CN112732804A - Cooperation 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: in response to detecting an instruction which is triggered by a user and used for inquiring the cooperation safety information of the target individual, acquiring relationship data representing the relationship between the target individual and other individuals; 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; determining a cooperative security score of the target individual based on the scoring weight; and generating cooperation safety information representing the cooperation safety degree of the target individual based on the cooperation safety score and outputting the cooperation safety information. The technical scheme disclosed by the invention can effectively generate the cooperation safety information which can be referred by the user based on the analysis of the relation between the target individual and other individuals, thereby providing an intuitive cooperation safety display system, being capable of simply and clearly knowing the other side and avoiding possible cooperation risks.
Description
Technical Field
The present disclosure relates to the field of computer technologies, and in particular, to a method and an apparatus for evaluating collaboration data, an electronic device, a computer-readable storage medium, and a computer program.
Background
In many cases, different individuals (e.g., enterprises, individuals, etc.) often collaborate with each other, and when selecting a partner, the person usually selects the partner based on basic information of other individuals to collaborate with. For example, when selecting an enterprise partner, it is common to select a qualified enterprise with a large scale, high integrity, a wide operating range, and the like. However, for various reasons, individuals may collaborate with individuals who are not familiar with or who do not meet the above-described conditions, and the need may indicate or represent a risk situation for the partner individual in the collaborative 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 the enterprises may need to cooperate with different enterprises in a plurality of different fields in various aspects, so that the cooperation among the enterprises has an uncertainty, when the enterprises start to carry out a new cooperation requirement, the enterprises of the cooperative parties need to be timely and accurately known about the basic conditions of the enterprises of the cooperative parties, and possible cooperation risks are avoided.
Disclosure of Invention
An object of the present disclosure is to provide a collaborative data evaluation method and apparatus, an electronic device, a computer-readable storage medium, and a computer program, thereby solving at least some of the technical problems described in the above background art.
Additional features and advantages of the disclosure will be set forth in the detailed description which follows, or in part will be obvious from the description, or may be learned by practice of the disclosure.
According to a first aspect of the present disclosure, there is provided a collaborative data evaluation method, comprising: in response to detecting an instruction which is triggered by a user and used for inquiring the cooperation safety information of the target individual, acquiring relationship data representing the relationship between the target individual and other individuals; 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; determining a cooperative security score of the target individual based on the scoring weight; and generating cooperation safety information representing the cooperation safety degree of the target individual based on the cooperation safety score and outputting the cooperation safety information.
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 relation data interval in which the relation data is located from at least two preset relation 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 corresponding relationship table of the corresponding relationship between the pre-established characterization relationship data and the scoring weight, wherein the step comprises the following steps: determining forward relation data from the relation data, and determining a forward scoring weight corresponding to the forward relation data based on the forward corresponding relation 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 corresponding relation 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 corresponding relationship table of the corresponding relationship between the pre-established characterization relationship data and the scoring weight, wherein the step comprises the following steps: determining the dimensionality 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 dimensionality respectively based on a pre-established corresponding relationship table representing the corresponding relationship between the sub-relationship data in different dimensionalities 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 relation data corresponding to individuals in a preset individual set; determining the number of individuals corresponding to the same relational data in each relational data; performing data fitting based on the corresponding relation between the same relation data and the number of individuals to obtain a fitting curve; dividing the fitted 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 at least two relation data intervals and corresponding grading weights.
In an exemplary embodiment of the present disclosure, segmenting the fitted curve to obtain at least two relational data intervals includes: dividing the region between the fitted curve and the horizontal axis into at least two sub-regions 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 collaborative data evaluation apparatus including: the acquiring module is used for responding to a detected instruction which is triggered by a user and used for inquiring the cooperation safety information of the target individual, and acquiring relationship data representing the relationship between the target individual and other individuals; the first determination module is used for determining the scoring weight corresponding to the relational data based on a corresponding relation table of the corresponding relation between the pre-established characterization relational data and the scoring weight; the second determination module is used for determining the cooperation safety score of the target individual based on the score weight; and the generating module is used for generating cooperation safety information representing the cooperation safety degree of the target individual based on the cooperation safety score and outputting the cooperation 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 determining module includes: the third determining unit is used for determining forward direction relation data from the relation data and determining a forward direction grading weight corresponding to the forward direction relation data according to the forward direction corresponding relation table; and/or a fourth determining unit, configured to determine negative-direction relationship data from the relationship data, and determine a negative-direction scoring weight corresponding to the negative-direction relationship data based on the negative-direction 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 dimensionality 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 dimensionality respectively based on a pre-established corresponding relationship table representing the corresponding relationship between the sub-relationship data in different dimensionalities 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 relation data corresponding to individuals in a preset individual set; determining the number of individuals corresponding to the same relational data in each relational data; performing data fitting based on the corresponding relation between the same relation data and the number of individuals to obtain a fitting curve; dividing the fitted 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 at least two relation data intervals and corresponding grading weights.
In an exemplary embodiment of the present disclosure, the at least two relational data intervals are obtained based on the following steps: dividing the region between the fitted curve and the horizontal axis into at least two sub-regions 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 for the processor; wherein the processor is configured to perform the above-described collaborative data evaluation method via execution of executable instructions.
According to a fourth aspect of the present disclosure, there is provided a computer storage medium having a computer program stored thereon, wherein 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, a processor in the device executes instructions for implementing the steps in the above-described collaborative data assessment method.
As can be seen from the foregoing technical solutions, the cooperation 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 cooperation data evaluation method and device, the electronic device, the computer readable storage medium and the computer program in the embodiment of the disclosure, when a user queries cooperation safety information of a target individual, a scoring weight corresponding to the relation data of the target individual is determined based on a pre-established corresponding relation table, then a cooperation safety score of the target individual is determined based on the scoring weight, and finally the cooperation safety information is generated and output based on the cooperation safety score, so that the cooperation safety information which can be referred by the user is generated based on analysis of the relation between the target individual and other individuals, an intuitive cooperation safety display system is provided, the opposite side can be simply and clearly known, and possible cooperation 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 present disclosure and together with the description, serve to explain the principles of the disclosure. It is to be understood that the drawings in the following description are merely exemplary of the disclosure, and that other drawings may be derived from those drawings by one of ordinary skill in the art without the exercise of inventive faculty.
FIG. 1 is a system diagram to which the present disclosure is applicable;
FIG. 2 is a schematic flow chart diagram of a collaboration data evaluation method provided by an exemplary embodiment of the present disclosure;
FIG. 3 is a schematic flow chart diagram of a collaboration data evaluation method provided by another exemplary embodiment of the present disclosure;
FIG. 4 is an exemplary diagram of a fitted curve provided by an exemplary embodiment of the present disclosure;
FIG. 5 is a schematic structural diagram of a collaboration data evaluation apparatus provided in an exemplary embodiment of the present disclosure;
fig. 6 is a schematic structural diagram of a cooperation data evaluation 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. Example embodiments may, however, be embodied in many different forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art. The drawings are merely schematic illustrations of the present disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus their repetitive description will be omitted.
Furthermore, the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the disclosure. One skilled in the relevant art will recognize, however, that the subject matter of the present disclosure can be practiced without one or more of the specific details, or with other methods, apparatus, steps, etc. In other instances, well-known structures, methods, devices, implementations, or operations are not shown or described in detail to avoid obscuring aspects of the disclosure.
Furthermore, the terms "first", "second", etc. are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of the present disclosure, "a plurality" means at least two, e.g., two, three, etc., unless explicitly specifically limited otherwise. The symbol "/" generally indicates a relationship where the contextually relevant individuals are an "or".
In the present disclosure, unless otherwise expressly specified or limited, the terms "connected" and the like are to be construed broadly, e.g., as meaning electrically connected or in communication with each other; may be directly connected or indirectly connected through an intermediate. The specific meaning of the above terms in the present disclosure can be understood by those of ordinary skill in the art as appropriate.
Exemplary System
Fig. 1 shows a schematic diagram of a system architecture 100 to which a collaborative data evaluation method or a collaborative data evaluation apparatus according to 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 serves as a medium for providing communication links between the terminal devices 101, 102, 103 and the server 105. Network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, 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, server 105 may be a server cluster comprised of multiple servers, or the like.
The user may use the terminal devices 101, 102, 103 to interact with the server 105 via the network 104 to receive or send messages or the like. The terminal devices 101, 102, 103 may have installed thereon various communication client applications, such as a search-type application, a web browser application, a shopping-type application, an instant messaging tool, and the like.
The terminal devices 101, 102, 103 may be a variety of electronic devices including, but not limited to, smart phones, tablets, portable and desktop computers, digital cinema projectors, and the like.
The server 105 may be a server that provides various services. For example, the user sends an instruction to query the server 105 for the cooperation security information of the target individual by using the terminal device 103 (or the terminal device 101 or 102). The server may perform processing based on the information of the target individual, resulting in a processing result (e.g., collaboration security information, etc.).
Exemplary method
Referring to fig. 2, a flowchart of a collaboration data evaluation method provided in an exemplary embodiment of the present disclosure, where the present embodiment is applicable to an electronic device (such as the terminal device 101, 102, 103 or the server 105 shown in fig. 1), includes the following steps:
s210, in response to detecting an instruction which is triggered by a user and used for inquiring the cooperation safety information of the target individual, acquiring relationship data representing the relationship between the target individual and other individuals.
And S220, determining the grading weight corresponding to the relational data based on a corresponding relational table of the corresponding relations between the pre-established characterization relational data and the grading weight.
And S230, determining the cooperative security score of the target individual based on the scoring weight.
And S240, generating cooperation safety information representing the cooperation safety degree of the target individual based on the cooperation safety score and outputting the cooperation safety information.
According to the cooperation data evaluation method provided by the embodiment of the disclosure, when the user queries the cooperation 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 cooperation safety score of the target individual is determined based on the scoring weight, and finally the cooperation safety information is generated and output based on the cooperation safety score, so that the analysis of the relation between the target individual and other individuals is realized, the cooperation safety information which can be referred by the user is generated, an intuitive cooperation safety display system is provided, the opposite side can be simply and clearly known, and the possible cooperation risk is avoided.
In S210, the electronic device may acquire relationship data representing a relationship between the target individual and other individuals in response to detecting an instruction for querying the cooperative security information of the target individual triggered by the user.
By way of example, a user may enter information (e.g., name, code, etc.) of a target individual using an electronic device such as that shown in FIG. 1, click a "query" button, and may generate a collaborative security information instruction. After the electronic device detects the instruction, the relationship data can be acquired.
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. By way of example, the collaboration relationship data may include a number of individuals collaborating with the target individual; the supply relationship data may include the number of individuals that are "customers" or "suppliers" in relationship to the target individual; the dispute relationship data may include the number of individuals having a relationship of "original" and "defendant" with the target individual.
In S220, the electronic device may determine a scoring weight corresponding to the relationship data based on a pre-established correspondence table of correspondence between the characterization relationship data and the scoring weight.
As an example, the correspondence table may be a table based on statistics of a large amount of relationship data in advance and setting a score weight 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 of 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. By way of example, the relationship data may include the number of other individuals having a relationship of "customer" with the target individual, the number of other individuals having a relationship of "supplier" with the target individual, the number of other individuals having a dispute relationship with the target individual, and the like.
Based on this, S220 may be performed as follows:
determining the dimensionality 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 dimensionality respectively based on a pre-established corresponding relationship table representing the corresponding relationship between the sub-relationship data in different dimensionalities and the scoring weight. Generally, each of the at least one dimension corresponds to a corresponding relationship table, and for the sub-relationship data of a certain dimension, the electronic device may determine, from the corresponding relationship 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 have prosecuted the target individual (referred to herein as original relationship data) and the number of other individuals who have been prosecuted by the target individual (referred to herein as subject relationship data). The electronic device may query the scoring weight corresponding to the original notification relationship data from the correspondence table of the original notification relationship dimension, and query the scoring weight corresponding to the notified relationship data from the correspondence table of the notified relationship dimension. In general, the scoring weight corresponding to the original relationship data and the scoring weight corresponding to the reported relationship data are negative values, and the absolute value of the scoring weight corresponding to the original relationship data is smaller than the absolute value of the scoring weight corresponding to the reported relationship data.
According to the implementation mode, different scoring weights are respectively extracted from the relationship data corresponding to at least one dimension, so that the relationship between the target individual and other individuals can be represented in more detail by using the sub-relationship data under different dimensions, the influence of the relationship data capable of reflecting various dimensions on the cooperative safety score can be calculated by the determined scoring weights, and the accuracy of outputting the cooperative safety information is improved.
In S230, the electronic device may determine a cooperative security score for the target individual based on the scoring weight.
The cooperation security score is used for representing the degree of risk of cooperation with the target individual, and generally, the higher the cooperation security score is, the higher the security of cooperation with the target individual is, that is, the lower the risk is. Specifically, the cooperation security score may be determined based on the score weight on the basis of a preset base score (e.g., 60 points).
For example, the scoring weight may be a specific score, such as +10, -5, etc., and the base score is directly added to the scoring weight to obtain the collaborative security score. As another example, the scoring weight may be a percentage, such as + 10%, -10%, etc., and 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 cooperation security information representing the cooperation security degree of the target individual based on the cooperation security score and output the cooperation security information.
The cooperation safety information is used for representing the cooperation safety degree of the target individual. The collaborative security information may include, but is not limited to, information in at least one of the following forms: text, numbers, images, symbols, and the like. The output form of the cooperative security information may include, but is not limited to, at least one of the following: the display is displayed on a display connected with the electronic equipment, the display is sent to other electronic equipment connected with the electronic equipment for display, and the display is stored in a preset storage area.
As an example, the above-described collaboration security score may be used as collaboration security information. Alternatively, information characterizing a collaborative security level (e.g., in the form of a five-star rating) may be determined as the collaborative security information based on the collaborative security score. In addition, the cooperative security information may also include some relevant information of the target individual to present to the user for reference. For example, the number of individuals in cooperative relationship with the target individual, the number of individuals in 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 relation data interval in which the relation data is located from at least two preset relation data intervals.
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 sections, each section corresponding to one scoring weight. For example, interval one is [0,10), the corresponding scoring weight is + 10; and if the relation data is 60, the corresponding scoring weight is determined to be +30, so that the basic score is +30, and the cooperation safety score can be obtained.
According to the implementation mode, at least two intervals are set, so that the data volume of the corresponding relation table can be reduced, and the efficiency of inquiring the scoring weight is improved.
In some optional implementations, 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 positive relation data are used for carrying out positive scoring, namely point adding, on the target individual, and the negative relation data are used for carrying out negative scoring, namely point subtracting, on the target individual. As an example, the positive-going relationship data may include a number of individuals having a cooperative relationship with the target individual, and the negative-going relationship data may include a number of individuals having a dispute relationship with the target individual.
Based on this, the above step 220 may be performed as follows:
and determining positive scoring weight corresponding to the positive relationship data from the relationship data based on the positive correspondence table, and/or determining negative scoring weight corresponding to the negative relationship data from the relationship data based on the negative correspondence table.
Specifically, the electronic device may determine whether the relationship data is positive-direction relationship data or negative-direction relationship data according to the type of the relationship data. For example, the positive-direction relationship data may have a corresponding positive-direction data type flag, the negative-direction relationship data may have a corresponding negative-direction data type flag, and the electronic device may determine whether each piece of relationship data is positive-direction relationship data or negative-direction relationship data according to the data type flag corresponding to each piece of relationship data.
As an example, the relationship data of the target individual includes positive-direction relationship data representing the number of individuals having a relationship with the target individual as a cooperative relationship, and the relationship data of the target individual further includes negative-direction relationship data representing the number of individuals having a relationship with the target individual as a dispute relationship. And 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 cooperation safety score is +20-10 of the basic score.
By setting the positive correspondence table and the negative correspondence table, the method can perform positive and negative scoring on the target individual, so that the target individual can be scored more comprehensively in cooperation safety, and the accuracy of outputting the cooperation safety information is improved.
With further reference to FIG. 3, a flow diagram 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, acquiring the corresponding relation data of the individuals in the preset individual set.
S320, determining the number of individuals corresponding to the same relation data in each relation data.
And S330, performing data fitting based on the corresponding relation between the same relation data and the number of the individuals to obtain a fitting curve.
And S340, segmenting the fitted curve to obtain at least two relation data intervals.
And S350, determining the scoring weight corresponding to the relation data interval in the at least two relation data intervals.
And S360, generating a corresponding relation table based on at least two relation data intervals and corresponding grading weights.
In the embodiment corresponding to fig. 3, the acquired relationship data corresponding to the individuals in the individual set is used for 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 cooperation safety degree of the individuals in a larger range can be reflected by the corresponding relationship table, and the accuracy of generating the corresponding relationship table is improved.
In S310, the electronic device may obtain relationship data corresponding to individuals in a preset individual set from a local place or a remote place. The individual set 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 meaning of the relationship data of each individual is the same as that of the relationship data of the target individual, and the description is omitted here.
In S320, the electronic device may determine the number of individuals corresponding to the same relationship data in each relationship data.
For example, 100 individuals each have 10 individuals in cooperative relationship, 200 individuals each have 20 individuals in cooperative relationship, and so on.
In S330, the electronic device may perform data fitting based on the same correspondence between the relationship data and the number of individuals to obtain a fitting curve.
As an example, it may be provided that the abscissa represents the relationship data and the ordinate represents the number of individuals having the 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, and the like. Since the gaussian curve can reflect the distribution of the relational data of a large number of individuals more accurately, it is preferable to perform curve fitting using a gaussian fitting algorithm.
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, the (x1, y1) point in fig. 4 indicates that the number of individuals having x1 individuals in the cooperative relationship is y1, and the (x2, y2) point indicates that the number of individuals having x2 individuals in the cooperative relationship is y 2.
It should be noted that, when the type of the relationship data is multiple dimensions, curve fitting may be performed on the relationship data of each dimension, and each fitted curve is processed according to the following method to obtain multiple correspondence tables.
In S340, the electronic device may segment the fitted curve to obtain at least two relationship data intervals.
The manner of dividing the fitted curve may include various manners, for example, dividing the abscissa of the curve by averaging to make the range of each relational data interval 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-regions D1, D2, and D3 divided by the coordinates (x1, y1) and (x1, y1) 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 intervals, 0 to x1, x1 to x2, and x2 to x3, are obtained.
According to the implementation mode, the regions under the fitting curve are divided into equal areas, and under the condition that a large number of individuals exist, the probabilities that different individuals fall into corresponding relation data intervals are approximately the same, so that the scoring weight corresponding to each determined sub-region is more reasonable, and the accuracy of generating the cooperation 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. In particular, the scoring weights may be set manually. When the type of the relationship data is multiple dimensions, a corresponding weight may be set for the relationship data interval corresponding to each dimension.
As an example, when the correlation 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 sections 0 to x1, a second scoring weight (e.g., +20) may be set for the sections x1 to x2, and a third scoring weight (e.g., +30) may be set for the sections x2 to x 3. When the relationship data represents the number of individuals of the dispute relationship, the scoring weight is set to be 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 score weights of the sections 0 to x1 are set to-10, the score weights of the sections x1 to x2 are set to-20, and the score weights of the sections x2 to x3 are set to-30.
In S360, the electronic device may generate a correspondence table based on the at least two relationship data intervals and the corresponding scoring weights. Specifically, the correspondence table includes a plurality of relationship data intervals and a scoring weight corresponding to each relationship data interval. It should be noted that, when the type of the relationship data is multiple dimensions, different correspondence tables may be set for the relationship data of each dimension.
Exemplary devices
Fig. 5 schematically shows a structural diagram of a cooperation data evaluation apparatus according to an embodiment of the present disclosure. The cooperation data evaluation apparatus provided by the embodiment of the present disclosure may be disposed on the terminal device, may also be disposed on the server, or may be partially disposed on the terminal device and partially disposed on the server, for example, may be disposed on the server 105 in fig. 1 (according to actual replacement), but the present disclosure is not limited thereto.
The cooperation data evaluation device provided by the embodiment of the disclosure may include: an obtaining module 510, configured to obtain, in response to detecting an instruction triggered by a user and used to query the cooperative security information of the target individual, relationship data representing a relationship between the target individual and another individual; a first determining module 520, configured to determine a scoring weight corresponding to the relationship data based on a correspondence table of correspondence between pre-established characterization relationship data and the scoring weight; a second determining module 530, configured to determine a cooperation security score of the target individual based on the scoring weight; and the generating module 540 is configured to generate cooperation safety information representing the cooperation safety degree of the target individual based on the cooperation safety score and output the cooperation safety information.
In this embodiment, the obtaining module 510 may obtain relationship data representing a relationship between the target individual and other individuals in response to detecting an instruction for querying the cooperative security information of the target individual triggered by the user. 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. By way of example, the collaboration relationship data may include a number of individuals collaborating with the target individual; the supply relationship data may include the number of individuals that are "customers" or "suppliers" in relationship to the target individual; the dispute relationship data may include the number of individuals having a relationship of "original" and "defendant" with the target individual.
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 of correspondence between the characterization relationship data and the scoring weight. As an example, the correspondence table may be a table based on statistics of a large amount of relationship data in advance and setting a score weight 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 determining module 530 may determine the cooperative security score of the target individual based on the scoring weight. The cooperation security score is used for representing the degree of risk of cooperation with the target individual, and generally, the higher the cooperation security score is, the higher the security of cooperation with the target individual is, that is, the lower the risk is. Specifically, the cooperation security score may be determined based on the score weight on the basis of a preset base score (e.g., 60 points).
In this embodiment, the generating module 540 may generate cooperation security information representing the cooperation security degree of the target individual based on the cooperation security score and output the cooperation security information. The cooperation safety information is used for representing the cooperation safety degree of the target individual. The collaborative security information may include, but is not limited to, information in at least one of the following forms: text, numbers, images, symbols, and the like.
Referring to fig. 6, fig. 6 is a schematic structural diagram of a data compression apparatus according to another exemplary embodiment of the present disclosure.
In some optional implementations, the first determining module 520 may include: a first determining unit 5201, configured to determine, from at least two preset relationship data intervals, a relationship data interval in which relationship data is located; the second determining unit 5202 is configured to determine a scoring weight corresponding to the relationship data section based on the correspondence table.
In some optional 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 direction relationship data from the relationship data, and determine a forward direction scoring weight corresponding to the forward direction relationship data based on the forward direction correspondence table; and/or the fourth determining unit 5204 is configured to determine negative-direction relationship data from the relationship data, and determine a negative-direction scoring weight corresponding to the negative-direction relationship data based on the negative-direction correspondence table.
In some alternative implementations, the relationship data of 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 dimensionality 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 dimensionality respectively based on a pre-established corresponding relationship table representing the corresponding relationship between the sub-relationship data in different dimensionalities and the scoring weight.
In some optional implementations, the correspondence table may be obtained in advance based on the following steps: acquiring relation data corresponding to individuals in a preset individual set; determining the number of individuals corresponding to the same relational data in each relational data; performing data fitting based on the corresponding relation between the same relation data and the number of individuals to obtain a fitting curve; dividing the fitted 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 at least two relation data intervals and corresponding grading weights.
In some optional implementations, the at least two relationship data intervals are obtained based on the following steps: dividing the region between the fitted curve and the horizontal axis into at least two sub-regions with equal areas; at least two relational data intervals are determined based on the boundaries of the respective sub-regions.
According to the cooperation data evaluation device provided by the embodiment of the disclosure, when a user queries the cooperation safety information of a 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 cooperation safety score of the target individual is determined based on the scoring weight, and finally the cooperation safety information is generated and output based on the cooperation safety score, so that the analysis of the relation between the target individual and other individuals is realized, the cooperation safety information which can be referred by the user is generated, a visual cooperation safety display system is provided, the other party can be simply and clearly known, and the possible cooperation risk is avoided.
The specific implementation of each module, unit, and subunit in the cooperative data evaluation apparatus provided in the embodiment of the present disclosure may refer to the content in the cooperative data evaluation method, and is not described herein again.
It should be noted that although several modules, units and sub-units of the apparatus for action execution are mentioned in the above detailed description, such division is not mandatory. Indeed, the features and functionality of two or more modules, units and sub-units described above may be embodied in one module, unit and sub-unit, in accordance with embodiments of the present disclosure. Conversely, the features and functions of one module, unit and sub-unit described above may be further divided into embodiments by a plurality of modules, units and sub-units.
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 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, or a network.
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 the removable storage unit 721 and the interface 720 include: a program cartridge (cartridge) and cartridge interface (such as that found in video game console devices), a removable memory chip (such as an EPROM, or PROM) and associated socket, and other removable storage units 721 and interfaces 720 which allow software and data to be transferred from the removable storage unit 721 to electronic device 70.
The electronic device 70 also includes at least one communication interface 740. Communications interface 740 allows software and data to be transferred between electronic device 70 and external devices via communications 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. The 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 electrical, magnetic, optical or other signals capable of being received by communications interface 740. These signals are provided to a communications interface via communications path 741.
As shown in fig. 7, the electronic device 70 also includes a display interface 731 and an audio interface 732, the display interface 731 performing operations for rendering images to an associated display 730, and the audio interface 732 for performing operations for playing audio content through an associated speaker 733.
In this document, the term "computer program product" may refer, in part, to: a removable storage unit 713, a removable storage unit 721, a hard disk installed in the hard disk drive 711, or a carrier wave carrying software to the communication interface 740 through a communication path 741 (wireless link or cable). 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 optical disks, hard drives, ROMs, or integrated circuits, USB memory, magneto-optical disks, or a computer-readable card, such as a PCMCIA card, among others, whether internal or external to the electronic device 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 including radio or infrared transmission channels, network connections to another computer or another networked device, and the internet or intranet including e-mail transmissions and information recorded on websites and the like.
Computer programs (also called computer program code) are stored in the main memory 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 embodiments discussed herein. In various embodiments, the computer programs, when executed, enable the processor 701 to perform the features of the embodiments described above. Accordingly, such computer programs 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 the removable storage drive 712, the hard drive 711 or the interface 720. Alternatively, the computer program product may be downloaded to computer system 70 over communications path 741. The software, 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 electronic device 70 may be omitted. Also, in some embodiments, one or more features of electronic device 70 may be combined together. Additionally, in some embodiments, one or more features of 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 means for performing the various functions and operations of the server described in the above embodiments.
In one embodiment, a server may be generally described as a physical device including at least one processor and at least one memory including computer program code. The at least one memory and the computer program code are configured to, with the at least one processor, cause the physical device to perform necessary operations.
Exemplary computer readable storage Medium
Embodiments of the present application also provide a computer-readable storage medium, on which a computer program is stored, which when executed by a processor implements the functions of the method 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 computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by an electronic device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
From the above description of the embodiments, it is clear to those skilled in the art that the embodiments of the present disclosure can be implemented by software plus necessary general hardware platform. Based on such understanding, the technical solutions of the embodiments of the present specification may be essentially or partially implemented in the form of a software product, which may be stored in a storage medium, such as a ROM/RAM, a magnetic disk, an optical disk, etc., and includes several instructions for enabling a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments of the present specification.
Exemplary computer program
The disclosed embodiments also provide a computer program product for storing computer readable instructions, which when executed cause a computer to execute the cooperation data evaluation method in any one of the above possible implementations.
The computer program product may be embodied in hardware, software or a combination thereof. In one alternative, the computer program product is embodied in a computer storage medium, and in another alternative, the computer program product is embodied in a Software product, such as a Software Development Kit (SDK), or the like.
The foregoing describes the general principles of the present disclosure in conjunction with specific embodiments, however, it is noted that the advantages, effects, etc. mentioned in the present disclosure are merely examples and are not limiting, and they should not be considered essential to the various embodiments of the present disclosure. Furthermore, the foregoing disclosure of specific details is for the purpose of illustration and description and is not intended to be limiting, since the disclosure is not intended to be limited to the specific details so described.
In the present specification, the embodiments are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same or similar parts in the embodiments are referred to each other. For the system embodiment, since it basically corresponds to the method embodiment, the description is relatively simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
Those of ordinary skill in the art will understand that: all or a portion of the steps of implementing the above-described method embodiments may be performed by hardware associated with program instructions.
In the present specification, the embodiments are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same or similar parts in the embodiments are referred to each other. For the system embodiment, since it basically corresponds to the method embodiment, the description is relatively simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
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, and firmware. The above-described order for the steps of the method is for illustration only, and the steps of the method of the present disclosure are not limited to the order specifically described above unless specifically stated otherwise. Further, in some embodiments, the present disclosure may also be embodied 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 practitioners skilled in this art. The embodiment was 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 (10)
1. A collaborative data evaluation method, comprising:
in response to detecting an instruction which is triggered by a user and used for inquiring the cooperation safety information of a target individual, acquiring relationship data representing the relationship between the target individual and other individuals;
determining a grading 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 grading weight;
determining a cooperation safety score of the target individual based on the scoring weight;
and generating cooperation safety information representing the cooperation safety degree of the target individual based on the cooperation safety score and outputting the cooperation safety information.
2. The method according to claim 1, wherein the determining the scoring weight corresponding to the relationship data based on a pre-established correspondence table of correspondence between the characterization relationship data and the scoring weight comprises:
determining a relation data interval in which the relation data is located from at least two preset relation data intervals;
and determining the scoring weight corresponding to the relation data interval based on the corresponding relation table.
3. The method of claim 1, wherein the relationship data comprises positive-going relationship data and/or negative-going relationship data, and the correspondence table comprises a positive-going correspondence table and/or a negative-going 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 includes:
determining forward relation data from the relation data, and determining a forward scoring weight corresponding to the forward relation data based on the forward corresponding relation table; and/or
Determining negative-direction relation data from the relation data, and determining a negative-direction scoring weight corresponding to the negative-direction relation data based on the negative-direction corresponding relation 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 includes:
determining the dimensionality 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 dimensionality respectively based on a pre-established corresponding relationship table representing the corresponding relationship between the sub-relationship data in different dimensionalities and the scoring weight.
5. The method according to one of claims 1 to 4, wherein the correspondence table is obtained in advance based on the following steps:
acquiring relation data corresponding to individuals in a preset individual set;
determining the number of individuals corresponding to the same relational data in each relational data;
performing data fitting based on the corresponding relation between the same relation data and the number of individuals 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 grading weights.
6. The method of claim 5, wherein the segmenting the fitted curve to obtain at least two relational data intervals comprises:
dividing the region between the fitted curve and the horizontal axis into at least two sub-regions with equal areas;
determining the at least two relational data intervals based on the boundaries of the respective sub-regions.
7. A collaborative data evaluation apparatus, comprising:
the acquiring module is used for responding to a detected instruction which is triggered by a user and used for inquiring the cooperation safety information of a target individual, and acquiring relationship data representing the relationship between the target individual and other individuals;
the first determination 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;
a second determination module, configured to determine a cooperation security score of the target individual based on the scoring weight;
and the generating module is used for generating cooperation safety information representing the cooperation safety degree of the target individual based on the cooperation safety score and outputting the cooperation safety information.
8. An electronic device, comprising:
a processor; and
a memory for storing executable instructions of the processor;
wherein the processor is configured to perform the collaboration data evaluation method of any of claims 1-6 via execution of the executable instructions.
9. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the cooperation data evaluation method of any one of claims 1 to 6.
10. A computer program comprising computer readable code for, when run on a device, a processor in the device executing instructions for carrying out the steps of the method according to any one of claims 1 to 6.
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