Method, device, equipment and medium for weight analysis of factors affecting resource acquisition

Abstract

The present application relates to the field of big data and can be used in the financial field. It provides a method, device, equipment, medium and program product for weight analysis of resource acquisition influencing factors. The present application can improve the accuracy of the analysis results of resource acquisition influencing factors. The method includes: obtaining data corresponding to multiple objects under multiple resource acquisition influencing factors as independent variable data; obtaining data corresponding to multiple influencing factor evaluation dimensions based on the independent variable data as dependent variable data; establishing a multiple regression analysis equation based on the independent variable data and the dependent variable data; using the stepwise regression method and regression model according to the multiple regression analysis equation to screen out a combination of resource acquisition influencing factors that pass the significance test; using the principal component regression analysis method to obtain the weights of each resource acquisition influencing factor in the combination of resource acquisition influencing factors; and obtaining the weight analysis results of resource acquisition influencing factors based on the weights of each resource acquisition influencing factor.

Description

Technical Field

[0001] The present application relates to the field of big data technology, and in particular to a method, device, computer equipment, storage medium and computer program product for weight analysis of factors affecting resource acquisition. Background Art

[0002] With the rapid development of big data technology, more and more users choose to conduct comprehensive analysis of various types of information based on big data technology. Among them, comprehensive analysis of various influencing factors of resource acquisition is conducive to helping users deal with problems in their technology development process in a timely manner, so as to take targeted and effective measures as soon as possible.

[0003] The current statistical analysis method for factors affecting resource acquisition is mainly based on research on specific issues to screen out relevant factors affecting resource acquisition. However, the results cover a small number of evaluation dimensions, resulting in low analysis accuracy. Summary of the invention

[0004] Based on this, it is necessary to provide a weight analysis method, device, computer equipment, computer-readable storage medium and computer program product that can analyze factors affecting resource acquisition in response to the above technical problems.

[0005] In a first aspect, the present application provides a method for weight analysis of factors affecting resource acquisition. The method comprises:

[0006] Obtaining data corresponding to multiple preset objects under multiple preset resource acquisition influencing factors as independent variable data;

[0007] Based on the independent variable data, data corresponding to multiple preset influencing factor evaluation dimensions are obtained as dependent variable data;

[0008] Establishing a multiple regression analysis equation based on the independent variable data and the dependent variable data;

[0009] According to the multivariate regression analysis equation, using the stepwise regression method and regression model, screen out the combination of resource acquisition influencing factors that pass the significance test;

[0010] Using principal component regression analysis, the weight of each resource acquisition influencing factor in the resource acquisition influencing factor combination is obtained;

[0011] Based on the weights of the various resource acquisition influencing factors, a weight analysis result of the resource acquisition influencing factors is obtained.

[0012] In a second aspect, the present application also provides a device for weight analysis of factors affecting resource acquisition. The device comprises:

[0013] A data acquisition module, which acquires data corresponding to multiple preset objects under multiple preset resource acquisition influencing factors as independent variable data;

[0014] A variable output module, which obtains data corresponding to multiple preset influencing factor evaluation dimensions based on the independent variable data as dependent variable data;

[0015] An equation building module is used to build a multiple regression analysis equation based on the independent variable data and the dependent variable data;

[0016] A variable screening module, based on the multivariate regression analysis equation, uses a stepwise regression method and a regression model to screen out combinations of resource acquisition influencing factors that pass the significance test;

[0017] The weight calculation module uses the principal component regression analysis method to obtain the weight of each resource acquisition influencing factor in the resource acquisition influencing factor combination;

[0018] The result analysis module obtains the weight analysis results of the resource acquisition influencing factors based on the weights of the various resource acquisition influencing factors.

[0019] In a third aspect, the present application further provides a computer device. The computer device includes a memory and a processor, the memory stores a computer program, and the processor implements the following steps when executing the computer program:

[0020] Obtain data corresponding to multiple preset resource acquisition influencing factors from multiple preset objects as independent variable data; obtain data corresponding to multiple preset influencing factor evaluation dimensions based on the independent variable data as dependent variable data; establish a multiple regression analysis equation based on the independent variable data and the dependent variable data; based on the multiple regression analysis equation, use the stepwise regression method and regression model to screen out a combination of resource acquisition influencing factors that pass the significance test; use the principal component regression analysis method to obtain the weight of each resource acquisition influencing factor in the combination of resource acquisition influencing factors; based on the weights of each resource acquisition influencing factor, obtain the weight analysis result of the resource acquisition influencing factors.

[0021] In a fourth aspect, the present application further provides a computer-readable storage medium. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the following steps are implemented:

[0022] Obtain data corresponding to multiple preset resource acquisition influencing factors from multiple preset objects as independent variable data; obtain data corresponding to multiple preset influencing factor evaluation dimensions based on the independent variable data as dependent variable data; establish a multiple regression analysis equation based on the independent variable data and the dependent variable data; based on the multiple regression analysis equation, use the stepwise regression method and regression model to screen out a combination of resource acquisition influencing factors that pass the significance test; use the principal component regression analysis method to obtain the weight of each resource acquisition influencing factor in the combination of resource acquisition influencing factors; based on the weights of each resource acquisition influencing factor, obtain the weight analysis result of the resource acquisition influencing factors.

[0023] In a fifth aspect, the present application further provides a computer program product. The computer program product includes a computer program, and when the computer program is executed by a processor, the following steps are implemented:

[0024] Obtain data corresponding to multiple preset resource acquisition influencing factors from multiple preset objects as independent variable data; obtain data corresponding to multiple preset influencing factor evaluation dimensions based on the independent variable data as dependent variable data; establish a multiple regression analysis equation based on the independent variable data and the dependent variable data; based on the multiple regression analysis equation, use the stepwise regression method and regression model to screen out a combination of resource acquisition influencing factors that pass the significance test; use the principal component regression analysis method to obtain the weight of each resource acquisition influencing factor in the combination of resource acquisition influencing factors; based on the weights of each resource acquisition influencing factor, obtain the weight analysis result of the resource acquisition influencing factors.

[0025] The above-mentioned weight analysis method, device, computer equipment, storage medium and computer program product of resource acquisition influencing factors first obtain data corresponding to multiple preset objects under multiple preset resource acquisition influencing factors as independent variable data. Then, based on the independent variable data, data corresponding to multiple preset influencing factor evaluation dimensions are obtained as dependent variable data. Secondly, a multivariate regression analysis equation is established based on the independent variable data and the dependent variable data. Then, based on the multivariate regression analysis equation, the stepwise regression method and regression model are used to screen out the combination of resource acquisition influencing factors that pass the significance test; then, the principal component regression analysis method is used to obtain the weight of each resource acquisition influencing factor in the combination of resource acquisition influencing factors; finally, based on the weights of each resource acquisition influencing factor, the weight analysis result of the resource acquisition influencing factor is obtained. This scheme can use the multivariate regression equation to screen out the combination of influencing factors with the best significance performance under different preset resource acquisition influencing factor evaluation dimensions, and further use the principal component regression analysis method to obtain the weight analysis results of each influencing factor under different preset resource acquisition influencing factor evaluation dimensions. This can not only enrich the number of evaluation dimensions covered by the resource acquisition influencing factor analysis results, but also further improve the accuracy of the resource acquisition influencing factor analysis results. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 A schematic flow chart of a method for weight analysis of factors affecting resource acquisition in one embodiment;

[0027] Figure 2 A schematic diagram of a flow chart of steps for obtaining the weights of various resource acquisition influencing factors using a principal component regression analysis method in one embodiment;

[0028] Figure 3 A schematic flow chart of the steps of screening resources to obtain a combination of influencing factors in one embodiment;

[0029] Figure 4 It is a structural block diagram of a weight analysis device for resource acquisition influencing factors in one embodiment;

[0030] Figure 5 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0031] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0032] The weight analysis method of resource acquisition influencing factors provided in the embodiment of the present application can be applied to a terminal for execution, wherein the terminal is but not limited to various personal computers, laptops, smart phones and tablet computers.

[0033] In one embodiment, Figure 1 As shown, a method for weight analysis of factors affecting resource acquisition is provided, and the method is applied to a terminal as an example for explanation, including the following steps:

[0034] Step S101 : obtaining data corresponding to a plurality of preset objects under a plurality of preset resource acquisition influencing factors as independent variable data.

[0035] In this step, the multiple preset objects can be different enterprises, and the enterprises can be from different or the same industries, such as different banks, etc. The multiple preset resource acquisition influencing factors refer to multiple preset influencing factors associated with resource acquisition, and the types of resources can be various. Therefore, when the multiple preset resource acquisition influencing factors are clarified, data corresponding to these preset resource acquisition influencing factors can be obtained from the multiple preset objects as independent variable data.

[0036] Step S102: obtaining data corresponding to a plurality of preset influencing factor evaluation dimensions based on the independent variable data as dependent variable data.

[0037] In this step, the multiple preset influencing factor evaluation dimensions can be obtained by a single or multiple independent variables in the above-mentioned independent variable data through a variety of different calculation methods, such as adding multiple independent variables, adding and averaging the corresponding dimensions. Therefore, in the case of clarifying the method for obtaining multiple preset influencing factor evaluation dimensions, the corresponding data under multiple preset influencing factor evaluation dimensions can be obtained according to the data corresponding to the multiple independent variables in the above-mentioned independent variable data, using the calculation method preset by the user, as the dependent variable data.

[0038] Step S103, establishing a multiple regression analysis equation based on the independent variable data and the dependent variable data.

[0039] In this step, the number of multiple regression analysis equations established may correspond to the number of dependent variables in the above-mentioned dependent variable data. For example, the above-mentioned dependent variable data may correspond to two dependent variables, namely dependent variable A and dependent variable B, which correspond to two preset influencing factor evaluation dimensions respectively. Then, a first multiple regression analysis equation may be established based on the independent variable data and dependent variable A, and a second multiple regression analysis equation may be established based on the independent variable data and dependent variable B.

[0040] Step S104: According to the multiple regression analysis equation, a stepwise regression method and a regression model are used to screen out combinations of resource acquisition influencing factors that pass the significance test.

[0041] In this step, according to the multiple regression analysis equation established in step 103, the object to be screened by the stepwise regression method and the regression model is the independent variable data used to establish the above-mentioned multiple regression analysis equation; the stepwise regression method can be a pre-step for using the regression model to perform a significance test on the independent variable data used to establish the above-mentioned multiple regression analysis equation, that is, the independent variable data used to establish the above-mentioned multiple regression analysis equation is first screened by the stepwise regression method, and then a new multiple regression analysis equation is established based on the independent variable data screened by the stepwise regression method, and the regression model is used to perform a significance test on the independent variable data used to establish the above-mentioned equation; the regression model can also be used alone to perform a significance test on the independent variable data used to establish the above-mentioned multiple regression analysis equation; the combination of resource acquisition influencing factors that passes the significance test refers to the independent variable combination that passes the significance test for establishing the above-mentioned multiple regression analysis equation, the number of independent variables in the above-mentioned independent variable combination can be single or multiple, and the evaluation dimension of the above-mentioned combination of resource acquisition influencing factors is the dependent variable used to establish the above-mentioned multiple regression analysis equation.

[0042] Furthermore, when it is clear that the number of the above-mentioned multiple regression analysis equations can correspond to the number of dependent variables in the dependent variable data, if the dependent variable data contains data corresponding to multiple dependent variables, multiple multiple regression analysis equations can be established respectively according to the multiple dependent variables, and the stepwise regression method and regression model can be used to screen out the combination of resource acquisition influencing factors that pass the significance test under multiple evaluation dimensions.

[0043] Step S105, using the principal component regression analysis method, obtain the weight of each resource acquisition influencing factor in the resource acquisition influencing factor combination.

[0044] In this step, the number of principal components used for principal component regression analysis can be single or multiple. The basis for determining the aforementioned number of principal components is the variance contribution rate of each principal component and a preset explanation rate threshold. The aforementioned explanation rate is obtained by successively adding up the variance contribution rates of each principal component. The weight of each resource acquisition influencing factor in the aforementioned combination of resource acquisition influencing factors refers to the degree of influence of each resource acquisition influencing factor in the combination of resource acquisition influencing factors.

[0045] Furthermore, when it is clear that multiple multivariate regression analysis equations can be established according to multiple dependent variables to screen out combinations of resource acquisition influencing factors that pass significance tests under multiple evaluation dimensions, the weights of each resource acquisition influencing factor in the combination of resource acquisition influencing factors can also be obtained based on multiple evaluation dimensions.

[0046] Step S106, based on the weights of the various resource acquisition influencing factors, obtain the weight analysis results of the resource acquisition influencing factors.

[0047] In this step, when it is clear that the weight of each resource acquisition influencing factor in the resource acquisition influencing factor combination can be obtained based on multiple evaluation dimensions, the weight analysis result of the above-mentioned resource acquisition influencing factors can also be the weight analysis result of the resource acquisition influencing factors obtained based on multiple evaluation dimensions.

[0048] The weight analysis method of the resource acquisition influencing factors mentioned above first obtains data corresponding to multiple preset resource acquisition influencing factors from multiple preset objects as independent variable data. Then, based on the independent variable data, data corresponding to multiple preset influencing factor evaluation dimensions are obtained as dependent variable data. Secondly, a multiple regression analysis equation is established according to the independent variable data and the dependent variable data. Then, according to the multiple regression analysis equation, the combination of resource acquisition influencing factors that pass the significance test is screened out by using the stepwise regression method and the regression model; then, the weight of each resource acquisition influencing factor in the combination of resource acquisition influencing factors is obtained by using the principal component regression analysis method; finally, based on the weight of each resource acquisition influencing factor, the weight analysis result of the resource acquisition influencing factor is obtained. This scheme can further use the principal component regression analysis method to obtain the weight analysis results of each influencing factor under different preset resource acquisition influencing factor evaluation dimensions on the basis of using the multiple regression equation to screen out the influencing factor combination with the highest correlation under different preset resource acquisition influencing factor evaluation dimensions, thereby increasing the number of evaluation dimensions covered by the resource acquisition influencing factor analysis results and improving the accuracy of the resource acquisition influencing factor analysis results.

[0049] Regarding obtaining the weight of each resource acquisition influencing factor in the resource acquisition influencing factor combination, in one embodiment, Figure 2 As shown, the above step S105 specifically includes:

[0050] Step S201 , standardize the data corresponding to each resource acquisition influencing factor in the resource acquisition influencing factor combination, and use the principal component regression analysis method to obtain the variance contribution rate of each principal component.

[0051] In this step, the operating principle of the principal component regression analysis method is to use the idea of ​​dimensionality reduction to convert a variable combination with correlation into a linearly uncorrelated variable combination through the orthogonal transformation method. The number of variables contained in the aforementioned linearly uncorrelated variable combination is usually less than the number of variables contained in the variable combination with correlation used to convert the variable combination. The aforementioned linearly uncorrelated variable combination is also called the principal component combination used to convert the variable combination with correlation; each principal component refers to the principal component included in a linearly uncorrelated principal component combination obtained by converting the resource acquisition influencing factor combination using the principal component regression analysis method. Therefore, when it is clarified that each principal component refers to the principal component included in a linearly uncorrelated principal component combination obtained by converting the resource acquisition influencing factor combination using the principal component regression analysis method, the variance contribution rate of each principal component can be the variance contribution rate of each principal component included in a linearly uncorrelated principal component combination obtained by converting the resource acquisition influencing factor combination using the principal component regression analysis method.

[0052] For example, the resource acquisition influencing factor combination includes three resource acquisition influencing factors, namely X1, X2 and X3. The data corresponding to X1, X2 and X3 are standardized respectively, and the standardized data shown in Table 1 can be obtained. By using the principal component regression analysis method, the above resource acquisition influencing factor combination is converted into a linearly unrelated principal component combination (such as the first principal component F1, the second principal component F2, and the third principal component F3 in Table 2), and the results of the principal component regression analysis of the standardized data shown in Table 1 can be obtained as shown in Table 2.

[0053]

[0054]

[0055] Table 1

[0056] principal component total Variance contribution rate (unit: %) The first principal component F1 1.672 55.723 The second principal component F2 1.079 35.956 The third principal component F3 0.250 8.321

[0057] Table 2

[0058] Step S202, the variance contribution rates of the principal components are sequentially accumulated to obtain the explanation rates of different numbers of principal components for explaining the combination of factors affecting resource acquisition.

[0059] In this step, after it is clarified that the variance contribution rate of each principal component can be the variance contribution rate of each principal component included in a linearly uncorrelated principal component combination obtained by transforming the combination of resource acquisition influencing factors using the principal component regression analysis method, the explanation rate of different numbers of principal components for explaining the combination of resource acquisition influencing factors refers to the result obtained by successively adding the variance contribution rates of each principal component included in the aforementioned principal component combination.

[0060] For example, a principal component combination converted from the combination of factors affecting resource acquisition includes three principal components, namely the first principal component, the second principal component and the third principal component. Among them, the variance contribution rate of the first principal component is 55.723%, the variance contribution rate of the second principal component is 35.956%, and the variance contribution rate of the third principal component is 8.321%. By adding up the variance contribution rates of the above principal components in turn, we can get the explanation rate of different numbers of principal components for explaining the combination of factors affecting resource acquisition as shown in Table 3.

[0061]

[0062]

[0063] Table 3

[0064] Step S203, selecting a corresponding number of principal components according to a preset threshold, and based on the selected principal components, using the principal component regression analysis method to obtain the component matrix corresponding to each resource acquisition influencing factor in the resource acquisition influencing factor combination in the selected principal component and the characteristic roots of the selected principal component.

[0065] In this step, the preset threshold refers to the set value of the explanation rate of the combination of influencing factors for obtaining explanation resources that the total of all principal components selected as the analysis dimension needs to reach; selecting a corresponding number of principal components according to the preset threshold refers to selecting a corresponding number of principal components from the above-mentioned principal component combination according to the set value of the explanation rate of the combination of influencing factors for obtaining explanation resources that the total of all principal components selected as the analysis dimension needs to reach; the selected principal component refers to the principal component among all principal components whose explanation rate meets the set value of the explanation rate of the combination of influencing factors for obtaining explanation resources that the total of all principal components selected as the analysis dimension needs to reach; the number of all principal components selected as the analysis dimension mentioned above can be single or multiple.

[0066] For example, the set value of the explanation rate of the combination of resource acquisition influencing factors that all the selected principal components need to reach is set to 80%. According to the data shown in Table 3, when the first principal component and the second principal component are used, the explanation rate of the principal component combination reaches 91.679%, which exceeds the set value of 80% of the above explanation rate. Therefore, the first principal component and the second principal component are selected as the analysis dimensions. As shown in Table 4, principal component regression analysis is performed on the data shown in Table 1 from the two analysis dimensions of the first principal component and the second principal component, and the component matrix corresponding to each resource acquisition influencing factor in the above resource acquisition influencing factor combination in the first principal component and the second principal component and the characteristic roots of the first principal component and the second principal component can be obtained.

[0067] The first principal component F1 The second principal component F2 <![CDATA[X1]]> 0.935 -0.088 <![CDATA[X2]]> 0.832 0.458 <![CDATA[X3]]> -0.322 0.928 Eigenvalues ​​of principal components 1.672 1.079

[0068] Table 4

[0069] Step S204, obtaining the coefficient of each resource acquisition influencing factor in the resource acquisition influencing factor combination in the selected principal component linear combination according to the corresponding component matrix and the characteristic root of the selected principal component.

[0070] In this step, when it is clear that the number of principal components selected as analysis dimensions can be single or multiple, the coefficients in the linear combination of each resource acquisition influencing factor in the resource acquisition influencing factor combination can also be obtained based on multiple different principal component dimensions.

[0071] Further, in one embodiment, step S204 specifically includes:

[0072] Obtain the standardized number of the component matrix; obtain the square root corresponding to the characteristic root of the selected principal component; and use the ratio of each standardized number of each selected principal component to the square root corresponding to the characteristic root of the corresponding selected principal component as the above coefficient.

[0073] For example, the standardized numbers corresponding to each resource acquisition influencing factor in the combination of resource acquisition influencing factors in the component matrices of the first principal component and the second principal component and the characteristic roots of the first principal component and the second principal component are obtained from the data shown in Table 4, and the ratio of each of the above standardized numbers to the square root of the characteristic root of the corresponding principal component is calculated, then the coefficients of the above-mentioned each resource acquisition influencing factor in the linear combination of the first and second principal components as shown in Table 5 can be obtained.

[0074] The coefficients in the linear combination of the first principal component The coefficients in the linear combination of the second principal component <![CDATA[X1]]> 0.7231 -0.08472 <![CDATA[X2]]> 0.6434 0.4409 <![CDATA[X3]]> -0.2490 0.8934

[0075] Table 5

[0076] Step S205, obtaining the coefficient of each resource acquisition influencing factor in the resource acquisition influencing factor combination in the comprehensive score model according to the coefficient of each resource acquisition influencing factor in the selected principal component linear combination in the resource acquisition influencing factor combination;

[0077] Further, in one embodiment, step S205 specifically includes:

[0078] Obtain the variance contribution rate of the selected principal component; multiply the coefficient of each resource acquisition influencing factor in the resource acquisition influencing factor combination in the selected principal component linear combination by the variance contribution rate of the selected principal component and then add them to obtain the first coefficient component; add the variance contribution rates of each selected principal component to obtain the second coefficient component; use the ratio of the first coefficient component to the second coefficient component as the coefficient of each resource acquisition influencing factor in the resource acquisition influencing factor combination in the comprehensive score model.

[0079] For example, according to the coefficients of each resource acquisition influencing factor in the linear combination of the first principal component and the second principal component in the resource acquisition influencing factor combination as shown in Table 5 and the variance contribution rates of the first principal component and the second principal component as shown in Table 2, the ratios of the first coefficient component to the second coefficient component of each of the above-mentioned resource acquisition influencing factors are calculated respectively, and the coefficients of the above-mentioned resource acquisition influencing factors in the comprehensive score model as shown in Table 6 can be obtained.

[0080] Coefficient of comprehensive score model (absolute value) <![CDATA[X1]]> 0.406 <![CDATA[X2]]> 0.564 <![CDATA[X3]]> 0.199

[0081] Table 6

[0082] Step S206, obtaining the weight of each resource acquisition influencing factor in the resource acquisition influencing factor combination according to the coefficient of each resource acquisition influencing factor in the comprehensive score model and the sum of the coefficients.

[0083] Further, in one embodiment, step S206 specifically includes:

[0084] Obtain the sum of the coefficients of each resource acquisition influencing factor in the resource acquisition influencing factor combination in the comprehensive scoring model; for each resource acquisition influencing factor, take the ratio of the coefficient of the resource acquisition influencing factor in the comprehensive scoring model to the above sum as the weight of the resource acquisition influencing factor.

[0085] For example, according to the coefficient of each resource acquisition influencing factor in the comprehensive scoring model as shown in Table 6, the sum of the coefficients of each resource acquisition influencing factor in the resource acquisition influencing factor combination in the comprehensive scoring model is obtained, and the ratio of the coefficient of each of the aforementioned resource acquisition influencing factors in the comprehensive scoring model to the aforementioned sum is calculated, and the weights of the aforementioned resource acquisition influencing factors as shown in Table 7 can be obtained.

[0086] The weight of each resource acquisition influencing factor <![CDATA[X1]]> 0.3473 <![CDATA[X2]]> 0.4825 <![CDATA[X3]]> 0.1702

[0087] Table 7

[0088] The above embodiment can accurately analyze the weight of each resource acquisition influencing factor in the resource acquisition influencing factor combination by utilizing the principal component regression analysis method, which is helpful to help users discover problems in the current resource acquisition strategy as early as possible and take targeted and effective measures in a timely manner.

[0089] For obtaining the combination of resource acquisition influencing factors that pass the significance test, in one embodiment, Figure 3 As shown, the above step S104 specifically includes:

[0090] Step S301, determining whether there are obviously insignificant independent variables in the regression model established by the multiple regression analysis equation;

[0091] In this step, the number of regression models established by the multiple regression analysis equation corresponds to the number of multiple regression equations; if it is determined that there are obviously insignificant independent variables in the regression model established by the multiple regression analysis equation, the next step of this step is step S302; if it is determined that there are obviously insignificant independent variables in the regression model established by the multiple regression analysis equation, the next step of this step is step S303.

[0092] Step S302: if there is, then use the stepwise regression method to eliminate the obviously insignificant independent variables, and re-establish the multiple regression analysis equation based on the remaining independent variables, and then perform a significance test on the set of independent variables screened out by the regression model of the equation. If the significance test passes, it means that there are no obviously insignificant independent variables in the regression model of the equation, and if the significance test fails, it means that there are still obviously insignificant independent variables in the regression model of the equation;

[0093] In this step, if the above-screened independent variable set passes the significance test, then the above-screened independent variable set does not contain any obviously insignificant independent variables, and the next step of this step is step S303; if the above-screened independent variable set does not pass the significance test, then the above-screened independent variable set still contains any obviously insignificant independent variables, and the next step of this step is step S301.

[0094] Step S303: If it does not exist, the corresponding independent variable set in the regression model established by the multiple regression analysis equation is used as the resource acquisition influencing factor combination that passes the significance test.

[0095] In this step, the combination of influencing factors of resource acquisition that passes the significance test refers to the combination of influencing factors of resource acquisition that passes the significance test selected from the combination of independent variables used to establish the above-mentioned multiple regression analysis equation; the preceding step of this step may be step S301 or step S302.

[0096] The above-mentioned embodiment establishes a regression model by analyzing the multiple regression analysis equation, and can accurately screen out independent variable combinations that pass the significance test from the independent variable set used to establish the multiple regression analysis equation, so as to ensure the significance of each independent variable in the combination of resource acquisition influencing factors, thereby improving the accuracy of the analysis results of resource acquisition influencing factors.

[0097] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0098] Based on the same inventive concept, the embodiment of the present application also provides a device for implementing the weight analysis of the resource acquisition influencing factors involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in the embodiment of the weight analysis device for one or more resource acquisition influencing factors provided below can be referred to the limitations of the weight analysis method for a resource acquisition influencing factor above, and will not be repeated here.

[0099] In one embodiment, Figure 4 As shown, a weight analysis device 400 for factors affecting resource acquisition is provided, comprising:

[0100] The data acquisition module 401 acquires data corresponding to multiple preset objects under multiple preset resource acquisition influencing factors as independent variable data;

[0101] A variable output module 402, based on the independent variable data, obtains data corresponding to multiple preset influencing factor evaluation dimensions as dependent variable data;

[0102] An equation building module 403 builds a multiple regression analysis equation according to the independent variable data and the dependent variable data;

[0103] The variable screening module 404 screens out the resource acquisition influencing factor combinations that pass the significance test by using the stepwise regression method and regression model according to the multivariate regression analysis equation;

[0104] The weight calculation module 405 obtains the weight of each resource acquisition influencing factor in the resource acquisition influencing factor combination by using the principal component regression analysis method;

[0105] The result analysis module 406 obtains the weight analysis result of the resource acquisition influencing factors based on the weights of the various resource acquisition influencing factors.

[0106] In one embodiment, the weight calculation module 405 is used to standardize the data corresponding to each resource acquisition influencing factor in the resource acquisition influencing factor combination, and obtain the variance contribution rate of each principal component by using the principal component regression analysis method; the variance contribution rates of each principal component are accumulated in sequence to obtain the explanation rate of different numbers of principal components for explaining the resource acquisition influencing factor combination; a corresponding number of principal components are selected according to a preset threshold, and based on the selected principal components, the principal component regression analysis method is used to obtain the component matrix corresponding to each resource acquisition influencing factor in the resource acquisition influencing factor combination in the selected principal component and the characteristic root of the selected principal component; according to the corresponding component matrix and the characteristic root of the selected principal component, the coefficient of each resource acquisition influencing factor in the resource acquisition influencing factor combination in the selected principal component linear combination is obtained; according to the coefficient of each resource acquisition influencing factor in the resource acquisition influencing factor combination in the selected principal component linear combination, the coefficient of each resource acquisition influencing factor in the resource acquisition influencing factor combination in the comprehensive score model is obtained; according to the coefficient of each resource acquisition influencing factor in the resource acquisition influencing factor combination in the comprehensive score model and the sum of the coefficients, the weight of each resource acquisition influencing factor in the resource acquisition influencing factor combination is obtained.

[0107] In one embodiment, the weight calculation module 405 is used to obtain the sum of the coefficients of each resource acquisition influencing factor in the resource acquisition influencing factor combination in the comprehensive scoring model; for each resource acquisition influencing factor, the ratio of the coefficient of the resource acquisition influencing factor in the comprehensive scoring model to the sum is used as the weight of the resource acquisition influencing factor.

[0108] In one embodiment, the weight calculation module 405 is used to obtain the standardized number of the component matrix; obtain the square root corresponding to the characteristic root of the selected principal component; and use the ratio of each standardized number of each selected principal component to the square root corresponding to the characteristic root of the corresponding selected principal component as the coefficient.

[0109] In one embodiment, the weight calculation module 405 is used to obtain the variance contribution rate of the selected principal component; multiply the coefficients of each resource acquisition influencing factor in the resource acquisition influencing factor combination in the selected principal component linear combination by the variance contribution rate of the selected principal component and add them to obtain a first coefficient component; add the variance contribution rates of each selected principal component to obtain a second coefficient component; and use the ratio of the first coefficient component to the second coefficient component as the coefficient of each resource acquisition influencing factor in the resource acquisition influencing factor combination in the comprehensive score model.

[0110] In one embodiment, the variable screening module 404 is used to determine whether there are obviously insignificant independent variables in the regression model established by the multiple regression analysis equation; if so, the stepwise regression method is used to eliminate the obviously insignificant independent variables, and the multiple regression analysis equation is established again based on the remaining independent variables, and then a significance test is performed on the set of independent variables screened out of the regression model of the equation. If the significance test passes, it means that there are no obviously insignificant independent variables in the regression model of the equation, and if the significance test fails, it means that there are still obviously insignificant independent variables in the regression model of the equation; if not, the current corresponding set of independent variables in the regression model established by the multiple regression analysis equation is used as the combination of resource acquisition influencing factors that passes the significance test.

[0111] Each module in the above-mentioned weight analysis device for factors affecting resource acquisition can be implemented in whole or in part by software, hardware, or a combination thereof. Each of the above-mentioned modules can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to each of the above modules.

[0112] It should be noted that the weight analysis method and device of factors affecting resource acquisition of the present application can be used for weight analysis of factors affecting resource acquisition in the financial field, and can also be used in any field other than the financial field. The application field of the weight analysis method and device of factors affecting resource acquisition of the present application is not limited.

[0113] In one embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as follows: Figure 5 As shown. The computer device includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a weight analysis method for factors affecting resource acquisition is implemented. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covered on the display screen, or a button, trackball or touchpad set on the computer device shell, or an external keyboard, touchpad or mouse.

[0114] Those skilled in the art will understand that Figure 5 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0115] In one embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above method embodiments when executing the computer program.

[0116] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0117] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0118] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited thereto.

[0119] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0120] The technical features of the above embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0121] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A weight analysis method for factors affecting resource acquisition, characterized in that: The method comprises: Obtaining data corresponding to multiple preset objects under multiple preset resource acquisition influencing factors as independent variable data; Based on the independent variable data, data corresponding to multiple preset influencing factor evaluation dimensions are obtained as dependent variable data; the multiple preset influencing factor evaluation dimensions are obtained by a single or multiple independent variables in the independent variable data through multiple calculation methods; the multiple calculation methods include adding the multiple independent variables and adding and averaging the multiple independent variables; Establishing a multiple regression analysis equation based on the independent variable data and the dependent variable data; According to the multiple regression analysis equation, the stepwise regression method and the regression model are used to screen out the combination of resource acquisition influencing factors that pass the significance test, including: judging whether there are obviously insignificant independent variables in the regression model established by the multiple regression analysis equation; if there are, the stepwise regression method is used to eliminate the obviously insignificant independent variables, and the multiple regression analysis equation is established again based on the remaining independent variables, and then the significance test is performed on the independent variable set screened out by the regression model of the equation, and the passing of the significance test means that there are no obviously insignificant independent variables in the regression model of the equation, and the failing of the significance test means that there are still obviously insignificant independent variables in the regression model of the equation; if there are no independent variables, the current corresponding independent variable set in the regression model established by the multiple regression analysis equation is used as the combination of resource acquisition influencing factors that pass the significance test; The principal component regression analysis method is used to obtain the weight of each resource acquisition influencing factor in the resource acquisition influencing factor combination, including: standardizing the data corresponding to each resource acquisition influencing factor in the resource acquisition influencing factor combination, and obtaining the variance contribution rate of each principal component by using the principal component regression analysis method; accumulating the variance contribution rates of each principal component in turn to obtain the explanation rate of different numbers of principal components for explaining the resource acquisition influencing factor combination; selecting a corresponding number of principal components according to a preset threshold, and obtaining the corresponding variance contribution rate of each resource acquisition influencing factor in the resource acquisition influencing factor combination in the selected principal component by using the principal component regression analysis method based on the selected principal component. The characteristic roots of the component matrix and the selected principal components; according to the corresponding component matrix and the characteristic roots of the selected principal components, the coefficients of each resource acquisition influencing factor in the resource acquisition influencing factor combination in the selected principal component linear combination are obtained; according to the coefficients of each resource acquisition influencing factor in the resource acquisition influencing factor combination in the selected principal component linear combination, the coefficients of each resource acquisition influencing factor in the resource acquisition influencing factor combination in the comprehensive score model are obtained; according to the coefficients of each resource acquisition influencing factor in the resource acquisition influencing factor combination in the comprehensive score model and the sum of the coefficients, the weights of each resource acquisition influencing factor in the resource acquisition influencing factor combination are obtained; Based on the weights of the various resource acquisition influencing factors, a weight analysis result of the resource acquisition influencing factors is obtained.

2. The method according to claim 1, characterized in that The weight of each resource acquisition influencing factor in the resource acquisition influencing factor combination is obtained according to the coefficient of each resource acquisition influencing factor in the comprehensive score model and the sum of the coefficients, including: Obtaining the sum of the coefficients of each resource acquisition influencing factor in the resource acquisition influencing factor combination in the comprehensive score model; For each resource acquisition influencing factor, the ratio of the coefficient of the resource acquisition influencing factor in the comprehensive score model to the total is used as the weight of the resource acquisition influencing factor.

3. The method according to claim 1, characterized in that The step of obtaining the coefficient of each resource acquisition influencing factor in the resource acquisition influencing factor combination in the selected principal component linear combination according to the corresponding component matrix and the characteristic root of the selected principal component includes: Obtaining the normalized number of the component matrix; Obtain the square root corresponding to the characteristic root of the selected principal component; The ratio of each standardized number of each selected principal component to the square root corresponding to the characteristic root of the corresponding selected principal component is used as the coefficient.

4. The method according to claim 1, characterized in that: The step of obtaining the coefficient of each resource acquisition influencing factor in the resource acquisition influencing factor combination in the comprehensive score model according to the coefficient of each resource acquisition influencing factor in the resource acquisition influencing factor combination in the selected principal component linear combination includes: Obtaining the variance contribution rate of the selected principal component; The coefficients of each resource acquisition influencing factor in the resource acquisition influencing factor combination in the selected principal component linear combination are multiplied by the variance contribution rate of the selected principal component and then added to obtain a first coefficient component; Add the variance contribution rates of each selected principal component to obtain the second coefficient component; The ratio of the first coefficient component to the second coefficient component is used as the coefficient of each resource acquisition influencing factor in the resource acquisition influencing factor combination in the comprehensive score model.

5. A weight analysis device for factors affecting resource acquisition, characterized in that: The device comprises: A data acquisition module, which acquires data corresponding to multiple preset objects under multiple preset resource acquisition influencing factors as independent variable data; A variable output module, based on the independent variable data, obtains data corresponding to multiple preset influencing factor evaluation dimensions as dependent variable data; the multiple preset influencing factor evaluation dimensions are obtained by a single or multiple independent variables in the independent variable data through multiple calculation methods; the multiple calculation methods include adding multiple independent variables and adding and averaging multiple independent variables; An equation building module is used to build a multiple regression analysis equation based on the independent variable data and the dependent variable data; The variable screening module uses the stepwise regression method and the regression model according to the multiple regression analysis equation to screen out the combination of resource acquisition influencing factors that pass the significance test, including: judging whether there are obviously insignificant independent variables in the regression model established by the multiple regression analysis equation; if there are, using the stepwise regression method to eliminate the obviously insignificant independent variables, and re-establishing the multiple regression analysis equation based on the remaining independent variables, and then performing a significance test on the independent variable set screened out by the regression model of the equation, and if the significance test passes, it means that there are no obviously insignificant independent variables in the regression model of the equation, and if the significance test fails, it means that there are still obviously insignificant independent variables in the regression model of the equation; if there are no independent variables, the current corresponding independent variable set in the regression model established by the multiple regression analysis equation is used as the combination of resource acquisition influencing factors that pass the significance test; The weight calculation module uses the principal component regression analysis method to obtain the weight of each resource acquisition influencing factor in the resource acquisition influencing factor combination, including: standardizing the data corresponding to each resource acquisition influencing factor in the resource acquisition influencing factor combination, and using the principal component regression analysis method to obtain the variance contribution rate of each principal component; accumulating the variance contribution rates of each principal component in turn to obtain the explanation rate of different numbers of principal components for explaining the resource acquisition influencing factor combination; selecting a corresponding number of principal components according to a preset threshold, and based on the selected principal components, using the principal component regression analysis method to obtain the variance contribution rate of each resource acquisition influencing factor in the resource acquisition influencing factor combination in the selected principal components. The corresponding component matrix and the characteristic roots of the selected principal components; according to the corresponding component matrix and the characteristic roots of the selected principal components, the coefficients of each resource acquisition influencing factor in the resource acquisition influencing factor combination in the selected principal component linear combination are obtained; according to the coefficients of each resource acquisition influencing factor in the resource acquisition influencing factor combination in the selected principal component linear combination, the coefficients of each resource acquisition influencing factor in the resource acquisition influencing factor combination in the comprehensive score model are obtained; according to the coefficients of each resource acquisition influencing factor in the resource acquisition influencing factor combination in the comprehensive score model and the sum of the coefficients, the weights of each resource acquisition influencing factor in the resource acquisition influencing factor combination are obtained; The result analysis module obtains the weight analysis results of the resource acquisition influencing factors based on the weights of the various resource acquisition influencing factors.

6. The device according to claim 5, characterized in that The weight calculation module is used to: Obtaining the sum of the coefficients of each resource acquisition influencing factor in the resource acquisition influencing factor combination in the comprehensive score model; For each resource acquisition influencing factor, the ratio of the coefficient of the resource acquisition influencing factor in the comprehensive score model to the total is used as the weight of the resource acquisition influencing factor.

7. The device according to claim 5, characterized in that The weight calculation module is used to: Obtaining the normalized number of the component matrix; Obtain the square root corresponding to the characteristic root of the selected principal component; The ratio of each standardized number of each selected principal component to the square root corresponding to the characteristic root of the corresponding selected principal component is used as the coefficient.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 4 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 4 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 4 are implemented.

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