Data processing method, device and equipment

Abstract

The embodiment of the invention discloses a data processing method, device and equipment. The method comprises the following steps: receiving an identification request of a target brand; in response to the identification request, performing full-duty investigation processing on an institution brand included in a database based on a full-duty investigation strategy set by performing risk prevention and control on a preset risk, and extracting brand information related to the institution brand from the database based on an obtained full-duty investigation result, determining organization subject information corresponding to the brand information based on the brand information; based on the incidence relation between different mechanism main bodies in the database and the mechanism main body information, a mechanism main body relation graph is constructed, the mechanism main body relation graph comprises nodes and edges, the nodes are constructed through the mechanism main body information, and the edges are constructed through the incidence relation between different mechanism main bodies; and based on the mechanism main body relation graph, determining a relation subgraph for the target brand.

Description

Technical Field

[0001] This document relates to the field of computer technology, and particularly to a data processing method, apparatus, and device. Background Art

[0002] In today's global economic environment, an institutional brand (or corporate brand) is not only a marker for market recognition but also a concentrated reflection of the reputation and value of that institution or enterprise. The identification of an institutional brand is of profound significance for maintaining brand security, promoting brand consistency (i.e., brand unification), and effectively managing risks and customer relationships. To this end, there is a need to provide an institutional brand identification mechanism that can strengthen the risk prevention and control mechanism from the source, optimize the customer management system, create a safe, efficient, and trustworthy online environment for participants, protect the privacy data and interests of users, and promote the common development of the platform and the institution. Summary of the Invention

[0003] The purpose of the embodiments of this specification is to provide an institutional brand identification mechanism that can strengthen the risk prevention and control mechanism from the source, optimize the customer management system, create a safe, efficient, and trustworthy online environment for participants, protect the interests of users, and promote the common development of the platform and the institution.

[0004] To achieve the above technical solution, the embodiments of this specification are implemented as follows: A data processing method provided by the embodiments of this specification, the method includes: receiving an identification request for a target brand. In response to the identification request, based on the due diligence strategy set for risk prevention and control of preset risks, conducting due diligence processing on the institutional brands included in the database, extracting brand information related to the institutional brand from the database based on the obtained due diligence results, and determining the institutional entity information corresponding to the brand information based on the brand information. Based on the association relationships between different institutional entities in the database and the institutional entity information, constructing an institutional entity relationship graph, where the institutional entity relationship graph includes nodes and edges, the nodes are constructed by the institutional entity information, and the edges are constructed by the association relationships between different institutional entities. Based on the institutional entity relationship graph, determining a relationship subgraph for the target brand.

[0005] A data processing device provided by an embodiment of this specification, the device includes: an identification request module, which receives an identification request for a target brand. A due diligence module, in response to the identification request, based on a due diligence strategy set for risk prevention and control of preset risks, conducts due diligence processing on the institutional brands included in the database, extracts brand information related to the institutional brands from the database based on the obtained due diligence results, and determines institutional entity information corresponding to the brand information based on the brand information. A graph construction module, based on the association relationships between different institutional entities in the database and the institutional entity information, constructs an institutional entity relationship graph, where the institutional entity relationship graph includes nodes and edges, the nodes are constructed through the institutional entity information, and the edges are constructed through the association relationships between different institutional entities. A brand relationship determination module, based on the institutional entity relationship graph, determines a relationship sub-graph for the target brand.

[0006] A data processing device provided by an embodiment of this specification, the data processing device includes: a processor; and a memory arranged to store computer-executable instructions, the executable instructions, when executed, cause the processor to: receive an identification request for a target brand. In response to the identification request, based on a due diligence strategy set for risk prevention and control of preset risks, conduct due diligence processing on the institutional brands included in the database, extract brand information related to the institutional brands from the database based on the obtained due diligence results, and determine institutional entity information corresponding to the brand information based on the brand information. Based on the association relationships between different institutional entities in the database and the institutional entity information, construct an institutional entity relationship graph, where the institutional entity relationship graph includes nodes and edges, the nodes are constructed through the institutional entity information, and the edges are constructed through the association relationships between different institutional entities. Based on the institutional entity relationship graph, determine a relationship sub-graph for the target brand.

[0007] An embodiment of this specification also provides a storage medium for storing computer-executable instructions. When the executable instructions are executed by a processor, the following processes are implemented: receiving an identification request for a target brand. In response to the identification request, based on a due diligence strategy set for risk prevention and control of preset risks, conducting due diligence processing on the institutional brands included in the database, extracting brand information related to the institutional brands from the database based on the obtained due diligence results, and determining institutional entity information corresponding to the brand information based on the brand information. Based on the association relationships between different institutional entities in the database and the institutional entity information, constructing an institutional entity relationship graph, where the institutional entity relationship graph includes nodes and edges, the nodes are constructed through the institutional entity information, and the edges are constructed through the association relationships between different institutional entities. Based on the institutional entity relationship graph, determining a relationship sub-graph for the target brand.

[0008] An embodiment of this specification also provides a computer program product, including a computer program. When the computer program is executed by a processor, the following processes are implemented: receiving an identification request for a target brand. In response to the identification request, based on a due diligence strategy set for risk prevention and control of preset risks, conducting due diligence processing on the institutional brands included in the database, extracting brand information related to the institutional brands from the database based on the obtained due diligence results, and determining institutional entity information corresponding to the brand information based on the brand information. Based on the association relationships between different institutional entities in the database and the institutional entity information, constructing an institutional entity relationship graph, where the institutional entity relationship graph includes nodes and edges, the nodes are constructed through the institutional entity information, and the edges are constructed through the association relationships between different institutional entities. Based on the institutional entity relationship graph, determining a relationship sub-graph for the target brand. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] To more clearly illustrate the technical solutions in the embodiments of this specification or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments recorded in this specification. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings. Figure 1 It is a schematic diagram of an embodiment of a data processing method in this specification; Figure 2 It is a schematic diagram of a brand identification page in this specification; Figure 3 It is a schematic diagram of the association relationship between different institutional entities in this specification; Figure 4Schematic diagram of another embodiment of the data processing method in this specification; Figure 5 Schematic diagram of yet another embodiment of the data processing method in this specification; Figure 6 Schematic diagram of yet another embodiment of the data processing method in this specification; Figure 7 Schematic diagram of yet another embodiment of the data processing method in this specification; Figure 8 Schematic diagram of yet another embodiment of the data processing method in this specification; Figure 9 Schematic diagram of the association relationship of another different institutional entity in this specification; Figure 10 Schematic diagram of a diagram of the institutional entity relationship in this specification; Figure 11 Schematic diagram of yet another embodiment of the data processing method in this specification; Figure 12 Schematic diagram of yet another embodiment of the data processing method in this specification; Figure 13 Schematic diagram of a graph pruning in this specification; Figure 14 Schematic diagram of a data processing device in this specification; Figure 15 Schematic diagram of a data processing device in this specification. Detailed implementation manners

[0010] The embodiments of this specification provide a data processing method, device and equipment.

[0011] In order to enable those skilled in the art to better understand the technical solutions in this specification, the following will clearly and completely describe the technical solutions in the embodiments of this specification with reference to the accompanying drawings in the embodiments of this specification. Obviously, the described embodiments are only a part of the embodiments of this specification, rather than all the embodiments. Based on the embodiments in this specification, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of this specification.

[0012] This embodiment of the specification provides a mechanism for identifying an institutional brand through a due diligence strategy set for risk prevention and control of illegal financial activities. In today's global economic environment, an institutional brand (or corporate brand) is not only a marker for market identification but also a concentrated reflection of the reputation and value of that institution or enterprise. Therefore, identifying an institutional brand through a due diligence strategy set for risk prevention and control of illegal financial activities (i.e., the act of preventing users from laundering their illegal proceeds through the financial system through a series of regulations, laws, and procedures, which require financial institutions and other regulated entities to take measures to identify, monitor, and prevent suspicious financial activities) is of profound significance for maintaining brand security, promoting brand consistency (i.e., brand unification), and effectively managing risks and customer relationships. Identifying an institutional brand through a due diligence strategy set for risk prevention and control of illegal financial activities is not only a legal obligation but also a way to strengthen the risk prevention and control mechanism at the source, optimize the customer management system, and create a safe, efficient, and trustworthy online environment for participants. This not only protects the interests of users but also promotes the common development of the platform and the institution. For specific processing, refer to the specific content in the following embodiments.

[0013] As Figure 1 shown, this embodiment of the specification provides a data processing method. The execution subject of this method can be a terminal device or a server, etc. The terminal device can be a mobile terminal device such as a mobile phone or a tablet computer, or a computer device such as a laptop or a desktop computer, or it can also be an IoT device (specifically, a smart watch, a vehicle-mounted device, etc.). The server can be an independent server or a server cluster composed of multiple servers. The server can be a background server for financial services or online shopping services, etc., or a background server for a certain application. In this embodiment, the server is used as an example for detailed description. For the case where the execution subject is a terminal device, refer to the following processing for the server case and will not be elaborated here. The method can specifically include the following steps: In step S102, receive an identification request for the target brand.

[0014] Among them, the target brand is the sum of the identification, image, and reputation established by an institution or enterprise in the market. The brand information of the target brand not only includes visual elements such as the institution name, trademark, and LOGO but also covers intangible resources such as corporate culture, values, product, and service quality. The target brand is the basis for consumers to identify and distinguish different institutions or enterprises and their products or services, and it is also an important reflection of the reputation and market position of an institution or enterprise. The target brand can be the institutional brand of a certain designated institution or enterprise.

[0015] In implementation, as Figure 2As shown, when it is necessary to identify a specified brand (i.e., the target brand) from the database, the user can use the terminal device to obtain the data of the brand identification page. Then, the terminal device can display the brand identification page, which may include an input box for relevant information of the target brand. For example, the relevant information of the target brand may include identification information such as the name of the target brand, trademark or logo, and the name of the institution corresponding to the target brand, etc., which can be specifically set according to the actual situation. In addition, the brand identification page may also include an output box for the identification result, a confirmation button, a cancellation button, etc. The user can input the relevant information of the target brand in the input box for the relevant information of the target brand on the above brand identification page. After the input is completed, the user can click the confirmation button on the brand identification page. At this time, the terminal device can obtain the relevant information of the target brand input by the user, and can generate an identification request for the target brand based on the obtained information, and can send the identification request for the target brand to the server, and the server can receive the identification request for the target brand.

[0016] In step S104, in response to the above identification request, based on the due diligence strategy set for risk prevention and control of preset risks, conduct due diligence on the institutional brands included in the database, extract brand information related to the institutional brand from the database based on the obtained due diligence results, and determine the institutional entity information corresponding to the brand information based on the brand information.

[0017] Among them, the preset risks can include various types. For example, the preset risks can include one or more of the risks of illegal financial activities, fraud risks, privacy leakage risks, etc. The due diligence strategy can be an investigation strategy set for preventing and controlling the preset risks. The due diligence strategy is a relevant strategy for a series of investigation and verification activities carried out by an institution before establishing a business relationship with another party or before conducting a specific transaction. The above-mentioned investigation and verification activities are to evaluate and reduce the possible preset risks involved, including customer risk identification and verification, risk assessment, continuous monitoring and information update, understanding the business and resource sources of all parties, etc. Based on this, the due diligence strategy can include various types. Specifically, the corresponding strategy content can be set based on the above content, or the corresponding strategy content can be set according to other methods, and it can be specifically set according to the actual situation. The embodiments of this specification do not make any limitations in this regard. The database can be the database corresponding to the platform where one or both parties are located when establishing a business relationship or conducting a specific transaction, etc. The database can include relevant data of multiple different institutions or individuals, and can include brand information, association relationship information, transaction information, organizational structure of the institution, etc., which can be specifically set according to the actual situation. The brand information can include visual elements such as the institution name, trademark, LOGO, etc., and can also include intangible resources such as corporate culture, values, product and service quality. The institution entity information can be information related to the entities of different institutions, and can include, for example, institution name, registered place information, business scope, investor relationship, partner information, supply chain relationship, etc., which can be specifically set according to the actual situation.

[0018] In implementation, in response to the above recognition request, due diligence strategies that meet specified conditions can be pre-selected, and based on such due diligence strategies, institutional brands contained therein can be identified from a database. Among them, brand identification is not only to confirm its surface identification information, but more importantly, to deeply verify the legitimacy and credibility of the institution, ensure that its business activities comply with legal and regulatory requirements, etc. Specifically, considering brand identification through due diligence strategies set for risk prevention and control of preset risks, it is of profound significance for maintaining brand security, promoting brand consistency, and effectively managing risks and customer relationships. Therefore, due diligence strategies set for risk prevention and control of preset risks can be selected to conduct due diligence on institutional brands contained in the database. Based on this, in response to the above recognition request, due diligence on institutional brands contained in the database can be carried out based on due diligence strategies set for risk prevention and control of preset risks. Specifically, data in the database can be processed based on due diligence strategies set for risk prevention and control of preset risks to conduct customer risk identification and verification, risk assessment, continuous monitoring and information update, determine the business and resource sources of all parties, etc., to verify the true identity, business nature, resource sources, etc. of the institution, and the final due diligence result can be determined based on the above information. The above due diligence result can be analyzed, and then brand information related to the institutional brand can be extracted from the database. For example, based on the above due diligence result, verified institutional identity information, trademarks, LOGOs, corporate cultures, values, product and service quality, etc. related to the institutional brand can be obtained from the database. Then, based on this brand information, institutional entity information corresponding to the brand information can be obtained from the above database.

[0019] In step S106, based on the association relationships and institutional entity information among different institutional entities in the database, an institutional entity relationship graph is constructed. The institutional entity relationship graph includes nodes and edges. The nodes are constructed through institutional entity information, and the edges are constructed through the association relationships among different institutional entities.

[0020] In implementation, different nodes can be constructed with institutional entity information, and based on the data in the database, the association relationships among different institutional entities can be determined. Edges between different nodes can be constructed based on the association relationships among different institutional entities. Through the above nodes and edges, a corresponding institutional entity relationship graph can be constructed. For example, as Figure 3 shown, if two institutions have the same investor or registered location, there is an edge between the two institutions. Through the above method, many association relationships among different institutional entities can be constructed to obtain the overall institutional entity relationship graph.

[0021] In step S108, based on the institutional entity relationship graph, a relationship sub-graph for the target brand is determined.

[0022] In implementation, the institutional entity relationship graph can be analyzed, and community detection algorithms can be used to partition the institutional entity relationship graph into one or more different communities (i.e., relationship sub-graphs). For each community, the same or similar institutional entities can be classified, and the institutional entities classified into the same category can be replaced with a specified identifier, thereby obtaining the classified community. The community containing the target brand can be retrieved from the classified community, and the retrieved community containing the target brand can be used as the relationship sub-graph for the target brand. Subsequently, based on the relationship sub-graph for the target brand, the complex context of the market structure can be penetrated, potential risks can be accurately located, cooperation opportunities can be insighted, and a data foundation can be laid for strategic decision-making.

[0023] The embodiments of this specification provide a data processing method. By receiving an identification request for a target brand, in response to the identification request, based on the due diligence strategy set for risk prevention and control of preset risks, due diligence processing is performed on the institutional brands included in the database, and brand information related to the institutional brands is extracted from the database based on the obtained due diligence results. Based on the brand information, the institutional entity information corresponding to the brand information is determined. Then, based on the association relationships and institutional entity information among different institutional entities in the database, an institutional entity relationship graph can be constructed. Finally, based on the institutional entity relationship graph, the relationship sub-graph for the target brand can be determined. In this way, identifying institutional brands through the due diligence strategy set for risk prevention and control of preset risks is of profound significance for maintaining brand security, promoting brand consistency (i.e., brand normalization), and effectively managing risks and customer relationships. In addition, identifying institutional brands through the due diligence strategy set for risk prevention and control of preset risks is not only fulfilling legal obligations but also strengthening the risk prevention and control mechanism from the source, optimizing the customer management system, creating a safe, efficient, and trustworthy online environment for participants, protecting the interests of users, and promoting the common development of the platform and institutions. Moreover, by deeply mining and verifying the multi-dimensional information of institutions through the due diligence strategy set for risk prevention and control of preset risks, the true normalization of the brand is achieved, a solid defense line is built, the interests of users are protected, and the mutual trust and common prosperity of the platform and institutions are promoted. On this basis, the graph structure data of the institutional entity relationship graph can penetrate the complex context of the market structure, accurately locate potential risks, insight cooperation opportunities, and lay a data foundation for strategic decision-making.

[0024] In practical applications, the above-mentioned due diligence strategy may include a legal entity verification sub-strategy. Based on this, the specific processing method of performing due diligence processing on the institutional brands included in the database based on the due diligence strategy set for risk prevention and control of preset risks in step S104 above can be various. Hereinafter, an optional processing method is provided, such as Figure 4As shown, it may specifically include the processing of steps S10402 to S10406.

[0025] In step S10402, based on the legal entity verification sub-strategy set for risk prevention and control of preset risks, obtain and verify the institutional entity data of different institutions from the database. The institutional entity data includes one or more of business license data, tax registration data, institutional code data, and articles of association data.

[0026] In implementation, for the above due diligence strategy, legal entity verification can be carried out first. For this purpose, a legal entity verification sub-strategy is set. Through the legal entity verification sub-strategy, the review of basic information can be carried out first. Specifically, the business license data, tax registration data, institutional code data (or unified social credit code data, etc.), and articles of association data of different institutions, etc. (which can be some official documents) can be obtained and verified from the database to ensure that each institution actually exists and is legally registered.

[0027] In step S10404, based on the legal entity verification sub-strategy set for risk prevention and control of preset risks, trace and verify the share distribution structure data of different institutions from the database, and conduct background investigations on the investors in the above share distribution structure data.

[0028] In implementation, secondly, through the legal entity verification sub-strategy, investigations can be carried out on the investors and actual controllers of different institutions. Specifically, the share distribution structure (i.e., equity structure data) of different institutions can be traced and verified from the database, and background investigations can be carried out on the investors in the above share distribution structure data, especially on the investors whose directly or indirectly held shares exceed a certain proportion, to identify whether they include sensitive persons or other high-risk related parties.

[0029] In step S10406, based on the obtained institutional entity data and the background investigation results of the investors, determine the due diligence results for the institutional brands included in the database for due diligence processing.

[0030] In implementation, the obtained institutional entity data and the background investigation results of the investors can be combined to obtain the final result. The obtained final result can be used as the due diligence result corresponding to the legal entity verification sub-strategy in the due diligence strategy.

[0031] In practical applications, the above due diligence strategy may include a business essence review sub-strategy. Based on this, the specific processing methods for conducting due diligence on the institutional brands included in the database in the above step S104 according to the due diligence strategy set for risk prevention and control of preset risks can be various. The following provides another optional processing method, such asFigure 5 As shown in the figure, it may specifically include the processing of steps S10408 to S10412.

[0032] In step S10408, based on the business essence review sub-strategy set for risk prevention and control of preset risks, obtain business model data from the database, and evaluate the business models of different institutions based on the business model data to obtain corresponding evaluation results. The business model data includes one or more of the main business data, profit model information, and supply chain data of different institutions.

[0033] In implementation, for the above due diligence strategy, secondly, business essence review can be carried out. For this purpose, a business essence review sub-strategy is set. Through the business essence review sub-strategy, the evaluation of the business model can be carried out first. Specifically, the business model data such as the main business data, profit model information, and supply chain data of different institutions can be obtained from the database, and the business models of different institutions can be evaluated based on the business model data to evaluate whether its business logic is reasonable and whether there are business characteristics with a relatively high risk of illegal financial activities.

[0034] In step S10410, based on the business essence review sub-strategy set for risk prevention and control of preset risks, obtain the historical transaction data of different institutions from the database, and analyze the transaction models of different institutions based on the historical transaction data of different institutions to obtain corresponding analysis results.

[0035] In implementation, secondly, through the business essence review sub-strategy, the analysis of the transaction model can be carried out. Specifically, the historical transaction data between different institutions can be obtained from the database, and the transaction models of different institutions can be analyzed based on the historical transaction data of different institutions to obtain corresponding analysis results, so as to identify whether the corresponding transaction model conforms to its claimed business and whether there are risk signs such as abnormal large-value transactions and frequent cross-border transactions.

[0036] In step S10412, based on the above evaluation results and the above analysis results, determine the due diligence results for the due diligence processing of the institutional brands included in the database.

[0037] In implementation, the above evaluation results and the above analysis results can be combined to obtain the final result. The obtained final result can be used as the due diligence result corresponding to the business essence review sub-strategy in the due diligence strategy.

[0038] In practical applications, the above due diligence strategy may include a data comparison sub-strategy. Based on this, for the due diligence strategy set for risk prevention and control of preset risks in step S104 above, the specific processing methods for conducting due diligence on the institutional brands included in the database can be diverse. Hereinafter, an optional processing method is provided again. For example, Figure 6 as shown, it may specifically include the processing of the following steps S10414 and step S10416.

[0039] In step S10414, based on the data comparison sub-strategy set for risk prevention and control of preset risks, obtain relevant data of different institutions from the designated data sources, and obtain relevant data of different institutions from the database. Compare the relevant data in the data sources with the relevant data in the database. The data sources include one or more of authoritative institution websites, social media, news data, and preset blacklist data.

[0040] In implementation, for the above due diligence strategy, public information and third-party data comparison can be carried out again. For this purpose, a data comparison sub-strategy is set. Through the data comparison sub-strategy, network information collection can be carried out first. Specifically, public channels such as authoritative institution websites, social media, news data, etc. can be used as data sources. Through the above public channel data sources, obtain relevant data of different institutions. In addition, blacklist data (which may include blacklist data of illegal financial activity risks, right restriction blacklist data, negative news data, etc.) can be queried from professional data sources. Relevant data of different institutions can be obtained from the database. Compare the relevant data in the data sources with the relevant data in the database to obtain the corresponding comparison results. Through the above processing, the relevant information of different institutions can be cross-validated, their public images and market feedback can be evaluated, and whether different institutions and their related parties are involved in illegal or irregular records can be screened.

[0041] In step S10416, based on the comparison results, determine the due diligence results for the due diligence of the institutional brands included in the database.

[0042] In implementation, the comparison results can be used as the due diligence results corresponding to the data comparison sub-strategy in the due diligence strategy.

[0043] In practical applications, the above due diligence strategy may include a monitoring and review sub-strategy. Based on this, for the due diligence strategy set for risk prevention and control of preset risks in step S above, the specific processing methods for conducting due diligence on the institutional brands included in the database can be diverse. Hereinafter, an optional processing method is provided again. For example, Figure 7 as shown, it may specifically include the processing of the following steps S10418 and step S10420.

[0044] In step S10418, based on the monitoring and review sub-strategy set for risk prevention and control of preset risks, the operation conditions of different institutions included in the database are monitored and reviewed to obtain corresponding monitoring and review results.

[0045] In implementation, for the above due diligence strategy, continuous monitoring and regular review can be finally carried out. For this purpose, a monitoring and review sub-strategy is set. Through the monitoring and review sub-strategy, dynamic risk assessment can be carried out first. Specifically, a continuous monitoring mechanism is established to regularly review the business activities, resource transactions, compliance records, etc. of different institutions, and timely capture any change information that may affect the risk level. Secondly, user interaction and feedback can be carried out. Specifically, relevant information about the operation conditions of different institutions is collected through user interviews, questionnaires, etc., and the reputation of the institutional brand can be evaluated in combination with user feedback, so as to realize the monitoring and review processing of the operation conditions of different institutions included in the database and finally obtain corresponding monitoring and review results.

[0046] In step S10420, based on the monitoring and review results, the due diligence results for the institutional brands included in the database to conduct due diligence are determined.

[0047] In implementation, the monitoring and review results can be used as the due diligence results corresponding to the monitoring and review sub-strategy in the due diligence strategy.

[0048] Based on the above-obtained due diligence results corresponding to the legal entity verification sub-strategy in the due diligence strategy, the due diligence results corresponding to the business essence review sub-strategy in the due diligence strategy, the due diligence results corresponding to the data comparison sub-strategy in the due diligence strategy, and the due diligence results corresponding to the monitoring and review sub-strategy in the due diligence strategy, a comprehensive evaluation is carried out. Finally, the due diligence results for the institutional brands included in the database to conduct due diligence can be obtained.

[0049] Brand identification through the above-mentioned due diligence strategy can prevent financial risks. Specifically, illegal financial activities seriously affect the global economic order and social security. Verifying the true identity, business nature and source of funds of the institution through corresponding due diligence strategies can effectively intercept attempts to use the platform to transfer illegal resources, cut off the illegal resource chain, and build a safe financial environment; in addition, it can also enhance the security of the system. Specifically, identifying and verifying the authenticity of the institution's brand helps to establish a trustworthy merchant network, which can not only reduce the occurrence of fraud and protect the rights and interests of consumers, but also improve the overall security level of the platform, prevent network security issues such as institutional brand impersonation, and maintain the platform's reputation; in addition, dynamic risk management can also be carried out. Specifically, through continuous due diligence strategies, it can dynamically monitor changes in the institution's operating conditions and trading patterns, promptly detect abnormal trading behaviors, take corresponding risk prevention and control measures, and effectively control and reduce potential risks.

[0050] Brand identification through the above-mentioned due diligence strategy can improve the service experience. Specifically, it can accurately identify and unify institutional brands, which helps to provide merchants with more personalized and precise services. For example, it can customize payment solutions based on the real business needs of the institution, optimize settlement processes, and improve resource flow efficiency, thereby increasing merchant satisfaction and loyalty. In addition, it can also promote compliant operations. Specifically, it can help merchants understand and comply with relevant laws and regulations, and enhance their awareness and ability of the risks of illegal financial activities through training and guidance. This not only helps merchants to operate in compliance, but also maintains the health and stability of the entire transaction ecosystem, forming a virtuous circle. In addition, it can also optimize the user structure. Specifically, due diligence strategies can be used to identify and eliminate high-risk or non-compliant institutions, retaining honest and high-quality institutional brand partners, which is conducive to optimizing the user base, concentrating resources to serve more valuable partners, and achieving long-term sustainable development.

[0051] In practical applications, the specific processing methods of the above step S106 can be varied. The following provides an optional processing method, that is, after identifying the institutional brands, building an institutional brand association graph through a graph theory algorithm is an effective way to visualize and analyze the relationship between institutional brands. The construction of the institutional subject relationship graph can be achieved by combining the due diligence results of the above due diligence strategy and the graph theory algorithm, such as Figure 8 As shown, the process may specifically include the following steps S1062 to S1068.

[0052] In step S1062, the types of association relationships existing between different institutional entities in the database are determined. The types of association relationships include one or more of ownership relationships, cooperative relationships, competitive relationships, supply chain relationships, and common investor relationships.

[0053] In implementation, after identifying the brand information of the institutional brand (including but not limited to the name of the institutional brand, the industry it belongs to, investor information, partners, supply chain relationships, transaction records, etc.) through the above methods, the above brand information can be organized into structured data. The above data can also be preprocessed. For example, the above data can be cleaned to remove duplicate data and repair error information to ensure data quality. Then, the types of association relationships existing between different institutional entities in the database that need to be displayed can be determined, such as, ownership relationships, cooperation relationships, competition relationships, supply chain relationships, co-investor relationships, etc. As Figure 9 shown, the association relationships between two different institutional entities can be found first. Among them, institutional entity A and institutional entity B are connected through investor 1 and institutional brand 1, institutional entity B and institutional entity C are connected through investor 2 and institutional brand 1, institutional entity A and institutional entity D are connected through investor 3 and institutional brand 1, etc.

[0054] In step S1064, the weight information corresponding to each type of association relationship existing between different institutional entities in the database is determined.

[0055] In implementation, based on factors such as trading volume, cooperation frequency, and the quantity of invested resources, the corresponding weight information can be set for each type of association relationship existing between different institutional entities in the database, and the corresponding intensity can be represented in the institutional entity relationship diagram in the form of the width, color, or label of the edge.

[0056] In step S1066, based on each type of association relationship existing between different institutional entities in the database, the determined weight information, and the institutional entity information, the relationship and influence distribution information between the institutional brands corresponding to different institutional entities in the database are determined through a preset graph theory algorithm.

[0057] Among them, there can be multiple graph theory algorithms, such as, the shortest path algorithm, community detection algorithms (such as the Louvain algorithm, etc.), centrality analysis algorithms (such as the PageRank algorithm, etc.).

[0058] In implementation, the hidden connections and influence distribution information between institutional brands can be revealed through the above graph theory algorithms. Specifically, the community detection algorithm can be used to help identify brand groups with close internal connections, reflecting market segmentation or industry clusters. Through the centrality analysis algorithm, the key nodes, that is, the institutional brands with greater influence or in the core position, can be found. Based on this, the processing of determining the relationship and influence distribution information between the institutional brands corresponding to different institutional entities in the database through a preset graph theory algorithm based on each type of association relationship existing between different institutional entities in the database, the determined weight information, and the institutional entity information can be realized.

[0059] In step S1068, based on the relationship and influence distribution information among institutional brands corresponding to different institutional entities in the database, the relationship among institutional brands corresponding to different institutional entities in the database is visualized to obtain an institutional entity relationship diagram.

[0060] In implementation, a visualization tool can be preset in advance, and the results obtained from the above graph theory algorithm analysis are visualized through the visualization tool. Specifically, visualization tools such as XMind, EdrawMax, Gephi, or graph libraries in programming languages (such as NetworkX in Python) can be used to visualize the results obtained from the above graph theory algorithm analysis. Among them, for the layout of the graph, a force-directed layout can be considered to make closely related institutional brands gather naturally, or a hierarchical layout can be used to show the superior-subordinate or time-series relationship among institutional brands. In addition, label information and explanatory information can be added to the nodes (relevant information of institutional brands) and edges to clearly show the specific meaning of each associated relationship. As Figure 10 shown, a multi-layer institutional entity association relationship connected graph can be constructed through a graph theory algorithm based on the above Figure 9 associated relationship.

[0061] In practical applications, the specific processing method of the above step S108 can be various. Hereinafter, an optional processing method is provided again. As Figure 11 shown, it can specifically include the processing of the following steps S1082 to S1088.

[0062] In step S1082, the connected components in the institutional entity relationship diagram are determined.

[0063] Among them, a connected component can be a maximal connected subgraph of an undirected graph, which is the connected component of the undirected graph. Any connected graph has only one connected component, which is itself, and a non-connected undirected graph has multiple connected components.

[0064] In implementation, the connected components of the constructed institutional entity relationship diagram can be calculated to find out each connected component, that is, the set of associated relationships between the institutional entities corresponding to the target brand and other institutional entities.

[0065] In step S1084, the different institutional entities included in each determined connected component are classified to obtain the classified connected component.

[0066] In implementation, the institutional entities in each connected component can be classified. Specifically, the institutional entities in each connected component can be clustered through the similarity between different institutional entities included in each connected component, so as to find out the institutional entities with similar attributes and classify the found institutional entities with similar attributes into one category to obtain the classified connected component.

[0067] In step S1086, based on the classified connected components, the institutional brands corresponding to different institutional entities included in the classified connected components are normalized to obtain normalized connected components.

[0068] In implementation, the classified connected components can be normalized, that is, the institutional entities in the same category are merged or one institutional entity is selected as a representative to represent the entire institutional brand, so as to realize the normalization of the institutional brands corresponding to different institutional entities included in the classified connected components based on the classified connected components and obtain normalized connected components.

[0069] In step S1088, based on the normalized connected components, a relational subgraph for the target brand is determined.

[0070] In implementation, the normalized connected components containing the target brand can be directly obtained from the normalized connected components, and the obtained normalized connected components can be determined as the relational subgraph for the target brand. Or, the normalized connected components can be further processed, such as deleting redundant or useless nodes or edges in the normalized connected components, etc., and then the relational subgraph for the target brand can be obtained from them. It can be specifically set according to the actual situation, and the embodiments of this specification do not limit this.

[0071] In practical applications, the specific processing methods of the above step S1088 can be various. Hereinafter, another optional processing method is provided, such as Figure 12 as shown, which specifically may include the processing of the following step S10882 and step S10884.

[0072] In step S10882, based on the normalized connected components, the importance information of the nodes and edges in the normalized connected components is determined through a graph network model.

[0073] Among them, there can be various graph network models. For example, the graph network model can be a graph convolutional neural network GCN model, a graph neural network GNN model, etc., which can be specifically set according to the actual situation.

[0074] In implementation, for a specified business scenario, after obtaining the normalized connected components, the normalized connected components can be further pruned and purified. Specifically, the importance information of the nodes and edges can be learned from the graph structure data of the normalized connected components based on the graph network model.

[0075] In step S10884, based on the importance information of the nodes and edges in the normalized connected components, the normalized connected components are pruned to obtain a relational subgraph for the target brand.

[0076] In implementation, such asFigure 13 As shown, based on the importance information of nodes and edges in the normalized connected components, it can be determined which edges and / or nodes should be retained or deleted for processing, so as to perform pruning processing on the normalized connected components, obtain the pruned connected components, and the pruned connected components containing the target brand can be obtained from the pruned connected components, and the obtained pruned connected components can be determined as the relationship subgraph for the target brand.

[0077] Through the above processing, the constructed relationship subgraph for the target brand can not only intuitively display the complex relationship network among institutional brands, but also assist decision-makers in understanding the market structure, analyzing the reasons for the formation of brand clusters, identifying potential risk areas of illegal financial activities or cooperation opportunities, effectively identifying and managing risks, and at the same time providing data support for institutional strategic planning.

[0078] The embodiment of this specification provides a data processing method. By receiving an identification request for a target brand, in response to the identification request, based on the due diligence strategy set for risk prevention and control of preset risks, due diligence processing is performed on the institutional brands included in the database, and based on the obtained due diligence results, brand information related to the institutional brands is extracted from the database, and based on the brand information, the institutional entity information corresponding to the brand information is determined. Then, based on the association relationships and institutional entity information among different institutional entities in the database, an institutional entity relationship graph can be constructed. Finally, based on the institutional entity relationship graph, a relationship subgraph for the target brand can be determined. In this way, identifying institutional brands through the due diligence strategy set for risk prevention and control of preset risks has far-reaching significance for maintaining brand security, promoting brand consistency (i.e., brand normalization), and effectively managing risks and customer relationships. In addition, identifying institutional brands through the due diligence strategy set for risk prevention and control of preset risks is not only to fulfill legal obligations, but also to strengthen the risk prevention and control mechanism from the source, optimize the customer management system, create a safe, efficient, and trustworthy online environment for participants, which can protect the interests of users and promote the common development of the platform and institutions. Moreover, by deeply mining and verifying the multi-dimensional information of institutions through the due diligence strategy set for risk prevention and control of preset risks, the true normalization of the brand is achieved, building a solid defense line to protect the interests of users and promote the mutual trust and common prosperity of the platform and institutions. On this basis, the graph structure data of the institutional entity relationship graph can penetrate the complex context of the market structure, accurately locate potential risks, and insight into cooperation opportunities, laying a data foundation for strategic decision-making.

[0079] The above is the data processing method provided by the embodiment of this specification. Based on the same idea, the embodiment of this specification also provides a data processing device, as Figure 14 shown.

[0080] The data processing device includes: an identification request module 1401, a due diligence module 1402, a graph construction module 1403, and a brand relationship determination module 1404, where: The identification request module 1401 receives an identification request for a target brand; The due diligence module 1402, in response to the identification request, based on a due diligence strategy set for risk prevention and control of preset risks, conducts due diligence processing on the institutional brands included in the database, extracts brand information related to the institutional brands from the database based on the obtained due diligence results, and determines institutional entity information corresponding to the brand information based on the brand information; The graph construction module 1403 constructs an institutional entity relationship graph based on the association relationships between different institutional entities in the database and the institutional entity information. The institutional entity relationship graph includes nodes and edges. The nodes are constructed through the institutional entity information, and the edges are constructed through the association relationships between different institutional entities; The brand relationship determination module 1404 determines a relationship sub-graph for the target brand based on the institutional entity relationship graph.

[0081] In an embodiment of the present specification, the due diligence strategy includes a legal entity verification sub-strategy. The due diligence module 1402 includes: A first data acquisition unit, based on a legal entity verification sub-strategy set for risk prevention and control of preset risks, acquires and verifies institutional entity data of different institutions from the database. The institutional entity data includes one or more of business license data, tax registration data, institutional code data, and institutional charter data; A second data acquisition unit, based on a legal entity verification sub-strategy set for risk prevention and control of preset risks, traces and verifies the share distribution structure data of different institutions from the database, and conducts a background investigation on the investors in the share distribution structure data; A first investigation result determination unit, based on the obtained institutional entity data and the background investigation results of the investors, determines the due diligence results of the due diligence processing on the institutional brands included in the database.

[0082] In an embodiment of the present specification, the due diligence strategy includes a business essence review sub-strategy. The due diligence module 1402 includes: A third data acquisition unit, based on a business essence review sub-strategy set for risk prevention and control of preset risks, acquires business model data from the database, evaluates the business models of different institutions based on the business model data to obtain corresponding evaluation results. The business model data includes one or more of the main business data, profit model information, and supply chain data of different institutions; A fourth data acquisition unit, based on a business substance review sub-strategy set for risk prevention and control of preset risks, obtains historical transaction data of different institutions from the database, and analyzes the transaction patterns of different institutions based on the historical transaction data of different institutions to obtain corresponding analysis results; A second investigation result determination unit, based on the evaluation result and the analysis result, determines a due diligence investigation result for the institutional brands included in the database.

[0083] In the embodiments of the present specification, the due diligence investigation strategy includes a data comparison sub-strategy, and the due diligence investigation module 1402 includes: A data comparison unit, based on a data comparison sub-strategy set for risk prevention and control of preset risks, obtains relevant data of different institutions from a specified data source, and obtains the relevant data of different institutions from the database, and compares the relevant data in the data source with the relevant data in the database, where the data source includes one or more of an authoritative institution website, social media, news data, and preset blacklist data; A third investigation result determination unit, based on the comparison result, determines a due diligence investigation result for the institutional brands included in the database.

[0084] In the embodiments of the present specification, the due diligence investigation strategy includes a monitoring and review sub-strategy, and the due diligence investigation module 1402 includes: A monitoring and review unit, based on a monitoring and review sub-strategy set for risk prevention and control of preset risks, monitors and reviews the operation conditions of different institutions included in the database to obtain corresponding monitoring and review results; A fourth investigation result determination unit, based on the monitoring and review result, determines a due diligence investigation result for the institutional brands included in the database.

[0085] In the embodiments of the present specification, the graph construction module 1403 includes: A type determination unit, determines the type of association relationship existing between different institutional entities in the database, and the type of association relationship includes one or more of an ownership relationship, a cooperation relationship, a competition relationship, a supply chain relationship, and a co-investor relationship; A weight determination unit, determines the weight information corresponding to each type of association relationship existing between different institutional entities in the database; An information determination unit determines the relationship and influence distribution information between the institutional brands corresponding to different institutional entities in the database through a preset graph theory algorithm based on each type of association relationship existing between different institutional entities in the database, the determined weight information, and the institutional entity information. A graph construction unit performs visualization processing on the relationship between the institutional brands corresponding to different institutional entities in the database based on the relationship and influence distribution information between the institutional brands corresponding to different institutional entities in the database, and obtains an institutional entity relationship graph.

[0086] In the embodiments of this specification, the brand relationship determination module 1404 includes: A connected component determination unit determines the connected components in the institutional entity relationship graph. A classification unit performs classification processing on the different institutional entities included in each determined connected component to obtain classified connected components. A normalization unit performs normalization processing on the institutional brands corresponding to the different institutional entities included in the classified connected components based on the classified connected components to obtain normalized connected components. A brand relationship determination unit determines a relationship subgraph for the target brand based on the normalized connected components.

[0087] In the embodiments of this specification, the brand relationship determination unit determines the importance information of the nodes and edges in the normalized connected components through a graph network model based on the normalized connected components; performs pruning processing on the normalized connected components based on the importance information of the nodes and edges in the normalized connected components to obtain a relationship subgraph for the target brand.

[0088] An embodiment of this specification provides a data processing device. By receiving an identification request for a target brand, in response to the identification request, based on a due diligence strategy set for risk prevention and control of preset risks, a due diligence process is performed on the institutional brands included in the database, and brand information related to the institutional brands is extracted from the database based on the obtained due diligence results. Then, based on the brand information, the institutional entity information corresponding to the brand information is determined. Subsequently, an institutional entity relationship graph can be constructed based on the association relationships and institutional entity information among different institutional entities in the database. Finally, based on the institutional entity relationship graph, a relationship sub-graph for the target brand can be determined. In this way, identifying institutional brands through a due diligence strategy set for risk prevention and control of preset risks is of profound significance for maintaining brand security, promoting brand consistency (i.e., brand normalization), and effectively managing risks and customer relationships. In addition, identifying institutional brands through a due diligence strategy set for risk prevention and control of preset risks is not only fulfilling legal obligations but also strengthening the risk prevention and control mechanism from the source, optimizing the customer management system, creating a safe, efficient, and trustworthy online environment for participants, protecting the interests of users, and promoting the common development of the platform and institutions. Moreover, by deeply mining and verifying multi-dimensional information of institutions through a due diligence strategy set for risk prevention and control of preset risks, the true normalization of the brand is achieved, building a solid defense line, protecting the interests of users, and promoting mutual trust and common prosperity between the platform and institutions. On this basis, the graph structure data of the institutional entity relationship graph can be used to penetrate the complex context of the market structure, accurately locate potential risks, and gain insights into cooperation opportunities, laying a data foundation for strategic decision-making.

[0089] The above is the data processing device provided by the embodiment of this specification. Based on the same idea, the embodiment of this specification also provides a data processing device, as Figure 15 shown.

[0090] The data processing device may be a terminal device or a server provided in the above embodiment, etc.

[0091] Data processing devices can vary significantly depending on their configuration or performance. They can include one or more processors 1501 and a memory 1502. The memory 1502 can store one or more stored application programs or data. Among them, the memory 1502 can be transient storage or persistent storage. The application programs stored in the memory 1502 can include one or more modules (not shown in the figure), and each module can include a series of computer-executable instructions for the data processing device. Further, the processor 1501 can be set to communicate with the memory 1502 and execute a series of computer-executable instructions in the memory 1502 on the data processing device. The data processing device can also include one or more power supplies 1503, one or more wired or wireless network interfaces 1504, one or more input / output interfaces 1505, and one or more keyboards 1506.

[0092] Specifically, in this embodiment, the data processing device includes a memory and one or more programs. One or more of the programs are stored in the memory, and one or more of the programs can include one or more modules. Each module can include a series of computer-executable instructions for the data processing device and is configured to be executed by one or more processors. The one or more programs include the following computer-executable instructions for: Receiving an identification request for a target brand; In response to the identification request, based on the due diligence strategy set for risk prevention and control of preset risks, conducting due diligence on the institutional brands included in the database, extracting brand information related to the institutional brands from the database based on the obtained due diligence results, and determining the institutional entity information corresponding to the brand information based on the brand information; Based on the association relationships between different institutional entities in the database and the institutional entity information, constructing an institutional entity relationship graph. The institutional entity relationship graph includes nodes and edges. The nodes are constructed through the institutional entity information, and the edges are constructed through the association relationships between different institutional entities; Based on the institutional entity relationship graph, determining a relationship sub-graph for the target brand.

[0093] Each embodiment in this specification is described in a progressive manner. The same or similar parts between the embodiments can be referred to each other. The key point of each embodiment is to illustrate the differences from other embodiments. In particular, for the data processing device embodiment, since it is basically similar to the method embodiment, the description is relatively simple. For the relevant parts, refer to the corresponding description in the method embodiment.

[0094] An embodiment of this specification provides a data processing device. By receiving an identification request for a target brand, in response to the identification request, based on a due diligence strategy set for risk prevention and control of preset risks, conduct due diligence on the institutional brands included in the database, extract brand information related to the institutional brands from the database based on the obtained due diligence results, and determine the institutional entity information corresponding to the brand information based on the brand information. Then, based on the association relationships between different institutional entities in the database and the institutional entity information, construct an institutional entity relationship graph. Finally, based on the institutional entity relationship graph, determine a relationship sub-graph for the target brand. In this way, using the due diligence strategy set for risk prevention and control of preset risks to identify institutional brands is of profound significance for maintaining brand security, promoting brand consistency (i.e., brand normalization), and effectively managing risks and customer relationships. In addition, using the due diligence strategy set for risk prevention and control of preset risks to identify institutional brands is not only to fulfill legal obligations, but also to strengthen the risk prevention and control mechanism from the source, optimize the customer management system, create a safe, efficient, and trustworthy online environment for participants, protect the interests of users, and promote the common development of the platform and institutions. Moreover, using the due diligence strategy set for risk prevention and control of preset risks to deeply explore and verify multi-dimensional information of institutions realizes the true normalization of brands, builds a solid defense line, protects the interests of users, and promotes the mutual trust and common prosperity of the platform and institutions. On this basis, the graph structure data of the institutional entity relationship graph can penetrate the complex context of the market structure, accurately locate potential risks, and insight into cooperation opportunities, laying a data foundation for strategic decision-making.

[0095] Further, based on the above Figures 1 to 13 , one or more embodiments of this specification also provide a storage medium for storing computer-executable instruction information. In a specific embodiment, the storage medium can be a USB flash drive, an optical disc, a hard disk, etc. When the computer-executable instruction information stored in the storage medium is executed by a processor, the following process can be achieved: Receive an identification request for a target brand; In response to the identification request, based on a due diligence strategy set for risk prevention and control of preset risks, conduct due diligence on the institutional brands included in the database, extract brand information related to the institutional brands from the database based on the obtained due diligence results, and determine the institutional entity information corresponding to the brand information based on the brand information. Based on the association relationships between different institutional entities in the database and the institutional entity information, construct an institutional entity relationship graph. The institutional entity relationship graph includes nodes and edges. The nodes are constructed through the institutional entity information, and the edges are constructed through the association relationships between different institutional entities. Based on the institutional entity relationship graph, determine the relationship sub-graph for the target brand.

[0096] Each embodiment in this specification is described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other. Each embodiment focuses on the differences from other embodiments. In particular, for the above-mentioned embodiment of a storage medium, since it is basically similar to the method embodiment, the description is relatively simple, and reference can be made to the partial description of the method embodiment for the relevant parts.

[0097] The embodiments of this specification provide a storage medium. By receiving an identification request for a target brand, in response to the identification request, based on a due diligence strategy set for risk prevention and control of preset risks, conduct due diligence on the institutional brands included in the database, extract brand information related to the institutional brands from the database based on the obtained due diligence results, and determine the institutional entity information corresponding to the brand information based on the brand information. Then, based on the association relationships between different institutional entities in the database and the institutional entity information, construct an institutional entity relationship graph. Finally, based on the institutional entity relationship graph, determine the relationship sub-graph for the target brand. In this way, by using the due diligence strategy set for risk prevention and control of preset risks to identify institutional brands, it has far-reaching significance for maintaining brand security, promoting brand consistency (i.e., brand normalization), and effectively managing risks and customer relationships. In addition, by using the due diligence strategy set for risk prevention and control of preset risks to identify institutional brands, it is not only fulfilling legal obligations, but also strengthening the risk prevention and control mechanism from the source, optimizing the customer management system, creating a safe, efficient, and trustworthy online environment for participants, protecting the interests of users, and promoting the common development of the platform and institutions. Moreover, by using the due diligence strategy set for risk prevention and control of preset risks to deeply mine and verify multi-dimensional information of institutions, it realizes the true normalization of brands, builds a solid defense line, protects the interests of users, and promotes the mutual trust and common prosperity of the platform and institutions. On this basis, the graph structure data of the institutional entity relationship graph can be used to penetrate the complex context of the market structure, accurately locate potential risks, and gain insights into cooperation opportunities, laying a data foundation for strategic decision-making.

[0098] Further, based on the above Figures 1 to 13 One or more embodiments of this specification also provide a computer program product, including a computer program. When the computer program in this computer program product is executed by a processor, it can implement the following process: Receive an identification request for a target brand; In response to the recognition request, a due diligence strategy is set based on risk prevention and control of preset risks, and due diligence processing is performed on the institutional brands included in the database. Brand information related to the institutional brand is extracted from the database based on the obtained due diligence results, and institutional entity information corresponding to the brand information is determined based on the brand information; Based on the association relationships between different institutional entities in the database and the institutional entity information, an institutional entity relationship graph is constructed. The institutional entity relationship graph includes nodes and edges. The nodes are constructed through the institutional entity information, and the edges are constructed through the association relationships between different institutional entities; Based on the institutional entity relationship graph, a relationship subgraph for the target brand is determined.

[0099] Each embodiment in this specification is described in a progressive manner. For the same or similar parts between the embodiments, reference can be made to each other. Each embodiment focuses on the differences from other embodiments. In particular, for the above-mentioned embodiment of a computer program product, since it is basically similar to the method embodiment, the description is relatively simple, and for the relevant parts, reference can be made to the partial description of the method embodiment.

[0100] An embodiment of this specification provides a computer program product. By receiving an identification request for a target brand, in response to the identification request, based on a due diligence strategy set for risk prevention and control of preset risks, a due diligence process is performed on the institutional brands included in the database, and brand information related to the institutional brands is extracted from the database based on the obtained due diligence results. Then, the institutional entity information corresponding to the brand information is determined based on the brand information. Subsequently, an institutional entity relationship graph can be constructed based on the association relationships between different institutional entities and the institutional entity information in the database. Finally, based on the institutional entity relationship graph, a relationship sub-graph for the target brand can be determined. In this way, identifying institutional brands through a due diligence strategy set for risk prevention and control of preset risks is of profound significance for maintaining brand security, promoting brand consistency (i.e., brand normalization), and effectively managing risks and customer relationships. In addition, identifying institutional brands through a due diligence strategy set for risk prevention and control of preset risks is not only fulfilling legal obligations but also strengthening the risk prevention and control mechanism from the source, optimizing the customer management system, creating a safe, efficient, and trustworthy online environment for participants, protecting the interests of users, and promoting the common development of the platform and institutions. Moreover, through a due diligence strategy set for risk prevention and control of preset risks to deeply mine and verify multi-dimensional information of institutions, the true normalization of brands is achieved, building a solid defense line, protecting the interests of users, and promoting mutual trust and common prosperity between the platform and institutions. On this basis, the graph structure data of the institutional entity relationship graph can be used to penetrate the complex context of the market structure, accurately locate potential risks, and gain insights into cooperation opportunities, laying a data foundation for strategic decision-making.

[0101] The above describes specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be performed in a different order than in the embodiments and still achieve the desired results. Additionally, the processes depicted in the drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multi-tasking and parallel processing are also possible or may be advantageous.

[0102] In the 1990s, it was obvious to distinguish whether an improvement to a technology was a hardware improvement (e.g., improvement to circuit structures such as diodes, transistors, switches, etc.) or a software improvement (improvement to method flows). However, with the development of technology, many improvements to method flows today can be regarded as direct improvements to hardware circuit structures. Almost all designers obtain the corresponding hardware circuit structure by programming the improved method flow into the hardware circuit. Therefore, it cannot be said that an improvement to a method flow cannot be implemented with a hardware entity module. For example, a Programmable Logic Device (PLD) (such as a Field Programmable Gate Array (FPGA)) is such an integrated circuit whose logical function is determined by the user programming the device. Designers can program themselves to "integrate" a digital system onto a single PLD, without having to ask a chip manufacturer to design and fabricate a dedicated integrated circuit chip. Moreover, nowadays, instead of manually fabricating integrated circuit chips, this programming is mostly implemented using "logic compiler" software, which is similar to the software compilers used in program development and writing. The original code before compilation also has to be written in a specific programming language, which is called a Hardware Description Language (HDL), and there is not only one kind of HDL, but many kinds, such as ABEL (Advanced Boolean Expression Language), AHDL (Altera Hardware Description Language), Confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), Lava, Lola, MyHDL, PALASM, RHDL (Ruby Hardware Description Language), etc. Currently, the most commonly used are VHDL (Very-High-Speed Integrated Circuit Hardware Description Language) and Verilog. Those skilled in the art should also be aware that by simply performing a little logical programming on the method flow with the above-mentioned several hardware description languages and programming it into the integrated circuit, it is easy to obtain the hardware circuit that implements the logical method flow.

[0103] The controller can be implemented in any suitable manner. For example, the controller can take the form of, for example, a microprocessor or a processor and a computer-readable medium storing computer-readable program code (such as software or firmware) executable by the (micro)processor, logic gates, switches, an application specific integrated circuit (ASIC), a programmable logic controller, and an embedded microcontroller. Examples of the controller include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicone Labs C8051F320. The memory controller can also be implemented as part of the control logic of the memory. Those skilled in the art also know that, in addition to implementing the controller in the form of pure computer-readable program code, it is entirely possible to implement the same function by logically programming the method steps so that the controller is in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers, etc. Therefore, such a controller can be considered a hardware component, and the devices included therein for implementing various functions can also be regarded as the structures within the hardware component. Or even, the devices for implementing various functions can be regarded as either software modules for implementing the method or structures within the hardware component.

[0104] The systems, devices, modules, or units illustrated in the above embodiments can be specifically implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, the computer can be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or any combination of these devices.

[0105] For the convenience of description, when describing the above devices, they are described separately as various units according to their functions. Of course, when implementing one or more embodiments of this specification, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0106] Those skilled in the art should understand that the embodiments of this specification can be provided as a method, a system, or a computer program product. Therefore, one or more embodiments of this specification can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, one or more embodiments of this specification can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memories, CD-ROMs, optical memories, etc.) containing computer-usable program code.

[0107] Embodiments of this specification are described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of this specification. It should be understood that each process and / or block in the flowchart and / or block diagram, and the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable serial-parallel devices for fraud cases to generate a machine, such that the instructions executed by the processor of the computer or other programmable serial-parallel devices for fraud cases generate means for implementing the functions specified in one process Figure 1 one process or multiple processes and / or blocks Figure 1 or means for implementing the functions specified in multiple blocks.

[0108] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable serial-parallel devices for fraud cases to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including instruction means, and the instruction means implement the functions specified in one process Figure 1 one process or multiple processes and / or blocks Figure 1 or means for implementing the functions specified in multiple blocks.

[0109] These computer program instructions can also be loaded onto a computer or other programmable serial-parallel devices for fraud cases, such that a series of operation steps are executed on the computer or other programmable devices to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable devices provide steps for implementing the functions specified in one process Figure 1 one process or multiple processes and / or blocks Figure 1 or means for implementing the functions specified in multiple blocks.

[0110] In a typical configuration, a computing device includes one or more processors (CPUs), an input / output interface, a network interface, and memory.

[0111] The memory may include non-permanent memory in the form of computer-readable media, random access memory (RAM), and / or non-volatile memory such as read-only memory (ROM) or flash memory (flash RAM). The memory is an example of computer-readable media.

[0112] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can be implemented using any method or technology for information storage. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change RAM (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory computer-readable media such as modulated data signals and carrier waves.

[0113] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0114] Those skilled in the art will appreciate that the embodiments of this specification may be provided as methods, systems, or computer program products. Thus, one or more embodiments of this specification may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, one or more embodiments of this specification may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROMs, optical storage, etc.) containing computer-usable program code.

[0115] One or more embodiments of this specification may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, and the like that perform specific tasks or implement specific abstract data types. One or more embodiments of this specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communications network. In a distributed computing environment, program modules may be located in local and remote computer storage media, including storage devices.

[0116] Each embodiment in this specification is described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other, and the differences between each embodiment and other embodiments are emphasized. In particular, for system embodiments, since they are basically similar to method embodiments, the description is relatively simple, and reference can be made to the relevant parts of the method embodiments for the relevant content.

[0117] The above description is only for the embodiments of this specification and is not intended to limit this document. For those skilled in the art, various changes and modifications can be made to this specification. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of this specification shall be included within the scope of the claims of this specification.

Claims

1. A data processing method, the method comprising: Receiving an identification request for a target brand; In response to the identification request, based on a due diligence strategy set for risk prevention and control of preset risks, conducting due diligence on institutional brands included in a database, extracting brand information related to the institutional brands from the database based on the obtained due diligence results, and determining institutional entity information corresponding to the brand information based on the brand information; Based on the association relationships between different institutional entities in the database and the institutional entity information, constructing an institutional entity relationship graph, where the institutional entity relationship graph includes nodes and edges, the nodes are constructed through the institutional entity information, and the edges are constructed through the association relationships between different institutional entities; Based on the institutional entity relationship graph, determining a relationship sub-graph for the target brand.

2. The method according to claim 1, wherein the due diligence strategy includes a legal entity verification sub-strategy, and the conducting due diligence on institutional brands included in the database based on the due diligence strategy set for risk prevention and control of preset risks includes: Based on the legal entity verification sub-strategy set for risk prevention and control of preset risks, obtaining and verifying institutional entity data of different institutions from the database, where the institutional entity data includes one or more of business license data, tax registration data, institutional code data, and institutional charter data; Based on the legal entity verification sub-strategy set for risk prevention and control of preset risks, tracing and verifying the share distribution structure data of different institutions from the database, and conducting background investigations on the investors in the share distribution structure data; Based on the obtained institutional entity data and the background investigation results of the investors, to determine the due diligence results of the due diligence on institutional brands included in the database.

3. The method according to claim 1, wherein the due diligence strategy includes a business essence review sub-strategy, and the conducting due diligence on institutional brands included in the database based on the due diligence strategy set for risk prevention and control of preset risks includes: Based on the business essence review sub-strategy set for risk prevention and control of preset risks, obtaining business model data from the database, evaluating the business models of different institutions based on the business model data to obtain corresponding evaluation results, where the business model data includes one or more of the main business data, profit model information, and supply chain data of different institutions; Based on the business essence review sub-strategy set for risk prevention and control of preset risks, obtaining historical transaction data of different institutions from the database, and analyzing the transaction models of different institutions based on the historical transaction data of different institutions to obtain corresponding analysis results; Based on the evaluation results and the analysis results, to determine the due diligence results of the due diligence on institutional brands included in the database.

4. The method according to claim 1, wherein the due diligence strategy includes a data comparison sub-strategy. The due diligence strategy set for risk prevention and control of preset risks is used to conduct due diligence on the institutional brands included in the database, including: Based on the data comparison sub-strategy set for risk prevention and control of preset risks, obtain relevant data of different institutions from a specified data source, and obtain relevant data of the different institutions from the database. Compare the relevant data in the data source with the relevant data in the database. The data source includes one or more of authoritative institution websites, social media, news data, and preset blacklist data; Based on the comparison result, determine the due diligence result of the due diligence on the institutional brands included in the database.

5. The method according to claim 1, wherein the due diligence strategy includes a monitoring and review sub-strategy. The due diligence strategy set for risk prevention and control of preset risks is used to conduct due diligence on the institutional brands included in the database, including: Based on the monitoring and review sub-strategy set for risk prevention and control of preset risks, monitor and review the operation conditions of different institutions included in the database to obtain corresponding monitoring and review results; Based on the monitoring and review results, determine the due diligence result of the due diligence on the institutional brands included in the database.

6. The method according to any one of claims 1-5, wherein constructing an institutional entity relationship graph based on the association relationships between different institutional entities in the database and the institutional entity information includes: Determine the types of association relationships existing between different institutional entities in the database. The types of association relationships include one or more of ownership relationships, cooperation relationships, competition relationships, supply chain relationships, and co-investor relationships; Determine the weight information corresponding to each type of association relationship existing between different institutional entities in the database; Based on each type of association relationship existing between different institutional entities in the database, the determined weight information, and the institutional entity information, determine the relationship and influence distribution information between the institutional brands corresponding to different institutional entities in the database through a preset graph theory algorithm; Based on the relationship and influence distribution information between the institutional brands corresponding to different institutional entities in the database, perform visualization processing on the relationship between the institutional brands corresponding to different institutional entities in the database to obtain an institutional entity relationship graph.

7. The method according to claim 6, wherein determining a relationship sub-graph for the target brand based on the institutional entity relationship graph includes: Determine the connected components in the institutional entity relationship graph; Classify the different institutional entities included in each determined connected component to obtain the classified connected components; Based on the classified connected components, normalize the institutional brands corresponding to the different institutional entities included in the classified connected components to obtain normalized connected components; Based on the normalized connected components, determine the relationship sub-graph for the target brand.

8. The method according to claim 7, wherein determining a relationship subgraph for the target brand based on the normalized connected components comprises: Based on the normalized connected components, determining importance information of nodes and edges in the normalized connected components through a graph network model; Performing pruning processing on the normalized connected components based on the importance information of nodes and edges in the normalized connected components to obtain a relationship subgraph for the target brand.

9. A data processing apparatus, the apparatus comprising: An identification request module, which receives an identification request for a target brand; A due diligence module, in response to the identification request, based on a due diligence strategy set for risk prevention and control of preset risks, performing due diligence processing on institutional brands included in a database, extracting brand information related to the institutional brands from the database based on the obtained due diligence results, and determining institutional entity information corresponding to the brand information based on the brand information; A graph construction module, based on the association relationships between different institutional entities in the database and the institutional entity information, constructing an institutional entity relationship graph, wherein the institutional entity relationship graph includes nodes and edges, the nodes are constructed through the institutional entity information, and the edges are constructed through the association relationships between different institutional entities; A brand relationship determination module, based on the institutional entity relationship graph, determining a relationship subgraph for the target brand.

10. A data processing device, the data processing device comprising: A processor; And A memory arranged to store computer-executable instructions, the executable instructions, when executed, causing the processor to: Receive an identification request for a target brand; In response to the identification request, based on a due diligence strategy set for risk prevention and control of preset risks, performing due diligence processing on institutional brands included in a database, extracting brand information related to the institutional brands from the database based on the obtained due diligence results, and determining institutional entity information corresponding to the brand information based on the brand information; Based on the association relationships between different institutional entities in the database and the institutional entity information, constructing an institutional entity relationship graph, wherein the institutional entity relationship graph includes nodes and edges, the nodes are constructed through the institutional entity information, and the edges are constructed through the association relationships between different institutional entities; Based on the institutional entity relationship graph, determining a relationship subgraph for the target brand.

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