CN112487209A - String mark behavior analysis method based on knowledge graph, terminal equipment and storage medium - Google Patents
String mark behavior analysis method based on knowledge graph, terminal equipment and storage medium Download PDFInfo
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- 230000006399 behavior Effects 0.000 claims abstract description 65
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- 238000010276 construction Methods 0.000 claims description 5
- 210000000689 upper leg Anatomy 0.000 claims description 2
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- 101150039322 outE gene Proteins 0.000 description 4
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- 238000007405 data analysis Methods 0.000 description 2
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/36—Creation of semantic tools, e.g. ontology or thesauri
- G06F16/367—Ontology
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/33—Querying
- G06F16/332—Query formulation
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
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- G06F16/951—Indexing; Web crawling techniques
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Abstract
The invention relates to a string mark behavior analysis method based on a knowledge graph, a terminal device and a storage medium, wherein the method comprises the following steps: s1: collecting bidding announcement data; s2: preprocessing bidding announcement data and identifying bidding related information in the data; s3: acquiring corresponding unit information of the bid according to the unit of the bid related information; s4: constructing a knowledge graph according to the association relationship between the bid related information and the bid unit information; s5: and judging whether the possibility of the existence of the string mark behavior exists according to the constructed knowledge graph. The method and the system use the text processing, knowledge map and other related technologies for analyzing the bidding data, realize the analysis of suspected bid string behaviors from the bidding announcement data, and provide reference for bidding units or enterprises.
Description
Technical Field
The invention relates to the field of bidding data analysis, in particular to a string bid behavior analysis method based on a knowledge graph, a terminal device and a storage medium.
Background
The cross bidding is also called as cross bidding, and generally refers to means and behaviors that several bidders in the bidding industry agree with each other, bid by raising or lowering bid quotations in a consistent manner, and bid by limiting competition to exclude other bidders so as to enable a certain interest related person to bid, thereby earning out benefits. The series bidding not only disturbs the market order and avoids the supervision of the competent department, but also directly hurts the legal rights and interests of other bidders. Therefore, the string marking behavior needs to be authenticated.
Disclosure of Invention
In order to solve the above problems, the present invention provides a method for analyzing string marking behavior based on a knowledge graph, a terminal device and a storage medium.
The specific scheme is as follows:
a string mark behavior analysis method based on knowledge graph includes the following steps:
s1: collecting bidding announcement data;
s2: preprocessing bidding announcement data and identifying bidding related information in the data;
s3: acquiring corresponding unit information of the bid according to the unit of the bid related information;
s4: constructing a knowledge graph according to the association relationship between the bid related information and the bid unit information;
s5: and judging whether the possibility of the existence of the string mark behavior exists according to the constructed knowledge graph.
Further, the unit information of the reference mark comprises enterprise and commercial information, enterprise legal personnel and stock control relation.
Further, the knowledge-graph comprises: setting three types of vertexes according to the types of the information, wherein the three types of vertexes are respectively as follows: a bidding announcement vertex, an enterprise information vertex and a personnel vertex; five types of edges are set from the three types of vertices, respectively: reference mark limit, well mark limit, accuse thigh limit, french man limit and high pipe limit, wherein: the bid-participating side points to the bid-inviting announcement vertex from the enterprise information vertex, the bid-winning side points to the bid-inviting announcement vertex from the enterprise information vertex, the stock-controlling side points to the enterprise information vertex from the enterprise information vertex of the investment enterprise or the personnel vertex of the investor, the legal person side points to the enterprise information vertex of the enterprise from the personnel vertex of the legal person, and the high-management side points to the enterprise information vertex of the enterprise from the high-management personnel vertex.
Further, the construction process of the knowledge graph comprises the following steps:
s401: establishing a bidding announcement vertex according to the bidding announcement data;
s402: establishing an enterprise information vertex according to the bid participation unit and the bid winning unit, and establishing a corresponding bid participation edge and a bid winning edge pointing to the bid inviting and bidding announcement vertex;
s403: establishing a staff vertex according to the acquired unit information of the reference mark, and establishing a legal side and a high management side which point to the top of the enterprise information;
s404: if the acquired unit information of the reference mark comprises a stock control relation, establishing a corresponding enterprise information vertex and a corresponding personnel vertex according to the stock control relation, and establishing a stock control edge pointing to the enterprise information vertex corresponding to the invested enterprise;
s405: and repeating the steps S401 to S404 until the construction of the knowledge graph is completed.
Further, the method for determining the string marking behavior in step S5 includes: and finding out all associated vertexes corresponding to each bidding bulletin vertex, and if a plurality of enterprise information vertexes have the same personnel vertex or have associated stock control edges, judging the possibility that the enterprises corresponding to the enterprise information vertexes have the serial bidding behavior.
Further, the method for determining the string marking behavior in step S5 includes: if an enterprise information vertex which is associated with a plurality of bidding announcement vertexes and does not have any edge as a winning edge exists, judging the possibility that an enterprise corresponding to the enterprise information vertex has a bid string behavior; and if the bidding notice vertex associated with the business information vertex has other business information vertices, judging that the business corresponding to the business information vertex has the possibility of the cross bidding behavior.
Further, the method for determining the string marking behavior in step S5 includes: and if a certain person vertex has a plurality of legal people edges which are associated to different enterprise information vertexes, and the associated enterprise information vertexes are associated to the same bidding announcement vertex, judging the possibility that the enterprises corresponding to the enterprise information vertexes have the cross bidding behaviors.
Further, the method for determining the string marking behavior in step S5 includes: and if two or more than two enterprise information vertexes are simultaneously associated to a plurality of personnel vertexes, if only one enterprise vertex has a winning bid edge, judging that the possibility of the cross-bidding behavior of the enterprise corresponding to the enterprise information vertexes exists.
A terminal device for analyzing string marking behavior based on knowledge graph comprises a processor, a memory and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the computer program to realize the steps of the method of the embodiment of the invention.
A computer-readable storage medium, in which a computer program is stored, which, when being executed by a processor, carries out the steps of the method as described above for an embodiment of the invention.
According to the technical scheme, the method and the system for analyzing the suspected cross bidding behavior in the bidding data use the text processing, knowledge map and other related technologies for analyzing the bidding data, realize the analysis of the suspected cross bidding behavior in the bidding announcement data, and provide reference for bidding units or enterprises.
Drawings
Fig. 1 is a flowchart illustrating a first embodiment of the present invention.
FIG. 2 is a schematic diagram of a knowledge-graph in this embodiment.
FIG. 3 is a diagram illustrating possible string marking behavior of the knowledge graph in this embodiment.
Detailed Description
To further illustrate the various embodiments, the invention provides the accompanying drawings. The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate embodiments of the invention and, together with the description, serve to explain the principles of the embodiments. Those skilled in the art will appreciate still other possible embodiments and advantages of the present invention with reference to these figures.
The invention will now be further described with reference to the accompanying drawings and detailed description.
The first embodiment is as follows:
the embodiment of the invention provides a string mark behavior analysis method based on a knowledge graph, which is a flow chart of the string mark behavior analysis method based on the knowledge graph, as shown in fig. 1, and the method comprises the following steps:
s1: bidding announcement data is collected.
In this embodiment, the bid/bid notice information collected by the internet crawler is formatted as bid/bid notice data.
S2: the bid notice data is preprocessed to identify bid related information therein.
The bidding related information in this embodiment includes website where the bulletin is located, bidding target, purchasing unit, agency, bidding participating unit, winning unit, and related personnel information, etc., and in other embodiments, other information may be included, which is not limited herein.
S3: and acquiring corresponding unit information of the bid according to the unit of the bid related information.
In the embodiment, some basic information of the reference units, such as corporate legal persons, enterprise business information (such as the names of persons highly managed by the enterprise) and stock control relations (such as the parent company and the subsidiary company of the enterprise), is acquired through the internet.
S4: and constructing a knowledge graph according to the association relation between the bid related information and the bid unit information.
The knowledge-graph in this embodiment comprises:
(1) according to the classification of information, the following three types of vertexes are defined, and the attributes of the vertexes can be set according to application requirements:
Bid-Vertex: and (4) bidding bulletin vertex.
Enterprise-Vertex: and (4) an enterprise information vertex.
Person-Vertex: and (4) a person vertex.
(2) The following five types of edges are set according to the three types of vertices, and the attributes of the edges can be set according to application needs:
In-Bid-Edge: reference edge, from Enterprise-Vertex to Bid-Vertex.
Win-Bid-Edge: and the winning edge points from Enterprise-Vertex to Bid-Vertex.
Invest-Edge: and the stock control edge points to the Enterprise-Verttex from the Enterprise-Vertex or the investor Person-Vertex.
LegalPerson-Edge: and a legal side, which points from the legal Person-Person to the Enterprise Person-Person.
Staff-Edge: and the high-pipe side points from the high-pipe Person-Vertex to the Enterprise-Vertex.
Based on the above knowledge graph structure, the construction process of the knowledge graph in this embodiment includes the following steps:
s401: establishing a bidding announcement vertex according to the bidding announcement data;
s402: establishing an enterprise information vertex according to the bid participation unit and the bid winning unit, and establishing a corresponding bid participation edge and a bid winning edge pointing to the bid inviting and bidding announcement vertex;
s403: establishing a staff vertex according to the acquired unit information of the reference mark, and establishing a legal side and a high management side which point to the top of the enterprise information;
s404: if the acquired unit information of the reference mark comprises a stock control relation, establishing a corresponding enterprise information vertex and a corresponding personnel vertex according to the stock control relation, and establishing a stock control edge pointing to the enterprise information vertex corresponding to the invested enterprise;
s405: and repeating the steps S401 to S404 until the knowledge graph is constructed, as shown in FIG. 2.
S5: and judging whether the possibility of the existence of the string mark behavior exists according to the constructed knowledge graph.
The string marking behavior is divided into the following 3 forms:
a) inviting other accompanying bidding units to bid simultaneously, and increasing the winning probability of the user;
b) combining several units, and taking turns to sit and crowd other bidding units;
c) the same person simultaneously depends on a plurality of units to participate in bidding.
In this embodiment, the following four determination methods are proposed:
(1) and finding out all associated vertexes corresponding to each bidding bulletin vertex, and if a plurality of enterprise information vertexes have the same personnel vertex or have associated stock control edges, judging the possibility that the enterprises corresponding to the enterprise information vertexes have the serial bidding behavior.
As shown in FIG. 3, a business A and a business B have an investment relationship and are all related to the same bidding bulletin vertex, i.e. the possibility of cross-bidding behavior exists.
In the specific operation, all the bidding bulletin vertexes can be analyzed one by one, one bidding bulletin vertex is taken out, all the personnel vertexes and stock control edges in the subtree of the enterprise information vertex are found out for each enterprise information vertex related to the bidding bulletin vertex, the personnel vertexes and the stock control edges in the subtree of the enterprise information vertex are collided with the vertexes and the stock control edges in the subtree of other enterprise information vertexes, and whether the same personnel vertexes or stock control edges exist or not is found, so that the analysis is carried out.
(2) If an enterprise information vertex which is associated with a plurality of bidding announcement vertexes and does not have any edge as a winning edge exists, the enterprise information vertex is represented that the enterprise participates bidding for a plurality of times and never wins the bid, and the possibility that the enterprise corresponding to the enterprise information vertex has a bid string behavior is judged; and if the bidding notice vertex associated with the business information vertex has other business information vertices, judging that the business corresponding to the business information vertex has the possibility of the cross bidding behavior.
The gremlin query statement used in the query is: g.V (). hasLabel ('Enterprise-Vertex'). where (outE ('In-Bid-Edge'). count (). is (gte (4)). and (). outE ('Win-Bid-Edge'). count (). is (0)); the query statement is used for finding all enterprise information vertexes which contain more than 4 referential edges and do not have the winning bid edge.
(3) And if a certain person vertex has a plurality of legal people edges which are associated to different enterprise information vertexes, and the associated enterprise information vertexes are associated to the same bidding announcement vertex, judging the possibility that the enterprises corresponding to the enterprise information vertexes have the cross bidding behaviors.
The gremlin query statement used in the query is: g.V (). hasLabel ('Person-Vertex'). filter (outE ('Legalperson-Edge'). countt (). is (gte (2))). out (). outE (); the query statement is used for finding out all enterprise information vertexes related to personnel vertexes containing more than 2 legal people sides.
(4) And if two or more than two enterprise information vertexes are simultaneously associated to a plurality of personnel vertexes, if only one enterprise vertex has a winning bid edge, judging that the possibility of the cross-bidding behavior of the enterprise corresponding to the enterprise information vertexes exists.
The string bid behavior analysis method based on the knowledge graph provided by the embodiment of the invention uses the text processing, the knowledge graph and other related technologies for bid and ask data analysis, realizes the analysis of suspected string bid behaviors from the bid and ask announcement data, and provides reference for bid and ask units or enterprises.
Example two:
the invention also provides a terminal device for analyzing string marking behavior based on knowledge graph, which comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the computer program to realize the steps of the above method embodiment of the first embodiment of the invention.
Further, as an executable scheme, the terminal device for analyzing string marking behavior based on the knowledge graph may be a desktop computer, a notebook, a palm computer, a cloud server, and other computing devices. The knowledge-graph-based string mark behavior analysis terminal device can comprise, but is not limited to, a processor and a memory. It will be understood by those skilled in the art that the above-mentioned constituent structure of the knowledgegraph-based string tagging behavior analysis terminal device is only an example of the knowledgegraph-based string tagging behavior analysis terminal device, and does not constitute a limitation of the knowledgegraph-based string tagging behavior analysis terminal device, and may include more or less components than the above-mentioned one, or combine some components, or different components, for example, the knowledgegraph-based string tagging behavior analysis terminal device may further include an input-output device, a network access device, a bus, etc., which is not limited by the embodiment of the present invention.
Further, as an executable solution, the Processor may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, a discrete Gate or transistor logic device, a discrete hardware component, and the like. The general-purpose processor may be a microprocessor or the processor may be any conventional processor, etc., and the processor is a control center of the terminal equipment for analyzing string marking behavior based on the knowledge graph, and various interfaces and lines are used to connect various parts of the terminal equipment for analyzing string marking behavior based on the knowledge graph.
The memory can be used for storing the computer program and/or the module, and the processor realizes various functions of the terminal equipment for analyzing the string marking behavior based on the knowledge graph by running or executing the computer program and/or the module stored in the memory and calling data stored in the memory. The memory can mainly comprise a program storage area and a data storage area, wherein the program storage area can store an operating system and an application program required by at least one function; the storage data area may store data created according to the use of the mobile phone, and the like. In addition, the memory may include high speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), at least one magnetic disk storage device, a Flash memory device, or other volatile solid state storage device.
The invention also provides a computer-readable storage medium, in which a computer program is stored, which, when being executed by a processor, carries out the steps of the above-mentioned method of an embodiment of the invention.
The module/unit integrated with the knowledge-graph-based string-marking behavior analysis terminal device can be stored in a computer-readable storage medium if the module/unit is implemented in the form of a software functional unit and sold or used as an independent product. Based on such understanding, all or part of the flow of the method according to the embodiments of the present invention may also be implemented by a computer program, which may be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the method embodiments may be implemented. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), software distribution medium, and the like.
While the invention has been particularly shown and described with reference to a preferred embodiment, it will be understood by those skilled in the art that various changes in form and detail may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (10)
1. A string mark behavior analysis method based on knowledge graph is characterized by comprising the following steps:
s1: collecting bidding announcement data;
s2: preprocessing bidding announcement data and identifying bidding related information in the data;
s3: acquiring corresponding unit information of the bid according to the unit of the bid related information;
s4: constructing a knowledge graph according to the association relationship between the bid related information and the bid unit information;
s5: and judging whether the possibility of the existence of the string mark behavior exists according to the constructed knowledge graph.
2. The method of knowledge-graph-based string-marking behavior analysis according to claim 1, characterized in that: the unit information of the reference mark comprises enterprise and commercial information, enterprise legal personnel and stock control relation.
3. The method of knowledge-graph-based string-marking behavior analysis according to claim 1, characterized in that: the knowledge graph comprises: setting three types of vertexes according to the types of the information, wherein the three types of vertexes are respectively as follows: a bidding announcement vertex, an enterprise information vertex and a personnel vertex; five types of edges are set from the three types of vertices, respectively: reference mark limit, well mark limit, accuse thigh limit, french man limit and high pipe limit, wherein: the bid-participating side points to the bid-inviting announcement vertex from the enterprise information vertex, the bid-winning side points to the bid-inviting announcement vertex from the enterprise information vertex, the stock-controlling side points to the enterprise information vertex from the enterprise information vertex of the investment enterprise or the personnel vertex of the investor, the legal person side points to the enterprise information vertex of the enterprise from the personnel vertex of the legal person, and the high-management side points to the enterprise information vertex of the enterprise from the high-management personnel vertex.
4. The method of knowledge-graph-based string-marking behavior analysis according to claim 3, characterized in that: the construction process of the knowledge graph comprises the following steps:
s401: establishing a bidding announcement vertex according to the bidding announcement data;
s402: establishing an enterprise information vertex according to the bid participation unit and the bid winning unit, and establishing a corresponding bid participation edge and a bid winning edge pointing to the bid inviting and bidding announcement vertex;
s403: establishing a staff vertex according to the acquired unit information of the reference mark, and establishing a legal side and a high management side which point to the top of the enterprise information;
s404: if the acquired unit information of the reference mark comprises a stock control relation, establishing a corresponding enterprise information vertex and a corresponding personnel vertex according to the stock control relation, and establishing a stock control edge pointing to the enterprise information vertex corresponding to the invested enterprise;
s405: and repeating the steps S401 to S404 until the construction of the knowledge graph is completed.
5. The method of knowledge-graph-based string-marking behavior analysis according to claim 1, characterized in that: the method for determining the string marking behavior in step S5 includes: and finding out all associated vertexes corresponding to each bidding bulletin vertex, and if a plurality of enterprise information vertexes have the same personnel vertex or have associated stock control edges, judging the possibility that the enterprises corresponding to the enterprise information vertexes have the serial bidding behavior.
6. The method of knowledge-graph-based string-marking behavior analysis according to claim 1, characterized in that: the method for determining the string marking behavior in step S5 includes: if an enterprise information vertex which is associated with a plurality of bidding announcement vertexes and does not have any edge as a winning edge exists, judging the possibility that an enterprise corresponding to the enterprise information vertex has a bid string behavior; and if the bidding notice vertex associated with the business information vertex has other business information vertices, judging that the business corresponding to the business information vertex has the possibility of the cross bidding behavior.
7. The method of knowledge-graph-based string-marking behavior analysis according to claim 1, characterized in that: the method for determining the string marking behavior in step S5 includes: and if a certain person vertex has a plurality of legal people edges which are associated to different enterprise information vertexes, and the associated enterprise information vertexes are associated to the same bidding announcement vertex, judging the possibility that the enterprises corresponding to the enterprise information vertexes have the cross bidding behaviors.
8. The method of knowledge-graph-based string-marking behavior analysis according to claim 1, characterized in that: the method for determining the string marking behavior in step S5 includes: and if two or more than two enterprise information vertexes are simultaneously associated to a plurality of personnel vertexes, if only one enterprise vertex has a winning bid edge, judging that the possibility of the cross-bidding behavior of the enterprise corresponding to the enterprise information vertexes exists.
9. A string mark behavior analysis terminal equipment based on knowledge graph is characterized in that: comprising a processor, a memory and a computer program stored in the memory and running on the processor, the processor implementing the steps of the method according to any one of claims 1 to 8 when executing the computer program.
10. A computer-readable storage medium storing a computer program, characterized in that: the computer program when executed by a processor implementing the steps of the method as claimed in any one of claims 1 to 8.
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