CN116956066A - Resource evaluation method, device, electronic equipment and storage medium - Google Patents
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Abstract
The present disclosure provides a resource assessment method, relates to the field of computer technology, and in particular, relates to a data processing technology. The specific implementation scheme is as follows: in response to receiving an evaluation request from the target object for evaluating the resource, determining object attribute data and object resource data based on the evaluation request; determining a target subset of base resource data from the set of base resource data based on the object attribute data; determining an object resource data set based on the object resource data and the target underlying resource data subset; and evaluating the target resource related to the target object based on the object resource data set to obtain target resource data.
Description
Technical Field
The present disclosure relates to the field of computer technologies, and in particular, to a data processing technology, and in particular, to a resource evaluation method, a device, an electronic apparatus, and a storage medium.
Background
With the rapid development of computers, the reconstruction and deep analysis of massive data are no longer insurmountable problems. In the process of providing the resource evaluation service, the user can quickly and accurately obtain the required information by continuously optimizing the advantages of intellectualization, individuation, simplicity and the like.
Disclosure of Invention
The present disclosure provides a resource evaluation method, apparatus, electronic device, storage medium, and program product.
According to an aspect of the present disclosure, there is provided a resource evaluation method including: in response to receiving an evaluation request from a target object for evaluating a resource, determining object attribute data and object resource data based on the evaluation request; determining a target basic resource data subset from the basic resource data set based on the object attribute data; determining an object resource data set based on the object resource data and the target underlying resource data subset; and evaluating the target resource related to the target object based on the object resource data set to obtain target resource data.
According to another aspect of the present disclosure, there is provided a resource evaluation apparatus including: a response module for determining object attribute data and object resource data based on an evaluation request received from a target object for evaluating the resource; a first determining module, configured to determine a target subset of basic resource data from the set of basic resource data based on the object attribute data; the second determining module is used for determining an object resource data set based on the object resource data and the target basic resource data subset; and an evaluation module for evaluating the target resource about the target object based on the object resource data set to obtain target resource data.
According to another aspect of the present disclosure, there is provided an electronic device including: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform a method as disclosed herein.
According to another aspect of the present disclosure, there is provided a non-transitory computer-readable storage medium storing computer instructions for causing a computer as described above to perform a method as disclosed herein.
According to another aspect of the present disclosure, there is provided a computer program product comprising a computer program which, when executed by a processor, implements a method as disclosed herein.
It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the disclosure, nor is it intended to be used to limit the scope of the disclosure. Other features of the present disclosure will become apparent from the following specification.
Drawings
The drawings are for a better understanding of the present solution and are not to be construed as limiting the present disclosure. Wherein:
FIG. 1 schematically illustrates an exemplary system architecture to which resource assessment methods and apparatus may be applied, according to embodiments of the present disclosure;
FIG. 2 schematically illustrates a flow chart of a resource assessment method according to an embodiment of the present disclosure;
FIG. 3 schematically illustrates a schematic diagram of acquiring a subset of underlying resource data, according to an embodiment of the disclosure;
FIG. 4 schematically illustrates a diagram of determining a set of object resource data, according to an embodiment of the disclosure;
FIG. 5A schematically illustrates an interactive schematic of a resource assessment method according to an embodiment of the present disclosure;
FIG. 5B schematically illustrates a schematic diagram of generating an evaluation request according to another embodiment of the present disclosure;
FIG. 6 schematically illustrates a block diagram of a resource assessment device according to an embodiment of the present disclosure; and
fig. 7 schematically illustrates a block diagram of an electronic device adapted to implement a resource assessment method according to an embodiment of the present disclosure.
Detailed Description
Exemplary embodiments of the present disclosure are described below in conjunction with the accompanying drawings, which include various details of the embodiments of the present disclosure to facilitate understanding, and should be considered as merely exemplary. Accordingly, one of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
The present disclosure provides a resource evaluation method, apparatus, electronic device, storage medium, and program product.
According to an embodiment of the present disclosure, there is provided a resource evaluation method including: in response to receiving an evaluation request from the target object for evaluating the resource, determining object attribute data and object resource data based on the evaluation request; determining a target subset of base resource data from the set of base resource data based on the object attribute data; determining an object resource data set based on the object resource data and the target underlying resource data subset; and evaluating the target resource related to the target object based on the object resource data set to obtain target resource data.
In the technical scheme of the disclosure, the related processes of collecting, storing, using, processing, transmitting, providing, disclosing, applying and the like of the personal information of the user all conform to the regulations of related laws and regulations, necessary security measures are adopted, and the public order harmony is not violated.
In the technical scheme of the disclosure, the authorization or consent of the user is obtained before the personal information of the user is obtained or acquired.
FIG. 1 schematically illustrates an exemplary system architecture to which resource assessment methods and apparatus may be applied, according to embodiments of the present disclosure.
It should be noted that fig. 1 is only an example of a system architecture to which embodiments of the present disclosure may be applied to assist those skilled in the art in understanding the technical content of the present disclosure, but does not mean that embodiments of the present disclosure may not be used in other devices, systems, environments, or scenarios. For example, in another embodiment, an exemplary system architecture to which the resource evaluation method and apparatus may be applied may include a terminal device, but the terminal device may implement the resource evaluation method and apparatus provided by the embodiments of the present disclosure without interaction with a server.
As shown in fig. 1, a system architecture 100 according to this embodiment may include 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 types, such as wired and/or wireless communication links, and the like.
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 may be installed on the terminal devices 101, 102, 103, such as a knowledge reading class application, a web browser application, a search class application, an instant messaging tool, a mailbox client and/or social platform software, etc. (as examples only).
The terminal devices 101, 102, 103 may be a variety of electronic devices having a display screen and supporting web browsing, including but not limited to smartphones, tablets, laptop and desktop computers, and the like.
The server 105 may be a server providing various services, such as a background management server (by way of example only) providing support for content browsed by the user using the terminal devices 101, 102, 103. The background management server may analyze and process the received data such as the evaluation request, and feed back the processing result (for example, the target resource data obtained according to the evaluation request) to the terminal device.
It should be noted that, the resource evaluation method provided by the embodiments of the present disclosure may be generally performed by the terminal device 101, 102, or 103. Accordingly, the resource evaluation apparatus provided by the embodiments of the present disclosure may also be provided in the terminal device 101, 102, or 103.
Alternatively, the resource assessment method provided by the embodiments of the present disclosure may also be generally performed by the server 105. Accordingly, the resource assessment device provided by the embodiments of the present disclosure may be generally provided in the server 105. The resource assessment method provided by the embodiments of the present disclosure may also be performed by a server or a server cluster that is different from the server 105 and is capable of communicating with the terminal devices 101, 102, 103 and/or the server 105. Accordingly, the resource evaluation device provided by the embodiments of the present disclosure may also be provided in a server or a server cluster that is different from the server 105 and is capable of communicating with the terminal devices 101, 102, 103 and/or the server 105.
For example, when a target object logs in an application for resource evaluation, the terminal device 101, 102, 103 may acquire object attribute data and object resource data input by the target object, then generate an evaluation request and send the evaluation request to the server 105, analyze the object attribute data and the object resource data based on the evaluation request, and determine a target base resource data subset from the base resource data set based on the object attribute data. An object resource data set is determined based on the object resource data and the target underlying resource data subset. And evaluating the target resources related to the target object based on the object resource data set to obtain target resource data. Or the object attribute data and the object resource data are analyzed by a server or a server cluster capable of communicating with the terminal devices 101, 102, 103 and/or the server 105, and finally the target resource about the target object is evaluated to obtain the target resource data.
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.
It should be noted that the sequence numbers of the respective operations in the following methods are merely representative of the operations for the purpose of description, and should not be construed as representing the order of execution of the respective operations. The method need not be performed in the exact order shown unless explicitly stated.
Fig. 2 schematically illustrates a flow chart of a resource assessment method according to an embodiment of the present disclosure.
As shown in fig. 2, the method includes operations S210 to S240.
In response to receiving an evaluation request for evaluating a resource from a target object, object attribute data and object resource data are determined based on the evaluation request in operation S210.
In operation S220, a target subset of base resource data is determined from the set of base resource data based on the object attribute data.
In operation S230, an object resource data set is determined based on the object resource data and the target underlying resource data subset.
In operation S240, a target resource with respect to a target object is evaluated based on the object resource data set, resulting in target resource data.
According to embodiments of the present disclosure, a resource may refer to an item of value. For example, items that can be circulated, redeemed, or the value can be changed or accumulated over time.
According to embodiments of the present disclosure, object attribute data may refer to data used to characterize the identity of a target object. The object attribute data includes at least one of: gender, age, location of the subject, age, school, and occupation data.
According to the embodiment of the present disclosure, the object resource data may refer to related data of a resource held by the target object, but is not limited thereto, and may refer to related data of a resource acquired by the target object at a predetermined frequency.
According to the embodiment of the disclosure, the collected mass data can be counted to obtain the basic resource data set. The base resource data set may include a plurality of base resource data subsets. Each subset of base resource data matches one of the object attribute data. A subset of target underlying resource data that matches the object attribute data may be determined from the set of underlying resource data based on the object attribute data. The basic resource data set is obtained by counting mass data, and has representativeness and accuracy. The target basic resource data subsets in the basic resource data sets are matched with the object attribute data, so that the accuracy is ensured, the pertinence is ensured, and the personalized requirements are met.
Taking the object resource data as the issue volume data of the book as an example. Author a was the target object, creating book a. Book a is a suspense type novel. A target underlying resource data subset of suspense type novels may be determined from the underlying resource data set associated with the novice release based on object attribute data, such as attribute data of author a, and attribute data of book a, such as suspense type. The target underlying resource data subset includes the average annual release of multiple suspense type novels over a period from the start of the year of publication to the next nth year. The issue number of each year of the object resource data set, for example, in a period from the start of the present year of publication to the next nth year, may be determined based on the target base resource data subset and the issue number of the object resource data, for example, book a, in the present year of publication. The total release of book a is determined based on the set of object resource data.
Taking the object resource data as the resource data of the financial product as an example. The target object invests in the financial product a. Product A is a pension insurance financial product. A subset of the target underlying resource data may be determined from the object attribute data. The target underlying resource data subset may include a plurality of types of pension insurance type financial products, an average annual rate of return over a period from the current year of participation to the next nth year. The annual rate of return of the set of object resource data, e.g. over a period from the current year of participation to the future nth year, may be determined based on the subset of target base resource data and the object resource data, e.g. the amount of participation in the current year. A total benefit rate is determined based on the set of object resource data.
According to the embodiment of the disclosure, the object resource data set is determined based on the object resource data and the target basic resource data subset, the object resource data set can be obtained by means of prediction based on single object resource data by utilizing the collected target basic resource data subset, and the data prediction capability is enlarged from single element to set. In addition, the object resource data set is obtained through determining the target basic resource data subset, and the matching degree of the object resource data set and the real object data set is high, so that the accuracy of target resource data obtained based on the object resource data set is high.
According to an embodiment of the present disclosure, before operation S210 shown in fig. 2, the resource evaluation method may further include: a base resource data set is obtained.
According to an embodiment of the present disclosure, obtaining the set of base resource data may include: a subset of base resource data is obtained. And obtaining a basic resource data set based on a plurality of basic resource data subsets corresponding to the plurality of resource attribute data one by one. Alternatively, a subset of base resource data that matches each resource attribute data may be obtained for each resource attribute data.
According to an embodiment of the present disclosure, the resource attribute data may include: attribute data for characterizing a resource and object attribute data corresponding to the resource. For example, attribute data used to characterize a resource may include the source of the resource, the number or value of the resource, the place of investment of the resource, and so forth. The object attribute data for characterizing the corresponding resource may include at least one of: identity, occupation data, age, sex, school, location of the subject.
According to an embodiment of the present disclosure, obtaining the subset of base resource data may include: a plurality of initial underlying resource data subsets in one-to-one correspondence with a plurality of points in time are obtained. The initial underlying resource data subset includes a plurality of initial underlying resource sub-data at the same point in time. And obtaining the basic resource sub-data based on the plurality of initial basic resource sub-data of the time point. And obtaining a basic resource data subset based on the plurality of basic resource sub-data which are in one-to-one correspondence with the plurality of time points. Alternatively, each initial underlying resource data subset may comprise a plurality of initial underlying resource sub-data at the same point in time. And obtaining the basic resource sub-data based on the plurality of initial basic resource sub-data of each time point.
Fig. 3 schematically illustrates a schematic diagram of acquiring a subset of underlying resource data, according to an embodiment of the disclosure.
As shown in fig. 3, professional data of an object may be used as resource attribute data. And acquiring the initial basic resource sub-data of each of a plurality of objects aiming at the same professional data and the same time point through the mass data. For example, the initial basic resource sub-data a1, a2, a3, a.sub.1, a.sub.2, a.sub.3 of the object i, respectively, are acquired at the same professional data and at the same time point a. i is an integer greater than 3. The plurality of initial base resource sub-data a1, a2, a3, &..once again, the base resource sub-data a is obtained by weighted summation of ai. The initial basic resource sub-data B1, B2, B3, bi of the subject n+1, subject n+2, subject n+3, and subject n+i, respectively, at the same professional data and the same time point B are acquired. And carrying out weighted summation on the plurality of initial basic resource sub-data b1, b2, b3 and the bi, and obtaining the basic resource sub-data b. The initial basic resource sub-data C1, C2, C3, ci of the object p+1, the object p+2, the object p+3, the object p+i, respectively, at the same professional data and the same time point C are acquired. And carrying out weighted summation on the plurality of initial basic resource sub-data c1, c2, c3 and the number of the basic resource sub-data c. Based on the basic resource sub-data a of the time point a, the basic resource sub-data B of the time point B, the basic resource sub-data C of the time point C, a basic resource data subset L is obtained.
According to an embodiment of the present disclosure, a set of base resource data is obtained based on a subset of base resource data of each of a different plurality of professional data.
According to the embodiment of the disclosure, each basic resource sub-data is determined by referring to the initial basic resource sub-data of each of a plurality of objects at the same time point for the same professional data, and has universality and statistics. And each basic resource data subset is matched with one professional data, so that pertinence and consistency are realized. The object resource data set is determined based on the target basic resource data subset in the basic resource data set, and the target basic resource data subset matched with the object attribute data such as professional data can be determined from the complex basic resource data set, so that the object resource data set for evaluating the target resource of the target object has universality and simultaneously has pertinence, rationally and accurately simulates the unknown object resource data of the target resource, and the accuracy of determining the object resource data set is improved.
According to an embodiment of the present disclosure, for operation S220 as shown in fig. 2, determining a target base resource data subset from the base resource data set based on the object attribute data may include: a target subset of base resource data is determined from the set of base resource data based on the object attribute data and the resource attribute data.
For example, the object attribute data may include professional data of the target object, but is not limited thereto, and may include object attribute data such as professional data of the target object and a place where the object is located. The resource attribute data may include professional data of the object, resource investment place, and the like, corresponding to the object attribute data. It is understood that the object site and the resource investment site may be the same site. The more data types that are included in the object property data, the higher the consistency between the target base resource data subset and the object resource data.
According to an embodiment of the present disclosure, the set of base resource data includes a plurality of subsets of base resource data, the plurality of subsets of base resource data differing from each other in resource attribute data. A subset of target underlying resource data that matches the object attribute data may be determined from the set of underlying resource data based on the object attribute data and the resource attribute data.
According to embodiments of the present disclosure, the underlying resource data set may be derived based on massive underlying resource data, with statistics and universality. And determining a target basic resource data subset matched with the object attribute data from the basic resource data set, so that the target basic resource data subset has consistency and pertinence with the object resource data.
According to an embodiment of the disclosure, for operation 230 as shown in fig. 2, determining the set of object resource data within the target period based on the object resource data and the target underlying resource data subset may include the following operations.
For example, target underlying resource sub-data is determined from a subset of target underlying resource data based on a point in time corresponding to the target resource data. Adjustment parameters are determined based on the target base resource sub-data and the object resource data. And adjusting a plurality of basic resource sub-data in the target basic resource data sub-set based on the adjustment parameters to obtain the object resource data set.
According to an embodiment of the present disclosure, the target underlying resource data subset includes a plurality of underlying resource sub-data and a plurality of time points in one-to-one correspondence with the plurality of underlying resource sub-data.
For example, the target underlying resource data subset includes { (B1, T1), (B2, T2),... The time point corresponding to the target resource data BD is T5, and the target base resource sub-data is B5 corresponding to T5.
According to the embodiment of the present disclosure, the adjustment parameter may be a difference between the object resource data BD and the target base resource sub-data B5, but is not limited thereto, and may be an absolute value of a difference between the object resource data BD and the target base resource sub-data B5, or a quotient of the object resource data BD and the target base resource sub-data B5.
According to an embodiment of the present disclosure, adjusting a plurality of base resource sub-data in a target base resource data subset based on an adjustment parameter to obtain an object resource data set may include: and (3) a plurality of time points corresponding to the plurality of basic resource sub-data one by one are unchanged, and based on the adjustment parameters, the numerical values of the plurality of basic resource sub-data are adjusted to obtain a plurality of transformed basic resource sub-data which are used as a plurality of object resource data in the object resource data set.
According to an embodiment of the present disclosure, adjusting the respective values of the plurality of base resource sub-data based on the adjustment parameters may include: and carrying out numerical transformation on each basic resource sub-data based on the adjustment parameters according to the determination mode of the adjustment parameters.
For example, the determination modes of the adjustment parameters include: and subtracting the target resource data BD from the target basic resource sub-data B5 to obtain the adjustment parameters. Performing a numerical transformation on each of the base resource sub-data based on the adjustment parameters includes: the base resource sub-data is added to the adjustment parameters.
For example, the adjustment parameter z=bd-B5, and the object resource data set includes { (B1 ', T1), (B2 ', T2),. The term, and (Bn ', tn). B1' =b1+bd-B5, b2' =b2+bd-B5, bn ' =bn+bd-B5.
According to an embodiment of the present disclosure, a difference between target base resource sub-data and object resource data is determined using target base resource sub-data corresponding to the object resource data in the target base resource data sub-set. The method has the advantages that the change trend of the object resource data set and the change trend of the target basic resource data subset are kept consistent along with the change of time points, meanwhile, the difference of the object resource data set and the target basic resource data subset in numerical value is made up by adjusting parameters, and the accuracy of unknown data prediction is improved.
FIG. 4 schematically illustrates a diagram of determining a set of object resource data, according to an embodiment of the disclosure.
As shown in fig. 4, a target subset of base resource data may be determined from the set of base resource data based on the object attribute data. A resource time map for the subset of target underlying resource data may be generated with the point in time as the abscissa and the underlying resource sub-data as the ordinate.
As shown in fig. 4, the object resource data BD may be determined in the resource time map, and the target base resource sub-data B5 corresponding to the object resource data BD may be determined based on the time point. The adjustment parameter Z is determined based on the absolute value of the difference between the target resource data BD and the target base resource sub-data B5. The adjustment parameter may refer to a translation parameter. The resource curve of the target base resource data subset L may be translated based on the adjustment parameter Z to obtain the object resource data set P.
According to the embodiment of the disclosure, by the method for determining the target resource data set as shown in fig. 4, the accuracy of determining the target resource data set can be improved while the processing efficiency is improved, and further the accuracy of evaluating the target resource data of the future period is improved.
According to an embodiment of the present disclosure, salaries of target objects may be taken as object resource data as an example, the initial base resource sub-data may refer to salaries of a plurality of objects in the same industry, and the base resource sub-data may refer to an average value of salaries of a plurality of objects in the same industry. Each time point can represent an age, and an average value of salaries of a plurality of objects in the same industry can be taken as an ordinate by taking the age as an abscissa to obtain a resource curve of the industry. The adjustment parameters are determined based on current year object resource data of the target object. And determining the resource curve of the object based on the adjustment parameters and the resource curve of the industry. Determining object resource data a_1, a_2 of each of the target objects 1 year, 2 years, and n years before retirement, based on the object's resource curve, thereby obtaining the object resource data set.
According to a related example, a resource transformation trend corresponding to a subset of base resource data may be generated based on a plurality of subsets of base resource data in the set of base resource data. The object resource transformation trend is obtained based on the resource transformation trend, object attribute data such as professional data of the target object, the place of the object, the academic, and the like. Based on the object resource data and the object resource transformation trend, an object resource data set can be obtained. A resource transformation trend or object resource transformation trend may be understood as a resource function that takes a point in time as a parameter and takes resource data as a result.
According to related examples, deriving the object resource transformation trend based on the resource transformation trend, the object attribute data may include: and inputting the resource transformation trend and the object attribute data into a trend determination model to obtain the object resource transformation trend. The network structure of the trend determining model is not limited, and may be, for example, one or a combination of a convolutional neural network, a cyclic neural network, and a long-short-term memory neural network.
Compared with the mode of determining the object resource data set based on the object resource transformation trend, the method has the advantages that the object resource data set is determined by utilizing the adjustment parameters, the accuracy of the object resource data set can be ensured, the processing efficiency is improved, the trend determination model is prevented from being trained, and the data processing amount is reduced.
According to the embodiment of the disclosure, the adjusted basic resource sub-data can be directly used as the target resource data, but is not limited to this, the adjusted basic resource sub-data can also be directly used as the initial target resource data, the comparison is performed based on the reference basic average resource sub-data and the initial target resource data, and the initial target resource data is further adjusted based on the comparison result, so as to obtain the target resource data.
For example, adjusting the plurality of base resource sub-data in the target base resource data subset based on the adjustment parameter to obtain the object resource data set may include: and respectively adjusting the plurality of basic resource sub-data based on the adjustment parameters to obtain a plurality of initial object resource data. Based on the point in time corresponding to the initial object resource data, reference base average resource sub-data matching the point in time is determined from the base resource data set. Object resource data is determined based on the reference base average resource sub-data and the initial object resource data. Based on the plurality of object resource data, an object resource data set is obtained.
The manner of obtaining the initial object resource data according to the embodiments of the present disclosure may refer to the manner of obtaining the object resource data shown in fig. 4, which is not described herein.
According to an embodiment of the present disclosure, the reference base average resource sub-data may be predetermined resource data, but is not limited thereto, and may be determined from a target base average resource data subset referred to in the following embodiment. For example, a time point corresponding to the initial target resource data is determined, and base average resource sub-data corresponding to the time point is determined from the target base average resource data sub-set as base average resource sub-data.
According to an embodiment of the present disclosure, a comparison result between the reference base average resource sub-data and the initial object resource data is determined for each of the initial object resource data. And determining the object resource data based on the comparison result and a preset mapping relation. The predetermined mapping relationship may be used to characterize the mapping relationship between the comparison result and the object resource data.
For example, the predetermined mapping relationship includes: when the comparison result is used for representing that the initial object resource data is larger than or equal to the product of the first preset weight and the basic average resource sub-data, the preset mapping relation is as follows: the object resource data is the product of the first predetermined weight and the reference base average resource sub-data. The first predetermined weight may include an integer greater than 1, for example 3.
For example, the predetermined mapping relationship includes: and the object resource data is the initial object resource data under the condition that the comparison result is used for representing that the initial object resource data is smaller than the product of the first preset weight and the basic average resource sub-data and larger than the product of the second preset weight and the basic average resource sub-data. The second predetermined weight may include greater than 0 and less than 1, such as 0.6.
For example, the predetermined mapping relationship includes: when the comparison result is used for representing that the initial object resource data is smaller than or equal to the product of the second preset weight and the reference basic average resource sub-data, the preset mapping relation is as follows: the object resource data is the product of the second predetermined weight and the reference base average resource sub-data.
According to the embodiment of the disclosure, the initial object resource data and the reference basic average resource sub-data are compared, so that the determination of the object resource data is combined with the actual, the standard matching of the object resource data and the actual reference is improved, and the accuracy of the target resource data determined based on the object resource data set is further improved.
According to an embodiment of the present disclosure, taking the object resource data as a social security payment base as an example, in the case where the initial object resource data of the current year is greater than or equal to 3×the average wages of the workers on the post of the social security payment location, the object resource data of the current year=3×the basic average resource sub-data of the basic, such as the average wages of the workers on the post of the social security payment location. Under the condition that average wages of on-duty workers in the annual social security payment place are less than 3, the on-duty object resource data=the on-duty initial object resource data. When the initial object resource data in the current year is less than or equal to 60% of the average wages of the on-Shift workers in the current year's social security payment place, the object resource data in the current year=60% of the average wages of the on-Shift workers in the current year's social security payment place.
According to an embodiment of the present disclosure, for operation S240 as shown in fig. 2, evaluating a target resource with respect to a target object based on a set of object resource data, resulting in target resource data, may include the following operations.
For example, a target subset of base average resource data is determined from the set of base resource data based on the object attribute data. And evaluating the target resources related to the target object based on the object resource data set and the target basic average resource data subset to obtain target resource data.
According to embodiments of the present disclosure, the base resource data set may include a plurality of base resource data subsets, but is not limited thereto, and the base resource data set may also include a plurality of base resource data subsets and a target base average resource data subset. The base resource data set may also include a plurality of base resource data subsets and a plurality of base average resource data subsets. Each of the plurality of sub-sets of base average resource data corresponds to one resource attribute data, such as resource investment. Based on resource attribute data, such as professional data, a plurality of sub-sets of basic resource data with the same resource investment and different professional data can be obtained. And weighting and summing the plurality of basic resource data subsets to obtain a target basic average resource data subset corresponding to the resource investment data. The resource attribute data, e.g., resource investment, of the target underlying average resource data subset is the same as the object attribute data, e.g., object location data.
According to an embodiment of the present disclosure, evaluating a target resource with respect to a target object based on a target resource data set and a target underlying average resource data subset, resulting in target resource data may include: the resource type of the target resource is determined. A target evaluation mode is determined based on the resource type of the target resource. And processing the object resource data set and the target basic average resource data subset according to the target evaluation mode to obtain target resource data.
According to other embodiments of the present disclosure, a target resource of a target object may be evaluated based on a set of object resource data, resulting in target resource data.
Compared with a mode of evaluating target resources based on the object resource data set, the method and the device for evaluating target resources based on the object resource data set and the target basic average resource data subset provided by the embodiment of the invention can combine the target basic average resource data subset with the object resource data set, and improve the accuracy of target resource data.
According to an embodiment of the present disclosure, determining a target underlying average resource data set from the underlying resource data set based on the object attribute data may include: a transition period is determined based on the starting point in time and the resource implementation point in time in the object attribute data. In the event that the transition period is determined to be less than or equal to the predetermined threshold, a target underlying average resource data subset is determined from the underlying resource data set based on the resource attribute data in the object attribute data and the target period.
According to embodiments of the present disclosure, the start time point in the object attribute data may be understood as the start year in which the target object participates in the job, for example, 1980. The resource implementation time point can be understood as the implementation time of the pension resource, such as the time of implementing social security, for example, 1993. Determining the transition period based on the resource implementation time point and the start time point in the object attribute data may include: the difference between the starting time point and the resource implementation time point in the object attribute data is taken as a transition period.
According to an embodiment of the present disclosure, the predetermined threshold may be set to 0. In the event that the transition period is greater than a predetermined threshold, it is determined that a transition period exists. In the case where the transition period is less than or equal to the predetermined threshold value, it is determined that there is no transition period. In the case where the transition period is determined to be less than or equal to the predetermined threshold, a first target underlying average resource data subset is determined from the underlying resource data set based on the object attribute data and the target period, and the first target underlying average resource data subset is cooperated to the target underlying average resource data subset. In the event that the transition period is determined to be greater than the predetermined threshold, a first target subset of base average resource data is determined from the set of base resource data based on the object attribute data and the target period. A second target subset of base average resource data is determined from the set of base resource data based on the transition period. And obtaining a target basic average resource data subset based on the first target basic average resource data subset and the second target basic average resource data subset.
According to an embodiment of the present disclosure, determining a first target subset of base average resource data from a set of base resource data based on object attribute data and a target period of time, comprises: the historical period and the future period are determined from the target period. A subset of historical base average resource data is determined from the set of base resource data based on the resource attribute data and the historical time period. Based on the resource attribute data and the future time period, a subset of future underlying average resource data is determined based on the subset of historical underlying average resource data. And obtaining a first target basic average resource data subset based on the historical basic average resource data subset and the future basic average resource data subset.
According to an embodiment of the present disclosure, the target underlying average resource data subset includes a plurality of underlying average resource sub-data in one-to-one correspondence with a plurality of time points. The plurality of time points are within the target period. The target period may include a future period and a history period. The target period may be determined based on object attribute data. For example, taking the target period as an example of the working period, the target period is obtained based on the already working period and the expected retirement period in the object attribute data. Also, for example, taking the target period as the holding time length, the target period is obtained based on the held time length and the predicted holding time length in the object attribute data.
According to an alternative embodiment of the present disclosure, for a historical period, a plurality of historical base average resource sub-data corresponding to a plurality of historical time points in the historical period one by one may be acquired by means of data acquisition. Based on the plurality of historical base average resource sub-data and the plurality of historical time points, a historical base average resource data sub-set is obtained. And further obtaining a historical basic average resource data subset. For a future period, a plurality of future base average resource sub-data may be derived that corresponds one-to-one to a plurality of future points in time in the future period based on the plurality of historical base average resource sub-data. A subset of future underlying average resource data is derived based on the plurality of future underlying average resource sub-data and the plurality of future points in time. And splicing the historical basic average resource data subset and the future basic average resource data subset to obtain a first target basic average resource data subset.
According to an alternative embodiment of the present disclosure, deriving a plurality of future base average resource sub-data in one-to-one correspondence with a plurality of future time points in a future period based on the plurality of historical base average resource sub-data may include: an average growth rate is determined based on the plurality of historical base average resource sub-data. Based on the average growth rate and the target historical base average resource sub-data in the historical period, future base average resource sub-data for each of a plurality of future time points in the future period is obtained. The historical base average resource sub-data corresponding to the point in time in the historical period closest to the current point in time may be taken as the target historical base average resource sub-data. But is not limited thereto. The historical base average resource sub-data corresponding to the current point in time may also be used as the target historical base average resource sub-data.
For example, the history base average resource sub-data b_1 corresponding to the history time point t_1 in the history period, the history base average resource sub-data b_2 corresponding to the history time point t_2, and the history base average resource sub-data b_3 corresponding to the history time point t_3. An average growth rate is determined based on the plurality of historical base average resource sub-data. For example, the rate of increase between B_2 and B_1, and the rate of increase between B_3 and B_2. The multiple growth rates are weighted and summed to obtain an average growth rate. And taking the historical basic average resource sub-data B_3 which is closest to the current time point as target historical basic average resource sub-data. Future underlying average resource sub-data for each of a plurality of future time points within the future period is determined based on the target historical underlying average resource sub-data and the average growth rate.
According to an alternative embodiment of the present disclosure, a predetermined growth rate may be used to determine future base average resource sub-data for each of a plurality of future time points within a future period of time based on the target historical base average resource data.
Compared with the method for obtaining the target basic average resource data sub-set based on the preset growth rate, the method for obtaining the target basic average resource data sub-set based on the average growth rate can be flexibly adjusted by combining mass data, so that the average growth rate is combined with reality, the precision of basic average resource sub-data in a future period in the target basic average resource data sub-set is improved, and the precision of target resource data obtained based on the target basic average resource data sub-set is further improved.
According to an embodiment of the present disclosure, determining a second target subset of base average resource data from the set of base resource data based on the transition period may include: at least one transition point in time is determined based on the transition period. At least one transition base average resource sub-data corresponding one-to-one to the at least one transition time point is determined from the base resource data set based on the professional data and the resource investment in the at least one transition time point and the object attribute data. A second subset of target underlying mean resource data is obtained based on the at least one transition time point and the at least one transition underlying mean resource sub-data. But is not limited thereto. At least one predetermined transition base average resource sub-data corresponding one-to-one to at least one transition time point may also be predetermined. A second subset of target underlying mean resource data is obtained based on the at least one transition time point and the at least one predetermined transition underlying mean resource sub-data.
FIG. 5A schematically illustrates an interactive schematic of a resource assessment method according to an embodiment of the present disclosure.
As shown in fig. 5A, a resource evaluation application may be loaded on the terminal device 510, object attribute data about a target object and object resource data are input on an input interface 511 of the resource evaluation application, and an evaluation request for evaluating a resource is generated.
As shown in fig. 5A, the terminal device transmits the evaluation request to the server 520, and the server 520 performs a resource evaluation method in response to receiving the evaluation request from the target object, resulting in target resource data.
As shown in fig. 5A, the server 520 transmits the target resource data to the terminal device 510, and the terminal device 510 generates a presentation interface 512 for presenting the target resource data.
According to the embodiment of the disclosure, as the server can store the basic resource data set in advance, the target basic resource data subset matched with the object attribute data can be determined through the basic resource data set, and then the object resource data set is determined, so that the content required to be filled in an input interface is simple, the data amount is small, and the user experience is improved.
According to an alternative embodiment of the present disclosure, a terminal device is provided with a target memory, a target processor and a display screen. The target memory may be one or more memories such as a dis (cache), but is not limited thereto as long as it is a device having a memory function. The target processor may be one or more processors such as a CPU (Central Processing Unit ), but is not limited thereto as long as it is a processing device capable of having an arithmetic function. The display screen may display an input interface and/or a display interface, may have a touch response function, but is not limited thereto, and may generate an evaluation request based on a touch operation by a user. For example, the display screen may be provided by rendering an input interface on which a screen is displayed so that a user can input contents to be filled in, such as object attribute data of a target object and object resource data, as required. An evaluation request can be generated through the display screen based on the object attribute data and the object resource data of the target object and sent to the target processor. Also for example, the target resource data is obtained with the target processor in response to the evaluation request. And transmitting the target resource data to the display screen through the target processor. And generating a display interface by the display screen to display the target resource data.
According to the embodiment of the disclosure, the request generation processor and the sensor are integrated in the display screen, so that the request generation processor can be utilized to directly respond and generate the evaluation request based on the input of a user or manual operation, the processing speed is improved, and the processing operation is simplified. In addition, the target processor is electrically connected with the display screen, so that the judgment operation of a receiver of an evaluation request can be avoided, and the processing procedure of transmission judgment is simplified. In addition, the evaluation request can be generated only based on the object attribute data and the object resource data, so that the manual operation process of the user is simplified, and the processing speed and the response accuracy of the display screen are improved.
According to an alternative embodiment of the present disclosure, the basic resource data set may be stored in advance in the target memory of the terminal device, so that after the target processor of the terminal device responds to the evaluation request for evaluating the resource, the target basic resource data subset matched with the object attribute data is determined through the basic resource data set stored in the target memory, and further the object resource data set is determined, thereby saving the computing resource of the terminal device, avoiding data leakage caused by data transmission between the terminal device and the server, improving data security, and improving data processing efficiency by reducing the data transmission amount.
Fig. 5B schematically illustrates a schematic diagram of generating an evaluation request according to another embodiment of the present disclosure.
As shown in fig. 5B, in the case where there are a plurality of resource types, the initial resource type may be filled in while the object attribute data on the target object and the object resource data are input on the input interface 511 of the terminal device 510. An evaluation request is generated based on the object attribute data, the object resource data, and the initial resource type.
According to an embodiment of the present disclosure, server 520 determines object attribute data and object resource data based on the evaluation request in response to receiving the evaluation request. Before performing operation S240 as shown in fig. 2, an operation of determining a resource type based on the evaluation request may be performed.
According to an embodiment of the present disclosure, determining a resource type based on an evaluation request may include: based on the evaluation request, an initial resource type of the target resource is determined. The resource type of the target resource is determined based on the object attribute data and the initial resource type.
According to embodiments of the present disclosure, the initial resource types may include a base resource type, an object resource type, a non-standard resource type, a transitional resource type, and the like. An evaluation request may be generated by populating an initial resource type determined from a plurality of selectable resource type boxes.
According to embodiments of the present disclosure, a predetermined field in an evaluation request may be parsed to obtain an initial resource type. The resource type can be determined based on the initial resource type and the object attribute data, so that the accurate and fine-grained determination of the resource type is realized.
For example, taking the resource type of a pension resource as an example, the initial resource type may be determined as the base resource type by a target object, such as user population. Based on the object attribute data of the target object, determining that the professional data of the target object includes professional data of enterprise employees can determine that the resource type is a basic resource type of the enterprise employees.
According to an embodiment of the present disclosure, evaluating a target resource with respect to a target object based on a target resource data set and a target underlying average resource data subset, resulting in target resource data, comprising: and determining target basic average resource sub-data based on the target basic average resource data sub-set under the condition that the resource type of the target resource is determined to be the basic resource type. A resource parameter is determined based on the object resource data set and the target underlying average resource data subset. And determining the target resource matched with the basic resource type based on the target basic average resource sub-data and the resource parameter.
According to embodiments of the present disclosure, the target underlying average resource data subset may include a plurality of underlying average resource sub-data in one-to-one correspondence with a plurality of points in time. The target base average resource sub-data may be determined from a plurality of base average resource sub-data based on object attribute data, such as the age of the target object. Taking the basic resource type as an example of the pension basic resource type, the target basic average resource sub-data may refer to basic average resource sub-data corresponding to a previous year of retirement of the target object. The underlying average resource sub-data may refer to average payroll for the on-duty job month of the social security payment.
According to an embodiment of the present disclosure, determining a resource parameter based on the object resource data set and the target underlying average resource data subset may include: for each time point, object resource data corresponding to the time point is determined from the object resource data set, and base average resource sub-data corresponding to the time point is determined from the target base average resource data subset. The resource sub-parameters are determined based on the object resource data corresponding to the point in time and the underlying average resource sub-data corresponding to the point in time. A resource parameter is determined based on a plurality of resource sub-parameters corresponding to a plurality of points in time. For example, a weighted sum of a plurality of resource sub-parameters corresponding to a plurality of points in time determines a resource parameter.
According to embodiments of the present disclosure, the plurality of time points may be a plurality of time points within the target period and the transition period. The target period and the transition period may be determined based on object attribute data. For example, the age of the target object to participate in the work and the age of the predetermined retirement are determined from the object attribute data. The target period and the transition period are determined based on the age of the target subject engaged in the work and the age of the predetermined retirement.
According to an embodiment of the present disclosure, determining a target resource that matches a base resource type based on target base average resource sub-data and resource parameters may include: and determining target resource data matched with the basic resource type based on the weight, the target basic average resource sub-data, the resource parameter, the target period and the transition period.
In accordance with an embodiment of the present disclosure, endowment resource data is exemplified on the basis of target resource data. Basic endowment resource data = target basic average resource sub-data such as average payroll of on Shift jobs (1 + resource parameters such as target object average payroll index) 2 (target period + transition period such as payment period) 1% of the last year (T-1) before retirement of social security payment.
In accordance with an embodiment of the present disclosure,
Wherein a is 1 、a 2 .....a n Object resource data such as a target object payment radix for 1 year, 2 years, respectively, of the first 1 year, 2 years, n years; a is that 1 、A 2 ......A n Average resource sub-data for 1 year, 2 years before retirement of the target object, respectively, such as average wages of on-Shift workers at social security payouts; n is a target period, for example, a period in which a basic endowment premium is actually paid=a paid period+a retired age-a current age. m is the transition period.
According to the embodiment of the disclosure, under the condition of determining the target resource data of the basic resource type, the target resource data set is determined through the target basic resource data subset, the target basic average resource data subset is determined through the basic resource data set, and the plurality of parameters are determined by utilizing different data statistics modes, so that the accuracy of the target resource data of the target object is high. In addition, in consideration of the transition period, the personalized requirements of various users of different ages can be improved, and the user experience can be improved while the accuracy of target resource data of a target object is improved.
According to an embodiment of the present disclosure, evaluating a target resource with respect to a target object based on a target resource data set and a target underlying average resource data subset, resulting in target resource data may include: and under the condition that the resource type of the target resource is determined to be the target resource type, determining the total resource data of the target investment based on the target resource data set and the target basic average resource data subset. Based on the object attribute data, a target consumption duration is determined. And determining target resource data matched with the object resource type based on the object input total resource data and the target consumption time length.
According to an embodiment of the present disclosure, determining object input total resource data based on the object resource data set and the target underlying average resource data subset may include: a target data set is determined from the target resource data set and the target underlying average resource data subset based on the professional data in the object attribute data. Based on the target data set, the object input total resource data is determined.
According to an embodiment of the present disclosure, a set mapping relationship may be predetermined, the predetermined set mapping relationship characterizing a mapping relationship between professional data and a target data set. For example, the occupation data characterizes free occupation, and the target underlying average resource data subset can be collaborated into a target data set. Also for example, the occupation data characterizes a non-free occupation, and the object resource data set may be regarded as a target data set.
According to embodiments of the present disclosure, the object resource type may refer to a personal care resource type. The target consumption time period may refer to a time period between a time point at which the input of the resource data is stopped and a target time point. The point in time at which to stop devoting resource data may be referred to as the beginning year of retirement. The target point in time may refer to the average lifetime. The point in time at which to stop devoting the resource data, such as the beginning year of retirement, may be determined based on the age in the object attribute data. The target time point can be obtained according to mass data statistics, and a time point can be preset as the target time point.
Taking the example of the object resource type as the personal pension resource type, the target resource data such as the personal account pension that matches the object resource type=the total resource data such as the personal account total storage amount/the target consumption time such as the number of months of issuance according to the embodiment of the present disclosure. Number of hair-count months= (population average life-retired age) ×12.
According to embodiments of the present disclosure, in the event that occupation data is determined to be representative of a non-free occupation, the subject invests total resource data, e.g., total storage of personal accounts = Σannual target subject payment base = local personal payment proportion (e.g., 8%) (1+ personal billing interest rate) T 。
Where T may be the year from retirement = retirement age-current age.
According to embodiments of the present disclosure, the number of issue months may be determined by querying the personal account pension Ji Fa month table 1 based on the object attribute data. The personal billing interest rate may be determined by querying personal billing interest rate table 2 based on the object attribute data.
According to embodiments of the present disclosure, in the event that occupation data is determined to characterize a free occupation, the annual target object payment base may be replaced with the average wages of the on Shift job and month of the last year social security payment. And determining the total resource input data of the object based on the average wages of the on-duty workers in the annual social security payment place.
| Age of retirement | Counting the number of months of hair | Age of retirement | Counting the number of months of hair |
| 40 | 233 | 56 | 164 |
| 41 | 230 | 57 | 158 |
| 42 | 226 | 58 | 152 |
| 43 | 223 | 59 | 145 |
| 44 | 220 | 60 | 139 |
| 45 | 216 | 61 | 132 |
| 46 | 212 | 62 | 125 |
| 47 | 207 | 63 | 117 |
| 48 | 204 | 64 | 109 |
| 49 | 199 | 65 | 101 |
| 50 | 195 | 66 | 93 |
| 51 | 190 | 67 | 84 |
| 52 | 185 | 68 | 75 |
| 53 | 180 | 69 | 65 |
| 54 | 175 | 70 | 56 |
| 55 | 170 |
TABLE 1
| Annual year | Accounting interest rate |
| 2016 | 8.31％ |
| 2017 | 7.12％ |
| 2018 | 8.29％ |
| 2019 | 7.61％ |
| 2020 | 6.04％ |
| 2021 | 6.69％ |
TABLE 2
According to the embodiment of the disclosure, the target resource data matched with the object resource type is used as target resource data, and the target data set matched with the object attribute data can be determined from the object resource data set and the target basic average resource data subset aiming at the object attribute data, so that the precision of the target resource data of the target object is improved, the personalized requirements of users are further met, and the user experience is improved.
According to an embodiment of the present disclosure, evaluating a target resource with respect to a target object based on a target resource data set and a target underlying average resource data subset, resulting in target resource data may include: and determining a qualification recognition result based on the object attribute data in the case that the resource type of the target resource is determined to be a non-standard resource type. In the case where it is determined that the qualifying recognition result is used to characterize the target object as enjoying resources of a non-standard resource type, target resource data matching the non-standard resource type is determined based on the set of object resource data and the subset of target underlying average resource data.
According to embodiments of the present disclosure, a non-standard resource type may refer to a pension resource as an annuity resource. Determining the qualifying recognition result based on the object attribute data may include: based on professional data in the object attribute data, determining whether the target object enjoys resources of a non-standard resource type, and obtaining a qualification recognition result. The target object may be determined to enjoy resources of a non-standard resource type in the case where the job data includes enterprise job data. In the case where the job data includes non-enterprise job data, it is determined that the target object does not enjoy resources of a non-standard resource type.
According to embodiments of the present disclosure, operations may be stopped in the event that a determination is made that the qualifying recognition result is used to characterize the target object as not enjoying resources of a non-standard resource type.
According to embodiments of the present disclosure, whether to perform a subsequent operation of determining target resource data that matches a non-standard resource type may be determined in advance by determining a qualification recognition result. The method has the advantages of ensuring the expansion of the target resource data types and the application range, simplifying the processing operation and reducing the processing efficiency.
According to an embodiment of the present disclosure, determining a target resource that matches a non-standard resource type based on a target resource data set and a target underlying average resource data subset, includes: non-standard total resource data is determined based on the object resource data set and the target underlying average resource data subset. A target consumption duration is determined based on the object attribute data and the target period. And determining target resource data matched with the nonstandard resource type based on the nonstandard total resource data and the target consumption duration.
According to an embodiment of the present disclosure, the target resource data that matches the non-standard resource type is exemplified by professional annuity. Professional annuity = non-standard total resource data such as total savings of professional annuity account/number of months of issuance.
According to embodiments of the present disclosure, professional annuity total deposit = Σannual target object payment base × predetermined ratio (1+personal billing interest rate) T 。
According to an embodiment of the present disclosure, after performing operation S240 for as shown in fig. 2, the resource evaluation method may further include the following operations.
For example, a resource type of the target resource is determined. In the case where it is determined that the resource type of the target resource includes a transitional resource type, a transitional period is determined based on the resource implementation time point and the start time point in the object attribute data. In the event that the transition period is determined to be greater than the predetermined threshold, target base average resource sub-data is determined based on the target base resource data sub-set. And obtaining target resource data matched with the transitional resource type based on the target basic average resource sub-data and the transitional period.
According to embodiments of the present disclosure, the resource type of the target resource includes a transitional resource type, which may be understood as the target resource is a transitional pension resource, such as a transitional pension.
According to embodiments of the present disclosure, the start time point in the object attribute data may be understood as the start year in which the target object participates in the job, for example, 1980. The resource implementation time point can be understood as the implementation time of the pension resource, such as the time of implementing social security, 1993. Determining the transition period based on the resource implementation time point and the start time point in the object attribute data may include: the difference between the starting time point and the resource implementation time point in the object attribute data is taken as a transition period.
According to an embodiment of the present disclosure, the predetermined threshold may be set to 0. In the event that the transition period is greater than a predetermined threshold, the presence of the transition period is evidenced, and the presence of target resource data matching the transitional resource type is determined. The determination of target underlying average resource sub-data based on the subset of underlying resource data may be performed. Based on the target average resource and the transition period, the operation of the target resource data matched with the transitional resource type is obtained. In the case where the transition period is equal to the predetermined threshold value, it is proved that there is no transition period, and the subsequent operation may be stopped.
According to an embodiment of the present disclosure, obtaining target resource data that matches a transitional resource type based on target base average resource sub-data and a transitional period may include: and taking the product between the target basic average resource sub-data and the transition period as target resource data matched with the transitional resource type. But is not limited thereto. The product of the target base average resource sub-data, the transition period, and the transition coefficient may also be used as target resource data that matches the transitional resource type.
Taking the example of transitional pension resources as target resource data matching the transitional resource type, according to embodiments of the present disclosure, target resource data matching the transitional resource type=target base average resource sub-data, e.g., average wages of on-duty wages of the last year before retirement of the resource location x co-payment wages index x transitional period x transitional coefficients.
According to embodiments of the present disclosure, the transition coefficient and the apparent payroll index may be predetermined weights. May be determined based on the location of the resource. And a mapping relation between the resource location and the preset weight can be established, and a transition coefficient and a pay-as-you-see payroll index are respectively determined based on the resource location and the mapping relation.
According to the embodiment of the disclosure, the target resource data matched with the transitional resource type is used as the target resource data, so that the target resource data of the older target object can be evaluated, the application range is improved, and the user experience is further improved.
According to the embodiment of the disclosure, the target resource data of the target resource can be obtained by weighted summation of the target resource data matched with the transitional resource type, the target resource data matched with the basic resource type, the target resource data matched with the target resource type and the target resource data matched with the nonstandard resource type.
According to the embodiment of the disclosure, the target resource data of the target resource has wide range and various types of references, so that the target resource data is comprehensive, accurate and effective.
Fig. 6 schematically illustrates a block diagram of a resource assessment apparatus according to an embodiment of the present disclosure.
As shown in fig. 6, the resource evaluation device 600 includes: a response module 610, a first determination module 620, a second determination module 630, and an evaluation module 640.
A response module 610 for determining object attribute data and object resource data based on the evaluation request in response to receiving an evaluation request from the target object for evaluating the resource.
The first determining module 620 is configured to determine a target subset of base resource data from the set of base resource data based on the object attribute data.
The second determining module 630 is configured to determine an object resource data set based on the object resource data and the target underlying resource data subset.
And an evaluation module 640, configured to evaluate the target resource related to the target object based on the object resource data set, to obtain target resource data.
According to an embodiment of the present disclosure, the target underlying resource data subset includes a plurality of underlying resource sub-data in one-to-one correspondence with a plurality of points in time.
According to an embodiment of the present disclosure, the second determining module includes: the first determining sub-module, the second determining sub-module and the set obtaining sub-module.
A first determination sub-module for determining target underlying resource sub-data from the target underlying resource data subset based on a point in time corresponding to the target resource data.
And a second determination sub-module that determines adjustment parameters based on the target base resource sub-data and the object resource data.
And the set acquisition sub-module is used for adjusting a plurality of basic resource sub-data in the target basic resource data sub-set based on the adjustment parameters to obtain the object resource data set.
According to an embodiment of the present disclosure, a set acquisition sub-module includes: the device comprises an initial acquisition unit, a matching unit, a data determination unit and a set acquisition unit.
The initial acquisition unit is used for respectively adjusting the plurality of basic resource sub-data based on the adjustment parameters to obtain a plurality of initial object resource data.
And a matching unit for determining reference basic average resource sub-data matched with the time point from the basic resource data set based on the time point corresponding to the initial object resource data.
And the data determining unit is used for determining the object resource data based on the reference basic average resource sub-data and the initial object resource data.
And the set acquisition unit is used for acquiring an object resource data set based on the plurality of object resource data.
According to an embodiment of the present disclosure, an evaluation module includes: and a third determination sub-module and an evaluation sub-module.
And a third determination sub-module for determining a target base average resource data subset from the base resource data set based on the object attribute data.
And the evaluation sub-module is used for evaluating the target resources related to the target object based on the object resource data set and the target basic average resource data subset to obtain target resource data.
According to an embodiment of the present disclosure, the third determining sub-module includes: a period determining unit and a threshold judging unit.
And a period determining unit for determining a transition period based on the start time point and the resource implementation time point in the object attribute data.
And the threshold judging unit is used for determining a first target basic average resource data subset from the basic resource data set based on the object attribute data and the target time period and combining the first target basic average resource data subset into the target basic average resource data subset under the condition that the transition time period is less than or equal to the preset threshold. The target period is determined based on the resource attribute data.
According to an embodiment of the present disclosure, the third determining sub-module further comprises: the first determining unit, the second determining unit and the subset determining unit.
A first determining unit for determining a first target subset of base average resource data from the set of base resource data based on the object attribute data and the target period, if the transition period is determined to be greater than the predetermined threshold.
And a second determining unit configured to determine a second target subset of base average resource data from the set of base resource data based on the transition period.
The subset determining unit is used for obtaining a target basic average resource data subset based on the first target basic average resource data subset and the second target basic average resource data subset.
According to an embodiment of the present disclosure, a first determination unit includes: a time period determining subunit, a first determining subunit, a second determining subunit, and a subset subunit.
A period determination subunit for determining a history period and a future period from the target period.
A first determination subunit for determining a historical base average subset of resource data from the base set of resource data based on the resource attribute data and the historical period.
A second determination subunit for determining a subset of future underlying average resource data based on the resource attribute data, the future time period, and the historical subset of underlying average resource data.
And the sub-set sub-unit is used for obtaining a first target basic average resource data sub-set based on the historical basic average resource data sub-set and the future basic average resource data sub-set.
According to an embodiment of the present disclosure, an evaluation sub-module includes: a resource determining unit, a parameter determining unit and a third determining unit.
And the resource determining unit is used for determining target basic average resource sub-data based on the target basic average resource data sub-set under the condition that the resource type of the resource is determined to be the basic resource type.
And the parameter determining unit is used for determining the resource parameter based on the object resource data set and the target basic average resource data subset.
And a third determining unit, configured to determine target resource data matched with the base resource type based on the target base average resource sub-data and the resource parameter.
According to an embodiment of the present disclosure, an evaluation sub-module includes: the system comprises a data input unit, a total resource determining unit, a duration determining unit and a fourth determining unit.
And the data input unit is used for determining a target data set based on the target resource data set and the target basic average resource data subset under the condition that the resource type of the resource is determined to be the target resource type.
And the total resource determining unit is used for determining the total resource data of the object input based on the target data set.
And the duration determining unit is used for determining the target consumption duration based on the object attribute data.
And the fourth determining unit is used for determining target resource data matched with the object resource type based on the total input resources and the target consumption time length of the object.
According to an embodiment of the present disclosure, an evaluation sub-module includes: and a result determination unit and a fifth determination unit.
And a result determination unit configured to determine a qualification recognition result based on the object attribute data in a case where it is determined that the resource type of the resource is a non-standard resource type.
And a fifth determining unit for determining target resource data matched with the nonstandard resource type based on the object resource data set and the target basic average resource data subset under the condition that the qualification recognition result is determined to be used for representing that the target object enjoys the resources of the nonstandard resource type.
According to an embodiment of the present disclosure, a fifth determining unit includes: the third determining subunit, the fourth determining subunit and the fifth determining subunit.
And a third determining subunit, configured to determine non-standard total resource data based on the object resource data set and the target base average resource data subset.
And a fourth determining subunit, configured to determine a target consumption duration based on the object attribute data and the target period.
And a fifth determining subunit, configured to determine, based on the nonstandard total resource and the target consumption duration, target resource data that matches the nonstandard resource type.
According to an embodiment of the present disclosure, the resource evaluation device further includes: the device comprises a time period determining module, a data determining module and a data obtaining module.
And a period determining module for determining a transition period based on the resource implementation time point and the start time point in the object attribute data in the case that the resource type of the resource is determined to include the transitional resource type.
And the data determining module is used for determining target average resource sub-data based on the target basic average resource data sub-set under the condition that the transition period is determined to be larger than the preset threshold value.
And the data acquisition module is used for acquiring target resource data matched with the transitional resource type based on the target average resource sub-data and the transitional period.
According to an embodiment of the present disclosure, the resource evaluation device further includes: the first type determining module and the second type determining module.
And the first type determining module is used for determining the initial resource type of the target resource based on the evaluation request.
And the second type determining module is used for determining the resource type of the target resource based on the object attribute data and the initial resource type.
According to an embodiment of the present disclosure, a first determination module includes: and a fourth determining sub-module.
And a fourth determination sub-module for determining a target subset of base resource data from the set of base resource data based on the object attribute data and the resource attribute data.
According to an embodiment of the present disclosure, the resource evaluation device further includes: the device comprises a first acquisition module, a second acquisition module, a third acquisition module and a fourth acquisition module.
The first acquisition module is used for acquiring a plurality of initial basic resource data subsets corresponding to a plurality of time points one by one. The initial underlying resource data subset includes a plurality of initial underlying resource sub-data at the same point in time.
And the second acquisition module is used for acquiring the basic resource sub-data based on the plurality of initial basic resource sub-data of the time point.
And the third acquisition module is used for acquiring a basic resource data subset based on a plurality of basic resource sub-data which are in one-to-one correspondence with a plurality of time points.
And the fourth acquisition module is used for acquiring the basic resource data set based on the plurality of basic resource data subsets.
According to embodiments of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium and a computer program product.
According to an embodiment of the present disclosure, an electronic device includes: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform a method as in an embodiment of the present disclosure.
According to an embodiment of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions for causing a computer to perform a method as in an embodiment of the present disclosure.
According to an embodiment of the present disclosure, a computer program product comprising a computer program which, when executed by a processor, implements a method as an embodiment of the present disclosure.
Fig. 7 illustrates a schematic block diagram of an example electronic device 700 that may be used to implement embodiments of the present disclosure. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the disclosure described and/or claimed herein.
As shown in fig. 7, the apparatus 700 includes a computing unit 701 that can perform various appropriate actions and processes according to a computer program stored in a Read Only Memory (ROM) 702 or a computer program loaded from a storage unit 708 into a Random Access Memory (RAM) 703. In the RAM 703, various programs and data required for the operation of the device 700 may also be stored. The computing unit 701, the ROM 702, and the RAM 703 are connected to each other through a bus 704. An input/output (I/O) interface 705 is also connected to bus 704.
Various components in device 700 are connected to an input/output (I/O) interface 705, including: an input unit 706 such as a keyboard, a mouse, etc.; an output unit 707 such as various types of displays, speakers, and the like; a storage unit 708 such as a magnetic disk, an optical disk, or the like; and a communication unit 709 such as a network card, modem, wireless communication transceiver, etc. The communication unit 709 allows the device 700 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.
The computing unit 701 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of computing unit 701 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 701 performs the respective methods and processes described above, such as a resource evaluation method. For example, in some embodiments, the resource assessment method may be implemented as a computer software program tangibly embodied on a machine-readable medium, such as storage unit 708. In some embodiments, part or all of the computer program may be loaded and/or installed onto device 700 via ROM 702 and/or communication unit 709. When the computer program is loaded into the RAM 703 and executed by the computing unit 701, one or more steps of the resource assessment method described above may be performed. Alternatively, in other embodiments, the computing unit 701 may be configured to perform the resource assessment method by any other suitable means (e.g. by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), complex Programmable Logic Devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
Program code for carrying out methods of the present disclosure may be written in any combination of one or more programming languages. These program code may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus such that the program code, when executed by the processor or controller, causes the functions/operations specified in the flowchart and/or block diagram to be implemented. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and pointing device (e.g., a mouse or trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), and the internet.
The computer system may include a client and a server. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server may be a cloud server, a server of a distributed system, or a server incorporating a blockchain.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps recited in the present disclosure may be performed in parallel or sequentially or in a different order, provided that the desired results of the technical solutions of the present disclosure are achieved, and are not limited herein.
The above detailed description should not be taken as limiting the scope of the present disclosure. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present disclosure are intended to be included within the scope of the present disclosure.
Claims (19)
1. A method of resource assessment, comprising:
in response to receiving an evaluation request from a target object for evaluating a resource, determining object attribute data and object resource data based on the evaluation request;
determining a target subset of base resource data from the set of base resource data based on the object attribute data;
determining an object resource data set based on the object resource data and the target underlying resource data subset; and
And evaluating target resources related to the target object based on the object resource data set to obtain target resource data.
2. The method of claim 1, wherein the target subset of base resource data comprises a plurality of base resource sub-data in one-to-one correspondence with a plurality of points in time;
the determining an object resource data set based on the object resource data and the target base resource data subset includes:
determining target underlying resource sub-data from the target underlying resource data sub-set based on a point in time corresponding to the object resource data;
determining an adjustment parameter based on the target base resource sub-data and the object resource data; and
and adjusting a plurality of basic resource sub-data in the target basic resource data sub-set based on the adjustment parameters to obtain the object resource data set.
3. The method of claim 2, wherein the adjusting the plurality of base resource sub-data in the target base resource data subset based on the adjustment parameter to obtain the object resource data set comprises:
based on the adjustment parameters, respectively adjusting the plurality of basic resource sub-data to obtain a plurality of initial object resource data;
Determining reference base average resource sub-data matching the point in time from the base resource data set based on the point in time corresponding to the initial object resource data;
determining object resource data based on the reference base average resource sub-data and the initial object resource data; and
and obtaining the object resource data set based on a plurality of object resource data.
4. A method according to any one of claims 1 to 3, wherein said evaluating a target resource with respect to said target object based on said set of object resource data resulting in target resource data comprises:
determining a target base average resource data subset from the base resource data set based on the object attribute data; and
and evaluating target resources related to the target object based on the object resource data set and the target basic average resource data subset to obtain target resource data.
5. The method of claim 4, wherein the determining a target subset of base average resource data from the set of base resource data based on the object attribute data comprises:
Determining a transition period based on a starting time point and a resource implementation time point in the object attribute data; and
and determining a first target basic average resource data subset from the basic resource data set based on the object attribute data and a target period, and taking the first target basic average resource data subset as the target basic average resource data subset, wherein the target period is determined based on the resource attribute data.
6. The method of claim 5, wherein the determining a target subset of base average resource data from the set of base resource data based on the object attribute data, further comprises:
determining a first target subset of base average resource data from the set of base resource data based on the object attribute data and the target period of time, if the transition period of time is determined to be greater than the predetermined threshold;
determining a second target subset of base average resource data from the set of base resource data based on the transition period; and
and obtaining the target basic average resource data subset based on the first target basic average resource data subset and the second target basic average resource data subset.
7. The method of claim 6, wherein the determining a first target subset of base average resource data from the set of base resource data based on the object attribute data and the target period of time comprises:
determining a history period and a future period from the target period;
determining a historical base average resource data subset from the base resource data set based on the resource attribute data and the historical period;
determining a subset of future underlying average resource data based on the resource attribute data, the future period of time, and the subset of historical underlying average resource data; and
and obtaining the first target basic average resource data subset based on the historical basic average resource data subset and the future basic average resource data subset.
8. The method of any of claims 4 to 7, wherein the evaluating the target resource for the target object based on the set of object resource data and the subset of target underlying average resource data to obtain the target resource data comprises:
determining target basic average resource sub-data based on the target basic average resource data sub-set under the condition that the resource type of the target resource is determined to be the basic resource type;
Determining a resource parameter based on the object resource data set and the target underlying average resource data subset; and
and determining target resource data matched with the basic resource type based on the target basic average resource sub-data and the resource parameter.
9. The method of any of claims 4 to 8, wherein the evaluating the target resource for the target object based on the set of object resource data and the subset of target underlying average resource data to obtain the target resource data comprises:
determining a target data set from the target resource data set and the target basic average resource data subset based on the object attribute data under the condition that the resource type of the target resource is determined to be the object resource type;
determining the total resource data input by the object based on the target data set;
determining a target consumption duration based on the object attribute data; and
and determining target resource data matched with the object resource type based on the object input total resource data and the target consumption duration.
10. The method of any of claims 4 to 9, wherein the evaluating the target resource for the target object based on the set of object resource data and the subset of target underlying average resource data to obtain target resource data comprises:
Determining a qualification recognition result based on the object attribute data under the condition that the resource type of the target resource is determined to be a non-standard resource type; and
and determining target resource data matched with the nonstandard resource type based on the object resource data set and the target basic average resource data subset under the condition that the qualification recognition result is determined to be used for representing that the target object enjoys the resources of the nonstandard resource type.
11. The method of claim 10, wherein the determining target resource data that matches the non-standard resource type based on the set of object resource data and the subset of target underlying average resource data comprises:
determining non-standard total resource data based on the object resource data set and the target underlying average resource data subset;
determining a target consumption duration based on the object attribute data and the target period; and
and determining the target resource data matched with the nonstandard resource type based on the nonstandard total resource and the target consumption duration.
12. The method of any one of claims 1 to 11, further comprising:
In the case that the resource type of the target resource is determined to comprise a transitional resource type, determining a transitional period based on a resource implementation time point and a starting time point in the object attribute data;
determining target base average resource sub-data based on the target base average resource data sub-set if the transition period is determined to be greater than a predetermined threshold; and
and obtaining target resource data matched with the transitional resource type based on the target basic average resource sub-data and the transitional period.
13. The method of claim 8, further comprising:
determining an initial resource type of the target resource based on the evaluation request; and
the resource type of the target resource is determined based on the object attribute data and the initial resource type.
14. The method of any of claims 1 to 13, wherein the determining a target subset of base resource data from a set of base resource data based on the object attribute data comprises:
the target subset of base resource data is determined from the set of base resource data based on the object attribute data and resource attribute data.
15. The method of any one of claims 1 to 14, further comprising:
acquiring a plurality of initial basic resource data subsets corresponding to a plurality of time points one by one, wherein the initial basic resource data subsets comprise a plurality of initial basic resource sub-data at the same time point;
obtaining basic resource sub-data based on the plurality of initial basic resource sub-data of the time point;
obtaining a basic resource data subset based on a plurality of basic resource sub-data which are in one-to-one correspondence with the plurality of time points; and
and obtaining the basic resource data set based on a plurality of the basic resource data subsets.
16. A resource assessment apparatus, comprising:
a response module for determining object attribute data and object resource data based on an evaluation request for evaluating a resource in response to receiving the evaluation request from a target object;
a first determining module, configured to determine a target subset of base resource data from a set of base resource data based on the object attribute data;
a second determining module configured to determine an object resource data set based on the object resource data and the target underlying resource data subset; and
And the evaluation module is used for evaluating the target resources related to the target object based on the object resource data set to obtain target resource data.
17. An electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein, the liquid crystal display device comprises a liquid crystal display device,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1 to 15.
18. A non-transitory computer readable storage medium storing computer instructions for causing the computer to perform the method of any one of claims 1 to 15.
19. A computer program product comprising a computer program which, when executed by a processor, implements the method according to any one of claims 1 to 15.
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