Transaction data model construction method, link traceability method and device

Abstract

The invention relates to the technical field of data supervision, and discloses a transaction data model construction method, a link tracing method and a link tracing device. On the basis of keeping a core structure of a PROV network topological graph, characteristics of a data element development process and a transaction process are combined; semantic extension is performed on a network topological graph again by defining a transaction entity, a transaction dynamic state and a dependency relationship between a transaction object and a node, abstract description is performed on a whole development link and a transaction link of data elements, transparency of a data element transaction process is improved, a blood relationship among different transaction data is constructed, and the transaction efficiency is improved. According to the technical scheme, end-to-end accurate traceability can be realized, so that the credibility and reliability of data can be better managed and maintained, the data security and data quality of a data supplier and a data demander in a transaction process can be guaranteed, and the data transaction risk can be reduced.

Description

Technical Field

[0001] The present invention relates to the field of data supervision technology, and in particular to a transaction data model building method, a link tracing method and a device. Background Art

[0002] As data-driven services and applications lead online and offline businesses, data elements are considered a new valuable asset and an indispensable driving force. In order to tap the true value of data, big data technology came into being.

[0003] With the rise of big data technology, data brokers, or data operators, have emerged, which collect and analyze data from data providers and sell it to other third parties, or data consumers. Data brokers collect data from various IoT devices (such as sensors, smartphones, or wearable devices) and service providers, perform big data analysis to extract new information and knowledge, and then sell the copied information to many other third-party services to improve service quality (for example, potential customer detection, identity verification, fraud detection, etc.). Data providers do not have the opportunity to know how their data is processed, delivered, and used, and the data transaction process lacks transparency, which also hinders the confirmation and supervision of data ownership.

[0004] In the relevant technologies, in the process of supervision, data elements are traceable objects, and there are operations such as fusion and splitting in the circulation transaction process. There is a lack of connection between the circulation nodes, and it is difficult to label the data through physical means. As a result, it is not only difficult to achieve end-to-end accurate traceability of the data element transaction process, but also increases the difficulty of data supervision. In addition, data transactions are different from general commodity transactions. Data elements are virtual assets that are extremely easy to spread. Existing regulatory measures cannot guarantee the data security and data quality of both data supply and demand parties in the transaction process, resulting in uneven data quality, high trial and error costs for both data supply and demand parties, and high transaction risks. Summary of the invention

[0005] In view of this, the present invention provides a transaction data model construction method, a link tracing method and a device to solve the problems that transaction data is difficult to supervise during the supervision process, the transaction risk is high, the transaction quality is low, the transaction reliability is low, and the source of data cannot be accurately traced.

[0006] In a first aspect, the present invention provides a method for constructing a transaction data model, comprising:

[0007] Acquire transaction entities, transaction dynamics, and transaction objects of various transaction data, wherein a first transaction relationship exists between the transaction entity and the transaction dynamics, a second transaction relationship exists between the transaction entity and the transaction object, and a third transaction relationship exists between the transaction dynamics and the transaction object;

[0008] Map transaction entities, transaction dynamics and transaction objects in the network topology diagram to obtain nodes corresponding to various transaction data;

[0009] Mapping the first transaction relationship, the second transaction relationship, and the third transaction relationship in the network topology graph respectively to obtain edges corresponding to the various transaction data;

[0010] Based on the nodes corresponding to the transaction entities and the edges corresponding to the transaction entities, a transaction data model that integrates multiple transaction data is constructed.

[0011] The transaction data model construction method in the disclosed embodiment, while retaining the core structure of the PROV network topology map, combines the characteristics of the data element development process and the transaction process, and re-semantically expands the network topology map by defining transaction entities, transaction dynamics, and dependencies between transaction objects and nodes. It abstractly describes the entire development link and transaction link of the data element, improves the transparency of the data element transaction process, and builds the blood relationship between different transaction data, which is conducive to achieving end-to-end accurate traceability, so as to better manage and maintain the credibility and reliability of data, and is also conducive to ensuring the data security and data quality of both data supply and demand parties in the transaction process, and reducing data transaction risks.

[0012] In some optional implementations, the transaction entity, transaction dynamics, and transaction object are respectively mapped in the network topology diagram to obtain nodes corresponding to various transaction data, including:

[0013] Creating an entity node expression, an active node expression, and a proxy node expression of a node in a network topology graph, wherein the entity node expression includes a first node identifier and a first node attribute, the active node expression includes a second node identifier and a second node attribute, and the proxy node expression includes a third node identifier and a third node attribute;

[0014] The transaction attribute information of the transaction entity is mapped to the first node attribute, the dynamic attribute information of the transaction dynamics is mapped to the second node attribute, and the object attribute information of the transaction object is mapped to the third node attribute to obtain nodes corresponding to various transaction data.

[0015] The disclosed embodiment maps transaction entities, transaction dynamics, and transaction objects respectively in a network topology diagram to obtain nodes corresponding to various transaction data, which can improve the transparency of the data element transaction process and facilitate end-to-end accurate traceability, so as to better manage and maintain the credibility and reliability of data.

[0016] In some optional implementations, the entity node expression is expressed by the following formula: Entity(id1,[attr_s1=value_s1,...]);

[0017] Wherein, Entity is the entity node, id1 is the first node identifier, attr_s1 is the first node information, and value_s1 is the transaction attribute information; the activity node expression is expressed by the following formula: Activity(id2,start,end,[para1=arg1,...]);

[0018] Among them, Activity is the activity node, id2 is the second node identifier, start is the activity start time, end is the activity end time, para1 is the second node attribute, and arg1 is the dynamic attribute information; the agent node expression is expressed by the following formula: Agent(id3,[[attr_d1=value_d1]);

[0019] Among them, Agent is the agent node, id3 is the third node identifier, attr_d1 is the third node attribute, and value_d1 is the object attribute information.

[0020] In some optional implementations, the first transaction relationship, the second transaction relationship, and the third transaction relationship are respectively mapped in a network topology graph to obtain edges corresponding to a variety of transaction data, including:

[0021] Create edge expressions for edges in the network topology graph. The edge expressions include edge identifiers, primary nodes, object nodes, and edge attributes.

[0022] Mapping the transaction entity corresponding to the first transaction relationship and any data in the transaction dynamics to the object node, mapping the remaining data to the main node, and mapping the first relationship attribute of the first transaction relationship to the edge attribute;

[0023] Mapping the transaction entity corresponding to the second transaction relationship and the transaction object in the transaction object to the object node, mapping the transaction entity to the master node, and mapping the second relationship attribute of the second transaction relationship to the edge attribute;

[0024] Mapping the transaction dynamics corresponding to establishing the third transaction relationship and the transaction object in the transaction object to the object node, mapping the transaction dynamics to the main node, and mapping the third relationship attribute of the third transaction relationship to the edge attribute;

[0025] Get the edges corresponding to various transaction data.

[0026] The disclosed embodiment maps the first transaction relationship, the second transaction relationship and the third transaction relationship respectively in the network topology diagram to obtain edges corresponding to various transaction data, which can improve the transparency of the data element transaction process, build the blood relationship of transaction data transactions, and facilitate end-to-end accurate traceability, so as to better manage and maintain the credibility and reliability of data.

[0027] In some optional implementations, the edge expression is expressed by the following formula: Dependency(id4, sub, obj, [attr_b1 = value_b1, ...])

[0028] Wherein, Dependency is the edge, id4 is the edge identifier, sub is the main node, obj is the object node, attr_b1 is the edge attribute, and value_b1 is the first transaction attribute, the second transaction attribute, or the third transaction attribute.

[0029] According to a second aspect, an embodiment of the present disclosure provides a link tracing method for transaction data, including:

[0030] Obtain target transaction data;

[0031] According to the graph theory traceability search algorithm, from the transaction data model in which multiple transaction data in the first aspect or any implementation scheme of the first aspect are integrated, the transaction information between the associated transaction entities, transaction dynamics and transaction objects of the target transaction data in the transaction process is searched.

[0032] According to a third aspect, an embodiment of the present disclosure provides a transaction data model building device, the device comprising:

[0033] A data acquisition module, used to acquire transaction entities, transaction dynamics and transaction objects of various transaction data, wherein a first transaction relationship exists between the transaction entity and the transaction dynamics, a second transaction relationship exists between the transaction entity and the transaction object, and a third transaction relationship exists between the transaction dynamics and the transaction object;

[0034] A first mapping module is used to map the transaction entity, transaction dynamics and transaction object in the network topology diagram to obtain nodes corresponding to various transaction data;

[0035] A second mapping module, used to map the first transaction relationship, the second transaction relationship and the third transaction relationship in the network topology graph respectively, so as to obtain edges corresponding to the various transaction data;

[0036] The model building module is used to build a transaction data model that integrates multiple transaction data based on the nodes corresponding to the transaction entities and the edges corresponding to the transaction entities.

[0037] According to a fourth aspect, an embodiment of the present disclosure provides a computer device, including:

[0038] The memory and the processor are communicatively connected with each other, the memory stores computer instructions, and the processor executes the model building method of transaction data in the first aspect or any embodiment of the first aspect, or the link tracing method of transaction data in the second aspect by executing the computer instructions.

[0039] In a fifth aspect, an embodiment of the present disclosure provides a computer-readable storage medium, on which computer instructions are stored, and the computer instructions are used to enable a computer to execute the transaction data model building method in the first aspect or any embodiment of the first aspect, or the transaction data link tracing method.

[0040] According to the sixth aspect, an embodiment of the present disclosure provides a computer program product, including computer instructions, which are used to enable a computer to execute the model building method of transaction data in the first aspect or any implementation of the first aspect, or the link tracing method of transaction data. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0042] Figure 1 is a flow chart of a method for constructing a model of transaction data according to an embodiment of the present invention;

[0043] Figure 2 is a schematic diagram of a DETM model structure according to an embodiment of the present invention;

[0044] Figure 3 is a schematic diagram of the blood relationship of data entities in the data asset processing process according to an embodiment of the present invention;

[0045] Figure 4 is a schematic diagram of a data element development and transaction link according to an embodiment of the present invention;

[0046] Figure 5 is a schematic diagram of a link tracing method for transaction data according to an embodiment of the present invention;

[0047] Figure 6 is a flow chart of a GSBTA traceability algorithm according to an embodiment of the present invention;

[0048] Figure 7 It is a schematic diagram of a data element traceability implementation architecture according to an embodiment of the present invention;

[0049] Figure 8 is a structural block diagram of a device for constructing a model of transaction data according to an embodiment of the present invention;

[0050] Fig. 9is a structural block diagram of a link tracing device for transaction data according to an embodiment of the present invention;

[0051] Fig.10 It is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0052] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0053] According to an embodiment of the present invention, an embodiment of a method for building a model of transaction data is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0054] In this embodiment, a transaction data model construction method is provided, which can be used in mobile terminals, such as mobile phones, tablet computers, etc. Figure 1 is a flow chart of a method for constructing a model of transaction data according to an embodiment of the present invention. Figure 1 As shown, the process includes the following steps:

[0055] Step S101, obtaining transaction entities, transaction dynamics and transaction objects of multiple transaction data, wherein a first transaction relationship exists between the transaction entity and the transaction dynamics, a second transaction relationship exists between the transaction entity and the transaction object, and a third transaction relationship exists between the transaction dynamics and the transaction object.

[0056] Specifically, the model building method of transaction data in the embodiments of the present disclosure can be applied to supply chains and transaction scenarios with high requirements on product quality (such as agricultural product quality traceability, data element transactions, etc.). Therefore, various transaction data can be business process transaction data in the supply chain, for example: including data resources in the database in the supply chain, such as procurement data or sales data or customer data; as well as data value in data assets; as well as e-books in data products; and agreement data / contract data in agreements / contracts.

[0057] Furthermore, if Figure 2 When a transaction entity is generated, it can be different types of transaction data generated in the actual transaction service process. For example, the transaction entity can be data assets, data products, data resources and agreements / contracts. Figure 2 Transaction dynamics can be the process of data transactions, aggregation, resource application, processing, listing, review, transaction, billing and settlement in the actual transaction service process. Figure 2 The transaction object can be the data provider, operator, service provider and consumer involved in the actual transaction service process, that is, Figure 2 The proxy subtype in .

[0058] Furthermore, there exists a first transaction relationship between the transaction entity and the transaction dynamics. Figure 2 The actual transaction relationship established between the entity node and the activity node shown in the figure. There is a second transaction relationship between the transaction entity and the transaction object. Figure 2 The actual transaction relationship established between the entity node and the proxy node shown in the figure. There is a third transaction relationship between the transaction dynamics and the transaction object. Figure 2 The actual transaction relationship established between the active node and the proxy node. Figure 2 The DETM model in is a transaction data model constructed by the transaction data model construction method in the embodiment of the present disclosure.

[0059] For example, in the process of processing data assets into data products, data, as an entity tracking object, has a one-to-one, one-to-many, many-to-one or many-to-many blood relationship, such as Figure 3 As shown in the figure, one-to-one and one-to-many blood relationships are described using the derived relationship in the core structure, and a new dependency composition relationship is defined by constructing a transaction data model, namely, the DETM model, to describe the many-to-one and many-to-many blood relationships between data entities.

[0060] Step S102, mapping the transaction entity, transaction dynamics and transaction object in the network topology diagram respectively, to obtain nodes corresponding to various transaction data.

[0061] Step S103: Map the first transaction relationship, the second transaction relationship, and the third transaction relationship in the network topology graph respectively to obtain edges corresponding to the various transaction data.

[0062] Specifically, the network topology diagram can be used as the most basic topological structure for data traceability, which can be a PROV (Provenance) data topology diagram, which is used to record and represent the source, transformation and derivative relationship of data. It aims to help users understand the evolution history, derivation method and relationship between data, so as to improve the credibility, repeatability and transparency of data, which is especially important in scientific research, data management and information security.

[0063] For example, Figure 2The transaction entities (entity subtype), transaction dynamics (activity subtype), and transaction objects (agent subtype) on the right side of the map are mapped to Figure 2 In the PROV data topology diagram on the left.

[0064] As data-driven services and applications lead online and offline businesses, data elements are a new valuable asset and an indispensable driving force. In order to tap the true value of data, big data technology has emerged. With the rise of big data technology, data agents, or data operators, have emerged, which collect and analyze data providers' data and sell it to other third parties, or data consumers. Data agents collect data from various IoT devices (such as sensors, smartphones, or wearable devices) and service providers, perform big data analysis to extract new information and knowledge, and then sell the copied information to many other third-party services to improve service quality (for example, potential customer detection, identity authentication, fraud detection, etc.). Data providers do not have the opportunity to know how their data is processed, delivered, and used, and the data transaction process lacks transparency, which also hinders data ownership and supervision. In the supervision process, data elements, as traceable objects, are subject to operations such as fusion and splitting in the circulation transaction process. There is a lack of association between circulation nodes, making it difficult to achieve end-to-end accurate traceability of the data element transaction process.

[0065] Therefore, in terms of data element supervision, there is still a lack of a data transaction model that can accurately describe data elements and their circulation and transaction processes. The disclosed embodiment semantically expands the PROV network topology diagram based on the actual transaction scenarios of transaction data elements, constructs a transaction data model for data element development and transaction links, and describes the blood relationship of data products, so as to better manage and maintain the credibility and reliability of data.

[0066] For example, the transaction data model (DETM model) in the embodiment of the present disclosure is applied in the transaction data development and transaction scenarios. Based on the PROV network topology diagram, combined with the characteristics of the data element development process and the transaction process, the core structure is semantically expanded and improved. The transaction data model can abstractly describe the entire development link and transaction link of the data element. Figure 4As shown in the figure, the user entities of the entire link include data providers, data service providers, data operators and data consumers. Data providers collect various data resources through sensors, smart devices, wearable devices or service providers, and upload them to the data collection and sharing platform as data assets; then, data service providers and operators process and package data assets into data element products and put them on the data trading service platform; data consumers purchase data products according to their respective needs. Data products are different from physical products. Data products are reproducible and easy to spread. It is difficult to label product content through physical means, and data rights confirmation and supervision are difficult.

[0067] Furthermore, the DETM model retains the core structure of the PROV network topology diagram and semantically extends the core structure by defining subtypes of three types of nodes: entities, activities, and agents, and new dependencies between nodes. In the model graphical representation of the embodiment of the present disclosure, entities are represented by ellipses, activities are represented by squares, and agents are represented by polygons. The extended relationship between the DETM model and the core structure is as follows: Figure 2 As shown. The development and transaction links of data elements are abstracted into three types of nodes: entity, activity, and agent, as well as eight types of dependencies between nodes. According to the actual business process, the subtypes of the three types of nodes are expanded. Among them, the entity subtype includes data resources, data assets, data products, and protocols\contracts; the activity subtype includes collection, aggregation, resource application, processing, listing, review, transaction, billing, and settlement; the agent subtype includes data providers, operators, service providers, and consumers. According to the type of object node, the dependency is divided into three types: generation, failure, and communication describe the dependency with activity type nodes as the object; attribution, association), and delegation describe the dependency with agent type nodes as the object; use, derivation, end (End), start, and composition describe the dependency with entity type nodes as the object.

[0068] Step S104, based on the nodes corresponding to the transaction entities and the edges corresponding to the transaction entities, a transaction data model in which multiple transaction data are integrated with each other is constructed.

[0069] Specifically, in Figure 3 In the data transaction service process, since the transaction data model defines a new dependency composition relationship to describe the many-to-one and many-to-many blood relationships between data entities, in the data transaction service process, data is used as an entity tracking object, and there are one-to-one, one-to-many, many-to-one or many-to-many blood relationships, thus forming a transaction data model in which multiple transaction data are integrated with each other.

[0070] Therefore, the model construction method of transaction data in the embodiment of the present invention, while retaining the core structure of the PROV network topology map, combines the characteristics of the data element development process and the transaction process, and re-semantically expands the network topology map by defining transaction entities, transaction dynamics, and the dependencies between transaction objects and nodes. It abstractly describes the entire development link and transaction link of the data element, improves the transparency of the data element transaction process, and builds the blood relationship between different transaction data, which is conducive to achieving end-to-end accurate traceability, so as to better manage and maintain the credibility and reliability of data, and is also conducive to ensuring the data security and data quality of both data supply and demand parties in the transaction process, and reducing data transaction risks.

[0071] In some optional implementations, the transaction entity, transaction dynamics, and transaction object are respectively mapped in the network topology diagram to obtain nodes corresponding to various transaction data, including:

[0072] Step a1, create entity node expressions, active node expressions and proxy node expressions of nodes in the network topology diagram, the entity node expressions include a first node identifier and a first node attribute, the active node expressions include a second node identifier and a second node attribute, and the proxy node expressions include a third node identifier and a third node attribute.

[0073] Step a2, mapping the transaction attribute information of the transaction entity to the first node attribute, mapping the dynamic attribute information of the transaction dynamics to the second node attribute, mapping the object attribute information of the transaction object to the third node attribute, to obtain nodes corresponding to multiple transaction data.

[0074] In a specific example, the entity node expression is expressed by the following formula: Entity(id1,[attr_s1=value_s1,...]);

[0075] Wherein, Entity is the entity node, id1 is the first node identifier, attr_s1 is the first node information, and value_s1 is the transaction attribute information; the activity node expression is expressed by the following formula: Activity(id2,start,end,[para1=arg1,...]);

[0076] Among them, Activity is the activity node, id2 is the second node identifier, start is the activity start time, end is the activity end time, para1 is the second node attribute, and arg1 is the dynamic attribute information; the agent node expression is expressed by the following formula: Agent(id3,[[attr_d1=value_d1]);

[0077] Among them, Agent is the agent node, id3 is the third node identifier, attr_d1 is the third node attribute, and value_d1 is the object attribute information.

[0078] Specifically, the DETM model uses RDF functional syntax to serialize model instances, where transaction attribute information defines the description information of data element nodes, dynamic attribute information defines the description information of development transaction actions, and object attribute information defines the description information of each agent participant.

[0079] In some optional implementations, mapping the first transaction relationship, the second transaction relationship, and the third transaction relationship in a network topology graph to obtain edges corresponding to a variety of transaction data includes:

[0080] Step b1, creating an edge expression of an edge in a network topology graph, wherein the edge expression includes an edge identifier, a primary node, an object node, and edge attributes.

[0081] In a specific embodiment, the edge expression is expressed by the following formula: Dependency(id4, sub, obj, [attr_b1 = value_b1, ...])

[0082] Wherein, Dependency is the edge, id4 is the edge identifier, sub is the main node, obj is the object node, attr_b1 is the edge attribute, and value_b1 is the first transaction attribute, the second transaction attribute, or the third transaction attribute.

[0083] Specifically, the first transaction attribute is the relationship description information between the transaction entity and the transaction dynamics, the second transaction attribute is the relationship description information between the transaction entity and the transaction object, and the third transaction attribute is the relationship description information between the transaction dynamics and the transaction object. The master node represents the transaction initiator, and the object node represents the transaction object.

[0084] Step b2, mapping the transaction entity corresponding to the first transaction relationship and any data in the transaction dynamics to the object node, mapping the remaining data to the main node, and mapping the first relationship attribute of the first transaction relationship to the edge attribute.

[0085] Specifically, in Figure 2 In the example, any data in the transaction entity (entity node) and the transaction dynamics (activity node) corresponding to the first transaction relationship is mapped to the object node, and the remaining data is mapped to the master node; for example, if the transaction entity (entity node) is mapped to the object node, the transaction dynamics (activity node) is mapped to the master node, or if the transaction dynamics (activity node) is mapped to the object node, the transaction entity (entity node) is mapped to the master node. Figure 2In the process, the active node can initiate transactions to the entity node, and the entity node can also initiate transactions to the active node.

[0086] In a specific example, when a transaction is dynamically mapped to an object node, the transaction relationship includes: one or more of a generation relationship, an invalidation relationship, and a communication relationship. When a transaction entity is mapped to an object node, the transaction relationship includes: one or more of a use relationship, a derivation relationship, an end relationship, a start relationship, and a composition relationship.

[0087] Step b3, mapping the transaction entity corresponding to the second transaction relationship and the transaction object in the transaction object to the object node, mapping the transaction entity to the main node, and mapping the second relationship attribute of the second transaction relationship to the edge attribute.

[0088] Specifically, in Figure 2 In the example, the transaction entity (entity node) corresponding to the second transaction relationship and the transaction object (proxy node) in the transaction object (proxy node) are mapped to the object node, for example, the transaction object (proxy node) is mapped to the object node, and the transaction entity (entity node) is mapped to the master node. Figure 2 In the process, the entity node initiates a transaction to the proxy node.

[0089] In a specific example, the transaction relationship when the transaction object is mapped to an object node includes: one or more of an affiliation relationship, an association relationship, and a delegation relationship.

[0090] Step b4, mapping the transaction dynamics corresponding to establishing the third transaction relationship and the transaction objects in the transaction object to the object node, mapping the transaction dynamics to the main node, and mapping the third relationship attribute of the third transaction relationship to the edge attribute.

[0091] Specifically, in Figure 2 In the process of establishing the transaction dynamics (active node) corresponding to the third transaction relationship and the transaction object (proxy node) in the transaction object (proxy node) are mapped to the object node. For example, the transaction object (proxy node) is mapped to the object node, and the transaction dynamics (active node) is mapped to the master node. Figure 2 In the process, the active node initiates a transaction to the proxy node.

[0092] In a specific example, the transaction relationship when the transaction object is mapped to an object node includes: one or more of an affiliation relationship, an association relationship, and a delegation relationship.

[0093] Step b5, obtaining edges corresponding to various transaction data.

[0094] In the embodiment of the present disclosure, the DETM model retains the core structure of the PROV network topology diagram, and semantically expands the core structure by defining subtypes of three types of nodes, namely entities, activities, and agents, and new dependencies between nodes. In the graphical representation of the DETM model of the embodiment of the present disclosure, entities are represented by ellipses, activities are represented by squares, and agents are represented by polygons. The extended relationship between the DETM model and the core structure is shown in the figure below: Figure 2 As shown. The development and transaction links of data elements are abstracted into three types of nodes: entity, activity, and agent, as well as eight types of dependencies between nodes. According to the actual business process, the subtypes of the three types of nodes are expanded. Among them, the entity subtype includes data resources, data assets, data products, and protocols\contracts; the activity subtype includes collection, aggregation, resource application, processing, listing, review, transaction, billing, and settlement; the agent subtype includes data providers, operators, service providers, and consumers. According to the type of object node, the dependency is divided into three types: generation, failure, and communication describe the dependency with activity type nodes as the object; attribution, association, and delegation describe the dependency with agent type nodes as the object; use, derivation, end, start, and composition describe the dependency with entity type nodes as the object.

[0095] Furthermore, in the process of processing data assets into data products, data, as an entity tracking object, has a one-to-one, one-to-many, many-to-one or many-to-many blood relationship, such as Figure 3 As shown in the figure, one-to-one and one-to-many blood relationships are described using the derived relationship in the core structure, and the DETM model defines a new dependency composition relationship to describe the many-to-one and many-to-many blood relationships between data entities.

[0096] Therefore, the disclosed embodiment maps the transaction entities, transaction dynamics and transaction objects respectively in the network topology diagram to obtain nodes corresponding to multiple transaction data, and maps the first transaction relationship, the second transaction relationship and the third transaction relationship respectively in the network topology diagram to obtain edges corresponding to multiple transaction data. This can improve the transparency of the data element transaction process, build the blood relationship of transaction data, and facilitate end-to-end accurate traceability, so as to better manage and maintain the credibility and reliability of data.

[0097] In this embodiment, a link tracing method for transaction data is provided, which can be used in mobile terminals, such as mobile phones, tablet computers, etc. Figure 5 is a flow chart of a link tracing method for transaction data according to an embodiment of the present invention. Figure 5 As shown, the process includes the following steps:

[0098] Step S501, obtaining target transaction data.

[0099] Specifically, the target transaction data is the transaction data to be traced.

[0100] Step S502, searching for transaction information between associated transaction entities, transaction dynamics and transaction objects of target transaction data in the transaction process from a transaction data model in which multiple transaction data are integrated according to a graph theory traceability search algorithm.

[0101] Specifically, for general commodity transactions, customers need to be informed in advance of the intrinsic and external attributes of the transaction subject matter, such as shape and quality. Data is different from physical assets. It is replicable and is a virtual asset that is extremely easy to spread. The cost of copying and distributing is extremely low. Due to the lack of transparency in the data transaction process and the inability of existing regulatory measures to guarantee data security and quality during the transaction process, data providers dare not circulate data, the quality of data varies, and the trial and error costs of data supply and demand parties are high, as well as the transaction risks.

[0102] In view of this, the embodiment of the present disclosure first uses the transaction market model constructed in the above embodiment to describe the transaction link of the data, and then searches for the target transaction data and the source information in the transaction process based on the breadth-first search algorithm of the graph, thereby improving the transparency of the transaction process, preventing the data from being tampered with during the transaction process, and reducing transaction risks.

[0103] In a specific embodiment, a graph data structure is used to process the transaction process data described by a transaction data model (DETM model), and a graph search traceability algorithm is constructed based on the breadth-first search strategy of the graph, Graph Search Based Traceability Algorithm, GSBTA for short), so as to obtain all source information of the target transaction data in the production transaction process, and the entire source information includes: transaction entities, transaction dynamics and transaction objects associated with the target transaction data, as well as the transaction process and transaction relationship of the target transaction data.

[0104] The DETM model is defined as a directed edge graph G, represented by a four-tuple, that is, G = (N, E, T, L), where N = <n1,n2,...,n m > represents the set of nodes in the graph, E = <e1,e2,...,e n > represents the set of directed edges in the graph, T and L correspond to the type label sets of nodes and directed edges in the graph respectively. Each directed edge e ∈E is defined as a triple e=(u,v,l), where u represents the starting point of the directed edge, v represents the end point of the directed edge, and l∈L represents the dependency type corresponding to the directed edge l=label(e). Next, define the forward neighborhood of each node. For each node n∈N in the graph, define the forward neighborhood node set of node n as n +, where n+={u|(v,u,l)∈E}. Next, a graph-theoretic traceability search algorithm is proposed based on the graph breadth-first search strategy, that is, obtaining the forward neighborhood set of the data node to be traced. The overall flow chart of the traceability algorithm is as follows: Figure 6 As shown, the graph theory traceability search algorithm is specifically described as shown in Algorithm 1 below.

[0105]

[0106]

[0107] The disclosed embodiments can be applied to data element trading platforms to accurately trace data element products end to end. Figure 7 As shown. First, the transaction information between data providers, data agents, and data consumers is aggregated through the web interface of the data element trading platform, and the data is stored in the cloud server to facilitate data sharing. Next, the transaction information metadata is described using the transaction data model proposed in the embodiment of the present disclosure, all data element entity instances are constructed, and the entity instances are managed using the graph data structure. Finally, the GSBTA traceability algorithm proposed in the embodiment of the present disclosure is packaged into a web interface to obtain the transaction process and source information of the data product. Before purchasing a data product, the consumer can obtain the transaction process and source information of the data product through the traceability query interface to determine the reliability of the data; the data agent can obtain the source information of the product before the data product is put on the shelf to determine whether the data product has been tampered with for secondary sales; the regulatory authorities can confirm the ownership of the data product through the transaction process to protect the legitimate rights and interests of the data provider.

[0108] In this embodiment, a model building device for transaction data is also provided, which is used to implement the above-mentioned embodiments and preferred implementation modes, and the descriptions that have been made will not be repeated. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0109] This embodiment provides a transaction data model building device, such as Figure 8 As shown, including:

[0110] The data acquisition module 801 is used to acquire transaction entities, transaction dynamics and transaction objects of various transaction data, wherein a first transaction relationship exists between the transaction entity and the transaction dynamics, a second transaction relationship exists between the transaction entity and the transaction object, and a third transaction relationship exists between the transaction dynamics and the transaction object;

[0111] A first mapping module 802 is used to map the transaction entity, transaction dynamics and transaction object in the network topology map to obtain nodes corresponding to various transaction data;

[0112] A second mapping module 803, used to map the first transaction relationship, the second transaction relationship and the third transaction relationship in the network topology graph respectively, so as to obtain edges corresponding to the various transaction data;

[0113] The model building module 804 is used to build a transaction data model that integrates multiple transaction data based on the nodes corresponding to the transaction entities and the edges corresponding to the transaction entities.

[0114] This embodiment provides a link tracing device for transaction data, such as Fig. 9 As shown, including:

[0115] Data acquisition module 901, used to acquire target transaction data;

[0116] The data tracing module 902 is used to search for the transaction information between the associated transaction entities, transaction dynamics and transaction objects of the target transaction data in the transaction process from the transaction data model in which multiple transaction data are integrated in the above-mentioned embodiment according to the graph theory tracing search algorithm.

[0117] In some optional implementations, the first mapping module 802 includes:

[0118] A data creation submodule, used to create an entity node expression, an active node expression and an agent node expression of a node in a network topology diagram, wherein the entity node expression includes a first node identifier and a first node attribute, the active node expression includes a second node identifier and a second node attribute, and the agent node expression includes a third node identifier and a third node attribute;

[0119] The data mapping submodule is used to map the transaction attribute information of the transaction entity to the first node attribute, map the dynamic attribute information of the transaction dynamics to the second node attribute, and map the object attribute information of the transaction object to the third node attribute to obtain nodes corresponding to multiple transaction data.

[0120] In some optional implementations, the entity node expression is expressed by the following formula: Entity(id1,[attr_s1=value_s1,...]);

[0121] Wherein, Entity is the entity node, id1 is the first node identifier, attr_s1 is the first node information, and value_s1 is the transaction attribute information; the activity node expression is expressed by the following formula: Activity(id2,start,end,[para1=arg1,...]);

[0122] Among them, Activity is the activity node, id2 is the second node identifier, start is the activity start time, end is the activity end time, para1 is the second node attribute, and arg1 is the dynamic attribute information; the agent node expression is expressed by the following formula: Agent(id3,[[attr_d1=value_d1]);

[0123] Among them, Agent is the agent node, id3 is the third node identifier, attr_d1 is the third node attribute, and value_d1 is the object attribute information.

[0124] In some optional implementations, the second mapping module 803 includes:

[0125] The data creation submodule is used to create edge expressions of edges in the network topology graph, and the edge expressions include edge identifiers, primary nodes, object nodes, and edge attributes;

[0126] A first mapping submodule is used to map the transaction entity corresponding to the establishment of the first transaction relationship and any data in the transaction dynamics to the object node, map the remaining data to the main node, and map the first relationship attribute of the first transaction relationship to the edge attribute;

[0127] A second mapping submodule is used to map the transaction entity corresponding to the second transaction relationship and the transaction object in the transaction object to the object node, map the transaction entity to the main node, and map the second relationship attribute of the second transaction relationship to the edge attribute;

[0128] A third mapping submodule, used to map the transaction dynamics corresponding to establishing the third transaction relationship and the transaction objects in the transaction object to the object node, map the transaction dynamics to the main node, and map the third relationship attribute of the third transaction relationship to the edge attribute;

[0129] Get the edges corresponding to various transaction data.

[0130] In some optional implementations, the edge expression is expressed by the following formula: Dependency(id4, sub, obj, [attr_b1 = value_b1, ...])

[0131] Wherein, Dependency is the edge, id4 is the edge identifier, sub is the main node, obj is the object node, attr_b1 is the edge attribute, and value_b1 is the first transaction attribute, the second transaction attribute, or the third transaction attribute.

[0132] The further functional description of each of the above modules and units is the same as that of the above corresponding embodiments and will not be repeated here.

[0133] The transaction data model building device and link tracing device in this embodiment are presented in the form of functional units, where the units refer to ASIC (Application Specific Integrated Circuit) circuits, processors and memories that execute one or more software or fixed programs, and / or other devices that can provide the above functions.

[0134] An embodiment of the present invention also provides a computer device having the above-mentioned transaction data model building device and link tracing device.

[0135] See also Fig.10 , Fig.10 is a schematic diagram of the structure of a computer device provided by an optional embodiment of the present invention, such as Fig.10 As shown, the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Various components are connected to each other using different buses for communication, and can be installed on a common mainboard or installed in other ways as needed. The processor can process the instructions executed in the computer device, including instructions stored in or on the memory to display the graphical information of the GUI on an external input / output device (such as, a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple computer devices can be connected, and each device provides some necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Fig.10 A processor 10 is taken as an example.

[0136] The processor 10 may be a central processing unit, a network processor or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be a dedicated integrated circuit, a programmable logic device or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic or any combination thereof.

[0137] The memory 20 stores instructions executable by at least one processor 10, so that the at least one processor 10 executes the method shown in the above embodiment.

[0138] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created according to the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely arranged relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0139] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid state drive; the memory 20 may also include a combination of the above types of memory.

[0140] The computer device further comprises a communication interface 30 for the computer device to communicate with other devices or a communication network.

[0141] The embodiment of the present invention also provides a computer-readable storage medium. The method according to the embodiment of the present invention can be implemented in hardware, firmware, or can be implemented as a computer code that can be recorded in a storage medium, or can be implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and will be stored in a local storage medium through a network download, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state hard disk, etc.; further, the storage medium can also include a combination of the above types of memories. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor, or hardware, the method shown in the above embodiment is implemented.

[0142] A part of the present invention may be applied as a computer program product, such as a computer program instruction, which, when executed by a computer, can call or provide the method and / or technical solution according to the present invention through the operation of the computer. Those skilled in the art should understand that the existence of the computer program instruction in a computer-readable medium includes, but is not limited to, a source file, an executable file, an installation package file, etc., and accordingly, the way in which the computer program instruction is executed by the computer includes, but is not limited to: the computer directly executes the instruction, or the computer compiles the instruction and then executes the corresponding compiled program, or the computer reads and executes the instruction, or the computer reads and installs the instruction and then executes the corresponding installed program. Here, the computer-readable medium may be any available computer-readable storage medium or communication medium accessible to the computer.

[0143] Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A method for constructing a model of transaction data, characterized in that: The method comprises: Acquire transaction entities, transaction dynamics, and transaction objects of multiple transaction data, wherein a first transaction relationship exists between the transaction entity and the transaction dynamics, a second transaction relationship exists between the transaction entity and the transaction object, and a third transaction relationship exists between the transaction dynamics and the transaction object; Mapping the transaction entity, the transaction dynamics and the transaction object in a network topology diagram respectively to obtain nodes corresponding to the multiple transaction data; Mapping the first transaction relationship, the second transaction relationship, and the third transaction relationship in a network topology graph respectively to obtain edges corresponding to the multiple transaction data; Based on the nodes corresponding to the transaction entities and the edges corresponding to the transaction entities, a transaction data model in which the multiple transaction data are integrated with each other is constructed.

2. The method according to claim 1, characterized in that Mapping the transaction entity, the transaction dynamics, and the transaction object in a network topology diagram to obtain nodes corresponding to the multiple transaction data includes: Creating an entity node expression, an active node expression, and a proxy node expression of a node in a network topology graph, wherein the entity node expression includes a first node identifier and a first node attribute, the active node expression includes a second node identifier and a second node attribute, and the proxy node expression includes a third node identifier and a third node attribute; The transaction attribute information of the transaction entity is mapped to the first node attribute, the dynamic attribute information of the transaction dynamics is mapped to the second node attribute, and the object attribute information of the transaction object is mapped to the third node attribute to obtain the nodes corresponding to the multiple transaction data.

3. The method according to claim 2, characterized in that The entity node expression is expressed by the following formula: Entity(id1,[attr_s1=value_s1,...]); Wherein, Entity is the entity node, id1 is the first node identifier, attr_s1 is the first node information, and value_s1 is the transaction attribute information; the activity node expression is expressed by the following formula: Activity(id2,start,end,[para1=arg1,...]); Wherein, Activity is the activity node, id2 is the second node identifier, start is the activity start time, end is the activity end time, para1 is the second node attribute, and arg1 is the dynamic attribute information; the agent node expression is expressed by the following formula: Agent(id3,[[attr_d1=value_d1]); Among them, Agent is the agent node, id3 is the third node identifier, attr_d1 is the third node attribute, and value_d1 is the object attribute information.

4. The method according to claim 1, characterized in that: Mapping the first transaction relationship, the second transaction relationship, and the third transaction relationship in a network topology graph respectively to obtain edges corresponding to the multiple transaction data includes: Creating an edge expression for an edge in a network topology graph, wherein the edge expression includes an edge identifier, a primary node, an object node, and edge attributes; Mapping any data in the transaction entity and the transaction dynamics corresponding to the establishment of the first transaction relationship to the object node, mapping the remaining data to the master node, and mapping the first relationship attribute of the first transaction relationship to the edge attribute; Mapping the transaction entity corresponding to the second transaction relationship and the transaction object in the transaction object to the object node, mapping the transaction entity to the master node, and mapping the second relationship attribute of the second transaction relationship to the edge attribute; Mapping the transaction dynamics corresponding to establishing the third transaction relationship and the transaction object in the transaction object to the object node, mapping the transaction dynamics to the master node, and mapping the third relationship attribute of the third transaction relationship to the edge attribute; Obtain edges corresponding to the multiple types of transaction data.

5. The method according to claim 4, characterized in that The edge expression is expressed by the following formula: Dependency(id4, sub, obj, [attr_b1 = value_b1, ...]) Wherein, Dependency is the edge, id4 is the edge identifier, sub is the main node, obj is the object node, attr_b1 is the edge attribute, and value_b1 is the first transaction attribute or the second transaction attribute or the third transaction attribute.

6. A link tracing method for transaction data, characterized in that: The method comprises: Obtain target transaction data; According to the graph theory traceability search algorithm, the transaction data model in which multiple transaction data are integrated with each other as described in any one of claims 1 to 5 is searched for transaction information between related transaction entities, transaction dynamics and transaction objects of the target transaction data in the transaction process.

7. A transaction data model building device, characterized in that: The device comprises: A data acquisition module, used to acquire transaction entities, transaction dynamics and transaction objects of various transaction data, wherein a first transaction relationship exists between the transaction entity and the transaction dynamics, a second transaction relationship exists between the transaction entity and the transaction object, and a third transaction relationship exists between the transaction dynamics and the transaction object; A first mapping module, used to map the transaction entity, the transaction dynamics and the transaction object in a network topology diagram to obtain nodes corresponding to the multiple transaction data; A second mapping module, configured to map the first transaction relationship, the second transaction relationship, and the third transaction relationship respectively in a network topology graph to obtain edges corresponding to the multiple transaction data; The model building module is used to build a transaction data model in which the multiple transaction data are integrated based on the nodes corresponding to the transaction entities and the edges corresponding to the transaction entities.

8. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the transaction data model building method described in any one of claims 1 to 5, or the transaction data link tracing method described in claim 6 by executing the computer instructions.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the transaction data model building method described in any one of claims 1 to 5, or the transaction data link tracing method described in claim 6.

10. A computer program product, characterized in that It includes computer instructions, which are used to enable a computer to execute the transaction data model building method described in any one of claims 1 to 5, or the transaction data link tracing method described in claim 6.

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