A method and device for calculating relationship intimacy based on multidimensional data

Abstract

The present invention discloses a method and device for calculating relationship intimacy based on multidimensional data. The method comprises: determining multiple relationships that affect the trust between a first entity and a second entity; quantifying the trust of the multiple relationships to obtain the intimacy value of each relationship; calculating the trust probability between the first entity and the second entity corresponding to each relationship based on the intimacy value of each relationship; clustering the multiple relationships using a relationship clustering method; obtaining the trust probability of each type of relationship after clustering based on the trust probability between the first entity and the second entity corresponding to each relationship; obtaining the trust probability between the first entity and the second entity based on the trust probability of each type of relationship; and obtaining the relationship intimacy between the first entity and the second entity based on the trust probability between the first entity and the second entity. The present invention can effectively identify the strength of relationships between entities based on multidimensional entity relationships.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a method and device for calculating relationship intimacy based on multidimensional data. Background Art

[0002] Measuring the strength of relationships between entities is a common concern for government administration and business operations. This is particularly true in areas like security and risk management, where the need to accurately identify the strength of relationships between entities is even more pressing. To this end, the industry has proposed various methods for measuring the closeness between individuals. For example, in the mobile sector, intimacy is determined by using call behavior data.

[0003] However, the current methods for calculating relationship intimacy have the following problems: First, the data source is single and data from a single source is often used; second, the data is highly industry-specific, so its calculation method often does not have broad reference value; third, the calculation method is simple, relying on data such as the time and frequency of a certain behavioral interaction between individuals, and judging the degree of intimacy between individuals through experience and rules; fourth, the method is mainly aimed at individuals and cannot evaluate the strength of relationships between enterprises and individuals, or between enterprises.

[0004] Therefore, how to effectively identify the strength of the relationship between entities is an urgent problem to be solved. Summary of the Invention

[0005] In view of this, the present invention provides a relationship intimacy calculation method based on multidimensional data, which can effectively identify the strength of the relationship between entities based on multidimensional entity relationships.

[0006] The present invention provides a method for calculating relationship intimacy based on multidimensional data, comprising:

[0007] determining a plurality of relationships affecting a level of trust between the first entity and the second entity;

[0008] Quantify the trust of multiple relationships separately to obtain the intimacy value of each relationship;

[0009] Based on the intimacy value of each relationship, respectively calculate the trust probability between the first entity and the second entity corresponding to each relationship;

[0010] clustering the multiple relationships using a relationship clustering method;

[0011] Based on the trust probabilities between the first entity and the second entity corresponding to each relationship, obtaining the trust probability of each type of relationship after clustering;

[0012] Obtaining a trust probability between the first entity and the second entity based on the trust probability of each type of relationship;

[0013] Based on the trust probability between the first entity and the second entity, the relationship intimacy between the first entity and the second entity is obtained.

[0014] Preferably, determining a plurality of relationships affecting the trust between the first entity and the second entity comprises:

[0015] A plurality of relationships affecting the trust between the first entity and the second entity are determined by traversing a graph database, wherein the graph database includes nodes and edges, the nodes represent entities, and the edges represent relationships between entities.

[0016] Preferably, the step of calculating the trust probability between the first entity and the second entity corresponding to each relationship based on the intimacy value of each relationship includes:

[0017] According to the formula Calculate the probability P that the first entity A trusts the second entity B through relationship i ABi , where S ABi is the closeness value of the relationship i between the first entity A and the second entity B.

[0018] Preferably, obtaining the trust probability of each type of relationship after clustering based on the trust probability between the first entity and the second entity corresponding to each relationship includes:

[0019] The maximum value of the trust probabilities between the first entity and the second entity corresponding to each relationship in each type of relationship is determined as the trust probability of the relationship in this type.

[0020] Preferably, obtaining the relationship closeness between the first entity and the second entity based on the trust probability between the first entity and the second entity includes:

[0021] According to the formula S AB =100*P AB Calculate the relationship intimacy S between the first entity and the second entity AB , where P AB is the trust probability between the first entity and the second entity.

[0022] A device for calculating relationship intimacy based on multidimensional data, comprising:

[0023] a relationship definition module, configured to determine a plurality of relationships affecting a degree of trust between a first entity and a second entity;

[0024] The single-degree relationship quantification module is used to quantify the trust of multiple relationships and obtain the intimacy value of each relationship;

[0025] A single-degree relationship trust probability calculation module is used to calculate the trust probability between the first entity and the second entity corresponding to each relationship based on the closeness value of each relationship;

[0026] A relationship clustering module, configured to cluster the plurality of relationships using a relationship clustering method;

[0027] A fusion module, configured to obtain a clustered trust probability of each type of relationship based on the trust probability between the first entity and the second entity corresponding to each relationship;

[0028] an obtaining module, configured to obtain a trust probability between the first entity and the second entity based on the trust probability of each type of relationship;

[0029] The calculation module is configured to calculate the relationship intimacy between the first entity and the second entity based on the trust probability between the first entity and the second entity.

[0030] Preferably, the relationship definition module is specifically used to:

[0031] A plurality of relationships affecting the trust between the first entity and the second entity are determined by traversing a graph database, wherein the graph database includes nodes and edges, the nodes represent entities, and the edges represent relationships between entities.

[0032] Preferably, the fusion module is specifically used to:

[0033] The maximum value of the trust probabilities between the first entity and the second entity corresponding to each relationship in each type of relationship is determined as the trust probability of the relationship in this type.

[0034] An electronic device includes: a processor and a memory, wherein a computer program is stored in the memory. When the processor runs the computer program stored in the memory, the processor executes the relationship intimacy calculation method based on multidimensional data as described above.

[0035] A computer-readable storage medium is characterized in that the computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are executed by a processor, they are used to implement the relationship intimacy calculation method based on multidimensional data as described above.

[0036] In summary, the present invention discloses a method for calculating relationship intimacy based on multidimensional data. When it is necessary to determine the relationship intimacy between entities, first determine multiple relationships that affect the trust between a first entity and a second entity, then quantify the trust of the multiple relationships respectively, obtain the intimacy value of each relationship, and calculate the trust probability between the first entity and the second entity corresponding to each relationship based on the intimacy value of each relationship; adopt a relationship clustering method to cluster the multiple relationships; based on the trust probability between the first entity and the second entity corresponding to each relationship, obtain the trust probability of each type of relationship after clustering; based on the trust probability of each type of relationship, obtain the trust probability between the first entity and the second entity; finally, based on the trust probability between the first entity and the second entity, obtain the relationship intimacy between the first entity and the second entity. The present invention can effectively identify the strength of the relationship between entities based on multidimensional entity relationships. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0038] Figure 1 This is a method flow chart of Example 1 of a method for calculating relationship intimacy based on multidimensional data disclosed in the present invention;

[0039] Figure 2 A schematic diagram of the relationship clustering between entity A and entity B disclosed in the present invention;

[0040] Figure 3 This is a method flow chart of Example 2 of a method for calculating relationship intimacy based on multidimensional data disclosed in the present invention;

[0041] Figure 4 This is a structural diagram of a first embodiment of a device for calculating relationship intimacy based on multidimensional data disclosed in the present invention;

[0042] Figure 5 This is a structural diagram of a second embodiment of a device for calculating relationship intimacy based on multidimensional data disclosed in the present invention;

[0043] Figure 6 The figure is a schematic structural diagram of an electronic device disclosed in the present invention. DETAILED DESCRIPTION

[0044] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0045] like Figure 1 FIG. 1 is a flowchart of a method for calculating relationship intimacy based on multidimensional data according to an embodiment of the present invention. The method may include the following steps:

[0046] S101, determining a plurality of relationships that affect the trust between a first entity and a second entity;

[0047] Relationship closeness is a quantification of the degree of closeness between two entities. When determining the closeness of a relationship between entities, we first determine multiple relationships that influence the level of trust between the first and second entities. The first and second entities are the entities for which the closeness of the relationship is to be determined. For example, the multiple relationships that influence the level of trust between first entity A and second entity B include, but are not limited to, blood relationships, financial transaction relationships, employment relationships, guarantee relationships, equity relationships, alumni relationships, fellow-townsmen relationships, neighbor relationships, business agency relationships, shared equipment relationships, and shared addresses between first entity A and second entity B.

[0048] S102, quantifying the trust of multiple relationships respectively to obtain the intimacy value of each relationship;

[0049] After determining a plurality of relationships that affect the trust between the first entity and the second entity, trust quantification is performed on each relationship that affects the trust between the first entity A and the second entity B to obtain a closeness value of the relationship.

[0050] Specifically, when trust is quantified, the trust can be quantified using the RFM model, or quantitative results can be obtained using an expert classification rating and scoring model.

[0051] For example, the RFM model can be used to quantify trust in capital transaction relationships, equity relationships, and guarantee relationships between entities A and B, yielding closeness values ​​for each relationship. For relationships between entities A and B that cannot be directly quantified using indicators, such as relatives, colleagues, and fellow townspeople, an expert classification rating and scoring model can be used to quantify these relationships. The results of this quantification can then be used to determine the closeness values ​​for each relationship.

[0052] S103, based on the intimacy value of each relationship, respectively calculating the trust probability between the first entity and the second entity corresponding to each relationship;

[0053] After obtaining the intimacy value of each relationship between the first entity and the second entity, the trust probability between the first entity and the second entity corresponding to each relationship is further calculated based on the intimacy value of each relationship.

[0054] Specifically, remember S ABi The score of the i-th relationship between the first entity A and the second entity B is the intimacy value of the i-th relationship between the first entity A and the second entity B. Then, the formula Get the probability that the first entity A trusts the second entity B through relationship i.

[0055] S104, clustering multiple relationships using a relationship clustering method;

[0056] Since the relationships are often not independent of each other, according to the five relationships proposed by Fei Xiaotong, namely blood relationship, kinship, geographical relationship, occupation relationship and interest relationship, the relationship clustering method is adopted, such as Figure 2 As shown, the n relationships between the first entity A and the second entity B can be clustered into k types of relationships according to their relevance, and these k types of relationships are independent of each other. Let the vth (v∈{1, 2, ...k})th type of relationship contain m v There are three types of relations, and the wth type of relation in the vth type is v_w.

[0057] S105: Obtain the trust probability of each type of relationship after clustering based on the trust probability between the first entity and the second entity corresponding to each relationship;

[0058] After calculating the trust probability between the first entity and the second entity corresponding to each relationship, the trust probability of each relationship after clustering is further calculated based on the trust probability between the first entity and the second entity corresponding to each relationship. For example, the trust probability of each relationship in k relationships is obtained.

[0059] S106. Obtain a trust probability between the first entity and the second entity based on the trust probability of each type of relationship;

[0060] Then, multiple relations are fused according to the trust probability of each type of relation to obtain the trust probability between the first entity A and the second entity B. For example, the trust probability P between the first entity A and the second entity B based on k types of relations is obtained. AB .

[0061] S107: Obtain the relationship intimacy between the first entity and the second entity based on the trust probability between the first entity and the second entity.

[0062] Finally, the relationship intimacy between the first entity and the second entity is obtained based on the trust probability between the first entity and the second entity.

[0063] Specifically, according to formula S AB =100*P AB The closeness of the relationship between the first entity A and the second entity B is calculated.

[0064] In summary, in the above embodiment, when it is necessary to determine the closeness of the relationship between entities, first determine multiple relationships that affect the trust between the first entity and the second entity, then quantify the trust of the multiple relationships to obtain the closeness value of each relationship, and calculate the trust probability between the first entity and the second entity corresponding to each relationship based on the closeness value of each relationship; adopt a relationship clustering method to cluster the multiple relationships; based on the trust probability between the first entity and the second entity corresponding to each relationship, obtain the trust probability of each type of relationship after clustering; based on the trust probability of each type of relationship, obtain the trust probability between the first entity and the second entity; finally, based on the trust probability between the first entity and the second entity, obtain the closeness of the relationship between the first entity and the second entity. It can effectively identify the strength of the relationship between entities based on multi-dimensional entity relationships.

[0065] like Figure 3 FIG. 1 is a flowchart of a second embodiment of a method for calculating relationship intimacy based on multidimensional data disclosed in the present invention. The method may include the following steps:

[0066] S301, determining a plurality of relationships that affect the trust between a first entity and a second entity by traversing a graph database;

[0067] Relationship closeness is a quantification of the degree of closeness between two entities. When determining the closeness of a relationship between entities, we first determine multiple relationships that influence the level of trust between the first and second entities. The first and second entities are the entities for which the closeness of the relationship is to be determined. For example, the multiple relationships that influence the level of trust between first entity A and second entity B include, but are not limited to, blood relationships, financial transaction relationships, employment relationships, guarantee relationships, equity relationships, alumni relationships, fellow-townsmen relationships, neighbor relationships, business agency relationships, shared equipment relationships, and shared addresses between first entity A and second entity B.

[0068] Specifically, when determining relationships that influence the level of trust between a first entity and a second entity, multiple relationships that influence the level of trust between the first entity and the second entity can be determined by traversing a graph database. A graph database includes nodes and edges, where nodes represent entities and edges represent relationships between entities. For example, established graph databases contain various types of nodes and edges. Nodes primarily fall into two categories: individuals and enterprises. Edge relationships include relationships between individuals, individuals and enterprises, enterprises and individuals, and enterprises and enterprises.

[0069] S302: quantify the trustworthiness of multiple relationships to obtain the intimacy value of each relationship;

[0070] After determining a plurality of relationships that affect the trust between the first entity and the second entity, trust quantification is performed on each relationship that affects the trust between the first entity A and the second entity B to obtain a closeness value of the relationship.

[0071] Specifically, when trust is quantified, the trust can be quantified using the RFM model, or quantitative results can be obtained using an expert classification rating and scoring model.

[0072] For example, the RFM model can be used to quantify trust in capital transaction relationships, equity relationships, and guarantee relationships between entities A and B, yielding closeness values ​​for each relationship. For relationships between entities A and B that cannot be directly quantified using indicators, such as relatives, colleagues, and fellow townspeople, an expert classification rating and scoring model can be used to quantify these relationships. The results of this quantification can then be used to determine the closeness values ​​for each relationship.

[0073] S303: Calculate the trust probability between the first entity and the second entity corresponding to each relationship based on the intimacy value of each relationship;

[0074] After obtaining the intimacy value of each relationship between the first entity and the second entity, the trust probability between the first entity and the second entity corresponding to each relationship is further calculated based on the intimacy value of each relationship.

[0075] Specifically, remember S ABi The score of the i-th relationship between the first entity A and the second entity B is the intimacy value of the i-th relationship between the first entity A and the second entity B. Then, the formula Get the probability that the first entity A trusts the second entity B through relationship i.

[0076] S304, clustering multiple relationships using a relationship clustering method;

[0077] Since the relationships are often not independent of each other, according to the five relationships proposed by Fei Xiaotong, namely blood relationship, kinship, geographical relationship, occupation relationship and interest relationship, the relationship clustering method is adopted, such as Figure 2 As shown, the n relationships between the first entity A and the second entity B can be clustered into k types of relationships according to their relevance, and these k types of relationships are independent of each other. Let the vth (v∈{1, 2, ...k})th type of relationship contain m v There are three types of relations, and the wth type of relation in the vth type is v_w.

[0078] S305: Determine the maximum value of the trust probabilities between the first entity and the second entity corresponding to each relationship in each type of relationship as the trust probability of the relationship in that type;

[0079] After respectively calculating the trust probability between the first entity and the second entity corresponding to each relationship, the trust probability of each type of relationship after clustering is obtained based on the trust probability between the first entity and the second entity corresponding to each relationship.

[0080] Specifically, the maximum value of the trust probabilities between the first entity and the second entity corresponding to each relationship in each type of relationship is determined as the trust probability of the relationship in that type.

[0081] For example, from each type of relationship, take the relationship v_max with the highest trust value, and the trust value is P v_max , then, P v_max This is the trust probability of this type of relationship.

[0082] S306: Obtain the trust probability between the first entity and the second entity based on the trust probability of each type of relationship;

[0083] Then, multiple relations are fused according to the trust probability of each type of relation to obtain the trust probability between the first entity A and the second entity B. For example, the trust probability P between the first entity A and the second entity B based on k types of relations is obtained. AB , where P AB =1-(1-P 1_max )(1-P 2_max )...(1-P k_max ).

[0084] S307: Obtain the relationship intimacy between the first entity and the second entity based on the trust probability between the first entity and the second entity.

[0085] Finally, the relationship intimacy between the first entity and the second entity is obtained based on the trust probability between the first entity and the second entity.

[0086] Specifically, according to formula S AB =100*P AB The closeness of the relationship between the first entity A and the second entity B is calculated.

[0087] In summary, the present invention proposes a relationship intimacy calculation method based on multidimensional data, which takes the generation of trust as its theoretical basis. It solves the problem of intimacy quantification by converting the direct or indirect relationship between entities into a trust probability between 0 and 1 and then performing relevant operations. Based on multidimensional entity relationships, it applies probabilistic statistical methods and graph database tools to effectively identify the strength of the relationship between entities.

[0088] like Figure 4 FIG. 1 is a schematic diagram of a first embodiment of a device for calculating relationship intimacy based on multidimensional data disclosed in the present invention. The device may include:

[0089] A relationship definition module 401 is configured to determine a plurality of relationships that affect the trust between a first entity and a second entity;

[0090] The single-degree relationship quantification module 402 is used to quantify the trust of multiple relationships and obtain the closeness value of each relationship;

[0091] A single-degree relationship trust probability calculation module 403 is configured to calculate the trust probability between the first entity and the second entity corresponding to each relationship based on the closeness value of each relationship;

[0092] The relationship clustering module 404 is used to cluster multiple relationships using a relationship clustering method;

[0093] A fusion module 405 is configured to obtain a clustered trust probability of each type of relationship based on the trust probability between the first entity and the second entity corresponding to each relationship;

[0094] An obtaining module 406 is configured to obtain a trust probability between the first entity and the second entity based on the trust probability of each type of relationship;

[0095] The calculation module 407 is configured to obtain the relationship intimacy between the first entity and the second entity based on the trust probability between the first entity and the second entity.

[0096] In summary, the working principle of the apparatus for calculating relationship intimacy based on multidimensional data disclosed in this embodiment is the same as the working principle of the method for calculating relationship intimacy based on multidimensional data in embodiment 1, and will not be described in detail here.

[0097] like Figure 5 FIG. 1 is a schematic diagram of a second embodiment of a device for calculating relationship intimacy based on multidimensional data disclosed in the present invention. The device may include:

[0098] A relationship definition module 501 is configured to determine a plurality of relationships affecting the trust between a first entity and a second entity by traversing a graph database;

[0099] The single-degree relationship quantification module 502 is used to quantify the trust of multiple relationships and obtain the closeness value of each relationship;

[0100] A single-degree relationship trust probability calculation module 503 is configured to calculate the trust probability between the first entity and the second entity corresponding to each relationship based on the closeness value of each relationship;

[0101] The relationship clustering module 504 is used to cluster multiple relationships using a relationship clustering method;

[0102] A fusion module 505 is configured to determine the maximum value of the trust probabilities between the first entity and the second entity corresponding to each relationship in each type of relationship as the trust probability of the relationship in that type;

[0103] An obtaining module 506 is configured to obtain a trust probability between the first entity and the second entity based on the trust probability of each type of relationship;

[0104] The calculation module 507 is configured to obtain the relationship intimacy between the first entity and the second entity based on the trust probability between the first entity and the second entity.

[0105] In summary, the present invention proposes a relationship intimacy calculation device based on multidimensional data, which takes the generation of trust as its theoretical basis. It solves the problem of intimacy quantification by converting the direct or indirect relationship between entities into a trust probability between 0 and 1 and then performing relevant operations. Based on multidimensional entity relationships, it applies probabilistic statistical methods and graph database tools to effectively identify the strength of the relationship between entities.

[0106] like Figure 6 As shown, it is a structural diagram of an electronic device disclosed in the present invention, and the electronic device includes a memory 10 and a processor 20. The memory 10 stores a computer program. When the processor 20 runs the computer program stored in the memory 10, the processor 20 executes the above-mentioned various possible relationship intimacy calculation methods based on multidimensional data.

[0107] The memory 10 is connected to the processor 20. The memory 10 may be a flash memory, a read-only memory, or other memory. The processor 20 may be a central processing unit or a single-chip microcomputer.

[0108] In addition, the present invention also provides a computer-readable storage medium, which stores computer execution instructions. When at least one processor of a user device executes the computer execution instructions, the user device executes the above-mentioned various possible relationship intimacy calculation methods based on multidimensional data.

[0109] Among them, computer-readable media include computer storage media and communication media, wherein communication media include any media that facilitates the transmission of computer programs from one place to another. The storage medium can be any available medium that can be accessed by a general-purpose or special-purpose computer. An exemplary storage medium is coupled to the processor so that the processor can read information from the storage medium and write information to the storage medium. Of course, the storage medium can also be an integral part of the processor. The processor and the storage medium can be located in an ASIC (Application Specific Integrated Circuit). In addition, the ASIC can be located in a user device. Of course, the processor and the storage medium can also exist in a communication device as discrete components.

[0110] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Reference can be made to the common and similar parts between the various embodiments. For the devices disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the method description.

[0111] Professionals may further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the above description has generally described the components and steps of each example according to their functions. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0112] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein may be implemented directly using hardware, a software module executed by a processor, or a combination of the two. The software module may be placed in a random access memory (RAM), internal memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

[0113] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not limited to the embodiments shown herein but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for calculating relationship intimacy based on multidimensional data, characterized in that: include: Determining a plurality of relationships that affect a degree of trust between a first entity and a second entity by traversing a graph database, wherein the graph database includes nodes and edges, the nodes representing entities, and the edges representing relationships between entities; the entities include individuals and businesses; Quantify the trust of multiple relationships separately to obtain the intimacy value of each relationship; Based on the intimacy value of each relationship, respectively calculate the trust probability between the first entity and the second entity corresponding to each relationship; clustering the multiple relationships using a relationship clustering method; Based on the trust probabilities between the first entity and the second entity corresponding to each relationship, obtaining the trust probability of each type of relationship after clustering; Obtaining a trust probability between the first entity and the second entity based on the trust probability of each type of relationship; Based on the trust probability between the first entity and the second entity, the relationship intimacy between the first entity and the second entity is obtained.

2. The method according to claim 1, characterized in that The step of calculating the trust probability between the first entity and the second entity corresponding to each relationship based on the intimacy value of each relationship includes: According to the formula Calculate the probability P that the first entity A trusts the second entity B through relationship i ABi , where S ABi is the closeness value of the relationship i between the first entity A and the second entity B.

3. The method according to claim 1, characterized in that The obtaining the clustered trust probability of each type of relationship based on the trust probability between the first entity and the second entity corresponding to each relationship includes: The maximum value of the trust probabilities between the first entity and the second entity corresponding to each relationship in each type of relationship is determined as the trust probability of the relationship in this type.

4. The method according to claim 1, wherein The obtaining of the relationship closeness between the first entity and the second entity based on the trust probability between the first entity and the second entity includes: According to the formula S AB =100*P AB Calculate the relationship intimacy S between the first entity and the second entity AB , where P AB is the trust probability between the first entity and the second entity.

5. A device for calculating relationship intimacy based on multidimensional data, characterized in that: include: a relationship definition module, configured to determine a plurality of relationships affecting the trust between a first entity and a second entity by traversing a graph database, wherein the graph database includes nodes and edges, the nodes representing entities and the edges representing relationships between entities; the entities include individuals and businesses; The single-degree relationship quantification module is used to quantify the trust of multiple relationships and obtain the intimacy value of each relationship; A single-degree relationship trust probability calculation module is used to calculate the trust probability between the first entity and the second entity corresponding to each relationship based on the closeness value of each relationship; A relationship clustering module, configured to cluster the plurality of relationships using a relationship clustering method; A fusion module, configured to obtain a clustered trust probability of each type of relationship based on the trust probability between the first entity and the second entity corresponding to each relationship; an obtaining module, configured to obtain a trust probability between the first entity and the second entity based on the trust probability of each type of relationship; The calculation module is configured to calculate the relationship intimacy between the first entity and the second entity based on the trust probability between the first entity and the second entity.

6. The device according to claim 5, characterized in that The fusion module is specifically used for: The maximum value of the trust probabilities between the first entity and the second entity corresponding to each relationship in each type of relationship is determined as the trust probability of the relationship in this type.

7. An electronic device, characterized in that: include: A processor and a memory, wherein a computer program is stored in the memory. When the processor runs the computer program stored in the memory, the processor executes the relationship intimacy calculation method based on multidimensional data as described in any one of claims 1 to 4.

Images