Data identification method, device, equipment and storage medium

Abstract

The present disclosure provides a data identification method, apparatus, device, and storage medium. The method relates to the field of data processing technology, and in particular to the fields of data mining and data analysis. A specific implementation scheme is as follows: obtaining first data of a first entity and second data of a second entity, wherein the first data includes features of a target object of the first entity, and the second data includes features of a target object of the second entity; obtaining relationship data between the first entity and the second entity; and identifying target objects with the same features included in the first and second entities as the same target object when the relationship data between the first and second entities meets a preset condition. According to the technical solution of the present disclosure, the accuracy of data identification can be improved.

Description

Technical Field

[0001] The present disclosure relates to the field of data processing technology, and in particular to the fields of data mining and data analysis. Background Art

[0002] The application of data recognition is becoming more and more extensive and important. In related technologies, it is often necessary to identify target objects in different entities, but the recognition accuracy is low. Summary of the Invention

[0003] The present disclosure provides a data identification method, apparatus, device, and storage medium.

[0004] According to one aspect of the present disclosure, a data identification method is provided, comprising:

[0005] Obtaining first data of a first entity and second data of a second entity, wherein the first data includes features of a target object of the first entity and the second data includes features of the target object of the second entity;

[0006] Obtaining relationship data between a first entity and a second entity;

[0007] When the relationship data between the first entity and the second entity meets a preset condition, target objects with the same features included in the first entity and the second entity are identified as the same target object.

[0008] According to another aspect of the present disclosure, there is provided a data identification device, comprising:

[0009] A first acquisition module is configured to acquire first data of a first entity and second data of a second entity, wherein the first data includes features of a target object of the first entity and the second data includes features of the target object of the second entity;

[0010] A second acquisition module is used to obtain the relationship data between the first entity and the second entity;

[0011] The identification module is configured to identify target objects with the same features included in the first entity and the second entity as the same target object when the relationship data between the first entity and the second entity meets a preset condition.

[0012] According to another aspect of the present disclosure, there is provided an electronic device, comprising:

[0013] at least one processor; and

[0014] a memory communicatively connected to the at least one processor; wherein,

[0015] The memory stores instructions that can be executed by the at least one processor. The instructions are executed by the at least one processor to enable the at least one processor to perform the method of any embodiment of the present disclosure.

[0016] According to yet another aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to enable the computer to execute the method of any embodiment of the present disclosure.

[0017] According to yet another aspect of the present disclosure, a computer program product is provided, including a computer program. When the computer program is executed by a processor, the method of any embodiment of the present disclosure is implemented.

[0018] According to the technical solution disclosed in the present invention, the accuracy of data recognition can at least be improved.

[0019] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present disclosure, nor are they intended to limit the scope of the present disclosure. Other features of the present disclosure will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The accompanying drawings are provided to facilitate a better understanding of the present invention and do not constitute a limitation of the present disclosure.

[0021] Figure 1 is a flow chart of a data identification method according to an embodiment of the present disclosure;

[0022] Figure 2 This is a schematic diagram of the entity relationship according to the embodiment of the present disclosure. Figure 1 ;

[0023] Figure 3 This is a schematic diagram of the entity relationship according to the embodiment of the present disclosure. Figure 2 ;

[0024] Figure 4 This is a schematic diagram of the entity relationship according to the embodiment of the present disclosure. Figure 3 ;

[0025] Figure 5 This is a schematic diagram of the entity relationship according to the embodiment of the present disclosure. Figure 4 ;

[0026] Figure 6 is a schematic diagram of two target objects with the same characteristics appearing in the same entity according to an embodiment of the present disclosure;

[0027] Figure 7 is an overall flow chart of data identification according to an embodiment of the present disclosure;

[0028] Figure 8 is a schematic diagram of a data identification device according to an embodiment of the present disclosure;

[0029] Figure 9 is a schematic diagram of a scenario of data recognition according to an embodiment of the present disclosure;

[0030] Figure 10 It is a block diagram of an electronic device used to implement the data identification method according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0031] The following description of exemplary embodiments of the present disclosure is made in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding. These details should be considered as merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0032] The terms "first," "second," and "third," etc., in the description, embodiments, claims, and accompanying figures of the present disclosure are used to distinguish similar items and are not necessarily used to describe a particular order or precedence. Furthermore, the terms "including," "having," and any variations thereof, are intended to cover non-exclusive inclusions, such as, for example, inclusion of a series of steps or elements. A method, system, product, or apparatus is not necessarily limited to the steps or elements explicitly listed, but may include other steps or elements not explicitly listed or inherent to the process, method, product, or apparatus.

[0033] As described in the background technology section above, data identification applications are becoming increasingly widespread and important. For example, with enterprises as the entity and natural persons as the target, identifying natural persons in industrial and commercial data is becoming increasingly important. In raw industrial and commercial data, natural persons are uniquely identified by their personal identification numbers. However, due to personal data privacy protections, industrial and commercial data sources cannot obtain the original identification numbers of natural persons. Data sources are primarily collected from national corporate credit information websites, and natural persons lack corresponding unique codes. Financial knowledge graphs structure financial knowledge. They require acquiring multi-source data, cleaning and structuring it to form a knowledge network, and finally presenting it in a visual graph format for users to search, manage, and reorganize, serving business scenarios such as financial risk management, marketing, and investment. The first step in building a financial knowledge graph is data preprocessing. The underlying data of a financial knowledge graph is industrial and commercial data. Multiple industrial and commercial data sources are involved in graph construction. Determining the uniqueness of natural persons within this heterogeneous multi-source data is crucial and directly impacts the accuracy and usability of the financial knowledge graph.

[0034] In order to at least partially solve one or more of the above-mentioned problems and other potential problems, the present disclosure proposes a data identification method. By utilizing the technical solutions of the embodiments of the present disclosure, the accuracy of data identification can be improved, the problem of poor uniqueness identification of target objects in data sources can be solved, and the accuracy of uniqueness identification of target objects in multiple data sources can be improved, thereby effectively ensuring the accuracy and availability of the financial knowledge graph.

[0035] The present disclosure provides a data identification method, which can be applied to electronic devices, including but not limited to fixed devices and / or mobile devices. For example, fixed devices include but are not limited to servers, and servers can be cloud servers or ordinary servers. For example, mobile devices include but are not limited to: one or more of a mobile phone or a tablet computer. Figure 1 As shown, the data identification method includes:

[0036] S101, obtaining first data of a first entity and second data of a second entity, where the first data includes features of a target object of the first entity and the second data includes features of the target object of the second entity;

[0037] S102, obtaining relationship data between the first entity and the second entity;

[0038] S103: When the relationship data between the first entity and the second entity meets a preset condition, target objects with the same features included in the first entity and the second entity are identified as the same target object.

[0039] In the embodiment of the present disclosure, the first entity and the second entity are not the same entity. For example, the first entity may be a first enterprise, and the second entity may be a second enterprise.

[0040] In the embodiments of the present disclosure, the first data includes entity data of a first entity, such as first enterprise data. The first enterprise data may include at least one of shareholder information, business license information, branch information, and business scope information. The second data includes entity data of a second entity, such as second enterprise data. The second enterprise data may further include at least one of shareholder information, business license information, branch information, and business scope information.

[0041] In the embodiment of the present disclosure, the target object is an important object in the entity, for example, a natural person, a device, etc. Here, the natural person includes the legal representative, shareholder or senior executive of the entity.

[0042] In the embodiment of the present disclosure, the characteristics of the target object include name characteristics, and may also include characteristics such as gender and age.

[0043] In the disclosed embodiments, the relationship data between a first entity and a second entity includes pre-set direct relationships and indirect relationships. A pre-set direct relationship is a direct relationship between the first and second entities. For example, a pre-set direct relationship can be an investment relationship, an employment relationship, or a branch structure relationship. An indirect relationship is a connection between the first and second entities through one or more third entities. For example, an indirect relationship can be an investment relationship, a competitive relationship, or the like.

[0044] In the embodiments of the present disclosure, the channels for obtaining the first data of the first entity and the second data of the second entity are not limited. Exemplarily, the first data of the first entity and the second data of the second entity are obtained from a public database. In another exemplary embodiment, the first data of the first entity and the second data of the second entity are obtained from content provided by a third-party device. In another exemplary embodiment, the first data of the first entity and the second data of the second entity are obtained by querying from a third-party application software such as a public platform. In another exemplary embodiment, the first data of the first entity and the second data of the second entity are obtained by analyzing data provided by one or more data sources.

[0045] In the embodiments of the present disclosure, the method for obtaining the relationship data between the first entity and the second entity is not limited. Exemplarily, the relationship data between the first entity and the second entity is obtained directly from a public database. In another exemplary embodiment, the relationship data between the first entity and the second entity is obtained by analyzing the first data of the first entity and the second data of the second entity. In another exemplary embodiment, the relationship data between the first entity and the second entity is obtained by analyzing data provided by one or more data sources.

[0046] In some embodiments, the preset condition is that there are two target objects with the same characteristics in the two entities. Exemplarily, taking the target object as a natural person and the characteristic as a name as an example, the preset condition is that the natural person with the same name appears in the first entity and the second entity, and further, when there are more than two natural persons with the same name in the first entity and the second entity, the natural persons with the same name appearing in the first entity and the second entity are identified as the same natural person. Another exemplary example, taking the target object as a natural person and the characteristic as a public photo as an example, the preset condition is: the natural person with the same public photo appears in the first entity and the second entity, and further, when there are more than two public photos that are the same in the first entity and the second entity, the natural persons with the same public photo appearing in the first entity and the second entity are identified as the same natural person.

[0047] In other embodiments, the preset condition is that there is a preset direct relationship between the two entities. Here, the preset direct relationship includes a direct association relationship between the first entity and the second entity. For example, the preset direct relationship can be a direct investment relationship, an employment relationship, etc. Exemplarily, taking the target object as a natural person and the feature as a name as an example, when the first entity and the second entity have a direct investment relationship (such as the first entity holds 50% of the equity of the second entity), the natural persons with the same name appearing in the first entity and the second entity are identified as the same natural person. As another example, taking the target object as a natural person and the feature as a name as an example, when the first entity and the second entity have an employment relationship (such as the chairman of the first entity is the general manager of the second entity), the natural persons with the same name appearing in the first entity and the second entity are identified as the same natural person.

[0048] In some other embodiments, the preset condition is that there is a preset indirect relationship between the two entities. Here, an indirect relationship refers to an association relationship between the first entity and the second entity caused by other entities as a link. For example, an indirect relationship can be an investment relationship, an employment relationship, etc. Exemplarily, taking the target object as a natural person and the feature as a name as an example, when there is an indirect relationship between the first entity and the second entity (such as the first entity holds 20% of the equity of the third entity, and the third entity holds 40% of the equity of the second entity), the natural persons with the same name appearing in the first entity and the second entity are identified as the same natural person. As another example, taking the target object as a natural person and the feature as a name as an example, when there is an employment relationship between the first entity and the second entity (such as the manager of the first entity is the supervisor of the third entity, and the manager of the third entity is the chairman of the second entity), the natural persons with the same name appearing in the first entity and the second entity are identified as the same natural person.

[0049] In actual applications, the relationship between the first entity and the second entity includes but is not limited to the situations listed above. For example, the first entity and the second entity may also have the following relationship: one party directly or indirectly holds more than 25% of the total shares of the other party, or both parties directly or indirectly hold more than 25% of the shares of the third party; the loan funds between one party and the other party (excluding independent financial institutions) account for more than 50% of the paid-in capital of one party, or more than 10% of the total loan funds of one party are guaranteed by the other party (excluding independent financial institutions); more than half of the senior management personnel (including board members and managers, hereinafter the same) of one party or at least one senior member of the board of directors who can control the board of directors is appointed by the other party, or more than half of the senior management personnel of both parties are guaranteed by the other party. The management personnel or at least one senior member of the board of directors who can control the board of directors are both appointed by a third party; more than half of the senior management personnel of one party also serve as senior management personnel of the other party, or at least one senior member of the board of directors who can control the board of directors of one party also serves as a senior member of the board of directors of the other party; the production and operation activities of one party can only be carried out normally with the industrial property rights, proprietary technology and other franchises provided by the other party; the purchase or sale activities of one party are mainly controlled by the other party; the acceptance or provision of services by one party is mainly controlled by the other party; one party has substantial control over the production, operation and transactions of the other party, or the two parties have other related interests.

[0050] In the embodiment of the present disclosure, the number of preset conditions is not limited. It is understood that the more preset conditions there are, the higher the recognition accuracy and the wider the recognition coverage.

[0051] Furthermore, the data identification method may also include: assigning a unique code to the identified same target object.

[0052] It should be noted that the present disclosure does not limit the encoding rules, and any rule that can assign unique codes to different objects can be used as an encoding rule.

[0053] The technical solution described in the embodiment of the present disclosure obtains the first data of the first entity and the second data of the second entity; obtains the relationship data between the first entity and the second entity; and identifies the target objects with the same features appearing in the first entity and the second entity as the same target object when the relationship data between the first entity and the second entity meets the preset conditions; in this way, the accuracy of data recognition can be improved, and the accuracy of unique identification of target objects in the data can be improved, thereby providing basic support for effectively ensuring the accuracy and availability of the subsequent graph generated based on this.

[0054] In some embodiments, when the relationship data between the first entity and the second entity meets a preset condition, the target objects with the same characteristics included in the first entity and the second entity are identified as the same target object, including: when the number of target objects with the same characteristics included in the first entity and the second entity is greater than a preset value, the target objects with the same characteristics included in the first entity and the second entity are identified as the same target object.

[0055] Here, the preset value can be set or adjusted according to actual conditions such as recognition accuracy and recognition breadth.

[0056] In some specific embodiments, if the relationship data between a first entity and a second entity meets a preset condition, target objects with the same characteristics appearing in the first and second entities are identified as the same target object. For example, if the target object is a natural person and the characteristic is a name, and if the first and second entities have two or more natural persons with the same names, the natural persons with the same name appearing in the first and second entities are identified as the same natural person.

[0057] For example, if there are two or more natural persons with the same name in two companies, the natural persons with the same name are deemed to be the same natural person. Figure 2 A schematic diagram showing that there are more than two natural persons with the same name in two companies, such as Figure 2 As shown in the example, if the chairman of company A is named Zhang San and the director is named Li Si, and the chairman of company B is named Zhang San and the director is named Li Si, then the two Zhang Sans and the two Li Sis in the two companies are determined to be the same natural person. This identification of natural persons with the same name in two companies as the same natural person can improve the accuracy of uniquely identifying natural persons in industrial and commercial data, thereby effectively ensuring the usability of financial knowledge graphs.

[0058] In this way, target objects with the same features appearing in the first entity and the second entity are identified as the same target object, which can improve the accuracy of unique identification of target objects in the data.

[0059] In some embodiments, when the relationship data between the first entity and the second entity meets a preset condition, target objects with the same characteristics included in the first entity and the second entity are identified as the same target object, including: when there is a preset direct relationship between the first entity and the second entity, target objects with the same characteristics included in the first entity and the second entity are identified as the same target object.

[0060] Here, the pre-set direct relationship includes a direct association relationship between the first entity and the second entity. For example, the pre-set direct relationship may be a direct investment relationship, an employment relationship, etc.

[0061] In some specific embodiments, if the relationship data between a first entity and a second entity satisfies a predetermined direct relationship, target objects with the same characteristics appearing in the first and second entities are identified as the same target object. For example, if the target object is a natural person and the characteristic is a name, and if the first and second entities have a predetermined direct relationship, natural persons with the same name appearing in the first and second entities are identified as the same natural person.

[0062] For example, if two natural persons with the same name appear in an enterprise with a pre-determined direct relationship, the two natural persons will be determined to be the same natural person. Figure 3 A schematic diagram showing two natural persons with the same name appearing in an enterprise with a preset direct relationship, such as Figure 3 As shown in the example, if Company A holds a 30% stake in Company B, and the chairman of Company A is named Zhang San, and the general manager of Company B is also named Zhang San, then these two Zhang Sans are determined to be the same natural person. This allows us to identify natural persons with the same name in both companies as the same person, even if there is a pre-defined direct relationship between them. This improves the recognition of natural persons in industrial and commercial data, thereby effectively ensuring the usability of the financial knowledge graph.

[0063] In this way, when the relationship data between the first entity and the second entity satisfies the preset direct relationship, the target objects with the same features appearing in the first entity and the second entity are identified as the same target object, which can improve the accuracy of unique identification of the target objects in the data.

[0064] In some embodiments, when the relationship data between the first entity and the second entity meets a preset condition, the target objects with the same characteristics included in the first entity and the second entity are identified as the same target object, including: when there is an indirect relationship within N degrees between the first entity and the second entity, the target objects with the same characteristics included in the first entity and the second entity are identified as the same target object, where N is an integer greater than 1.

[0065] Here, the value of N can be set or adjusted according to actual needs such as recognition coverage or recognition accuracy.

[0066] Here, an indirect relationship refers to an association relationship between a first entity and a second entity caused by another entity as a link.

[0067] In some specific embodiments, when a first entity and a second entity have an indirect relationship within three degrees, target objects with the same characteristics as those in the first and second entities are identified as the same target object. For example, if the target object is a natural person and the characteristic is a name, and when the first entity and the second entity have an indirect relationship within three degrees, natural persons with the same name appearing in the first and second entities are identified as the same natural person.

[0068] For example, if two natural persons with the same name appear in an enterprise with an indirect relationship within three degrees (the weighted equity ratio is greater than 5%, and non-public equity relationships are deemed to be 100%), then the two natural persons will be determined to be the same natural person. Figure 4 A diagram showing two natural persons with the same name appearing in an enterprise with an indirect relationship within 3 degrees, such as Figure 4 As shown in the example, if Company A holds a 13.5% weighted equity stake in Company C, and the chairman of Company A is named Zhang San, and the general manager of Company C is also named Zhang San, then the Zhang Sans of both companies are considered to be the same individual. This allows us to identify individuals with the same name in both companies as the same individual, even when there is an indirect relationship between them. This improves the system's ability to identify individuals in industrial and commercial data, thereby effectively ensuring the usability of the financial knowledge graph.

[0069] The following is an example to illustrate the calculation of indirect investment ratio. Figure 5 As shown, penetration Figure 5 For all layers in the investment chain, multiply the proportions on the investment chain to get the indirect investment proportion at that depth (when calculating the control proportion, if the actual proportion is > 50%, it is calculated as 100%). If there are multiple investment chains with the same depth, the sum is calculated, such as Figure 5 Based on the direct investment ratio shown in the figure, the indirect investment ratios of different depths between Enterprise A and Enterprise D can be calculated as follows:

[0070] 1) The indirect investment ratio of enterprise A and enterprise D with a depth of 2 is (A—>B—>D; A—>E—>D): 1*0.3+0.4*0.3=0.42;

[0071] 2) The indirect investment ratio of Enterprise A and Enterprise D with a depth of 3 is (A—>B—>C—>D): 1*1*0.4=0.4;

[0072] 3) The total indirect investment ratio of Enterprise A and Enterprise D is (A—>B—>D; A—>E—>D; A—>B—>C—>D): 1*0.3+0.4*0.3+1*1*0.4=0.82.

[0073] In this way, when there is a certain indirect relationship between the first entity and the second entity, target objects with the same features appearing in the first entity and the second entity are identified as the same target object, which can improve the accuracy of unique identification of target objects in the data.

[0074] In some embodiments, the method may further include: identifying multiple target objects with the same characteristics included in the first entity as the same target object; and identifying multiple target objects with the same characteristics included in the second entity as the same target object.

[0075] In some specific embodiments, natural persons with the same characteristics appearing in the same entity are identified as the same natural person. For example, if the target object is a natural person and the characteristic is a name, two or more natural persons with the same name appearing in a first entity are identified as the same natural person, and two or more natural persons with the same name appearing in a second entity are identified as the same natural person.

[0076] For example, if two natural persons with the same name appear in the same enterprise, they are deemed to be the same natural person. Figure 6 A schematic diagram showing two natural persons with the same name in the same enterprise, such as Figure 6 As shown in the figure, among the executives of Company A, the legal representative's name is Zhang San and the chairman's name is Zhang San. Therefore, these two Zhang Sans are determined to be the same natural person. In this way, identifying natural persons with the same name in the same company as the same natural person can improve the accuracy of uniquely identifying natural persons in industrial and commercial data, thereby helping to effectively ensure the usability of the financial knowledge graph.

[0077] In this way, target objects with the same features appearing in the same entity are identified as the same target object, which can improve the accuracy of unique identification of target objects in the data.

[0078] In some embodiments, the data identification method may further include: after identifying the target objects with the same features included in the first entity and the second entity as the same target object, generating a relationship graph between the first entity and the second entity based on the identification result.

[0079] In some specific implementations, after identifying natural persons with the same name in a first enterprise and a second enterprise as the same person, a relationship graph between the first and second enterprises is drawn based on the identification results. In this way, integrating natural persons belonging to the same person in different enterprises on the graph can improve the accuracy of the relationship graph and help effectively ensure the usability of the financial knowledge graph.

[0080] For example, before identification, natural person 1 of the first enterprise is represented by one circle in the graph, and natural person 1 of the second enterprise is represented by another circle in the graph; after identification, if natural person 1 of the first enterprise and natural person 1 of the second enterprise are identified as the same natural person, then natural person 1 is merged into one circle in the graph.

[0081] Among them, the relationship diagram can be referred to as Figures 2 to 7 The diagram shown includes different shapes and arrows. Different arrows are used to represent different directional relationships, and different shapes are used to represent target objects and entities.

[0082] In this way, by identifying the target objects having the same features as the first entity and the second entity as the same target object, the accuracy of unique identification of the target object in the data can be improved.

[0083] In some embodiments, when the first target object of the first entity and the second target object of the second entity are identified as the same target object, the data identification method may further include: obtaining public image information corresponding to the first target object of the first entity and the second target object of the second entity respectively; identifying the public image information corresponding to the first target object and the second target object respectively, analyzing the credibility of the first target object and the second target object belonging to the same target object, and adding a numerical value or label representing the credibility to the same target object corresponding to the first target object and the second target object.

[0084] The embodiment of the present disclosure does not limit how to obtain the public image information corresponding to the first target object and the second target object respectively.

[0085] In some specific embodiments, when a first natural person of a first enterprise and a second natural person of a second enterprise are identified as the same natural person, the identification method may further include: obtaining publicly available image information corresponding to the first natural person of the first enterprise and the second natural person of the second enterprise; identifying the publicly available image information corresponding to the first natural person and the second natural person, analyzing the credibility of the first natural person and the second natural person as the same natural person, and adding a numerical value or label representing the credibility of the same natural person corresponding to the first natural person and the second natural person. In this way, by adding a credibility value or credibility level label to the same natural person identified in the industrial and commercial data, the display dimension of the same natural person is increased, the credibility of the identification result is improved, and the usability of the financial knowledge graph is effectively guaranteed.

[0086] For example, natural person A1 in enterprise A and natural person A1 in enterprise B are identified as the same natural person (assuming their assigned code is 00001), and the public image information of natural person A1 in enterprise A and natural person A1 in enterprise B is obtained. Based on the public image information, it is identified that the similarity between natural person A1 in enterprise A and natural person A1 in enterprise B is 100%. Then, the credibility that natural person A1 in enterprise A and natural person A1 in enterprise B are the same natural person is very high, and a credibility level label can be added to natural person A1 coded as 00001. In this case, the credibility level of natural person A1 coded as 00001 is high.

[0087] In this way, by adding a credibility value or credibility level label to the same target object identified by the data, the display dimension of the same target object is increased. Since the credibility of the recognition result is provided, the usability of the generated map is effectively guaranteed.

[0088] It should be understood that the above Figures 2 to 6 The schematic diagram shown is only exemplary and not restrictive, and it is scalable, and those skilled in the art can Figures 2 to 6 Various obvious changes and / or substitutions can be made to the examples, and the resulting technical solutions still fall within the scope of the disclosure of the embodiments of the present disclosure.

[0089] Below, data identification is explained by taking the target object as a natural person, the feature as a name, the first entity as a first enterprise, and the second entity as a second enterprise as an example. Figure 7 A flow chart of the unique identification of natural persons is shown in FIG. Figure 7 As shown, the process includes:

[0090] S701, obtaining public industrial and commercial data;

[0091] S702, determining the first enterprise and the second enterprise;

[0092] Here, the first enterprise and the second enterprise to be analyzed are determined based on the public industrial and commercial data.

[0093] S703, determine whether there are two natural persons with the same name, if yes, execute S704, if not, execute S705;

[0094] S704, identifying natural persons with the same name in the same enterprise as the same natural person, and then proceeding to S705;

[0095] S705: Determine whether there are two natural persons with the same name in the two companies. If yes, execute S708; if not, execute S706.

[0096] S706, determine whether the two enterprises have a direct relationship, if yes, execute S708, if not, execute S707;

[0097] S707, determine whether the two enterprises have an indirect relationship within N degrees. If yes, execute S708; if not, execute S709;

[0098] S708, identifying natural persons with the same name appearing in two enterprises as the same natural person;

[0099] S709: Determine that the first enterprise and the second enterprise do not have the same natural person.

[0100] It should be noted that the present disclosure does not impose a mandatory limitation on the execution order of S703 , S705 , S706 , and S707 .

[0101] It should be understood that Figure 7 The schematic diagram shown is only exemplary and not restrictive, and it is scalable, and those skilled in the art can Figure 7 Various obvious changes and / or substitutions can be made to the examples, and the resulting technical solutions still fall within the scope of the disclosure of the embodiments of the present disclosure.

[0102] In this way, the accuracy and coverage of unique identification of natural persons in data can be greatly improved.

[0103] The data identification method provided in this application can be used in projects such as data mining and data analysis. For example, the execution subject of the method can be an electronic device, which can be various search engine devices or query engine servers.

[0104] Figure 8 is a structural diagram of a data identification device according to an embodiment of the present disclosure, such as Figure 8 As shown, the data identification device may include:

[0105] A first acquisition module 810 is configured to acquire first data of a first entity and second data of a second entity, wherein the first data includes features of a target object of the first entity and the second data includes features of the target object of the second entity;

[0106] A second acquisition module 820 is used to acquire relationship data between the first entity and the second entity;

[0107] The identification module 830 is configured to identify target objects with the same features included in the first entity and the second entity as the same target object when the relationship data between the first entity and the second entity meets a preset condition.

[0108] In some embodiments, the identification module 830 includes a first identification submodule, which is configured to:

[0109] When the number of target objects with the same characteristics included in the first entity and the second entity is greater than a preset value, the target objects with the same characteristics included in the first entity and the second entity are identified as the same target object.

[0110] In some embodiments, the identification module 830 includes a second identification submodule, which is configured to:

[0111] In a case where a preset direct relationship exists between the first entity and the second entity, target objects with the same features included in the first entity and the second entity are identified as the same target object.

[0112] In some embodiments, the identification module 830 includes a third identification submodule, which is configured to:

[0113] When there is an indirect relationship within N degrees between the first entity and the second entity, target objects having the same features as those included in the first entity and the second entity are identified as the same target object, where N is an integer greater than 1.

[0114] In some embodiments, the identification module 830 includes a fourth identification submodule, which is configured to:

[0115] identifying a plurality of target objects having the same features as the first entity as the same target object;

[0116] A plurality of target objects having the same features included in the second entity are identified as the same target object.

[0117] In some embodiments, the data identification device may further include:

[0118] Generate Module( Figure 8 (not shown) for identifying target objects with the same features included in the first entity and the second entity as the same target object, and generating a relationship graph between the first entity and the second entity based on the identification result.

[0119] Those skilled in the art should understand that the functions of each processing module in the data identification device of the embodiment of the present disclosure can be understood with reference to the relevant description of the aforementioned data identification method. Each processing module in the data identification device of the embodiment of the present disclosure can be implemented by an analog circuit that implements the functions described in the embodiment of the present disclosure, or can be implemented by running software that executes the functions described in the embodiment of the present disclosure on an electronic device.

[0120] The data identification device of the embodiment of the present disclosure can improve the accuracy of data identification and the accuracy of unique identification of target objects in the data, thereby providing basic support for effectively ensuring the accuracy and usability of the graph generated based on it.

[0121] Figure 9 A schematic diagram of the data recognition scenario is shown. Figure 9 It can be seen that electronic devices such as cloud servers mine data from multiple data sources, obtain the first data of the first entity and the second data of the second entity from the mined data; analyze the target objects included in the first entity and the second entity based on the first data and the second data; if the relationship between the first entity and the second entity meets the preset conditions, identify the target objects with the same characteristics appearing in the first entity and the second entity as the same target object, and add a unique code to the same target object; finally, generate a relationship graph based on the identification results. The electronic device provides entity-related information query services for the terminal, and also provides relationship graph query services for the terminal; provides sufficient knowledge association for the implementation of intelligent applications, and provides accurate and efficient domain query services.

[0122] It should be understood that Figure 9 The scene diagram shown is only illustrative and not restrictive. Those skilled in the art can Figure 9 Various obvious changes and / or substitutions can be made to the examples, and the resulting technical solutions still fall within the scope of the disclosure of the embodiments of the present disclosure.

[0123] In the technical solutions disclosed herein, the acquisition, storage, and application of user personal information involved comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0124] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0125] Figure 10 A schematic block diagram of an example electronic device 1000 that can be used to implement embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0126] like Figure 10As shown, the device 1000 includes a computing unit 1001, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 1002 or a computer program loaded from a storage unit 1008 into a random access memory (RAM) 1003. Various programs and data required for the operation of the device 1000 can also be stored in the RAM 1003. The computing unit 1001, the ROM 1002, and the RAM 1003 are connected to each other via a bus 1004. An input / output (I / O) interface 1005 is also connected to the bus 1004.

[0127] Various components in device 1000 are connected to I / O interface 1005, including an input unit 1006, such as a keyboard, mouse, etc.; an output unit 1007, such as various types of displays, speakers, etc.; a storage unit 1008, such as a magnetic disk, optical disk, etc.; and a communication unit 1009, such as a network card, modem, wireless communication transceiver, etc. The communication unit 1009 allows device 1000 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0128] The computing unit 1001 can be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 1001 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units that run machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 1001 performs the various methods and processes described above, such as the data identification method. For example, in some embodiments, the data identification method can be implemented as a computer software program that is tangibly contained in a machine-readable medium, such as a storage unit 1008. In some embodiments, part or all of the computer program can be loaded and / or installed on the device 1000 via the ROM 1002 and / or the communication unit 1009. When the computer program is loaded into the RAM 1003 and executed by the computing unit 1001, one or more steps of the data identification method described above can be performed. Alternatively, in other embodiments, the computing unit 1001 may be configured to execute the data identification method in any other appropriate manner (eg, by means of firmware).

[0129] Various embodiments of the systems and techniques described herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system on chips (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0130] The program code for implementing the method of the present disclosure can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device so that when the program code is executed by the processor or controller, the functions / operations specified in the flow chart and / or block diagram are implemented. The program code can be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0131] In the context of the present disclosure, a machine-readable medium can be a tangible medium that can contain or store a program for use by an instruction execution system, device or equipment or used in combination with an instruction execution system, device or equipment. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared or semiconductor system, device or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium can include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory, a read-only memory, an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0132] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a cathode ray tube (CRT) or a liquid crystal display (LCD) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0133] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), and the Internet.

[0134] A computer system may include a client and a server. The client and server are generally remote from each other and typically interact through a communication network. The client-server relationship arises through computer programs running on the respective computers and having a client-server relationship with each other. The server may be a cloud server, a server in a distributed system, or a server integrated with a blockchain.

[0135] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in this disclosure can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved. This is not limited herein.

[0136] The above specific embodiments do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure shall be included within the scope of protection of this disclosure.

Claims

1. A data recognition method, applied to the cleaning of industrial and commercial data in a financial knowledge graph, comprising: Acquire first business data of a first entity and second business data of a second entity, wherein the first business data includes features of a target object of the first entity, and the second business data includes features of the target object of the second entity; Obtaining relationship data between the first entity and the second entity; the relationship data includes a preset direct relationship and an indirect relationship; the preset direct relationship includes a direct investment relationship and an employment relationship; The indirect relationship includes indirect equity relationship; If the relationship data between the first entity and the second entity meets a preset condition, identifying target objects with the same characteristics included in the first entity and the second entity as the same target object, where the target object is a natural person; The identifying target objects having the same features as those included in the first entity and the second entity as the same target object when the relationship data between the first entity and the second entity meets a preset condition includes: If the number of target objects with the same characteristics included in the first entity and the second entity is greater than a preset value, identifying the target objects with the same characteristics included in the first entity and the second entity as the same target object; In the case that there is an indirect relationship within N degrees between the first entity and the second entity, target objects with the same features included in the first entity and the second entity are identified as the same target object, where N is an integer greater than 1.

2. The method according to claim 1, wherein The identifying target objects having the same features as those included in the first entity and the second entity as the same target object when the relationship data between the first entity and the second entity meets a preset condition includes: In a case where there is a preset direct relationship between the first entity and the second entity, target objects having the same features as those included in the first entity and the second entity are identified as the same target object.

3. The method according to claim 1, further comprising: identifying a plurality of target objects having the same features as the first entity as the same target object; A plurality of target objects with the same features included in the second entity are identified as the same target object.

4. The method according to any one of claims 1 to 3, further comprising: After identifying the target objects having the same features as those included in the first entity and the second entity as the same target object, a relationship graph between the first entity and the second entity is generated based on the identification result.

5. A data recognition device, used for cleaning business data in a financial knowledge graph, comprising: A first acquisition module is configured to acquire first business data of a first entity and second business data of a second entity, wherein the first business data includes features of a target object of the first entity, and the second business data includes features of the target object of the second entity; A second acquisition module is configured to acquire relationship data between the first entity and the second entity; the relationship data includes a preset direct relationship and an indirect relationship; the preset direct relationship includes a direct investment relationship and an employment relationship; The indirect relationship includes indirect equity relationship; an identification module, configured to identify target objects with the same characteristics included in the first entity and the second entity as the same target object if the relationship data between the first entity and the second entity meets a preset condition, wherein the target object is a natural person; The identification module includes a first identification submodule, and the first identification submodule is configured to: If the number of target objects with the same characteristics included in the first entity and the second entity is greater than a preset value, identifying the target objects with the same characteristics included in the first entity and the second entity as the same target object; The identification module includes a third identification submodule, and the third identification submodule is used to: In the case that there is an indirect relationship within N degrees between the first entity and the second entity, target objects with the same features included in the first entity and the second entity are identified as the same target object, where N is an integer greater than 1.

6. The device according to claim 5, wherein The identification module includes a second identification submodule, and the second identification submodule is configured to: In a case where there is a preset direct relationship between the first entity and the second entity, target objects having the same features as those included in the first entity and the second entity are identified as the same target object.

7. The apparatus according to claim 5, wherein the identification module comprises a fourth identification submodule, wherein the fourth identification submodule is configured to: identifying a plurality of target objects having the same features as the first entity as the same target object; A plurality of target objects with the same features included in the second entity are identified as the same target object.

8. The apparatus according to any one of claims 5 to 7, further comprising: A generation module is used to generate a relationship graph between the first entity and the second entity based on the recognition result after identifying the target objects with the same features included in the first entity and the second entity as the same target object.

9. An electronic device comprising: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 4.

10. A non-transitory computer-readable storage medium storing computer instructions, wherein: The computer instructions are used to cause the computer to execute the method according to any one of claims 1 to 4.

11. A computer program product comprising a computer program, which, when executed by a processor, implements the method according to any one of claims 1 to 4.

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