CN113191784A

CN113191784A - Abnormal enterprise identification method and device, electronic equipment and storage medium

Info

Publication number: CN113191784A
Application number: CN202110442841.8A
Authority: CN
Inventors: 鲁良; 黄文瀚; 程浩
Original assignee: Beijing Jindi Credit Service Co ltd
Current assignee: Beijing Jindi Credit Service Co ltd
Priority date: 2021-04-23
Filing date: 2021-04-23
Publication date: 2021-07-30

Abstract

The embodiment of the disclosure provides an abnormal enterprise identification method and device, electronic equipment and a storage medium, and relates to the technical field of data processing. The specific implementation scheme is as follows: acquiring enterprise main body data, wherein the enterprise main body data comprises an enterprise main body object and enterprise information data corresponding to the enterprise main body object; extracting at least one characteristic data in the enterprise information data, wherein the characteristic data is associated with an enterprise main body object corresponding to the enterprise information data; judging whether the characteristic data associated with the enterprise subject object meets preset abnormal enterprise conditions or not; when the characteristic data associated with the enterprise main body object accords with the preset abnormal enterprise condition, the abnormal enterprise label corresponding to the preset abnormal enterprise condition is identified to the enterprise main body object associated with the characteristic data according to the preset scheme, so that the situation that a user manually inquires and analyzes the enterprise information data can be avoided, whether the enterprise has a certain abnormal enterprise label or not can be directly determined, the inquiry efficiency is high, and the utilization rate of the enterprise main body data is high.

Description

Abnormal enterprise identification method and device, electronic equipment and storage medium

Technical Field

The present disclosure relates to the field of data processing technologies, and in particular, to a method and an apparatus for identifying an abnormal enterprise, an electronic device, and a storage medium.

Background

At present, enterprise main body data exists in the form of an enterprise main body object table and a plurality of characteristic data tables, when a user inquires characteristic data of a certain enterprise, the enterprise main body object is required to obtain each characteristic data of the enterprise from the plurality of characteristic data tables, the user is required to manually analyze each characteristic data of the enterprise to judge whether the enterprise is an abnormal enterprise, investment is carried out according to an analysis result, and the like, the inquiry efficiency is poor, and the utilization rate of the enterprise main body data is low.

Disclosure of Invention

The invention provides an abnormal enterprise identification method, an abnormal enterprise identification device, electronic equipment and a storage medium, so that a user is prevented from manually inquiring enterprise information data and analyzing the enterprise information data at least to a certain extent, whether an enterprise has a certain abnormal enterprise label or not can be directly determined, the inquiry efficiency is high, and the utilization rate of enterprise main body data is high.

Additional features and advantages of the disclosure will be set forth in the detailed description which follows, or in part will be obvious from the description, or may be learned by practice of the disclosure.

According to a first aspect of the present disclosure, there is provided an abnormal enterprise identification method, including: acquiring enterprise main body data, wherein the enterprise main body data comprises an enterprise main body object and enterprise information data corresponding to the enterprise main body object; extracting at least one characteristic data from the enterprise information data, wherein the characteristic data is associated with the enterprise main body object corresponding to the enterprise information data; judging whether the characteristic data associated with the enterprise subject object meets preset abnormal enterprise conditions or not; and when the characteristic data associated with the enterprise main body object meets the preset abnormal enterprise condition, identifying the abnormal enterprise label corresponding to the met preset abnormal enterprise condition to the enterprise main body object associated with the characteristic data according to a preset scheme.

In an exemplary embodiment of the present disclosure, the feature data includes: enterprise legal data and enterprise establishment time data;

the judging whether the characteristic data associated with the enterprise main body meets the preset abnormal enterprise condition or not comprises the following steps: screening enterprise subject objects with the same associated enterprise legal data to form a first suspected data set; judging whether the number of the enterprise subject objects in the first suspected data set is larger than a first preset number of enterprise subject objects; when the number of the enterprise subject objects in the first suspected data set is larger than the first preset number of the enterprise subject objects, comparing whether the maximum difference value of enterprise establishment time data associated with the enterprise subject objects in the first suspected data set is smaller than a first standard time difference or not; and when the maximum difference value is smaller than the first standard time difference, judging that the enterprise subject object in the first suspected data set meets a preset abnormal enterprise condition corresponding to a first abnormal enterprise label.

In an exemplary embodiment of the present disclosure, the feature data further includes: at least one enterprise high-management personnel data and at least one enterprise shareholder data; the method further comprises the following steps:

acquiring a plurality of enterprise subject objects marked with the first abnormal enterprise tags to form a second suspected data set; in the second suspected data set, acquiring a candidate enterprise subject object which is associated with the same corporate data, the same shareholder data or the same enterprise high-management data and has at least one contact relation according to a preset algorithm; and determining that the candidate enterprise subject object meets a preset abnormal enterprise condition corresponding to a second abnormal enterprise tag.

In an exemplary embodiment of the present disclosure, the method further comprises: and filtering enterprise main body objects which are related to the second suspected data set and have the same enterprise legal person data, enterprise high management person data and enterprise shareholder number.

In an exemplary embodiment of the present disclosure, the shareholder data comprises shareholder identification data and shareholder type data; the determination method of whether the at least one stock of east data is the same comprises the following steps: when the shareholder identification data are the same, judging whether the shareholder type data corresponding to the same shareholder identification data are natural persons or not; when the person is a natural person, the shareholder data is determined to be the same.

In an exemplary embodiment of the present disclosure, the feature data further includes: enterprise legal person data, enterprise establishment time data, enterprise operation range data, stockholder data and registration address data; the enterprise legal person data comprises enterprise legal person type data and legal person identification data;

the judging whether the characteristic data associated with the enterprise main body meets preset abnormal enterprise conditions or not comprises the following steps: screening enterprise subject objects which have the same associated enterprise corporate data, are natural persons and do not comprise the enterprise corporate data in the shareholder data to form a third suspected data set; judging whether the number of the enterprise subject objects in the third suspected data set is larger than the number of second preset enterprise subject objects; if the number of the enterprise subject objects is larger than the second preset number of the enterprise subject objects, judging whether enterprise operation range data, registered address data and enterprise establishment time data associated with the enterprise subject objects in the third suspected data set meet preset abnormal judgment conditions or not; and if the preset abnormal judgment condition is met, judging that the enterprise subject object in the third suspected data set meets the preset abnormal enterprise condition corresponding to a third abnormal enterprise label.

In an exemplary embodiment of the present disclosure, the determining whether the enterprise operation range data, the registered address data, and the enterprise establishment time data associated with the enterprise subject object in the third suspected data set meet preset abnormal determination conditions includes: comparing whether the maximum difference value of the enterprise establishment time data associated with the enterprise subject object in the third suspected data set is smaller than the second enterprise establishment time difference; if the time difference is smaller than the second enterprise establishment time difference, screening enterprise main body objects which accord with similar conditions of the operation range among enterprise operation range data associated with the third suspected data set to form a first suspected data subset; screening enterprise main body objects which accord with similar conditions of the registered addresses among the registered address data associated with the third suspected data set to form a second suspected data subset; determining that the enterprise subject object in the union of the first suspected data subset and the second suspected data subset meets the preset abnormality determination condition.

In an exemplary embodiment of the present disclosure, the determining whether the feature data associated with the enterprise subject object meets a preset abnormal enterprise condition includes: judging whether the characteristic data associated with the enterprise main body object contains enterprise abnormal operation data and enterprise abnormal operation data existing time data corresponding to the enterprise abnormal operation data; and if the characteristic data contains enterprise abnormal operation data and the existing time data of the enterprise abnormal operation data corresponding to the enterprise abnormal operation data is larger than the preset existing time data, judging that the enterprise main body object meets the preset abnormal enterprise condition corresponding to the fourth abnormal enterprise label.

In an exemplary embodiment of the present disclosure, the determining whether the feature data associated with the enterprise subject object meets a preset abnormal enterprise condition includes: judging whether the characteristic data associated with the enterprise main body object contains enterprise abnormal operation data and enterprise abnormal operation data existing time data corresponding to the enterprise abnormal operation data; if the characteristic data contains enterprise abnormal operation data and the existing time data of the enterprise abnormal operation data corresponding to the enterprise abnormal operation data is larger than the preset existing time data, judging whether the real payment amount data and the registration address state in the characteristic data meet abnormal conditions or not; and if the abnormal conditions are met, judging that the enterprise subject object meets preset abnormal enterprise conditions corresponding to a fifth abnormal enterprise label.

In an exemplary embodiment of the present disclosure, the determining whether the real payment amount data and the registered address state in the feature data meet an abnormal condition includes: judging whether the real payment amount data is preset abnormal real payment data or not, and judging that the registration address state is a preset abnormal state; and if the real payment amount data is the preset abnormal real payment data or the registration address state is the preset abnormal state, determining that the real payment amount data and the registration address state in the feature data meet the abnormal condition.

In an exemplary embodiment of the present disclosure, the method further comprises: and filtering out the enterprise main body object and the enterprise information data, wherein the corresponding enterprise information data meets the preset filtering condition, in the enterprise main body data.

In an exemplary embodiment of the present disclosure, the business information data includes one or more of industry data, historical name data, and business scoring data.

According to a second aspect of the present disclosure, there is provided an abnormal business identification apparatus, including: the system comprises an acquisition module, a processing module and a display module, wherein the acquisition module is used for acquiring enterprise main body data, and the enterprise main body data comprises an enterprise main body object and enterprise information data corresponding to the enterprise main body object; the extraction module is used for extracting at least one piece of feature data in the enterprise information data, wherein the feature data is associated with the enterprise main body object corresponding to the enterprise information data; the judging module is used for judging whether the characteristic data associated with the enterprise main body object meets preset abnormal enterprise conditions or not; and the processing module is used for identifying the abnormal enterprise tag corresponding to the preset abnormal enterprise condition to the enterprise main body object associated with the characteristic data according to a preset scheme when the characteristic data associated with the enterprise main body object conforms to the preset abnormal enterprise condition.

In an exemplary embodiment of the present disclosure, the feature data includes: enterprise legal data and enterprise establishment time data; the judgment module is specifically used for screening enterprise subject objects with the same associated enterprise legal data to form a first suspected data set; judging whether the number of the enterprise subject objects in the first suspected data set is larger than a first preset number of enterprise subject objects; when the number of the enterprise subject objects in the first suspected data set is larger than the first preset number of the enterprise subject objects, comparing whether the maximum difference value of enterprise establishment time data associated with the enterprise subject objects in the first suspected data set is smaller than a first standard time difference or not; and when the maximum difference value is smaller than the first standard time difference, judging that the enterprise subject object in the first suspected data set meets a preset abnormal enterprise condition corresponding to a first abnormal enterprise label.

In an exemplary embodiment of the present disclosure, the feature data further includes: at least one enterprise high-management personnel data and at least one enterprise shareholder data; the processing module may be further configured to obtain a plurality of enterprise subject objects identified with the first abnormal enterprise tag, and form a second suspected data set; in the second suspected data set, acquiring a candidate enterprise subject object which is associated with the same corporate data, the same shareholder data or the same enterprise high-management data and has at least one contact relation according to a preset algorithm; and determining that the candidate enterprise subject object meets a preset abnormal enterprise condition corresponding to a second abnormal enterprise tag.

In an exemplary embodiment of the disclosure, the processing module is further configured to filter out enterprise subject objects with the same enterprise corporate data, enterprise high manager data, and enterprise shareholder number associated in the second suspected data set.

In an exemplary embodiment of the present disclosure, the feature data further includes: enterprise legal person data, enterprise establishment time data, enterprise operation range data, stockholder data and registration address data; the enterprise legal person data comprises enterprise legal person type data and legal person identification data; the judgment module is specifically configured to screen enterprise subject objects, of which the associated enterprise corporate data are the same, of which the enterprise corporate type data are natural persons and which do not include the enterprise corporate data, to form a third suspected data set; judging whether the number of the enterprise subject objects in the third suspected data set is larger than the number of second preset enterprise subject objects; if the number of the enterprise subject objects is larger than the second preset number of the enterprise subject objects, judging whether enterprise operation range data, registered address data and enterprise establishment time data associated with the enterprise subject objects in the third suspected data set meet preset abnormal judgment conditions or not; and if the preset abnormal judgment condition is met, judging that the enterprise subject object in the third suspected data set meets the preset abnormal enterprise condition corresponding to a third abnormal enterprise label.

In an exemplary embodiment of the disclosure, the determining module may be specifically configured to compare whether a maximum difference value of enterprise establishment time data associated with the enterprise subject object in the third suspected dataset is smaller than a second enterprise establishment time difference; if the time difference is smaller than the second enterprise establishment time difference, screening enterprise main body objects which accord with similar conditions of the operation range among enterprise operation range data associated with the third suspected data set to form a first suspected data subset; screening enterprise main body objects which accord with similar conditions of the registered addresses among the registered address data associated with the third suspected data set to form a second suspected data subset; determining that the enterprise subject object in the union of the first suspected data subset and the second suspected data subset meets the preset abnormality determination condition.

In an exemplary embodiment of the disclosure, the determining module may be specifically configured to determine whether the feature data associated with the enterprise subject object includes abnormal enterprise operation data and time data of the abnormal enterprise operation data corresponding to the abnormal enterprise operation data; and if the characteristic data contains enterprise abnormal operation data and the existing time data of the enterprise abnormal operation data corresponding to the enterprise abnormal operation data is larger than the preset existing time data, judging that the enterprise main body object meets the preset abnormal enterprise condition corresponding to the fourth abnormal enterprise label.

In an exemplary embodiment of the disclosure, the determining module may be specifically configured to determine whether the feature data associated with the enterprise subject object includes abnormal enterprise operation data and time data of the abnormal enterprise operation data corresponding to the abnormal enterprise operation data; if the characteristic data contains enterprise abnormal operation data and the existing time data of the enterprise abnormal operation data corresponding to the enterprise abnormal operation data is larger than the preset existing time data, judging whether the real payment amount data and the registration address state in the characteristic data meet abnormal conditions or not; and if the abnormal conditions are met, judging that the enterprise subject object meets preset abnormal enterprise conditions corresponding to a fifth abnormal enterprise label.

In an exemplary embodiment of the present disclosure, the determining module may be specifically configured to determine whether the real payment amount data is preset abnormal real payment data, and determine that the status of the registered address is a preset abnormal status; and if the real payment amount data is the preset abnormal real payment data and the registration address state is the preset abnormal state, determining that the real payment amount data and the registration address state in the feature data meet the abnormal condition.

In an exemplary embodiment of the disclosure, the processing module is further configured to filter out, from the enterprise subject data, an enterprise subject object and enterprise information data, where the corresponding enterprise information data meets a preset filtering condition.

According to a third aspect, there is provided an electronic device comprising: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to perform the abnormal enterprise identification method as described above via execution of the executable instructions.

According to a fourth aspect, there is provided a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the abnormal business identification method as described above.

According to a fifth aspect, there is provided a computer program product, which when executed by an instruction processor implements the method of abnormal business identification as described above.

According to the technical scheme, the abnormal enterprise identification method, the abnormal enterprise identification device, the electronic equipment and the storage medium in the exemplary embodiment of the disclosure have at least the following advantages and positive effects:

the abnormal enterprise identification method in the embodiment of the disclosure includes the steps of firstly, acquiring enterprise main body data, wherein the enterprise main body data comprises enterprise main body objects and enterprise information data corresponding to the enterprise main body objects; extracting at least one characteristic data in the enterprise information data, wherein the characteristic data is associated with an enterprise main body object corresponding to the enterprise information data; judging whether the characteristic data associated with the enterprise subject object meets preset abnormal enterprise conditions or not; when the characteristic data associated with the enterprise main body object accords with the preset abnormal enterprise condition, the abnormal enterprise label corresponding to the preset abnormal enterprise condition is identified to the enterprise main body object associated with the characteristic data according to the preset scheme, so that the situation that a user manually inquires and analyzes the enterprise information data can be avoided, whether the enterprise has a certain abnormal enterprise label or not can be directly determined, the inquiry efficiency is high, and the utilization rate of the enterprise main body data is high.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and together with the description, serve to explain the principles of the disclosure. It is to be understood that the drawings in the following description are merely exemplary of the disclosure, and that other drawings may be derived from those drawings by one of ordinary skill in the art without the exercise of inventive faculty.

FIG. 1 is a schematic diagram illustrating a system architecture to which the abnormal enterprise identification method of the disclosed embodiments may be applied;

FIG. 2 shows a flow diagram of an abnormal business identification method in an exemplary embodiment of the present disclosure;

FIG. 3 is a schematic diagram of an empty shell party;

FIG. 4 illustrates a block diagram of an abnormal business identification apparatus in an exemplary embodiment of the present disclosure;

fig. 5 shows a block diagram of an electronic device of an abnormal business identification method in an exemplary embodiment of the present disclosure.

Detailed Description

Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many different forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art. The drawings are merely schematic illustrations of the present disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus their repetitive description will be omitted.

Furthermore, the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the disclosure. One skilled in the relevant art will recognize, however, that the subject matter of the present disclosure can be practiced without one or more of the specific details, or with other methods, apparatus, steps, etc. In other instances, well-known structures, methods, devices, implementations, or operations are not shown or described in detail to avoid obscuring aspects of the disclosure.

Furthermore, the terms "first", "second", etc. are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of the present disclosure, "a plurality" means at least two, e.g., two, three, etc., unless explicitly specifically limited otherwise. The symbol "/" generally indicates that the former and latter associated objects are in an "or" relationship.

In the present disclosure, unless otherwise expressly specified or limited, the terms "connected" and the like are to be construed broadly, e.g., as meaning electrically connected or in communication with each other; may be directly connected or indirectly connected through an intermediate. The specific meaning of the above terms in the present disclosure can be understood by those of ordinary skill in the art as appropriate.

Fig. 1 is a schematic diagram illustrating a system architecture to which the abnormal enterprise identification method according to the embodiment of the present disclosure may be applied.

As shown in fig. 1, the system architecture 100 may include one or more of

terminal devices

101, 102, 103, a network 104, and a server 105. The network 104 serves as a medium for providing communication links between the

terminal devices

101, 102, 103 and the server 105. Network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.

It should be understood that the number of terminal devices, networks, and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation. For example, server 105 may be a server cluster comprised of multiple servers, or the like.

The user may use the

terminal devices

101, 102, 103 to interact with the server 105 via the network 104 to receive or send messages or the like. The

terminal devices

101, 102, 103 may be various electronic devices having display screens including, but not limited to, smart phones, tablets, portable and desktop computers, digital cinema projectors, and the like.

The server 105 may be a server that provides various services. For example, the server 105 acquires enterprise subject data, wherein the enterprise subject data includes an enterprise subject object and enterprise information data corresponding to the enterprise subject object; extracting at least one characteristic data in the enterprise information data, wherein the characteristic data is associated with an enterprise main body object corresponding to the enterprise information data; judging whether the characteristic data associated with the enterprise subject object meets preset abnormal enterprise conditions or not; and when the characteristic data associated with the enterprise main body object accords with the preset abnormal enterprise condition, identifying the abnormal enterprise tag corresponding to the met preset abnormal enterprise condition to the enterprise main body object associated with the characteristic data according to a preset scheme.

Terminal device 103 (which may also be terminal device 101 or 102) may send a query request to server 105 over network 104, querying for enterprise subject objects having anomalous enterprise tag identifications.

Fig. 2 shows a flowchart of an abnormal enterprise identification method in an exemplary embodiment of the present disclosure. It should be noted that the execution subject of the embodiment of the present application is the abnormal enterprise recognition device, and the abnormal enterprise recognition device may specifically be the terminal device or the server in fig. 1, or software in the terminal device or the server, or the like.

As shown in fig. 2, the specific implementation process of the abnormal enterprise identification method is as follows:

s201, enterprise main body data is obtained, wherein the enterprise main body data comprises enterprise main body objects and enterprise information data corresponding to the enterprise main body objects.

In this embodiment of the application, there may be two types of enterprise subject objects, one type is an identifier cid of the enterprise itself, and the other type is a serial number gid of the enterprise in an enterprise subject object table (company _ graph), where the two types of identifiers correspond to each other one to one. The characteristic data corresponding to the enterprise main body object are stored in each characteristic data table in a scattered mode, the identification cid and the corresponding characteristic data are stored in some characteristic data tables, the identification gid and the corresponding characteristic data are stored in some characteristic data tables, and the various characteristic data corresponding to the enterprise main body object can be obtained by inquiring each characteristic data table according to the enterprise main body object, and then aggregation is carried out to obtain the enterprise information data corresponding to the enterprise main body object.

In the embodiment of the application, a certain number of enterprise main body objects can be divided from the enterprise main body object table every time, and then each characteristic data table is inquired to obtain the characteristic data corresponding to each enterprise main body object; after the treatment is finished, a certain number of enterprise main body objects are divided again for treatment. The number may be 1000, for example.

In the embodiment of the present application, each feature data table may be, for example, an enterprise type table (company), an enterprise organization information table (company _ clean _ info), an enterprise employee table (company _ human _ relationship), an enterprise shareholder table (equality _ ratio), an enterprise legal person table, an enterprise old industry classification table (company _ category _20170411), an enterprise new industry classification table (company _ category _ new), an enterprise history name table (company _ history _ names), an enterprise geographic location table (company _ gps), an enterprise evaluation table (company _ score), an enterprise illegal exception table (company _ industrial _ info), an enterprise business exception table (company _ abnormal _ info), and the like.

In the embodiment of the application, in order to reduce the data volume that needs to be processed, the enterprise main body data can be filtered, and in the filtering enterprise main body data, the corresponding enterprise information data meets the enterprise main body object and the enterprise information data of the preset filtering condition. Wherein the preset filtering condition comprises at least one of the following conditions: the enterprise type is industrial and commercial, the enterprise operation state is a preset state, and the enterprise organization type is not a preset organization type.

In this embodiment of the present application, in order to further reduce the data amount that needs to be processed, the preset filtering condition includes: the enterprise type is the industry and commerce, the enterprise operation state is the preset state, and the enterprise organization type is not the preset organization type, the filtering can be carried out in a hierarchical manner, and correspondingly, the process of filtering the enterprise main body data by the abnormal enterprise identification device can be, for example, according to the enterprise type in the enterprise information data corresponding to each enterprise main body object, filtering out the enterprise main body object of which the corresponding enterprise type is not the industry and commerce; and according to the enterprise operation state and the enterprise organization type in the enterprise information data corresponding to each enterprise main body object, filtering out the enterprise operation state which is not a preset state and the enterprise organization type which is a preset organization type enterprise main body object. The preset state may be, for example, presence, persistence, immigration, emigration, or the like. The predetermined organization type may be, for example, an individual business entity, a collective ownership system, a branch office, and the like.

In this embodiment of the present application, the enterprise type is business, and it may also be determined whether the property2 field in the enterprise type table is an empty character string. Correspondingly, the type of the enterprise in the preset filtering condition is a business, specifically, the type of the enterprise is a business and the property2 field is an empty character string.

In the embodiment of the application, in order to further reduce the data amount required to be processed, after the enterprise subject objects of which the corresponding enterprise information data do not meet the preset filtering condition are filtered, the various feature data corresponding to the enterprise subject objects are aggregated for each enterprise subject object, so that the enterprise information data corresponding to the enterprise subject objects are obtained, and the data amount required to be processed during aggregation is reduced.

In the embodiment of the application, in order to facilitate user query and improve query efficiency, the enterprise information data may include one or more of industry data, historical name data and enterprise rating data. Specifically, the abnormal enterprise identification device may query the old industry classification table, the new industry classification table, the geographic location table and the enterprise evaluation table according to each enterprise subject object, so as to obtain the old industry type, the new industry type, the geographic location data and the enterprise evaluation data corresponding to each enterprise subject object; and updating the enterprise information data corresponding to each enterprise according to the old industry type, the new industry type, the geographic position data and the enterprise evaluation data corresponding to each enterprise main body object. The enterprise evaluation data may be, for example, the eye-of-day score of the enterprise.

The old industry classification table can comprise three levels of classifications, and the new industry classification table can also comprise three levels of classifications.

S202, extracting at least one characteristic data in the enterprise information data, wherein the characteristic data is associated with an enterprise main body object corresponding to the enterprise information data.

S203, judging whether the characteristic data associated with the enterprise subject object meets the preset abnormal enterprise condition.

In the embodiment of the application, a plurality of preset abnormal enterprise conditions can be provided, and the preset abnormal enterprise conditions correspond to different abnormal enterprise tags respectively. Wherein the abnormal enterprise tag may include at least one of the following tags: a zombie enterprise label, a remote operation enterprise label, a fake plate enterprise label, a puppet enterprise label, a blank gang label, and the like.

In the embodiment of the present application, in a first example, the feature data includes: enterprise legal data and enterprise establishment time data; correspondingly, the process of the abnormal enterprise identification device executing step 203 may be, for example, screening enterprise subject objects with the same associated enterprise legal data to form a first suspected data set; judging whether the number of the enterprise subject objects in the first suspected data set is larger than the number of the first preset enterprise subject objects; when the number of the enterprise subject objects in the first suspected data set is larger than the number of the first preset enterprise subject objects, comparing whether the maximum difference value of enterprise establishment time data associated with the enterprise subject objects in the first suspected data set is smaller than a first standard time difference or not; and when the maximum difference value is smaller than the first standard time difference, judging that the enterprise subject object in the first suspected data set meets the preset abnormal enterprise condition corresponding to the first abnormal enterprise label. Wherein, the first abnormal enterprise label is a fake-licensed enterprise label.

In a second example, the feature data may further include: at least one enterprise high-management personnel data and at least one enterprise shareholder data; correspondingly, in step 203, after the abnormal enterprise recognition device determines whether the enterprise subject object meets the preset abnormal enterprise condition corresponding to the first abnormal enterprise tag, the abnormal enterprise recognition device may further perform the following steps: acquiring a plurality of enterprise subject objects marked with first abnormal enterprise tags to form a second suspected data set; in the second suspected data set, acquiring a candidate enterprise subject object which is associated with the same corporate data, the same shareholder data or the same enterprise high-management data and has at least one contact relation according to a preset algorithm; and determining that the candidate enterprise subject object meets the preset abnormal enterprise condition corresponding to the second abnormal enterprise tag. Wherein the second abnormal business label is referred to as an empty hull partnership label.

Wherein the shareholder data comprises shareholder identification data and shareholder type data; the method for determining whether the at least one stock item data is the same comprises the following steps: when the shareholder identification data are the same, judging whether the shareholder type data corresponding to the same shareholder identification data are natural persons or not; when the person is a natural person, the shareholder data is determined to be the same.

In order to avoid determining that the enterprise subject objects with the identical associated enterprise legal data, enterprise high-master data and enterprise shareholder number accord with the preset abnormal enterprise conditions corresponding to the empty-shell group partner label, the accuracy of abnormal enterprise identification is improved, and the enterprise subject objects with the identical associated enterprise legal data, enterprise high-master data and enterprise shareholder number in the second suspected data set can be filtered.

Wherein, a schematic diagram of the empty shell group can be shown in fig. 3, for example. In fig. 3, the a-trade company and the B-trade company have the same employees of the enterprise, three and four; zhang III is a legal person of the trade company A and is a manager of the trade company B; LiIV is the manager of the trade company A, and is the legal person of the trade company B, and is the manager of the trade company C, and is the stockholder of the trade company D; c trade company and D trade company have the same employee king five and preside six of enterprise; wang is a stockholder of C trading company, and is a legal person of D trading company, and is a stockholder of A trading company, and is a stockholder of B trading company; DongLiu is a legal person of the C trading company and is a manager of the D trading company. The same stock LiIV and Wangwu exist in the trade company A, the trade company B, the trade company C and the trade company D, so that 4 trade companies belong to the same empty-shell partnership, and the 4 trade companies meet preset abnormal enterprise conditions corresponding to the empty-shell partnership labels.

In a third example, the process of the abnormal enterprise recognizing apparatus executing step 203 may be, for example, determining whether the characteristic data associated with the enterprise subject object includes the abnormal enterprise operation data and the time data of the abnormal enterprise operation data corresponding to the abnormal enterprise operation data; and if the characteristic data contains enterprise abnormal operation data and the existing time data of the enterprise abnormal operation data corresponding to the enterprise abnormal operation data is larger than the preset existing time data, judging that the enterprise main body object meets the preset abnormal enterprise condition corresponding to the fourth abnormal enterprise label. Wherein the fourth abnormal enterprise tag refers to a zombie enterprise tag. The preset lifespan data may be, for example, 3 years or the like. The enterprise abnormal operation data can be obtained from an enterprise abnormal operation table, and can mean that the enterprise is listed in an abnormal operation name list.

In a fourth example, the feature data further includes: enterprise legal person data, enterprise establishment time data, enterprise operation range data, stockholder data and registration address data; the enterprise legal data comprises enterprise legal type data and legal identification data; correspondingly, the process of the abnormal enterprise identification device executing step 203 may be, for example, screening enterprise subject objects, of which the associated enterprise corporate data are the same, the enterprise corporate type data are natural persons, and the stakeholder data do not include the enterprise corporate data, to form a third suspected data set; judging whether the number of the enterprise subject objects in the third suspected data set is larger than the number of the second preset enterprise subject objects; if the number of the enterprise main body objects is larger than the second preset number of the enterprise main body objects, judging whether enterprise operation range data, registration address data and enterprise establishment time data related to the enterprise main body objects in the third suspected data set meet preset abnormal judgment conditions or not; and if the preset abnormal judgment condition is met, judging that the enterprise subject object in the third suspected data set meets the preset abnormal enterprise condition corresponding to the third abnormal enterprise label. Wherein the third unusual enterprise tag is a puppet company tag.

The process of judging whether the enterprise business range data, the registration address data and the enterprise establishment time data associated with the enterprise subject object in the third suspected data set meet the preset abnormal judgment condition by the abnormal enterprise identification device may be, for example, comparing whether a maximum difference value of the enterprise establishment time data associated with the enterprise subject object in the third suspected data set is smaller than a second enterprise establishment time difference; if the time difference is smaller than the second enterprise establishment time difference, screening enterprise main body objects which accord with similar conditions of the business scope between enterprise business scope data associated with the third suspected data set to form a first suspected data subset; screening enterprise main body objects which accord with similar conditions of the registration addresses among the registration address data associated with the third suspected data set to form a second suspected data subset; and determining that the enterprise subject objects in the union set of the first suspected data subset and the second suspected data subset meet preset abnormal judgment conditions.

In the fifth example, the abnormal enterprise recognizing apparatus may execute the process of step 203, for example, to determine whether the characteristic data associated with the enterprise main body object includes the abnormal enterprise operation data and the time data of existence of the abnormal enterprise operation data corresponding to the abnormal enterprise operation data; if the characteristic data contains enterprise abnormal operation data and the existing time data of the enterprise abnormal operation data corresponding to the enterprise abnormal operation data is larger than the preset existing time data, judging whether the real payment amount data and the registration address state in the characteristic data meet abnormal conditions or not; and if the abnormal conditions are met, judging that the enterprise subject object meets the preset abnormal enterprise conditions corresponding to the fifth abnormal enterprise label. Wherein, the fifth abnormal enterprise tag refers to a remote operation enterprise tag. The abnormal condition refers to that the registered address state is not available for contact, or the registered address state contains a preset keyword and the real payment amount data is null.

The process of the abnormal enterprise identification device determining whether the real payment amount data and the registered address state in the feature data meet the abnormal condition may be, for example, determining whether the real payment amount data is preset abnormal real payment data and determining that the registered address state is a preset abnormal state; and if the real payment amount data is preset abnormal real payment data or the registration address state is a preset abnormal state, determining that the real payment amount data and the registration address state in the characteristic data meet abnormal conditions.

And S204, when the characteristic data associated with the enterprise main body object meets the preset abnormal enterprise conditions, identifying the abnormal enterprise tag corresponding to the met preset abnormal enterprise conditions to the enterprise main body object associated with the characteristic data according to a preset scheme.

In the embodiment of the present application, the preset scheme may refer to updating the enterprise subject object, the associated feature data, and the abnormal enterprise tag corresponding to the preset abnormal enterprise condition into the abnormal enterprise table.

In addition, after the abnormal enterprise identification device updates the enterprise main body object, the associated feature data and the abnormal enterprise label corresponding to the preset abnormal enterprise condition which is met into the abnormal enterprise table, after the abnormal enterprise query request of the user is received, the abnormal enterprise table can be queried according to the enterprise main body object to be queried in the abnormal enterprise query request, so that the abnormal enterprise label corresponding to the enterprise main body object to be queried and the associated feature data are obtained and displayed, the user is prevented from manually analyzing whether the enterprise meets the preset abnormal enterprise condition corresponding to a certain abnormal enterprise label, the query efficiency is improved, and the utilization rate of the enterprise main body data is improved.

In this embodiment of the present application, in order to ensure the accuracy of the abnormal business table, the method may further include the following steps: monitoring the change condition of the enterprise main body data, and acquiring a first enterprise main body object with the changed corresponding enterprise information data in the enterprise main body data; for the first enterprise main body object, acquiring enterprise information data corresponding to the first enterprise main body object again; and when the enterprise information data corresponding to the first enterprise subject object meets the preset abnormal enterprise conditions, updating the abnormal labels corresponding to the met abnormal enterprise conditions and the updated enterprise information data into the abnormal enterprise table.

In the embodiment of the application, the change condition of the enterprise main body data includes, for example, the change of employees, the change of shareholders, the change of illegal abnormal data, the change of operation abnormal data, and the like of the existing enterprise; for another example, a certain enterprise subject object and its enterprise information data are newly added.

In summary, enterprise subject data is acquired, wherein the enterprise subject data includes an enterprise subject object and enterprise information data corresponding to the enterprise subject object; extracting at least one characteristic data in the enterprise information data, wherein the characteristic data is associated with an enterprise main body object corresponding to the enterprise information data; judging whether the characteristic data associated with the enterprise subject object meets preset abnormal enterprise conditions or not; when the characteristic data associated with the enterprise main body object accords with the preset abnormal enterprise conditions, the abnormal enterprise tags corresponding to the preset abnormal enterprise conditions are identified to the enterprise main body object associated with the characteristic data according to the preset scheme, so that the situation that a user manually inquires and analyzes the enterprise information data can be avoided, whether the enterprise accords with a certain abnormal enterprise tag or not can be judged, whether the enterprise has a certain abnormal enterprise tag or not can be directly determined, the inquiry efficiency is high, and the utilization rate of the enterprise main body data is high.

Fig. 4 schematically shows a block diagram of an abnormal business identification apparatus according to an embodiment of the present disclosure. The abnormal enterprise recognition device provided in the embodiment of the present disclosure may be disposed on a terminal device, may also be disposed on a server, or may be partially disposed on a terminal device and partially disposed on a server, for example, may be disposed on the server 105 in fig. 1 (according to actual replacement), but the present disclosure is not limited thereto.

The abnormal enterprise recognition apparatus 400 provided in the embodiment of the present disclosure includes: an acquisition module 410, a lifting module 420, a judgment module 430 and a processing module 440.

The obtaining module 410 is configured to obtain enterprise subject data, where the enterprise subject data includes an enterprise subject object and enterprise information data corresponding to the enterprise subject object;

an extracting module 420, configured to extract at least one feature data in the enterprise information data, where the feature data is associated with the enterprise subject object corresponding to the enterprise information data;

a judging module 430, configured to judge whether feature data associated with the enterprise subject object meets preset abnormal enterprise conditions;

and the processing module 440 is configured to, when the feature data associated with the enterprise subject object meets the preset abnormal enterprise condition, identify, according to a preset scheme, an abnormal enterprise tag corresponding to the met preset abnormal enterprise condition to the enterprise subject object associated with the feature data.

As a possible implementation manner of the embodiment of the present application, the feature data includes: corporate data and enterprise establishment time data. Correspondingly, the judgment module 430 may be specifically configured to screen enterprise subject objects with the same associated enterprise legal data to form a first suspected data set; judging whether the number of the enterprise subject objects in the first suspected data set is larger than a first preset number of enterprise subject objects; when the number of the enterprise subject objects in the first suspected data set is larger than the first preset number of the enterprise subject objects, comparing whether the maximum difference value of enterprise establishment time data associated with the enterprise subject objects in the first suspected data set is smaller than a first standard time difference or not; and when the maximum difference value is smaller than the first standard time difference, judging that the enterprise subject object in the first suspected data set meets a preset abnormal enterprise condition corresponding to a first abnormal enterprise label.

As a possible implementation manner of the embodiment of the present application, the feature data further includes: at least one enterprise high-management personnel data and at least one enterprise shareholder data; the processing module 440 may be further configured to obtain a plurality of enterprise subject objects identified with the first abnormal enterprise tag to form a second suspected data set; in the second suspected data set, acquiring a candidate enterprise subject object which is associated with the same corporate data, the same shareholder data or the same enterprise high-management data and has at least one contact relation according to a preset algorithm; and determining that the candidate enterprise subject object meets a preset abnormal enterprise condition corresponding to a second abnormal enterprise tag.

As a possible implementation manner of the embodiment of the application, the processing module 440 is further configured to filter out enterprise subject objects that are associated with the second suspected data set and have the same enterprise corporate data, enterprise high manager data, and enterprise shareholder number.

As a possible implementation manner of the embodiment of the present application, the shareholder data includes shareholder identification data and shareholder type data; the determination method of whether the at least one stock of east data is the same comprises the following steps: when the shareholder identification data are the same, judging whether the shareholder type data corresponding to the same shareholder identification data are natural persons or not; when the person is a natural person, the shareholder data is determined to be the same.

As a possible implementation manner of the embodiment of the present application, the feature data further includes: enterprise legal person data, enterprise establishment time data, enterprise operation range data, stockholder data and registration address data; the enterprise corporate data includes enterprise corporate type data and corporate identification data. The determining module 430 may be specifically configured to screen enterprise subject objects, of which the associated enterprise corporate data are the same, of which the enterprise corporate type data are natural persons, and of which the shareholder data do not include the enterprise corporate data, to form a third suspected data set; judging whether the number of the enterprise subject objects in the third suspected data set is larger than the number of second preset enterprise subject objects; if the number of the enterprise subject objects is larger than the second preset number of the enterprise subject objects, judging whether enterprise operation range data, registered address data and enterprise establishment time data associated with the enterprise subject objects in the third suspected data set meet preset abnormal judgment conditions or not; and if the preset abnormal judgment condition is met, judging that the enterprise subject object in the third suspected data set meets the preset abnormal enterprise condition corresponding to a third abnormal enterprise label.

As a possible implementation manner of the embodiment of the application, the determining module 430 may be specifically configured to compare whether a maximum difference value of enterprise establishment time data associated with the enterprise subject object in the third suspected data set is smaller than a second enterprise establishment time difference; if the time difference is smaller than the second enterprise establishment time difference, screening enterprise main body objects which accord with similar conditions of the operation range among enterprise operation range data associated with the third suspected data set to form a first suspected data subset; screening enterprise main body objects which accord with similar conditions of the registered addresses among the registered address data associated with the third suspected data set to form a second suspected data subset; determining that the enterprise subject object in the union of the first suspected data subset and the second suspected data subset meets the preset abnormality determination condition.

As a possible implementation manner of the embodiment of the present application, the determining module 430 may be specifically configured to determine whether the feature data associated with the enterprise main body object includes abnormal enterprise operation data and time data of the abnormal enterprise operation data corresponding to the abnormal enterprise operation data; and if the characteristic data contains enterprise abnormal operation data and the existing time data of the enterprise abnormal operation data corresponding to the enterprise abnormal operation data is larger than the preset existing time data, judging that the enterprise main body object meets the preset abnormal enterprise condition corresponding to the fourth abnormal enterprise label.

As a possible implementation manner of the embodiment of the present application, the determining module 430 may be specifically configured to determine whether the feature data associated with the enterprise main body object includes abnormal enterprise operation data and time data of the abnormal enterprise operation data corresponding to the abnormal enterprise operation data; if the characteristic data contains enterprise abnormal operation data and the existing time data of the enterprise abnormal operation data corresponding to the enterprise abnormal operation data is larger than the preset existing time data, judging whether the real payment amount data and the registration address state in the characteristic data meet abnormal conditions or not; and if the abnormal conditions are met, judging that the enterprise subject object meets preset abnormal enterprise conditions corresponding to a fifth abnormal enterprise label.

As a possible implementation manner of the embodiment of the application, the determining module 430 may be specifically configured to determine whether the real payment amount data is preset abnormal real payment data, and determine that the registration address state is a preset abnormal state; and if the real payment amount data is the preset abnormal real payment data and the registration address state is the preset abnormal state, determining that the real payment amount data and the registration address state in the feature data meet the abnormal condition.

As a possible implementation manner of the embodiment of the application, the processing module 440 is further configured to filter out, from the enterprise subject data, an enterprise subject object and enterprise information data, of which corresponding enterprise information data meets a preset filtering condition.

As a possible implementation manner of the embodiment of the present application, the enterprise information data includes one or more of industry data, historical name data, and enterprise rating data.

The specific implementation of each module, unit and subunit in the abnormal enterprise identification apparatus provided in the embodiment of the present disclosure may refer to the content in the abnormal enterprise identification method, and is not described herein again.

It should be noted that although several modules, units and sub-units of the apparatus for action execution are mentioned in the above detailed description, such division is not mandatory. Indeed, the features and functionality of two or more modules, units and sub-units described above may be embodied in one module, unit and sub-unit, in accordance with embodiments of the present disclosure. Conversely, the features and functions of one module, unit and sub-unit described above may be further divided into embodiments by a plurality of modules, units and sub-units.

As shown in fig. 5, the example electronic device 50 includes a processor 501 for executing software routines although a single processor is shown for clarity, the electronic device 50 may include a multi-processor system. The processor 501 is connected to a communication infrastructure 502 for communicating with other components of the electronic device 50. The communication infrastructure 502 may include, for example, a communication bus, a crossbar, or a network.

Electronic device 50 also includes Memory, such as Random Access Memory (RAM), which may include a main Memory 503 and a secondary Memory 510. The secondary memory 510 may include, for example, a hard disk drive 511 and/or a removable storage drive 512, and the removable storage drive 512 may include a floppy disk drive, a magnetic tape drive, an optical disk drive, etc. The removable storage drive 512 reads from and/or writes to a removable storage unit 513 in a conventional manner. Removable storage unit 513 may comprise a floppy disk, magnetic tape, optical disk, etc. which is read by and written to by removable storage drive 512. As will be appreciated by one skilled in the relevant art, the removable storage unit 513 includes a computer-readable storage medium having stored thereon computer-executable program code instructions and/or data.

In an alternative embodiment, secondary memory 510 may additionally or alternatively include other similar means for allowing computer programs or other instructions to be loaded into electronic device 50. Such means may include, for example, a removable storage unit 521 and an interface 520. Examples of the removable storage unit 521 and the interface 520 include: a program cartridge and cartridge interface (such as that found in video game console devices), a removable memory chip (such as an EPROM, or PROM) and associated socket, and other removable storage units 521 and interfaces 520 which allow software and data to be transferred from the removable storage unit 521 to electronic device 50.

The electronic device 50 also includes at least one communication interface 540. Communications interface 540 allows software and data to be transferred between electronic device 50 and external devices via communications path 541. In various embodiments of the present invention, communication interface 540 allows data to be transferred between electronic device 50 and a data communication network, such as a public data or private data communication network. The communication interface 540 may be used to exchange data between different electronic devices 50, which electronic devices 50 form part of an interconnected computer network. Examples of communication interface 540 may include a modem, a network interface (such as an ethernet card), a communication port, an antenna with associated circuitry, and so forth. The communication interface 540 may be wired or may be wireless. Software and data transferred via communications interface 540 are in the form of signals which may be electronic, magnetic, optical or other signals capable of being received by communications interface 540. These signals are provided to a communications interface via a communications path 541.

As shown in fig. 5, the electronic device 50 also includes a display interface 531 for performing operations to render images to an associated display 530, and an audio interface 532 for performing operations to play audio content through associated speakers 533.

In this document, the term "computer program product" may refer, in part, to: a removable storage unit 513, a removable storage unit 521, a hard disk installed in the hard disk drive 511, or a carrier wave carrying software through a communication path 541 (wireless link or cable) to a communication interface 540. Computer-readable storage media refers to any non-transitory tangible storage medium that provides recorded instructions and/or data to electronic device 50 for execution and/or processing. Examples of such storage media include floppy disks, magnetic tape, CD-ROMs, DVDs, Blu-ray optical disks, hard drives, ROMs, or integrated circuits, USB memory, magneto-optical disks, or a computer-readable card, such as a PCMCIA card, among others, whether internal or external to the electronic device 50. Transitory or non-tangible computer-readable transmission media may also participate in providing software, applications, instructions, and/or data to the electronic device 50, examples of such transmission media including radio or infrared transmission channels, network connections to another computer or another networked device, and the internet or intranet including e-mail transmissions and information recorded on websites and the like.

Computer programs (also called computer program code) are stored in the main memory 503 and/or the secondary memory 510. Computer programs may also be received via communications interface 540. Such computer programs, when executed, enable the electronic device 50 to perform one or more features of embodiments discussed herein. In various embodiments, the computer programs, when executed, enable the processor 501 to perform the features of the embodiments described above. Accordingly, such computer programs represent controllers of the computer system 50.

The software may be stored in a computer program product and loaded into electronic device 50 using removable storage drive 512, hard disk drive 511, or interface 520. Alternatively, the computer program product may be downloaded to computer system 50 over communications path 541. The software, when executed by the processor 501, causes the electronic device 50 to perform the functions of the embodiments described herein.

It should be understood that the embodiment of fig. 5 is given by way of example only. Accordingly, in some embodiments, one or more features of electronic device 50 may be omitted. Also, in some embodiments, one or more features of electronic device 50 may be combined together. Additionally, in some embodiments, one or more features of electronic device 50 may be separated into one or more components.

It will be appreciated that the elements shown in fig. 5 serve to provide a means for performing the various functions and operations of the server described in the above embodiments.

In one embodiment, a server may be generally described as a physical device including at least one processor and at least one memory including computer program code. The at least one memory and the computer program code are configured to, with the at least one processor, cause the physical device to perform necessary operations.

Embodiments of the present application also provide a computer-readable storage medium, on which a computer program is stored, where the computer program is executed by a processor to implement the functions of the method shown in fig. 2.

Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (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 technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by an electronic device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.

From the above description of the embodiments, it is clear to those skilled in the art that the embodiments of the present disclosure can be implemented by software plus necessary general hardware platform. Based on such understanding, the technical solutions of the embodiments of the present specification may be essentially or partially implemented in the form of a software product, which may be stored in a storage medium, such as a ROM/RAM, a magnetic disk, an optical disk, etc., and includes several instructions for enabling a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments of the present specification.

The basic principles of the present invention have been described above with reference to specific embodiments, but it should be noted that the advantages, effects, etc. mentioned in the present invention are only examples and are not limiting, and the advantages, effects, etc. must not be considered to be possessed by various embodiments of the present invention. Furthermore, the foregoing disclosure of specific details is for the purpose of illustration and description and is not intended to be limiting, since the invention is not limited to the specific details described above.

In the present specification, the embodiments are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same or similar parts in the embodiments are referred to each other. For the system embodiment, since it basically corresponds to the method embodiment, the description is relatively simple, and for the relevant points, reference may be made to the partial description of the method embodiment.

Those of ordinary skill in the art will understand that: all or a portion of the steps of implementing the above-described method embodiments may be performed by hardware associated with program instructions.

The method and apparatus of the present invention may be implemented in a number of ways. For example, the methods and apparatus of the present invention may be implemented in software, hardware, firmware, or any combination of software, hardware, and firmware. The above-described order for the steps of the method is for illustrative purposes only, and the steps of the method of the present invention are not limited to the order specifically described above unless specifically indicated otherwise. Furthermore, in some embodiments, the present invention may also be embodied as a program recorded in a recording medium, the program including machine-readable instructions for implementing a method according to the present invention. Thus, the present invention also covers a recording medium storing a program for executing the method according to the present invention.

The description of the present invention has been presented for purposes of illustration and description, and is not intended to be exhaustive or limited to the invention in the form disclosed. Many modifications and variations will be apparent to practitioners skilled in this art. The embodiment was chosen and described in order to best explain the principles of the invention and the practical application, and to enable others of ordinary skill in the art to understand the invention for various embodiments with various modifications as are suited to the particular use contemplated.

Claims

1. An abnormal enterprise identification method is characterized by comprising the following steps:

acquiring enterprise main body data, wherein the enterprise main body data comprises an enterprise main body object and enterprise information data corresponding to the enterprise main body object;

extracting at least one characteristic data from the enterprise information data, wherein the characteristic data is associated with the enterprise main body object corresponding to the enterprise information data;

judging whether the characteristic data associated with the enterprise subject object meets preset abnormal enterprise conditions or not;

and when the characteristic data associated with the enterprise main body object meets the preset abnormal enterprise condition, identifying the abnormal enterprise label corresponding to the met preset abnormal enterprise condition to the enterprise main body object associated with the characteristic data according to a preset scheme.

2. The abnormal business identification method of claim 1, wherein the characteristic data comprises: enterprise legal data and enterprise establishment time data;

the judging whether the characteristic data associated with the enterprise main body meets the preset abnormal enterprise condition or not comprises the following steps:

screening enterprise subject objects with the same associated enterprise legal data to form a first suspected data set;

judging whether the number of the enterprise subject objects in the first suspected data set is larger than a first preset number of enterprise subject objects;

when the number of the enterprise subject objects in the first suspected data set is larger than the first preset number of the enterprise subject objects, comparing whether the maximum difference value of enterprise establishment time data associated with the enterprise subject objects in the first suspected data set is smaller than a first standard time difference or not;

and when the maximum difference value is smaller than the first standard time difference, judging that the enterprise subject object in the first suspected data set meets a preset abnormal enterprise condition corresponding to a first abnormal enterprise label.

3. The abnormal business identification method of claim 2, wherein the characterization data further comprises: at least one enterprise high-management personnel data and at least one enterprise shareholder data;

the method further comprises the following steps:

acquiring a plurality of enterprise subject objects marked with the first abnormal enterprise tags to form a second suspected data set;

in the second suspected data set, acquiring a candidate enterprise subject object which is associated with the same corporate data, the same shareholder data or the same enterprise high-management data and has at least one contact relation according to a preset algorithm;

and determining that the candidate enterprise subject object meets a preset abnormal enterprise condition corresponding to a second abnormal enterprise tag.

4. The abnormal business identification method of claim 3, further comprising:

and filtering enterprise main body objects which are related to the second suspected data set and have the same enterprise legal person data, enterprise high management person data and enterprise shareholder number.

5. The abnormal business identification method of claim 3, wherein the shareholder data comprises shareholder identification data and shareholder type data;

the determination method of whether the at least one stock of east data is the same comprises the following steps:

when the shareholder identification data are the same, judging whether the shareholder type data corresponding to the same shareholder identification data are natural persons or not;

when the person is a natural person, the shareholder data is determined to be the same.

6. The abnormal business identification method of claim 1, wherein the characterization data further comprises: enterprise legal person data, enterprise establishment time data, enterprise operation range data, stockholder data and registration address data; the enterprise legal person data comprises enterprise legal person type data and legal person identification data;

the judging whether the characteristic data associated with the enterprise main body meets preset abnormal enterprise conditions or not comprises the following steps:

screening enterprise subject objects which have the same associated enterprise corporate data, are natural persons and do not comprise the enterprise corporate data in the shareholder data to form a third suspected data set;

judging whether the number of the enterprise subject objects in the third suspected data set is larger than the number of second preset enterprise subject objects;

if the number of the enterprise subject objects is larger than the second preset number of the enterprise subject objects, judging whether enterprise operation range data, registered address data and enterprise establishment time data associated with the enterprise subject objects in the third suspected data set meet preset abnormal judgment conditions or not;

and if the preset abnormal judgment condition is met, judging that the enterprise subject object in the third suspected data set meets the preset abnormal enterprise condition corresponding to a third abnormal enterprise label.

7. The abnormal enterprise identification method according to claim 6, wherein the determining whether the enterprise business range data, the registered address data and the enterprise establishment time data associated with the enterprise subject object in the third suspected data set meet a preset abnormal determination condition includes:

comparing whether the maximum difference value of the enterprise establishment time data associated with the enterprise subject object in the third suspected data set is smaller than the second enterprise establishment time difference;

if the time difference is smaller than the second enterprise establishment time difference, screening enterprise main body objects which accord with similar conditions of the operation range among enterprise operation range data associated with the third suspected data set to form a first suspected data subset;

screening enterprise main body objects which accord with similar conditions of the registered addresses among the registered address data associated with the third suspected data set to form a second suspected data subset;

determining that the enterprise subject object in the union of the first suspected data subset and the second suspected data subset meets the preset abnormality determination condition.

8. The abnormal enterprise identification method according to claim 1, wherein the determining whether the feature data associated with the enterprise subject object meets preset abnormal enterprise conditions comprises:

judging whether the characteristic data associated with the enterprise main body object contains enterprise abnormal operation data and enterprise abnormal operation data existing time data corresponding to the enterprise abnormal operation data;

and if the characteristic data contains enterprise abnormal operation data and the existing time data of the enterprise abnormal operation data corresponding to the enterprise abnormal operation data is larger than the preset existing time data, judging that the enterprise main body object meets the preset abnormal enterprise condition corresponding to the fourth abnormal enterprise label.

9. The abnormal enterprise identification method according to claim 1, wherein the determining whether the feature data associated with the enterprise subject object meets a preset abnormal enterprise condition comprises:

if the characteristic data contains enterprise abnormal operation data and the existing time data of the enterprise abnormal operation data corresponding to the enterprise abnormal operation data is larger than the preset existing time data, judging whether the real payment amount data and the registration address state in the characteristic data meet abnormal conditions or not;

and if the abnormal conditions are met, judging that the enterprise subject object meets preset abnormal enterprise conditions corresponding to a fifth abnormal enterprise label.

10. The abnormal enterprise identification method according to claim 9, wherein the determining whether the real payment amount data and the registered address status in the feature data meet abnormal conditions includes:

judging whether the real payment amount data is preset abnormal real payment data or not, and judging that the registration address state is a preset abnormal state;

and if the real payment amount data is the preset abnormal real payment data or the registration address state is the preset abnormal state, determining that the real payment amount data and the registration address state in the feature data meet the abnormal condition.

11. The abnormal business identification method of claim 1, further comprising:

and filtering out the enterprise main body object and the enterprise information data, wherein the corresponding enterprise information data meets the preset filtering condition, in the enterprise main body data.

12. The abnormal enterprise identification method of claim 1, wherein the enterprise information data comprises one or more of industry data, historical name data and enterprise rating data.

13. An abnormal business identification apparatus, comprising:

the system comprises an acquisition module, a processing module and a display module, wherein the acquisition module is used for acquiring enterprise main body data, and the enterprise main body data comprises an enterprise main body object and enterprise information data corresponding to the enterprise main body object;

the extraction module is used for extracting at least one piece of feature data in the enterprise information data, wherein the feature data is associated with the enterprise main body object corresponding to the enterprise information data;

the judging module is used for judging whether the characteristic data associated with the enterprise main body object meets preset abnormal enterprise conditions or not;

and the processing module is used for identifying the abnormal enterprise tag corresponding to the preset abnormal enterprise condition to the enterprise main body object associated with the characteristic data according to a preset scheme when the characteristic data associated with the enterprise main body object conforms to the preset abnormal enterprise condition.

14. An electronic device, comprising:

a processor; and

a memory for storing executable instructions of the processor;

wherein the processor is configured to perform the method of any of claims 1-12 via execution of the executable instructions.

15. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the method of any one of claims 1-12.