Data processing system and method for digital platform optimization

Abstract

The invention discloses a data processing system and method for digital platform optimization, and belongs to the technical field of data processing. Calculating a similarity coefficient of the to-be-evaluated platform and the benchmark platform to obtain a similar benchmark platform of the to-be-evaluated platform; constructing a product set, and calculating a sales index of a product on the to-be-evaluated platform so as to calculate a stability index of the to-be-evaluated platform; calculating the value improvement potential of the to-be-evaluated platform by calculating the actual platform value of the to-be-evaluated platform; according to a digital platform optimization scheme selected by the to-be-evaluated platform, calculating a satisfaction index of the user to the digital platform optimization scheme so as to calculate an evaluation index of the user to the digital platform optimization scheme; generating an optimization scheme report, and sending the optimization scheme report to the to-be-evaluated platform; according to the method, through calculation of the stability index and the value improvement potential, the evaluation accuracy is improved; by calculating the satisfaction index of the user, the user experience after platform optimization is improved; and providing an optimization reference for the to-be-evaluated platform through the optimization scheme report.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a data processing system and method for digital platform optimization. Background Art

[0002] As the market environment and user needs continue to change, digital platforms need to be continuously optimized to maintain their competitive advantages and adapt to new market trends and user needs; digital platform optimization refers to the process of continuously improving and perfecting the established digital platforms.

[0003] In the field of data processing, since there are many types of optimization solutions for digital platforms, the efficiency of optimization solution selection can be improved by optimizing the data processing process. However, in the actual optimization process of the data processing process, the existing technology often over-pursues the innovativeness of the optimization solution when selecting the optimization solution, ignoring the real experience of the digital platform users, thus violating the original intention of digital platform optimization. Summary of the invention

[0004] The object of the present invention is to provide a data processing system and method for digital platform optimization to solve the problems raised in the above-mentioned background technology.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0006] A data processing method for digital platform optimization, the method comprising the following steps:

[0007] Step S100: According to the platform type of the platform to be evaluated, the platform name of the benchmark platform of the same platform type in the background database is obtained, a benchmark platform set is constructed, and the similarity coefficient between the platform to be evaluated and the benchmark platform is calculated to obtain a similar benchmark platform of the platform to be evaluated;

[0008] Step S200: extract product names of the platform to be evaluated, construct a product set, calculate the sales index of the product on the platform to be evaluated, and calculate the stress resistance value of the product; count the stress resistance values ​​of all products in the platform to be evaluated, and calculate the stability index of the platform to be evaluated;

[0009] Step S300: based on the stability index of the platform to be evaluated, extract the preset platform value of the platform to be evaluated, and calculate the actual platform value of the platform to be evaluated; extract the platform value of similar benchmark platforms of the platform to be evaluated, and calculate the value enhancement potential of the platform to be evaluated;

[0010] Step S400: Based on the digital platform optimization solution selected by the platform to be evaluated, an optimization solution set is constructed to construct a set of business model innovation points; user evaluation data of the business model innovation points are extracted, and the user satisfaction value of the business model innovation points is calculated to calculate the user satisfaction index of the digital platform optimization solution, and obtain the user evaluation index of the digital platform optimization solution; the optimization solution set is evaluated, an optimization solution report is generated, and sent to the platform to be evaluated.

[0011] Furthermore, the specific implementation process of step S100 includes:

[0012] Step S101: The backend database records the platform data and case data of the digital platform;

[0013] The platform data includes benchmark platform data and to-be-evaluated platform data, wherein the benchmark platform data includes the platform name, platform type, platform value and growth parameters of the benchmark platform; the to-be-evaluated platform data includes the platform name, platform type, preset platform value, growth parameters and product supply and marketing data of the to-be-evaluated platform, wherein the product supply and marketing data includes the product name, product sales volume and the number of product supply channels;

[0014] The case data includes business model innovation points and user evaluation data of the business model innovation points, and the user evaluation data includes the number of positive reviews and the number of negative reviews;

[0015] It should be explained that the platform value of the benchmark platform is obtained by technical personnel in the field of value assessment to conduct multi-faceted assessments of the benchmark platform, such as revenue and profitability, number of users and activity, brand value and reputation, assets and liabilities, etc.; the preset platform value is assessed by benchmark enterprise comparison method, that is, the platform value is divided into multiple dimensions, such as profitability, growth ability, operational efficiency, etc., and the impact of each dimension on the overall value is determined by expert scoring, and the corresponding weight is determined. The benchmark platform value is used as a benchmark, and the platform value of the platform to be assessed is calculated according to the performance of the platform to be assessed in each dimension and the corresponding weight. The platform value of the platform to be assessed = (a×the platform to be assessed) Platform dimension 1 of the platform to be evaluated / platform dimension 1 of the benchmark platform + b × platform dimension 2 of the platform to be evaluated / platform dimension 2 of the benchmark platform +...) × platform value of the benchmark platform; Growth parameter is an important indicator in platform financial analysis. It is used to measure the average growth of the platform's operating income in a certain period of time. This indicator is obtained by calculating the platform's operating income growth rate for many consecutive years and taking the average value; Average operating income growth rate = [(current year's operating income - operating income a few years ago) / operating income a few years ago × 100%] / number of years; Business model innovations include nine major innovations in business model user segmentation, value proposition, channel access, user relationship, value capture, core resources, key business, important cooperation, and cost structure.

[0016] Step S102: According to the platform type of the platform to be evaluated, the platform name of the benchmark platform of the same platform type in the background database is obtained, and the benchmark platform set G = {g e |e∈[1,E]}, where g e represents the platform name of the e-th benchmark platform of the same platform type as the platform to be evaluated, and E represents the total number of benchmark platforms of the same platform type as the platform to be evaluated;

[0017] Extract the platform to be evaluated and the benchmark platform g e The growth parameters of the platform to be evaluated and the benchmark platform g are calculated. e The similarity coefficient SVM = (a|g e -a) / a, where, a|g e Indicates benchmark platform g e , a represents the growth parameter of the platform to be evaluated;

[0018] A similar benchmark platform sequence is generated in ascending order of similarity coefficients between the platform to be evaluated and the benchmark platforms in the benchmark platform set, and the benchmark platform with the smallest similarity coefficient is extracted as the similar benchmark platform of the platform to be evaluated.

[0019] It should be explained that the benchmark platform is a landmark platform preset by the system. There are multiple benchmark platforms of different platform types. As the similarity coefficient between the platform to be evaluated and the benchmark platform increases, the difference between the growth coefficient of the platform to be evaluated and the growth coefficient of the benchmark platform becomes larger and the similarity becomes smaller. Therefore, the benchmark platform with the smallest similarity coefficient is extracted as a similar benchmark platform of the platform to be evaluated.

[0020] Furthermore, the specific implementation process of step S200 includes:

[0021] Step S201: Based on the background database, extract the product names of the platform to be evaluated and construct a product set U = {u m |m∈[1,M]}, where u m represents the product name of the mth product of the platform to be evaluated, and M represents the total number of product types of the platform to be evaluated;

[0022] Extract products within preset time m The sales volume on the platform to be evaluated is used to calculate the product u m Sales index on the platform to be evaluated in, Indicates the product u within the preset time m The sales volume on the platform to be evaluated, Q represents the total sales volume of the product on the platform to be evaluated within a preset period of time;

[0023] Extract product m The number of supply channels of product u m The compressive value Among them, θ represents the preset channel influence coefficient, c(τ) represents the piecewise function, and τ represents the number of supply channels;

[0024] Step S202: Count the compression resistance values ​​of all products in the platform to be evaluated and calculate the stability index of the platform to be evaluated

[0025] It needs to be explained that the products of the platforms to be evaluated are different, and the users’ preferences are different, so the degree of influence on the platforms to be evaluated is also different. The higher the users’ preference, that is, the greater the sales volume of the product, the higher the sales index and the greater the stability index of the platform to be evaluated. The stability of the platform to be evaluated is not only related to the users’ preference, but also to the supply channel. When there is only one supply channel for the same product, changes in the supply channel, such as product chain collapse, can directly affect the stability of the platform to be evaluated, thereby reducing the stability index of the platform to be evaluated. When there are multiple supply channels for the same product, if the product chain of one supply channel collapses, there are still other supply channels supplying the product, and the degree or probability of affecting the stability of the platform to be evaluated is relatively small.

[0026] Furthermore, the specific implementation process of step S300 includes:

[0027] Step S301: based on the stability index of the platform to be evaluated, extract the preset platform value of the platform to be evaluated, and calculate the actual platform value DS'=DS×ρ of the platform to be evaluated, where DS represents the preset platform value of the platform to be evaluated;

[0028] It should be explained that the preset platform value of the platform to be evaluated is not the actual platform value. It is affected by the stability index of the platform to be evaluated. The smaller the stability index, the lower the actual platform value of the platform to be evaluated. Therefore, the actual platform value of the platform to be evaluated is calculated by the stability index of the platform to be evaluated.

[0029] Step S302: extracting the platform value of similar benchmark platforms of the platform to be evaluated, and calculating the value enhancement potential k=DS' / ds of the platform to be evaluated, where ds represents the platform value of similar benchmark platforms of the platform to be evaluated.

[0030] It needs to be explained that the relative value ratio = platform value of the platform to be evaluated / platform value of similar benchmark platforms of the platform to be evaluated. The larger the ratio, the closer the platform value of the platform to be evaluated is to the platform value of similar benchmark platforms of the platform to be evaluated, and the greater the potential for value enhancement, which will affect the evaluation of the platform to be evaluated.

[0031] Furthermore, the specific implementation process of step S400 includes:

[0032] Step S401: Based on the digital platform optimization solution selected by the platform to be evaluated, the digital platform optimization solution is composed of business model innovation points, and an optimization solution set Y = {y r |r∈[1,R]}, where y r represents the rth digital platform optimization solution selected by the platform to be evaluated, and R represents the total number of digital platform optimization solutions selected by the platform to be evaluated;

[0033] Digital platform optimization solution selected based on the platform to be evaluated r , construct the business model innovation point set P = {p j |j∈[1,J]}, where p j Represents the digital platform optimization solution y r The name of the j-th business model innovation point in , where J represents the digital platform optimization solution y r The total number of innovation points in the business model;

[0034] Step S402: Extracting business model innovation points p j User evaluation data, calculate the business model innovation point p j User satisfaction value in, Indicates the innovation point of business model j Number of favorable comments, Indicates the innovation point of business model j The number of negative reviews;

[0035] According to the business model innovation point j Calculate the user satisfaction value of the digital platform optimization solution y r Satisfaction index in, Represents the digital platform optimization solution y r The total number of positive reviews for the innovative points of the business model;

[0036] It needs to be explained that the digital platform solicits user opinions before conducting an optimization evaluation. The digital platform proposes optimization directions (business model innovations), such as introducing new third-party users, providing users with new products and services, adding new transaction channels, and expanding new businesses. Users evaluate the optimization directions, generate evaluation data for the digital platform, and record the user's evaluation data in the case data. The digital platform thereby obtains the user's satisfaction index for the optimization direction.

[0037] Based on the value enhancement potential of the platform to be evaluated and the user's optimization plan for the digital platform r Satisfaction index, calculate the digital platform optimization solution y r The evaluation index m = k × o;

[0038] Step S403: Evaluate the optimization solution set, generate an optimization solution report in descending order of the evaluation index of the digital platform optimization solution, wherein the optimization solution includes the digital platform optimization solution and the evaluation index of the digital platform optimization solution, and send the optimization solution report to the platform to be evaluated.

[0039] A data processing system for digital platform optimization, the system includes: a data acquisition module, a stabilization module, an improvement potential module and a solution evaluation module;

[0040] The data storage module is used to calculate the similarity coefficient between the platform to be evaluated and the benchmark platform to obtain a similar benchmark platform of the platform to be evaluated; the stability module constructs a product set and calculates the sales index of the product on the platform to be evaluated to calculate the stability index of the platform to be evaluated; the improvement potential module calculates the value improvement potential of the platform to be evaluated by calculating the actual platform value of the platform to be evaluated; the solution evaluation module calculates the user satisfaction index of the digital platform optimization solution selected by the platform to be evaluated to calculate the user evaluation index of the digital platform optimization solution; generates an optimization solution report and sends it to the platform to be evaluated;

[0041] The data storage module is electrically connected to the stabilization module, and the output end of the data storage module is connected to the input end of the stabilization module; the stabilization module is electrically connected to the potential enhancement module, and the output end of the stabilization module is connected to the input end of the potential enhancement module; the potential enhancement module is electrically connected to the solution evaluation module, and the output end of the potential enhancement module is connected to the input end of the solution evaluation module.

[0042] Further, the data storage module includes a storage unit and a growth parameter unit;

[0043] The storage unit records the platform data and case data of the digital platform through the background database; the growth parameter unit is used to obtain the platform name of the benchmark platform of the same platform type in the background database, build a benchmark platform set, calculate the similarity coefficient between the platform to be evaluated and the benchmark platform, so as to obtain a similar benchmark platform of the platform to be evaluated;

[0044] The storage unit is electrically connected to the growth parameter unit, and the output end of the storage unit is connected to the input end of the growth parameter unit.

[0045] Further, the stability module includes a pressure resistance unit and a stability index unit;

[0046] The stress resistance unit extracts product names of the platform to be evaluated, constructs a product set, and calculates the sales index of the product on the platform to be evaluated to calculate the stress resistance value of the product; the stability index unit calculates the stability index of the platform to be evaluated by counting the stress resistance values ​​of all products in the platform to be evaluated;

[0047] The pressure-resistant unit is electrically connected to the stability index unit, and an output end of the pressure-resistant unit is connected to an input end of the stability index unit.

[0048] Further, the improvement potential module includes a value unit and an improvement potential unit;

[0049] The value unit extracts the preset platform value of the platform to be evaluated based on the stability index of the platform to be evaluated, and calculates the actual platform value of the platform to be evaluated; the improvement potential unit calculates the value improvement potential of the platform to be evaluated by extracting the platform value of similar benchmark platforms of the platform to be evaluated;

[0050] The value unit is electrically connected to the potential enhancement unit, and the output end of the value unit is connected to the input end of the potential enhancement unit.

[0051] Further, the scheme evaluation module includes a satisfaction index unit and an optimization evaluation unit;

[0052] The satisfaction index unit constructs an optimization solution set according to the digital platform optimization solution selected by the platform to be evaluated, so as to construct a business model innovation point set; extracts user evaluation data of the business model innovation point, calculates the user satisfaction value of the business model innovation point, so as to calculate the user satisfaction index of the digital platform optimization solution; the optimization evaluation unit evaluates the optimization solution set by calculating the user's evaluation index of the digital platform optimization solution, generates an optimization solution report, and sends it to the platform to be evaluated;

[0053] The satisfaction index unit is electrically connected to the optimization evaluation unit, and the output end of the satisfaction index unit is connected to the input end of the optimization evaluation unit.

[0054] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: in a data processing system and method for digital platform optimization provided by the present invention, a similarity coefficient between the platform to be evaluated and the benchmark platform is calculated to obtain a similar benchmark platform of the platform to be evaluated; a product set is constructed, and the sales index of the product on the platform to be evaluated is calculated to calculate the stability index of the platform to be evaluated; the value enhancement potential of the platform to be evaluated is calculated by calculating the actual platform value of the platform to be evaluated; according to the digital platform optimization solution selected by the platform to be evaluated, the user satisfaction index of the digital platform optimization solution is calculated to calculate the user evaluation index of the digital platform optimization solution; an optimization solution report is generated and sent to the platform to be evaluated; the present invention improves the accuracy of the evaluation by calculating the stability index and the value enhancement potential; further improves the user experience after the platform optimization is improved by calculating the user satisfaction index; and provides an optimization reference for the platform to be evaluated through the optimization solution report. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] The accompanying drawings are used to provide further understanding of the present invention and constitute a part of the specification. They are used to explain the present invention together with the embodiments of the present invention and do not constitute a limitation of the present invention.

[0056] Figure 1 It is a structural schematic diagram of a data processing system for digital platform optimization according to the present invention;

[0057] Figure 2 It is a schematic diagram of the steps of a data processing method for digital platform optimization of the present invention. DETAILED DESCRIPTION

[0058] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0059] See also Figure 1 , in the first embodiment: a data processing system for digital platform optimization is provided, the system comprising: a data acquisition module, a stabilization module, an improvement potential module and a solution evaluation module;

[0060] The data storage module is used to calculate the similarity coefficient between the platform to be evaluated and the benchmark platform to obtain a similar benchmark platform for the platform to be evaluated; the stability module builds a product set and calculates the sales index of the product on the platform to be evaluated to calculate the stability index of the platform to be evaluated; the improvement potential module calculates the value improvement potential of the platform to be evaluated by calculating the actual platform value of the platform to be evaluated; the solution evaluation module calculates the user satisfaction index of the digital platform optimization solution selected by the platform to be evaluated to calculate the user evaluation index of the digital platform optimization solution; generates an optimization solution report and sends it to the platform to be evaluated;

[0061] The data storage module is electrically connected to the stabilization module, and the output end of the data storage module is connected to the input end of the stabilization module; the stabilization module is electrically connected to the potential enhancement module, and the output end of the stabilization module is connected to the input end of the potential enhancement module; the potential enhancement module is electrically connected to the solution evaluation module, and the output end of the potential enhancement module is connected to the input end of the solution evaluation module.

[0062] Specifically, the data storage module includes a storage unit and a growth parameter unit;

[0063] The storage unit records the platform data and case data of the digital platform through the background database; the growth parameter unit is used to obtain the platform name of the benchmark platform of the same platform type in the background database, build a benchmark platform set, and calculate the similarity coefficient between the platform to be evaluated and the benchmark platform to obtain a similar benchmark platform of the platform to be evaluated;

[0064] The storage unit is electrically connected to the growth parameter unit, and the output end of the storage unit is connected to the input end of the growth parameter unit.

[0065] Specifically, the stability module includes a compression resistance unit and a stability index unit;

[0066] The stress resistance unit extracts the product names of the platform to be evaluated, constructs a product set, and calculates the sales index of the product on the platform to be evaluated to calculate the stress resistance value of the product; the stability index unit calculates the stability index of the platform to be evaluated by counting the stress resistance values ​​of all products on the platform to be evaluated;

[0067] The pressure-resistant unit is electrically connected to the stability index unit, and an output end of the pressure-resistant unit is connected to an input end of the stability index unit.

[0068] Specifically, the improvement potential module includes value units and improvement potential units;

[0069] The value unit extracts the preset platform value of the platform to be evaluated based on the stability index of the platform to be evaluated, and calculates the actual platform value of the platform to be evaluated. The potential improvement unit calculates the value improvement potential of the platform to be evaluated by extracting the platform value of similar benchmark platforms of the platform to be evaluated.

[0070] The value unit is electrically connected to the potential enhancement unit, and the output end of the value unit is connected to the input end of the potential enhancement unit.

[0071] Specifically, the program evaluation module includes a satisfaction index unit and an optimization evaluation unit;

[0072] The satisfaction index unit constructs an optimization solution set according to the digital platform optimization solution selected by the platform to be evaluated, so as to construct a business model innovation point set; extracts user evaluation data of the business model innovation points, calculates the user satisfaction value of the business model innovation points, and calculates the user satisfaction index of the digital platform optimization solution; the optimization evaluation unit evaluates the optimization solution set by calculating the user's evaluation index of the digital platform optimization solution, generates an optimization solution report, and sends it to the platform to be evaluated;

[0073] The satisfaction index unit is electrically connected to the optimization evaluation unit, and an output end of the satisfaction index unit is connected to an input end of the optimization evaluation unit.

[0074] See also Figure 2 In the second embodiment, a data processing method for optimizing a digital platform is provided, the method comprising the following steps:

[0075] Step S100: According to the platform type of the platform to be evaluated, the platform name of the benchmark platform of the same platform type in the background database is obtained, a benchmark platform set is constructed, and the similarity coefficient between the platform to be evaluated and the benchmark platform is calculated to obtain a similar benchmark platform of the platform to be evaluated;

[0076] Step S200: extract product names of the platform to be evaluated, construct a product set, calculate the sales index of the product on the platform to be evaluated, and calculate the stress resistance value of the product; count the stress resistance values ​​of all products in the platform to be evaluated, and calculate the stability index of the platform to be evaluated;

[0077] Step S300: based on the stability index of the platform to be evaluated, extract the preset platform value of the platform to be evaluated, and calculate the actual platform value of the platform to be evaluated; extract the platform value of similar benchmark platforms of the platform to be evaluated, and calculate the value enhancement potential of the platform to be evaluated;

[0078] Step S400: Based on the digital platform optimization solution selected by the platform to be evaluated, an optimization solution set is constructed to construct a set of business model innovation points; user evaluation data of the business model innovation points are extracted, and the user satisfaction value of the business model innovation points is calculated to calculate the user satisfaction index of the digital platform optimization solution, and obtain the user evaluation index of the digital platform optimization solution; the optimization solution set is evaluated, an optimization solution report is generated, and sent to the platform to be evaluated.

[0079] Preferably, the specific implementation process of step S100 includes:

[0080] Step S101: The backend database records the platform data and case data of the digital platform;

[0081] Platform data includes benchmark platform data and platform data to be evaluated. Benchmark platform data includes the platform name, platform type, platform value and growth parameters of the benchmark platform; platform data to be evaluated includes the platform name, platform type, preset platform value, growth parameters and product supply and marketing data of the platform to be evaluated. Product supply and marketing data includes product name, product sales volume and number of product supply channels;

[0082] The case data includes the innovative points of the business model and the user evaluation data of the innovative points of the business model. The user evaluation data includes the number of positive reviews and the number of negative reviews.

[0083] Step S102: According to the platform type of the platform to be evaluated, the platform name of the benchmark platform of the same platform type in the background database is obtained, and the benchmark platform set G = {g e |e∈[1,E]}, where g e represents the platform name of the e-th benchmark platform of the same platform type as the platform to be evaluated, and E represents the total number of benchmark platforms of the same platform type as the platform to be evaluated;

[0084] Extract the platform to be evaluated and the benchmark platform g e The growth parameters of the platform to be evaluated and the benchmark platform g are calculated. e The similarity coefficient SVM = (a|g e -a) / a, where, a|g e Indicates benchmark platform g e , a represents the growth parameter of the platform to be evaluated;

[0085] A similar benchmark platform sequence is generated in ascending order of similarity coefficients between the platform to be evaluated and the benchmark platforms in the benchmark platform set, and the benchmark platform with the smallest similarity coefficient is extracted as the similar benchmark platform of the platform to be evaluated.

[0086] Preferably, the specific implementation process of step S200 includes:

[0087] Step S201: Based on the background database, extract the product names of the platform to be evaluated and construct a product set U = {u m |m∈[1,M]}, where u m represents the product name of the mth product of the platform to be evaluated, and M represents the total number of product types of the platform to be evaluated;

[0088] Extract products within preset time m The sales volume on the platform to be evaluated is used to calculate the product u m Sales index on the platform to be evaluated in, Indicates the product u within the preset time m The sales volume on the platform to be evaluated, Q represents the total sales volume of the product on the platform to be evaluated within a preset period of time;

[0089] Extract product m The number of supply channels of product u m The compressive value Among them, θ represents the preset channel influence coefficient, c(τ) represents the piecewise function, and τ represents the number of supply channels;

[0090] Step S202: Count the compression resistance values ​​of all products in the platform to be evaluated and calculate the stability index of the platform to be evaluated

[0091] Preferably, the specific implementation process of step S300 includes:

[0092] Step S301: based on the stability index of the platform to be evaluated, extract the preset platform value of the platform to be evaluated, and calculate the actual platform value DS'=DS×ρ of the platform to be evaluated, where DS represents the preset platform value of the platform to be evaluated;

[0093] Step S302: extracting the platform value of similar benchmark platforms of the platform to be evaluated, and calculating the value enhancement potential k=DS' / ds of the platform to be evaluated, where ds represents the platform value of similar benchmark platforms of the platform to be evaluated.

[0094] Preferably, the specific implementation process of step S400 includes:

[0095] Step S401: Based on the digital platform optimization solution selected by the platform to be evaluated, the digital platform optimization solution is composed of business model innovation points, and an optimization solution set Y = {y r |r∈[1,R]}, where y r represents the rth digital platform optimization solution selected by the platform to be evaluated, and R represents the total number of digital platform optimization solutions selected by the platform to be evaluated;

[0096] Digital platform optimization solution selected based on the platform to be evaluated r , construct the business model innovation point set P = {p j |j∈[1,J]}, where p j Represents the digital platform optimization solution y r The name of the j-th business model innovation point in , where J represents the digital platform optimization solution y r The total number of innovation points in the business model;

[0097] Step S402: Extracting business model innovation points p j User evaluation data, calculate the business model innovation point p j User satisfaction value in, Indicates the innovation point of business model j Number of favorable comments, Indicates the innovation point of business model j The number of negative reviews;

[0098] According to the business model innovation point j Calculate the user satisfaction value of the digital platform optimization solution y r Satisfaction index in, Represents the digital platform optimization solution y r The total number of positive reviews for the innovative points of the business model;

[0099] For example: the number of favorable comments for the nine business model innovations are 8, 2, 4, 7, 6, 5, 4, 1, 9; the number of unfavorable comments are 2, 8, 6, 3, 4, 5, 6, 9, 1;

[0100] The user satisfaction values ​​of the business model innovation points of the digital platform optimization solution y1 are 0.8, 0.2, 0.4, 0.7, 0.6, 0.5, 0.4, 0.1, and 0.9 respectively. The satisfaction index of the digital platform optimization solution y1 is

[0101] The user satisfaction values ​​of the business model innovation points of the digital platform optimization solution y2 are 0.8 and 0.2 respectively. The satisfaction index of the digital platform optimization solution y2 is

[0102] The user satisfaction values ​​of the business model innovation points of the digital platform optimization solution y3 are 0.8 and 0.9 respectively. The satisfaction index of the digital platform optimization solution y3

[0103] The user satisfaction values ​​of the business model innovation points of the digital platform optimization solution y4 are 0.2, 0.3, and 0.1 respectively. The satisfaction index of the digital platform optimization solution y4

[0104] The user satisfaction values ​​of the business model innovation points of the digital platform optimization solution y5 are 0.8, 0.7, and 0.9 respectively. The satisfaction index of the digital platform optimization solution y5

[0105] Based on the value enhancement potential of the platform to be evaluated and the user's optimization plan for the digital platform r Satisfaction index, calculate the digital platform optimization solution y r The evaluation index m = k × o;

[0106] Step S403: Evaluate the optimization solution set, generate an optimization solution report in descending order of the evaluation index of the digital platform optimization solution, the optimization solution includes the digital platform optimization solution and the evaluation index of the digital platform optimization solution, and send the optimization solution report to the platform to be evaluated.

[0107] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0108] Finally, it should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A data processing method for digital platform optimization, characterized in that: The method comprises the following steps: Step S100: According to the platform type of the platform to be evaluated, the platform name of the benchmark platform of the same platform type in the background database is obtained, a benchmark platform set is constructed, and the similarity coefficient between the platform to be evaluated and the benchmark platform is calculated to obtain a similar benchmark platform of the platform to be evaluated; Step S200: extract product names of the platform to be evaluated, construct a product set, calculate the sales index of the product on the platform to be evaluated, and calculate the stress resistance value of the product; count the stress resistance values ​​of all products in the platform to be evaluated, and calculate the stability index of the platform to be evaluated; Step S300: based on the stability index of the platform to be evaluated, extract the preset platform value of the platform to be evaluated, and calculate the actual platform value of the platform to be evaluated; extract the platform value of similar benchmark platforms of the platform to be evaluated, and calculate the value enhancement potential of the platform to be evaluated; Step S400: Based on the digital platform optimization solution selected by the platform to be evaluated, an optimization solution set is constructed to construct a set of business model innovation points; user evaluation data of the business model innovation points are extracted, and the user satisfaction value of the business model innovation points is calculated to calculate the user satisfaction index of the digital platform optimization solution, and obtain the user evaluation index of the digital platform optimization solution; the optimization solution set is evaluated, an optimization solution report is generated, and sent to the platform to be evaluated.

2. A data processing method for digital platform optimization according to claim 1, characterized in that: The specific implementation process of step S100 includes: Step S101: The backend database records the platform data and case data of the digital platform; The platform data includes benchmark platform data and to-be-evaluated platform data, wherein the benchmark platform data includes the platform name, platform type, platform value and growth parameters of the benchmark platform; the to-be-evaluated platform data includes the platform name, platform type, preset platform value, growth parameters and product supply and marketing data of the to-be-evaluated platform, wherein the product supply and marketing data includes the product name, product sales volume and the number of product supply channels; The case data includes business model innovation points and user evaluation data of the business model innovation points, and the user evaluation data includes the number of positive reviews and the number of negative reviews; Step S102: According to the platform type of the platform to be evaluated, the platform name of the benchmark platform of the same platform type in the background database is obtained, and the benchmark platform set G = {g e |e∈[1,E]}, where g e represents the platform name of the e-th benchmark platform of the same platform type as the platform to be evaluated, and E represents the total number of benchmark platforms of the same platform type as the platform to be evaluated; Extract the platform to be evaluated and the benchmark platform g e The growth parameters of the platform to be evaluated and the benchmark platform g are calculated. e The similarity coefficient SVM = (a|g e -a) / a, where, a|g e Indicates benchmark platform g e , a represents the growth parameter of the platform to be evaluated; A similar benchmark platform sequence is generated in ascending order of similarity coefficients between the platform to be evaluated and the benchmark platforms in the benchmark platform set, and the benchmark platform with the smallest similarity coefficient is extracted as the similar benchmark platform of the platform to be evaluated.

3. A data processing method for digital platform optimization according to claim 2, characterized in that: The specific implementation process of step S200 includes: Step S201: Based on the background database, extract the product names of the platform to be evaluated and construct a product set U = {u m |m∈[1,M]}, where u m represents the product name of the mth product of the platform to be evaluated, and M represents the total number of product types of the platform to be evaluated; Extract products within preset time m The sales volume on the platform to be evaluated is used to calculate the product u m Sales index on the platform to be evaluated in, Indicates the product u within the preset time m The sales volume on the platform to be evaluated, Q represents the total sales volume of the product on the platform to be evaluated within a preset period of time; Extract product m The number of supply channels of product u m The compressive value 0<θ<1, Among them, θ represents the preset channel influence coefficient, c(τ) represents the piecewise function, and τ represents the number of supply channels; Step S202: Count the compression resistance values ​​of all products in the platform to be evaluated and calculate the stability index of the platform to be evaluated 4. A data processing method for digital platform optimization according to claim 3, characterized in that: The specific implementation process of step S300 includes: Step S301: based on the stability index of the platform to be evaluated, extract the preset platform value of the platform to be evaluated, and calculate the actual platform value DS'=DS×ρ of the platform to be evaluated, where DS represents the preset platform value of the platform to be evaluated; Step S302: extracting the platform value of similar benchmark platforms of the platform to be evaluated, and calculating the value enhancement potential k=DS' / ds of the platform to be evaluated, where ds represents the platform value of similar benchmark platforms of the platform to be evaluated.

5. A data processing method for digital platform optimization according to claim 4, characterized in that: The specific implementation process of step S400 includes: Step S401: Based on the digital platform optimization solution selected by the platform to be evaluated, the digital platform optimization solution is composed of business model innovation points, and an optimization solution set Y = {y r |r∈[1,R]}, where y r represents the rth digital platform optimization solution selected by the platform to be evaluated, and R represents the total number of digital platform optimization solutions selected by the platform to be evaluated; Digital platform optimization solution selected based on the platform to be evaluated r , construct the business model innovation point set P = {p j |j∈[1,J]}, where p j Represents the digital platform optimization solution y r The name of the j-th business model innovation point in , where J represents the digital platform optimization solution y r The total number of business model innovation points in the Step S402: Extracting business model innovation points p j User evaluation data, calculate the business model innovation point p j User satisfaction value in, Indicates the innovation point of business model j Number of favorable comments, Indicates the innovation point of business model j The number of negative reviews; According to the business model innovation point j Calculate the user satisfaction value of the digital platform optimization solution y r Satisfaction index in, Represents the digital platform optimization solution y r The total number of positive reviews for the innovative points of the business model; Based on the value enhancement potential of the platform to be evaluated and the user's optimization plan for the digital platform r Satisfaction index, calculate the digital platform optimization solution y r The evaluation index m = k × o; Step S403: Evaluate the optimization solution set, generate an optimization solution report in descending order of the evaluation index of the digital platform optimization solution, wherein the optimization solution includes the digital platform optimization solution and the evaluation index of the digital platform optimization solution, and send the optimization solution report to the platform to be evaluated.

6. A data processing system for digital platform optimization, using a data processing method for digital platform optimization as claimed in any one of claims 1 to 5, characterized in that: The system includes: a data storage module, a stabilization module, a potential improvement module and a solution evaluation module; The data storage module is used to calculate the similarity coefficient between the platform to be evaluated and the benchmark platform to obtain a similar benchmark platform of the platform to be evaluated; the stability module constructs a product set and calculates the sales index of the product on the platform to be evaluated to calculate the stability index of the platform to be evaluated; the improvement potential module calculates the value improvement potential of the platform to be evaluated by calculating the actual platform value of the platform to be evaluated; the solution evaluation module calculates the user satisfaction index of the digital platform optimization solution selected by the platform to be evaluated to calculate the user evaluation index of the digital platform optimization solution; generates an optimization solution report and sends it to the platform to be evaluated; The data storage module is electrically connected to the stabilization module, and the output end of the data storage module is connected to the input end of the stabilization module; the stabilization module is electrically connected to the potential enhancement module, and the output end of the stabilization module is connected to the input end of the potential enhancement module; the potential enhancement module is electrically connected to the solution evaluation module, and the output end of the potential enhancement module is connected to the input end of the solution evaluation module.

7. A data processing system for digital platform optimization according to claim 6, characterized in that: The data storage module includes a storage unit and a growth parameter unit; The storage unit records the platform data and case data of the digital platform through the background database; the growth parameter unit is used to obtain the platform name of the benchmark platform of the same platform type in the background database, build a benchmark platform set, calculate the similarity coefficient between the platform to be evaluated and the benchmark platform, so as to obtain a similar benchmark platform of the platform to be evaluated; The storage unit is electrically connected to the growth parameter unit, and the output end of the storage unit is connected to the input end of the growth parameter unit.

8. A data processing system for digital platform optimization according to claim 7, characterized in that: The stability module includes a pressure resistance unit and a stability index unit; The stress resistance unit extracts product names of the platform to be evaluated, constructs a product set, and calculates the sales index of the product on the platform to be evaluated to calculate the stress resistance value of the product; the stability index unit calculates the stability index of the platform to be evaluated by counting the stress resistance values ​​of all products in the platform to be evaluated; The pressure-resistant unit is electrically connected to the stability index unit, and an output end of the pressure-resistant unit is connected to an input end of the stability index unit.

9. A data processing system for digital platform optimization according to claim 8, characterized in that: The improvement potential module includes a value unit and an improvement potential unit; The value unit extracts the preset platform value of the platform to be evaluated based on the stability index of the platform to be evaluated, and calculates the actual platform value of the platform to be evaluated; The potential improvement unit calculates the value improvement potential of the platform to be evaluated by extracting the platform value of similar benchmark platforms of the platform to be evaluated; The value unit is electrically connected to the potential enhancement unit, and the output end of the value unit is connected to the input end of the potential enhancement unit.

10. A data processing system for digital platform optimization according to claim 9, characterized in that: The program evaluation module includes a satisfaction index unit and an optimization evaluation unit; The satisfaction index unit constructs an optimization solution set according to the digital platform optimization solution selected by the platform to be evaluated, so as to construct a business model innovation point set; Extract user evaluation data of business model innovation points, calculate user satisfaction values ​​of business model innovation points, and calculate user satisfaction index of digital platform optimization solutions; The optimization evaluation unit evaluates the optimization solution set by calculating the user's evaluation index for the digital platform optimization solution, generates an optimization solution report, and sends it to the platform to be evaluated; The satisfaction index unit is electrically connected to the optimization evaluation unit, and the output end of the satisfaction index unit is connected to the input end of the optimization evaluation unit.

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