Replacement and subsidy consistency verification method and system, terminal and medium

Abstract

The invention belongs to the technical field of data verification, and particularly discloses a replacement and subsidy consistency verification method and system, a terminal and a medium. Comprising the steps of obtaining replacement subsidy application data; executing data matching and validity verification on the application data to generate verification state data; performing consistency verification on the replacement process based on the verification state data to generate consistency verification result data; constructing an associated data structure based on the application data and the consistency verification result data, and executing associated feature analysis to extract cross-application abnormal associated features; performing fusion processing on the verification state data, the consistency verification result data and the abnormal association features to form feature data, and generating a verification evaluation result based on the feature data; and outputting a processing result of the replacement subsidy application. By performing consistency verification and correlation analysis on the multi-source data of the replacement and subsidy application, comprehensive evaluation of the replacement and subsidy application state is realized, and the method is suitable for data verification processing of a complex replacement and subsidy service scene.

Description

Technical Field

[0001] This invention belongs to the field of data verification technology, specifically relating to a method, system, terminal, and medium for verifying the consistency of replacement subsidies. Background Technology

[0002] With the continued advancement of policies promoting trade-in programs, consumer spending, and green travel, various trade-in subsidy programs have been widely adopted in transportation, home appliances, and digital products. Trade-in subsidies typically involve multiple types of information, including the applicant, disposal of the old product, acquisition of the new product, and the transaction entity. Data collection, verification, and subsidy disbursement must be completed within certain business process constraints. To ensure the compliant use of subsidy funds and the standardized operation of business processes, relevant business systems usually need to verify and evaluate multi-source data during the trade-in subsidy application process to identify applications that do not meet business requirements or pose risks.

[0003] In existing technologies, the verification process for replacement subsidies or similar subsidies typically employs methods based on field matching, rule validation, or single-process verification to compare the applicant's information, object identification information, or transaction information item by item. For example, by verifying the validity of the applicant's identity, comparing the basic attributes of the old and new objects, or checking whether the application time and transaction time meet preset conditions, a judgment is made as to whether a single application meets the subsidy conditions.

[0004] However, as the scale of the replacement subsidy business expands and the business scenarios become more complex, relying solely on rule verification or field comparison at the single application level is no longer sufficient to effectively address the complex issues arising from cross-application and multi-entity associations: different replacement subsidy applications may have potential associations at the level of the applicant entity, object identifier, or transaction entity, and verifying only a single application makes it difficult to identify such abnormal cross-application patterns. Summary of the Invention

[0005] This invention addresses the problems in the prior art by providing a method, system, terminal, and medium for verifying the consistency of replacement subsidies. This solves the problem that, in the aforementioned background art, different replacement subsidy applications may have potential connections at the level of the applicant, object identifier, or transaction entity, and it is difficult to identify such cross-application anomalies by verifying only a single application.

[0006] The technical solution adopted in this invention is as follows: Firstly, this application provides a method for verifying the consistency of replacement subsidies, which includes the following steps: Step S1: Obtain replacement subsidy application data and perform structured processing on the application data. The application data shall include at least the applicant's identification information, the old object's identification information, the new object's identification information, and the transaction entity information corresponding to the replacement subsidy behavior. Step S2: Based on the multi-source external data interface, perform data matching and validity verification on the applicant entity identification information, old object identification information, new object identification information and transaction entity information involved in the application data, and generate corresponding verification status data; Step S3: Based on the verification status data, perform consistency verification on the replacement process data between the disposal of old objects, the acquisition of new objects and the application for replacement subsidies. The consistency verification includes at least the consistency verification of time sequence relationship, the consistency verification of subject association and the uniqueness verification of object identifier, and generates consistency verification result data. Step S4: Based on the application data and consistency verification results, construct a data structure to represent the relationship between the applicant, object identifier and transaction entity, perform association feature analysis on the data structure, and extract abnormal association features across applications; Step S5: The verification status data, consistency verification result data, and abnormal correlation features are fused to form feature data that characterizes the status of the replacement subsidy application, and the corresponding verification evaluation results are generated based on the feature data. Step S6: Based on the verification and evaluation results, output the processing result of the replacement subsidy application.

[0007] Furthermore, the consistency check in step S3 includes: Based on the time data corresponding to the disposal of old objects, the acquisition of new objects, and the application for replacement subsidies, the consistency of the timing relationship in the replacement process is verified. Based on the applicant's identification information, the object's identification information, and the transaction entity's information, the consistency of the entities executing the replacement process is verified. Based on the old object identification information and the new object identification information, the object identification uniqueness verification is performed during the replacement process; Determine the consistency status of the replacement process in terms of time, subject association, and object identification, and generate consistency verification result data.

[0008] Furthermore, the consistency status of the replacement process across the time dimension, subject association dimension, and object identification dimension is determined, including: Obtain the verification status information corresponding to the time sequence consistency verification, subject association consistency verification, and object identifier uniqueness verification respectively; The verification status information corresponding to the time sequence consistency verification, the verification status information corresponding to the subject association consistency verification, and the verification status information corresponding to the object identifier uniqueness verification are aligned according to the preset consistency status mapping structure and mapped to the corresponding status fields respectively. The verification status information mapped to each status field is uniformly encoded to form a consistent status data structure that includes consistency status in the time dimension, consistency status in the subject association dimension, and consistency status in the object identifier dimension. Output the consistent state data structure as the consistency verification result data.

[0009] Furthermore, step S4 includes: Based on application data and consistency verification results, an association data structure is constructed to represent the relationship between the applicant, object identifier and transaction entity. Different types of applicant, object identifier and transaction entity are mapped to different types of structure nodes, and the permutation behavior relationship between the applicant, object identifier and transaction entity is mapped to the structure association relationship. In the associated data structure, association feature analysis is performed on the structural nodes and structural relationships corresponding to multiple replacement subsidy applications to extract abnormal association features across applications. Abnormal association features include at least shared identifier features, abnormal clustering features, and abnormal association frequency features.

[0010] Furthermore, association feature analysis is performed on the associated data structure to extract abnormal association features across applications, including: For object identifier nodes or subject identifier nodes, count the number of replacement subsidy applications that have established a relationship with the identifier node, calculate the proportion or concentration distribution of the number of applications in the associated data structure, and generate shared identifier features based on the number of applications and their distribution characteristics to characterize the degree to which multiple replacement subsidy applications share the same identifier; Based on the connection relationships between structural nodes, the connection density, node clustering degree, or substructure concentration index of the applicant entity node, object identifier node, or transaction entity node within the local structural scope is calculated. Based on the connection density, clustering degree, or substructure concentration index, abnormal clustering features are generated to characterize the degree of concentrated association of multiple replacement subsidy applications at the structural level. Within a preset time range, the number of times the applicant entity node, object identifier node, or transaction entity node participates in structural association relationships is counted. The frequency distribution characteristics of the number of occurrences over time are calculated. Based on the frequency distribution characteristics, abnormal association frequency characteristics are generated to characterize the abnormally frequent association relationships of multiple replacement subsidy applications.

[0011] Furthermore, step S5 includes: Feature alignment processing is performed on the verification status data, consistency verification result data, and anomaly correlation features to map data from different sources to a unified feature dimension. Feature encoding is performed on the data mapped to each feature dimension to generate corresponding feature fields. The feature fields are used to characterize the feature information of the data in the dimensions of real state, consistency state and associated structure state. The feature fields are combined to form a feature data structure that represents the status of replacement subsidy applications; Verification and evaluation results are generated based on the feature data structure.

[0012] Furthermore, based on the feature data structure, verification and evaluation results are generated, including: Based on the correlation between feature fields corresponding to different state dimensions in the feature data structure, a state correlation model is constructed to characterize the mutual influence relationship between each state dimension. In the state association model, the feature fields corresponding to each state dimension in the feature data structure are used as the initial state. State propagation calculation is performed on the feature fields of each state dimension according to the state association model. The state propagation calculation includes state aggregation based on the feature fields of adjacent state dimensions and state update based on the aggregation result. The steady-state results obtained through state propagation calculation are processed by state readout to generate evaluation feature quantities that characterize the combined effect of each state dimension. Verification and evaluation results are generated based on the evaluation features.

[0013] Secondly, this application provides a replacement subsidy consistency verification system for implementing the replacement subsidy consistency verification method as described in the first aspect. The system includes: The data acquisition and processing unit is configured to acquire replacement subsidy application data, perform structured processing on the application data, and the application data includes at least the applicant entity identification information, the old object identification information, the new object identification information, and the transaction entity information corresponding to the replacement subsidy behavior; The verification status generation unit is connected to the data acquisition and processing unit and is configured to perform data matching and validity verification on the applicant entity identification information, old object identification information, new object identification information and transaction entity information involved in the application data based on multi-source external data interfaces, and generate corresponding verification status data. The consistency verification unit, connected to the verification status generation unit, is configured to perform consistency verification on the replacement process data between the disposal of old objects, the acquisition of new objects and the application for replacement subsidies based on the verification status data, and generate consistency verification result data. The association analysis unit, connected to the consistency verification unit, is configured to construct a data structure based on application data and consistency verification result data to represent the relationship between the applicant, object identifier and transaction entity, perform association feature analysis on the data structure, and extract abnormal association features across applications; The evaluation result generation unit is connected to the correlation analysis unit and is configured to perform fusion processing on the verification status data, consistency verification result data and abnormal correlation features to form feature data that characterizes the status of the replacement subsidy application, and generate corresponding verification evaluation results based on the feature data. The processing result output unit is connected to the evaluation result generation unit and is configured to output the processing result of the replacement subsidy application based on the verification evaluation result.

[0014] Thirdly, this application provides a terminal, including: Memory, used to store the replacement subsidy consistency verification program; A processor is configured to implement the steps of the replacement subsidy consistency verification method as described in the first aspect when executing the replacement subsidy consistency verification device.

[0015] Fourthly, this application provides a computer-readable storage medium that stores computer instructions. When a computer reads the computer instructions in the storage medium, the computer executes the replacement subsidy consistency verification method as described in the first aspect.

[0016] As can be seen from the above technical solutions, the advantages of the present invention are: The replacement subsidy consistency verification method described in this application can perform structured processing on multi-source data involved in replacement subsidy applications, and on this basis, combine external data interfaces to complete authenticity and validity verification, thereby providing a reliable data foundation for subsequent consistency analysis and comprehensive evaluation. Compared with methods based solely on a single data source or static field verification, this application can improve the completeness and credibility of application information at the data level.

[0017] By performing consistency verification on the replacement process between the disposal of old objects, the acquisition of new objects, and the application for replacement subsidies, and by uniformly modeling the replacement process from the dimensions of time, subject association, and object identification, this application can form a structured representation of the consistency status of a single replacement subsidy application, effectively avoiding the limitations of judging based on a single condition or isolated rule, and improving the ability to characterize the rationality of the replacement process.

[0018] By aligning, encoding, and combining the multi-dimensional verification status information obtained during the consistency verification process into a consistency status data structure, this application achieves a unified expression of results from different verification dimensions, enabling consistency verification results to participate in subsequent analysis and processing in a standardized data form, which is beneficial to improving the overall data processing efficiency and scalability of the system.

[0019] By constructing a relational data structure to characterize the relationships between applicants, object identifiers, and transaction entities, and performing cross-application relational feature analysis within this structure, this application can identify abnormal relational patterns among multiple replacement subsidy applications in terms of identifier sharing, structural clustering, and relational frequency. This overcomes the limitations of single-application verification and enables technical analysis of cross-application behavioral characteristics.

[0020] By calculating and extracting shared identifier features, abnormal clustering features, and abnormal association frequency features, this application can quantitatively describe the relationship between multiple applications from a structural and temporal perspective, providing distinctive association structure features for subsequent comprehensive evaluation and improving adaptability to complex replacement subsidy application scenarios.

[0021] By uniformly aligning, encoding, and combining the verification status data, consistency verification result data, and anomaly correlation features, this application can form a feature data structure that characterizes the overall status of the replacement subsidy application. This allows information from different processing stages to be comprehensively expressed in the same feature space, which helps reduce information fragmentation between different processing stages.

[0022] By constructing a state association model based on the feature data structure and performing state propagation calculation, this application can characterize the mutual influence relationship between real state, consistent state and associated structural state, thereby generating a verification and evaluation result that reflects the comprehensive effect of multiple state dimensions, avoiding reliance on simple linear combination or fixed rules to output evaluation results, and improving the stability and interpretability of the evaluation results. Attached Figure Description

[0023] To more clearly illustrate the technical solution of the present invention, the accompanying drawings used in the description will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 This is a flowchart of the replacement subsidy consistency verification method of the present invention; Figure 2 This is an architecture diagram of the replacement subsidy consistency verification system of the present invention. Detailed Implementation

[0025] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and 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.

[0026] Please see Figure 1 As shown, this application provides a method for verifying the consistency of replacement subsidies, including the following steps: Step S1: Obtain replacement subsidy application data and perform structured processing on the application data. The application data shall include at least the applicant's identification information, the old object's identification information, the new object's identification information, and the transaction entity information corresponding to the replacement subsidy behavior. In a specific implementation, replacement subsidy application data can be obtained through front-end submission of the business system, API push, or batch import. The structured processing includes parsing, normalizing, and standardizing the data types of information from different sources and in different formats within the application data, ensuring that applicant identification information, old object identification information, new object identification information, and transaction entity information correspond to preset data fields. In one embodiment, applicant identification information may include identity identifiers, contact information, or account identifiers; old object identification information and new object identification information may include unique object numbers or registration identifiers; and transaction entity information may include the identifier of the seller or recycler. Through structured processing, all types of information can be subsequently processed within a unified data structure.

[0027] Step S2: Based on the multi-source external data interface, perform data matching and validity verification on the applicant entity identification information, old object identification information, new object identification information and transaction entity information involved in the application data, and generate corresponding verification status data; In specific implementations, multi-source external data interfaces may include identity information verification interfaces, object registration information query interfaces, or transaction entity information verification interfaces. By calling these external data interfaces, various identification information in the application data is matched with corresponding information in the external data source to confirm the existence, consistency, or current validity of the relevant information. In one embodiment, corresponding verification status identifiers are generated for different types of identification information to characterize the verification result of the identification information, enabling subsequent steps to further analyze the replacement process based on the verification status data.

[0028] Step S3: Based on the verification status data, perform consistency verification on the replacement process data between the disposal of old objects, the acquisition of new objects and the application for replacement subsidies. The consistency verification includes at least the consistency verification of time sequence relationship, the consistency verification of subject association and the uniqueness verification of object identifier, and generates consistency verification result data. In a specific implementation, the consistency verification comprehensively analyzes multiple stages involved in the replacement subsidy application based on verification status data. The temporal relationship consistency verification analyzes whether the chronological order of the old object disposal, new object acquisition, and subsidy application forms a reasonable replacement process sequence; the subject association consistency verification analyzes whether a stable and coherent association is formed between the applicant, transaction entity, and object identifier; and the object identifier uniqueness verification analyzes the usage of the old and new object identifiers in the replacement subsidy application. In one embodiment, corresponding verification statuses are generated for each of the above consistency verifications, and these are combined to form consistency verification result data, which characterizes the consistency status of a single replacement process across multiple dimensions.

[0029] Step S4: Based on the application data and consistency verification results, construct a data structure to represent the relationship between the applicant, object identifier and transaction entity, perform association feature analysis on the data structure, and extract abnormal association features across applications; In a specific implementation, an association data structure describing the relationships between multiple applications is constructed by mapping the applicants, object identifiers, and transaction entities in multiple replacement subsidy applications to different nodes in an association data structure, and mapping the relationships formed by replacement behaviors to the association relationships between nodes. In one embodiment, the nodes corresponding to multiple replacement subsidy applications and their association relationships are analyzed in this association data structure to identify abnormal patterns among multiple applications in terms of identifier sharing, structural clustering, or association frequency, thereby extracting abnormal association features across applications and providing structural-level feature information for subsequent evaluation.

[0030] Step S5: The verification status data, consistency verification result data, and abnormal correlation features are fused to form feature data that characterizes the status of the replacement subsidy application, and the corresponding verification evaluation results are generated based on the feature data. In a specific implementation, the fusion process includes uniformly aligning and encoding data from different processing stages, enabling verification status data, consistency verification result data, and anomaly correlation features to be combined within the same feature space. In one embodiment, by mapping the aforementioned data to feature fields characterizing authenticity status, consistency status, and correlation structure status, and combining these feature fields to form a feature data structure, a verification evaluation result reflecting the overall status of the replacement subsidy application is generated based on the feature data structure.

[0031] Step S6: Based on the verification and evaluation results, output the processing result of the replacement subsidy application.

[0032] In a specific implementation, the processing result can be used to indicate the subsequent processing method for the replacement subsidy application. In one embodiment, the system can mark the replacement subsidy application as different processing statuses based on the verification and evaluation results, and output the corresponding processing results to the business system or management system for subsequent process invocation, thereby completing the overall verification and processing of the replacement subsidy application.

[0033] In some embodiments, the consistency check in step S3 includes: Based on the time data corresponding to the disposal of old objects, the acquisition of new objects, and the application for replacement subsidies, the consistency of the timing relationship in the replacement process is verified. Based on the applicant's identification information, the object's identification information, and the transaction entity's information, the consistency of the entities executing the replacement process is verified. Based on the old object identification information and the new object identification information, the object identification uniqueness verification is performed during the replacement process; Determine the consistency status of the replacement process in terms of time, subject association, and object identification, and generate consistency verification result data.

[0034] In a specific implementation, the consistency verification in step S3 involves a comprehensive analysis of multiple business processes related to the replacement subsidy application, based on the obtained verification status data. The temporal consistency verification obtains the old object disposal time, the new object acquisition time, and the subsidy application submission time, processes the time data uniformly, and uses this data to depict the overall temporal sequence of the replacement process. The entity association consistency verification analyzes the association between the applicant entity, the transaction entity, and the object identifier to reflect the completeness of the association between different business elements in the replacement process. The object identifier uniqueness verification analyzes the usage of the old and new object identifiers in the replacement subsidy application to depict the independence and distinctiveness of the object identifier in the replacement process. In one embodiment, each of the above verification methods generates corresponding verification status information, which serves as the basis for subsequent consistency status generation.

[0035] In some embodiments, determining the consistency status of the replacement process across the time dimension, subject association dimension, and object identification dimension includes: Obtain the verification status information corresponding to the time sequence consistency verification, subject association consistency verification, and object identifier uniqueness verification respectively; The verification status information corresponding to the time sequence consistency verification, the verification status information corresponding to the subject association consistency verification, and the verification status information corresponding to the object identifier uniqueness verification are aligned according to the preset consistency status mapping structure and mapped to the corresponding status fields respectively. The verification status information mapped to each status field is uniformly encoded to form a consistent status data structure that includes consistency status in the time dimension, consistency status in the subject association dimension, and consistency status in the object identifier dimension. Output the consistent state data structure as the consistency verification result data.

[0036] In a specific implementation, different types of consistency verification results, due to their different sources and semantics, are first aligned to occupy fixed positions in a unified data structure. The consistency state mapping structure defines the correspondence between the time dimension, subject association dimension, and object identifier dimension in the data structure, enabling verification states of different dimensions to be expressed within the same structure. In one embodiment, the mapped verification state information undergoes unified encoding processing, representing the consistency state of each dimension in a standardized data form, thereby forming a consistency state data structure, which serves as the consistency verification result data for subsequent steps.

[0037] In some embodiments, step S4 includes: Based on application data and consistency verification results, an association data structure is constructed to represent the relationship between the applicant, object identifier and transaction entity. Different types of applicant, object identifier and transaction entity are mapped to different types of structure nodes, and the permutation behavior relationship between the applicant, object identifier and transaction entity is mapped to the structure association relationship. In the associated data structure, association feature analysis is performed on the structural nodes and structural relationships corresponding to multiple replacement subsidy applications to extract abnormal association features across applications. Abnormal association features include at least shared identifier features, abnormal clustering features, and abnormal association frequency features.

[0038] In a specific implementation, the associated data structure is used to uniformly describe the relationship information between multiple replacement subsidy applications. In one embodiment, the applicant, object identifier, and transaction entity involved in different replacement subsidy applications are introduced into the associated data structure as independent nodes, and the relationship formed by the replacement behavior is used as the association relationship between nodes. By introducing multiple application data into this structure, the association between different applications can be analyzed at the structural level, thereby providing a basis for extracting abnormal association features across applications.

[0039] In some embodiments, association feature analysis is performed in the associated data structure to extract anomalous association features across applications, including: For object identifier nodes or subject identifier nodes, count the number of replacement subsidy applications that have established a relationship with the identifier node, calculate the proportion or concentration distribution of the number of applications in the associated data structure, and generate shared identifier features based on the number of applications and their distribution characteristics to characterize the degree to which multiple replacement subsidy applications share the same identifier; Based on the connection relationships between structural nodes, the connection density, node clustering degree, or substructure concentration index of the applicant entity node, object identifier node, or transaction entity node within the local structural scope is calculated. Based on the connection density, clustering degree, or substructure concentration index, abnormal clustering features are generated to characterize the degree of concentrated association of multiple replacement subsidy applications at the structural level. Within a preset time range, the number of times the applicant entity node, object identifier node, or transaction entity node participates in structural association relationships is counted. The frequency distribution characteristics of the number of occurrences over time are calculated. Based on the frequency distribution characteristics, abnormal association frequency characteristics are generated to characterize the abnormally frequent association relationships of multiple replacement subsidy applications.

[0040] In a specific implementation, the shared identifier feature is used to characterize situations where the same identifier is associated with multiple replacement subsidy applications. By analyzing the number and distribution of associations, the degree of sharing can be expressed in the form of features. The abnormal clustering feature is used to characterize situations where multiple applications appear in clusters at the structural level. By analyzing the connections of nodes in the local structure, the degree of clustering is reflected. The abnormal association frequency feature is used to characterize situations where the applicant, object identifier, or transaction entity repeatedly participates in replacement subsidy applications within a certain time frame. The frequency distribution reflects the activity level of the association relationship. In one embodiment, the above features collectively constitute a cross-application abnormal association feature set.

[0041] In some embodiments, step S5 includes: Feature alignment processing is performed on the verification status data, consistency verification result data, and anomaly correlation features to map data from different sources to a unified feature dimension. Feature encoding is performed on the data mapped to each feature dimension to generate corresponding feature fields. The feature fields are used to characterize the feature information of the data in the dimensions of real state, consistency state and associated structure state. The feature fields are combined to form a feature data structure that represents the status of replacement subsidy applications; Verification and evaluation results are generated based on the feature data structure.

[0042] In a specific implementation, step S5 is used to perform unified modeling of data from different processing stages. In one embodiment, alignment processing is used to express verification status data, consistency verification result data, and anomaly correlation features under the same feature dimension system, and then feature encoding is used to transform them into composable feature fields. By combining the feature fields to form a feature data structure, information from multiple status dimensions of the replacement subsidy application can participate in the evaluation in a holistic manner.

[0043] In some embodiments, generating verification evaluation results based on feature data structures includes: Based on the correlation between feature fields corresponding to different state dimensions in the feature data structure, a state correlation model is constructed to characterize the mutual influence relationship between each state dimension. In the state association model, the feature fields corresponding to each state dimension in the feature data structure are used as the initial state. State propagation calculation is performed on the feature fields of each state dimension according to the state association model. The state propagation calculation includes state aggregation based on the feature fields of adjacent state dimensions and state update based on the aggregation result. The steady-state results obtained through state propagation calculation are processed by state readout to generate evaluation feature quantities that characterize the combined effect of each state dimension. Verification and evaluation results are generated based on the evaluation features.

[0044] In a specific implementation, a state association model is used to characterize the interaction between authentic states, consistent states, and associated structural states. In one embodiment, feature fields of different state dimensions in the feature data structure are used as initial state inputs to the state association model. Through multiple rounds of state propagation calculations, the information between each state dimension influences each other and gradually stabilizes. Subsequently, the stabilized state results are read out to form an evaluation feature quantity reflecting the overall state of the replacement subsidy application. Based on the evaluation feature quantity, a verification evaluation result is generated for use in subsequent processing steps.

[0045] Please see Figure 2 As shown, in some embodiments, this application provides a replacement subsidy consistency verification system for implementing a replacement subsidy consistency verification method. The system includes: The data acquisition and processing unit is configured to acquire replacement subsidy application data, perform structured processing on the application data, and the application data includes at least the applicant entity identification information, the old object identification information, the new object identification information, and the transaction entity information corresponding to the replacement subsidy behavior; The verification status generation unit is connected to the data acquisition and processing unit and is configured to perform data matching and validity verification on the applicant entity identification information, old object identification information, new object identification information and transaction entity information involved in the application data based on multi-source external data interfaces, and generate corresponding verification status data. The consistency verification unit, connected to the verification status generation unit, is configured to perform consistency verification on the replacement process data between the disposal of old objects, the acquisition of new objects and the application for replacement subsidies based on the verification status data, and generate consistency verification result data. The association analysis unit, connected to the consistency verification unit, is configured to construct a data structure based on application data and consistency verification result data to represent the relationship between the applicant, object identifier and transaction entity, perform association feature analysis on the data structure, and extract abnormal association features across applications; The evaluation result generation unit is connected to the correlation analysis unit and is configured to perform fusion processing on the verification status data, consistency verification result data and abnormal correlation features to form feature data that characterizes the status of the replacement subsidy application, and generate corresponding verification evaluation results based on the feature data. The processing result output unit is connected to the evaluation result generation unit and is configured to output the processing result of the replacement subsidy application based on the verification evaluation result.

[0046] In some embodiments, this application provides a terminal, including: Memory, used to store the replacement subsidy consistency verification program; A processor is used to implement the steps of the replacement subsidy consistency verification method when executing the replacement subsidy consistency verification system.

[0047] In some embodiments, this application provides a computer-readable storage medium that stores computer instructions. When a computer reads the computer instructions in the storage medium, the computer executes the replacement subsidy consistency verification method.

[0048] The above description is merely a preferred embodiment of one or more embodiments of this specification and is not intended to limit the scope of one or more embodiments of this specification. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of one or more embodiments of this specification should be included within the protection scope of one or more embodiments of this specification.

Claims

1. A method for verifying the consistency of replacement subsidies, characterized in that, Includes the following steps: Step S1: Obtain replacement subsidy application data and perform structured processing on the application data. The application data shall include at least the applicant identification information, the old object identification information, the new object identification information, and the transaction entity information corresponding to the replacement subsidy behavior. Step S2: Based on the multi-source external data interface, perform data matching and validity verification on the applicant entity identification information, old object identification information, new object identification information and transaction entity information involved in the application data, and generate corresponding verification status data; Step S3: Based on the verification status data, perform consistency verification on the replacement process data between the disposal of old objects, the acquisition of new objects and the application for replacement subsidies. The consistency verification includes at least the consistency verification of time sequence relationship, the consistency verification of subject association and the uniqueness verification of object identifier, and generates consistency verification result data. Step S4: Based on the application data and consistency verification results, construct a data structure to represent the relationship between the applicant, object identifier and transaction entity, perform association feature analysis on the data structure, and extract abnormal association features across applications; Step S5: The verification status data, consistency verification result data, and abnormal correlation features are fused to form feature data that characterizes the status of the replacement subsidy application, and the corresponding verification evaluation results are generated based on the feature data. Step S6: Based on the verification and evaluation results, output the processing results of the replacement subsidy application.

2. The replacement subsidy consistency verification method according to claim 1, characterized in that, The consistency check in step S3 includes: Based on the time data corresponding to the disposal of old objects, the acquisition of new objects, and the application for replacement subsidies, the consistency of the timing relationship in the replacement process is verified. Based on the applicant's identification information, the object's identification information, and the transaction entity's information, the consistency of the entities executing the replacement process is verified. Based on the old object identification information and the new object identification information, the object identification uniqueness verification is performed during the replacement process; Determine the consistency status of the replacement process in terms of time, subject association, and object identification, and generate consistency verification result data.

3. The replacement subsidy consistency verification method according to claim 2, characterized in that, Determine the consistency status of the replacement process across the time dimension, subject association dimension, and object identification dimension, including: Obtain the verification status information corresponding to the time sequence consistency verification, subject association consistency verification, and object identifier uniqueness verification, respectively; The verification status information corresponding to the time sequence consistency verification, the verification status information corresponding to the subject association consistency verification, and the verification status information corresponding to the object identifier uniqueness verification are aligned according to the preset consistency status mapping structure and mapped to the corresponding status fields respectively. The verification status information mapped to each status field is uniformly encoded to form a consistent status data structure that includes consistency status in the time dimension, consistency status in the subject association dimension, and consistency status in the object identifier dimension. Output the consistent state data structure as the consistency verification result data.

4. The method for verifying the consistency of replacement subsidies according to any one of claims 1-3, characterized in that, Step S4 includes: Based on application data and consistency verification results, an association data structure is constructed to represent the relationship between the applicant, object identifier and transaction entity. Different types of applicant, object identifier and transaction entity are mapped to different types of structure nodes, and the permutation behavior relationship between the applicant, object identifier and transaction entity is mapped to the structure association relationship. In the associated data structure, association feature analysis is performed on the structural nodes and structural relationships corresponding to multiple replacement subsidy applications to extract abnormal association features across applications. Abnormal association features include at least shared identifier features, abnormal clustering features, and abnormal association frequency features.

5. The replacement subsidy consistency verification method according to claim 4, characterized in that, Perform association feature analysis on the associated data structure to extract anomalous association features across applications, including: For object identifier nodes or subject identifier nodes, count the number of replacement subsidy applications that have established a relationship with the identifier node, calculate the proportion or concentration distribution of the number of applications in the associated data structure, and generate shared identifier features based on the number of applications and their distribution characteristics to characterize the degree to which multiple replacement subsidy applications share the same identifier; Based on the connection relationships between structural nodes, the connection density, node clustering degree, or substructure concentration index of the applicant entity node, object identifier node, or transaction entity node within the local structural scope is calculated. Based on the connection density, clustering degree, or substructure concentration index, abnormal clustering features are generated to characterize the degree of concentrated association of multiple replacement subsidy applications at the structural level. Within a preset time range, the number of times the applicant entity node, object identifier node, or transaction entity node participates in structural association relationships is counted. The frequency distribution characteristics of the number of occurrences over time are calculated. Based on the frequency distribution characteristics, abnormal association frequency characteristics are generated to characterize the abnormally frequent association relationships of multiple replacement subsidy applications.

6. The replacement subsidy consistency verification method according to claim 5, characterized in that, Step S5 includes: Feature alignment processing is performed on the verification status data, consistency verification result data, and anomaly correlation features to map data from different sources to a unified feature dimension. Feature encoding is performed on the data mapped to each feature dimension to generate corresponding feature fields. The feature fields are used to characterize the feature information of the data in the dimensions of real state, consistency state and associated structure state. The feature fields are combined to form a feature data structure that represents the status of replacement subsidy applications; Verification and evaluation results are generated based on the feature data structure.

7. The replacement subsidy consistency verification method according to claim 6, characterized in that, The verification evaluation results are generated based on the feature data structure, including: Based on the correlation between feature fields corresponding to different state dimensions in the feature data structure, a state correlation model is constructed to characterize the mutual influence relationship between each state dimension. In the state association model, the feature fields corresponding to each state dimension in the feature data structure are used as the initial state. State propagation calculation is performed on the feature fields of each state dimension according to the state association model. The state propagation calculation includes state aggregation based on the feature fields of adjacent state dimensions and state update based on the aggregation result. The steady-state results obtained through state propagation calculation are processed by state readout to generate evaluation feature quantities that characterize the combined effect of each state dimension. Verification and evaluation results are generated based on the evaluation features.

8. A system for verifying the consistency of replacement subsidies, used to implement the method for verifying the consistency of replacement subsidies as described in claim 1, characterized in that, The system includes: The data acquisition and processing unit is configured to acquire replacement subsidy application data, perform structured processing on the application data, and the application data includes at least the applicant entity identification information, the old object identification information, the new object identification information, and the transaction entity information corresponding to the replacement subsidy behavior; The verification status generation unit is connected to the data acquisition and processing unit and is configured to perform data matching and validity verification on the applicant entity identification information, old object identification information, new object identification information and transaction entity information involved in the application data based on multi-source external data interfaces, and generate corresponding verification status data. The consistency verification unit, connected to the verification status generation unit, is configured to perform consistency verification on the replacement process data between the disposal of old objects, the acquisition of new objects and the application for replacement subsidies based on the verification status data, and generate consistency verification result data. The association analysis unit, connected to the consistency verification unit, is configured to construct a data structure based on application data and consistency verification result data to represent the association relationship between the applicant, object identifier and transaction entity, perform association feature analysis on the data structure, and extract abnormal association features across applications; The evaluation result generation unit is connected to the correlation analysis unit and is configured to perform fusion processing on the verification status data, consistency verification result data and abnormal correlation features to form feature data that characterizes the status of the replacement subsidy application, and generate corresponding verification evaluation results based on the feature data. The processing result output unit is connected to the evaluation result generation unit and is configured to output the processing result of the replacement subsidy application based on the verification evaluation result.

9. A terminal, characterized in that, include: Memory, used to store the replacement subsidy consistency verification program; A processor is configured to implement the steps of the replacement subsidy consistency verification method as described in any one of claims 1-7 when executing the replacement subsidy consistency verification device.

10. A computer-readable storage medium, characterized in that, The storage medium stores computer instructions. When the computer reads the computer instructions from the storage medium, the computer executes the replacement subsidy consistency verification method as described in any one of claims 1-7.

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