CN115049237A

CN115049237A - Evaluation processing method and device for product resources, electronic equipment and storage medium

Info

Publication number: CN115049237A
Application number: CN202210631843.6A
Authority: CN
Inventors: 马源; 赵惠玲; 茹语嫣
Original assignee: Shenzhen Futu Network Technology Co Ltd
Current assignee: Shenzhen Futu Network Technology Co Ltd
Priority date: 2022-06-02
Filing date: 2022-06-02
Publication date: 2022-09-13

Abstract

The embodiment of the application discloses a method and a device for evaluating and processing product resources, electronic equipment and a storage medium, wherein the method comprises the following steps: if the touch operation of entering the evaluation page corresponding to the specified product resource is detected, acquiring first multi-dimensional feature data of the specified product resource and second multi-dimensional feature data of a reference product resource; calculating the similarity between the designated product resource and the reference product resource based on the first multi-dimensional feature data and the second multi-dimensional feature data; calculating a comprehensive index value of the designated product resource based on the multi-dimensional index value and the similarity of the designated product resource; and determining a file corresponding to the comprehensive index value of the specified product resource, and displaying the file in an evaluation page. The technical scheme of the embodiment of the application can improve the accuracy of calculating the index value of the specified product resource.

Description

Evaluation processing method and device for product resources, electronic equipment and storage medium

Technical Field

The present application relates to the field of computer technologies, and in particular, to a method and an apparatus for evaluating and processing product resources, an electronic device, and a computer-readable storage medium.

Background

Valuation of product resources is an important reference criterion for investors in conducting ground-based analyses. At present, the estimation of the product resources mainly adopts a relative estimation method to calculate the corresponding estimation, in the relative estimation method, the product resources to be estimated are compared with a plurality of other product resources, and if the value is lower than the average value of the corresponding index values of a comparison system, the product resources to be estimated are considered to be underestimated. However, in this way, the determination of other product resources and the determination of the average value of the index values are rough, resulting in a low accuracy of the final estimate of the product resource to be estimated.

Disclosure of Invention

In order to solve the above technical problem, embodiments of the present application provide a method and an apparatus for evaluating and processing a product resource, an electronic device, and a computer-readable storage medium, which can provide accuracy of an estimate of a product resource to be estimated.

Other features and advantages of the present application will be apparent from the following detailed description, or may be learned by practice of the application.

According to an aspect of an embodiment of the present application, there is provided a method for evaluating and processing a product resource, including:

if the touch operation of entering the evaluation page corresponding to the specified product resource is detected, acquiring first multi-dimensional feature data of the specified product resource and second multi-dimensional feature data of a reference product resource;

calculating the similarity between the designated product resource and the reference product resource based on the first multi-dimensional feature data and the second multi-dimensional feature data;

calculating a comprehensive index value of the specified product resource based on the multi-dimensional index value and the similarity of the specified product resource;

and determining a file corresponding to the comprehensive index value of the specified product resource, and displaying the file in an evaluation page.

According to an aspect of an embodiment of the present application, there is provided an evaluation processing apparatus for a product resource, including:

the acquisition module is configured to acquire first multi-dimensional feature data of the specified product resource and second multi-dimensional feature data of the reference product resource if touch operation entering an evaluation page corresponding to the specified product resource is detected;

the first calculation module is configured to calculate the similarity between the specified product resource and the reference product resource based on the first multi-dimensional feature data and the second multi-dimensional feature data;

the second calculation module is configured to calculate a comprehensive index value of the specified product resource based on the multi-dimensional index value and the similarity of the specified product resource;

and the display module is configured to determine a file corresponding to the comprehensive index value of the specified product resource and display the file on the evaluation page.

According to an aspect of an embodiment of the present application, there is provided an electronic device including: one or more processors; and a storage device for storing one or more programs, which when executed by the one or more processors, cause the electronic apparatus to implement the aforementioned evaluation processing method of the product resource.

According to an aspect of embodiments of the present application, there is provided a computer-readable storage medium having stored thereon computer-readable instructions which, when executed by a processor of a computer, cause the computer to execute the above evaluation processing method of a product resource.

According to an aspect of embodiments herein, there is provided a computer program product or computer program comprising computer instructions stored in a computer readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the evaluation processing method of the product resource provided in the above-described various alternative embodiments.

In the technical solution provided in the embodiment of the present application:

on one hand, the similarity between the designated product resource and the reference product resource is calculated, wherein the similarity measures the similarity between the reference product resource and the designated product resource, so that the comprehensive index value of the designated product resource can be calculated based on the reference product resource with emphasis, the accuracy of the calculated comprehensive index value of the designated product resource is higher, correspondingly, the file corresponding to the comprehensive index value is more accurate, and the user can know the designated product resource by checking the comprehensive index value and the corresponding file;

on one hand, when the similarity between the specified product resource and the reference product resource is calculated, the characteristic data (such as the total market value, the daily handover rate and the like) of a plurality of dimensions are comprehensively considered, so that the evaluation value of the specified product resource can be more suitable for the condition of the specified product resource;

on one hand, when the similarity of the designated product resource and the reference product resource is calculated, index data of multiple dimensions (such as a first index value related to the price and the profit of the product resource, a second index value related to the price and the net asset of the product resource, and a third index value related to the price and the income of the product resource) are comprehensively considered, so that the comprehensive index value is calculated through the dimension index values of the multiple dimensions, the industrial characteristics are better utilized to carry out emphasis on the multiple dimension index values, and the calculated comprehensive dimension value can more accurately reflect the condition of the designated product resource.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the application.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and together with the description, serve to explain the principles of the application. It is obvious that the drawings in the following description are only some embodiments of the application, and that for a person skilled in the art, other drawings can be derived from them without inventive effort. In the drawings:

FIG. 1 is a schematic illustration of an implementation environment to which the present application relates;

FIG. 2 is a flow chart of a method of evaluating a product resource in accordance with the present application;

FIG. 3 is a flowchart of step S210 in one embodiment to which the present application relates;

FIG. 4 is a flowchart of step S220 in one embodiment to which the present application relates;

FIG. 5 is a flowchart of step S420 in one embodiment to which the present application relates;

FIG. 6 is a flowchart of step S430 in one embodiment to which the present application relates;

FIG. 7 is a flowchart of step S230 in one embodiment to which the present application relates;

FIG. 8 is a schematic diagram of an evaluation page for a specified product resource to which the present application relates;

FIG. 9 is a diagram of a detail page specifying a first index value for a product resource to which the present application relates;

FIG. 10 is a flowchart of calculating a similarity between a specified product resource and a reference product resource in a method of evaluating a product resource according to an embodiment of the present disclosure;

FIG. 11 is a flowchart of calculating a composite indicator value for a given product resource in the evaluation processing method for the product resource according to an embodiment of the present application;

FIG. 12 is a block diagram of an evaluation processing device for a product resource according to the present application;

FIG. 13 illustrates a schematic structural diagram of a computer system suitable for use in implementing the electronic device of an embodiment of the present application.

Detailed Description

Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.

The block diagrams shown in the figures are functional entities only and do not necessarily correspond to physically separate entities. I.e. these functional entities may be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor means and/or microcontroller means.

The flowcharts shown in the figures are illustrative only and do not necessarily include all of the contents and operations/steps, nor do they necessarily have to be performed in the order described. For example, some operations/steps may be decomposed, and some operations/steps may be combined or partially combined, so that the actual execution sequence may be changed according to the actual situation.

It should also be noted that: reference to "a plurality" in this application means two or more. "and/or" describe the association relationship of the associated objects, meaning that there may be three relationships, e.g., A and/or B may mean: a exists alone, A and B exist simultaneously, and B exists alone. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship.

The evaluation processing method of product resources provided by the embodiment of the application relates to the field of Artificial Intelligence (AI), which is a theory, method, technology and application system for simulating, extending and expanding human Intelligence, sensing environment, acquiring knowledge and obtaining optimal results by using knowledge by using a digital computer or a machine controlled by the digital computer. In other words, artificial intelligence is a comprehensive technique of computer science that attempts to understand the essence of intelligence and produce a new intelligent machine that can react in a manner similar to human intelligence. Artificial intelligence is the research of the design principle and the implementation method of various intelligent machines, so that the machines have the functions of perception, reasoning and decision making.

Referring to fig. 1, fig. 1 is a schematic diagram of an implementation environment related to the present application. The implementation environment includes a terminal 110 and a server 120, and the terminal 110 and the server 120 communicate with each other through a wired or wireless network.

The server 120 stores corresponding data of product resources, the terminal 110 runs a related application program for displaying the product resources, and a user can check related information of the product resources in the application program, wherein the terminal 110 may be any electronic device capable of running a video playing client, such as a smart phone, a tablet, a notebook computer, and a computer, the server 120 may be an independent physical server, may also be a server cluster or a distributed system formed by a plurality of physical servers, and may also be a cloud server providing basic cloud computing services, such as a cloud service, a cloud database, cloud computing, a cloud function, cloud storage, a Network service, cloud communication, middleware service, a domain name service, a security service, a CDN (Content Delivery Network), a big data and an artificial intelligence platform, and the like, which is not limited herein.

Product resources described in this application include, but are not limited to, virtual product resources such as stocks, futures, options, securities, virtual currency, funds or foreign exchange, and the like.

FIG. 2 is a flow diagram illustrating a method for evaluation processing of a product resource in accordance with an exemplary embodiment. The method for evaluating and processing the product resource can be applied to the implementation environment shown in fig. 1, and is specifically executed by the terminal 110 in the embodiment environment shown in fig. 1.

As shown in fig. 2, in an exemplary embodiment, the method for evaluating and processing product resources may include steps S210 to S240, which are described in detail as follows:

step S210, if a touch operation entering an evaluation page corresponding to a specified product resource is detected, acquiring first multidimensional feature data of the specified product resource and second multidimensional feature data of a reference product resource.

In the embodiment of the application, each product resource is correspondingly provided with an evaluation page, the evaluation page is accessed by clicking a corresponding control, the first multidimensional feature data of the specified product resource and the second multidimensional feature data of the reference product resource are correspondingly obtained, the reference product resource and the specified product resource are in the same industry, and the specified product resource and the reference product resource comprise but not limited to stocks corresponding to a stock in harbor stocks, U.S. stocks and Shanghai-Shenzhong.

On the evaluation page, a data chart of the specified product resource in a certain time period is displayed, and meanwhile, the data chart supports selection of the displayed time period, such as a data chart of the specified product resource in the last 3 months, a data chart of the last year and the like. In one embodiment, the time period of the product resources in the hong Kong and Shanghai markets is selected differently from the time period of the product resources in the Mei Guo markets, specifically, the time period of the product resources in the hong Kong and Shanghai markets includes approximately 3 months, approximately 1 year, approximately 3 years, approximately 5 years and approximately 10 years, and the time period of the product resources in the Mei Guo markets includes approximately 3 months, approximately 6 months, approximately 1 year and approximately 2 years.

The first multidimensional feature data and the second multidimensional feature data comprise but are not limited to various types of total market value, daily handover rate, net profit rate (TTM), ROE (Return On profit, net asset profitability) TTM, EPS (Earnings Perre, surplus Per stock) ShaTTM and composite growth rate; wherein the total market value represents an average value of market values of the product resource in the last year, the daily average hand-exchange rate represents a daily average hand-exchange rate of the product resource in the last year, the net profit TTM represents the net profit/business income of the product resource in the last 12 months, the ROTTM represents the equity profit/business income of the product resource in the last 12 months, the net profit/business end total equity of the product resource in the last 12 months, and the compound growth rate represents a compound growth rate of the business income of the product resource in the last three years; when the various feature data are calculated according to the specified product resources, first multi-dimensional feature data are obtained, and when the various feature data are calculated according to the related data of the reference product resources, second multi-dimensional feature data are obtained.

In an embodiment of the present application, referring to fig. 3, the step S210 of obtaining the first multidimensional feature data of the specified product resource and the second multidimensional feature data of the reference product resource includes steps S310 to S340, which are described in detail as follows:

step S310, acquiring first multi-dimensional feature data of a specified product resource; and the number of the first and second groups,

in the embodiment of the application, after the touch operation of entering the evaluation page corresponding to the specified product resource is detected, first multi-dimensional feature data of the specified product resource is obtained, the first multi-dimensional feature data can be pre-calculated according to a certain updating frequency and stored in the server, when the first multi-dimensional feature data is needed, the first multi-dimensional feature data can be directly called from the server, when the first multi-dimensional feature data is needed to be updated, corresponding data is obtained according to corresponding calculation rules to carry out calculation, for example, the first multi-dimensional feature data is calculated by obtaining corresponding data of the specified product resource in the last 3 months, the last 1 year, the last 3 years, the last 5 years or the last 10 years, and the first multi-dimensional feature data stored in the server is replaced by the recalculated first multi-dimensional feature data.

Step S320, determining the type of the specified product resource;

in the embodiment of the application, the type of the specified product resource is determined, namely the industry of the specified product resource is determined. In the embodiment, industry of the product resources can be divided in advance by adopting a Shenten thousand classification standard, and the industry where each product resource is located is determined. In other embodiments, the classification criteria for product resources may be more refined as needed.

Step S330, acquiring product resources matched with the type of the specified product resources from the plurality of product resources, and taking the acquired product resources as reference product resources;

in the embodiment of the application, a product resource in the same industry as a specified product resource is obtained from a plurality of product resources and is used as a reference product resource of the specified product resource, and the reference product resource may include a plurality of reference product resources.

Step S340, obtain second multidimensional feature data of the reference product resource.

In the embodiment of the present application, the second multidimensional feature data of the reference product resource is obtained, and the second multidimensional feature data of the reference product resource may be processed in the manner described in step S310, and is directly called from the server when needed, without performing corresponding calculation each time.

It should be noted that step S310 and steps S320 to S340 may be executed in parallel, or may be executed in any order.

Step S220, calculating a similarity between the designated product resource and the reference product resource based on the first multidimensional feature data and the second multidimensional feature data.

In the embodiment of the application, the similarity between the specified product resource and each reference product resource is calculated according to the first multi-dimensional feature data and the second multi-dimensional feature data, the similarity between the specified product resource and each reference product resource is characterized through the similarity, and the higher the similarity is, the more similar the similarity is.

In an embodiment of the present application, referring to fig. 4, in step S220, calculating a similarity between the specified product resource and the reference product resource based on the first multidimensional feature data and the second multidimensional feature data includes steps S410 to S440, which are described in detail as follows:

step S410, the first multi-dimensional feature data is standardized to obtain first multi-dimensional standard feature data, and the second multi-dimensional feature data is standardized to obtain second multi-dimensional standard feature data.

In the embodiment of the application, the first multi-dimensional feature data and the second multi-dimensional feature data are respectively subjected to standardization processing, and meanwhile, extreme values in the first multi-dimensional feature data and the second multi-dimensional feature data are removed to obtain corresponding first multi-dimensional standard feature data and second multi-dimensional standard feature data.

Step S420, calculating an integral distance standard value between standard feature data of the same dimensionality based on the first multi-dimensional standard feature data and the second multi-dimensional standard feature data.

In the embodiment of the application, the first multidimensional standard feature data and the second multidimensional standard feature data correspond to each other, that is, have the same dimensionality, and the overall distance standard value corresponding to each dimensionality is calculated according to the first multidimensional standard feature data and the second multidimensional standard feature data, wherein the overall distance standard value can represent the average level of the specified product resources and the reference product resources and is not affected by the extreme values of the standard feature data in the specified product resources and the reference product resources.

Step S430, calculating a target distance value between the designated product resource and the reference product resource based on the overall distance standard value, the first multi-dimensional standard feature data and the second multi-dimensional standard feature data.

In the embodiment of the application, a target distance value between the specified product resource and the reference product resource is calculated according to the overall distance standard value, the first multi-dimensional standard feature data and the second multi-dimensional standard feature data, the target distance value represents the similarity degree between the specified product resource and the reference product resource to a certain extent, and the larger the target distance value is, the smaller the similarity degree is.

Step S440, calculating the similarity between the designated product resource and the reference product resource based on the target distance value.

In the embodiment of the application, the similarity and the target distance value are in a reverse relation, a corresponding transformation function is arranged, and the similarity between the designated product resource and the reference product resource can be calculated by inputting the target distance value into the transformation function.

In an embodiment of the present application, referring to fig. 5, in step S420, based on the first multidimensional standard feature data and the second multidimensional standard feature data, an overall distance standard value between standard feature data of the same dimension is calculated, which includes step S510 and step S520, and the following details are introduced as follows:

step S510, performing difference calculation on the standard feature data with the same dimension between the first multidimensional standard feature data and the second multidimensional standard feature data, respectively, to obtain a difference value for a plurality of standard feature data with the same dimension.

In the embodiment of the application, product resources and each reference product resource are specified, difference calculation is performed under the same dimension between first multi-dimensional standard feature data and second multi-dimensional standard feature data, the first multi-dimensional standard feature data and the second multi-dimensional standard feature data comprise six standard feature data of total market value, daily hand-changing rate, net profit rate TTM, ROE TTM, EPS TTM and composite growth rate (revenue), and in the dimension of the total market value, the first multi-dimensional standard feature data and the second multi-dimensional standard feature data respectivelyCalculating the absolute value of the difference between every two of the 6 product resources, and calculating the total market value on the dimension of total market value when n product resources are appointed and reference product resources are added

And calculating the difference of the standard characteristic data on the remaining five dimensions in the same way.

Step S520, determining a target difference value from the differences of the plurality of standard characteristic data according to a median determination rule, and taking the target difference value as an integral distance standard value of the corresponding dimension.

In the embodiment of the application, in each dimension, the corresponding dimension is

The method comprises the steps of sorting according to the sequence from small to large, determining corresponding medians according to the sorting, taking the medians as target difference values of corresponding dimensions, further taking the target difference values as overall distance standard values, determining an overall distance standard value for each dimension, calculating the specified product resources and each reference product resource, and calculating the overall distance standard value in each dimension.

In an embodiment of the present application, referring to fig. 6, in step S430, a target distance value between a designated product resource and a reference product resource is calculated based on the overall distance standard value, the first multidimensional standard feature data and the second multidimensional standard feature data, including steps S610 to S630, which are described in detail as follows:

step S610, calculating the Manhattan distance between the standard feature data with the same dimension according to the first multi-dimension standard feature data and the second multi-dimension standard feature data.

In the embodiment of the application, the Manhattan distance between the designated product resource and each reference product resource is calculated according to the first multi-dimensional standard feature data and the second multi-dimensional standard feature data, and when each designated product resource has n-1 reference product resources and m dimensions of feature data, the (n-1) m Manhattan distances can be calculated.

Step S620, carrying out quotient calculation on the Manhattan distances of the plurality of standard characteristic data and the integral distance standard value of the corresponding dimensionality respectively to obtain the space ratio of the plurality of dimensionalities.

In the embodiment of the present application, between the designated product resource a and the reference product resource B, the manhattan distance of each dimension is divided by the corresponding overall distance standard value, for example, between a and B, in the total market value dimension, the manhattan distance between a and B in the total market value dimension is divided by the overall distance standard value of a and B in the total market value dimension, so as to obtain the distance ratio of a and B in the total market value dimension, and the distance ratio of the designated product resource and each reference product resource in all dimensions is calculated.

Step S630, based on the distance ratio of the multiple dimensions, calculates a target distance value between the designated product resource and the reference product resource.

In the embodiment of the application, the arithmetic mean of the spacing ratios of the designated product resources and the reference product resources in multiple dimensions is calculated, and the arithmetic mean is used as the target distance value of the designated product resources and the reference product resources.

Step S230, calculating a comprehensive index value of the designated product resource based on the multi-dimensional index value and the similarity of the designated product resource.

In the embodiment of the present application, the multi-dimensional index value includes a first index value related to the price and profit of the product resource, a second index value related to the price and net assets of the product resource, and a third index value related to the price and income of the product resource. And calculating the comprehensive index value of the designated product resource according to the multi-dimensional index value and the similarity between the designated product resource and the reference product resource.

In an embodiment of the present application, referring to fig. 7, in step S230, a comprehensive index value of the designated product resource is calculated based on the multidimensional index value and the similarity of the designated product resource, which includes steps S710 to S730, and the detailed description is as follows:

step S710, calculating a first evaluation index value according to the multi-dimensional index value of the designated product resource; and the number of the first and second groups,

in the embodiment of the application, the multi-dimensional index value of the designated product resource is the current multi-dimensional index value of the designated product resource at the current moment, then the historical multi-dimensional index value of the designated product resource in the historical time period is obtained, and the first evaluation index value is calculated according to the current multi-dimensional index value and the historical multi-dimensional index value. Further, the historical time period of the specified product resource can be determined according to the market cycle characteristics of the specified product resource, and if the market situation of the specified product resource in the current 2022 year is determined to be similar to the market situation of the specified product resource in 2015-2016 year after analysis, 2015-2016 year is taken as the historical time period of the specified product resource.

Specifically, the current multidimensional index value includes PE (Price Earnings market rate), PB (Price Book value, average market rate), and PS (Price-to-sales rate) of the specified product resource at the current time, and the historical multidimensional index value includes PE, PB, and PS of the specified product resource at a historical time period, where the historical time period may be one month before the current time. Comparing the PE at the current moment with the PE in the historical time period, and calculating a first evaluation index value in the PE dimension according to the comparison result; then, the PB of the current time is compared with the PB of the historical time period, and a first evaluation index value in the PB dimension is calculated according to the comparison result; the first evaluation index value in the PS dimension is calculated in the same manner. When comparing, the corresponding first evaluation index value can be determined according to the difference value between the index values of the current time and the historical time period.

Step S720, the multi-dimensional index value of the reference product resource and the similarity are subjected to product calculation to obtain a second evaluation index value.

In the embodiment of the present application, the multidimensional index value of the reference product resource is a current multidimensional index value of the reference product resource at the current time. And performing product operation according to the current multi-dimensional index value and the similarity of the reference product resource, and calculating to obtain a second evaluation index value.

In particular, it can be represented by the formula

Calculating a second evaluation index value for each dimension, wherein Q represents the second evaluation index value, x _n Representing the similarity, s, of the reference product resource n to the specified product resource _n And representing the dimension index value corresponding to the reference product resource n.

Taking PE dimension as an example, if a product resource is designated as a, there are 5 reference product resources, which are B, C, D, E, F respectively. B. C, D, E, F have similarities of 0.2, 0.4, 0.8, 0.5, 0.7 with a, and the PEs of the five reference product resources at the current time are 10, 5, 20, 12, 15, respectively, then the second evaluation index value of the product resource in the PE dimension is specified as:

according to the same manner, a second evaluation index value of the designated product resource in the PB dimension and the PS dimension is calculated.

Step S730, calculating a comprehensive index value of the designated product resource based on the first evaluation index value and the second evaluation index value.

In the embodiment of the application, the comprehensive index value of the designated product resource is calculated according to the first evaluation index value and the second evaluation index value. In this embodiment, the first evaluation index value combines the index value of the specified product resource at the current time and the index value of the historical time period, and the second evaluation index value combines the current index value of the industry reference product resource, so that the calculated comprehensive index value comprehensively considers the influence of the specified product resource at the historical level and the industry level, and the calculated comprehensive index value can more accurately reflect the basic situation of the specified product resource.

It should be noted that step S710 and steps S720 to S730 may be executed in parallel, or may be executed in any order.

In an embodiment of the present application, the step S730 of calculating the composite index value of the designated product resource based on the first evaluation index value and the second evaluation index value includes the steps S810 and S820, which are described in detail as follows:

step S810, summing the first evaluation index value and the second evaluation index value of the same dimension to obtain dimension index values of multiple dimensions.

In the embodiment of the application, the first evaluation index value and the second evaluation index value of the same dimension are summed, that is, the first evaluation index value and the second evaluation index value of the PE dimension are added to obtain the dimension index value of the PE dimension, the first evaluation index value and the second evaluation index value of the PB dimension are added to obtain the dimension index value of the PB dimension, and the first evaluation index value and the second evaluation index value of the PS dimension are added to obtain the dimension index value of the PS dimension.

In step S820, a comprehensive index value of the designated product resource is calculated based on the dimension index values of the plurality of dimensions.

In the embodiment of the application, the comprehensive index value is calculated according to the dimension index values of multiple dimensions. Specifically, the three dimensional index values are correspondingly displayed on a detail page of the specified product resource, but when the three dimensional index values are displayed, a dimensional index value is determined in advance as a recommended dimensional index value to be displayed preferentially, the recommended dimensional index value has a first preset weight, and if the dimensional index value of the PE dimension is taken as the recommended dimensional index value, the first preset weight is given to the dimensional index value of the PE dimension; meanwhile, a second preset weight is set, in the same industry, dimension index values of multiple dimensions for each product resource are correspondingly calculated, a recommended dimension index value is correspondingly determined for each product resource, the recommended probability of each dimension index value serving as the recommended dimension index value in the reference product resource of the specified product resource is counted, the second preset weight is given to each dimension index value of the specified product resource according to the recommended probability, and particularly, the second preset weight can be given to each dimension index value of the specified product resource through a formula

Calculating a composite index value, wherein W in the above formula represents the composite index value, ω ₁ Representing a first predetermined weight, ω ₂ Representing a second predetermined weight, P _t Representing a value of a recommended dimension index, P ₁ To P _y The index values of the y dimensions are represented,

to

And representing the recommendation probability corresponding to each of the y dimension index values. If 28 reference product resources are set, and the product resource quantity ratio of the PE dimension, the PB dimension and the PS dimension recommended in the 28 reference product resources is 4:8:16, the second preset weight is weighted to the dimension index values of the PE dimension, the PB dimension and the PS dimension according to 4:8:16, and therefore the comprehensive index value of the specified product resources is obtained through calculation. For example, the first preset weight and the second preset weight are respectively set to 0.5, the dimension index values of the PE dimension, the PB dimension and the PS dimension are respectively 3, 4 and 5, and the comprehensive index value of the specified product resource is calculated to be

Step S240, determining the file corresponding to the comprehensive index value of the specified product resource, and displaying the file in the evaluation page.

In the embodiment of the application, corresponding documents are preset, each comprehensive index value corresponds to one document, and the documents and the comprehensive index values are displayed on an evaluation page at the same time for a user to check. As shown in fig. 8, fig. 8 is an evaluation page of a specified product resource, where the evaluation page shows a comprehensive index value and a corresponding document of the specified product resource, and also shows a first index value, a second index value, and a third index value, where the "first index value", "second index value", and "third index value" in fig. 8 are three corresponding controls, which can be clicked, and by clicking the control, information of the corresponding index value is shown below the control, where the current first index value is shown below the control, and the trade average of the reference product resource calculated according to the current first index value, and the probability that the current first index value exceeds the historical data are shown. Meanwhile, by clicking to view details, a detail page corresponding to three index values can be entered, for example, the detail page of the first index value shown in fig. 9, a line graph of the first index value of the specified product resource changing with time is displayed in the detail page of the first index value, and meanwhile, an average first index value in the industry where the specified product resource is located is also displayed in the line graph; further, the detail page also displays an industry distribution module of the specified product resource based on the first index value, and an industry ranking of the first index value in the obstetrical product resource and an industry mean value of the first index value in the industry are displayed in the industry distribution module.

In an embodiment of the application, when the dimension index values are not calculated or are all negative numbers, the case corresponding to the comprehensive index value does not need to be determined and displayed, and when the dimension index values are positive numbers, the interpretation content corresponding to the positive dimension index values can be displayed.

In an embodiment of the present application, the determining of the document corresponding to the composite index value of the designated product resource in step S240 includes steps S910 and S920, which are described in detail as follows:

step S910, determining a score interval where a comprehensive index value of the specified product resource is located based on a preset score interval and a pattern mapping table;

in the embodiment of the application, the calculated comprehensive index value is a numerical value, a score interval is preset, an interval corresponding to the comprehensive index value is determined, and a document mapping table is arranged between the preset score interval and the reading document, specifically, the document mapping table is as shown in table 1 below:

TABLE 1

And step S920, acquiring the file corresponding to the score interval.

In the embodiment of the application, after the comprehensive index value is obtained, the corresponding file is obtained and displayed in the evaluation page.

In one embodiment of the present application, when the following is satisfied: and when the comprehensive index value is equal to a certain dimension index value, or when the first evaluation index value exceeds a threshold value and the second evaluation index value exceeds a threshold value, the supplementary interpretation meeting the corresponding conditions is further provided on the evaluation page.

Specifically, when the composite index value is equal to one of the PE dimension, the PB dimension, or the PS dimension, an algorithm conclusion of adding a dimension index value equivalent to the composite index value to the evaluation page may be further performed:

under the condition that the recommended dimension index value exists, when the recommended dimension index value is equal to the comprehensive index value, an algorithm conclusion of the recommended dimension index value can be directly displayed on an evaluation page to serve as a case of the comprehensive index value; and when the recommended dimension index value does not exist and only one dimension index value in the 3 dimension index values is a positive number or a corresponding value exists, displaying the algorithm conclusion of the dimension index value which is the positive number or the corresponding value exists.

In another embodiment of the application, a cash flow discount model or a stock discount model is trained in advance, and the operation data of the specified product resources are input into the model for calculation, so that a corresponding comprehensive index value is obtained, without depending on the similarity between the index values of the historical multiple dimensions of the specified product resources in the historical time period and the reference product resources.

In an exemplary embodiment of the present application, referring to fig. 10 and 11, a method for evaluating and processing product resources is provided, which is described in detail as follows:

referring to fig. 10, fig. 10 provides a flowchart for calculating the similarity between the designated product resource and the reference product resource, including steps S1010 to S1040, which are described in detail as follows:

step S1010, preprocessing the characteristic data of the designated product resource and the reference product resource to obtain standard characteristic data; the characteristic data comprises a total market value, a daily handover rate, a net profit rate TTM, a ROE TTM, an EPS TTM and a composite growth rate;

in the embodiment of the present application, the 6 kinds of feature data are normalized, and then subjected to a maximum value removing process, that is, a maximum value and a minimum value in the feature data are removed, and by removing an extreme value in the feature data, the influence of the extreme value on an index value is reduced.

Step S1020, calculating an integral distance standard value according to the standard characteristic data of the specified product resource and the reference product resource.

In the embodiment of the application, the absolute difference value between every two product resources in the product resource set of the specified product resource and the reference product resource between each standard characteristic data is calculated, and the median of the absolute difference value in each standard characteristic data is selected as the integral distance standard value of the corresponding standard characteristic data.

And step S1030, calculating a target distance value between the designated product resource and the reference product resource according to the overall distance standard value.

In the embodiment of the application, the Manhattan distance between the specified product resource and the reference product resource and between each kind of standard characteristic data are calculated, the Manhattan distance of each kind of standard characteristic data is divided by the corresponding integral distance standard value to obtain the distance between the corresponding standard characteristic data, and then the arithmetic mean value is calculated according to the distance between each kind of standard characteristic data to obtain the target distance value between the specified product resource and the reference product resource.

And step S1040, calculating the similarity between the designated product resource and the reference product resource according to the target distance value.

In the embodiment of the application, a reverse relation exists between the target distance value and the similarity, namely the similarity is smaller when the target distance value is larger, and the similarity between the designated product resource and each reference product resource is calculated according to the target distance value.

Referring to fig. 11, fig. 11 provides a flowchart for calculating a composite index value for a specified product resource, including steps S1110 through S1140, as described in detail below:

step S1110, respectively calculating first evaluation index values in a PE dimension, a PB dimension and a PS dimension according to the multi-dimensional index values of the specified product resources; and respectively calculating second evaluation index values in the PE dimension, the PB dimension and the PS dimension according to the similarity of the specified product resources and the reference product resources and the multi-dimension index values of the reference product resources.

In the embodiment of the present application, the first evaluation index value and the second evaluation index value in the PE dimension, the PB dimension, and the PS dimension are calculated according to the manner described in steps S610 to S630 and steps S710 to S730, the first evaluation index value combines the multidimensional index values of the designated product resource in the history time period, and the second evaluation value combines the multidimensional index values of the reference product resource, so that the two aspects of history and industry are considered comprehensively, and the case of the designated product resource can be described more accurately.

In step S1120, a dimension index value in the PE dimension, PB dimension, and PS dimension is calculated from the first evaluation index value and the second evaluation index value in the PE dimension, PB dimension, and PS dimension.

In the embodiment of the application, the first evaluation index value and the second evaluation index value of the PE dimension are directly added to obtain the dimension index value of the PE dimension, and the dimension index values of the PB dimension and the PS dimension are obtained through the same calculation.

Step S1130, calculating a comprehensive index value of the designated product resource according to the dimension index values of the PE dimension, the PB dimension and the PS dimension.

In the embodiment of the present application, the comprehensive index value of the designated product resource is calculated in the manners described in step S810 and step S820, which is not described herein again.

Step S1140, determine the file corresponding to the comprehensive index value of the specified product resource.

In the embodiment of the present application, the comprehensive index value of the designated product resource is calculated in the manners described in step S910 and step S920, which is not described herein again.

In the embodiment of the application, when the similarity between the specified product resource and the reference product resource is calculated, a plurality of feature data are comprehensively considered, and meanwhile, the feature data are preprocessed, so that the subsequent calculation is not easily influenced by abnormal values. The determination of the overall distance standard value utilizes the difference value between a plurality of product resources, and can more accurately reflect the basic situation in the industry. Further, in the embodiment, the comprehensive index value is calculated through the 3-dimensional index values, and meanwhile, the multi-dimensional index values of the specified product resources in the historical time period and the multi-dimensional index values of the reference product resources are combined, so that the industrial characteristics are better utilized to emphasize the multi-dimensional index values, the calculated comprehensive dimension value can better reflect the condition of the specified product resources.

In an exemplary embodiment of the present application, please refer to fig. 12, fig. 12 is a block diagram illustrating an evaluation processing apparatus of a product resource according to an exemplary embodiment, including:

the obtaining module 1210 is configured to obtain first multidimensional feature data of a specified product resource and second multidimensional feature data of a reference product resource if a touch operation of entering an evaluation page corresponding to the specified product resource is detected;

a first calculation module 1220 configured to calculate a similarity between the designated product resource and the reference product resource based on the first multi-dimensional feature data and the second multi-dimensional feature data;

the second calculation module 1230 is configured to calculate a comprehensive index value of the specified product resource based on the multi-dimensional index value and the similarity of the specified product resource;

and the display module 1240 is configured to determine the file corresponding to the comprehensive index value of the specified product resource and display the file in the evaluation page.

In an exemplary embodiment, the obtaining module 1210 includes:

the first acquisition submodule is configured to acquire first multi-dimensional feature data of a specified product resource; and the number of the first and second groups,

a first determining submodule configured to determine a type of a specified product resource;

the second acquisition sub-module is configured to acquire a product resource matched with the type of the specified product resource from the plurality of product resources and take the acquired product resource as a reference product resource;

and the third acquisition submodule is configured to acquire second multi-dimensional feature data of the reference product resource.

In an exemplary embodiment, the first calculation module 1220 includes:

the standardization processing submodule is configured to standardize the first multi-dimensional feature data to obtain first multi-dimensional standard feature data, and standardize the second multi-dimensional feature data to obtain second multi-dimensional standard feature data;

the first calculation submodule is configured to calculate an overall distance standard value between standard feature data of the same dimensionality based on the first multi-dimensional standard feature data and the second multi-dimensional standard feature data;

the second calculation submodule is configured to calculate a target distance value between the designated product resource and the reference product resource based on the overall distance standard value, the first multi-dimensional standard feature data and the second multi-dimensional standard feature data;

a third computing submodule configured to compute a similarity between the specified product resource and the reference product resource based on the target distance value.

In an exemplary embodiment, the first computation submodule includes:

the difference calculation unit is configured to perform difference calculation on the standard feature data with the same dimensionality between the first multi-dimensional standard feature data and the second multi-dimensional standard feature data respectively to obtain difference values of the plurality of standard feature data with the same dimensionality;

and the determining unit is configured to determine a target difference value from the differences of the plurality of standard feature data according to a median determination rule, and take the target difference value as an integral distance standard value of the corresponding dimension.

In an exemplary embodiment, the second computation submodule includes:

the first calculation unit is configured to calculate the Manhattan distance between the standard feature data with the same dimensionality according to the first multi-dimensional standard feature data and the second multi-dimensional standard feature data;

the quotient calculation unit is configured to perform quotient calculation on the Manhattan distances of the plurality of standard feature data and the overall distance standard value of the corresponding dimensionality respectively to obtain a distance ratio of the plurality of dimensionalities;

and the second calculation unit is configured to calculate a target distance value of the specified product resource and the reference product resource based on the distance ratio of the plurality of dimensions.

In an exemplary embodiment, the second calculating module 1230 includes:

the fourth calculation submodule is configured to calculate a first evaluation index value according to the multi-dimensional index value of the specified product resource; and the number of the first and second groups,

the product operation sub-module is configured to perform product operation on the multi-dimensional index value of the reference product resource and the similarity to obtain a second evaluation index value;

and the fifth calculation submodule is configured to calculate a comprehensive index value of the specified product resource based on the first evaluation index value and the second evaluation index value.

In an exemplary embodiment, a fifth computation submodule includes:

the summation operation unit is configured to respectively perform summation operation on the first evaluation index value and the second evaluation index value with the same dimensionality to obtain dimensionality index values of multiple dimensionalities;

and the third calculation unit is configured to calculate a comprehensive index value of the specified product resource based on the dimension index values of the plurality of dimensions.

In an exemplary embodiment, the display module 1240 includes:

the second determining submodule is configured to determine a score interval where the comprehensive index value of the specified product resource is located based on the preset score interval and the file mapping table;

and the fourth obtaining submodule is configured to obtain the file corresponding to the score interval.

It should be noted that the apparatus provided in the foregoing embodiment and the method provided in the foregoing embodiment belong to the same concept, and specific ways for the modules, sub-modules, and units to perform operations have been described in detail in the method embodiment, and are not described herein again.

An embodiment of the present application further provides an electronic device, including: one or more processors; a storage device, configured to store one or more programs, which when executed by the one or more processors, cause the electronic device to implement the evaluation processing method for product resources provided in the above-described embodiments.

It should be noted that the computer system 1300 of the electronic device shown in fig. 13 is only an example, and should not bring any limitation to the functions and the application scope of the embodiments of the present application.

As shown in fig. 13, the computer system 1300 includes a Central Processing Unit (CPU)1301, which can perform various appropriate actions and processes, such as performing the methods described in the above embodiments, according to a program stored in a Read-Only Memory (ROM) 1302 or a program loaded from a storage portion 1308 into a Random Access Memory (RAM) 1303. In the RAM 1303, various programs and data necessary for system operation are also stored. The CPU1301, the ROM 1302, and the RAM 1303 are connected to each other via a bus 1304. An Input/Output (I/O) interface 1305 is also connected to bus 1304.

The following components are connected to the I/O interface 1305: an input portion 1306 including a keyboard, a mouse, and the like; an output section 1307 including a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, a speaker, and the like; a storage portion 1308 including a hard disk and the like; and a communication section 1309 including a Network interface card such as a LAN (Local Area Network) card, a modem, or the like. The communication section 1309 performs communication processing via a network such as the internet. A drive 1310 is also connected to the I/O interface 1305 as needed. A removable medium 1311 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 1310 as necessary, so that a computer program read out therefrom is mounted into the storage portion 1308 as necessary.

In particular, according to embodiments of the present application, the processes described above with reference to the flow diagrams may be implemented as computer software programs. For example, embodiments of the present application include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising a computer program for performing the method illustrated by the flow chart. In such embodiments, the computer program may be downloaded and installed from a network via communications component 1309 and/or installed from removable media 1311. The computer program executes various functions defined in the system of the present application when executed by a Central Processing Unit (CPU) 1301.

It should be noted that the computer readable medium shown in the embodiments of the present application may be a computer readable signal medium or a computer readable storage medium or any combination of the two. The computer readable storage medium may be, for example, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having 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), a 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. In the present application, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In this application, however, a computer readable signal medium may include a propagated data signal with a computer program embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. The computer program embodied on the computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wired, etc., or any suitable combination of the foregoing.

The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. Each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

The units described in the embodiments of the present application may be implemented by software, or may be implemented by hardware, and the described units may also be disposed in a processor. Wherein the names of the elements do not in some way constitute a limitation on the elements themselves.

Another aspect of the present application also provides a computer-readable storage medium on which a computer program is stored, which, when executed by a processor, implements the method for evaluation processing of a product resource as described above. The computer-readable storage medium may be included in the electronic device described in the above embodiment, or may exist separately without being incorporated in the electronic device.

Another aspect of the application also provides a computer program product or computer program comprising computer instructions stored in a computer readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the evaluation processing method of the product resource provided in the above-described embodiments.

The above description is only a preferred exemplary embodiment of the present application, and is not intended to limit the embodiments of the present application, and those skilled in the art can easily make various changes and modifications according to the main concept and spirit of the present application, so that the protection scope of the present application shall be subject to the protection scope of the claims.

Claims

1. An evaluation processing method for a product resource, comprising:

if touch operation entering an evaluation page corresponding to a specified product resource is detected, acquiring first multi-dimensional feature data of the specified product resource and second multi-dimensional feature data of a reference product resource;

calculating the similarity of the designated product resource and the reference product resource based on the first multi-dimensional feature data and the second multi-dimensional feature data;

calculating a comprehensive index value of the specified product resource based on the multi-dimensional index value of the specified product resource and the similarity;

and determining a file corresponding to the comprehensive index value of the specified product resource, and displaying the file in the evaluation page.

2. The method of claim 1, wherein the obtaining first multidimensional feature data for the specified product resource and second multidimensional feature data for a reference product resource comprises:

acquiring first multi-dimensional feature data of the specified product resource; and the number of the first and second groups,

determining a type of the specified product resource;

acquiring a product resource matched with the type of the specified product resource from a plurality of product resources, and taking the acquired product resource as the reference product resource;

and acquiring second multi-dimensional characteristic data of the reference product resource.

3. The method of claim 1, wherein said calculating a similarity of the specified product resource to the reference product resource based on the first multi-dimensional feature data and the second multi-dimensional feature data comprises:

the first multi-dimensional feature data are subjected to standardization processing to obtain first multi-dimensional standard feature data, and the second multi-dimensional feature data are subjected to standardization processing to obtain second multi-dimensional standard feature data;

calculating an overall distance standard value between standard feature data of the same dimension based on the first multi-dimensional standard feature data and the second multi-dimensional standard feature data;

calculating a target distance value of the designated product resource from the reference product resource based on the overall distance criterion value, the first multi-dimensional criterion feature data and the second multi-dimensional criterion feature data;

calculating a similarity between the designated product resource and a reference product resource based on the target distance value.

4. The method of claim 3, wherein calculating an overall distance criterion value between standard feature data of the same dimension based on the first multi-dimensional standard feature data and the second multi-dimensional standard feature data comprises:

performing difference calculation on the standard feature data with the same dimensionality between the first multi-dimensional standard feature data and the second multi-dimensional standard feature data respectively to obtain difference values of a plurality of standard feature data with the same dimensionality;

and determining a target difference value from the differences of the plurality of standard characteristic data according to a median determination rule, and taking the target difference value as an integral distance standard value of the corresponding dimension.

5. The method of claim 3, wherein said calculating a target distance value for the specified product resource from the reference product resource based on the overall distance criterion value, the first multi-dimensional criterion feature data, and the second multi-dimensional criterion feature data comprises:

calculating the Manhattan distance between the standard feature data with the same dimensionality according to the first multi-dimensional standard feature data and the second multi-dimensional standard feature data;

respectively carrying out quotient calculation on the Manhattan distances of the plurality of standard characteristic data and the integral distance standard values of the corresponding dimensionalities to obtain the space ratio of the plurality of dimensionalities;

calculating a target distance value of the designated product resource and the reference product resource based on the spacing ratio of the plurality of dimensions.

6. The method of claim 1, wherein said calculating a composite merit value for the specified product resource based on the multi-dimensional merit values for the specified product resource and the similarity comprises:

calculating a first evaluation index value according to the multi-dimensional index value of the specified product resource; and the number of the first and second groups,

performing product calculation on the multi-dimensional index value of the reference product resource and the similarity to obtain a second evaluation index value;

and calculating a comprehensive index value of the specified product resource based on the first evaluation index value and the second evaluation index value.

7. The method of claim 6, wherein the calculating a composite merit value for the specified product resource based on the first merit indicator value and the second merit indicator value comprises:

respectively carrying out summation operation on the first evaluation index value and the second evaluation index value with the same dimensionality to obtain dimensionality index values of multiple dimensionalities;

and calculating a comprehensive index value of the specified product resource based on the dimension index values of the plurality of dimensions.

8. The method of any one of claims 1 to 7, wherein said determining a case corresponding to a composite merit value for the specified product resource comprises:

determining a score interval where the comprehensive index value of the specified product resource is located based on a preset score interval and a file mapping table;

and acquiring the file corresponding to the score interval.

9. An evaluation processing apparatus for a product resource, comprising:

the acquisition module is configured to acquire first multi-dimensional feature data of a specified product resource and second multi-dimensional feature data of a reference product resource if touch operation entering an evaluation page corresponding to the specified product resource is detected;

a first calculation module configured to calculate a similarity of the specified product resource and the reference product resource based on the first multi-dimensional feature data and the second multi-dimensional feature data;

the second calculation module is configured to calculate a comprehensive index value of the specified product resource based on the multi-dimensional index value of the specified product resource and the similarity;

and the display module is configured to determine a file corresponding to the comprehensive index value of the specified product resource and display the file in the evaluation page.

10. An electronic device, comprising:

one or more processors;

storage means for storing one or more programs that, when executed by the one or more processors, cause the electronic device to implement the evaluation processing method of a product resource according to any one of claims 1 to 8.

11. A computer-readable storage medium having stored thereon computer-readable instructions which, when executed by a processor of a computer, cause the computer to execute the evaluation processing method of a product resource according to any one of claims 1 to 8.