Operation index analysis method, device and equipment

Abstract

The invention relates to the technical field of operation index analysis, and provides an operation index analysis method, device and equipment, and the method is applied to an operation index analysis system with a preset operator analysis module. Operation data and index dimensions corresponding to the to-be-analyzed operation indexes are obtained from a database; the operation data and the index dimensions are input to a preset operator analysis module, corresponding contribution degree, sensitivity and time sequence analysis results are obtained, and the preset operator analysis module is constructed according to a preset analysis rule; and according to the contribution degree, the sensitivity and the time sequence analysis result, analyzing the to-be-analyzed operating index in at least any two dimensions of the downward dimension, the transverse dimension and the longitudinal dimension. According to the method, the to-be-analyzed operating indexes are analyzed in the downward dimension, the transverse dimension and the longitudinal dimension according to the contribution degree, the sensitivity and the time sequence analysis result, and the accuracy of multi-dimensional operating index analysis is improved.

Description

Technical Field

[0001] The present application relates to the technical field of business indicator analysis, and in particular to a business indicator analysis method, device and equipment. Background Art

[0002] In today's business environment, business indicator analysis has become an important means for corporate decision makers to understand business operations, evaluate performance and formulate strategic plans. With the advent of the big data era, companies have accumulated a large amount of business data, which contains rich information. How to effectively mine and analyze this data has become the key to improving the competitiveness of enterprises.

[0003] Currently, statistical analysis software is generally used for data analysis, such as Statistical Package for the Social Sciences (SPSS). Since these statistical software focus on single-dimensional data analysis, they lack accuracy when conducting multi-dimensional business indicator analysis and cannot effectively perform complex multi-dimensional calculations and in-depth analysis.

[0004] The above contents are only used to assist in understanding the technical solution of the present application and do not constitute an admission that the above contents are prior art. Summary of the invention

[0005] The main purpose of this application is to provide a business indicator analysis method, device and equipment, aiming to solve the technical problem of insufficient accuracy of the prior art when conducting multi-dimensional business indicator analysis.

[0006] To achieve the above purpose, the present application proposes a business indicator analysis method, which is applied to a business indicator analysis system with a preset operator analysis module, and the method includes:

[0007] When receiving the indicator analysis instruction, the operating data and indicator dimensions corresponding to the operating indicator to be analyzed are obtained from the database;

[0008] Inputting the operating data and the indicator dimension into the preset operator analysis module to obtain corresponding contribution, sensitivity and time series analysis results, wherein the preset operator analysis module is a module constructed according to preset analysis rules;

[0009] The operating indicator to be analyzed is analyzed in at least any two dimensions of a downward dimension, a horizontal dimension, and a vertical dimension according to the contribution, the sensitivity, and the time series analysis result.

[0010] In one embodiment, the step of inputting the operating data and the indicator dimension into the preset operator analysis module to obtain corresponding contribution, sensitivity and time series analysis results includes:

[0011] Inputting the operating data and the indicator dimension into the preset operator analysis module to obtain the corresponding contribution degree;

[0012] Inputting the operating data into the preset operator analysis module to obtain corresponding sensitivity;

[0013] The operating data and the indicator dimensions are input into the preset operator analysis module to obtain corresponding time series analysis results.

[0014] In one embodiment, the preset analysis rule includes a preset contribution analysis rule, and the step of inputting the operating data and the indicator dimension into the preset operator analysis module to obtain the corresponding contribution includes:

[0015] Divide the operating data into upper and lower dimensions according to the indicator dimensions to obtain upper and lower dimensional operating data corresponding to each indicator dimension;

[0016] The upper and lower dimensional business data are input into the preset operator analysis module, so that the preset operator analysis module obtains the corresponding contribution of the business indicator in each indicator dimension by using the preset contribution analysis rule.

[0017] In one embodiment, the preset analysis rule includes a preset sensitivity analysis rule, and the step of inputting the business data into the preset operator analysis module to obtain the corresponding sensitivity includes:

[0018] Obtaining composite indicators and corresponding composite indicator data in the operating indicators to be analyzed;

[0019] Inputting each of the composite index data and the operating data into the preset operator analysis module, so that the preset operator analysis module obtains the sensitive parameters between the index to be analyzed and each of the composite indexes by using the preset sensitivity analysis rule;

[0020] A corresponding sensitivity is obtained according to the sensitive parameter.

[0021] In one embodiment, the preset analysis rule includes a preset stationarity analysis rule, and the step of inputting the operating data and the indicator dimension into the preset operator analysis module to obtain the corresponding time series analysis result includes:

[0022] Segmenting the business data according to preset time intervals to obtain a number of segmented business data;

[0023] Inputting each of the segmented business data and the indicator dimension into the preset operator analysis module, so that the preset operator analysis module obtains the stability test result of the business indicator to be analyzed by using the preset stability analysis rule;

[0024] The corresponding time series analysis result is obtained according to the stationarity test result.

[0025] In one embodiment, the preset analysis rule includes a preset month-on-month analysis rule, and the step of inputting the operating data and the indicator dimension into the preset operator analysis module to obtain the corresponding time series analysis result includes:

[0026] Divide the operating data based on preset time nodes to obtain current period data and previous period data;

[0027] Inputting the current period data, the previous period data and the indicator dimension into the preset operator analysis module, so that the preset operator analysis module obtains the month-on-month growth rate of the operating indicator to be analyzed by using the preset month-on-month analysis rule;

[0028] A corresponding time series analysis result is obtained according to the month-on-month growth rate.

[0029] In one embodiment, the preset analysis rule includes a preset correlation analysis rule, and the step of inputting the business data and the indicator dimension into the preset operator analysis module to obtain the corresponding time series analysis result includes:

[0030] Based on the indicator dimension and a number of preset business nodes, the business indicator is divided to obtain a number of business node data under the same upper and lower dimensions;

[0031] Inputting the data of each of the business nodes into the preset operator analysis module, so that the preset operator analysis module obtains the correlation of each of the preset business nodes by using the preset correlation analysis rule;

[0032] The corresponding timing analysis results are obtained according to the correlation of each of the preset business nodes.

[0033] In one embodiment, the preset analysis rule includes a preset periodic analysis rule, and the step of inputting the operating data and the indicator dimension into the preset operator analysis module to obtain the corresponding time series analysis result includes:

[0034] Sorting the business data based on a preset time sequence to obtain a sorting result;

[0035] Inputting the sorting result and the indicator dimension into the preset operator analysis module, so that the preset operator analysis module obtains the periodicity of the operating indicator by using the preset periodicity analysis rule;

[0036] The corresponding time series analysis results are obtained according to the periodicity of the operating indicators.

[0037] In addition, to achieve the above purpose, the present application also proposes a business indicator analysis device, the device comprising:

[0038] The indicator acquisition module is used to obtain the business data and indicator dimensions corresponding to the business indicator to be analyzed from the database when receiving the indicator analysis instruction;

[0039] An indicator analysis module, used to input the operating data and the indicator dimension into a preset operator analysis module to obtain corresponding contribution, sensitivity and time series analysis results, wherein the preset operator analysis module is a module constructed according to preset analysis rules;

[0040] The result integration module is used to analyze the business indicator to be analyzed in at least any two dimensions of the downward dimension, the horizontal dimension and the vertical dimension according to the contribution, the sensitivity and the time series analysis result.

[0041] In addition, to achieve the above-mentioned purpose, the present application also proposes a business indicator analysis device, which includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, and the computer program is configured to implement the steps of the business indicator analysis method described above.

[0042] The present application proposes a method, device and equipment for analyzing business indicators. The method is applied to a business indicator analysis system with a preset operator analysis module. The method includes: when receiving an indicator analysis instruction, obtaining business data and indicator dimensions corresponding to the business indicator to be analyzed from a database; inputting the business data and indicator dimensions into the preset operator analysis module to obtain corresponding contribution, sensitivity and timing analysis results, the preset operator analysis module is a module constructed according to preset analysis rules; analyzing the business indicator to be analyzed in at least any two dimensions of the downward dimension, horizontal dimension and vertical dimension according to the contribution, sensitivity and timing analysis results. This shows that the present application analyzes the business indicator to be analyzed in the downward dimension, horizontal dimension and vertical dimension according to the contribution, sensitivity and timing analysis results, thereby improving the accuracy of multi-dimensional business indicator analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0044] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0045] Figure 1 This is a flow chart of the first embodiment of the business indicator analysis method proposed in this embodiment;

[0046] Figure 2 This is a dimensional analysis diagram of the business indicator analysis method proposed in this embodiment;

[0047] Figure 3 This is a flow chart of a second embodiment of the business indicator analysis method proposed in this embodiment;

[0048] Figure 4 A technical architecture diagram of the business indicator analysis method provided in this embodiment;

[0049] Figure 5 A diagram of the operating indicator analysis device provided for this embodiment;

[0050] Figure 6 It is a schematic diagram of the structure of the business indicator analysis device suitable for implementing the present embodiment.

[0051] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0052] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0053] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0054] It should be noted that all directional indications in the embodiments of the present application (such as up, down, left, right, front, back, etc.) are only used to explain the relative position relationship, movement status, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.

[0055] It is understandable that in today's business environment, business indicator analysis has become an important means for corporate decision makers to understand business operations, evaluate performance and formulate strategic plans. With the advent of the big data era, companies have accumulated a large amount of business data, which contains rich information. How to effectively mine and analyze these data has become the key to improving the competitiveness of companies. In this context, statistical analysis software such as SPSS came into being, which provides companies with a set of systematic data analysis methods. These software can help companies quantify business indicators, reveal the laws behind the data by building models, calculating correlation coefficients, and other means, and provide strong support for corporate decision-making.

[0056] Although statistical analysis software such as SPSS has met the needs of enterprises for business indicator analysis to a certain extent, existing data analysis methods still have certain limitations. These technologies often focus on single-dimensional data analysis, such as statistical analysis of single indicators such as sales and profit margins. However, in actual business activities, enterprises need to face a complex and changing business environment, and single-dimensional analysis is difficult to fully reflect the true situation of the enterprise. In addition, existing technologies are unable to cope with multi-dimensional calculations and analysis, and are unable to deeply explore the internal connections between data, resulting in a certain one-sidedness of the analysis results. This makes it possible for enterprises to fall into difficulties when making decisions due to failure to fully consider various factors, limiting the application value of business indicator analysis in enterprises.

[0057] Therefore, in order to solve the technical problem of insufficient accuracy of the prior art when performing multi-dimensional business indicator analysis, this embodiment proposes a business indicator analysis method, device, equipment and storage medium, the method comprising: when receiving an indicator analysis instruction, obtaining the business data and indicator dimensions corresponding to the business indicator to be analyzed from the database; inputting the business data and indicator dimensions into a preset operator analysis module to obtain the corresponding contribution, sensitivity and timing analysis results, the preset operator analysis module is a module constructed according to preset analysis rules; analyzing the business indicator to be analyzed in at least any two dimensions of the downward dimension, the horizontal dimension and the vertical dimension according to the contribution, sensitivity and timing analysis results. This shows that the present application obtains the corresponding contribution, sensitivity and timing analysis results through the preset operator analysis module, and analyzes and treats the business indicator to be analyzed in at least any two dimensions of the downward dimension, the horizontal dimension and the vertical dimension according to the contribution, sensitivity and timing analysis results, thereby improving the accuracy of the multi-dimensional business indicator analysis.

[0058] For ease of understanding, the following combination Figures 1 to 6 The operating indicator analysis method provided in the embodiment of the present application and the operating indicator analysis method, device, equipment and storage medium provided in the following embodiments are specifically introduced.

[0059] The present application embodiment provides a method for analyzing business indicators. Figure 1 , Figure 1 This is a flow chart of the first embodiment of the business indicator analysis method proposed in this embodiment.

[0060] like Figure 1 As shown, the method includes:

[0061] Step S10: When an indicator analysis instruction is received, the business data and indicator dimensions corresponding to the business indicator to be analyzed are obtained from the database.

[0062] It should be noted that the execution subject of this embodiment can be a computing service device with business indicator analysis, network communication and program running functions, such as a tablet computer, a personal computer, a mobile phone, etc., or an electronic device capable of realizing the above functions, etc. The following takes the business indicator analysis device (hereinafter referred to as the device) as an example to illustrate this embodiment and the following embodiments.

[0063] It should be noted that the above-mentioned indicator analysis instruction can be an instruction issued by the user according to demand, or it can be an instruction automatically generated by the above-mentioned device according to the real-time analysis instruction, which is used to start the business instruction analysis function of the above-mentioned device to perform subsequent steps of the subsequent business instruction analysis method. The above-mentioned database can be a library for storing indicator dimensions and business data corresponding to the business indicators. The above-mentioned business indicators to be analyzed can be business indicators selected by the user according to the above-mentioned indicator analysis instructions that need to be analyzed in multiple dimensions. The above-mentioned business data can be historical business data of the above-mentioned business indicators to be analyzed. The above-mentioned data can correspond to time. The above-mentioned indicator dimensions can be dimensions in which the above-mentioned business indicators to be analyzed exist (for example, the head office of the proprietary loan bank exists, and the bank branch also exists, where the head office of the bank and the bank branch are the indicator dimensions of proprietary loans).

[0064] It should be noted that the above-mentioned operating indicators to be analyzed can be indicators of the same type in all dimensions (for example, proprietary loans of all banks), or can refer to operating indicators of a certain dimension (for example, proprietary loans of Beijing branch), can be all operating indicators of a certain indicator dimension (such as all operating indicators of Beijing branch), or can be all indicators of all indicator dimensions in the database (such as all operating indicators of all banks). The selection can be made according to user needs, and this embodiment does not impose any specific restrictions.

[0065] In a specific implementation, when the above-mentioned device receives an indicator analysis instruction issued by a user, it analyzes the indicator analysis instruction to obtain the operating indicator to be analyzed, and extracts the various dimensions of the above-mentioned indicator to be analyzed and the historical data of the above-mentioned indicator to be analyzed in each dimension from the database.

[0066] Step S20: Input the operating data and the indicator dimensions into the preset operator analysis module to obtain corresponding contribution, sensitivity and timing analysis results. The preset operator analysis module is a module constructed according to preset analysis rules.

[0067] It should be noted that the preset operator module may be a module constructed according to preset analysis rules, and the preset analysis rules may be rules composed of formulas for calculating the contribution, sensitivity, and timing analysis results, etc. In order to construct the preset operator analysis module, an operator service and a formula interpreter may be used.

[0068] It is clear that an operator service can be a service provided in the field of data processing and analysis, where an operator refers to a functional unit that performs specific calculations or operations, and all operators in the operator service are independent. It is a general service that is not limited to the current application. The implementation of the operator service mainly relies on the time series data processing capabilities of pandas, and also integrates other data analysis packages. The operator service can be implemented through the PythonFastAPI framework, calling packages related to the Python data analysis ecosystem to meet all the complex computing needs of the application. In one example, a custom framework can be used and the definition of the operator calculation function can be added. The framework can use the Python typing capabilities and FastAPI's support for types to automatically generate API interfaces, interface documents, input and output verification, etc.

[0069] In a specific implementation, after determining the above-mentioned business indicators to be analyzed, the above-mentioned device inputs the business data and indicator dimensions corresponding to the above-mentioned business indicators to be analyzed into a constructed preset operator analysis module. The above-mentioned preset operator analysis module obtains the corresponding contribution, sensitivity and time series analysis results for the above-mentioned business data and indicator dimensions through preset analysis rules.

[0070] Further, in order to obtain corresponding contribution, sensitivity and timing analysis results, step S20 includes:

[0071] Step S21: inputting the operating data and the indicator dimension into the preset operator analysis module to obtain the corresponding contribution degree;

[0072] In a specific implementation, the above-mentioned device inputs the business data and indicator dimensions corresponding to the above-mentioned business indicators to be analyzed into a constructed preset operator analysis module, and the above-mentioned preset operator analysis module obtains the corresponding contribution degree of the above-mentioned business data and indicator dimensions through preset analysis rules.

[0073] Further, step S21 includes:

[0074] Step S211: Divide the operating data into upper and lower dimensions according to the indicator dimensions, and obtain upper and lower dimensional operating data corresponding to each indicator dimension.

[0075] Step S212: Input the upper and lower dimensional business data into the preset operator analysis module, so that the preset operator analysis module obtains the corresponding contribution of the business indicator in each indicator dimension using a preset contribution analysis rule.

[0076] It should be understood that the above-mentioned upper and lower dimension operating data can be the classification of the operating data of the above-mentioned operating instructions to be analyzed according to different dimensions (for example, self-operated loans, the head office has it, the branches also have it, and the head office and branches form a upper and lower dimension). The above-mentioned preset contribution analysis rules can be contribution algorithm rules constructed according to the definition of contribution.

[0077] In addition, it should be understood that the above contribution algorithm rules may include: fixed base method (difference ratio between base period and current period), elimination method (indicator change after eliminating one dimension), weighted proportion method (division method in which both numerator and denominator can be added), relative entropy method, based on the distribution difference between the base period and the current dimension of the current period, the overall root cause (not the contribution of a single dimension) can be seen.

[0078] In one example, the above-mentioned fixed base method can be divided into the algorithm of additive indicators and the algorithm of composite indicators. The algorithm of additive indicators is the proportion of the difference of dimension i to the total difference. You can refer to the following calculation formula for the contribution rate of additive indicators:

[0079]

[0080] And the contribution rate of all values ​​under the same dimension = 100%, among which today's total value is the current value of the above-mentioned upper-dimensional operating data, yesterday's total value is the historical value of the above-mentioned upper-dimensional operating data, today's value of dimension value 1 is the current value of the above-mentioned lower-dimensional operating data, and yesterday's value of dimension value 1 is the historical value of the above-mentioned lower-dimensional operating data. The above-mentioned current value and the above-mentioned historical value can be changed according to user needs. When the user has no demand, the operating data value at the time of detecting the above-mentioned analysis instruction will be used as the current value, and the time point 24 hours before the time of detecting the above-mentioned analysis instruction will be used as the historical value.

[0081] It should be understood that the composite index is obtained by combining multiple basic indicators. The difference method can be used to calculate the changes brought about by the dimension to the composite index. Refer to the following composite index contribution rate calculation formula:

[0082]

[0083] Among them, the numerator of the base period is the operating data of the upper dimension basic indicator in the upper dimension composite indicator, and the denominator of the base period is the operating data corresponding to the upper dimension composite indicator. The above-mentioned numerator change of dimension i in this period and the operating data corresponding to the basic indicator of the lower dimension composite indicator, the denominator of the base period is the operating data corresponding to the lower dimension composite indicator. After calculating the contribution value of each i, the contribution value is normalized to get the contribution rate:

[0084]

[0085] In another example, the above elimination method refers to eliminating the data of the dimension value of the base period and the current period at the same time, and checking the month-on-month difference change of the market after elimination (such as the market index after elimination of the current period - the market index after elimination of the base period); if the market of the current period is rising month-on-month, then the greater the month-on-month decline of the market after eliminating the dimension value indicates the greater contribution of the dimension value; if the market of the current period is falling month-on-month, then the greater the month-on-month increase of the market after eliminating the dimension value indicates the greater contribution of the dimension value. The calculation method is:

[0086] The market rose month-on-month:

[0087] The score of dimension value i = the month-on-month difference without excluding dimension value i - the month-on-month difference after excluding dimension value i;

[0088] The market fell month-on-month:

[0089] Score three of dimension value i: the month-on-month difference without excluding dimension value i - the month-on-month difference without excluding dimension value i;

[0090] Contribution rate:

[0091]

[0092] In another example, the weighted proportion method is to use the impact of changes within the category relative to the unchanged whole. Assume that the base period proportion of the denominator of dimension i is w1, and the current period proportion is w2; the base period indicator of dimension i is q1, and the current period is q2; the base period of the market is indicator Q1, and the current period is Q2. Then, the contribution value within the category: w2(q2-q1) = the current period proportion of the denominator of dimension i * (the current period indicator of dimension i - the base period indicator of dimension i) = the current period proportion of the denominator of dimension i * the change value of the dimension i indicator; the contribution value between categories: (q1-Q1)(w2-w1) = (the base period indicator of dimension i - the base period indicator of the market) - (the current period proportion of the denominator of dimension i - the base period proportion of the denominator of dimension i) = (the base period indicator of dimension i - the base period indicator of the market) * the change in the denominator proportion; the comprehensive contribution value: the contribution value within the category + the contribution value between categories; the total comprehensive contribution value: Q2-Q1 = the change in the market indicator.

[0093] In a specific implementation, after obtaining the upper and lower dimensional business data, the preset operator analysis module selects the corresponding preset contribution analysis rule according to the business indicator to be analyzed to analyze the upper and lower dimensional business data and obtain the corresponding contribution.

[0094] Step S22: input the business data into the preset operator analysis module to obtain the corresponding sensitivity.

[0095] In a specific implementation, the above-mentioned device inputs the business data and indicator dimensions corresponding to the above-mentioned business indicators to be analyzed into a constructed preset operator analysis module, and the above-mentioned preset operator analysis module obtains the corresponding sensitivity of the above-mentioned business data and indicator dimensions through preset analysis rules.

[0096] Further, step 22 includes:

[0097] Step S221: obtaining the composite indicator and corresponding composite indicator data in the business indicator to be analyzed;

[0098] Step S222: inputting each of the composite indicator data and the operating data into the preset operator analysis module, so that the preset operator analysis module uses the preset sensitivity analysis rule to obtain the sensitive parameters between the indicator to be analyzed and each of the composite indicators;

[0099] Step S223: Obtain corresponding sensitivity according to the sensitive parameter.

[0100] It should be noted that the diversity of indicator calculation formulas makes it difficult to express the degree of dependence between indicators through analysis (accurate formula). In this embodiment, sensitivity analysis (SA) is used to obtain the degree of influence of input on output. SA is suitable for complex mathematical models. Its basic principle is to generate a large number of input samples (depending on the definition of the input range), calculate the output results, and then use specific statistical methods (such as Sobol, Morris, FAST, etc.) to analyze the input and output, and finally obtain the sensitivity index (Sensitivity Index).

[0101] Since the derivative is a linear approximation of a nonlinear relationship, for example, the pricing of a bond is related to the yield to maturity, duration is the sensitivity of the percentage change in security value to the change in the yield to maturity (first-order partial derivative), and convexity is a second-order partial derivative, in this embodiment, the first-order and second-order derivatives of the above functions with respect to each variable can be directly obtained through the gradient and Hessian functions to obtain the sensitivity.

[0102] It should be understood that the above-mentioned composite indicator can be an indicator composed of a combination of multiple indicators (for example, net profit margin = (turnover-cost) / cost, where net profit margin is a composite indicator), and the above-mentioned preset sensitivity analysis rules can be rules for sensitivity analysis according to the above-mentioned SA method, or can be rules for obtaining sensitivity through second-order derivatives. In a specific implementation, after determining that the above-mentioned operating indicator to be analyzed is a composite indicator, the above-mentioned device obtains the operating data of the above-mentioned composite indicator and obtains the operating data of the basic indicators constituting the composite indicator, and inputs the above-mentioned composite indicator operating data and the above-mentioned basic indicator operating data into the above-mentioned preset operator analysis module, so that the above-mentioned preset operator analysis module obtains the sensitivity of the above-mentioned basic indicators corresponding to the above-mentioned composite indicator as the corresponding sensitivity based on the corresponding relationship and change between the operating data of the above-mentioned basic indicators and the operating data corresponding to the above-mentioned composite indicator according to the above-mentioned preset sensitivity analysis rules.

[0103] Step S23: input the operating data and the indicator dimension into the preset operator analysis module to obtain corresponding time series analysis results.

[0104] In a specific implementation, the above-mentioned device inputs the business data and indicator dimensions corresponding to the above-mentioned business indicators to be analyzed into a constructed preset operator analysis module, and the above-mentioned preset operator analysis module obtains corresponding time series analysis results for the above-mentioned business data and indicator dimensions through preset analysis rules.

[0105] Furthermore, the time series analysis may be a stationary analysis of the indicator to be analyzed. To implement the stationary analysis, step S23 includes:

[0106] Segmenting the business data according to preset time intervals to obtain a number of segmented business data;

[0107] Inputting each of the segmented business data and the indicator dimension into the preset operator analysis module, so that the preset operator analysis module obtains the stability test result of the business indicator to be analyzed by using the preset stability analysis rule;

[0108] The corresponding time series analysis result is obtained according to the stationarity test result.

[0109] It should be understood that the above-mentioned stationarity test can be a test method for judging whether the characteristic value of the operating data of the above-mentioned indicator to be analyzed changes with time, thereby judging whether the above-mentioned indicator to be analyzed is stable, and the above-mentioned characteristic value can be a statistical characteristic value such as the mean and variance of the above-mentioned operating data. The above-mentioned preset stationarity analysis rule can be a rule constructed according to the definition of stationarity, wherein the above-mentioned definition of stationarity means that the statistical characteristics such as the mean and variance of the sequence will not change significantly over time. In this embodiment, the Augmented Dickey-Fuller test (ADF) unit root hypothesis test method can be used as the above-mentioned preset stationarity analysis rule.

[0110] It should also be understood that the above-mentioned preset time interval can be a time interval preset by a user (eg, every year, every month, every day, etc.) and is used to segment the above-mentioned experience data in order to calculate statistical features.

[0111] In a specific implementation, the above-mentioned device can segment the above-mentioned business data according to preset time intervals, and input each segment of the business data into the above-mentioned preset operator analysis module, so that the above-mentioned preset operator analysis module can calculate the statistical characteristic values ​​(such as mean, variance, etc.) of each segment of the business data, and then analyze the above-mentioned statistical characteristic values ​​through preset stability algorithm rules to judge the stability of the above-mentioned business indicators to be analyzed.

[0112] Step S30: Analyze the business indicator to be analyzed in at least any two dimensions of a downward dimension, a horizontal dimension, and a vertical dimension according to the contribution, the sensitivity, and the time series analysis result.

[0113] It is important to understand that reference Figure 2 , Figure 2 This is a dimensional analysis diagram of the business indicator analysis method proposed in this embodiment. The downward dimension can be a multi-dimensional decomposition of the above business indicators based on graph visualization (i.e. Figure 2 The dimensions that can achieve flexible operations such as multiple levels, different perspectives, horizontal / vertical comparisons, etc. are obtained by decomposing the horizontal dimensions from the middle to the lower dimensions. There is no need to limit one dimension to one level. The horizontal dimensions can be the dimensions composed of the composite indicators and the association relationship between the composite indicators (i.e. Figure 2 The horizontal correlation dimension is the relationship between the composite index and the basic index. Figure 2 EVA = pre-tax net profit - (economic capital x capital cost rate)), the above vertical dimension can be the dimension formed by the relationship between the operating data of the above operating indicators at different times (i.e. Figure 2 The dimension of the direction of longitudinal time series analysis).

[0114] In a specific implementation, after obtaining the contribution, sensitivity and time series analysis results corresponding to the above-mentioned business indicators to be analyzed, the above-mentioned device classifies the above-mentioned contribution, sensitivity and time series analysis results, wherein the above-mentioned contribution represents the analysis result of the above-mentioned business indicators in the downward dimension, the above-mentioned sensitivity represents the analysis of the above-mentioned business indicators in the horizontal dimension, and the above-mentioned time series analysis results represent the analysis of the above-mentioned business indicators in the vertical dimension.

[0115] This embodiment proposes a business indicator analysis method, which is applied to a business indicator analysis system with a preset operator analysis module, and the method includes: when the indicator analysis instruction is received, the business data and indicator dimensions corresponding to the business indicator to be analyzed are obtained from the database; the business data and indicator dimensions are input into the preset operator analysis module to obtain the corresponding contribution, sensitivity and time series analysis results, and the preset operator analysis module is a module constructed according to the preset analysis rules; according to the contribution, sensitivity and time series analysis results, the business indicator to be analyzed is analyzed in at least any two dimensions of the downward dimension, the horizontal dimension and the vertical dimension. This shows that this embodiment can analyze the business indicator to be analyzed in the downward dimension, the horizontal dimension and the vertical dimension according to the contribution, sensitivity and time series analysis results, thereby improving the accuracy of multi-dimensional business indicator analysis.

[0116] Based on the first embodiment, in the second embodiment, the same or similar contents as those in the above-mentioned embodiment 1 can be referred to the above introduction, and will not be described in detail later. Figure 3 , Figure 3 This is a flow chart of the second embodiment of the business indicator analysis method proposed in this embodiment. The time series analysis result may be the month-on-month growth rate of the indicator to be analyzed. In order to obtain the month-on-month growth rate, the preset analysis rule includes a preset month-on-month analysis rule. The step of inputting the business data and the indicator dimension into the preset operator analysis module to obtain the corresponding time series analysis result includes:

[0117] Step S231: dividing the operating data based on preset time nodes to obtain current period data and previous period data;

[0118] Step S232: inputting the current period data, the previous period data and the indicator dimension into the preset operator analysis module, so that the preset operator analysis module obtains the month-on-month growth rate of the operating indicator to be analyzed by using the preset month-on-month analysis rule;

[0119] Step S233: Obtain corresponding timing analysis results according to the month-on-month growth rate.

[0120] It should be understood that the above-mentioned month-on-month growth may refer to the growth compared with the previous period, where the previous period and the next period may be time periods distinguished by the user through preset time nodes (e.g., the transaction amount in May and the transaction amount in June, when the current period is June, the previous period is May). The above-mentioned preset month-on-month analysis rule may be a rule for calculating the month-on-month growth rate, where the month-on-month growth rate = (this period data - previous period data) / previous period data.

[0121] In a specific implementation, the above-mentioned device splits the above-mentioned operating data according to preset time nodes to obtain current period data and previous period data, and inputs the current period data and previous period data into a preset operator analysis module to obtain the month-on-month growth rate of the above-mentioned operating indicator to be analyzed, and uses the month-on-month growth rate as the corresponding time series analysis result.

[0122] In addition, the time series analysis result may be the year-on-year growth rate of the indicator to be analyzed. In order to obtain the year-on-year growth rate, the preset analysis rule includes a preset year-on-year analysis rule. The step of inputting the operating data and the indicator dimension into the preset operator analysis module to obtain the corresponding time series analysis result includes:

[0123] Divide the operating data based on preset time nodes to obtain current period data and historical current period data;

[0124] Inputting the current period data, the historical current period data and the indicator dimension into the preset operator analysis module, so that the preset operator analysis module obtains the year-on-year growth rate of the operating indicator to be analyzed by using the preset year-on-year analysis rule;

[0125] The corresponding time series analysis results are obtained according to the year-on-year growth rate.

[0126] It is important to understand that year-on-year growth (rate) can refer to the growth (rate) between this year and the same period last year. Here, the meaning of the so-called "same period" should be processed according to data of different frequencies. Monthly, quarterly, and annual frequencies are relatively straightforward, but daily frequency data needs to consider the impact of leap years. Data with a frequency lower than daily frequency is directly switched to annual data, and the rest remain unchanged. Among them, year-on-year growth rate = (current period data - historical current period data) / historical current period data.

[0127] In a specific implementation, the above-mentioned device splits the above-mentioned operating data according to preset time nodes to obtain current period data and historical period data, and inputs the current period data and historical period data into a preset operator analysis module to obtain the year-on-year growth rate of the above-mentioned operating indicator to be analyzed, and uses the year-on-year growth rate as the corresponding time series analysis result.

[0128] Furthermore, the time series analysis result may be a correlation growth rate of the indicator to be analyzed. In order to obtain the correlation growth rate, the preset analysis rule includes a preset correlation analysis rule. The step of inputting the business data and the indicator dimension into the preset operator analysis module to obtain the corresponding time series analysis result includes:

[0129] Based on the indicator dimension and a number of preset business nodes, the business indicator is divided to obtain a number of business node data under the same upper and lower dimensions;

[0130] Inputting the data of each of the business nodes into the preset operator analysis module, so that the preset operator analysis module obtains the correlation of each of the preset business nodes by using the preset correlation analysis rule;

[0131] The corresponding timing analysis results are obtained according to the correlation of each of the preset business nodes.

[0132] It should be understood that the above correlation only analyzes two or more variable elements with correlation, calculates the correlation coefficient between each variable element, and thus measures the degree of correlation between two variable factors. That is, before the above correlation calculation, it is necessary to determine whether there are multiple nodes of the above business indicators. The above preset business nodes can be nodes where the above business indicators to be analyzed exist (for example, the turnover of Beijing Branch, the turnover of Wuhan Branch, where Beijing Branch and Wuhan Branch are the nodes of the above turnover indicators). When calculating the above correlation coefficient, methods such as Pearson, Kendall, and Spearman can be used. This embodiment uses Pearson to calculate the correlation coefficient, but no specific restrictions are made.

[0133] In a specific implementation, the above-mentioned device divides the above-mentioned business data through the above-mentioned indicator dimension, classifies the above-mentioned business data according to the nodes, obtains the business data corresponding to the above-mentioned indicator to be analyzed of each node, and inputs the above-mentioned classified business data into a preset operator analysis module, so that the above-mentioned preset operator analysis module can calculate the correlation coefficient between each node for the above-mentioned business indicator to be analyzed through the Pearsond method.

[0134] In addition, the time series analysis result may be a periodic analysis growth rate of the indicator to be analyzed. In order to obtain the periodic analysis growth rate, the preset analysis rule includes a preset periodic analysis rule. The step of inputting the operating data and the indicator dimension into the preset operator analysis module to obtain the corresponding time series analysis result includes:

[0135] Sorting the business data based on a preset time sequence to obtain a sorting result;

[0136] Inputting the sorting result and the indicator dimension into the preset operator analysis module, so that the preset operator analysis module obtains the periodicity of the operating indicator by using the preset periodicity analysis rule;

[0137] The corresponding time series analysis results are obtained according to the periodicity of the operating indicators.

[0138] It should be noted that the above-mentioned preset time sequence can be in chronological order or in reverse chronological order or in a specific chronological order. This embodiment uses the chronological order for illustration, but does not impose specific restrictions on this embodiment. The above-mentioned periodic analysis rule can be a rule for obtaining a curve chart corresponding to the above-mentioned business data based on the above-mentioned sorting result and performing periodic decomposition on the curve chart.

[0139] In a specific implementation, after obtaining the above-mentioned business data, the above-mentioned device sorts the above-mentioned business data according to chronological order to obtain sorted business data, and inputs the above-mentioned business data into a preset operator analysis module, so that the above-mentioned preset operator analysis module can obtain a curve graph corresponding to the above-mentioned business data according to the above-mentioned sorting, and perform periodic decomposition (for example, periodic decomposition based on multivariate seasonal decomposition method (Multivariate Seasonal and Trend decomposition using Loess, MSTL)) to analyze whether the above-mentioned sorted business data has a certain periodicity.

[0140] In one example, reference Figure 4 , Figure 4 The technical architecture diagram of the business indicator analysis method provided in this embodiment is as follows. Users can select and configure indicators through the framework provided by the API service and analyze the indicators selected by the user through the operator service. When receiving the user's indicator analysis instruction, firstly, from the data layer (such as Figure 4 Clickhous, Hive, GuassDB) shown in the figure retrieves the relevant business data and configures the original data through the configuration layer, where the configuration includes indicator attribute configuration (such as Figure 4 Single indicator, multiple indicators, composite indicators), single dimension configuration (such as Figure 4 asset type, card level, institution level, statistical caliber) and dimension combination configuration (such as Figure 4 In the asset type + card level, asset type + statistical caliber, asset type + institution, and card level + institution), the business data corresponding to the indicators required by the user is obtained through the query layer, and then the user's scenario for indicator analysis is obtained (such as Figure 4The data source / indicator set / analysis scenario in the query layer is used to query the engine in real time, summarize in real time, and call operators. After confirmation, the above business data and user-configured parameters are used as operators through the API interface to process data through the operator service (i.e. Figure 4 The operator implementation, data analysis and acceleration) in the middle obtain the analysis results of the analysis indicators required by the user in various dimensions, and through the graph view (i.e. Figure 4 The medium-dimensional decomposition interface displays the indicator analysis results to users by decomposing the head office AUM into branches such as Beijing Branch and Shanghai Branch.

[0141] In addition, the above-mentioned preset operator analysis module can also perform data aggregation, which can be grouped and aggregated in time, such as aggregating daily frequency data to monthly frequency, grouping time series data into each month, each group is a shorter time series, and using some aggregation methods (such as summing, taking the end of the month) to convert each group of time series into an aggregated scalar, and then merge the aggregation results of each group to become a new time series, and the frequency of this time series also becomes monthly frequency. At the same time, the above-mentioned preset operator analysis module can also fill in the missing values ​​of business data, including: forward filling (that is, filling with the value of the previous time point of the missing value. Backward filling: filling with the value of the next time point of the missing value); average filling (that is, after removing the missing values ​​from the sequence data, calculate the average value and fill it with this value); median filling (after removing the missing values ​​from the sequence data, calculate the median and fill it with this value); linear interpolation (that is, fitting the linear relationship between the values ​​before and after the missing value, such as the previous one is 1, the next one is 3, and the missing value is filled with 2); custom filling (that is, the user enters a value and fills it with this value. For example, the commonly used method of filling missing values ​​with 0).

[0142] This embodiment also provides a business indicator analysis device, please refer to Figure 5 , Figure 5 The operating indicator analysis device provided in this embodiment includes:

[0143] The indicator acquisition module is used to obtain the business data and indicator dimensions corresponding to the business indicator to be analyzed from the database when receiving the indicator analysis instruction;

[0144] An indicator analysis module, used to input the operating data and the indicator dimension into a preset operator analysis module to obtain corresponding contribution, sensitivity and time series analysis results, wherein the preset operator analysis module is a module constructed according to preset analysis rules;

[0145] The result integration module is used to analyze the business indicator to be analyzed in at least any two dimensions of the downward dimension, the horizontal dimension and the vertical dimension according to the contribution, the sensitivity and the time series analysis result.

[0146] The operating indicator analysis device provided in this embodiment adopts the operating indicator analysis method in the above embodiment, which can solve the technical problem of insufficient accuracy of the prior art when performing multi-dimensional operating indicator analysis. Compared with the prior art, the beneficial effects of the operating indicator analysis device provided in this embodiment are the same as the beneficial effects of the operating indicator analysis method provided in the above embodiment, and other technical features in the operating indicator analysis device are the same as the features disclosed in the above embodiment method, which will not be repeated here.

[0147] This embodiment provides a business indicator analysis device, which includes: at least one processor; and a memory that is communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the business indicator analysis method in the above-mentioned embodiment one.

[0148] Reference below Figure 6 , Figure 6 Schematic diagram of the structure of the business indicator analysis device suitable for implementing the present embodiment. The business indicator analysis device in the present embodiment may include but is not limited to mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle terminals (such as vehicle navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 6 The business indicator analysis device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0149] like Figure 6As shown, the business indicator analysis device may include a processing device 1001 (e.g., a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM: Read Only Memory) 1002 or a program loaded from a storage device 1003 to a random access memory (RAM: Random Access Memory) 1004. In RAM1004, various programs and data required for the operation of the business indicator analysis device are also stored. The processing device 1001, ROM1002, and RAM1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Generally, the following systems can be connected to the I / O interface 1006: an input device 1007 including, for example, a touch screen, a touch pad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD: Liquid Crystal Display), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 can allow the business indicator analysis device to communicate with other devices wirelessly or by wire to exchange data. Although the figure shows a business indicator analysis device with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems can be implemented or have alternatively.

[0150] In particular, according to the present embodiment, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the present embodiment includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program contains program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through a communication device, or installed from the storage device 1003, or installed from the ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present embodiment are executed.

[0151] The business indicator analysis device provided in this embodiment adopts the business indicator analysis method in the above embodiment, which can solve the technical problem of insufficient accuracy of the prior art when performing multi-dimensional business indicator analysis. Compared with the prior art, the beneficial effects of the business indicator analysis device provided in this embodiment are the same as the beneficial effects of the business indicator analysis method provided in the above embodiment, and other technical features in the business indicator analysis device are the same as the features disclosed in the method of the previous embodiment, which will not be repeated here.

[0152] It should be understood that the various parts disclosed in this embodiment can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0153] The above is only a specific implementation of this embodiment, but the protection scope of this embodiment is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in this embodiment, which should be included in the protection scope of this embodiment. Therefore, the protection scope of this embodiment should be based on the protection scope of the claims.

[0154] This embodiment provides a computer-readable storage medium having computer-readable program instructions (ie, computer programs) stored thereon, and the computer-readable program instructions are used to execute the business indicator analysis method in the above embodiment.

[0155] The computer-readable storage medium provided in this embodiment may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM: Random Access Memory), a read-only memory (ROM: Read Only Memory), an erasable programmable read-only memory (EPROM: Erasable Programmable Read Only Memory or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM: CD-Read Only Memory), an optical storage device, a magnetic storage device, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency: Radio Frequency), etc., or any suitable combination thereof.

[0156] The computer-readable storage medium may be included in the business indicator analysis device; or may exist independently without being assembled into the business indicator analysis device.

[0157] The computer-readable storage medium carries one or more programs. When the one or more programs are executed by the business indicator analysis device, the business indicator analysis device performs business indicator analysis.

[0158] The computer program code for performing the operation of the present embodiment can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as an independent software package, partially on the user's computer and partially on the remote computer, or entirely on the remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (for example, using an Internet service provider to connect through the Internet).

[0159] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present embodiment. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0160] The modules involved in the description of this embodiment may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0161] The readable storage medium provided in this embodiment is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned business indicator analysis method, and is intended to solve the technical problem of insufficient accuracy in the prior art when performing multi-dimensional business indicator analysis. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this embodiment are the same as the beneficial effects of the business indicator analysis method provided in the above-mentioned embodiment, and will not be repeated here.

[0162] This embodiment also provides a computer program product, including a computer program, which implements the steps of the above-mentioned business indicator analysis method when executed by a processor.

[0163] The computer program product provided in this embodiment can solve the technical problem of insufficient accuracy in the prior art when performing multi-dimensional business indicator analysis. Compared with the prior art, the beneficial effects of the computer program product provided in this embodiment are the same as the beneficial effects of the business indicator analysis method provided in the above embodiment, which will not be described in detail here.

[0164] The above descriptions are only some embodiments, and are not intended to limit the patent scope of this embodiment. All equivalent structural changes made using the contents of the specification and drawings of this application under the technical concept of this application, or directly / indirectly applied in other related technical fields are included in the patent protection scope of this application.

Claims

1. A method for analyzing business indicators, characterized in that: The method is applied to a business indicator analysis system with a preset operator analysis module, and the method comprises: When receiving the indicator analysis instruction, the operating data and indicator dimensions corresponding to the operating indicator to be analyzed are obtained from the database; Inputting the operating data and the indicator dimension into the preset operator analysis module to obtain corresponding contribution, sensitivity and time series analysis results, wherein the preset operator analysis module is a module constructed according to preset analysis rules; The operating indicator to be analyzed is analyzed in at least any two dimensions of a downward dimension, a horizontal dimension, and a vertical dimension according to the contribution, the sensitivity, and the time series analysis result.

2. The method according to claim 1, characterized in that The step of inputting the operating data and the indicator dimension into the preset operator analysis module to obtain corresponding contribution, sensitivity and time series analysis results includes: Inputting the operating data and the indicator dimension into the preset operator analysis module to obtain the corresponding contribution degree; Inputting the operating data into the preset operator analysis module to obtain corresponding sensitivity; The operating data and the indicator dimensions are input into the preset operator analysis module to obtain corresponding time series analysis results.

3. The method according to claim 2, characterized in that The preset analysis rule includes a preset contribution analysis rule, and the step of inputting the operating data and the indicator dimension into the preset operator analysis module to obtain the corresponding contribution includes: Divide the operating data into upper and lower dimensions according to the indicator dimensions to obtain upper and lower dimensional operating data corresponding to each indicator dimension; The upper and lower dimensional business data are input into the preset operator analysis module, so that the preset operator analysis module obtains the corresponding contribution of the business indicator in each indicator dimension by using the preset contribution analysis rule.

4. The method according to claim 2, characterized in that The preset analysis rules include preset sensitivity analysis rules, and the step of inputting the business data into the preset operator analysis module to obtain the corresponding sensitivity includes: Obtaining composite indicators and corresponding composite indicator data in the operating indicators to be analyzed; Inputting each of the composite index data and the operating data into the preset operator analysis module, so that the preset operator analysis module obtains the sensitive parameters between the index to be analyzed and each of the composite indexes by using the preset sensitivity analysis rule; A corresponding sensitivity is obtained according to the sensitive parameter.

5. The method according to claim 2, characterized in that The preset analysis rule includes a preset stationarity analysis rule, and the step of inputting the operating data and the indicator dimension into the preset operator analysis module to obtain the corresponding time series analysis result includes: Segmenting the business data according to preset time intervals to obtain a number of segmented business data; Inputting each of the segmented business data and the indicator dimension into the preset operator analysis module, so that the preset operator analysis module obtains the stability test result of the business indicator to be analyzed by using the preset stability analysis rule; The corresponding time series analysis result is obtained according to the stationarity test result.

6. The method according to claim 2, characterized in that The preset analysis rule includes a preset month-on-month analysis rule, and the step of inputting the operating data and the indicator dimension into the preset operator analysis module to obtain the corresponding time series analysis result includes: Divide the operating data based on preset time nodes to obtain current period data and previous period data; Inputting the current period data, the previous period data and the indicator dimension into the preset operator analysis module, so that the preset operator analysis module obtains the month-on-month growth rate of the operating indicator to be analyzed by using the preset month-on-month analysis rule; A corresponding time series analysis result is obtained according to the month-on-month growth rate.

7. The method according to claim 2, characterized in that The preset analysis rule includes a preset correlation analysis rule, and the step of inputting the business data and the indicator dimension into the preset operator analysis module to obtain the corresponding time series analysis result includes: Based on the indicator dimension and a number of preset business nodes, the business indicator is divided to obtain a number of business node data under the same upper and lower dimensions; Inputting the data of each of the business nodes into the preset operator analysis module, so that the preset operator analysis module obtains the correlation of each of the preset business nodes by using the preset correlation analysis rule; The corresponding timing analysis results are obtained according to the correlation of each of the preset business nodes.

8. The method according to claim 2, characterized in that The preset analysis rule includes a preset periodic analysis rule, and the step of inputting the operating data and the indicator dimension into the preset operator analysis module to obtain the corresponding time series analysis result includes: Sorting the business data based on a preset time sequence to obtain a sorting result; Inputting the sorting result and the indicator dimension into the preset operator analysis module, so that the preset operator analysis module obtains the periodicity of the operating indicator by using the preset periodicity analysis rule; The corresponding time series analysis results are obtained according to the periodicity of the operating indicators.

9. A business indicator analysis device, characterized in that: The device comprises: The indicator acquisition module is used to obtain the business data and indicator dimensions corresponding to the business indicator to be analyzed from the database when receiving the indicator analysis instruction; An indicator analysis module, used to input the operating data and the indicator dimension into a preset operator analysis module to obtain corresponding contribution, sensitivity and time series analysis results, wherein the preset operator analysis module is a module constructed according to preset analysis rules; The result integration module is used to analyze the business indicator to be analyzed in at least any two dimensions of the downward dimension, the horizontal dimension and the vertical dimension according to the contribution, the sensitivity and the time series analysis result.

10. A business indicator analysis device, characterized in that: The device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the business indicator analysis method according to any one of claims 1 to 8.

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