A green code right and interest attribute mapping verification method and system

Abstract

The application discloses a kind of green code right interest attribute mapping verification method and system.The method is first to multi-source data access and standardization, each object is modeled as structured multiset, and it is mapped to uniform representation space by multi-channel feature coding.In training phase, the feature encoder is trained by constraint perception weighted contrast loss function.In inference phase, high-precision candidate mapping set is generated using the encoder, and a plurality of constraint checks are performed in turn.If the candidate mapping passes all constraints, output the only compliant mapping, and update the life cycle state of green code, green certificate and consumption declaration;If it does not meet the constraints, the system outputs an interpretable exception blocking result containing exception type, trigger constraint and processing suggestion.The application solves the problems of candidate recall difficulty of multi-source right interest data, lack of compliance guarantee for deep matching, and difficulty in automatic blocking of repeated mapping, and can be used in green electricity consumption authentication, degree electricity code verification and green certificate uniqueness audit scenarios.

Description

Technical Field

[0001] This invention relates to the technical fields of green electricity metering, electricity consumption coding, green certificate verification, data rights mapping, machine learning and compliance reasoning, specifically a green code rights attribute mapping and verification method and system. Background Technology

[0002] The green code, as a digital identifier of green rights at the kilowatt-hour level, can record information such as power generation time, power generation entity, power source type, metering point, and rights status; the green certificate is a certificate of the environmental attributes of green electricity; and the consumption declaration reflects the user's claim on the amount and source attributes of green electricity consumption within a specific settlement period.

[0003] In practice, the aforementioned data is scattered across power generation companies, trading platforms, green certificate systems, metering systems, and user declaration systems. Due to inconsistent coding standards, different data update frequencies, asynchronous trading and verification processes, and the complexity of distributed green energy consumption scenarios, mapping misalignments can easily occur between green codes, green certificates, power generation attributes, and consumption declarations. Typical problems include: the same green code being mapped to multiple consumption declarations; the same consumption declaration being repeatedly endorsed by multiple mutually exclusive green certificates; the number of consumption declarations exceeding the supportable net green equity; certificates being used again even after verification; and incorrect matching of time, region, or power type.

[0004] Existing systems mostly employ primary key exact matching or manual rule validation. Exact matching struggles to handle issues such as missing fields, synonyms, and inconsistencies in cross-platform identifiers; while manual rules are interpretable, their recall capabilities are weak, making it difficult to promptly detect complex hidden conflicts. Using only deep learning similarity matching lacks compliance rigidity and cannot guarantee that the mapping results satisfy uniqueness, sales limits, and business rules.

[0005] In summary, the existing technology has the following shortcomings: First, semantic inconsistencies in multi-source equity data make it difficult to recall candidate mappings; Second, deep learning candidate matching lacks compliance guarantees and cannot ensure the business compliance of the mapping results; Third, duplicate mapping, conflicting mapping, and excessive write-offs are difficult to identify and block automatically.

[0006] Therefore, a green code rights attribute mapping and verification method that combines intelligent candidate recognition capability with compliance reasoning and interpretation capability is needed. Summary of the Invention

[0007] The technical solution of this invention is used to solve the problems of difficulty in recalling candidates of multi-source rights data, lack of compliance guarantee for deep matching, and difficulty in automatically blocking duplicate mapping.

[0008] The present invention solves the above-mentioned technical problems through the following technical means: A method for verifying the mapping of green code rights attributes includes: firstly, accessing and standardizing multi-source green electricity rights data, modeling each rights data as a structured tuple, and mapping the structured tuple to a unified representation space through multi-channel feature encoding to obtain a dense initial feature vector; during the training phase, constructing positive and negative sample pairs based on historical rights confirmation records, introducing time consistency, power type consistency, certificate status, and electricity boundary constraint-aware weights into the dense initial feature vector, and training the feature encoder through a constraint-aware weighted contrast loss function to make objects in the same rights link closer in the representation space; during the inference phase, using the trained encoder to generate a high-confidence candidate mapping set, and sequentially performing minimum satisfaction constraint verification, mutual exclusion unique constraint verification, and sales volume boundary constraint verification; if the candidate mapping passes all constraints, outputting a unique compliant mapping through constraint optimization, and updating the lifecycle status of the green code, green certificate, and consumption declaration; if the constraints are not met, outputting an interpretable anomaly blocking result containing the anomaly type, triggering constraints, and processing suggestions.

[0009] Furthermore, the specific process of modeling each rights data into a structured plural is as follows: each green code rights are regarded as a rights atom that can be mapped, locked, cancelled, and traced; each green code rights atom is modeled as a structured plural containing a unique identifier, power generation time, power generation entity, power source type, initial rights amount, cancelled electricity amount, frozen rights amount, available rights amount, current ownership entity, and business status; the green certificate object is modeled as a plural containing the green certificate number, issuing entity, cancelled power generation amount, validity period, and cancellation status; the power generation attribute object is modeled as a plural containing the grid connection metering serial number, physical power generation unit identifier, power source type, power generation area, metering settlement cycle, and on-grid electricity amount; the consumption declaration object is modeled as a plural containing the declaration number, consumer entity identifier, consumption cycle, declared electricity amount, power source type preference, declared purpose, and declared business status.

[0010] Furthermore, the available equity amount The calculation method is as follows:

[0011] in, Green code The total green equity quota included in the initial issuance; Indicates green code The cumulative amount of electricity consumed and deducted in historical records; The amount of frozen equity that is temporarily locked during the transaction or review process; For cross-regional and cross-network transmission, the loss correction scalar is calculated and deducted based on the network loss topology coefficient of the physical power grid node.

[0012] Furthermore, the method of mapping structured tuples to a unified representation space through multi-channel feature encoding is as follows: (1) For numeric fields, the maximum-minimum normalization method is used to map them to... interval; (2) For category fields, a learnable embedding matrix is ​​used for dimensionality reduction and dense representation; (3) For the time field, time position encoding based on sine and cosine functions is adopted; (4) For text fields, a pre-trained language model is used to extract global semantic features; (5) For the state field, construct an independent state transition feature space; Finally, the feature vectors of each channel are concatenated and mapped to a unified dimension representation space using a multilayer perceptron.

[0013] Furthermore, the method for constructing positive and negative samples is as follows: Based on historical write-off records, confirmed green certificate relationships, and transaction contract links, a positive sample set and a negative sample set are constructed. The negative sample set includes hard negative samples that are close in time but different in subject, have excessive electricity consumption, have invalid green certificate status, and have different write-off cycles.

[0014] Furthermore, the hard negative samples include at least one of the following categories: object pairs with similar time but different consumer entities; object pairs with the same consumer entity but declared electricity exceeding the available equity amount; object pairs with similar power generation types but invalid green certificate status; and object pairs with similar text descriptions but different write-off cycles.

[0015] Furthermore, the constraint-aware weighted contrastive learning method is as follows: the obtained feature vector is mapped to a unit hypersphere through a nonlinear projection head to obtain normalized latent variables; based on time consistency, power type consistency, voucher status, reconciliation status, and power boundary, the comprehensive compliance constraint weight between sample pairs is calculated; through hard negative sample adaptive mining and constraint-aware weighted contrastive loss function, the feature encoder and projection head are trained so that objects of the same rights and interests link are closer in the representation space and objects of different rights and interests link are farther apart, and the output parameters are fixed for a multi-channel unified feature encoder.

[0016] Furthermore, the constraint-aware weighted contrastive loss function Defined as:

[0017] in: This represents the set of anchor indexes in the current training batch. Represents an object When used as an anchor point, it is the set of object indexes that form a positive sample relationship with it; correspondingly, This represents the set of negative sample indices corresponding to the anchor point; This represents the index of the current anchor point. Indicates the positive sample index. Represents the negative sample index, and the corresponding specific objects are respectively , and ; Based on the base temperature hyperparameter; and Representing anchor objects respectively and negative sample objects After the aforementioned nonlinear projection mapping and After norm normalization, the output is a continuous latent variable eigenvector on the unit hypersphere; exp represents the exponential function; For adaptive temperature scaling; compliance between positive samples The higher the value, the lower the effective temperature, and the more severe the penalty for the model to narrow down the two values. The amplification factor for difficult negative samples is defined as follows: This is the penalty coefficient; Represents the anchor object With negative sample objects The overall compliance constraint weights between them; when negative samples Serious violation of business constraints )hour, The surge forces the network to push it away in the hidden space, thereby decoupling "soft semantic similarity" from "hard rule compliance".

[0018] Furthermore, the method for generating the candidate mapping set is as follows: the real-time anchor object and the full set of target candidate objects are vectorized using the trained encoder, the cosine similarity is calculated and the candidate mapping set with similarity higher than the threshold is initially screened out; the candidate mapping risk score is further calculated and the high-confidence candidate mapping set that simultaneously meets the requirements of similarity threshold and risk threshold is filtered out.

[0019] Furthermore, the method for calculating the candidate mapping risk score is as follows:

[0020] in, , Representing anchor objects respectively Candidates , Representing anchor objects respectively With candidate The scores for time consistency, power type consistency, and state compatibility are as follows: Indicates the candidate mapping risk score. This indicates a risk of exceeding the battery limit. Indicates the risk of mutual exclusion and conflict. to This indicates the weight of each risk item.

[0021] Furthermore, the logic for determining the minimum satisfied constraint is as follows:

[0022] in, Indicates candidate mapping pairs Does it meet the basic requirements? This serves as a time window compliance indicator, used to determine whether the power generation time and the consumption declaration time are within the same settlement cycle allowed by the system or within a valid matching tolerance period; This is a power type matching identifier used to determine whether the physical power type strictly meets the demand preferences and legal definition domain; It serves as a status validity identifier, used to determine whether the current lifecycle status of the green code, green certificate, and consumption declaration allows for the execution of the rights confirmation mapping; It serves as a link reachability identifier, used to determine whether there is a traceable physical topology connection or a legitimate transaction credential chain between the power generation entity and the consumer entity in distributed transactions or cross-network scenarios; It serves as a qualification identifier to determine whether the declaring consumer has the legal qualifications to receive and verify green electricity environmental rights within the regulatory system; Indicates whether the consumer or declaring entity is eligible to receive rights; only when Only when the candidate mapping relationship is established can it proceed to subsequent mutual exclusion and power boundary checks.

[0023] Furthermore, the mutually exclusive unique constraint is expressed as:

[0024] in, For Boolean decision variables, if and only if the green code Mapped to consumer statement The value is 1 at time. Represents a collection of green code objects; The nuclear sales volume boundary constraint is expressed as:

[0025] in, Indicates green code The relevant subset of candidate consumption statements is determined if and only if the constraint inference engine decides to include the green code. Map and assign to consumption declarations The value is 1 if the condition is met, otherwise it is 0. Consumer Statement The declared power scalar; This indicates that before this mapping verification was performed, the green code... The cumulative amount of electricity consumed and deducted in historical records; Indicates green code The current actual available equity level boundary.

[0026] This invention also provides a green code rights attribute mapping verification system, comprising: Multi-source data access and standardization module: used to access and standardize multi-source green electricity rights data; Equity Attribute Object Modeling Module: Used to model each equity data as a structured tuple; Multi-source attribute feature encoding module: used to map structured tuples to a unified representation space through multi-channel feature encoding to obtain dense initial feature vectors; Positive and negative sample mapping construction module: used to construct positive and negative sample pairs based on historical rights confirmation records; Constraint-aware weighted contrastive learning module: used to introduce time consistency, power type consistency, voucher status and power boundary constraint-aware weights into the dense initial feature vector, and train the feature encoder through constraint-aware weighted contrastive loss function to make objects with the same rights link closer in the representation space; Candidate Mapping Generation and Filtering Module: Used to generate a set of high-confidence candidate maps using the trained encoder; The constraint reasoning verification module is used to sequentially perform minimum satisfaction constraint verification, mutual exclusion and unique constraint verification, and core sales boundary constraint verification. Unique Compliance Mapping Solver: Within a defined compliance solution space, with the goal of maximizing semantic similarity and minimizing risk score, the module solves the unique compliance mapping decision result through constrained optimization and outputs the optimal decision variable alignment mapping pair. The anomaly blocking and status write-back module: If a candidate mapping passes all constraints, it outputs a unique compliant mapping result and performs status write-back, updating the remaining green code rights and the lifecycle status of the green code, green certificate, and consumption declaration; if a candidate mapping does not meet any constraint, it generates an anomaly blocking result, which includes the anomaly object identifier, trigger constraint rule code, anomaly type, anomaly reason, involved electricity consumption, and processing suggestions.

[0027] The advantages of this invention are: First, it improves the candidate recall capability of multi-source rights data. Traditional primary key matching methods rely on completely identical IDs, which is difficult to handle issues such as cross-platform identifier differences, missing fields, and inconsistent text descriptions. This invention maps green codes, green certificates, power generation attributes, and consumption declarations to a unified representation space through multi-source attribute feature encoding and comparative learning. This can discover potential correspondences and significantly improve the candidate mapping recall capability in complex scenarios.

[0028] Second, it improves the compliance and interpretability of mapping results. Relying solely on deep learning similarity can easily lead to erroneous matches that are "semantically similar but have illegal business operations." This invention introduces minimum satisfaction constraints, mutually exclusive unique constraints, and sales volume boundary rules after candidate generation, ensuring that each mapping result is explicitly verified by rules and can provide specific reasons for passing or being blocked, thus meeting the audit interpretability requirements.

[0029] Third, it reduces the risks of duplicate mapping and excessive redemption. This invention treats green code rights as the smallest unit of rights confirmation, and restricts the same green code rights to be occupied by only one final consumption declaration within the same redemption cycle through mutual exclusion and unique constraints. It also controls the cumulative redemption volume to not exceed the available rights volume through redemption volume boundary constraints, effectively preventing multiple uses of one certificate, multiple recognitions of one telephone number, and excessive declarations.

[0030] Fourth, it balances intelligent recognition capabilities with rule rigidity. The learning model is used to solve the problem of difficult discovery of complex candidate relationships, while constraint reasoning is used to ensure that compliance boundaries cannot be broken. The two are linked to form a technical route of "soft recognition + hard verification", which improves the degree of automation while retaining the rule auditing capability.

[0031] Fifth, it is applicable to distributed green electricity and multi-platform collaborative scenarios. When distributed photovoltaics, green electricity trading, green certificate verification, and user declarations are scattered across different systems, this invention can achieve cross-system consistent verification through unified field coding, candidate recall, and constraint reasoning, which is beneficial to improving the credibility of green electricity consumption certification and electricity-based coding applications.

[0032] Sixth, it facilitates regulatory verification and subsequent traceability. The system can output the cause of abnormal blocking, verification time, involved objects, and status change records, and can optionally generate a trusted evidence summary to provide a basis for business review, regulatory verification, green electricity consumption certification, and dispute resolution. Attached Figure Description

[0033] Figure 1 This is a method architecture diagram in an embodiment of the present invention; Figure 2 This is a flowchart of the green code rights attribute mapping verification process based on contrastive learning and constraint reasoning, as described in an embodiment of the present invention. Figure 3 This is a flowchart illustrating the multi-source attribute feature encoding and candidate mapping generation process for green code rights in an embodiment of the present invention. Figure 4 This is a flowchart of the constraint reasoning verification engine according to an embodiment of the present invention; Figure 5 This is the unique compliant mapping output and abnormal blocking process flowchart of this invention embodiment. Detailed Implementation

[0034] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0035] Figure 1 This diagram illustrates the overall technical approach of the present invention. It comprises a data input layer, a feature encoding layer, a candidate mapping generation layer, a constraint reasoning verification layer, a result output layer, and an optional trusted evidence storage layer. The data input layer receives green codes, green certificates, power generation attributes, user consumption declarations, redemption records, and necessary path constraint data. The feature encoding layer performs field standardization, semantic encoding, and vectorization representation on the aforementioned multi-source heterogeneous objects. The candidate mapping generation layer calculates the semantic similarity between different objects using a contrastive learning model, forming candidate mapping relationships between green code rights and target attributes. The constraint reasoning verification layer performs compliance judgments based on minimum satisfaction constraints, mutually exclusive unique constraints, and redemption volume boundary rules. The result output layer outputs a unique compliant mapping result, or outputs an abnormal blocking result when the candidate relationship does not meet the constraints.

[0036] Figure 1 In this invention, the layers are connected sequentially in the order of "multi-source data input - unified feature encoding - candidate mapping generation - constraint reasoning verification - mapping result output - state update and optional evidence storage". This indicates that the invention does not simply rely on manual rules or single similarity matching, but connects learning-based candidate recall with rule-based compliance verification to form an identifiable, interpretable, and blocking green code rights attribute mapping verification closed loop.

[0037] Figure 2 The core process used to illustrate this invention is the green code rights attribute mapping verification process based on comparative learning and constraint reasoning. The system first reads green code data, green certificate data, power generation attribute data, consumption declaration data, and verification status data, and performs time alignment, numbering unification, field cleaning, and status verification on the above data. Subsequently, the system extracts key attribute fields for various objects, including power generation time, power generation entity, power source type, power generation area, corresponding electricity volume, green certificate number, ownership entity, consumption entity, declared electricity volume, and verification status.

[0038] After data standardization, the system constructs positive and negative sample mapping pairs. Positive sample mapping pairs represent green codes, green certificates, generation attributes, and consumption declarations originating from the same green electricity right; negative sample mapping pairs represent objects that should not be matched under different batches, entities, times, or electricity limits. The system then inputs these positive and negative sample pairs into a constraint-aware contrastive learning model, training a feature encoder capable of distinguishing between "same right relationship" and "non-same right relationship." The trained encoder is used to calculate the similarity between objects to be matched and to generate candidate mapping pairs and their corresponding confidence scores.

[0039] Figure 2 This also illustrates that after candidate mappings are generated, the system does not directly output the final result. Instead, it compares the candidate mappings with the input constraint inference and verification engine. The constraint inference and verification engine sequentially executes the minimum satisfaction constraint, the mutual exclusion and unique constraint, and the core volume boundary rule. If all candidate mapping relationships pass the constraint verification, a unique compliant mapping result is output; if any constraint is not satisfied, an anomaly blocking result is output, and the reason for the anomaly is recorded. This process reflects the technical feature of this invention: "intelligent identification first, then compliance verification."

[0040] Figure 3 This system is used to illustrate the multi-source attribute feature encoding and candidate mapping generation process for green code rights. First, the system establishes attribute field sets for data objects from different sources. The green code attributes include green code number, power generation time, power generation batch, power source type, initial rights amount, and status identifier; the green certificate attributes include green certificate number, issuing entity, power generation amount, validity period, transaction status, and cancellation status; the power generation attributes include power generation company, power generation project, power generation area, power generation time, power source type, and on-grid electricity volume; and the consumption declaration attributes include consumption entity, declaration time, declared electricity volume, declared purpose, contract number, and cancellation application status.

[0041] During feature encoding, the system encodes numerical fields, categorical fields, time fields, and text fields separately. Numerical fields undergo normalization, categorical fields are embedded and encoded, time fields are converted into time window features, and text fields are converted into semantic vectors using a semantic encoding model. Subsequently, the encoding results of each type of field are concatenated to form green code vectors, green certificate vectors, power generation attribute vectors, and consumption declaration vectors.

[0042] Figure 3To further explain, the contrastive learning model learns a unified mapping representation space by narrowing the vector distance between the same green rights object and widening the vector distance between different rights objects. For any green code object, the system can retrieve the closest green certificate, power generation attribute, and consumption declaration objects in this representation space and form a candidate mapping set. The candidate mapping set is not the final conclusion, but rather an object to be reviewed in subsequent constraint reasoning and verification.

[0043] Figure 4 This describes the specific workflow of the constraint reasoning verification engine. The constraint reasoning verification engine includes a minimum constraint satisfaction verification unit, a mutually exclusive unique constraint verification unit, and a sales volume boundary verification unit. The minimum constraint satisfaction verification unit is used to determine whether a candidate mapping relationship meets the basic conditions for validity. These basic conditions include at least: a valid green code status, a valid green certificate status, the power generation time and the consumption declaration time being within the allowed matching window, the power generation type and the declaration type being consistent, the power generation entity or transaction link being traceable, and the declaring entity having the corresponding rights and interests to receive. If any of these basic conditions are not met, the candidate mapping relationship is determined to be invalid and enters the anomaly blocking process.

[0044] The mutually exclusive unique constraint verification unit is used to prevent the same green code benefit from being repeatedly used by multiple consumption claims. The system treats the green code benefit as the smallest unit of rights confirmation and restricts the same green code benefit to only pointing to one final consumption claim within the same redemption period. When it is detected that a green code is mapped to two or more consumption entities at the same time, or that the same green code benefit is repeatedly bound to multiple mutually exclusive consumption claims, the system triggers a mutual exclusion conflict alarm and blocks the relevant mapping.

[0045] The declared electricity volume boundary verification unit is used to determine whether the cumulative declared electricity volume exceeds the available green rights volume. The system calculates the current available rights volume based on the initial green code rights volume, the already redeemed electricity volume, the frozen rights volume, the network loss correction volume, or other deductions. If the sum of the newly declared electricity volume of the target consumption declaration and the historically redeemed electricity volume exceeds the available rights volume, the system determines that the candidate mapping relationship has an over-redemption risk and outputs a blocking result. This verification unit is used to prevent multiple uses of one certificate, multiple recognitions of one electricity amount, and over-declaration.

[0046] Figure 5 This is used to illustrate the unique compliant mapping output and the anomaly blocking handling process. When a candidate mapping relationship passes... Figure 4 After verifying all constraints, the system generates a unique compliance mapping result. This result includes at least the green code number, the corresponding green certificate number, the power generation attribute identifier, the consumption declaration number, the amount of electricity cancelled, the remaining equity amount, the mapping status, and the verification time. Based on this result, the system updates the green code equity status, marking it as mapped, locked, partially cancelled, or cancelled, and simultaneously updates the corresponding green certificate and consumption declaration status.

[0047] When a candidate mapping relationship fails constraint validation, the system generates an anomaly blocking result. The anomaly blocking result includes at least the anomaly object number, anomaly type, triggering constraint, anomaly reason, electricity consumption involved, processing suggestions, and recording time. Anomaly types include, but are not limited to, time mismatch, inconsistent entities, invalid green certificate status, already redeemed green code, duplicate mapping, mutual exclusion conflict, excessive cumulative redeemed sales, and missing consumption declaration information. The system can feed back the anomaly blocking result to the business review end for manual review, re-matching, or risk alerts.

[0048] Figure 5 This also illustrates that the mapping results and anomaly blocking results can be further written into the trusted evidence storage module. The trusted evidence storage module is not the core judgment step of this invention, but rather used to record verification results, anomaly causes, and key status changes, facilitating subsequent regulatory verification, green electricity consumption certification, and rights traceability inquiries. This evidence storage process can be implemented using hash digests, timestamps, or blockchain registration methods.

[0049] This embodiment revolves around four core aspects: unified representation of multi-source rights data, constraint-aware comparative learning for candidate recall, rule-based constraint reasoning verification, and unique mapping output and anomaly blocking. Unlike traditional methods that rely solely on manual rules or precise primary key matching, this embodiment first utilizes a learning model to improve the recall capability of candidate relationships, and then uses explicit constraint rules to ensure compliance and interpretability. This makes it suitable for scenarios involving cross-system, cross-entity, and cross-time window collaborative verification of green codes, green certificates, power generation attributes, and consumption declarations.

[0050] In order to clearly understand the execution sequence of this embodiment, each step of this implementation method is strictly connected in series according to the data flow direction, and the output of the previous step is used as the input of the next step, together forming a state machine that can be audited in a closed loop.

[0051] The relationship between global data flow and timing mapping is shown in Table 1: Table 1. Relationship between global data flow and timing mapping

[0052] Step 1. Access and Standardization of Multi-Source Green Electricity Rights Data This implementation first establishes a multi-source green electricity rights data access unit. The access objects include green code records, green certificate records, power generation attribute records, transaction contract records, user consumption declaration records, and verification status records. The green code record describes the smallest green rights atom in the electricity kilowatt-hour coding system; the green certificate record describes the renewable energy green electricity certificate and its status; the power generation attribute record describes the power generation entity, power generation project, power source type, power generation time, and power generation amount; and the user consumption declaration record describes the consumer entity, declared electricity amount, declaration time, declaration purpose, and verification application information.

[0053] Since the data may originate from different platforms, the system first performs field standardization, identifier mapping, time alignment, and status cleaning. Field standardization unifies the naming of synonymous fields across different systems; for example, it maps "Project Number," "Power Plant Number," and "Power Generation Entity Identifier" to the same power generation entity field. Identifier mapping establishes a traceable index between green code numbers, green certificate numbers, transaction contract numbers, and consumption declaration numbers. Time alignment maps power generation time, transaction time, and declaration time at different granularities to a unified settlement window. Status cleaning removes records that are invalid, revoked, void, or have missing statuses.

[0054] During the standardization process, the system establishes a unified set of objects for each object:

[0055] in, Represents a collection of green code objects. Let A represent the set of green certificate objects, and let A represent the set of power generation attribute objects. Represents a collection of objects that are consumed. Any object All include numeric fields, category fields, time fields, text fields, and status fields. Within the unified object collection, green code objects, green certificate objects, power generation attribute objects, and consumption declaration objects can all be encoded as... During the training phase, these objects take turns serving as anchor objects in constructing positive and negative sample pairs. During the inference phase, the system selects the object to be verified as the anchor object based on the specific verification task.

[0056] Step 2. Modeling the Green Code Rights Attribute Object The system treats each green code benefit as a benefit atom that can be mapped, locked, cancelled, and traced. Each green code benefit atom includes at least the following fields: unique identifier, power generation time, power generation entity, power source type, initial benefit amount, cancelled electricity amount, frozen benefit amount, available benefit amount, current ownership entity, and business status.

[0057] (1) Green Certificate Object Modeling Represent a green certificate object as a tuple containing its lifecycle states:

[0058] in, A unique identifier for the green certificate; This indicates the entity that issued the green certificate; This indicates the amount of green electricity represented by the green certificate, typically 1 MWh per green certificate. This indicates the valid time window for the green certificate; This indicates the current transaction and write-off status of the green certificate, and its value set includes at least the following: .

[0059] (2) Modeling of power generation attribute objects The power generation attribute object records the objective fact of physical power grid connection, and is represented as:

[0060] in, For grid connection metering serial number; Physical power generation units, such as specific generator set or inverter number; The physical power supply type must match the power supply type set specified in the green code. Maintain mapping consistency; The corresponding physical area for power generation at the network topology node; For the measurement and settlement cycle; This refers to the actual physical power consumption when connected to the network.

[0061] (3) Modeling of consumer declaration objects The consumer statement reflects the user's claim to green attributes, and is represented as:

[0062] in, The business number indicating the consumer declaration; The identifier of the consumer entity that initiated the statement, such as the unified social credit code; This indicates the consumption cycle advocated in the statement; This indicates the green battery level that the user expects to match; Indicates the user-specified power type preference (empty or the entire set if there is no preference); This indicates the intended use, and its set of possible values ​​must include at least: ; This indicates a declared business state, and its value set must include at least: .

[0063] The purpose of the above object modeling is to transform scattered, heterogeneous, and non-uniform data into computable, comparable, and inferable data objects, providing a foundation for subsequent comparative learning and constraint verification.

[0064] Step 3. Multi-source attribute feature encoding The system performs multi-channel feature encoding on each object, and the specific implementation of each sub-encoder is as follows: (1) For numerical fields (such as electricity data), the maximum and minimum value normalization method is used to map them to An interval is defined to eliminate the influence of different units. Its encoding function is expressed as:

[0065] (2) For category fields (such as power type and subject identifier), a learnable embedding matrix is ​​used for dimensionality reduction and density representation. Its encoding function is expressed as:

[0066] in, This is the embedding parameter matrix that the system optimizes synchronously during the comparative learning training process.

[0067] (3) For time fields (such as timestamps) To capture the continuity and periodicity of the time cycle, a time position encoding based on sine and cosine functions is employed. Its first... and The dimension encoding calculation is as follows:

[0068]

[0069] in, Define the dimensions for time characteristics.

[0070] (4) For text fields (such as declarations of purpose, contract descriptions), a pre-trained language model is used to extract global semantic features, and the output feature vector is used as the text encoding:

[0071] (5) For the state field, the system constructs an independent state transition feature space: Finally, the system concatenates the feature vectors from each channel and maps them to a unified-dimensional representation space using a multilayer perceptron (MLP). Each object is ultimately encoded by an encoder. The eigenvectors are represented as:

[0072] in, This represents the anchor object to be validated. The final high-dimensional feature vector in the unified representation space after multi-channel feature extraction; For a unified collection of objects The first in An input object, i.e., an anchor object; This represents the global object feature encoder function. The square brackets on the right side of the formula describe the concatenation process of the sub-features for each channel. This represents a vector concatenation operation. Specifically, This represents a numerical feature encoder that maps numeric fields of an object to continuous vectors; Represents a category feature encoder; Indicates a time field encoder; This represents a text semantic encoder; This represents the state-space feature encoder. Through the above multi-channel mapping and concatenation, heterogeneous multi-source data is uniformly transformed into dense feature vectors that support matrix operations.

[0073] After feature vectorization, for any two objects to be matched across source databases... and (For example, a green health code object and a consumption declaration object,) (Representing any candidate object in the database), the system calculates the feature distance between the two in a unified representation space. This embodiment uses cosine similarity for measurement, and its calculation formula is:

[0074] in, express and Cosine similarity between them. This represents the anchor object to be validated. After the aforementioned multi-channel feature encoder After mapping, dense feature vectors are generated in a unified representation space; Indicates candidate objects The corresponding dense feature vector generated after mapping by the isomorphic encoder; Representing the eigenvector and The inner product of the two is used to measure their absolute projective correlation in the representation space. This similarity is used to preliminarily determine whether the two may correspond to the same green rights, but it is not used as a final compliance conclusion.

[0075] Step 4. Constructing positive and negative sample mapping pairs To train the candidate mapping generation model, the system constructs positive and negative sample mapping pairs based on historical write-off records, confirmed green certificate relationships, transaction contract links, and manual review results. Positive sample mapping pairs represent objects that have been confirmed to belong to the same green rights link, such as the relationship between the same green code, corresponding green certificate, and corresponding consumption declaration; negative sample mapping pairs represent objects that should not match, such as inconsistent time windows, inconsistent entities, inconsistent power types, or relationships where green certificates have been written off or power boundaries are clearly not met.

[0076] The system represents the positive sample set and the negative sample set as follows:

[0077] in, Representation Object and Belonging to the same rights and interests chain, This indicates that the two do not belong to the same rights and interests chain.

[0078] To avoid overly simplistic negative samples that prevent the model from learning the boundaries, the system prioritizes constructing hard negative samples. Hard negative samples include objects with similar timeframes but different subjects, objects with the same subject but exceeding their electricity limits, objects with similar power generation types but invalid green certificates, and objects with similar textual descriptions but different write-off cycles. By introducing hard negative samples, the model can learn more granular mapping boundaries.

[0079] Step 5. Constraint-aware weighted contrastive learning This embodiment introduces constraint-aware weights on top of ordinary contrastive learning, enabling the model to perceive compliance factors such as time consistency, power type consistency, voucher status, reimbursement status, and electricity limits during the training phase. The network maps multi-source green electricity objects to the same metric space through nonlinear projection mapping, adaptive mining of difficult negative samples, and a customized loss function that incorporates compliance constraint penalties. The input to this step is the initial feature vector extracted from the training samples in step 3. and the set of positive and negative sample pairs mined and constructed in step 4. and The specific implementation steps are as follows: (1) Nonlinear projection mapping and feature normalization To avoid information loss or overfitting caused by directly calculating the contrastive loss in the original high-dimensional feature space, the system first processes the object feature vector after multi-channel encoding and fusion. The input is fed into a nonlinear projection head. This projection head consists of a multilayer perceptron (MLP) containing a ReLU activation function.

[0080] in, Represents the projection mapping function; and These represent the learnable weight matrices of the first and second layers of the network, respectively. and These represent the learnable bias vectors of the corresponding network layers; This represents the ReLU nonlinear activation function.

[0081] Subsequently, the projection output was... conduct Norm normalization maps the latent variables to the unit hypersphere, yielding the final latent variables for comparison. This operation ensures that subsequent dot product operations are equivalent to cosine similarity calculations, improving the model's robustness to the absolute scale of features.

[0082] (2) Construction of positive and negative sample sets based on business rules For any anchor object within the current training batch The system is based on the global positive and negative sample set constructed in step 4. and ,for The sampling extracts a specific subset of absolute positive samples and a candidate subset of negative samples, which are represented as follows:

[0083]

[0084] Furthermore, the system calculates anchor point objects in real time. Its target objects (including positive sample objects) With negative sample objects The overall compliance constraint weights between the anchor objects. With negative sample objects For example, the system extracts the original business attribute fields of both and calculates their constraint consistency score vector:

[0085] Based on this score vector, the system calculates the anchor object. With negative sample objects Comprehensive compliance constraint weights between :

[0086] in, . The closer to 1, the higher the compliance with business rules; the closer to 0, the more serious the violation of core business constraints. This represents the Sigmoid function, used to smoothly map the results of linear calculations to the interval (0, 1). This represents the learnable global bias term of the weighted network; M represents the total number of business constraint types introduced. Indicates the specific constraint dimension index (such as time, type, etc.); Represents the above score vector The specific score component of the m-th dimension in the dataset; This indicates that the model adaptively learns the first [number] [unit] during training. The importance weighting parameter for each type of business constraint.

[0087] (3) Adaptive mining strategy for difficult negative samples In the green electricity mapping scenario, the most confusing candidates are those with "extremely similar semantic descriptions but invalid business boundaries (such as time windows and credit limits)." To address this, the system incorporates dynamic difficulty scoring during comparative learning training. To mine these hard negative samples:

[0088] and Representing anchor objects respectively and negative sample objects After the aforementioned nonlinear projection mapping and After norm normalization, the output is a continuous latent variable eigenvector on the unit hypersphere; exp represents the exponential function; This represents the anchor object calculated in step (2). With negative sample objects The overall compliance constraint weights among them.

[0089] When negative sample object With anchor object Extremely similar in the current latent space ( However, its business compliance weight is extremely low ( When ), its difficulty score The system has reached its peak value. Then sample or assign higher loss gradient weights.

[0090] (4) Constraint-aware supervised contrastive loss function To overcome the limitation of traditional InfoNCE loss functions in failing to perceive rigid business constraints, this embodiment constructs a constraint-aware weighted contrastive loss function. This function not only shortens the distance between positive samples, but more importantly, it exerts an exponentially amplified repulsive force on difficult negative samples that are "semantically similar but not compliant". Its definition is as follows:

[0091] in: This represents the set of anchor indexes in the current training batch. Represents an object When used as an anchor point, it is the set of object indexes that form a positive sample relationship with it; correspondingly, This represents the set of negative sample indices corresponding to the anchor point. This represents the index of the current anchor point. Indicates the positive sample index. Represents the negative sample index, and the corresponding specific objects are respectively , and ; Based on the base temperature hyperparameter; For adaptive temperature scaling. Compliance between positive samples. The higher the value, the lower the effective temperature, and the more severe the penalty for the model to narrow down the two values. The amplification factor for difficult negative samples is defined as follows: This is the penalty coefficient. When the negative sample object... Serious violation of business discipline ( )hour, The surge forces the network to push it away in the hidden space, thereby decoupling "soft semantic similarity" from "hard rule compliance".

[0092] The system minimizes the above loss function using the backpropagation algorithm. The feature encoder is continuously updated using constructed positive and negative samples. The network parameters of the projection head are then adjusted until the model converges, at which point the offline training phase ends. The final output of this step is a multi-channel unified feature encoder with fixed parameters that has been trained. and nonlinear projection head network The output will be directly input into step 6 as the core operator, used for feature mapping of real-time data to be verified in actual online business scenarios.

[0093] Step 6. Candidate Mapping Pair Generation and Low-Confidence Filtering After model training is complete, the system enters the online inference and verification phase, where it utilizes a fixed encoder. The system vectorizes both the anchor point objects to be verified input from real-world business scenarios and all target candidate objects in the database (covering green certificates, power generation attributes, and consumption declaration objects). Subsequently, the system calculates the anchor point objects. With any candidate object Feature cosine similarity between For each green code to be verified The system initially filters out objects with similarity higher than a set threshold from a large set of candidate targets, generating an initial candidate mapping set:

[0094] in, Indicates green code The set of candidate mappings This represents the similarity threshold. i The index of the anchor point to be verified (such as a green code). j It is an index of any candidate object in the full target library (such as a green certificate or consumption statement).

[0095] To further reduce the probability of unreliable candidates entering the inference engine, the system calculates a candidate mapping risk score:

[0096] in, These represent the anchor objects to be verified. With candidate The scores are time consistency, power type consistency, and state compatibility. Indicates the candidate mapping risk score, This indicates a risk of exceeding the battery limit. Indicates the risk of mutual exclusion and conflict. to This indicates the weight of each risk item.

[0097] The system only retains candidate objects that simultaneously meet both similarity and risk threshold requirements:

[0098] in, This represents the set of filtered candidate maps. This indicates the risk threshold.

[0099] This step outputs the filtered set of high-confidence candidate maps. (where the explicit content includes the semantic similarity of each candidate pair) Risk Score This set serves as the base dataset for review and is then passed to steps 7 and 8.

[0100] Step 7. Constraint Reasoning Verification After candidate generation, the system performs rule-based constraint inference. The constraint inference verification engine extracts the set. The elements in the set are sequentially subjected to minimum constraint satisfaction verification, mutual exclusion and uniqueness constraint verification, and core sales boundary verification. A compliant feasible solution space is defined using explicit Boolean logic.

[0101] The minimum satisfaction constraint is used to determine whether the basic conditions of a candidate mapping relationship are met, and can be expressed as:

[0102] in, Indicates candidate mapping pairs Does it meet the basic requirements? This serves as a time window compliance indicator, used to determine whether the power generation time and the consumption declaration time are within the same settlement cycle allowed by the system or within a valid matching tolerance period; This is a power type matching identifier used to determine whether the physical power type strictly meets the demand preference and the legal definition domain (i.e., whether the power type is in the legal set). (and meet the requirements of supply and demand). It serves as a status validity identifier, used to determine whether the current lifecycle status of the green code, green certificate, and consumption declaration allows for the execution of the rights confirmation mapping; It serves as a link reachability identifier, used to determine whether there is a traceable physical topology connection or a legitimate transaction credential chain between the power generation entity and the consumer entity in distributed transactions or cross-network scenarios; This serves as a qualification identifier, used to determine whether the declaring consumer possesses the legal qualifications to receive and verify green energy environmental rights within the regulatory system. In the compliance determination logic, this type constraint factor... It not only requires that the supply and demand types be consistent, but also plays a strict algebraic domain verification role, that is, it forcibly prevents any power type variable from escaping the standard set (i.e., Illegal mapping relationship; This indicates whether the consumer or declaring entity is eligible to receive rights. Only when... Only when the candidate mapping relationship is established can it proceed to subsequent mutual exclusion and power boundary checks.

[0103] Mutually exclusive and unique constraints are used to ensure that the same green code equity atom can be mapped to at most one final consumption declaration within the same settlement cycle:

[0104] in, Indicates green code Does it map to a consumption statement? If mapping ,otherwise This constraint is designed to prevent the same green interest from being claimed by multiple entities.

[0105] The nuclear power sales limit constraint is used to ensure that the cumulative nuclear power sales do not exceed the available green equity amount:

[0106] in, Indicates green code A subset of relevant candidate consumer statements. This set is generated by the aforementioned constraint-aware contrastive learning and risk filtering steps, wherein... ,in This is the filtered set of candidate mappings. For the collection of objects declared for full consumption; For Boolean decision variables, if and only if the constraint inference engine decides to give the green code Map and assign to consumption declarations The value is 1 if the condition is met, otherwise it is 0. Consumer Statement The declared energy scalar (unit: MWh); This indicates that before this mapping verification was performed, the green code... The cumulative amount of electricity consumed and deducted in historical records; This indicates the current actual available rights and benefits boundary of the green code.

[0107] If we consider the amount of frozen equity, deductions, or network loss corrections, the currently available equity can be expressed as:

[0108] in, This refers to the total amount of green electricity consumption rights included when the green code is initially issued; The amount of frozen equity that is temporarily locked during the transaction or review process; For cross-regional and cross-network transmission, the loss correction scalar is calculated and deducted based on the network loss topology coefficient of the physical power grid node.

[0109] Step 8. Solving for the unique compliance mapping The input for this step consists of two parts: first, the semantic similarity calculated in step 6. Risk penalty coefficient Second, the compliant solution space control boundary defined in step 7, namely... .

[0110] When multiple candidate mappings simultaneously satisfy some constraints, the system selects a unique compliant mapping relationship through constrained optimization. The optimization objective is to maximize similarity and minimize risk while ensuring that the constraints hold.

[0111] The constraints include:

[0112] Through this optimization process, the system can select a unique compliance mapping that has the lowest risk and satisfies the business boundaries from multiple similar candidates. It outputs a unique compliance mapping decision result matrix (i.e., the optimal decision variables) that represents the lowest risk compliance mapping that fully satisfies the business boundaries. (The alignment mapping pair). This mapping conclusion serves as the sole basis for rights confirmation and library writing, and is input into step 9.

[0113] Step 9. Abnormal Blocking and State Rewrite If a candidate mapping fails to satisfy any core constraint, the system does not output a compliant mapping but instead generates an exception blocking result. The exception blocking result includes the exception object identifier, the triggering constraint rule code, and the specific exception type. To facilitate automated processing, the system pre-defines an exception type enumeration set, which includes at least: ={ERR01: Settlement time windows do not intersect; ERR02: Power type constraint mismatch; ERR03: Green certificate has expired or been frozen; ERR04: Consumer entity lacks redemption qualification; ERR05: Mutually exclusive allocation conflict for the same green code; ERR06: Cumulative declared redemption volume exceeds available quota}. For different exception codes, the system will output differentiated processing suggestions to the business review end.

[0114] If the candidate mapping passes all constraints, the system outputs a unique compliant mapping result and performs a status write-back. The remaining equity is updated as follows:

[0115] like If so, the green code status will be updated to "verified"; if If the green code status is partially verified, the mapping result is only locked but not verified, and the green code status is updated to fully locked. The corresponding green certificate status, consumption declaration status, and verification record are updated synchronously.

[0116] Step 10. Optional trusted evidence storage and traceability query The core of this embodiment lies in the mapping and verification of green code rights attributes. To facilitate subsequent regulatory verification and business traceability, the mapping result, anomaly blocking result, status update time, operator identifier, and key digest value can optionally be written into the trusted evidence storage module. The trusted evidence storage module can be implemented using hash digests, timestamps, consortium blockchains, or other tamper-proof recording methods.

[0117] The system can generate a summary of the verification results:

[0118] in, Indicates a summary of evidence storage. Indicates the green code number. Indicates the green certificate number. Indicates the consumer declaration number, Indicates the amount of electricity to be reimbursed. Indicates the verification time. Indicates the mapping state. This summary indicates the cause of the anomaly or verification explanation. It is used to prevent subsequent tampering but does not change the core technical essence of this embodiment, which is based on mapping verification.

[0119] The working principle of this embodiment can be summarized as "unified representation of multi-source rights objects—comparative learning to generate candidate relationships—constraint reasoning for compliance screening—outputting a unique mapping or anomaly blocking". When the system is running, it first reads data from the green code system, green certificate system, power generation metering system, trading system, and consumption declaration system, and transforms data from different sources, formats, and time granularities into a unified object representation.

[0120] Based on a unified object representation, the system extracts numerical, categorical, temporal, textual, and status features from green codes, green certificates, power generation attributes, and consumption declarations, and forms vectorized representations through an encoder. The contrastive learning model constructs positive samples through historical confirmation relationships and negative samples through inconsistencies in time, subject, electricity consumption, and status, bringing objects within the same green rights chain closer together in the representation space and distancing objects outside the same rights chain.

[0121] Unlike conventional contrastive learning, this embodiment introduces constraint-aware weights during sample pair training and candidate generation. Factors such as time consistency, power type consistency, credential status, ownership link, and power boundary are not only used for subsequent inference but also participate in the training of the candidate recall model. This can reduce semantically similar but non-compliant erroneous candidates and improve candidate quality.

[0122] After candidate mappings are generated, the system enters the constraint reasoning phase. The constraint reasoning engine first executes the minimum satisfaction constraint to determine whether the candidate mapping has a basis for validity in terms of time, type, subject qualification, certificate status, and link integrity; secondly, it executes the mutual exclusion and uniqueness constraint to ensure that the same green code right atom can be occupied by at most one consumption declaration within the same redemption cycle; finally, it executes the redemption volume boundary constraint to ensure that the sum of the new redemption volume and the historical redemption volume does not exceed the current available green rights.

[0123] If a candidate mapping simultaneously meets the above constraints, the system outputs a unique compliant mapping and synchronously updates the green code, green certificate, and consumption declaration status. If a candidate mapping does not meet the constraints, the system outputs an anomaly blocking report, clearly specifying the triggering constraints, conflicting objects, involved electricity consumption, and suggested handling methods. Through this mechanism, this embodiment can ensure the uniqueness, compliance, and explainability of green electricity rights mapping while guaranteeing candidate recall capabilities.

[0124] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for verifying the mapping of green health code rights attributes, characterized in that, The methods include: First, the multi-source green electricity rights data is accessed and standardized. Each rights data is modeled as a structured tuple, and the structured tuple is mapped to a unified representation space through multi-channel feature encoding to obtain a dense initial feature vector. During the training phase, positive and negative sample pairs are constructed based on historical rights confirmation records. Time consistency, power type consistency, certificate status and power boundary constraint perception weights are introduced into the dense initial feature vector. The feature encoder is trained by the constraint perception weighted contrast loss function so that objects with the same rights link are closer in the representation space. During the inference phase, a set of high-confidence candidate mappings is generated using the trained encoder, and minimum satisfaction constraint verification, mutual exclusion unique constraint verification, and sales volume boundary constraint verification are performed sequentially. If the candidate mapping passes all constraints, a unique compliant mapping is output through constraint optimization, and the lifecycle status of the green code, green certificate, and consumption declaration is updated. If the constraints are not met, an interpretable anomaly blocking result containing the anomaly type, triggering constraints, and handling suggestions is output.

2. The green code rights attribute mapping verification method according to claim 1, characterized in that, The specific process of modeling each rights data into a structured plural is as follows: each green code rights are regarded as a rights atom that can be mapped, locked, cancelled, and traced; each green code rights atom is modeled as a structured plural containing a unique identifier, power generation time, power generation entity, power source type, initial rights amount, cancelled electricity amount, frozen rights amount, available rights amount, current ownership entity, and business status; the green certificate object is modeled as a plural containing the green certificate number, issuing entity, cancelled power generation amount, validity period, and cancellation status; the power generation attribute object is modeled as a plural containing the grid connection metering serial number, physical power generation unit identifier, power source type, power generation area, metering settlement cycle, and on-grid electricity amount; the consumption declaration object is modeled as a plural containing the declaration number, consumer entity identifier, consumption cycle, declared electricity amount, power source type preference, declared purpose, and declared business status.

3. The green code rights attribute mapping verification method according to claim 2, characterized in that, The amount of available equity The calculation method is as follows: in, Green code The total green equity quota included in the initial issuance; Indicates green code The cumulative amount of electricity consumed and deducted in historical records; The amount of frozen equity that is temporarily locked during the transaction or review process; For cross-regional and cross-network transmission, the loss correction scalar is calculated and deducted based on the network loss topology coefficient of the physical power grid node.

4. A method for verifying green code rights attributes according to any one of claims 1 to 3, characterized in that, The method of mapping structured tuples to a unified representation space through multi-channel feature encoding is as follows: (1) For numeric fields, the maximum-minimum normalization method is used to map them to... interval; (2) For category fields, a learnable embedding matrix is ​​used for dimensionality reduction and dense representation; (3) For the time field, time position encoding based on sine and cosine functions is adopted; (4) For text fields, a pre-trained language model is used to extract global semantic features; (5) For the state field, construct an independent state transition feature space; Finally, the multi-channel feature vectors are concatenated and mapped to a unified-dimensional representation space using a multilayer perceptron.

5. A method for verifying the mapping of green code rights attributes according to any one of claims 1 to 3, characterized in that, The method for constructing positive and negative samples is as follows: Based on historical write-off records, confirmed green certificate relationships, and transaction contract links, a positive sample set and a negative sample set are constructed. The negative sample set includes hard negative samples that are close in time but different in subject, have excessive electricity consumption, have invalid green certificate status, and have different write-off cycles.

6. The green code rights attribute mapping verification method according to claim 5, characterized in that, The hard negative samples include at least one of the following categories: pairs of objects with similar time but different consumer entities; pairs of objects with the same consumer entity but whose declared electricity volume exceeds the available equity amount; pairs of objects with similar power generation types but invalid green certificate status; and pairs of objects with similar text descriptions but different write-off cycles.

7. A method for verifying green code rights attributes according to any one of claims 1 to 3, characterized in that, The constraint-aware weighted contrastive learning method is as follows: the obtained feature vector is mapped to a unit hypersphere through a nonlinear projection head to obtain normalized latent variables; based on time consistency, power type consistency, voucher status, reimbursement status, and power boundary, the comprehensive compliance constraint weight between sample pairs is calculated; through hard negative sample adaptive mining and constraint-aware weighted contrastive loss function, the feature encoder and projection head are trained to make objects of the same rights and interests link closer in the representation space and objects of different rights and interests link farther apart, and the output parameters are fixed for a multi-channel unified feature encoder.

8. The green code rights attribute mapping verification method according to claim 7, characterized in that, Constraint-aware weighted contrastive loss function Defined as: in: This represents the set of anchor indices in the current training batch, indicating the object... When used as an anchor point, it is the set of object indexes that form a positive sample relationship with it; correspondingly, Represents the anchor object The corresponding set of negative sample indices, This represents the index of the current anchor point. Indicates the positive sample index. Represents the negative sample index, and the corresponding specific objects are respectively , and ; Based on the base temperature hyperparameter; and Representing anchor objects respectively and negative sample objects After nonlinear projection mapping and After norm normalization, the output is a continuous latent variable eigenvector on the unit hypersphere; exp represents the exponential function; Represents the anchor object With negative sample objects The overall compliance constraint weights among them; For adaptive temperature scaling; compliance between positive samples The higher the value, the lower the effective temperature, and the more severe the penalty for the model to narrow down the two values. The amplification factor for difficult negative samples is defined as follows: This is the penalty coefficient; Serious violation of business discipline ( )hour, The surge forces the network to push it away in the hidden space, thereby decoupling "soft semantic similarity" from "hard rule compliance".

9. A method for verifying green code rights attributes according to any one of claims 1 to 3, characterized in that, The method for generating the candidate mapping set is as follows: the real-time anchor object and the full set of target candidate objects are vectorized using the trained encoder, the cosine similarity is calculated and the candidate mapping set with similarity higher than the threshold is initially screened out; the candidate mapping risk score is further calculated and the high-confidence candidate mapping set that meets both the similarity threshold and the risk threshold requirements is filtered out.

10. The green code rights attribute mapping verification method according to claim 9, characterized in that, The method for calculating the candidate mapping risk score is as follows: in, Representing anchor objects respectively With candidate The scores for time consistency, power type consistency, and state compatibility are as follows: Indicates the candidate mapping risk score. This indicates a risk of exceeding the battery limit. Indicates the risk of mutual exclusion and conflict. to This indicates the weight of each risk item.

11. A green code rights attribute mapping verification method according to any one of claims 1 to 3, characterized in that, The logic for determining the minimum satisfaction constraint is as follows: in, Indicates candidate mapping pairs Does it meet the basic requirements? This serves as a time window compliance indicator, used to determine whether the power generation time and the consumption declaration time are within the same settlement cycle allowed by the system or within a valid matching tolerance period; This is a power type matching identifier used to determine whether the physical power type strictly meets the demand preferences and legal definition domain; It serves as a status validity identifier, used to determine whether the current lifecycle status of the green code, green certificate, and consumption declaration allows for the execution of the rights confirmation mapping; It serves as a link reachability identifier, used to determine whether there is a traceable physical topology connection or a legitimate transaction credential chain between the power generation entity and the consumer entity in distributed transactions or cross-network scenarios; It serves as a qualification identifier to determine whether the declaring consumer has the legal qualifications to receive and verify green electricity environmental rights within the regulatory system; Indicates whether the consumer or declaring entity is eligible to receive rights; only when Only when the candidate mapping relationship is established can it proceed to subsequent mutual exclusion and power boundary checks.

12. A green code rights attribute mapping verification method according to any one of claims 1 to 3, characterized in that, The mutually exclusive unique constraint is represented as follows: in, For Boolean decision variables, if and only if the green code Mapped to consumer statement The value is 1 at time. Represents a collection of green code objects; The nuclear sales volume boundary constraint is expressed as follows: in, Indicates green code The relevant subset of candidate consumption statements is determined if and only if the constraint inference engine decides to include the green code. Map and assign to consumption declarations The value is 1 if the condition is met, otherwise it is 0. Consumer Statement The declared power scalar; This indicates that before this mapping verification was performed, the green code... The cumulative amount of electricity consumed and deducted in historical records; Indicates green code The current actual available equity level boundary.

13. A green code rights attribute mapping verification system, characterized in that, include: Multi-source data access and standardization module: used to access and standardize multi-source green electricity rights data; Equity Attribute Object Modeling Module: Used to model each equity data as a structured tuple; Multi-source attribute feature encoding module: used to map structured tuples to a unified representation space through multi-channel feature encoding to obtain dense initial feature vectors; Positive and negative sample mapping construction module: used to construct positive and negative sample pairs based on historical rights confirmation records; Constraint-aware weighted contrastive learning module: used to introduce time consistency, power type consistency, voucher status and power boundary constraint-aware weights into the dense initial feature vector, and train the feature encoder through constraint-aware weighted contrastive loss function to make objects with the same rights link closer in the representation space; Candidate Mapping Generation and Filtering Module: Used to generate a set of high-confidence candidate maps using the trained encoder; The constraint reasoning verification module is used to sequentially perform minimum satisfaction constraint verification, mutual exclusion and unique constraint verification, and core sales boundary constraint verification. Unique Compliance Mapping Solver: Within a defined compliance solution space, with the goal of maximizing semantic similarity and minimizing risk score, the module solves the unique compliance mapping decision result through constrained optimization and outputs the optimal decision variable alignment mapping pair. The anomaly blocking and status write-back module: If a candidate mapping passes all constraints, it outputs a unique compliant mapping result and performs status write-back, updating the remaining green code rights and the lifecycle status of the green code, green certificate, and consumption declaration; if a candidate mapping does not meet any constraint, it generates an anomaly blocking result, which includes the anomaly object identifier, trigger constraint rule code, anomaly type, anomaly reason, involved electricity consumption, and processing suggestions.

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