CN111930961B - Competitive relationship analysis method, device, electronic equipment and storage medium - Google Patents
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Abstract
The invention relates to a data analysis technology, and discloses a competitive relationship analysis method, which comprises the following steps: acquiring original user data of a plurality of users and constructing an original knowledge graph according to the original user data; according to the similarity relation between the data in the original knowledge graph complemented by the neighbor search, obtaining an initial knowledge graph; carrying out suspicious analysis on the initial knowledge graph by using a preset knowledge representation algorithm, finding out suspicious competition data and suspicious competition users, and carrying out completion operation on the initial knowledge graph to obtain a standard knowledge graph; and scoring the suspected competitive users in the standard knowledge graph to obtain the competition relationship among the users. The invention also relates to blockchain technology, in which the raw user data may be stored. The invention also discloses a competitive relationship analysis processing device, electronic equipment and a storage medium. The invention can improve the accuracy of judging the competition relationship.
Description
Technical Field
The present invention relates to the field of data analysis technologies, and in particular, to a competitive relationship analysis method, a competitive relationship analysis device, an electronic device, and a computer readable storage medium.
Background
At present, the law, regulation and judicial explanation does not clearly explain what is the 'competition relationship', and the limitation of the 'competition relationship' in the labor contract law is limited to the general 'same kind of products are produced or managed by the unit, and similar business is performed', but the connotation and extension of the 'same kind of products and similar business' are not clear, and the enterprise is required to be evaluated according to the standards, so that the situation that the judgment of the competition relationship is inaccurate is caused.
Disclosure of Invention
The invention provides a competitive relationship analysis method, a device, an electronic device and a computer readable storage medium, and mainly aims to improve the efficiency of the competitive relationship analysis method.
In order to achieve the above object, the present invention provides a competitive relationship analysis method, including:
Acquiring original user data of a plurality of users from a database in communication connection with the electronic equipment, and constructing an original knowledge graph according to the original user data;
according to the similarity relation between the data in the original knowledge graph complemented by the neighbor search, obtaining an initial knowledge graph;
carrying out suspicious analysis on the initial knowledge graph by using a preset knowledge representation algorithm, finding out suspicious competition data and suspicious competition users, and carrying out completion operation on the initial knowledge graph according to the suspicious competition data and the suspicious competition users to obtain a standard knowledge graph;
And scoring the suspected competitive users according to the standard knowledge graph, obtaining the competition relationship among the users according to the scoring, and outputting the competition relationship through a display screen of the electronic equipment.
Optionally, the constructing an original knowledge graph according to the original user data includes:
carrying out structuring treatment on the original user data to obtain structured data;
Performing entity extraction on the structured data to obtain entity information, and performing relation extraction on the structured data to obtain a correlation;
and carrying out information fusion processing on the entity information and the related relationship to obtain the original knowledge graph.
Optionally, the obtaining the initial knowledge graph by complementing the similarity relationship between the data in the original knowledge graph according to the neighbor search includes:
Sentence vector processing is carried out on the data in the original user data, and a data vector is generated;
And calculating the distance between the data vectors, analyzing the similarity between the data vectors according to the distance, and complementing the similarity relation between the data in the original knowledge graph according to the similarity to obtain an initial knowledge graph.
Optionally, the sentence vector processing is performed on the data in the original user data, so as to generate a data vector, which includes:
the data vector is generated using the following calculation formula:
Where sen vec denotes the data vector, m denotes the number of words contained in each piece of data in the original user data, vec i denotes the word vector of each word.
Optionally, the calculating the distance between the data vectors, analyzing the similarity between the data vectors according to the distance, and complementing the similarity relationship between the data in the original knowledge-graph according to the similarity to obtain an initial knowledge-graph, including:
randomly selecting K data vectors from the data vectors to serve as K clusters;
Randomly selecting one data vector from unselected data vectors as a target data vector, and respectively calculating the square Euclidean distance between the target data vector and K clusters;
If the square Euclidean distance is greater than or equal to a preset distance threshold, adding a similarity relationship between data corresponding to the target data vector in the original knowledge graph and data corresponding to the K data vectors in the original knowledge graph;
And if the square Euclidean distance is smaller than a preset distance threshold value, maintaining the original knowledge graph unchanged.
Optionally, the performing the suspicious analysis on the initial knowledge graph by using a preset knowledge representation algorithm, to find suspicious competitive data and suspicious competitive users, includes:
converting the entity and the related relationship in the initial knowledge graph into vectors with the same dimension;
Performing distance calculation on the vectors with the same dimension according to a preset distance formula, and judging the similarity between the vectors according to the calculated distance;
and determining suspected competition data in the initial knowledge graph according to the similarity, and determining the suspected competition user according to the suspected competition data.
Optionally, the calculating the distance of the vectors in the same dimension according to a preset distance formula includes:
the distance d (h, t) between the vectors is calculated using the following method:
Wherein h is the front entity in a triplet, t is the rear entity in the triplet, r is the correlation in the triplet, and d represents the relationship distance between two entities in the triplet.
In order to solve the above problems, the present invention also provides a competitive relationship analysis method apparatus, the apparatus comprising:
The system comprises an original knowledge graph construction module, a database management module and a database management module, wherein the original knowledge graph construction module is used for acquiring original user data of at least two users from a database in communication connection with the electronic equipment and constructing an original knowledge graph of each user according to the original user data;
The initial knowledge graph construction module is used for complementing the similarity relation between the data in the initial knowledge graph according to the neighbor search to obtain an initial knowledge graph;
The standard knowledge graph construction module is used for carrying out suspicious analysis on the initial knowledge graph by utilizing a preset knowledge representation algorithm, finding out suspicious competition data and suspicious competition users, and carrying out completion operation on the initial knowledge graph by the suspicious competition data and the suspicious competition users to obtain a standard knowledge graph;
And the competition relationship judging module is used for scoring the suspected competition users in the standard knowledge graph to obtain competition relationships among the users, and outputting the competition relationships through a display screen of the electronic equipment.
In order to solve the above-mentioned problems, the present invention also provides an electronic apparatus including:
at least one processor; and
A memory communicatively coupled to the at least one processor; wherein,
The memory stores instructions executable by the at least one processor to cause the at least one processor to perform the competitive relationship analysis method described above.
In order to solve the above-mentioned problems, the present invention also provides a computer-readable storage medium storing a computer program which, when executed by a processor, implements the above-mentioned competitive relationship analysis method.
According to the embodiment of the invention, an original knowledge graph is constructed according to the original user data of a plurality of users, the similarity relation of the data in the original knowledge graph is complemented by utilizing neighbor retrieval to obtain an initial knowledge graph, and the initial knowledge graph is further subjected to suspicious analysis and complementation operation by utilizing a preset knowledge representation algorithm to obtain a standard knowledge graph. The relationship among the users can be more intuitively presented by utilizing the standard knowledge graph, and the suspected competitive users in the standard knowledge graph are scored, so that the competitive relationship among the users can be obtained. Therefore, the competitive relationship analysis method, the device and the computer readable storage medium can improve the efficiency of the competitive relationship analysis method.
Drawings
FIG. 1 is a flow chart of a competitive relationship analysis method according to an embodiment of the present invention;
FIG. 2 is a flowchart illustrating one of the steps in a competitive relationship analysis method according to an embodiment of the present invention;
FIG. 3 is a flowchart illustrating one of the steps in a competitive relationship analysis method according to an embodiment of the present invention;
FIG. 4 is a flowchart illustrating one of the steps in the competitive relationship analysis method according to an embodiment of the present invention;
FIG. 5 is a flowchart illustrating one of the steps in a competitive relationship analysis method according to an embodiment of the present invention;
FIG. 6 is a schematic block diagram illustrating a competitive relationship analysis method according to an embodiment of the present invention;
fig. 7 is a schematic diagram of an internal structure of an electronic device for implementing a competitive relationship analysis method according to an embodiment of the present invention;
the achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
The execution subject of the competitive relationship analysis method provided by the embodiment of the present application includes, but is not limited to, at least one of a server, a terminal, and the like, which can be configured to execute the method provided by the embodiment of the present application. In other words, the competitive relationship analysis method may be performed by software or hardware installed in a terminal device or a server device, and the software may be a blockchain platform. The service end includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, and the like.
Referring to fig. 1, a flow chart of a competitive relationship analysis method according to an embodiment of the present invention is shown. As described above, in the embodiment of the present invention, the competitive relationship analysis method is applied to an electronic device, and includes the following steps:
S1, acquiring original user data of a plurality of users from a database in communication connection with the electronic equipment, and constructing an original knowledge graph according to the original user data.
Preferably, in the embodiment of the present invention, the user includes different enterprises, and the original user data includes patent-related data of the enterprises. Wherein each piece of the original user data represents a patent document. The embodiment of the invention analyzes the competition relationship among the enterprise units by using the patent related data.
In detail, the embodiment of the present invention acquires the patent-related data including, but not limited to, published patent information, enterprise information, inventor information, etc., from a database storing the patent-related data using a python statement having a data crawling function.
In detail, referring to fig. 2, in an embodiment of the present invention, the constructing an original knowledge graph according to the original user data includes:
S11, carrying out structuring treatment on the original user data to obtain structured data;
s12, carrying out entity extraction on the structured data to obtain entity information, and carrying out relation extraction on the structured data to obtain a correlation;
and S13, carrying out information fusion processing on the entity information and the related relationship to obtain the original knowledge graph.
Specifically, in the embodiment of the present invention, the structuring process is to define the patent-related data to obtain structured data.
For example, the patent-related data includes a Hua-as-technology limited company and an Xin-as-communication limited company, and the Hua-as-technology limited company and the Xin-as-communication limited company are defined as enterprises to realize the structuring processing of the patent-related data.
The structuring treatment can enable the structured data obtained after the treatment to be regularly stored and arranged, and subsequent operation is convenient.
Further, the entity information includes, but is not limited to, the name, application number, inventor, applicant, family, and reference fields of the patent. Further, the related relationships include an invention relationship between an inventor and a patent, an application relationship between an enterprise and a patent, a family relationship between a patent and a patent, a citation relationship, and the like. Wherein, the same family relation refers to a group of patents with the same or basically the same content and applied, published or approved by the patent organization for multiple times in different countries or regions and between regions based on the same priority file.
Specifically, in the embodiment of the invention, after the entity information and the correlation are fused, a plurality of triples are obtained, and the original knowledge graph is obtained according to the triples. The triplet is an information expression of "entity+relationship=entity", for example: the owner of patent a is company B, denoted by a triplet as "patent a+possession relationship=company B". The inventor of patent a is C, denoted by a triplet as "patent a+invention relation=inventor C".
The graph structure of the original knowledge graph may be used to provide a basic data structure for subsequent operations.
In the embodiment of the invention, the original knowledge graph is constructed according to the patent related data, so that the related relations among a plurality of entities in the original knowledge graph can be intuitively reflected, and the efficiency of further analysis by using the original knowledge graph is improved.
S2, complementing the similarity relation between the data in the original knowledge graph according to the neighbor search to obtain an initial knowledge graph.
In the embodiment of the invention, the neighbor search is to search the data most similar to the target data from the original knowledge graph according to the distance between the data, thereby obtaining the similarity relation between the data.
In detail, referring to fig. 3, in the embodiment of the present invention, the obtaining an initial knowledge graph according to the similarity relationship between patents in the neighbor search completion original knowledge graph includes:
s20, carrying out sentence vector processing on the data in the original user data to generate a data vector;
S21, calculating the distance between the data vectors, analyzing the similarity between the data vectors according to the distance, and complementing the similarity relation between the data in the original knowledge graph according to the similarity to obtain an initial knowledge graph.
Specifically, the sentence vector processing is performed on the data in the original user data to generate a data vector, which includes:
the data vector is generated using the following calculation formula:
Where sen vec denotes the data vector, m denotes the number of words contained in each piece of data in the original user data, vec i denotes the word vector of each word.
Further, referring to fig. 4, the calculating the distance between the data vectors, analyzing the similarity between the data vectors according to the distance, and complementing the similarity relationship between the data in the original knowledge-graph according to the similarity, to obtain an initial knowledge-graph includes:
s210, randomly selecting K data vectors from the data vectors to serve as K clusters;
s211, randomly selecting one data vector from unselected data vectors as a target data vector, and respectively calculating square Euclidean distances between the target data vector and K clusters;
And if the square Euclidean distance is greater than or equal to a preset distance threshold, executing S212, and adding a similar relation between data corresponding to the target data vector in the original knowledge graph and data corresponding to the K data vectors in the original knowledge graph.
And if the square Euclidean distance is smaller than a preset distance threshold, executing S213, and maintaining the original knowledge graph unchanged.
Specifically, the squared euclidean distance formula includes:
wherein d (x, y) 2 is the distance from the unselected data vector to the K cluster, x is the K cluster, y is the unselected data vector, and j is the number of unselected data vectors.
In the embodiment of the invention, if the square euclidean distance is greater than or equal to the preset distance threshold, the similarity between the two patents is high, and the similarity relationship between the two patents needs to be marked, so that the original knowledge graph is complemented, and the initial knowledge graph is obtained.
S3, carrying out suspicious analysis on the initial knowledge graph by using a preset knowledge representation algorithm, finding out suspicious competition data and suspicious competition users, and carrying out completion operation on the initial knowledge graph according to the suspicious competition data and the suspicious competition users to obtain a standard knowledge graph.
Preferably, the preset knowledge representation algorithm in the embodiment of the present invention may adopt a Trans algorithm disclosed at present, and the knowledge representation algorithm may represent the entity vector in a low-dimensional dense vector space, so as to facilitate calculation and reasoning of the entity vector.
Specifically, referring to fig. 5, the performing the suspicious analysis on the initial knowledge graph by using a preset knowledge representation algorithm to find suspicious competitive data and suspicious competitive users includes:
S31, converting the entity and the related relationship in the initial knowledge graph into vectors with the same dimension;
S32, carrying out distance calculation on the vectors with the same dimension according to a preset distance formula, and judging the similarity between the vectors according to the calculated distance;
S33, determining suspected competition data in the initial knowledge graph according to the similarity, and determining the suspected competition user according to the suspected competition data.
Wherein, the distance formula is as follows:
Wherein h is the entity in front of a triplet, t is the entity in back of the triplet, r is the correlation in the triplet, d represents the relationship distance between two entities in the triplet, wherein the closer d is to 0, the higher the similarity between two vectors.
Further, the determining the similarity between the vectors according to the calculation result includes:
Comparing the calculation result with a preset similarity threshold;
if the calculation result is smaller than a preset similarity threshold, judging that the vectors have similarity;
if the calculation result is greater than or equal to a preset similarity threshold, judging that the vectors have no similarity.
In the embodiment of the invention, the entities in the initial knowledge graph have different types and attributes, and the correlation relationship among the entities also has different types, but a certain relationship exists among a plurality of entities but is not reflected, so that the embodiment of the invention further performs suspicious analysis on the initial knowledge graph, finds the relationship to be complemented which does not exist in the initial knowledge graph, finds the suspicious competition data and the suspicious competition user, and complements the initial knowledge graph to obtain the standard knowledge graph.
And S4, scoring the suspected competitive users according to the standard knowledge graph, obtaining the competition relationship among the users according to the scoring, and outputting the competition relationship through a display screen of the electronic equipment.
In the embodiment of the present invention, when the suspected competing users in the standard knowledge graph are scored, the following scoring formula may be adopted:
wherein Score AB refers to the Score between user a and user B, score A refers to the Score of user a, score B refers to the Score of user B, score A and Score B are preset scoring values, d (h, t) refers to the relation distance between two entities in the triplet, N is the total number of nodes in the standard knowledge graph, α is a damping coefficient, and is a preset constant.
And after the suspected competitive user relationship score is obtained, judging the suspected competitive user relationship score as a competitor when the suspected competitive user relationship score is larger than a preset score threshold.
According to the embodiment of the invention, the original knowledge graph is constructed according to the original user data of a plurality of users, the similarity relation of the data in the original knowledge graph is complemented by utilizing neighbor search to obtain the initial knowledge graph, and the initial knowledge graph can be subjected to suspected analysis and complementation operation by further utilizing a preset knowledge representation algorithm to obtain the standard knowledge graph. The relationship between users can be more intuitively presented by using the standard knowledge graph. And scoring the suspected competitive users in the standard knowledge graph to obtain the competition relationship among the users. Therefore, the competitive relationship analysis method, the device and the computer readable storage medium can improve the efficiency of the competitive relationship analysis method, and solve the problems that the enterprise needs to be relatively complicated to evaluate according to the definition of the competitive relationship and the judgment of the competitive relationship is not prepared.
FIG. 6 is a schematic block diagram of the competitive relationship analysis apparatus according to the present invention.
The competitive relationship analysis apparatus 100 of the present invention may be mounted in an electronic device. The competitive relationship analysis apparatus 100 may include an original knowledge-graph construction module 101, an initial knowledge-graph construction module 102, a standard knowledge-graph construction module 103, and a competitive relationship determination module 104, depending on the functions implemented. The module of the invention, which may also be referred to as a unit, refers to a series of computer program segments, which are stored in the memory of the electronic device, capable of being executed by the processor of the electronic device and of performing a fixed function.
In the present embodiment, the functions concerning the respective modules/units are as follows:
The original knowledge graph construction module 101 is configured to acquire original user data of at least two users from a database communicatively connected to the electronic device, and construct an original knowledge graph of each user according to the original user data;
The initial knowledge graph construction module 102 is configured to complement a similarity relationship between data in the initial knowledge graph according to neighbor search, so as to obtain an initial knowledge graph;
The standard knowledge graph construction module 103 is configured to perform a suspected analysis on the initial knowledge graph by using a preset knowledge representation algorithm, find suspected competition data and a suspected competition user, and perform a completion operation on the initial knowledge graph by using the suspected competition data and the suspected competition user to obtain a standard knowledge graph;
The competition relationship determination module 104 is configured to score the suspected competition users in the standard knowledge graph to obtain a competition relationship between the users, and output the competition relationship through a display screen of the electronic device.
In detail, the specific embodiments of the modules of the competitive relationship analysis apparatus 100 are as follows:
Step one, the original knowledge graph construction module 101 acquires original user data of a plurality of users from a database communicatively connected with the electronic device, and constructs an original knowledge graph according to the original user data.
Preferably, in the embodiment of the present invention, the user includes different enterprises, and the original user data includes patent-related data of the enterprises. Wherein each piece of the original user data represents a patent document. The embodiment of the invention analyzes the competition relationship among the enterprise units by using the patent related data.
In detail, the original knowledge graph construction module 101 according to the embodiment of the present invention obtains the patent-related data from the database storing the patent-related data, including but not limited to published patent information, enterprise information, inventor information, etc., by using a python sentence with a data crawling function.
In detail, in the embodiment of the present invention, the original knowledge-graph construction module 101 constructs an original knowledge-graph by:
carrying out structuring treatment on the original user data to obtain structured data;
Performing entity extraction on the structured data to obtain entity information, and performing relation extraction on the structured data to obtain a correlation;
and carrying out information fusion processing on the entity information and the related relationship to obtain the original knowledge graph.
Specifically, in the embodiment of the present invention, the structuring process is to define the patent-related data to obtain structured data.
For example, the patent-related data includes a Hua-as-technology limited company and an Xin-as-communication limited company, and the Hua-as-technology limited company and the Xin-as-communication limited company are defined as enterprises to realize the structuring processing of the patent-related data.
The structuring treatment can enable the structured data obtained after the treatment to be regularly stored and arranged, and subsequent operation is convenient.
Further, the entity information includes, but is not limited to, the name, application number, inventor, applicant, family, and reference fields of the patent. Further, the related relationships include an invention relationship between an inventor and a patent, an application relationship between an enterprise and a patent, a family relationship between a patent and a patent, a citation relationship, and the like. Wherein, the same family relation refers to a group of patents with the same or basically the same content and applied, published or approved by the patent organization for multiple times in different countries or regions and between regions based on the same priority file.
Specifically, in the embodiment of the present invention, the original knowledge graph construction module 101 performs fusion processing on the entity information and the correlation relationship to obtain a plurality of triples, and obtains the original knowledge graph according to the triples. The triplet is an information expression of "entity+relationship=entity", for example: the owner of patent a is company B, denoted by a triplet as "patent a+possession relationship=company B". The inventor of patent a is C, denoted by a triplet as "patent a+invention relation=inventor C".
The graph structure of the original knowledge graph may be used to provide a basic data structure for subsequent operations.
In the embodiment of the invention, the original knowledge graph is constructed according to the patent related data, so that the related relations among a plurality of entities in the original knowledge graph can be intuitively reflected, and the efficiency of further analysis by using the original knowledge graph is improved.
And step two, the initial knowledge graph construction module 102 complements the similarity relation between the data in the original knowledge graph according to neighbor retrieval to obtain an initial knowledge graph.
In the embodiment of the invention, the neighbor search is to search the data most similar to the target data from the original knowledge graph according to the distance between the data, thereby obtaining the similarity relation between the data.
In detail, referring to fig. 3, in the embodiment of the present invention, the initial knowledge-graph construction module 102 complements the similarity relationship between the patents in the initial knowledge-graph to obtain the initial knowledge-graph by:
Sentence vector processing is carried out on the data in the original user data, and a data vector is generated;
And calculating the distance between the data vectors, analyzing the similarity between the data vectors according to the distance, and complementing the similarity relation between the data in the original knowledge graph according to the similarity to obtain an initial knowledge graph.
Specifically, the sentence vector processing is performed on the data in the original user data to generate a data vector, which includes:
the data vector is generated using the following calculation formula:
Where sen vec denotes the data vector, m denotes the number of words contained in each piece of data in the original user data, vec i denotes the word vector of each word.
Further, the calculating the distance between the data vectors, analyzing the similarity between the data vectors according to the distance, and complementing the similarity relationship between the data in the original knowledge graph according to the similarity to obtain an initial knowledge graph, including:
randomly selecting K data vectors from the data vectors to serve as K clusters;
Randomly selecting one data vector from unselected data vectors as a target data vector, and respectively calculating the square Euclidean distance between the target data vector and K clusters;
and if the square Euclidean distance is greater than or equal to a preset distance threshold, adding the similarity relationship between the data corresponding to the target data vector in the original knowledge graph and the data corresponding to the K data vectors in the original knowledge graph.
And if the square Euclidean distance is smaller than a preset distance threshold value, maintaining the original knowledge graph unchanged.
Specifically, the squared euclidean distance formula includes:
wherein d (x, y) 2 is the distance from the unselected data vector to the K cluster, x is the K cluster, y is the unselected data vector, and j is the number of unselected data vectors.
In the embodiment of the invention, if the square euclidean distance is greater than or equal to the preset distance threshold, the similarity between the two patents is high, and the similarity relationship between the two patents needs to be marked, so that the original knowledge graph is complemented, and the initial knowledge graph is obtained.
And thirdly, the standard knowledge graph construction module 103 performs suspicious analysis on the initial knowledge graph by using a preset knowledge representation algorithm, finds suspicious competition data and suspicious competition users, and performs completion operation on the initial knowledge graph according to the suspicious competition data and the suspicious competition users to obtain the standard knowledge graph.
Preferably, the preset knowledge representation algorithm in the embodiment of the present invention may adopt a Trans algorithm disclosed at present, and the knowledge representation algorithm may represent the entity vector in a low-dimensional dense vector space, so as to facilitate calculation and reasoning of the entity vector.
Specifically, referring to fig. 5, the standard knowledge graph construction module 103 finds the suspected competition data and the suspected competition user by:
converting the entity and the related relationship in the initial knowledge graph into vectors with the same dimension;
Performing distance calculation on the vectors with the same dimension according to a preset distance formula, and judging the similarity between the vectors according to the calculated distance;
and determining suspected competition data in the initial knowledge graph according to the similarity, and determining the suspected competition user according to the suspected competition data.
Wherein, the distance formula is as follows:
Wherein h is the entity in front of a triplet, t is the entity in back of the triplet, r is the correlation in the triplet, d represents the relationship distance between two entities in the triplet, wherein the closer d is to 0, the higher the similarity between two vectors.
Further, the determining the similarity between the vectors according to the calculation result includes:
Comparing the calculation result with a preset similarity threshold;
if the calculation result is smaller than a preset similarity threshold, judging that the vectors have similarity;
if the calculation result is greater than or equal to a preset similarity threshold, judging that the vectors have no similarity.
In the embodiment of the invention, the entities in the initial knowledge graph have different types and attributes, and the correlation relationship among the entities also has different types, but a certain relationship exists among a plurality of entities but is not reflected, so that the embodiment of the invention further performs suspicious analysis on the initial knowledge graph, finds the relationship to be complemented which does not exist in the initial knowledge graph, finds the suspicious competition data and the suspicious competition user, and complements the initial knowledge graph to obtain the standard knowledge graph.
And step four, the competition relationship determination module 104 scores the suspected competition users according to the standard knowledge graph, obtains the competition relationship between the users according to the score, and outputs the competition relationship through a display screen of the electronic device.
In the embodiment of the present invention, when the competition relationship determination module 104 performs scoring on the suspected competing users in the standard knowledge graph, the following scoring formula may be adopted:
wherein Score AB refers to the Score between user a and user B, score A refers to the Score of user a, score B refers to the Score of user B, score A and Score B are preset scoring values, d (h, t) refers to the relation distance between two entities in the triplet, N is the total number of nodes in the standard knowledge graph, α is a damping coefficient, and is a preset constant.
After obtaining the score of the relationship between the suspected competing users, the competition relationship determination module 104 determines the score of the relationship between the suspected competing users as a competitor when the score of the relationship between the suspected competing users is greater than a preset score threshold.
According to the embodiment of the invention, the original knowledge graph is constructed according to the original user data of a plurality of users, the similarity relation of the data in the original knowledge graph is complemented by utilizing neighbor search to obtain the initial knowledge graph, and the initial knowledge graph can be subjected to suspected analysis and complementation operation by further utilizing a preset knowledge representation algorithm to obtain the standard knowledge graph. The relationship between users can be more intuitively presented by using the standard knowledge graph. And scoring the suspected competitive users in the standard knowledge graph to obtain the competition relationship among the users. Therefore, the competitive relationship analysis method, the device and the computer readable storage medium can improve the efficiency of the competitive relationship analysis method, and solve the problems that the enterprise needs to be relatively complicated to evaluate according to the definition of the competitive relationship and the judgment of the competitive relationship is not prepared.
Fig. 7 is a schematic structural diagram of an electronic device implementing the competitive relationship analysis method according to the present invention.
The electronic device 1 may comprise a processor 10, a memory 11 and a bus, and may further comprise a computer program, such as a competitive relationship analysis program 12, stored in the memory 11 and executable on the processor 10.
The memory 11 includes at least one type of readable storage medium, including flash memory, a mobile hard disk, a multimedia card, a card memory (e.g., SD or DX memory, etc.), a magnetic memory, a magnetic disk, an optical disk, etc. The memory 11 may in some embodiments be an internal storage unit of the electronic device 1, such as a removable hard disk of the electronic device 1. The memory 11 may in other embodiments also be an external storage device of the electronic device 1, such as a plug-in mobile hard disk, a smart memory card (SMART MEDIA CARD, SMC), a Secure Digital (SD) card, a flash memory card (FLASH CARD) or the like, which are provided on the electronic device 1. Further, the memory 11 may also include both an internal storage unit and an external storage device of the electronic device 1. The memory 11 may be used not only for storing application software installed in the electronic device 1 and various types of data, such as codes of the competitive relationship analysis program 12, but also for temporarily storing data that has been output or is to be output.
The processor 10 may be comprised of integrated circuits in some embodiments, for example, a single packaged integrated circuit, or may be comprised of multiple integrated circuits packaged with the same or different functions, including one or more central processing units (Central Processing unit, CPU), microprocessors, digital processing chips, graphics processors, combinations of various control chips, and the like. The processor 10 is a Control Unit (Control Unit) of the electronic device, connects respective components of the entire electronic device using various interfaces and lines, executes or executes programs or modules (for example, executes a competitive relationship analysis program or the like) stored in the memory 11, and invokes data stored in the memory 11 to perform various functions of the electronic device 1 and process data.
The bus may be a peripheral component interconnect standard (PERIPHERAL COMPONENT INTERCONNECT, PCI) bus, or an extended industry standard architecture (extended industry standard architecture, EISA) bus, among others. The bus may be classified as an address bus, a data bus, a control bus, etc. The bus is arranged to enable a connection communication between the memory 11 and at least one processor 10 etc.
Fig. 7 shows only an electronic device with components, it being understood by a person skilled in the art that the structure shown in fig. 7 does not constitute a limitation of the electronic device 1, and may comprise fewer or more components than shown, or may combine certain components, or may be arranged in different components.
For example, although not shown, the electronic device 1 may further include a power source (such as a battery) for supplying power to each component, and preferably, the power source may be logically connected to the at least one processor 10 through a power management device, so that functions of charge management, discharge management, power consumption management, and the like are implemented through the power management device. The power supply may also include one or more of any of a direct current or alternating current power supply, recharging device, power failure detection circuit, power converter or inverter, power status indicator, etc. The electronic device 1 may further include various sensors, bluetooth modules, wi-Fi modules, etc., which will not be described herein.
Further, the electronic device 1 may also comprise a network interface, optionally the network interface may comprise a wired interface and/or a wireless interface (e.g. WI-FI interface, bluetooth interface, etc.), typically used for establishing a communication connection between the electronic device 1 and other electronic devices.
The electronic device 1 may optionally further comprise a user interface, which may be a Display, an input unit, such as a Keyboard (Keyboard), or a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch, or the like. The display may also be referred to as a display screen or display unit, as appropriate, for displaying information processed in the electronic device 1 and for displaying a visual user interface.
It should be understood that the embodiments described are for illustrative purposes only and are not limited to this configuration in the scope of the patent application.
The competitive relationship analysis program 12 stored in the memory 11 in the electronic device 1 is a combination of instructions that, when executed in the processor 10, may implement:
Acquiring original user data of a plurality of users from a database in communication connection with the electronic equipment, and constructing an original knowledge graph according to the original user data;
according to the similarity relation between the data in the original knowledge graph complemented by the neighbor search, obtaining an initial knowledge graph;
carrying out suspicious analysis on the initial knowledge graph by using a preset knowledge representation algorithm, finding out suspicious competition data and suspicious competition users, and carrying out completion operation on the initial knowledge graph according to the suspicious competition data and the suspicious competition users to obtain a standard knowledge graph;
And scoring the suspected competitive users according to the standard knowledge graph, obtaining the competition relationship among the users according to the scoring, and outputting the competition relationship through a display screen of the electronic equipment.
Further, the modules/units integrated in the electronic device 1 may be stored in a computer readable storage medium if implemented in the form of software functional units and sold or used as separate products. The computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM).
Further, the computer-usable storage medium may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function, and the like; the storage data area may store data created from the use of blockchain nodes, and the like.
In the several embodiments provided in the present invention, it should be understood that the disclosed apparatus, device and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is merely a logical function division, and there may be other manners of division when actually implemented.
The modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical units, may be located in one place, or may be distributed over multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional module in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units can be realized in a form of hardware or a form of hardware and a form of software functional modules.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof.
The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any accompanying diagram representation in the claims should not be considered as limiting the claim concerned.
Furthermore, it is evident that the word "comprising" does not exclude other elements or steps, and that the singular does not exclude a plurality. A plurality of units or means recited in the system claims can also be implemented by means of software or hardware by means of one unit or means. The terms second, etc. are used to denote a name, but not any particular order.
Finally, it should be noted that the above-mentioned embodiments are merely for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications and equivalents may be made to the technical solution of the present invention without departing from the spirit and scope of the technical solution of the present invention.
Claims (7)
1. A method for analyzing a competitive relationship, the method being applied to an electronic device and comprising:
Acquiring original user data of a plurality of users from a database in communication connection with the electronic equipment, and constructing an original knowledge graph according to the original user data;
according to the similarity relation between the data in the original knowledge graph complemented by the neighbor search, obtaining an initial knowledge graph;
carrying out suspicious analysis on the initial knowledge graph by using a preset knowledge representation algorithm, finding out suspicious competition data and suspicious competition users, and carrying out completion operation on the initial knowledge graph according to the suspicious competition data and the suspicious competition users to obtain a standard knowledge graph;
scoring the suspected competitive users according to the standard knowledge graph, obtaining competition relations among the users according to the scoring, and outputting the competition relations through a display screen of the electronic equipment;
The step of obtaining an initial knowledge graph by complementing the similarity relation between the data in the original knowledge graph according to the neighbor search comprises the following steps: sentence vector processing is carried out on the data in the original user data, and a data vector is generated; calculating the distance between the data vectors, analyzing the similarity between the data vectors according to the distance, and complementing the similarity relation between the data in the original knowledge graph according to the similarity to obtain an initial knowledge graph;
The calculating the distance between the data vectors, analyzing the similarity between the data vectors according to the distance, and complementing the similarity relation between the data in the original knowledge graph according to the similarity to obtain an initial knowledge graph, including: randomly selecting K data vectors from the data vectors to serve as K clusters; randomly selecting one data vector from unselected data vectors as a target data vector, and respectively calculating the square Euclidean distance between the target data vector and K clusters; if the square Euclidean distance is greater than or equal to a preset distance threshold, adding a similarity relationship between data corresponding to the target data vector in the original knowledge graph and data corresponding to the K data vectors in the original knowledge graph; if the square Euclidean distance is smaller than a preset distance threshold, maintaining the original knowledge graph unchanged;
The performing suspicious analysis on the initial knowledge graph by using a preset knowledge representation algorithm to find suspicious competitive data and suspicious competitive users, including: converting the entity and the related relationship in the initial knowledge graph into vectors with the same dimension; performing distance calculation on the vectors with the same dimension according to a preset distance formula, and judging the similarity between the vectors according to the calculated distance; determining suspected competition data in the initial knowledge graph according to the similarity, and determining the suspected competition user according to the suspected competition data;
And scoring the suspected competing users according to the standard knowledge graph by adopting the following scoring formula:
Wherein Score AB refers to the Score between user a and user B, score A refers to the Score of user a, score B refers to the Score of user B, score A and Score B are preset scoring values, d (h, t) refers to the relation distance between two entities in the triples in the standard knowledge graph, N is the total number of nodes in the standard knowledge graph, α is a damping coefficient, and is a preset constant;
And obtaining the competition relationship among the users according to the scores, wherein the method comprises the following steps: and when the score is larger than a preset score threshold value, judging that the competition relationship between the users is a competitor.
2. The competitive relationship analysis method of claim 1, wherein said constructing an original knowledge-graph from said original user data includes:
carrying out structuring treatment on the original user data to obtain structured data;
Performing entity extraction on the structured data to obtain entity information, and performing relation extraction on the structured data to obtain a correlation;
and carrying out information fusion processing on the entity information and the related relationship to obtain the original knowledge graph.
3. The competitive relationship analysis method of claim 1, wherein said performing sentence vector processing on data in said raw user data to generate a data vector includes:
the data vector is generated using the following calculation formula:
Where sen vec denotes the data vector, m denotes the number of words contained in each piece of data in the original user data, vec i denotes the word vector of each word.
4. The competitive relationship analysis method of claim 1, wherein said performing a distance calculation on said vector of the same dimension according to a preset distance formula comprises:
the distance d (h, t) between the vectors is calculated using the following method:
Wherein h is the front entity in a triplet, t is the rear entity in the triplet, r is the correlation in the triplet, and d represents the relationship distance between two entities in the triplet.
5. A competitive relationship analysis apparatus for implementing the competitive relationship analysis method of any one of claims 1 to 4, said apparatus comprising:
the system comprises an original knowledge graph construction module, a database and a data processing module, wherein the original knowledge graph construction module is used for acquiring original user data of at least two users in the database and constructing an original knowledge graph of each user according to the original user data;
The initial knowledge graph construction module is used for complementing the similarity relation between the data in the initial knowledge graph according to the neighbor search to obtain an initial knowledge graph;
The standard knowledge graph construction module is used for carrying out suspicious analysis on the initial knowledge graph by utilizing a preset knowledge representation algorithm, finding out suspicious competition data and suspicious competition users, and carrying out completion operation on the initial knowledge graph by the suspicious competition data and the suspicious competition users to obtain a standard knowledge graph;
and the competition relationship judging module is used for scoring the suspected competition users in the standard knowledge graph to obtain competition relationships among the users, and outputting the competition relationships through a display screen of the electronic equipment.
6. An electronic device, the electronic device comprising:
at least one processor; and
A memory communicatively coupled to the at least one processor; wherein,
The memory stores instructions executable by the at least one processor to cause the at least one processor to perform the competitive relationship analysis method of any one of claims 1 to 4.
7. A computer readable storage medium storing a computer program, wherein the computer program when executed by a processor implements the competitive relationship analysis method of any one of claims 1 to 4.
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