CN107093103B - Brand value evaluation method and system based on big data statistical analysis - Google Patents

Abstract

The invention provides a brand value evaluation method and system based on big data statistical analysis, wherein the method comprises the following steps: acquiring first income data in the current period, second income data in the current period and first cost data in the current period; calculating to obtain current-stage first profit data according to the current-stage first income data, the current-stage second income data and the current-stage first cost data; obtaining profit discount rate, increase rate and second profit data, wherein the increase rate is long-term forecast inflation rate, and the second profit data is obtained according to the first profit data in the current period; and calculating to generate an evaluation result according to the first profit data, the profit discount rate, the increase rate and the second profit data in the current period. According to the invention, a big data statistical analysis-based mode is adopted, the influence of various influencing factors on the brand value is considered, the rapid and effective evaluation on the brand value is realized, and the user experience is improved.

Description

Brand value evaluation method and system based on big data statistical analysis

Technical Field

The invention relates to the field of data processing, in particular to a brand value evaluation method and system based on big data statistical analysis.

Background

Brands can be classified into product brands and enterprise brands according to their attributes. The product brand is carried on a product (or service), a series of element combinations (including but not limited to names, words, symbols, images, identifications, designs or combinations thereof, and cultures, proprietary technologies, processes and the like related to the element combinations) for distinguishing the same product (or service) or entity can be perceived by consumers and influence the purchasing choices of the consumers, and represent a series of utilities, functions, tastes, forms, prices, convenience, services and other added values of the product. The enterprise brand is an enterprise name or other short names, represents an image of certain characteristics of an organization or a group, shows the culture, concept and development direction of the enterprise, and transmits the value and the individuality of the enterprise to the stakeholders, so that the goodness and loyalty of the audience to the enterprise are established, and the quality differentiation core embodiment is realized.

Brands are the most important assets of enterprises and are the only assets capable of directly contributing to the product benefits (including physical products and service products) of enterprises. The method can bring contributions exceeding general (expected) benefits to enterprises, including more stable market share, more loyal grand clients and more suitable consumption habit preference, so that higher product price overflow, wider benefit expansion space and stronger cost transfer capability are created, a firmer product structure is created, competitiveness of brands serving as cores is built, and the leading effect of the brands in enterprise value realization is fully exerted. The value of a brand measures the economic value that the brand can bring to the owner.

In the prior art, the considered factors of the evaluation method of the brand value are unilateral, the adopted modes are manual evaluation, and the evaluated brand value has subjective factors of evaluators, so that the evaluated brand value cannot be accurately reflected.

Therefore, the existing brand value evaluation method has the defects that the considered influence factors are single, the evaluation mode is not scientific and intelligent enough, and the brand value cannot be quickly and effectively given.

Disclosure of Invention

Aiming at the technical problems, the invention provides a brand value evaluation method and system based on big data statistical analysis, which adopts a big data statistical analysis-based mode, considers the influence of various influencing factors on the brand value, realizes quick and effective evaluation on the brand value and improves the user experience.

In order to solve the technical problems, the technical scheme provided by the invention is as follows:

in a first aspect, the invention provides a brand value evaluation method based on big data statistical analysis, which comprises the following steps:

step S1, acquiring current-period first income data, current-period second income data and current-period first cost data, wherein the current-period second income data is obtained by calculating current-period second cost data and current-period first business cost rate, and the current-period first cost data is obtained by calculating previous-period first cost data and cost increase rate;

step S2, calculating current early-stage first profit data according to the current early-stage first income data, the current early-stage second income data and the current-stage first cost data;

step S3, obtaining profit discount rate, increase rate and second profit data, wherein the increase rate is long-term forecast inflation rate, and the second profit data is obtained according to the current early-stage first profit data;

and step S4, calculating and generating an evaluation result according to the current early-stage first profit data, the profit discount rate, the increase rate and the second profit data.

The invention provides a brand value evaluation method based on big data statistical analysis, which has the technical scheme that: acquiring current-stage first income data, current-stage second income data and current-stage first cost data, wherein the current-stage second income data is obtained by calculating current-stage second cost data and current-stage first business cost rate, and the current-stage first cost data is obtained by calculating previous-stage first cost data and cost increase rate; calculating to obtain current-stage first profit data according to the current-stage first income data, the current-stage second income data and the current-stage first cost data; obtaining profit discount rate, increase rate and second profit data, wherein the increase rate is long-term forecast inflation rate, and the second profit data is obtained according to the first profit data in the current period; and calculating to generate an evaluation result according to the current early-stage first profit data, the profit discount rate, the increase rate and the second profit data.

According to the brand value evaluation method based on big data statistical analysis, a big data statistical analysis-based mode is adopted, the influence of influence factors on the brand value in multiple aspects is considered, the brand value is evaluated quickly and effectively, and the user experience is improved.

Further, in step S1, the current early-stage first revenue data is obtained through calculation, specifically:

acquiring second cost data in the current period and second business cost rate in the current period;

and calculating to obtain the first income data in the current period according to the second cost data in the current period and the second business cost rate in the current period.

Further, in step S1, the current early-stage first revenue data is obtained through calculation, specifically:

acquiring second cost data of the current period, third cost data of the current period and fourth cost data of the current period;

calculating to obtain third profit data according to the current-stage second cost data;

and calculating to obtain the current-stage first income data according to the current-stage second cost data, the current-stage third cost data, the current-stage fourth cost data and the third profit data.

Further, the current-stage second cost data is obtained through calculation, and specifically:

obtaining a unit cost change coefficient, previous unit cost data, previous sales data and a sales growth rate;

and calculating to obtain second cost data in the current period according to the unit cost change coefficient, the unit cost data in the previous period, the sales data in the previous period and the sales growth rate.

Further, the step S1 further includes correcting the current early-stage first business cost rate, specifically:

obtaining the grading data of the current stage influence factors and the grading data of the previous stage influence factors;

calculating to obtain an influence coefficient according to the grading data of the influence factors in the current stage and the grading data of the influence factors in the previous stage;

calculating to obtain a correction coefficient according to the influence coefficient;

and correcting and calculating the second business cost rate in the current period through the correction coefficient to obtain the corrected first business cost rate in the current period.

In a second aspect, the present invention further provides a brand value evaluation system based on big data statistical analysis, including:

the system comprises a first data acquisition module, a second data acquisition module and a first cost calculation module, wherein the first data acquisition module is used for acquiring current-period first income data, current-period second income data and current-period first cost data, the current-period second income data is obtained by calculating current-period second cost data and current-period first business cost rate, and the current-period first cost data is obtained by calculating previous-period first cost data and cost increase rate;

the data processing module is used for calculating to obtain current-stage first profit data according to the current-stage first income data, the current-stage second income data and the current-stage first cost data;

the second data acquisition module is used for acquiring profit discount rate, increase rate and second profit data, wherein the increase rate is long-term forecast inflation rate, and the second profit data is obtained according to the first profit data in the current period;

and the evaluation result generation module is used for calculating and generating an evaluation result according to the current early-stage first profit data, the profit discount rate, the increase rate and the second profit data.

The invention provides a brand value evaluation system based on big data statistical analysis, which adopts the technical scheme that: the method comprises the steps that first early-stage first income data, second early-stage second income data and first cost data in the current stage are obtained through a first data obtaining module, the second early-stage income data are obtained through second cost data in the current stage and first business cost rate in the current stage in a calculating mode, and the first cost data in the current stage are obtained through first cost data in the previous stage and cost increase rate in a calculating mode; calculating to obtain current-stage first profit data according to the current-stage first income data, the current-stage second income data and the current-stage first cost data through a data processing module; obtaining profit discount rate, increase rate and second profit data through a second data obtaining module, wherein the increase rate is long-term forecast inflation rate, and the second profit data is obtained according to the first profit data in the current period; and calculating and generating an evaluation result according to the current early-stage first profit data, the profit discount rate, the increase rate and the second profit data through an evaluation result generation module.

According to the brand value evaluation system based on big data statistical analysis, the brand value is evaluated quickly and effectively by adopting a big data statistical analysis-based mode and considering the influence of various influencing factors on the brand value, and the user experience is improved.

Further, the first data obtaining module is specifically configured to calculate and obtain the current early-stage first revenue data:

acquiring second cost data in the current period and second business cost rate in the current period;

and calculating to obtain the first income data in the current period according to the second cost data in the current period and the second business cost rate in the current period.

Further, the first data obtaining module is specifically configured to calculate and obtain the current early-stage first revenue data:

acquiring second cost data of the current period, third cost data of the current period and fourth cost data of the current period;

calculating to obtain third profit data according to the current-stage second cost data;

and calculating to obtain the current-stage first income data according to the current-stage second cost data, the current-stage third cost data, the current-stage fourth cost data and the third profit data.

Further, the first data obtaining module is specifically configured to calculate and obtain the current earlier-stage second cost data:

obtaining a unit cost change coefficient, previous unit cost data, previous sales data and a sales growth rate;

and calculating to obtain second cost data in the current period according to the unit cost change coefficient, the unit cost data in the previous period, the sales data in the previous period and the sales growth rate.

Further, the first data obtaining module further includes a modification submodule, specifically configured to modify the current first business cost rate:

obtaining the grading data of the current stage influence factors and the grading data of the previous stage influence factors;

calculating to obtain an influence coefficient according to the grading data of the influence factors in the current stage and the grading data of the influence factors in the previous stage;

calculating to obtain a correction coefficient according to the influence coefficient;

and correcting and calculating the second business cost rate in the current period through the correction coefficient to obtain the corrected first business cost rate in the current period.

Drawings

In order to more clearly illustrate the detailed description of the invention or the technical solutions in the prior art, the drawings that are needed in the detailed description of the invention or the prior art will be briefly described below.

FIG. 1 is a flow chart of a brand value evaluation method based on big data statistical analysis according to an embodiment of the present invention;

FIG. 2 is a schematic diagram of a brand value evaluation system based on big data statistical analysis according to an embodiment of the present invention.

Detailed Description

Embodiments of the present invention will be described in detail below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and therefore are only examples, and the protection scope of the present invention is not limited thereby.

Example one

In a first aspect, fig. 1 is a flowchart illustrating a brand value evaluation method based on big data statistical analysis according to an embodiment of the present invention; the brand value evaluation method and system based on big data statistical analysis provided by the invention are used for evaluating the value of a certain brand, firstly, the brand value in a period of time is selected for evaluation, the period of time is set as t, and the unit of t is year; then, parameters are set corresponding to each data related in the invention, and the meaning represented by each data is explained;

R'_tis first revenue data for the current term, which represents the t-th term, R'_tThe income created by non-brand elements in the t period is shown, wherein the non-brand elements refer to various investment elements which do not directly form brand contribution during enterprise production and operation activities.

R_tFor the current term second revenue data, i.e. R_tIndicating revenue for the product (or service) at the t-th stage.

BP_tFor the current term first profit data, i.e. BP_tRepresenting a forecast period brand profit, where the brand profit is equal to a brand contribution minus a brand cost, a fee, the brand contribution referring to excess revenue achieved due to brand differentiation, the brand cost referring to creation orManaging costs invested by brands.

I is profit discount rate, i.e. I is brand profit discount rate, wherein discount rate refers to the remuneration rate used for moist discount of future brand interest to present value. The brand value discount rate I is the industry average asset return rate, can be obtained by calculating the market-appearing enterprise average asset return rate of similar industries, types and scales, and can also be obtained by means of statistical investigation and the like.

g is the growth rate, i.e. g represents the sustainable growth rate, and the long-term expected inflation rate can be adopted.

BP_n+1For second profit data, i.e. BP_n+1Indicating the expected brand profit for stage n + 1.

n is the preset prediction period number, and the value range of n is more than or equal to 3 and less than or equal to 5 years.

VB is an evaluation result, namely VB represents brand value, and the brand value refers to the economic value which can be transferred and is expressed by currency units and is brought to brand owners; the calculated brand value may be a certain value or range of values; the economic value refers to the value of the direct future cash flow (including principal and actual interest) obtained by the economic behavior from the product and/or the service, which is discounted to the current time point.

Based on the above description, in combination with the brand value evaluation method based on big data statistical analysis in the present embodiment,

step S1, acquiring current first income data, current second income data and current first cost data, wherein the current second income data is obtained by calculating current second cost data and current first business cost rate, and the current first cost data is obtained by calculating current first cost data and cost increase rate;

wherein the current period second revenue data may be calculated by the following formula:

C_tfor the second cost data of the current period, i.e. C_tRepresenting the production element (brand elimination) cost of the product (or service) of the t th period;

α_tfor the first business cost rate of the current period, i.e. alpha_tAnd (3) business cost rate representing brand cost culled for the product (or service) of the t < th > period.

The current-stage first cost data can be obtained by calculating according to the following formula:

BC_t＝BC_t-1(1+γ_t)

BC_t-1the first cost data of the previous stage is expressed as brand cost of the t-1 stage;

γ_tthe cost increase rate is expressed as the brand cost increase rate in the t stage, and is generally predicted according to enterprise brand management plans, brand input-output conditions and other factors in a comprehensive manner.

Step S2, calculating current early-stage first profit data according to the current early-stage first income data, the current early-stage second income data and the current-stage first cost data;

the calculation can be made according to the following formula:

BP_t＝R_t-R′_t-BC_t

step S3, obtaining profit discount rate, increase rate and second profit data, wherein the increase rate is long-term forecast inflation rate, and the second profit data is obtained according to the first profit data in the earlier stage;

wherein, BP_n+1Can be based on BP_tAnd n is calculated.

And step S4, calculating and generating an evaluation result according to the current early-stage first profit data, the profit discount rate, the increase rate and the second profit data.

The evaluation result can be calculated by the following formula:

according to the brand value evaluation method based on big data statistical analysis, the big data statistical analysis-based mode is adopted, the influence of various influencing elements on the brand value is considered, the brand is used as the element with the same value creation capability as human resources, production data, technologies, land and the like, the value created by the element is gradually stripped from the income of brand products, the value of the brand is finally determined, the rapid and effective evaluation on the brand value is realized, and the user experience is improved.

It should be noted that, in the prior art, the calculation of the brand profits is performed by taking the brand as the asset and subtracting the total profit from the profits created by other assets, and the brand cost is not distinguished, but the brand profits are calculated by subtracting the brand cost from the brand income in the invention, and the biggest characteristic in the brand income is that the brand is not only taken as the asset but also taken as a value creation element, the total income is separated from the income created by non-brand elements, and the income created by the brand is remained. Specifically, stripping the brand cost means stripping the brand cost from the production cost (business cost) and the period cost of the enterprise; and then, determining the profit of the brand, stripping the income created by the brand from the income of the brand product according to a value creation theory, deducting the cost of brand investment, and calculating the profit of the brand. Therefore, the method of the invention evaluates the brand value, considers the brand as the asset and also as the element of value creation, and makes the evaluation result of the brand value more accurate.

The profit of the brand is equal to the income created by subtracting other elements from the income of the product (or service), and then the cost of the brand is deducted.

Calculated by the following formula:

BP＝R-C-S-G-M-BC

wherein, R is the income of the product (or service);

c is the production factor (brand rejection) cost consumed by the product (or service); the cost of elements (eliminating brands) in the production process can adopt the production cost in financial data.

S is the cost of selling elements (eliminating brands) of products (or services); the cost of sales elements (culling brands) is equal to culling brand-related expenses (including advertising expenses, sales expenses, promotional expenses, etc.) from sales expenses.

G is the management element (brand rejection) cost of the product (or service); the administrative factor (brand culling) cost is equal to the culling of brand-related administrative fees from administrative fees (including administrative fees for brand management, brand design fees, etc.).

M is the profit created by the production element (brand rejection) consumed by the product (or service); profits created by elements (brand removal) in the production process can be calculated by referring to the profit margins of the substitute processing enterprises in the same industry.

BC is the brand cost, including advertising expenses, sales expenses, promotional expenses, brand design expenses, brand planning expenses, management expenses of brand management departments, and the like.

Based on the above principle, BP is obtained_t＝R_t-R′_t-BC_tAnd the calculation is convenient.

Specifically, the current-period first revenue data may be calculated by the following two ways, namely, the first way, which is known to set the current-period second cost data to C_t，C_tRepresenting the production element (brand elimination) cost of the product (or service) of the t th period; production factors refer to the various input factors required for a product production campaign, including the brand, labor, land, production equipment, capital, technology, etc. associated with the production campaign. If the current early-stage second business cost rate is alpha'_t，α′_tRepresenting industry operating cost rates of non-branded elements. The calculation formula of the current period first revenue data is as follows:

a second method of calculating the current first revenue data is: it is known that the third cost data at the current stage is S_t，S_tRepresenting a t-th product (or service) sale element (brand elimination) cost; the sales elements refer to various investment elements required in the product sales activity, including investments such as brands, sales channels, sales personnel, product packages, and the like related to the product sales activity. Let the fourth cost data in the current earlier stage be G_t，G_tManaging element (brand elimination) cost on behalf of the product (or service) of the t period; the management elements refer to various investment elements required by enterprises for managing and managing activities, and include investment of brands, office sites, equipment, managers, capital and the like related to management.

Let the third profit data be M_t，M_tRepresenting profit created by production factor (brand rejection) of product (or service) at the t-th stage, M_tAccording to C_tAnd (4) obtaining. The calculation formula of the current period first revenue data is as follows:

R′_t＝C_t+S_t+G_t+M_t

thus, by the formula

Calculating to obtain current early-stage first income data, and using current early-stage first income data and current early-stage second cost data C_tThe third profit data M can be calculated_tLikewise, formula R'_t＝C_t+S_t+G_t+M_tAnd also through the third profit data M_tAnd calculating to obtain the current early-stage first income data, thus forming the recycling of the data.

Wherein, the current earlier stage second cost data is obtained by calculating according to the following formula:

C_t＝C_t-1×Z_uc×(1+_Q)

wherein, C_t-1＝UC_t-1×Q_t-1；

Is provided with Z_ucA coefficient representing a unit cost change caused by objective factors such as the market;

UC_t-1the unit cost data of the previous period represents the unit cost of the product (or service) of the t-1 th period, and the unit cost refers to the average cost consumed for producing the unit product. Generally, the second cost data can be obtained by removing the second cost data from the total production, and the total cost is divided into a unit product cost according to different consumption levels, which reflects the cost level of the same product.

Q_t-1The sales data of the previous period represents the sales volume of the product (or service) of the t-1 period;

_Qthe sales increase rate is an important index for evaluating the growth condition and the development capability of the enterprise. The calculation formula is as follows: the sales increase rate is the sales increase amount in the current year ÷ total sales in the last year ÷ (sales amount in the current year-total sales amount in the last year) ÷ total sales in the last year.

Preferably, in step S1, the method further includes modifying the current first business cost rate, specifically:

α_t＝α′_t-β_tα′_t

wherein, beta_tThe correction coefficient is expressed as the correction coefficient of the t-th brand to the business cost rate, and the correction coefficient of the business cost rate refers to the correction of the expected business cost rate according to the brand influence coefficient

Wherein, K_tjTo influence the coefficient, K_tjAnd (3) a brand influence coefficient representing the jth aspect in the tth stage, wherein the brand influence coefficient refers to a factor influencing the brand contribution, and the brand influence coefficient refers to an influence value of the factor influencing the brand contribution.

Preferably, the first and second electrodes are formed of a metal,

the calculation result is generally between 0.5 and 1.5;

X_tjfor scoring the current term influence factor, i.e. X_tjRepresenting brand influence factor j during period tScoring, wherein the value range is (0-1000); in the same way, X_(t-1)jScore data for previous term influencing factor, i.e. X_(t-1)jRepresents the score of the t-1 stage brand impact factor j.

The brand influence coefficient is influenced by a plurality of factors, including product conditions (X1), an identification system (X2), brand culture (X3), brand vision (X4), brand positioning (X5), brand management (X6) and the like, and the scoring of each factor can adopt an expert scoring method, not only referring to the conditions of own brands at different periods, but also referring to comparative changes among different brands at the same period and at the same level.

In a second aspect, fig. 2 is a schematic diagram illustrating a brand value evaluation system based on big data statistical analysis according to an embodiment of the present invention. As shown in FIG. 2, one embodiment provides a brand value evaluation system 10 based on big data statistical analysis, including:

the first data acquisition module 101 is configured to acquire current-period first revenue data, current-period second revenue data, and current-period first cost data, where the current-period second revenue data is obtained by calculating current-period second cost data and current-period first business cost rate, and the current-period first cost data is obtained by calculating previous-period first cost data and cost increase rate;

wherein the current period second revenue data may be calculated by the following formula:

C_tfor the second cost data of the current period, i.e. C_tRepresenting the production element (brand elimination) cost of the product (or service) of the t th period;

α_tfor the first business cost rate of the current period, i.e. alpha_tAnd (3) business cost rate representing brand cost culled for the product (or service) of the t < th > period.

The current-stage first cost data can be obtained by calculating according to the following formula:

BC_t＝BC_t-1(1+γ_t)

BC_t-1the first cost data of the previous stage is expressed as brand cost of the t-1 stage;

γ_tthe cost increase rate is expressed as the brand cost increase rate in the t stage, and is generally predicted according to enterprise brand management plans, brand input-output conditions and other factors in a comprehensive manner.

The data processing module 102 is configured to calculate to obtain current-stage first profit data according to the current-stage first revenue data, the current-stage second revenue data, and the current-stage first cost data;

the calculation can be made according to the following formula:

BP_t＝R_t-R′_t-BC_t

the second data acquisition module 103 is used for acquiring profit discount rate, increase rate and second profit data, wherein the increase rate is long-term forecast inflation rate, and the second profit data is acquired according to the first profit data in the current period;

wherein, BP_n+1Can be based on BP_tAnd n is calculated.

And the evaluation result generation module 104 is used for calculating and generating an evaluation result according to the current early-stage first profit data, the profit discount rate, the increase rate and the second profit data.

The evaluation result can be calculated by the following formula:

the brand value evaluation system 10 based on big data statistical analysis provided by the invention has the technical scheme that: acquiring current-stage first revenue data, current-stage second revenue data and current-stage first cost data through a first data acquisition module 101; calculating to obtain current-stage first profit data according to the current-stage first income data, the current-stage second income data and the current-stage first cost data through the data processing module 102; obtaining profit discount rate, increase rate and second profit data through a second data obtaining module 103, wherein the increase rate is long-term forecast inflation rate, and the second profit data is obtained according to the first profit data in the current earlier stage; and calculating and generating an evaluation result according to the current early-stage first profit data, the profit discount rate, the increase rate and the second profit data through the evaluation result generating module 104.

The brand value evaluation system 10 based on big data statistical analysis provided by the invention adopts a big data statistical analysis-based mode, considers the influence of various influence elements on the brand value, takes the brand as the element with the same value creation capability as human resources, production data, technology, land and the like, gradually peels off from the brand product income according to the value created by the element, finally determines the brand value, realizes quick and effective evaluation on the brand value, and improves the user experience.

The profit of the brand is equal to the income created by subtracting other elements from the income of the product (or service), and then the cost of the brand is deducted.

It can also be calculated by the following formula:

BP＝R-C-S-G-M-BC

wherein, R is the income of the product (or service);

c is the production factor (brand rejection) cost consumed by the product (or service); the cost of elements (eliminating brands) in the production process can adopt the production cost in financial data.

S is the cost of selling elements (eliminating brands) of products (or services); the cost of sales elements (culling brands) is equal to culling brand-related expenses (including advertising expenses, sales expenses, promotional expenses, etc.) from sales expenses.

G is the management element (brand rejection) cost of the product (or service); the administrative factor (brand culling) cost is equal to the culling of brand-related administrative fees from administrative fees (including administrative fees for brand management, brand design fees, etc.).

M is the profit created by the production element (brand rejection) consumed by the product (or service); profits created by elements (brand removal) in the production process can be calculated by referring to the profit margins of the substitute processing enterprises in the same industry.

BC is the brand cost, including advertising expenses, sales expenses, promotional expenses, brand design expenses, brand planning expenses, management expenses of brand management departments, and the like.

Specifically, the first data obtaining module 101, specifically configured to calculate and obtain current early-stage first revenue data, may be calculated and obtained in the following two ways, where in the first way, it is known that the current-stage second cost data is C_t， C_tRepresenting the production element (brand elimination) cost of the product (or service) of the t th period; production factors refer to the various input factors required for a product production campaign, including the brand, labor, land, production equipment, capital, technology, etc. associated with the production campaign. If the current early-stage second business cost rate is alpha'_t，α′_tRepresenting industry operating cost rates of non-branded elements. The calculation formula of the current period first revenue data is as follows:

a second method of calculating the current first revenue data is: it is known that the third cost data at the current stage is S_t，S_tRepresenting a t-th product (or service) sale element (brand elimination) cost; the sales elements refer to various investment elements required in the product sales activity, including investments such as brands, sales channels, sales personnel, product packages, and the like related to the product sales activity. Let the fourth cost data in the current earlier stage be G_t，G_tManaging element (brand elimination) cost on behalf of the product (or service) of the t period; the management elements refer to various investment elements required by enterprises for managing and managing activities, and include investment of brands, office sites, equipment, managers, capital and the like related to management.

Let the third profit data be M_t，M_tRepresenting profit created by production factor (brand rejection) of product (or service) at the t-th stage, M_tAccording to C_tAnd (4) obtaining. The calculation formula of the current period first revenue data is as follows:

R′_t＝C_t+S_t+G_t+M_t

thus, by the formula

Calculating to obtain current early-stage first income data, and using current early-stage first income data and current early-stage second cost data C_tThe third profit data M can be calculated_tLikewise, formula R'_t＝C_t+S_t+G_t+M_tAnd also through the third profit data M_tAnd calculating to obtain the current early-stage first income data, thus forming the recycling of the data.

The first data obtaining module 101 is specifically configured to obtain current-period second cost data through calculation according to the following formula:

C_t＝C_t-1×Z_uc×(1+_Q)

wherein, C_t-1＝UC_t-1×Q_t-1；

Is provided with Z_ucA coefficient representing a unit cost change caused by objective factors such as the market;

UC_t-1the unit cost data of the previous period represents the unit cost of the product (or service) of the t-1 th period, and the unit cost refers to the average cost consumed for producing the unit product. Generally, the second cost data can be obtained by removing the second cost data from the total production, and the total cost is divided into a unit product cost according to different consumption levels, which reflects the cost level of the same product.

Q_t-1The sales data of the previous period represents the sales volume of products (or services) of the t-1 period, and the sales growth rate is an important index for evaluating the growth condition and the development capability of the enterprise. The calculation formula is as follows: the sales increase rate is the sales increase amount in the current year ÷ total sales in the last year ÷ (sales amount in the current year-total sales amount in the last year) ÷ total sales in the last year.

_QThe sales increase rate indicates a product (or service) sales increase rate.

Preferably, the first data obtaining module 101 further includes a modification submodule 1011, specifically configured to modify the current first business cost rate by the following formula:

α_t＝α′_t-β_tα′_t

wherein, beta_tThe correction coefficient is expressed as the correction coefficient of the t-th brand to the business cost rate, and the correction coefficient of the business cost rate refers to the correction of the expected business cost rate according to the brand influence coefficient

Wherein, K_tjTo influence the coefficient, K_tjAnd (3) a brand influence coefficient representing the jth aspect in the tth stage, wherein the brand influence coefficient refers to a factor influencing the brand contribution, and the brand influence coefficient refers to an influence value of the factor influencing the brand contribution.

Preferably, the first and second electrodes are formed of a metal,

the calculation result is generally between 0.5 and 1.5;

X_tjfor scoring the current term influence factor, i.e. X_tjThe score of the brand influence factor j in the t stage is represented, and the value range is (0-1000); in the same way, X_(t-1)jScore data for previous term influencing factor, i.e. X_(t-1)jRepresents the score of the t-1 stage brand impact factor j.

The brand influence coefficient is influenced by a plurality of factors, including product conditions (X1), an identification system (X2), brand culture (X3), brand vision (X4), brand positioning (X5), brand management (X6) and the like, and the scoring of each factor can adopt an expert scoring method, not only referring to the conditions of own brands at different periods, but also referring to comparative changes among different brands at the same period and at the same level.

Example two

The brand value evaluation method and system based on big data statistical analysis in the first embodiment are used for evaluating brand value, and not only need to consider factors such as investment cost and profit creation of the brand, but also need to consider public praise influence factors of the brand to evaluate the brand value more comprehensively, so that the online evaluation content is combined into the brand value evaluation method to further evaluate the brand value comprehensively, and the specific method is as follows:

capturing brand-related content from the internet;

quantitatively analyzing the captured content for determining a first brand value of the brand;

filtering the captured content to extract content related to a brand-related subject;

evaluating the content of the extracted subject matter to determine a second brand value of the brand;

a value of the brand is determined in conjunction with the first brand value and the second brand value.

In particular, a computer system accesses the internet to obtain online content relating to a brand. Some non-limiting examples of online content (including "social media" resources) may include social networks, blogs, internet forums, wikis, weblogs, social blogs, and podcasts. Thus, a computer system may obtain news articles, analyst reports, stock market applications, blog reviews, tweets (tweets), etc., which may be related to a brand. Any online content that mentions, discusses, reviews, talks about, or provides any reference or observation about a brand, owner of the brand, competitors of the brand, or industry of the brand may be interpreted as "referring to" or relating to the brand. Additionally, the user may select brands for searching for related online content. For example, the user may select to search for and capture content related to "XY". In another case, a search for content related to a brand may be predefined in the system. The captured content may be stored on a computer system used to search for and capture online content related to the brand, or in another computer system.

Wherein the captured content is quantitatively analyzed for determining a first brand value of the brand. Quantitative analysis involves obtaining various types of metrics related to the captured content (i.e., measures that facilitate quantification of certain specific characteristics). Some non-limiting examples of quantitative analysis that may be performed on captured content (related to a brand) include a "sound share" analysis of the brand, a count of tweets or tweets (re-tweets) containing Uniform Resource Locators (URLs) for particular blogs of the brand, a count of web page views containing content related to the brand, a count of "likes" related to the brand, and comments on blogs related to the brand. The quantitative analysis of the captured content determines a first brand value of the brand, which may be any, all, or a combination of the metric(s) described above.

Wherein the captured content is filtered to extract content that is subject matter related to the brand. Sometimes, the entire captured content may not be brand related. For example, a news article may include only transitive references (such as sentences) to brands that are in a survey. The remaining subject matter may not relate to branding. In this embodiment, the captured content may be filtered to extract content that is subject matter related to the brand. The captured content may contain certain claims or portions that may tend to affect the reader's mind when creating a certain brand value (e.g., certain claims create the impression that the brand is "innovative"). In this case, the captured content may be filtered to extract similar claims. Filtering helps identify key impact declarations from the captured content. Such claims tend to have an impact on the reader's thoughts due to their semantic attributes, and are likely to affect an individual's perception of a brand. Some non-limiting examples of key impact declarations that may be extracted from captured content may include: (a) the title of the article; (b) the first segment of the article, which is itself an abstract of the article, should not only provide a glance at the article, but also generate a reader's interest in news; (c) brand-associated references from influencers, which often convey value increases crudely to end customers, and also increase the overall credibility to promotions; (d) comparing the branded product with the competitor product's statement; and

(e) declarations describing the merchant's future plans.

The brand value determined by the method is combined with the brand value obtained by evaluation in the first embodiment to carry out comprehensive consideration, so that the evaluation of the brand value is more accurate and comprehensive.

EXAMPLE III

Based on the second embodiment, a method for evaluating brand value by capturing content related to a brand from the internet is adopted, wherein a theme related to the brand is extracted from the captured content, the theme extracted from the content is related to the brand, and the theme is extracted from the dynamic page content, which affects a theme extraction result due to the fact that the extracted theme is not related to the brand, the extraction speed is slow, the extraction is inaccurate, and the like, and therefore, based on this, the method for acquiring the content related to the brand from the dynamic page content based on the theme includes:

establishing a JavaScript filter library and a JavaScript local library at a capturing server side;

acquiring page information of each captured page, and generating a DOM (document object model) of the current page; if the host object is used in the current page, the capture server instantiates the host object as a corresponding object;

checking an external JavaScript file requested in the current page according to the JavaScript filter library, if the external JavaScript file is irrelevant to the theme, setting a loading-free mark at a corresponding position of a DOM object of the current page, and otherwise, setting a normal loading mark; wherein the theme is a theme related to the brand;

for the external JavaScript file marked as normal loading, if the currently processed JavaScript file exists in a JavaScript local library, setting a local loading mark, otherwise, setting a normal loading mark;

executing JavaScript in the current page to obtain dynamic page information; wherein, the external JavaScript file is loaded according to the loading mark;

and checking whether each acquired dynamic page loses part of information in the original page or not, if so, adding the lost part into the dynamic page again to obtain integrated page information, namely the content of the theme related to the brand.

The method comprises the steps of obtaining the content of a theme related to a brand in a dynamic page, and filtering the JavaScript file irrelevant to the theme by establishing a JavaScript filter library so as to reduce the loading of an external JavaScript file irrelevant to the theme; the method comprises the steps that a JavaScript local library is built so as to locally load a JavaScript file which originally needs to be loaded from a remote host, so that interaction with the remote host is reduced, and time for loading an external JavaScript file is further reduced; the integrity of the dynamic page is improved by adding the information which exists in the original page and is lacked in the dynamic page analyzed by the JavaScript analyzer into the dynamic page.

The JavaScript filter library is established based on a theme and is irrelevant to target content (brand), and the JavaScript filter library mainly comprises two types of executable files: 1. JavaScript files that are apparently unrelated to subject matter, such as JavaScript files used to change page layout; 2. the system is used for online statistics of customer satisfaction, insertion of third-party advertisement promotion codes and other functions, such as online statistics of customer satisfaction codes ForSee Results Survey Code, Baidu alliance, Taobao alliance and advertisement services provided by Google AdSense and realized in a JavaScript form.

The selection of the content in the library mainly uses the pages which are analyzed one by one and added into the capture URL set, and whether the external JavaScript file contained in the page is related to the capture subject is determined to be stored in the JavaScript filter library.

The JavaScript local library should initially add jQuery, Ext, Dojo, Google Web ToolKit, ProtoType, YUI and other file sets, and establish a one-to-one mapping relationship between keywords and the file sets for each file set according to the keywords, so as to be accurate and rapid during loading. Then, whether the keyword part of the name of the JavaScript file requested outside the current site exists in the JavaScript local library is checked according to each time, if not, an Ajax request is sent to acquire the file and the file is stored in the JavaScript local library; if the local download exists, the local download is directly carried out without sending a request. In order to facilitate the subsequent links to use the Java Script local library, a maintenance function for adding and deleting checks and a function for marking whether to load from the library are added to the library.

The URL address required to be captured for acquiring the page information is derived from a URL set specific to a subject, and the code acquisition part mainly comprises two parts:

A. identifying page codes

Firstly, acquiring a Content-Type field through an HTTP response header, if the field does not contain charset characters, taking any one of character sets such as GBK and UTF-8 as a part of codes of a current character set reading page, then searching charset character strings in the character set reading page to intercept charset, and if the character set can not be determined, defaulting the current character set to be UTF-8.

B. Reading pages

And reading the page code content corresponding to the address through a Uniform Resource Locator (URL) address.

The method for generating the DOM object of the current page specifically comprises the following steps: analyzing the current page by using an HTML resolver such as HTMLParser, wherein the resolver has the function of adding a flag attribute on each javascript node of the DOM tree, and the flag meaning is as follows: 0 represents normal; 1 represents filtration is required; and 2 represents that the file needs to be requested from the local JavaScript framework library.

Wherein, whether the JavaScript file is required to be filtered is marked, which specifically comprises the following steps: and traversing the DOM tree of the current page, and setting a filtering identifier for the encountered JavaScript node.

Wherein, whether the mark requests a JavaScript local library specifically comprises the following steps: and traversing the DOM tree of the current page, and setting whether the JavaScript nodes meet the set mark to be loaded from the JavaScript local library.

The method comprises the following steps of executing JavaScript codes in a page, specifically: all the described host objects should be loaded first by using the existing JavaScript parsers, such as spidermondey, Rhino or Google v8, and any of them. The JavaScript code to be executed is divided into two parts:

(1) loading an external JavaScript file in the process of constructing the DOM object, or executing code segments existing in a page or existing in attribute values;

(2) after the DOM is constructed, triggering codes contained in registration events beginning with on, such as onload and the like, wherein the events comprise events such as calling registration onload in a page and the like, and events such as onload and the like contained in a loaded external JavaScript file.

The JavaScript file is an external JavaScript file loaded by the src attribute of the HTML tag, the code segment is a code existing between the HTML tag < script > </script >, and the code statement is a code existing in the attribute value of the HTML tag and is expressed by JavaScript: the sentence at the beginning of the way.

In the code executing process, whether the file needs to be loaded or not and whether the file needs to be loaded from the JavaScirpt local library or not are determined according to the flag attribute on the DOM node corresponding to each JavaScirpt file which needs to be loaded from the outside, if so, the file is loaded from the JavaScript local library, otherwise, the remote host is requested according to the original mode.

The method for saving the dynamic page information specifically comprises the following steps: and saving the page acquired by the Ajax request sent by all JavaScript codes related to the current page as a dynamic page library of the current page. For each page in the library, only the internal part of the < body > tag is meaningful to the subject, so the content inside each page < body > tag is extracted, nested within the < div > tag.

The acquiring of the integrated page information specifically includes: establishing a dynamic page library for each current page, traversing the dynamic page library of the current dynamic page, and executing the following algorithm for a root < div > tag of each dynamic page, wherein the algorithm takes the next tag according to the depth-first order, and an empty stack needs to be initialized:

(1) taking a first element under a root div label;

(2) if the element does not exist, popping up the stack top element, then acquiring the stack top element, if the stack top element is empty, turning to the step (7), otherwise, taking out the next element in the element, if the next element of the element is empty, popping up the stack top element, and if the stack is empty, turning to the step (7); if the element is text content, turning to the step (4);

(3) if the label contains the label, the current label is pressed into the stack, the first element under the label (namely the current label) is taken out, the step (2) is carried out, and otherwise, the text content of the label is taken out;

(4) querying the text content in the DOM (namely the DOM tree of the current page) which is constructed in the front;

(5) if the stack top element is found, acquiring the stack top element, if the stack top element is empty, turning to the step (7), otherwise, taking out the next element in the stack top element, and turning to the step (2);

(6) placing the text content in a root tag < div > and inserting the text content into the position in front of a current page main body tag </body >, acquiring a stack top element, taking the next element, and turning to the step 2);

(7) taking the root < div > of the next dynamic page;

(8) and (4) if the next dynamic page exists, turning to the step (1), otherwise, ending the processing.

And finally, acquiring combined page information of the dynamic page source code acquired by the JavaScript parser after the whole DOM operation is executed and the content which is not inserted into the current DOM in the execution process, wherein the page information provides more complete page information compared with the page analyzed by the traditional JavaScript.

By the method for extracting the theme from the dynamic page content, the content of the theme related to the brand can be extracted more accurately, comprehensively and quickly, so that the evaluation of the brand value based on the online evaluation content is more accurate and comprehensive.

Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit and scope of the present invention, and they should be construed as being included in the following claims and description.

Claims (8)

1. A brand value evaluation method based on big data statistical analysis is characterized by comprising the following steps:

step S1, acquiring current-period first income data, current-period second income data and current-period first cost data, wherein the current-period second income data is obtained by calculating current-period second cost data and current-period first business cost rate, and the current-period first cost data is obtained by calculating previous-period first cost data and cost increase rate; the current-stage second cost data is obtained by calculating a unit cost change coefficient, previous-stage unit cost data, previous-stage sales data and a sales increase rate; in step S1, the method further includes correcting the current first business cost rate, specifically: obtaining the grading data of the current stage influence factors and the grading data of the previous stage influence factors; calculating to obtain an influence coefficient according to the grading data of the influence factors in the current stage and the grading data of the influence factors in the previous stage; calculating to obtain a correction coefficient according to the influence coefficient; correcting and calculating the second business cost rate in the current period through the correction coefficient to obtain the corrected first business cost rate in the current period;

step S2, calculating current early-stage first profit data according to the current early-stage first income data, the current early-stage second income data and the current-stage first cost data;

step S3, obtaining profit discount rate, long-term forecast inflation rate and second profit data, wherein the second profit data is obtained according to the first profit data in the current period;

step S4, calculating the brand value based on cost and profit according to the current early stage first profit data, the profit discount rate, the long-term forecast inflation rate and the second profit data;

accessing the internet and capturing content related to the brand;

quantitatively analyzing the captured content for determining a first brand value of the brand;

the method for filtering the captured content, extracting the content of the theme related to the brand and acquiring the content related to the brand in the dynamic page content based on the theme comprises the following steps:

establishing a JavaScript filter library and a JavaScript local library at a capturing server side; the JavaScript filter library is established based on a theme and is irrelevant to target content, and the JavaScript filter library mainly comprises two types of executable files: 1. a JavaScript file that is obviously unrelated to subject; 2. a file for online statistics of customer satisfaction and insertion of third party advertisement promotion codes; the method comprises the steps that a JavaScript local library is initially added with file sets of jQuery, Ext, Dojo, Google Web ToolKit, ProtoType and YUI, and a one-to-one mapping relation between keywords and the file sets is established for each file set according to the keywords; checking whether a file set of jQuery, Ext, Dojo, Google Web ToolKit, ProtoType and YUI initially added to a JavaScript local library exists in the JavaScript local library according to a keyword part of a JavaScript file name requested outside the current site every time, and if the file set does not exist, sending an Ajax request to acquire the file set and storing the file set in the JavaScript local library; if yes, directly carrying out local downloading;

acquiring page information of each captured page, and generating a DOM (document object model) of the current page; if the host object is used in the current page, the capture server instantiates the host object as a corresponding object;

checking an external JavaScript file requested in the current page according to the JavaScript filter library, if the external JavaScript file is irrelevant to the theme, setting a loading-free mark at a corresponding position of a DOM object of the current page, and otherwise, setting a normal loading mark; wherein the theme is a theme related to the brand;

for the external JavaScript file marked as normal loading, if the currently processed JavaScript file exists in a JavaScript local library, setting a local loading mark, otherwise, setting a normal loading mark;

executing JavaScript in the current page to obtain dynamic page information; wherein, the external JavaScript file is loaded according to the loading mark;

checking whether each obtained dynamic page loses part of information in the original page or not, if so, adding the lost part into the dynamic page again to obtain integrated page information, namely content of a theme related to the brand;

wherein, whether each dynamic page obtained by the test loses part of the information in the original page, if so, the lost part is added to the dynamic page again to obtain the integrated page information, namely the content of the theme related to the brand, and the method comprises the following steps:

establishing a dynamic page library for each current dynamic page;

traversing a dynamic page library of a current dynamic page, executing the following algorithm on a root < div > tag of the current dynamic page, wherein the algorithm acquires the next tag according to a depth-first sequence and initializes an empty stack at the same time, and the method comprises the following steps:

step a1, taking the first element under the root < div > tag of the current dynamic page;

step a2, judging whether the first element exists; popping a top stack element if the first element does not exist, and then acquiring the top stack element, at this time if the top stack element is an idle step a 7; if the first element exists, taking out the next element in the first element, at this time, popping up the top element if the next element is empty, at this time, if the top element is an idle step a7, and if the above element is text content, directly turning to step a 4;

step a3, if the root < div > tag of the current dynamic page contains another < div > tag, then the root < div > tag of the current dynamic page is pressed into the stack, the first element under the root < div > tag of the current dynamic page is taken out, the step a2 is switched, otherwise, the text content of the root < div > tag of the current dynamic page is taken out;

step a4, inquiring the text content in the DOM tree of the current dynamic page;

step a5, if the text content is found, acquiring the stack top element, if the stack top element is empty, turning to step a7, otherwise, taking out the next element in the current element, and turning to step a 2;

a6, placing the text content in a root label < div > and inserting the text content into the position in front of the current page main body label </body >, obtaining the stack top element, if the stack top element is empty, turning to a7, otherwise, taking out the next element in the current element, and turning to a 2;

step a7, taking the root < div > of a next dynamic page;

step a8, if the next dynamic page exists, turning to step a1, otherwise ending the processing;

finally, acquiring combined page information of the dynamic page source code acquired by the JavaScript parser after the whole DOM operation is executed and the content which is not inserted into the current DOM in the execution process;

evaluating the content of the extracted subject matter to determine a second brand value of the brand;

determining a brand value based on a public praise impact in conjunction with the first brand value and the second brand value;

a final brand value is determined based on the cost and profit based brand value and the public praise impact based brand value.

2. The brand value evaluation method based on big data statistical analysis according to claim 1,

in step S1, the obtaining the current-stage first revenue data specifically includes:

acquiring second cost data in the current period and second business cost rate in the current period;

and calculating to obtain the first income data in the current period according to the second cost data in the current period and the second business cost rate in the current period.

3. The brand value evaluation method based on big data statistical analysis according to claim 1,

in step S1, the obtaining the current-stage first revenue data specifically includes:

acquiring second cost data of the current period, third cost data of the current period and fourth cost data of the current period;

calculating to obtain third profit data according to the current-stage second cost data;

and calculating to obtain the current-stage first income data according to the current-stage second cost data, the current-stage third cost data, the current-stage fourth cost data and the third profit data.

4. The brand value evaluation method based on big data statistical analysis according to claim 2 or 3,

obtaining the current-stage second cost data through calculation, specifically:

obtaining a unit cost change coefficient, previous unit cost data, previous sales data and a sales growth rate;

and calculating to obtain second cost data in the current period according to the unit cost change coefficient, the unit cost data in the previous period, the sales data in the previous period and the sales growth rate.

5. A brand value evaluation system based on big data statistical analysis is characterized by comprising:

the system comprises a first data acquisition module, a second data acquisition module and a first cost calculation module, wherein the first data acquisition module is used for acquiring current-period first income data, current-period second income data and current-period first cost data, the current-period second income data is obtained by calculating current-period second cost data and current-period first business cost rate, and the current-period first cost data is obtained by calculating previous-period first cost data and cost increase rate; the current-stage second cost data is obtained by calculating a unit cost change coefficient, previous-stage unit cost data, previous-stage sales data and a sales increase rate; the first data acquisition module further comprises a correction submodule, which is specifically used for correcting the first business cost rate in the current period: obtaining the grading data of the current stage influence factors and the grading data of the previous stage influence factors; calculating to obtain an influence coefficient according to the grading data of the influence factors in the current stage and the grading data of the influence factors in the previous stage; calculating to obtain a correction coefficient according to the influence coefficient; correcting and calculating the second business cost rate in the current period through the correction coefficient to obtain the corrected first business cost rate in the current period;

the data processing module is used for calculating to obtain current-stage first profit data according to the current-stage first income data, the current-stage second income data and the current-stage first cost data;

the second data acquisition module is used for acquiring profit discount rate, long-term forecast currency expansion rate and second profit data, and the second profit data is acquired according to the first profit data in the current period;

the evaluation result generation module is used for calculating the brand value based on cost and profit according to the current early-stage first profit data, the profit discount rate, the long-term forecast currency expansion rate and the second profit data; accessing the internet and capturing content related to the brand; quantitatively analyzing the captured content for determining a first brand value of the brand; the method for filtering the captured content, extracting the content of the theme related to the brand and acquiring the content related to the brand in the dynamic page content based on the theme comprises the following steps:

establishing a JavaScript filter library and a JavaScript local library at a capturing server side; the JavaScript filter library is established based on a theme and is irrelevant to target content, and the JavaScript filter library mainly comprises two types of executable files: 1. a JavaScript file that is obviously unrelated to subject; 2. a file for online statistics of customer satisfaction and insertion of third party advertisement promotion codes; the method comprises the steps that a JavaScript local library is initially added with file sets of jQuery, Ext, Dojo, Google Web ToolKit, ProtoType and YUI, and a one-to-one mapping relation between keywords and the file sets is established for each file set according to the keywords; checking whether a file set of jQuery, Ext, Dojo, Google Web ToolKit, ProtoType and YUI initially added to a JavaScript local library exists in the JavaScript local library according to a keyword part of a JavaScript file name requested outside the current site every time, and if the file set does not exist, sending an Ajax request to acquire the file set and storing the file set in the JavaScript local library; if yes, directly carrying out local downloading;

acquiring page information of each captured page, and generating a DOM (document object model) of the current page; if the host object is used in the current page, the capture server instantiates the host object as a corresponding object;

checking an external JavaScript file requested in the current page according to the JavaScript filter library, if the external JavaScript file is irrelevant to the theme, setting a loading-free mark at a corresponding position of a DOM object of the current page, and otherwise, setting a normal loading mark; wherein the theme is a theme related to the brand;

for the external JavaScript file marked as normal loading, if the currently processed JavaScript file exists in a JavaScript local library, setting a local loading mark, otherwise, setting a normal loading mark;

executing JavaScript in the current page to obtain dynamic page information; wherein, the external JavaScript file is loaded according to the loading mark;

checking whether each obtained dynamic page loses part of information in the original page or not, if so, adding the lost part into the dynamic page again to obtain integrated page information, namely content of a theme related to the brand;

wherein, whether each dynamic page obtained by the test loses part of the information in the original page, if so, the lost part is added to the dynamic page again to obtain the integrated page information, namely the content of the theme related to the brand, and the method comprises the following steps:

establishing a dynamic page library for each current page;

traversing a dynamic page library of a current page, executing the following algorithm according to a root < div > tag of the current page, wherein the algorithm acquires the next tag according to a depth-first sequence and initializes an empty stack at the same time, and the method comprises the following steps:

step a1, taking the first element under the root < div > tag of the current dynamic page;

step a2, judging whether the first element exists; popping a top stack element if the first element does not exist, and then acquiring the top stack element, at this time if the top stack element is an idle step a 7; if the first element exists, taking out the next element in the first element, at this time, popping up the top element if the next element is empty, at this time, if the top element is an idle step a7, and if the above element is text content, directly turning to step a 4;

step a3, if the root < div > tag of the current dynamic page contains another < div > tag, then the root < div > tag of the current dynamic page is pressed into the stack, the first element under the root < div > tag of the current dynamic page is taken out, the step a2 is switched, otherwise, the text content of the root < div > tag of the current dynamic page is taken out;

step a4, inquiring the text content in the DOM tree of the current dynamic page;

step a5, if the text content is found, acquiring the stack top element, if the stack top element is empty, turning to step a7, otherwise, taking out the next element in the current element, and turning to step a 2;

a6, placing the text content in a root label < div > and inserting the text content into the position in front of the current page main body label </body >, obtaining the stack top element, if the stack top element is empty, turning to a7, otherwise, taking out the next element in the current element, and turning to a 2;

step a7, taking the root < div > of a next dynamic page;

step a8, if the next dynamic page exists, turning to step a1, otherwise ending the processing;

finally, acquiring combined page information of the dynamic page source code acquired by the JavaScript parser after the whole DOM operation is executed and the content which is not inserted into the current DOM in the execution process;

evaluating the content of the extracted subject matter to determine a second brand value of the brand; determining a brand value based on a public praise impact in conjunction with the first brand value and the second brand value; a final brand value is determined based on the cost and profit based brand value and the public praise impact based brand value.

6. The big-data-statistics-analysis-based brand value evaluation system of claim 5,

the first data obtaining module is specifically configured to obtain the current-stage first revenue data:

acquiring second cost data in the current period and second business cost rate in the current period;

and calculating to obtain the first income data in the current period according to the second cost data in the current period and the second business cost rate in the current period.

7. The big-data-statistics-analysis-based brand value evaluation system of claim 5,

the first data obtaining module is specifically configured to obtain the current-stage first revenue data:

acquiring second cost data of the current period, third cost data of the current period and fourth cost data of the current period;

calculating to obtain third profit data according to the current-stage second cost data;

and calculating to obtain the current-stage first income data according to the current-stage second cost data, the current-stage third cost data, the current-stage fourth cost data and the third profit data.

8. The brand value rating system based on big data statistical analysis of claim 6 or 7,

the first data obtaining module is specifically configured to calculate and obtain the current-stage second cost data:

obtaining a unit cost change coefficient, previous unit cost data, previous sales data and a sales growth rate;

and calculating to obtain second cost data in the current period according to the unit cost change coefficient, the unit cost data in the previous period, the sales data in the previous period and the sales growth rate.

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