0130] Step 5: Collaborative embedding of multiple structural sub-modules to obtain the fusion result of the multi-structure symbiotic network. Specifically, the multi-structure sub-modules include: a transaction topology sub-module for analyzing blockchain flow paths; a compliance standard change sub-module for monitoring logs of old and new standards; and a cross-regional traceability correction sub-module for fusing information from different dimensions and outputting regional correction embedding vectors. The fusion result of the multi-structure symbiotic network is obtained based on the multi-dimensional embedding vectors.

[0131] Step 6: Explainable Large Model and Adaptive Tuning. Specifically, the model inference process is explained hierarchically and traceably to identify which causal coupling segments contribute to the compliance score of the carbon offset nodes; the compliance scores of the carbon offset nodes are evaluated, and if the compliance scores are within the critical range, an adaptive tuning mechanism is triggered to dynamically adjust the accounting method and / or trading strategy of the carbon offset nodes; if the compliance scores meet the compliance requirements, traceable management is completed.

[0132] This application preprocesses multi-scenario data and extracts causal coupling fragments, mapping different business transaction stages and regulatory rules through cross-scenario compliance causal chains, and deepening causal relationships during the causal coupling fragment recombination stage. Subsequently, it integrates various information into a unified deep representation by focusing on transaction topology, compliance standard changes, and cross-regional differences through a multi-structured symbiotic network. Finally, the interpretable large model utilizes these multi-dimensional embedded vectors to generate hierarchical source-tracing explanations, tracks compliance scores of business nodes in real time, and dynamically adjusts offsetting amounts and transaction strategies through an adaptive tuning mechanism.

[0133] By using multiple structural sub-modules in synergy, and combining parallel sub-modules with internal connection hubs, a network structure that is compatible with multiple scenarios and has real-time response capabilities is formed. Among them, the transaction topology sub-module aggregates blockchain transaction paths and offset certificate circulation information, and outputs topology embeddings for complex node relationships; the compliance change sub-module collects time-series regulatory update logs to extract features of standard adjustments and element offsets; and the cross-regional traceability sub-module matches nodes to different groups or regions based on regional regulatory preferences.

[0134] By selectively extracting causal coupling fragments related to compliance and reorganizing and indexing them across cross-scenario causal chains, the evolution of node compliance information can be tracked in a multi-dimensional environment. Combining fragmented causal relationships with large-scale transaction structures enables precise identification of compliance risk sources at different stages or in different scenarios, reducing over-reliance on single rules or network structures.

[0135] By employing hierarchical source attribution explanation and adaptive optimization, the large model can output hierarchical source attribution information for specific causal segments after completing the inference for node compliance scoring. This enables regulators and business stakeholders to quickly identify the source and influencing factors of the scoring, and dynamically adjust the offset calculation method or trading permissions based on the node's federal governance level, risk level, and criticality to the carbon offset target. This mechanism also provides real-time switching capabilities across multiple workloads or cross-regional collaborations, offering flexible and controllable technical support for the compliance management system.

[0136] The compliance management method presented in this application exhibits more systematic improvements in hierarchical tracing and adaptive optimization of interpretable large models. By analyzing cross-scenario causal chains and weightedly fusing different compliance segments, this application can display the contribution of each causal segment when scoring nodes, thereby maintaining high transparency in multi-dimensional compliance scenarios.

[0137] Regarding dynamic adaptation capabilities, this application introduces an adaptive optimization mechanism. After completing the compliance score calculation, it can automatically generate compliance strategy correction instructions based on node risk levels and changes in transaction data. This allows for temporary or phased constraint management of offset amounts or transaction permissions, enabling timely responses to regulatory adjustments. In terms of data processing depth, supported by multi-structure symbiotic learning networks and causal coupling fragment recombination, this application integrates cross-regional differences and dynamic regulatory updates with core compliance logic into the scoring and reasoning process, enhancing compatibility with complex business scenarios. At the visualization and traceability level, this application provides regulatory agencies or business entities with a hierarchical traceability process from the causal chain level to the fragment level by analyzing the specific contribution of each node to the causal coupling fragment. This helps users quickly locate problematic links and make timely management decisions when anomalies occur.

[0138] By combining multi-structured symbiotic networks with interpretable large models, the system can respond to dynamic regulatory requirements across regions with high accuracy and flexibility. When policies change frequently, the proposed solution can shorten the compliance adjustment cycle and improve the efficiency of conducting business in different markets or regulatory regions, thereby potentially saving operating costs. The modular design of causal coupling fragment recombination and hierarchical source tracing interpretation helps lower the barriers to integrating external data and connecting with third-party audits or regulatory agencies. When enterprises implement the compliance management system, they do not need to make large-scale changes to their existing data or business processes; they only need to connect to the causal fragment parsing and transaction topology sub-modules as needed to meet the requirements of their respective operational stages and compliance depths. This flexibility reduces initial investment at the implementation level while leaving sufficient room for future expansion. In addition, the proposed solution can conduct a refined assessment of the value and potential risks of carbon offsetting activities during the calculation process, providing more transparent and verifiable compliance support for multi-party transactions. Finally, the adaptive optimization mechanism can ensure real-time policy correction for abnormal or critical nodes, which can not only reduce violation penalties or credit losses but also lay a more robust environment for market transactions.

[0139] like Figure 7 As shown in the embodiment of this application, a compliance management device 700 is also provided, including: a first processing module 710, used to construct a carbon offset multidimensional compliance consensus base.

[0140] The second processing module 720 is used to generate a scenario-based compliance identifier vector for each carbon offset scenario based on the carbon offset multidimensional compliance consensus base.

[0141] The third processing module 730 is used to aggregate the compliance identifier vectors corresponding to different types of carbon offset scenarios to obtain a global compliance representation vector.

[0142] The fourth processing module 740 is used to obtain a compliance causal chain based on the global compliance representation vector, wherein the compliance causal chain is formed by splicing together multiple causal coupling segments.

[0143] The fifth processing module 750 is used to perform multi-structure sub-module collaborative embedding on the basis of the compliant causal chain to obtain the fusion result of the multi-structure symbiotic network.

[0144] The compliance management module 760 is used to perform hierarchical source tracing interpretation of carbon offset nodes based on the fusion results of the multi-structure symbiotic network, and to make compliance determination for each carbon offset node.

[0145] In some embodiments, the first processing module is specifically used to: obtain compliance feature parameters of multiple dimensions based on the carbon offset business system; the compliance feature parameters include: compliance constraint information extracted from different types of carbon offset scenarios, transaction and regulatory correlation parameters extracted from historical transaction behavior and corresponding review status, and cross-regional regulatory difference information; and integrate and calculate the compliance feature parameters using a global function, wherein the calculation result of the global function is a global reference value of the carbon offset multidimensional compliance consensus base.

[0146] In some embodiments, the integration calculation of the compliance feature parameters using a global function includes: integrating and calculating the compliance feature parameters using the following global function: ;in, This represents the result of a global function calculation. A vector representing the compliance feature parameters; Represents the importance weights of compliance feature parameters across different dimensions; These represent coefficients reflecting the sensitivity to change among compliance feature parameters of different dimensions; Indicates the nonlinear transformation index; Representing a scene , .

[0147] In some embodiments, the apparatus further includes: a first storage module, configured to store the calculation result of the global function, the compliance feature parameters, and the weight information of the compliance feature parameters in a blockchain after hashing.

[0148] In some embodiments, the second processing module is specifically used to: extract scenario semantic vectors corresponding to different types of carbon offset scenarios; select compliance feature parameters associated with different types of carbon offset scenarios from the carbon offset multidimensional compliance consensus base; map the scenario semantic vectors to the compliance feature parameters to obtain a matching result between the scenario semantic vectors and the compliance feature parameters under the mapping; and generate a scenario-based compliance identifier vector for each carbon offset scenario based on the matching result.

[0149] In some embodiments, mapping the scene semantic vector to the compliance feature parameters to obtain a matching result between the scene semantic vector and the compliance feature parameters under the mapping includes: mapping the scene semantic vector to the compliance feature parameters using the following formula to obtain a matching result between the scene semantic vector and the compliance feature parameters under the mapping: .

[0150] in, This indicates the matching result between the scene semantic vector and the compliance feature parameters; Indicates the first Business semantic vectors for various carbon offsetting scenarios This indicates that the selection of scenarios is based on the multidimensional compliance consensus base of carbon offsetting. The set of associated compliance feature parameters, This indicates the number of compliance feature parameters selected. ; This indicates the importance of compliance feature parameters in the current scenario. Indicates compliance feature parameters Importance in the current context; This indicates adjusting different compliance feature parameters. Amplification factor for compliance matching degree; This indicates the factor that affects scene adaptation.

[0151] In some embodiments, the third processing module is specifically used to: record the compliance identification vectors corresponding to different types of carbon offset scenarios to the iteration management module; and during multiple iterations, aggregate the compliance identification vectors corresponding to different types of carbon offset scenarios with compliance conflict information and change point information, and obtain the global compliance representation vector after the iteration is completed.

[0152] In some embodiments, the aggregation of compliance identification vectors, compliance conflict information, and change point information corresponding to different types of carbon offset scenarios during multiple iterations includes: aggregating compliance identification vectors, compliance conflict information, and change point information corresponding to different types of carbon offset scenarios using the following formula.

[0153] ;in, Indicates the first The global compliance representation vector obtained from rounds of iteration; Indicates the first Round iteration for scenarios The calculated base weights; Indicates the compliance evolution rate coefficient; Denotes the adaptive correction function, where Representative at the Scenes detected in round iteration Compliance conflict level Represents the current round (number) The standard changes identified by the wheel; This is the magnification factor, used to characterize the scene. Impact on the integration process; Representing a scene In the Core components of the compliance identifier vector generated in each iteration; It is a power exponent.

[0154] In some embodiments, the apparatus further includes: a recording module for recording change information and convergence index during each iteration; wherein the change information includes standard change information identified during the iteration and adjustment information caused by the standard change to the compliance identifier vector fusion process corresponding to different types of carbon offset scenarios; the convergence index is used to represent the stability of the global compliance representation vector in continuous iterations.

[0155] In some embodiments, the apparatus further includes a second storage module for storing the global compliance representation vector to a blockchain and associating it with parameters contained in the carbon offset multidimensional compliance consensus base.

[0156] In some embodiments, the fourth processing module is specifically used to: split the inherent causal relationship of carbon offset certificates into causal coupling segments based on the global compliance representation vector; calculate the comprehensive score of the causal coupling segments in terms of structural similarity and regulatory coupling degree; sort the causal coupling segments according to the comprehensive score; splice and combine the causal coupling segments with high comprehensive scores according to the sorting; and perform compliance annotation on each spliced ​​and combined causal coupling segment to obtain a compliance causal chain.

[0157] In some embodiments, the step of splitting the inherent causal relationship of carbon offset certificates into causal coupling segments based on the global compliance representation vector includes: extracting primary causal coupling segments from offset amount accounting logs; extracting intermediate causal coupling segments from blockchain historical transactions; and marking the primary causal coupling segments and the intermediate causal coupling segments with regulatory conflict causes and regulatory features based on the global compliance representation vector.

[0158] In some embodiments, calculating the comprehensive score of the causal coupling segment in terms of structural similarity and regulatory coupling includes: calculating the comprehensive score of the causal coupling segment in terms of structural similarity and regulatory coupling using the following formula.

[0159] .

[0160] .

[0161] in, This represents the combined score of the set of causal coupling segments in terms of both structural similarity and regulatory coupling. This is a set of causal coupling fragments to be spliced ​​together. This represents the structural similarity reference information corresponding to each causal coupling segment; This indicates the regulatory coupling degree reference information corresponding to each causal coupling segment; The coefficient is used to measure structural similarity. This is a coefficient measuring the degree of regulatory coupling; A represents the number of causal coupling segments to be spliced ​​together. ; For causal coupling segments With structural similarity reference information The similarity function; For causal coupling segments Coupling with regulation reference information The coupling degree function; This is a weighting term for the position of causal coupling segments. It is a power exponent; , These are weighting coefficients, used to control the contribution ratio of different similarity measures in the final result; and They respectively represent: the causal coupling segment in the k-th structural feature dimension. With structural similarity reference information Their respective metrics; Represents causal coupling segments With structural similarity The distance or mismatch between referenced information; , These are weighting coefficients, used to balance the contribution and importance of causal coupling segments in various regulatory metrics; These represent the causal coupling segments at the b-th (b=1,2,…B) regulatory feature dimension. Coupling with regulation reference information The measurement value; Demonstrating causal coupling fragments Coupling with regulation reference information Constraint deviations or stage differences between them; It is a dynamic adjustment term based on sine function correction; It is a constant. Used to Projected to [0, The range forms a fluctuation cycle.

[0162] In some embodiments, the fifth processing module is specifically used to: determine the set of embedded vectors corresponding to each sub-module based on the compliance causal chain, wherein each vector in the set of embedded vectors corresponds to a vectorized representation of the compliance elements in the compliance causal chain by the sub-module; and perform weighted transformation, activation and norm operation on the vectors in the set of embedded vectors to obtain the fusion result of the multi-structure co-occurrence network.

[0163] In some embodiments, the step of performing weighted transformation, activation, and norm operation on the vectors in the embedded vector set to obtain the fusion result of the multi-structure co-occurrence network includes: performing weighted transformation, activation, and norm operation on the vectors in the embedded vector set using the following formula to obtain the fusion result of the multi-structure co-occurrence network.

[0164] ;in, This represents the fusion result of multi-structured symbiotic networks; This represents the set of embedding vectors output from D submodules; This represents the embedding vector generated by the d-th submodule; Let represent the set of learnable weight matrices, where For the first submodule d Transformation parameters of each activation unit; For the set of bias terms, For the first submodule d Bias terms for each activation unit; Represents a nonlinear activation function; This is the weight vector for cross-submodule fusion. It is the weight vector of submodule d. It is a power exponent.

[0165] In some embodiments, the compliance management module is specifically used to: retrieve accounting logic, transaction tracing paths, and regulatory judgment information related to each carbon offset node from the causal coupling fragment library; perform hierarchical tracing interpretation of the carbon offset node based on the fusion result of the multi-structure symbiotic network and the accounting logic, transaction tracing paths, and regulatory judgment information related to the carbon offset node, and perform compliance judgment on each carbon offset node.

[0166] In some embodiments, based on the fusion result of the multi-structure symbiotic network and the accounting logic, transaction traceability path, and regulatory judgment information related to the carbon offsetting node, a hierarchical traceability interpretation is performed on the carbon offsetting node, and a compliance judgment is made for each carbon offsetting node. This includes: using the fusion result of the multi-structure symbiotic network as the representation vector of the carbon offsetting node in the multi-structure symbiotic network; reading the representation vector using the multi-structure symbiotic network; and combining the accounting logic, transaction traceability path, and regulatory judgment information related to the carbon offsetting node to output a characterization vector of the feature output of the multi-structure symbiotic network for the carbon offsetting node; the characterization vector is used to represent the comprehensive attributes of the carbon offsetting node in accounting logic, transaction traceability, and regulatory judgment; calculating the compliance score of the carbon offsetting node based on the characterization vector of the feature output of the multi-structure symbiotic network for the carbon offsetting node; and making a compliance judgment on the carbon offsetting node based on the compliance score.

[0167] In some embodiments, calculating the compliance score of the carbon offset node based on the characterization vector of the feature output of the multi-structure symbiotic network for the carbon offset node includes: calculating the compliance score of the carbon offset node using the following formula based on the characterization vector of the feature output of the multi-structure symbiotic network for the carbon offset node.

[0168] .

[0169] .

[0170] in, Indicates the first Compliance score of each carbon offset node; For the first One carbon offset node, For this carbon offset node The compliance impact coefficients associated with each causal coupling segment in the compliance causal chain; For multi-structure symbiotic networks targeting carbon offset nodes The feature output characterization vector; For hierarchical traceability, the first The weights of the contributions of each causal coupling segment; It is a power exponent; This represents a function that performs a hybrid mapping between the coefficients of the causal coupling segments and the characterization vector of the network output; This is an exponentiation operation on the norm of the embedded vector; No. The carbon offsetting node and the first The compliance impact coefficient between causal coupling segments; For multi-structured symbiotic networks to the first The carbon offsetting node at the _th ... Embedded values ​​on each feature dimension; This represents a nonlinear control factor used to control the slope and saturation value of the piecewise response; It is an exponential operation used to construct a nonlinear mapping of logistic regression. The range of values ​​for the output result is: .

[0171] In some embodiments, determining the compliance of the carbon offset node based on the compliance score includes: if the compliance score of the carbon offset node is within a preset range, switching the carbon offset accounting method of the carbon offset node based on the carbon offset node's level in the federal governance system, risk level, and impact on the overall carbon offset target.

[0172] It should be noted that the apparatus provided in this application embodiment can implement all the method steps implemented in the above method embodiment and can achieve the same technical effect. Here, the parts that are the same as those in the method embodiment and the beneficial effects will not be described in detail.

[0173] Another embodiment of the electronic device of this application, such as... Figure 8 As shown, it includes a transceiver 810, a processor 800, a memory 820, and a program or instructions stored in the memory 820 and executable on the processor 800; when the processor 800 executes the program or instructions, it implements the above-mentioned compliance management method.

[0174] The transceiver 810 is used to receive and send data under the control of the processor 800.

[0175] Among them, Figure 8 In this context, the bus architecture can include any number of interconnected buses and bridges, specifically linking various circuits together, represented by one or more processors (processor 800) and memory (memory 820). The bus architecture can also link various other circuits such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and therefore will not be described further herein. The bus interface provides an interface. The transceiver 810 can be multiple elements, including transmitters and receivers, providing a unit for communicating with various other devices over a transmission medium. The processor 800 is responsible for managing the bus architecture and general processing, and the memory 820 can store data used by the processor 800 during operation.

[0176] This application also provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it implements the steps in the above-described compliance management method and achieves the same technical effect. To avoid repetition, it will not be described again here.

[0177] The processor mentioned above is the processor in the electronic device described in the above embodiments. The readable storage medium includes computer-readable storage media, such as computer read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk.

[0178] This application also provides a computer program product that stores programs or instructions, including computer instructions. When these computer instructions are executed by a processor, they implement the steps in the compliance management method described above and achieve the same technical effect. To avoid repetition, further details are omitted here.

[0179] In this embodiment, the module can be implemented in software so that it can be executed by various types of processors. For example, an identified executable code module may include one or more physical or logical blocks of computer instructions, which may be constructed as objects, procedures, or functions. Nevertheless, the executable code of the identified module does not need to be physically located together, but may include different instructions stored in different bits, which, when logically combined, constitute the module and achieve the module's intended purpose.

[0180] In practice, an executable code module can be a single instruction or many instructions, and can even be distributed across multiple different code segments, different programs, and across multiple memory devices. Similarly, operational data can be identified within the module and can be implemented in any suitable form and organized within any suitable data structure. This operational data can be collected as a single dataset or distributed across different locations (including different storage devices), and can exist, at least in part, solely as electronic signals within the system or network.

[0181] When a module can be implemented using software, considering the current level of hardware technology, modules that can be implemented in software can be implemented using hardware circuits by those skilled in the art to achieve the corresponding functions, without considering cost. These hardware circuits include conventional Very Large Scale Integration (VLSI) circuits or gate arrays, as well as existing semiconductors such as logic chips and transistors, or other discrete components. Modules can also be implemented using programmable hardware devices, such as field-programmable gate arrays, programmable array logic, and programmable logic devices.

[0182] The exemplary embodiments described above are with reference to the accompanying drawings. Many different forms and embodiments are feasible without departing from the spirit and teachings of this application. Therefore, this application should not be construed as limiting the exemplary embodiments set forth herein. Rather, these exemplary embodiments are provided to make this application complete and convey the scope of this application to those skilled in the art. In these drawings, component dimensions and relative dimensions may be exaggerated for clarity. The terminology used herein is for the purpose of describing particular exemplary embodiments only and is not intended to be limiting. As used herein, unless clearly indicated otherwise, the singular forms “a,” “an,” and “the” are intended to include all such forms. It will be further understood that the terms “comprising” and / or “including”, when used in this specification, indicate the presence of the stated features, integers, steps, operations, components, and / or elements, but do not exclude the presence or addition of one or more other features, integers, steps, operations, components, and / or groups thereof. Unless otherwise indicated, when stated, a range of values ​​includes the upper and lower limits of the range and any subranges in between.

[0183] The embodiments of this application have been described above with reference to the accompanying drawings. However, this application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of this application.

Claims

1. A compliance management method, characterized in that, The method includes: Building a multi-dimensional compliance consensus base for carbon offsetting; Based on the aforementioned multidimensional compliance consensus base for carbon offsetting, a scenario-based compliance identifier vector is generated for each carbon offsetting scenario. The compliance identifier vectors corresponding to different types of carbon offsetting scenarios are aggregated to obtain a global compliance representation vector. The compliance causal chain is obtained based on the global compliance representation vector, and the compliance causal chain is formed by splicing together multiple causal coupling segments; Based on the compliant causal chain, multi-structure sub-modules are collaboratively embedded to obtain the fusion result of a multi-structure symbiotic network; Based on the fusion results of the multi-structure symbiotic network, the carbon offset nodes are interpreted in a hierarchical manner, and compliance is determined for each carbon offset node. The construction of the multidimensional compliance consensus base for carbon offsetting includes: The compliance feature parameters are obtained from the carbon offset business system. These compliance feature parameters include: compliance constraint information extracted from different types of carbon offset scenarios, transaction and regulatory correlation parameters extracted from historical transaction behavior and corresponding review status, and cross-regional regulatory difference information. The compliance feature parameters are integrated and calculated using a global function, and the result of the calculation of the global function is the global reference value of the carbon offset multidimensional compliance consensus base. The process of obtaining the compliance causal chain based on the global compliance representation vector includes: Based on the global compliance representation vector, the inherent causal relationship of carbon offset certificates is split into causal coupling segments; Calculate the combined score of the causal coupling segment in terms of both structural similarity and regulatory coupling degree; The causal coupling segments are sorted according to the comprehensive score. According to the sorting, the causal coupling segments with high comprehensive scores are spliced ​​and combined. For each causal coupling segment of the spliced ​​combination, a compliant causal chain is obtained by performing compliant annotation.

2. The method according to claim 1, characterized in that, The process of generating scenario-specific compliance identifier vectors for each carbon offset scenario based on the aforementioned multidimensional compliance consensus base includes: Extract scene semantic vectors corresponding to different types of carbon offset scenarios; Select compliance feature parameters associated with different types of carbon offset scenarios from the aforementioned multidimensional compliance consensus base for carbon offsetting; The scene semantic vector is mapped to the compliance feature parameters to obtain the matching result between the scene semantic vector and the compliance feature parameters under the mapping. Based on the matching results, a scenario-specific compliance identifier vector is generated for each carbon offset scenario.

3. The method according to claim 1, characterized in that, The aggregation of compliance identifier vectors corresponding to different types of carbon offsetting scenarios to obtain a global compliance representation vector includes: Record the compliance identification vectors corresponding to different types of carbon offset scenarios into the iteration management module; In multiple iterations, the compliance identification vectors, compliance conflict information, and change point information corresponding to different types of carbon offset scenarios are aggregated, and the global compliance representation vector is obtained after the iteration is completed.

4. The method according to claim 1, characterized in that, Based on the aforementioned compliant causal chain, multi-structure sub-module collaborative embedding is performed to obtain the fusion result of a multi-structure symbiotic network, including: Based on the compliance causal chain, an embedding vector set corresponding to each sub-module is determined, and each vector in the embedding vector set corresponds to a vectorized representation of the compliance element in the compliance causal chain by the sub-module. The vectors in the embedded vector set are subjected to weighted transformation, activation, and norm operation to obtain the fusion result of the multi-structure co-occurrence network.

5. The method according to claim 1, characterized in that, The step of performing hierarchical source tracing interpretation of carbon offset nodes based on the fusion results of the multi-structure symbiotic network, and making compliance determination for each carbon offset node, includes: Retrieve the accounting logic, transaction traceability path, and regulatory judgment information related to each carbon offset node from the causal coupling fragment library; Based on the fusion results of the multi-structure symbiotic network, as well as the accounting logic, transaction traceability path, and regulatory judgment information related to the carbon offset nodes, the carbon offset nodes are subjected to hierarchical traceability interpretation, and compliance judgment is made for each carbon offset node.

6. A compliance management device, characterized in that, include: The first processing module is used to build a multi-dimensional compliance consensus base for carbon offsetting. The second processing module is used to generate a scenario-based compliance identifier vector for each carbon offset scenario based on the carbon offset multidimensional compliance consensus base. The third processing module is used to aggregate the compliance identifier vectors corresponding to different types of carbon offset scenarios to obtain a global compliance representation vector. The fourth processing module is used to obtain the compliance causal chain based on the global compliance representation vector, wherein the compliance causal chain is formed by splicing together multiple causal coupling segments; The fifth processing module is used to perform multi-structure sub-module collaborative embedding on the basis of the compliant causal chain to obtain the fusion result of the multi-structure symbiotic network; The compliance management module is used to perform hierarchical source tracing interpretation of carbon offset nodes based on the fusion results of the multi-structure symbiotic network, and to make compliance judgments for each carbon offset node. The first processing module is specifically used to: obtain compliance feature parameters from multiple dimensions based on the carbon offset business system; The compliance feature parameters include: compliance constraint information extracted from different types of carbon offset scenarios, transaction and regulatory correlation parameters extracted from historical transaction behavior and corresponding review status, and cross-regional regulatory difference information; the compliance feature parameters are integrated and calculated using a global function, and the calculation result of the global function is the global reference value of the carbon offset multidimensional compliance consensus base; The fourth processing module is specifically used for: splitting the inherent causal relationship of carbon offset certificates into causal coupling segments based on the global compliance representation vector; calculating the comprehensive score of the causal coupling segments in terms of structural similarity and regulatory coupling degree; sorting the causal coupling segments according to the comprehensive score; splicing and combining the causal coupling segments with high comprehensive scores according to the sorting; and performing compliance annotation on each spliced ​​and combined causal coupling segment to obtain a compliance causal chain.

7. An electronic device, characterized in that, include: A processor, a memory, and a program stored in the memory and executable on the processor, wherein the program, when executed by the processor, implements the steps of the method as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the method as described in any one of claims 1 to 5.

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