CN112732804B - Cooperative data evaluation method and device, electronic equipment and readable storage medium - Google Patents
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Abstract
Embodiments of the present disclosure provide a collaborative data evaluation method and apparatus, a computer-readable storage medium, an electronic device, and a computer program. The method comprises the following steps: responding to the detected instruction triggered by the user for inquiring the cooperative security information of the target individual, and acquiring relationship data representing the relationship between the target individual and other individuals; determining a scoring weight corresponding to the relationship data based on a corresponding relationship table of a pre-established corresponding relationship between the characterization relationship data and the scoring weight; determining a cooperative security score for the target individual based on the scoring weights; based on the cooperative security score, cooperative security information characterizing the cooperative security degree of the target individual is generated and output. According to the technical scheme, the cooperative security information which can be referred by the user can be effectively generated based on the analysis of the relation between the target individual and other individuals, so that an intuitive cooperative security display system is provided, the other party can be simply and clearly known, and possible cooperative risks are avoided.
Description
Technical Field
The present disclosure relates to the field of computer technology, and in particular, to a collaborative data evaluation method and apparatus, an electronic device, a computer readable storage medium, and a computer program.
Background
In many cases, different individuals (e.g., enterprises, individuals, etc.) often cooperate, and when selecting a partner, the selection is typically performed manually based on basic information of other individuals to cooperate. For example, when selecting an enterprise partner, a business with large scale, high integrity, wide business scope, etc. is usually selected. However, for various reasons, an individual may cooperate with an individual who is unfamiliar or who does not meet the above conditions, and there is a need to indicate or embody the risk of the partner individual in terms of cooperative business.
Taking enterprises as an example, because the business ranges of the enterprises are different, the development projects are different, the cooperation among the enterprises is not fixed and stable, and multiple aspects of cooperation with different enterprises in different fields may be needed, so the cooperation among the enterprises has an unorientation, and when the enterprises start to perform a new cooperation requirement, the basic situation of the enterprises of the cooperation counterpart needs to be known timely and accurately, so that possible cooperation risks are avoided.
Disclosure of Invention
The present disclosure aims to provide a collaborative data evaluation method and apparatus, an electronic device, a computer-readable storage medium, and a computer program, so as to solve at least some of the technical problems described in the background art.
Other features and advantages of the present disclosure will be apparent from the following detailed description, or may be learned in part by the practice of the disclosure.
According to a first aspect of the present disclosure, there is provided a collaborative data evaluation method, comprising: responding to the detected instruction triggered by the user for inquiring the cooperative security information of the target individual, and acquiring relationship data representing the relationship between the target individual and other individuals; determining a scoring weight corresponding to the relationship data based on a corresponding relationship table of a pre-established corresponding relationship between the characterization relationship data and the scoring weight; determining a cooperative security score for the target individual based on the scoring weights; based on the cooperative security score, cooperative security information characterizing the cooperative security degree of the target individual is generated and output.
In an exemplary embodiment of the present disclosure, determining a scoring weight corresponding to relationship data based on a correspondence table of correspondence between pre-established characterization relationship data and scoring weights, includes: determining a relationship data interval in which the relationship data is located from at least two preset relationship data intervals; and determining the scoring weight corresponding to the relation data interval based on the corresponding relation table.
In an exemplary embodiment of the present disclosure, the relationship data includes positive relationship data and/or negative relationship data, and the correspondence table includes a positive correspondence table and/or a negative correspondence table; determining the scoring weight corresponding to the relationship data based on a pre-established corresponding relationship table representing the corresponding relationship between the relationship data and the scoring weight, wherein the method comprises the following steps: determining forward relation data from the relation data, and determining forward scoring weights corresponding to the forward relation data based on a forward correspondence table; and/or determining negative relation data from the relation data, and determining a negative scoring weight corresponding to the negative relation data based on the negative correspondence table.
In an exemplary embodiment of the present disclosure, the relationship data of the target individual includes sub-relationship data in at least one dimension; determining the scoring weight corresponding to the relationship data based on a pre-established corresponding relationship table representing the corresponding relationship between the relationship data and the scoring weight, wherein the method comprises the following steps: determining the dimension of the sub-relationship data included in the relationship data, and determining the scoring weight corresponding to the sub-relationship data in at least one dimension based on a pre-established corresponding relationship table representing the corresponding relationship between the sub-relationship data and the scoring weight in different dimensions.
In an exemplary embodiment of the present disclosure, the correspondence table is obtained in advance based on the following steps: acquiring corresponding relation data of individuals in a preset individual set; determining the number of individuals corresponding to the same relationship data in each relationship data; based on the corresponding relation between the same relation data and the number of individuals, carrying out data fitting to obtain a fitting curve; dividing the fitting curve to obtain at least two relation data intervals; determining scoring weights corresponding to the relationship data intervals in the at least two relationship data intervals; and generating a corresponding relation table based on the at least two relation data intervals and the corresponding scoring weights.
In an exemplary embodiment of the present disclosure, segmenting a fitted curve to obtain at least two relational data intervals includes: dividing the area between the fitting curve and the transverse axis into at least two subareas with equal areas; at least two relational data intervals are determined based on the boundaries of the respective sub-regions.
According to a second aspect of the present disclosure, there is provided a cooperative data evaluating apparatus including: the acquisition module is used for responding to the instruction triggered by the detected user and used for inquiring the cooperative security information of the target individual and acquiring the relationship data representing the relationship between the target individual and other individuals; the first determining module is used for determining the scoring weight corresponding to the relationship data based on a corresponding relationship table of the pre-established corresponding relationship between the characterization relationship data and the scoring weight; the second determining module is used for determining cooperative security scores of the target individuals based on the scoring weights; and the generation module is used for generating cooperative safety information representing the cooperative safety degree of the target individual based on the cooperative safety score and outputting the cooperative safety information.
In an exemplary embodiment of the present disclosure, the first determining module includes: the first determining unit is used for determining a relation data interval in which the relation data is located from at least two preset relation data intervals; and the second determining unit is used for determining the scoring weight corresponding to the relation data interval based on the corresponding relation table.
In an exemplary embodiment of the present disclosure, the relationship data includes positive relationship data and/or negative relationship data, and the correspondence table includes a positive correspondence table and/or a negative correspondence table; the first determination module includes: the third determining unit is used for determining forward relation data based on the relation data and determining forward scoring weights corresponding to the forward relation data according to the forward corresponding relation table; and/or a fourth determining unit, configured to determine negative relationship data from the relationship data, and determine a negative scoring weight corresponding to the negative relationship data based on the negative correspondence table.
In an exemplary embodiment of the present disclosure, the relationship data of the target individual includes sub-relationship data in at least one dimension; the first determination module is further to: determining the dimension of sub-relationship data included in the relationship data, and determining the scoring weight corresponding to the sub-relationship data in at least one dimension based on a pre-established corresponding relationship table representing the corresponding relationship between the sub-relationship data in different dimensions and the scoring weight.
In an exemplary embodiment of the present disclosure, the correspondence table is obtained in advance based on the following steps: acquiring corresponding relation data of individuals in a preset individual set; determining the number of individuals corresponding to the same relationship data in each relationship data; based on the corresponding relation between the same relation data and the number of individuals, carrying out data fitting to obtain a fitting curve; dividing the fitting curve to obtain at least two relation data intervals; determining scoring weights corresponding to the relationship data intervals in the at least two relationship data intervals; and generating a corresponding relation table based on the at least two relation data intervals and the corresponding scoring weights.
In an exemplary embodiment of the present disclosure, at least two relational data intervals are derived based on the steps of: dividing the area between the fitting curve and the transverse axis into at least two subareas with equal areas; at least two relational data intervals are determined based on the boundaries of the respective sub-regions.
According to a third aspect of the present disclosure, there is provided an electronic device comprising: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to perform the collaborative data evaluation method described above via execution of the executable instructions.
According to a fourth aspect of the present disclosure, there is provided a computer storage medium having a computer program stored thereon, characterized in that the computer program, when executed by a processor, implements the above-described collaborative data evaluation method.
According to a fifth aspect of the present disclosure, there is provided a computer program comprising computer readable code which, when run on a device, causes a processor in the device to execute instructions for carrying out the steps of the above-described collaborative data evaluation method.
As can be seen from the above technical solutions, the cooperative data evaluation method and apparatus, the computer-readable storage medium, the electronic device, and the computer program in the exemplary embodiments of the present disclosure have at least the following advantages and positive effects:
According to the collaborative data evaluation method and device, the electronic equipment, the computer readable storage medium and the computer program, when the collaborative safety information of the target individual is queried by the user, the scoring weight corresponding to the relation data of the target individual is determined based on the pre-established corresponding relation table, then the collaborative safety score of the target individual is determined based on the scoring weight, and finally the collaborative safety information is generated and output based on the collaborative safety score, so that the collaborative safety information which can be referred by the user is generated based on the analysis of the relation between the target individual and other individuals, an intuitive collaborative safety display system is provided, the opposite party can be simply and clearly known, and possible collaborative risks are avoided.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the disclosure and together with the description, serve to explain the principles of the disclosure. It will be apparent to those of ordinary skill in the art that the drawings in the following description are merely examples of the disclosure and that other drawings may be derived from them without undue effort.
FIG. 1 is a system diagram to which the present disclosure is applicable;
FIG. 2 is a flow chart of a collaborative data assessment method provided by an exemplary embodiment of the present disclosure;
FIG. 3 is a flow chart of a collaborative data assessment method provided by another exemplary embodiment of the present disclosure;
FIG. 4 is an exemplary schematic of a fitted curve provided by exemplary embodiments of the present disclosure;
FIG. 5 is a schematic diagram of a collaborative data assessment apparatus provided in an exemplary embodiment of the present disclosure;
fig. 6 is a schematic structural view of a cooperative data evaluating apparatus provided in another exemplary embodiment of the present disclosure;
fig. 7 is a block diagram of an electronic device provided in an exemplary embodiment of the present disclosure.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. However, the exemplary embodiments may be embodied in many different forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the example embodiments to those skilled in the art. The drawings are merely schematic illustrations of the present disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus a repetitive description thereof will be omitted.
Furthermore, the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the disclosure. One skilled in the relevant art will recognize, however, that the aspects of the disclosure may be practiced without one or more of the specific details, or with other methods, apparatus, steps, etc. In other instances, well-known structures, methods, devices, implementations, or operations are not shown or described in detail to avoid obscuring aspects of the disclosure.
Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more such feature. In the description of the present disclosure, the meaning of "a plurality" is at least two, such as two, three, etc., unless explicitly specified otherwise. The symbol "/" generally indicates that the associated individuals are an "or" relationship.
In the present disclosure, unless explicitly specified and limited otherwise, terms such as "connected" and the like are to be construed broadly and, for example, may be electrically connected or may communicate with each other; can be directly connected or indirectly connected through an intermediate medium. The specific meaning of the terms in this disclosure will be understood by those of ordinary skill in the art as the case may be.
Exemplary System
Fig. 1 shows a schematic diagram of a system architecture 100 to which a collaborative data evaluation method or collaborative data evaluation apparatus of an embodiment of the present disclosure may be applied.
As shown in fig. 1, the system architecture 100 may include one or more of terminal devices 101, 102, 103, a network 104, and a server 105. The network 104 is used as a medium to provide communication links between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection categories such as wired, wireless communication links, or fiber optic cables, among others.
It should be understood that the number of terminal devices, networks and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation. For example, the server 105 may be a server cluster formed by a plurality of servers.
The user may interact with the server 105 via the network 104 using the terminal devices 101, 102, 103 to receive or send messages or the like. Various communication client applications, such as a search class application, a web browser application, a shopping class application, an instant messaging tool, etc., may be installed on the terminal devices 101, 102, 103.
The terminal devices 101, 102, 103 may be a variety of electronic devices including, but not limited to, smartphones, tablets, laptop and desktop computers, digital cinema projectors, and the like.
The server 105 may be a server providing various services. For example, the user transmits an instruction to inquire about cooperative security information of the target individual to the server 105 using the terminal device 103 (may be the terminal device 101 or 102). The server may perform processing based on information of the target individual to obtain a processing result (e.g., cooperative security information, etc.).
Exemplary method
Referring to fig. 2, a flowchart of a cooperative data assessment method according to an exemplary embodiment of the present disclosure is provided, and the present embodiment may be applied to an electronic device (such as the terminal device 101, 102, 103 or the server 105 shown in fig. 1), and the method includes the following steps:
s210, acquiring relation data representing the relation between the target individual and other individuals in response to the detection of the user-triggered instruction for querying the cooperative security information of the target individual.
S220, determining the scoring weight corresponding to the relationship data based on a corresponding relationship table of the corresponding relationship between the pre-established characterization relationship data and the scoring weight.
S230, determining cooperative security scores of the target individuals based on the scoring weights.
S240, based on the cooperative security score, generating cooperative security information representing the cooperative security degree of the target individual and outputting the cooperative security information.
According to the collaborative data evaluation method provided by the embodiment of the disclosure, when the user inquires the collaborative safety information of the target individual, the scoring weight corresponding to the relation data of the target individual is determined based on the pre-established corresponding relation table, then the collaborative safety score of the target individual is determined based on the scoring weight, and finally the collaborative safety information is generated and output based on the collaborative safety score, so that the collaborative safety information which can be used for reference by the user is generated based on the analysis of the relation between the target individual and other individuals, an intuitive collaborative safety display system is provided, the opponent can be simply and clearly known, and possible collaborative risks are avoided.
In S210, the electronic device may obtain relationship data characterizing a relationship between the target individual and other individuals in response to detecting the user-triggered instruction for querying the collaborative security information of the target individual.
As an example, a user may enter information (e.g., name, code, etc.) of a target individual using an electronic device as shown in fig. 1, click on a "query" button, and a collaborative security information instruction may be generated. After the electronic device detects the instruction, the relationship data may be obtained.
The relationship data may include, but is not limited to, at least one of: collaboration relationship data, provisioning relationship data, dispute relationship data, and the like. As an example, the collaboration relationship data may include a number of individuals collaborating with the target individual; the provisioning relationship data may include a number of individuals in relationship to the target individual as a "customer" or "provider"; the dispute data may include the number of individuals in relationship to the target individual as "original" and "advertised".
In S220, the electronic device may determine the scoring weight corresponding to the relationship data based on a pre-established correspondence table that characterizes the correspondence between the relationship data and the scoring weight.
As an example, the above-described correspondence table may be a table based in advance on counting a large amount of relationship data and setting scoring weights corresponding to the relationship data. The electronic device may query the scoring weight corresponding to the relationship data from the correspondence table.
In some alternative implementations, the relationship data for the target individual includes sub-relationship data in at least one dimension. Where a certain dimension corresponds to a certain relationship with the target individual. As an example, the relationship data may include the number of other individuals who are in a "customer" relationship with the target individual, the number of other individuals who are in a "provider" relationship with the target individual, the number of other individuals who have a dispute with the target individual, and the like.
Based on this, S220 may be performed as follows:
Determining the dimension of sub-relationship data included in the relationship data, and determining the scoring weight corresponding to the sub-relationship data in at least one dimension based on a pre-established corresponding relationship table representing the corresponding relationship between the sub-relationship data in different dimensions and the scoring weight. In general, each dimension of the at least one dimension corresponds to a correspondence table, and for sub-relationship data of a certain dimension, the electronic device may determine, from the correspondence table of the dimension, a scoring weight corresponding to the sub-relationship data. As an example, the dispute relationship data may include the number of other individuals who complain about the target individual (referred to herein as original relationship data) and the number of other individuals who complain about the target individual (referred to herein as perceived relationship data). The electronic device may query the scoring weight corresponding to the original report relationship data from the corresponding relationship table of the original report relationship dimension, and query the scoring weight corresponding to the report relationship data from the corresponding relationship table of the report relationship dimension. In general, the scoring weights corresponding to the original report relationship data and the scoring weights corresponding to the report relationship data are negative, and the absolute value of the scoring weights corresponding to the original report relationship data is smaller than the absolute value of the scoring weights corresponding to the report relationship data.
According to the method, different scoring weights are respectively extracted from the relationship data corresponding to at least one dimension, so that the relationship between a target individual and other individuals can be represented in more detail by using the sub-relationship data in different dimensions, the determined scoring weights can calculate the influence of the relationship data capable of reflecting various dimensions on the composite security score, and the accuracy of outputting the cooperative security information is improved.
In S230, the electronic device may determine a collaborative security score for the target individual based on the scoring weights.
Wherein the cooperative security score is used to characterize the degree of risk of cooperation with the target individual, in general, the higher the cooperative security score, the higher the security of cooperation with the target individual, i.e., the less the risk. Specifically, the collaborative security score may be determined based on a scoring weight based on a preset base score (e.g., 60 points).
For example, the scoring weights may be specific scores, such as +10, -5, and the like, and the base score may be directly added to the scoring weights to obtain the collaborative security score. For another example, the scoring weight may be a percentage, such as +10%, -10%, etc., where the product of the base score and the scoring weight is a score that increases or decreases based on the base score.
In S240, the electronic device may generate cooperative security information characterizing a degree of cooperative security of the target individual based on the cooperative security score and output the cooperative security information.
The cooperative security information is used for representing the cooperative security degree of the target individual. The collaboration security information may include, but is not limited to, information in at least one of the following forms: text, numbers, images, symbols, etc. The output form of the cooperative security information may include, but is not limited to, at least one of: and displaying on a display connected with the electronic equipment, transmitting to other electronic equipment connected with the electronic equipment for displaying, storing in a preset storage area and the like.
As an example, the above-described cooperative security score may be used as cooperative security information. Or may determine a piece of information characterizing the cooperative security level (e.g., in the form of a five star level) as cooperative security information based on the cooperative security score. In addition, the collaborative security information may also include some relevant information of the target individual to be presented to the user for reference. For example, the number of individuals in a cooperative relationship with the target individual, the number of individuals in a dispute relationship with the target individual, and the like may be output.
In some alternative implementations, step 220 may be performed as follows:
firstly, determining a relationship data section in which the relationship data is located from at least two preset relationship data sections.
Then, based on the correspondence table, a scoring weight corresponding to the relationship data section is determined.
As an example, the relationship data is the number of individuals in cooperative relationship with the target individual, and the correspondence table includes at least two number intervals, each interval corresponding to one scoring weight. For example, interval one is [0,10 ], and the corresponding scoring weight is +10; and if the relation data is 60, determining that the corresponding scoring weight is +30, thereby obtaining the basic score +30 and obtaining the cooperative security score.
According to the implementation mode, the data size of the corresponding relation table can be reduced by setting at least two intervals, and the efficiency of inquiring the scoring weight is improved.
In some alternative implementations, the relationship data includes positive-going relationship data and/or negative-going relationship data, and the correspondence table includes a positive-going correspondence table and/or a negative-going correspondence table. The positive relationship data is used for positively scoring the target individual, namely adding score, and the negative relationship data is used for negatively scoring the target individual, namely subtracting score. As an example, the positive relationship data may include a number of individuals having a partnership with the target individual, and the negative relationship data may include a number of individuals having a dispute with the target individual.
Based on this, the above step 220 may be performed as follows:
Positive relationship data is determined from the relationship data, positive scoring weights corresponding to the positive relationship data are determined based on the positive correspondence table, and/or negative relationship data is determined from the relationship data, and negative scoring weights corresponding to the negative relationship data are determined based on the negative correspondence table.
Specifically, the electronic device may determine whether the relationship data is positive relationship data or negative relationship data according to the type of the relationship data. For example, positive relationship data may have a corresponding positive data type label, negative relationship data may have a corresponding negative data type label, and the electronic device may determine whether each piece of relationship data is positive relationship data or negative relationship data according to the data type label corresponding to each piece of relationship data.
As an example, the relationship data of the target individual includes positive relationship data representing the number of individuals in cooperative relationship with the target individual, and the relationship data of the target individual further includes negative relationship data representing the number of individuals in dispute relationship with the target individual. If the positive scoring weight corresponding to the positive relationship data is +20 and the negative scoring weight corresponding to the negative relationship data is-10, the final cooperative security score is +20-10.
By setting the positive correspondence table and the negative correspondence table, the implementation mode can score the target individuals positively and negatively, so that the target individuals can be scored in a more comprehensive way, and the accuracy of outputting the cooperation safety information is improved.
With further reference to fig. 3, a flow chart of a collaborative data evaluation method according to another embodiment of the present disclosure is schematically shown. On the basis of the embodiment shown in fig. 2, the correspondence table is obtained based on the following steps:
S310, obtaining corresponding relation data of individuals in a preset individual set.
S320, determining the number of individuals corresponding to the same relationship data in each relationship data.
S330, based on the corresponding relation between the same relation data and the number of individuals, performing data fitting to obtain a fitting curve.
And S340, dividing the fitting curve to obtain at least two relation data sections.
S350, determining scoring weights corresponding to the relationship data intervals in the at least two relationship data intervals.
S360, generating a corresponding relation table based on at least two relation data intervals and corresponding scoring weights.
In the corresponding embodiment of fig. 3, the obtained relationship data corresponding to the individuals in the individual set is utilized to perform data fitting in advance, and then at least two relationship data intervals and corresponding scoring weights are determined based on the fitting curve, so that the corresponding relationship table can reflect the cooperative security degree of the individuals in a larger range, and the accuracy of generating the corresponding relationship table is improved.
In S310, the electronic device may obtain, locally or remotely, relationship data corresponding to an individual in a preset set of individuals. The set of individuals may be a set of individuals of a certain type, for example, a set of a large number of enterprises, each enterprise having corresponding relationship data. The relationship data of each individual is the same as the meaning of the relationship data representation of the target individual, and is not described here again.
In S320, the electronic device may determine the number of individuals corresponding to the same relationship data in the respective relationship data.
For example, 100 individuals each have 10 cooperative individuals, 200 individuals each have 20 cooperative individuals, and so on.
In S330, the electronic device may perform data fitting based on the correspondence between the same relationship data and the number of individuals, to obtain a fitting curve.
As an example, an abscissa may be set to represent relationship data, and an ordinate to represent the number of individuals having corresponding relationship data. The electronic device may perform curve fitting based on various fitting algorithms, such as gaussian curve fitting, straight line fitting, quadratic curve fitting, etc. Since a gaussian curve can more accurately reflect the distribution of relational data for a large number of individuals, it may be preferable to use a gaussian fitting algorithm for curve fitting.
As shown in fig. 4, the x-axis represents the number of individuals in the cooperative relationship, and the y-axis represents the number of individuals having the corresponding number of individuals in the cooperative relationship. For example, a point (x 1, y 1) in fig. 4 indicates that the number of individuals having x1 cooperative relationships is y1, and a point (x 2, y 2) indicates that the number of individuals having x2 cooperative relationships is y2.
When the relationship data is of a plurality of dimensions, curve fitting may be performed on the relationship data of each dimension, and each fitting curve may be processed according to the following method to obtain a plurality of correspondence tables.
In S340, the electronic device may segment the fitted curve to obtain at least two relationship data intervals.
The method of dividing the fitted curve may include various methods, for example, average dividing the abscissa of the curve so that the range of each relationship data section is the same.
In some alternative implementations, S340 may be performed as follows:
First, the region between the fitted curve and the horizontal axis is divided into at least two sub-regions of equal area. As shown in fig. 4, the areas of the sub-areas D1, D2, D3 divided by the coordinates (x 1, y 1) and (x 1, y 1) are the same.
Then, at least two relational data intervals are determined based on the boundaries of the respective sub-regions. As shown in fig. 4, three relational data sections, 0 to x1, x1 to x2, and x2 to x3, respectively, can be obtained.
According to the method, the equal area division is carried out on the area under the fitting curve, and under the condition that a large number of individuals exist, the probability that different individuals fall into corresponding relation data intervals is approximately the same, so that the scoring weight corresponding to each determined subarea is more reasonable, and the accuracy of generating cooperative safety information is improved.
In S350, the electronic device may determine a scoring weight corresponding to a relationship data interval of the at least two relationship data intervals. Specifically, the scoring weights may be manually set. When the types of the relational data are multiple dimensions, a corresponding weight can be set for the relational data interval corresponding to each dimension.
As an example, when the relationship data indicates the number of individuals of the cooperative relationship, as shown in fig. 4, a first scoring weight (e.g., +10) may be set for the intervals 0 to x1, a second scoring weight (e.g., +20) may be set for the intervals x1 to x2, and a third scoring weight (e.g., +30) may be set for the intervals x2 to x 3. When the relationship data indicates the number of individuals of the dispute, the scoring weight is set to a negative number, and the change rule is set such that the absolute value decreases as the relationship data increases. As shown in fig. 4, the scoring weights of the intervals 0 to x1 may be set to-10, the scoring weights of the intervals x1 to x2 may be set to-20, and the scoring weights of the intervals x2 to x3 may be set to-30.
In S360, the electronic device may generate a correspondence table based on at least two relationship data intervals and corresponding scoring weights. Specifically, the correspondence table includes a plurality of relationship data sections and scoring weights corresponding to each relationship data section. When the relationship data is of a plurality of dimensions, different correspondence tables may be set for each dimension of the relationship data.
Exemplary apparatus
Fig. 5 schematically illustrates a structural diagram of a cooperative data evaluating apparatus according to an embodiment of the present disclosure. The cooperative data evaluating apparatus provided in the embodiment of the present disclosure may be provided on the terminal device, may be provided on the server side, or may be provided partially on the terminal device, and may be provided partially on the server side, for example, may be provided on the server 105 (according to actual substitution) in fig. 1, but the present disclosure is not limited thereto.
The cooperative data evaluating apparatus provided by the embodiment of the present disclosure may include: an obtaining module 510, configured to obtain relationship data that characterizes a relationship between the target individual and other individuals in response to detecting a user-triggered instruction for querying cooperative security information of the target individual; a first determining module 520, configured to determine a scoring weight corresponding to the relationship data based on a pre-established correspondence table that characterizes a correspondence between the relationship data and the scoring weight; a second determination module 530 for determining a collaborative security score for the target individual based on the scoring weights; and a generation module 540 for generating cooperative security information characterizing the cooperative security degree of the target individual based on the cooperative security score and outputting the cooperative security information.
In this embodiment, the obtaining module 510 may obtain relationship data characterizing a relationship between the target individual and other individuals in response to detecting a user-triggered instruction for querying cooperative security information of the target individual. The relationship data may include, but is not limited to, at least one of: collaboration relationship data, provisioning relationship data, dispute relationship data, and the like. As an example, the collaboration relationship data may include a number of individuals collaborating with the target individual; the provisioning relationship data may include a number of individuals in relationship to the target individual as a "customer" or "provider"; the dispute data may include the number of individuals in relationship to the target individual as "original" and "advertised".
In this embodiment, the first determining module 520 may determine the scoring weight corresponding to the relationship data based on a pre-established correspondence table that characterizes the correspondence between the relationship data and the scoring weight. As an example, the above-described correspondence table may be a table based in advance on counting a large amount of relationship data and setting scoring weights corresponding to the relationship data. The first determining module may query the scoring weight corresponding to the relationship data from the correspondence table.
In this embodiment, the second determination module 530 may determine the collaborative security score for the target individual based on the scoring weights. Wherein the cooperative security score is used to characterize the degree of risk of cooperation with the target individual, in general, the higher the cooperative security score, the higher the security of cooperation with the target individual, i.e., the less the risk. Specifically, the collaborative security score may be determined based on a scoring weight based on a preset base score (e.g., 60 points).
In this embodiment, the generation module 540 may generate cooperative security information characterizing the degree of cooperative security of the target individual and output the cooperative security information based on the cooperative security score. The cooperative security information is used for representing the cooperative security degree of the target individual. The collaboration security information may include, but is not limited to, information in at least one of the following forms: text, numbers, images, symbols, etc.
Referring to fig. 6, fig. 6 is a schematic structural view of a data compression apparatus provided in another exemplary embodiment of the present disclosure.
In some alternative implementations, the first determining module 520 may include: a first determining unit 5201, configured to determine a relationship data interval in which the relationship data is located from at least two preset relationship data intervals; the second determining unit 5202 is configured to determine a scoring weight corresponding to the relational data interval based on the correspondence table.
In some alternative implementations, the relationship data may include positive relationship data and/or negative relationship data, and the correspondence table may include a positive correspondence table and/or a negative correspondence table; the first determination module 520 may include: a third determining unit 5203, configured to determine forward relationship data from the relationship data, and determine a forward scoring weight corresponding to the forward relationship data based on the forward correspondence table; and/or a fourth determining unit 5204, configured to determine negative relationship data from the relationship data, and determine a negative scoring weight corresponding to the negative relationship data based on the negative correspondence table.
In some alternative implementations, the relationship data for the target individual may include sub-relationship data in at least one dimension; the first determination module 520 may be further configured to: determining the dimension of the sub-relationship data included in the relationship data, and determining the scoring weight corresponding to the sub-relationship data in at least one dimension based on a pre-established corresponding relationship table representing the corresponding relationship between the sub-relationship data and the scoring weight in different dimensions.
In some alternative implementations, the correspondence table may be obtained in advance based on the following steps: acquiring corresponding relation data of individuals in a preset individual set; determining the number of individuals corresponding to the same relationship data in each relationship data; based on the corresponding relation between the same relation data and the number of individuals, carrying out data fitting to obtain a fitting curve; dividing the fitting curve to obtain at least two relation data intervals; determining scoring weights corresponding to the relationship data intervals in the at least two relationship data intervals; and generating a corresponding relation table based on the at least two relation data intervals and the corresponding scoring weights.
In some optional implementations, the at least two relationship data intervals are obtained based on the following steps: dividing the area between the fitting curve and the transverse axis into at least two subareas with equal areas; at least two relational data intervals are determined based on the boundaries of the respective sub-regions.
According to the cooperative data evaluation device provided by the embodiment of the disclosure, when the user inquires the cooperative safety information of the target individual, the scoring weight corresponding to the relationship data of the target individual is determined based on the pre-established corresponding relationship table, then the cooperative safety score of the target individual is determined based on the scoring weight, and finally the cooperative safety information is generated and output based on the cooperative safety score, so that the cooperative safety information which can be referred by the user is generated based on the analysis of the relationship between the target individual and other individuals, an intuitive cooperative safety display system is provided, the opponent can be simply and clearly known, and possible cooperative risks are avoided.
The specific implementation of each module, unit and subunit in the cooperative data evaluating apparatus provided in the embodiments of the present disclosure may refer to the content in the above cooperative data evaluating method, which is not described herein again.
It should be noted that although in the above detailed description several modules, units and sub-units of the apparatus for action execution are mentioned, this division is not mandatory. Indeed, the features and functions of two or more modules, units, and sub-units described above may be embodied in one module, unit, and sub-unit, in accordance with embodiments of the present disclosure. Conversely, the features and functions of one module, unit, and sub-unit described above may be further divided into ones that are embodied by a plurality of modules, units, and sub-units.
Exemplary electronic device
As shown in fig. 7, the example electronic device 70 includes a processor 701 for executing software routines, although a single processor is shown for clarity, the electronic device 70 may also include a multi-processor system. The processor 701 is connected to a communication infrastructure 702 for communicating with other components of the electronic device 70. The communication infrastructure 702 may include, for example, a communication bus, a crossbar switch, or a network.
The electronic device 70 also includes memory, such as random access memory (Random Access Memory, RAM), which may include a main memory 703 and a secondary memory 710. The secondary memory 710 may include, for example, a hard disk drive 711 and/or a removable storage drive 712, and the removable storage drive 712 may include a floppy disk drive, a magnetic tape drive, an optical disk drive, etc. Removable storage drive 712 reads from and/or writes to a removable storage unit 713 in a conventional manner. Removable storage unit 713 may comprise a floppy disk, magnetic tape, optical disk, etc. which is read by and written to by removable storage drive 712. One skilled in the relevant art will appreciate that removable storage unit 713 includes a computer-readable storage medium having stored thereon computer-executable program code instructions and/or data.
In an alternative embodiment, secondary memory 710 may additionally or alternatively include other similar means for allowing computer programs or other instructions to be loaded into electronic device 70. Such means may include, for example, a removable storage unit 721 and an interface 720. Examples of removable storage unit 721 and interface 720 include: program cartridge (cartridge) and cartridge interfaces (such as those found in video game console devices), removable memory chips (such as EPROM or PROM) and associated sockets, and other removable storage units 721 and interfaces 720 which allow software and data to be transferred from removable storage unit 721 to electronic device 70.
The electronic device 70 also includes at least one communication interface 740. Communication interface 740 allows software and data to be transferred between electronic device 70 and external devices via communication path 741. In various embodiments of the present disclosure, the communication interface 740 allows data to be transferred between the electronic device 70 and a data communication network, such as a public data or private data communication network. The communication interface 740 may be used to exchange data between different electronic devices 70, which electronic devices 70 form part of an interconnected computer network. Examples of communication interface 740 may include a modem, a network interface (such as an ethernet card), a communication port, an antenna with associated circuitry, and so forth. Communication interface 740 may be wired or may be wireless. Software and data transferred via communications interface 740 are in the form of signals which may be electronic, magnetic, optical, or other signals capable of being received by communications interface 740. These signals are provided to a communication interface via a communication path 741.
As shown in fig. 7, the electronic device 70 further includes a display interface 731 and an audio interface 732, the display interface 731 performing operations for rendering images to an associated display 730, the audio interface 732 performing operations for playing audio content through an associated speaker 733.
In this document, the term "computer program product" may refer, in part, to: removable storage unit 713, removable storage unit 721, a hard disk installed in hard disk drive 711, or a carrier wave carrying software over communications path 741 (wireless link or cable) to communications interface 740. Computer-readable storage media refers to any non-transitory tangible storage medium that provides recorded instructions and/or data to electronic device 70 for execution and/or processing. Examples of such storage media include floppy disks, magnetic tape, CD-ROMs, DVDs, blu-ray (TM) optical disks, hard disk drives, ROMs or integrated circuits, USB memory, magneto-optical disks, or computer-readable cards such as PCMCIA cards, etc., whether internal or external to electronic device 70. Transitory or non-tangible computer readable transmission media may also participate in providing software, applications, instructions, and/or data to the electronic device 70, examples of such transmission media include radio or infrared transmission channels, network connections to another computer or another networked device, and the internet or intranets including email transmissions and information recorded on websites, and the like.
Computer programs (also called computer program code) are stored in the main memory 703 and/or the secondary memory 710. Computer programs may also be received via communications interface 740. Such computer programs, when executed, enable the electronic device 70 to perform one or more features of the embodiments discussed herein. In various embodiments, a computer program, when executed, enables the processor 701 to perform the features of the embodiments described above. Such computer programs thus represent controllers of the computer system 70.
The software may be stored in a computer program product and loaded into the electronic device 70 using a removable storage drive 712, a hard drive 711 or an interface 720. Alternatively, the computer program product may be downloaded to computer system 70 via communication path 741. Which when executed by the processor 701, causes the electronic device 70 to perform the functions of the embodiments described herein.
It should be understood that the embodiment of fig. 7 is given by way of example only. Accordingly, in some embodiments, one or more features of the electronic device 70 may be omitted. Moreover, in some embodiments, one or more features of the electronic device 70 may be combined together. Additionally, in some embodiments, one or more features of the electronic device 70 may be separated into one or more components.
It will be appreciated that the elements shown in fig. 7 serve to provide a way to perform the various functions and operations of the servers described in the above embodiments.
In one embodiment, a server may be generally described as a physical device comprising at least one processor and at least one memory including computer program code. The at least one memory and the computer program code are configured to, with the at least one processor, cause the physical device to perform the necessary operations.
Exemplary computer-readable storage Medium
Embodiments of the present application also provide a computer readable storage medium having stored thereon a computer program which, when executed by a processor, performs the functions of the methods shown in fig. 2-6.
Computer readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by an electronic device. Computer-readable media, as defined herein, does not include transitory computer-readable media (transmission media), such as modulated data signals and carrier waves.
From the foregoing description of embodiments, it will be apparent to those skilled in the art that the present embodiments may be implemented in software plus a necessary general purpose hardware platform. Based on such understanding, the technical solutions of the embodiments of the present specification may be embodied in essence or what contributes to the prior art in the form of a software product, which may be stored in a storage medium, such as a ROM/RAM, a magnetic disk, an optical disk, etc., including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method described in the embodiments or some parts of the embodiments of the present specification.
Exemplary computer program
The disclosed embodiments also provide a computer program product for storing computer readable instructions that, when executed, cause a computer to perform the collaborative data evaluation method in any of the possible implementations described above.
The computer program product may be realized in particular by means of hardware, software or a combination thereof. In one alternative, the computer program product is embodied as a computer storage medium, and in another alternative, the computer program product is embodied as a software product, such as a software development kit (Software Development Kit, SDK), or the like.
The basic principles of the present disclosure have been described above in connection with specific embodiments, but it should be noted that the advantages, benefits, effects, etc. mentioned in the present disclosure are merely examples and not limiting, and these advantages, benefits, effects, etc. are not to be considered as necessarily possessed by the various embodiments of the present disclosure. Furthermore, the specific details disclosed herein are for purposes of illustration and understanding only, and are not intended to be limiting, since the disclosure is not necessarily limited to practice with the specific details described.
In this specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different manner from other embodiments, so that the same or similar parts between the embodiments are mutually referred to. For system embodiments, the description is relatively simple as it essentially corresponds to method embodiments, and reference should be made to the description of method embodiments for relevant points.
Those of ordinary skill in the art will appreciate that: all or part of the steps for implementing the above method embodiments may be performed by hardware associated with program instructions.
In this specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different manner from other embodiments, so that the same or similar parts between the embodiments are mutually referred to. For system embodiments, the description is relatively simple as it essentially corresponds to method embodiments, and reference should be made to the description of method embodiments for relevant points.
The methods and apparatus of the present disclosure may be implemented in a number of ways. For example, the methods and apparatus of the present disclosure may be implemented by software, hardware, firmware, or any combination of software, hardware, firmware. The above-described sequence of steps for the method is for illustration only, and the steps of the method of the present disclosure are not limited to the sequence specifically described above unless specifically stated otherwise. Furthermore, in some embodiments, the present disclosure may also be implemented as programs recorded in a recording medium, the programs including machine-readable instructions for implementing the methods according to the present disclosure. Thus, the present disclosure also covers a recording medium storing a program for executing the method according to the present disclosure.
The description of the present disclosure has been presented for purposes of illustration and description, and is not intended to be exhaustive or limited to the disclosure in the form disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art. The embodiments were chosen and described in order to best explain the principles of the disclosure and the practical application, and to enable others of ordinary skill in the art to understand the disclosure for various embodiments with various modifications as are suited to the particular use contemplated.
Claims (9)
1. A method of collaborative data assessment, comprising:
In response to detecting a user-triggered instruction for querying cooperative security information of a target individual, obtaining relationship data characterizing a relationship between the target individual and other individuals;
Determining a scoring weight corresponding to the relationship data based on a corresponding relationship table of a pre-established corresponding relationship between the characterization relationship data and the scoring weight;
determining a collaborative security score for the target individual based on the scoring weights;
generating cooperative security information representing the cooperative security degree of the target individual based on the cooperative security score and outputting the cooperative security information;
The corresponding relation table is obtained in advance based on the following steps: acquiring corresponding relation data of individuals in a preset individual set; determining the number of individuals corresponding to the same relationship data in each relationship data; based on the corresponding relation between the same relation data and the number of individuals, carrying out data fitting to obtain a fitting curve; dividing the fitting curve to obtain at least two relation data intervals; determining scoring weights corresponding to the relationship data intervals in the at least two relationship data intervals; and generating the corresponding relation table based on the at least two relation data intervals and the corresponding scoring weights.
2. The method according to claim 1, wherein the determining the scoring weight corresponding to the relationship data based on the pre-established correspondence table representing the correspondence between the relationship data and the scoring weight comprises:
determining a relationship data interval in which the relationship data is located from at least two preset relationship data intervals;
and determining the scoring weight corresponding to the relation data interval based on the corresponding relation table.
3. The method according to claim 1, wherein the relationship data comprises positive relationship data and/or negative relationship data, and the correspondence table comprises a positive correspondence table and/or a negative correspondence table;
The determining the scoring weight corresponding to the relationship data based on the corresponding relationship table of the corresponding relationship between the pre-established characterization relationship data and the scoring weight comprises the following steps:
Determining forward relation data from the relation data, and determining forward scoring weights corresponding to the forward relation data based on the forward correspondence table; and/or
And determining negative relation data from the relation data, and determining a negative scoring weight corresponding to the negative relation data based on the negative correspondence table.
4. The method of claim 1, wherein the relationship data of the target individual comprises sub-relationship data in at least one dimension;
The determining the scoring weight corresponding to the relationship data based on the corresponding relationship table of the corresponding relationship between the pre-established characterization relationship data and the scoring weight comprises the following steps:
Determining the dimension of the sub-relationship data included in the relationship data, and determining the scoring weight corresponding to the sub-relationship data in at least one dimension based on a pre-established corresponding relationship table representing the corresponding relationship between the sub-relationship data and the scoring weights in different dimensions.
5. The method of claim 1, wherein the segmenting the fitted curve results in at least two relational data intervals, comprising:
dividing the area between the fitting curve and the transverse axis into at least two subareas with equal areas;
the at least two relational data intervals are determined based on the boundaries of the respective sub-regions.
6. A cooperative data evaluating apparatus, comprising:
The acquisition module is used for responding to the instruction triggered by the detected user and used for inquiring the cooperative security information of the target individual and acquiring relationship data representing the relationship between the target individual and other individuals;
The first determining module is used for determining the scoring weight corresponding to the relationship data based on a corresponding relationship table of the corresponding relationship between the pre-established characterization relationship data and the scoring weight;
The correspondence table may be obtained in advance based on the following steps: acquiring corresponding relation data of individuals in a preset individual set; determining the number of individuals corresponding to the same relationship data in each relationship data; based on the corresponding relation between the same relation data and the number of individuals, carrying out data fitting to obtain a fitting curve; dividing the fitting curve to obtain at least two relation data intervals; determining scoring weights corresponding to the relationship data intervals in the at least two relationship data intervals; generating the corresponding relation table based on the at least two relation data intervals and the corresponding scoring weights;
a second determination module for determining a collaborative security score for the target individual based on the scoring weights;
And the generation module is used for generating cooperative safety information representing the cooperative safety degree of the target individual based on the cooperative safety score and outputting the cooperative safety information.
7. An electronic device, comprising:
A processor; and
A memory for storing executable instructions of the processor;
wherein the processor is configured to perform the collaborative data evaluation method of any one of claims 1-5 via execution of the executable instructions.
8. A computer readable storage medium having stored thereon a computer program, wherein the computer program, when executed by a processor, implements the collaborative data evaluation method of any one of claims 1-5.
9. A computer program product comprising a computer program, characterized in that a processor in a device executes instructions for carrying out the steps of the method according to any one of claims 1-5 when said computer program is run on the device.
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