CN111311105A - Combined product scoring method, device, equipment and readable storage medium - Google Patents
Combined product scoring method, device, equipment and readable storage medium Download PDFInfo
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Abstract
The invention discloses a method, a device, equipment and a readable storage medium for scoring a combined product, wherein the method comprises the following steps: reading product parameters of each product in a combined product, and determining the product weight of each product according to the product parameters of each product; determining a product score for each product according to the raw data for each product; and acquiring the factor parameter of each product, and determining the score of the combined product according to the product weight of each product, the product score of each product and the factor parameter of each product. The invention can realize re-scoring by modifying the products in the combined product, and the scoring flexibility is good; meanwhile, the score of the determined combined product reflects historical score performance, and the score accuracy is improved.
Description
Technical Field
The invention relates to the technical field of financial technology (Fintech), in particular to a method, a device, equipment and a readable storage medium for grading a combined product.
Background
With the continuous development of financial technology (Fintech), especially internet technology and finance, more and more technologies (such as artificial intelligence, big data analysis, cloud storage and the like) are applied to the financial field, but the financial field also puts higher requirements on various technologies, and for example, the risk possibility of a user owning a product of each company is required to be accurately analyzed according to the data of each company.
Currently, the analysis of the risk occurrence probability generally includes scoring each company product selected by a user according to a scoring mode of an index system, and integrating each company product to obtain an overall score so as to represent the risk occurrence probability. Once the user needs to replace other products, the selection and scoring operation needs to be performed again, and the scoring flexibility is poor.
Disclosure of Invention
The invention mainly aims to provide a combined product scoring method, a combined product scoring device, combined product scoring equipment and a readable storage medium, and aims to solve the technical problem that in the prior art, the flexibility of scoring products of companies is poor.
In order to achieve the above object, the present invention provides a combination product scoring method, comprising the steps of:
reading product parameters of each product in a combined product, and determining the product weight of each product according to the product parameters of each product;
determining a product score for each product according to the raw data for each product;
and acquiring the factor parameter of each product, and determining the score of the combined product according to the product weight of each product, the product score of each product and the factor parameter of each product.
Optionally, the step of determining the score of the combined product according to the product weight of each product, the product score of each product, and the factor parameter of each product comprises:
multiplying the product weight, the product score and the factor parameter of each product to update the product score of each product;
and adding the updated product scores of the products to generate the score of the combined product.
Optionally, the step of determining a product score for each product according to the raw data of each product comprises:
acquiring original data, and screening the original data to obtain product data corresponding to each product;
classifying the product data corresponding to each product according to preset classification factors to generate classification data of each product;
and according to the factor parameters of each product, carrying out grading processing on the classification data of each product to obtain the product grade of each product.
Optionally, the step of screening the original data to obtain product data corresponding to each product includes:
screening the original data according to the association factors of the products to generate initial data of the products;
and screening the initial data of each product according to a preset content factor to generate the product data of each product.
Optionally, the step of scoring the classification data of each product according to the factor parameter of each product to obtain a product score of each product includes:
classifying the products to generate a plurality of product type groups;
and adjusting the product score of each product in the plurality of product type groups according to the group factors of the plurality of product type groups so as to update the product score of each product.
Optionally, the product parameter includes a first parameter value and a second parameter value, and the step of determining the product weight of each product according to the product parameter of each product includes:
multiplying the first parameter value and the second parameter value of each product to generate a third parameter value of each product;
adding the third parameter values of the products to generate a combined parameter value of the combined product;
and respectively making a ratio of the third parameter value of each product to the combined parameter value to generate the product weight of each product.
Optionally, the step of reading the product parameters of each product in the combined product is preceded by:
when a combined editing instruction is received, determining the instruction type of the combined editing instruction;
if the instruction type is a newly-added type, jumping to a newly-added combined interface to newly add the combined product;
if the instruction type is a first modification type, performing combined modification on the combined product, wherein the combined modification comprises operations of combined copying, combined editing and combined deleting;
and if the instruction type is a second modification type, performing product modification on each product in the combined product, wherein the product modification comprises operations of product addition, product parameter modification and product deletion.
Further, to achieve the above object, the present invention also provides a combination product scoring apparatus, including:
the reading module is used for reading the product parameters of all products in the combined product and determining the product weight of each product according to the product parameters of each product;
the determining module is used for determining the product score of each product according to the original data of each product;
and the acquisition module is used for acquiring the factor parameters of the products and determining the score of the combined product according to the product weight of the products, the product score of the products and the factor parameters of the products.
Further, to achieve the above object, the present invention also provides a combined product scoring apparatus including a memory, a processor, and a combined product scoring program stored on the memory and operable on the processor, wherein when executed by the processor, the combined product scoring program implements the steps of the combined product scoring method as described above.
Further, to achieve the above object, the present invention also provides a readable storage medium having stored thereon a combination product scoring program, which when executed by a processor, implements the steps of the combination product scoring method as described above.
The scoring method of the combined product of the invention forms the product selected by the user into the combined product and sets a combined scoring mechanism; reading product parameters of products in the combined product, and determining the product weight of each product according to the product parameters of each product; obtaining the product score of each product according to the original data of each product, and obtaining the factor parameter of each product; determining the grade of the combined product according to the product weight, the product grade and the factor parameter of each product; therefore, the overall score of the product selected by the user is obtained, and the risk of the product held by the user is represented. If the user has the requirement of replacing other products for scoring, products in the combined product can be modified to realize re-scoring, and the scoring flexibility is good.
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FIG. 1 is a schematic structural diagram of a hardware operating environment of a device according to an embodiment of the combined product scoring device of the present invention;
FIG. 2 is a schematic flow chart of a first embodiment of the combined product scoring method of the present invention;
FIG. 3 is a schematic flow chart of the method for determining the product score of each product according to the second embodiment of the combined product scoring method of the present invention;
FIG. 4 is a functional block diagram of a preferred embodiment of the combined product scoring device of the present invention.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The invention provides a combined product scoring device, and referring to fig. 1, fig. 1 is a schematic structural diagram of a device hardware operating environment related to an embodiment of the combined product scoring device of the invention.
As shown in fig. 1, the combination product scoring apparatus may include: a processor 1001, such as a CPU, a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. Wherein a communication bus 1002 is used to enable connective communication between these components. The user interface 1003 may include a Display screen (Display), an input unit such as a Keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface, a wireless interface. The network interface 1004 may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface). The memory 1005 may be a high-speed RAM memory or a non-volatile memory (e.g., a magnetic disk memory). The memory 1005 may alternatively be a memory device separate from the processor 1001 described above.
Those skilled in the art will appreciate that the hardware configuration of the combination product scoring device shown in fig. 1 does not constitute a limitation of the combination product scoring device and may include more or fewer components than shown, or some components in combination, or a different arrangement of components.
As shown in fig. 1, a memory 1005, which is a readable storage medium, may include therein an operating system, a network communication module, a user interface module, and a combination product scoring program. The operating system is a program for managing and controlling the combined product scoring equipment and software resources, and supports the operation of a network communication module, a user interface module, a combined product scoring program and other programs or software; the network communication module is used to manage and control the network interface 1004; the user interface module is used to manage and control the user interface 1003.
In the hardware structure of the combined product scoring device shown in fig. 1, the network interface 1004 is mainly used for connecting with a background server and performing data communication with the background server; the user interface 1003 is mainly used for connecting a client (user side) and performing data communication with the client; the processor 1001 may invoke the combined product scoring program stored in the memory 1005 and perform the following operations:
reading product parameters of each product in a combined product, and determining the product weight of each product according to the product parameters of each product;
determining a product score for each product according to the raw data for each product;
and acquiring the factor parameter of each product, and determining the score of the combined product according to the product weight of each product, the product score of each product and the factor parameter of each product.
Further, the step of determining the score of the combination product according to the product weight of each of the products, the product score of each of the products, and the factor parameter of each of the products comprises:
multiplying the product weight, the product score and the factor parameter of each product to update the product score of each product;
and adding the updated product scores of the products to generate the score of the combined product.
Further, the step of determining a product score for each of the products based on the raw data for each of the products comprises:
acquiring original data, and screening the original data to obtain product data corresponding to each product;
classifying the product data corresponding to each product according to preset classification factors to generate classification data of each product;
and according to the factor parameters of each product, carrying out grading processing on the classification data of each product to obtain the product grade of each product.
Further, the step of screening the original data to obtain product data corresponding to each of the products includes:
screening the original data according to the association factors of the products to generate initial data of the products;
and screening the initial data of each product according to a preset content factor to generate the product data of each product.
Further, after the step of performing a scoring process on the classification data of each product according to the factor parameter of each product to obtain a product score of each product, the processor 1001 may call a combined product scoring program stored in the memory 1005, and perform the following operations:
classifying the products to generate a plurality of product type groups;
and adjusting the product score of each product in the plurality of product type groups according to the group factors of the plurality of product type groups so as to update the product score of each product.
Further, the product parameters include a first parameter value and a second parameter value, and the step of determining the product weight of each product according to the product parameters of each product includes:
multiplying the first parameter value and the second parameter value of each product to generate a third parameter value of each product;
adding the third parameter values of the products to generate a combined parameter value of the combined product;
and respectively making a ratio of the third parameter value of each product to the combined parameter value to generate the product weight of each product.
Further, before the step of reading the product parameters of each product in the combined product, the processor 1001 may call a combined product scoring program stored in the memory 1005, and perform the following operations:
when a combined editing instruction is received, determining the instruction type of the combined editing instruction;
if the instruction type is a newly-added type, jumping to a newly-added combined interface to newly add the combined product;
if the instruction type is a first modification type, performing combined modification on the combined product, wherein the combined modification comprises operations of combined copying, combined editing and combined deleting;
and if the instruction type is a second modification type, performing product modification on each product in the combined product, wherein the product modification comprises operations of product addition, product parameter modification and product deletion.
The specific implementation of the combined product scoring device of the present invention is substantially the same as the following embodiments of the combined product scoring method, and is not described herein again.
The invention also provides a combined product scoring method.
Referring to fig. 2, fig. 2 is a flowchart illustrating a first embodiment of the method for scoring a combination product according to the present invention.
While a logical order is shown in the flow chart, in some cases, the steps shown or described may be performed in an order different than presented herein. Specifically, the method for scoring a combination product in this embodiment includes:
step S10, reading the product parameters of each product in the combined product, and determining the product weight of each product according to the product parameters of each product;
the combined product scoring method in the embodiment is applied to a server, and is suitable for scoring the combined product formed by user selection through the server, and evaluating the risk of the product selected by the user through the grade. The product is preferably the stock, fund, option and the like issued by each company, and the user selects the stock, fund or option of different companies to form a combined product; the server measures the operation condition of each company through the data of each company, reflects the scores of the products of each company according to the operation condition of each company, and further obtains the scores of the combined products according to the scores of the products in the combined products and the actual conditions of the products. In this embodiment, the operation condition of the company is mainly reflected by an ESG (Environment, Society, and gooenance, environmental, social, and corporate Governance) evaluation system. ESG requires companies to pay attention to environmental protection, travel social responsibility and perfect company governance in development, and the company with good ESG data has stronger risk resistance and is more prone to long-term stable development, so that ESG is gradually an index for reference in investment analysis and decision making. The method mainly comprises environmental indexes such as pollution reduction, energy conservation and green, social indexes such as staff management, supply chain management, customer management and public welfare donation, and related indexes of company management such as commercial morality and information disclosure.
Further, the server provides a scoring system for realizing the ESG evaluation system externally, and meanwhile, the server is in communication connection with terminal equipment such as a computer and a mobile phone with a display screen. After a user of the terminal equipment registers a user account of the scoring system, the user accesses the server through a network link address of the scoring system, and the first page of the scoring system is displayed in a display screen of the terminal equipment. In addition, the server is connected with a database or is provided with a storage unit to store products which can be scored. A user selects a required product from the stored products to form a combined product by operating a home page of the scoring system in a display screen; and editing operations such as addition, modification, deletion and the like can be performed on the combined product through an editing interface corresponding to the combined product. Specifically, the step of reading the product parameters of each product in the combined product is preceded by:
step a1, when a combination editing instruction is received, determining the instruction type of the combination editing instruction;
step a2, if the instruction type is a new type, jumping to a new combination interface to newly add the combination product;
step a3, if the instruction type is a first modification type, performing combined modification on the combined product, wherein the combined modification comprises operations of combined copying, combined editing and combined deleting;
step a4, if the instruction type is a second modification type, performing product modification on each product in the combined product, wherein the product modification comprises operations of product addition, product parameter modification and product deletion.
Furthermore, multiple virtual keys for triggering different types of editing operations are arranged in the editing interface, a user transmits an editing instruction to the server by triggering the virtual keys, and the server uniformly processes the received editing instruction as a combined editing instruction. Different virtual instructions carry different identifiers, after the server receives the combined editing instruction, the identifiers in the combined editing instruction are compared and identified, and the instruction type of the combined editing instruction is determined, so that the editing type required by a user is represented through the instruction type. And if the instruction type is determined to be the new type through comparison, the requirement of the user for adding other combinations is represented, the user jumps to a new combination interface, and the user operates in the new combination interface to realize a new combination product. The newly added combined interface is provided with a search box so that a user can search products required by the user to add the products to form a combined product, and meanwhile, product parameter editing is supported on the added products, such as setting the holding number of the products, setting product names, short names and the like; the product can also be deleted.
Furthermore, if the instruction type is determined to be a first modification type for modifying the combined product through comparison, the combined product is modified according to the modification operation of the first modification type. Wherein the modification operation includes at least a rename operation to modify a name of a combination, a combination copy operation to copy a combination product, a combination delete operation to delete a combination product, a combination edit operation to edit a combination product, and the like. And if the instruction type is determined to be a second modification type used for modifying the products in the combined product through comparison, modifying a certain product in the combined product according to the product modification operation of the second modification type. The product modification operation at least comprises a product addition operation for adding a new product, a product parameter modification operation for modifying product parameters, a product deletion operation for deleting a product and the like. The products and the parameters thereof in the combined product can be changed by different operations of the combined product, the combined product is flexibly adjusted, and the quick scoring of different combined products is realized.
Further, when the combined product is evaluated, product parameters of each product in the combined product are read respectively, and the read product parameters are the holding quantity of each product set by a user. In addition, each product selected by a user in the combined product has a respective determined unit value in the market, and the product weight of each product is determined by combining the holding quantity and the respective unit value of each product in the combined product, so as to represent the importance degree of each product in the combined product and reflect the influence degree of each product on the user risk.
Step S20, determining the product score of each product according to the original data of each product;
understandably, different products originate from different companies, which have different operational aspects, which have different effects on the scores characterizing the risks of the respective products. Therefore, in order to represent the influence of the operation condition of each company on each product, various data such as past and current disclosure data (such as financial report data, mandatory disclosure data, some alternative disclosure data and the like), public opinion data (such as related news, administrative penalty, announcement information and the like of the company), product relation data (such as information of other companies related to the company and own share right structure and the like), product parameters (such as information of share price information of listed companies, market value and the like) and the like of each company are obtained and used as original data of each product, and the product score of each product is obtained by processing each original data through a preset data processing model, so that the risk influence of the operation condition of each company on each company product is reflected.
Step S30, obtaining the factor parameter of each product, and determining the score of the combined product according to the product weight of each product, the product score of each product, and the factor parameter of each product.
Furthermore, the preset data processing model generates different factor parameters of products of each company according to the previous disclosure data, public opinion data, product relation data and product parameters of each company. In the process of scoring the combined products, the factor parameters of each product are acquired, and the score of the combined product is determined by combining the product weight of each product, the product score of each product and the factor parameters of each product. And adjusting the scores of the products through the factor parameters of the products to obtain the scores of the combined products, so that the risk influence degree of the historical operation conditions of the companies on the combined products is reflected. Specifically, the step of determining the score of the combination product according to the product weight of each product, the product score of each product, and the factor parameter of each product includes:
step S31, multiplying the product weight, the product score, and the factor parameter of each product to update the product score of each product;
and step S32, adding the updated product scores of the products to generate the score of the combined product.
Furthermore, the product weight, the product score and the factor parameter of each product are multiplied to obtain a new product score of each product, and the new product scores of each product are added to obtain the score of the combined product. For example, the combination comprises a first product and a second product N, wherein the product weights of the first product and the second product N are w1 and w2 wn, the product scores of the first product and the second product N are s1 and s2 sn, respectively, and the factor parameters of the first product and the second product N are a1 and a2 sn, respectively, so that the generated combined residual score F (w1, w2, s1, s2 wn, sn) a1 w1 s1+ a2 w2 s2+ an sn.
The scoring method of the combined product of the invention forms the product selected by the user into the combined product and sets a combined scoring mechanism; reading product parameters of products in the combined product, and determining the product weight of each product according to the product parameters of each product; obtaining the product score of each product according to the original data of each product, and obtaining the factor parameter of each product; determining the grade of the combined product according to the product weight, the product grade and the factor parameter of each product; therefore, the overall score of the product selected by the user is obtained, and the risk of the product held by the user is represented. If the user has the requirement of replacing other products for scoring, products in the combined product can be modified to realize re-scoring, and the scoring flexibility is good.
Further, in a specific embodiment of a terminal device, the scoring system may be installed in the terminal device in the form of application software, or may be provided for the terminal device in the form of a web page link. For application software, the user launches the home page by triggering their application icon, and for web page links, the home page is launched by entering the web page link into a search engine. The home page has an input box for registration or login for the user to register or login to the scoring system.
Furthermore, after logging in the scoring system, for a user who does not select a product to form a combined product, an entry for generating the combined product is arranged in a home page of the scoring system; the user enters the interface for generating the combined product through the entrance, and selects the required product from the stored products to form the combined product. For a user who has selected products to form a combined product, the editing interface of the combined product can be accessed through an entry for editing the combined product, which is set in the top page of the scoring system. The editing interface at least displays the name of each combined product, the score of each combined product, the editing operation supported by each combined product and a newly added combined virtual key. The name of the combined product supports modification, a new combination virtual key is used for adding a new combination, and the editing operation at least comprises a copying option, an editing option and a deleting option; the copy option is used for realizing that the combination copies the same combination as the existing combination, the edit option is used for modifying each product in the combined products, and the delete option is used for deleting the existing combination.
It should be noted that, triggering an editing option for any combination product in the editing interface of the combination product jumps to the product editing interface of the combination product. At least the name of each product contained in the combined product, the abbreviation of each product, the weight of each product, the holding quantity of each product, the supported editing operation, the search box and the like are displayed in the product editing interface. The name of the product, the abbreviation of the product and the holding data of the product all support modification, the supported editing operation can be a deleting operation of deleting a certain product from the combined product, and the search box can be used for searching other products and adding the products into the combined product so as to update the combined product. Therefore, the combination product and the products in the combination product can be freely changed through the editing interface of the combination product and the product editing interface, the combination product can be flexibly adjusted, and the quick scoring of different combination products can be realized.
Further, based on the first embodiment of the combined product scoring method, the second embodiment of the combined product scoring method is provided.
The second embodiment of the combination product scoring method differs from the first embodiment of the combination product scoring method in that the step of determining a product score for each of the products from the raw data for each of the products comprises:
step S21, acquiring original data, and screening the original data to obtain product data corresponding to each product;
in the embodiment, in the process of determining the product score of each product according to the original data of each product, the disclosure data, public opinion data, product relationship data and product parameters of each company are firstly obtained from the internet as the original data of each product. Since the internet includes data of various companies, there is a possibility that the acquired data is erroneous due to similarity of keywords used for acquisition in the acquisition process. Therefore, in this embodiment, after the original data are acquired, the original data are screened, and data irrelevant to each product in the combined product are removed, so as to obtain product data corresponding to each product. Specifically, the step of screening the original data to obtain product data corresponding to each product includes:
step S211, screening the original data according to the association factor of each product to generate initial data of each product;
step S212, according to a preset content factor, screening the initial data of each product, and generating product data of each product.
Further, a correlation factor is generated for each product in the combined product, and the company from which each product is derived and other companies having correlation with each company are characterized. And screening the data of the source company and other related companies from the original data to serve as initial data of the product. If stock a in the combined product is determined to be M in the source company, and the company M has a share right association relationship with the companies M1 and M2, the companies M, M1 and M2 are formed as the association factors of the stock a, and the data of the companies M, M1 and M2 are screened from the original data as the initial data of the stock a.
In addition, preset content factors are preset aiming at each product in the combined product, and data types having influences on product scores are represented. And screening the initial data of each product, and searching data matched with the preset content factors, namely searching data belonging to the data type as product data of each product. The preset content factor is generally set as data types such as disclosure data, product price, public opinion data, product association relation, and the like. For the stock A, the preset content factors are set as the type of the disclosure data k1, the type of the stock price k2, the type of the public opinion data k3 and the type of the stock right association relation k 3; then, the initial data of the stock a is screened according to the preset content factor, and the data with the data types of K1, K2, K3 and K4 are screened out as the product data of the stock a. And screening the original data of each product through the association factor and the preset content factor respectively to obtain the product data of each product.
Step S22, classifying the product data corresponding to each product according to a preset classification factor to generate classification data of each product;
step S23, scoring the classification data of each product according to the factor parameter of each product, to obtain a product score of each product.
Furthermore, type identifiers representing data types included in the preset content factors are preset to distinguish the data types, for example, the type identifiers of the exposure data, the product price, the public opinion data and the product association relationship are respectively set as f1, f2, f3 and f 4. In the process of screening the initial data to obtain the product data, different types of identifiers are allocated to different types of product data; if one of the initial data is recognized as public opinion data, it is assigned with a type identifier f3, and for the recognized product price, it is assigned with a type identifier f 2. After the initial data of each product is screened and the product data of each product is obtained, each item of data in the product data forming each product carries a type identifier representing the data type of the item.
Furthermore, the type identifier is used as a preset classification factor, the product data of each product is classified separately according to the preset classification factor, and the data with the same type identifier in the product data of each product is divided into certain type of data of the product. And after the product data of each product is independently classified, obtaining the classification data of each product. As for the above stock a and the type identifiers f1, f2, f3 and f4, if the product data of the stock a contains a1, a2, a3, a4, a5 and a6, where a1 and a3 carry the type identifier f4, a2 and a5 carry the type identifier f2, and a4 and a6 carry the type identifier f1, a1 and a3 are divided into the same class, a2 and a5 are divided into the same class, a4 and a6 are divided into the same class, and classification data [ a1, a3], [ a2, a5], [ a4, a6] of the stock a are obtained.
Further, after the products of each product in the combined product are classified and the respective classification data is obtained, the score of each product can be generated according to the factor parameter of each product. Specifically, the data processing model processes the classification data of each product individually, and performs classification evaluation and overall evaluation on the classification data of each product through model parameters to obtain a classification score and an overall score of each product on the classification data of each product. The classification scores are the scores of the products on each classification data, and one classification data corresponds to one score; the overall score is a score that characterizes the overall risk of a product, with one overall score for each product, and multiple classification scores. And then, multiplying the integral score and the classification score of each product by using the factor parameter of each product to obtain a new integral score and a new classification score of each product. The updated overall score and classification score of each product are the product score of each product, and the risk of each product in different types of dimensions, the overall risk and the influence degree of the historical operation condition of a company from which each product is obtained on the risk of each product are reflected.
Understandably, the products in the combined product originate from companies in different industries, which have different risks influenced by market environments. In order to represent the risk between companies of the same type, a mechanism for classifying each product in the combined product according to the type of the company from which the product is sourced and updating the product score according to the risk of the classified industry type is arranged. Specifically, the step of performing scoring processing on the classification data of each product according to the factor parameter of each product to obtain the product score of each product comprises the following steps:
step S24, classifying each product to generate a plurality of product type groups;
step S25, adjusting the product score of each product in the plurality of product type groups according to the group factor of the plurality of product type groups, so as to update the product score of each product.
Classifying products according to the industry type of a company from which each product in the combined product is sourced, the marketing mode of each product or the product type of each product, dividing the products of the companies with the same industry, the products with the same marketing mode or the products with the same product type into the same type, and forming a plurality of product type groups after the division of each product in the combined product is completed. Wherein products in the same product type group all originate from the same industry, have the same marketing patterns, or have the same product types, and products in different product type groups originate from different industries, have different marketing patterns, or have different product types.
Further, in order to represent the risk of each industry, generating an influence factor for representing the risk of each industry according to the influence degree of the market on each industry, and taking the influence factor as a group factor of a product type group; and determining the group factors of the product type groups according to the industries corresponding to the product type groups. And then, the product scores of the products in the product type groups are adjusted through the group factors of the product type groups, so that the risk of products from different companies in the same industry is reflected. Specifically, for each product type group, the respective group factor is multiplied by the product score of the respective product, so as to obtain a new product score of each product. And then the scores of all products in the same product type group are arranged in a sorting, normal distribution, median absolute value and other modes, and the risk difference of products from different companies in the same industry is reflected by the arrangement sequence. It should be noted that, since the product score includes an integral score and a classification score, the integral score and the classification score are multiplied by a group factor respectively during the multiplication, so as to ensure the accuracy and completeness of the update of the product score.
It should be noted that, please refer to fig. 3 for the process of obtaining the product score of each product by screening, classifying and updating the raw data of each product. And, in the process of determining the score of the combination product, it can be performed according to the classification score and the overall score, respectively. Calculating the classification scores of the same classification of each product in the combined product to obtain the scores of the combined product in different classifications; and calculating the integral scores of all products in the combined product to obtain the integral scores of the combined product. The risk of the combined product formed by the user selected products in each classification dimension and the risk of the combination product as a whole are embodied by the scores on the different classifications and the overall score.
In the process of generating the product scores of the products according to the original data of the products, the data accuracy for generating the product scores of the products is ensured through screening of the association factors and the preset content factors. Meanwhile, the product data of each product is divided into each classification data, and the factor parameters of each product are used for grading the classification data, so that the risk of each product can be reflected from multiple dimensions by combining the historical operation condition of a company. In addition, all products in the combined product are classified into product type groups according to industries of companies from which the products are respectively sourced, and product scores in all product classification groups are adjusted by using group factors representing industry risks, so that comparison of product risks of all companies in the same industry is realized. The risk of the product can be reflected from multiple dimensions of the product and dimensions of different industries on the whole, so that the risk can be reflected more accurately and comprehensively by the product grade of each product.
Further, a third embodiment of the scoring method for a combination product of the present invention is proposed based on the first or second embodiment of the scoring method for a combination product of the present invention.
The third embodiment of the method for scoring a combination product differs from the first or second embodiment of the method for scoring a combination product in that the product parameters include a first parameter value and a second parameter value, and the step of determining the product weight for each of the products based on the product parameters for each of the products comprises:
step S11, multiplying the first parameter value and the second parameter value of each product to generate a third parameter value of each product;
step S12, the third parameter values of the products are added to generate a combined parameter value of the combined product;
step S13, comparing the third parameter value of each product with the combined parameter value to generate a product weight of each product.
The product parameters in this embodiment include a first parameter value and a second parameter value, where the first parameter value is the holding quantity of each product set by the user, and the second parameter value is the unit price of each product in the current market. And in the process of determining the product weight of each product, multiplying the first parameter value and the second parameter value of each product to obtain a third parameter value of each product, wherein each third parameter value represents the value of each product held by the user. And adding the third parameter values to generate a combined parameter value of the combined product, and representing the total price of the combined product. And then, the third parameter values of all the products are respectively compared with the combined parameter values to obtain a ratio result, namely the ratio of the value of each product to the total price of the combined product. And taking the ratio of each product as the product weight of each product to represent the importance degree of each product in the combined product.
It should be noted that, the user can set the first parameter value of each product according to the requirement, and the product value of each product and the total price of the combined product change with the difference of the first parameter value of each product, so as to reflect the risk change condition of the user holding different numbers of products, and facilitate the user to refer to and select different products and numbers thereof to form the combined product.
The invention also provides a combined product scoring device.
Referring to fig. 4, fig. 4 is a functional module diagram of a first embodiment of the combined product scoring device according to the present invention. The combination product scoring device comprises:
a generating module 10, configured to read a module, configured to read product parameters of each product in a combined product, and determine a product weight of each product according to the product parameters of each product;
a determining module 20, configured to determine a product score of each product according to the raw data of each product;
an obtaining module 30, configured to obtain the factor parameter of each product, and determine the score of the combined product according to the product weight of each product, the product score of each product, and the factor parameter of each product.
Further, the obtaining module 30 further includes:
an updating unit, configured to multiply the product weight, the product score, and the factor parameter of each product to update the product score of each product;
and the first generation unit is used for summing the updated product scores of the products to generate the score of the combined product.
Further, the determining module 20 further includes:
the screening unit is used for acquiring original data and screening the original data to obtain product data corresponding to each product;
the first classification unit is used for classifying the product data corresponding to each product according to a preset classification factor to generate classification data of each product;
and the processing unit is used for carrying out grading processing on the classification data of each product according to the factor parameter of each product to obtain the product grade of each product.
Further, the screening unit is further configured to:
screening the original data according to the association factors of the products to generate initial data of the products;
and screening the initial data of each product according to a preset content factor to generate the product data of each product.
Further, the determining module 20 further includes:
the second classification unit is used for classifying the products to generate a plurality of product type groups;
and the adjusting unit is used for adjusting the product scores of the products in the product type groups according to the group factors of the product type groups so as to update the product scores of the products.
Further, the product parameter includes a first parameter value and a second parameter value, and the reading module 10 further includes:
a second generation unit, configured to multiply the first parameter value and the second parameter value of each product to generate a third parameter value of each product;
a third generating unit, configured to add the third parameter values of the products to generate a combined parameter value of the combined product;
and the fourth generating unit is used for making a ratio of the third parameter value of each product to the combined parameter value to generate the product weight of each product.
Further, the combination product scoring apparatus further includes:
the device comprises a determining unit, a judging unit and a judging unit, wherein the determining unit is used for determining the instruction type of a combined editing instruction when the combined editing instruction is received;
the skip unit is used for skipping to a newly-added combined interface to newly add the combined product if the instruction type is the newly-added type;
the first modification unit is used for performing combined modification on the combined product if the instruction type is a first modification type, wherein the combined modification comprises operations of combined copying, combined editing and combined deleting;
and the second modification unit is used for modifying products of the combined product if the instruction type is a second modification type, wherein the product modification comprises operations of product addition, product parameter modification and product deletion.
The specific implementation of the combined product scoring device of the present invention is substantially the same as the above-mentioned embodiments of the combined product scoring method, and is not described herein again.
In addition, the embodiment of the invention also provides a readable storage medium.
The readable storage medium has stored thereon a combination product scoring program which, when executed by the processor, implements the steps of the combination product scoring method as described above.
The readable storage medium of the present invention may be a computer readable storage medium, and the specific implementation manner thereof is substantially the same as that of each embodiment of the above-mentioned combined product scoring method, and will not be described herein again.
The present invention is described in connection with the accompanying drawings, but the present invention is not limited to the above embodiments, which are only illustrative and not restrictive, and those skilled in the art can make various changes without departing from the spirit and scope of the invention as defined by the appended claims, and all changes that come within the meaning and range of equivalency of the specification and drawings that are obvious from the description and the attached claims are intended to be embraced therein.
Claims (10)
1. A method for scoring a combination product, the method comprising the steps of:
reading product parameters of each product in a combined product, and determining the product weight of each product according to the product parameters of each product;
determining a product score for each product according to the raw data for each product;
and acquiring the factor parameter of each product, and determining the score of the combined product according to the product weight of each product, the product score of each product and the factor parameter of each product.
2. The combination product scoring method of claim 1, wherein said step of determining a score for said combination product based on said product weight for each of said products, said product score for each of said products, and said factor parameter for each of said products comprises:
multiplying the product weight, the product score and the factor parameter of each product to update the product score of each product;
and adding the updated product scores of the products to generate the score of the combined product.
3. The combination product scoring method of claim 1, wherein said step of determining a product score for each of said products based on raw data for each of said products comprises:
acquiring original data, and screening the original data to obtain product data corresponding to each product;
classifying the product data corresponding to each product according to preset classification factors to generate classification data of each product;
and according to the factor parameters of each product, carrying out grading processing on the classification data of each product to obtain the product grade of each product.
4. The combination product scoring method of claim 3, wherein said step of screening said raw data to obtain product data corresponding to each of said products comprises:
screening the original data according to the association factors of the products to generate initial data of the products;
and screening the initial data of each product according to a preset content factor to generate the product data of each product.
5. The combination product scoring method of claim 3, wherein said step of scoring said classification data of each of said products according to said factor parameter of each of said products to obtain a product score for each of said products is followed by the step of:
classifying the products to generate a plurality of product type groups;
and adjusting the product score of each product in the plurality of product type groups according to the group factors of the plurality of product type groups so as to update the product score of each product.
6. A method of scoring a combination product according to any one of claims 1 to 5, wherein the product parameters include a first parameter value and a second parameter value, and wherein determining the product weight for each product based on the product parameters for each product comprises:
multiplying the first parameter value and the second parameter value of each product to generate a third parameter value of each product;
adding the third parameter values of the products to generate a combined parameter value of the combined product;
and respectively making a ratio of the third parameter value of each product to the combined parameter value to generate the product weight of each product.
7. A method of scoring a combination product according to any one of claims 1 to 5, wherein the step of reading product parameters for each product in the combination product is preceded by:
when a combined editing instruction is received, determining the instruction type of the combined editing instruction;
if the instruction type is a newly-added type, jumping to a newly-added combined interface to newly add the combined product;
if the instruction type is a first modification type, performing combined modification on the combined product, wherein the combined modification comprises operations of combined copying, combined editing and combined deleting;
and if the instruction type is a second modification type, performing product modification on each product in the combined product, wherein the product modification comprises operations of product addition, product parameter modification and product deletion.
8. A combination product scoring device, the combination product scoring device comprising:
the reading module is used for reading the product parameters of all products in the combined product and determining the product weight of each product according to the product parameters of each product;
the determining module is used for determining the product score of each product according to the original data of each product;
and the acquisition module is used for acquiring the factor parameters of the products and determining the score of the combined product according to the product weight of the products, the product score of the products and the factor parameters of the products.
9. A combination product scoring device comprising a memory, a processor, and a combination product scoring program stored on the memory and executable on the processor, the combination product scoring program when executed by the processor implementing the steps of the combination product scoring method of any one of claims 1-7.
10. A readable storage medium having stored thereon a combination product scoring program which, when executed by a processor, implements the steps of the combination product scoring method according to any one of claims 1-7.
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