Business strategy generation method and device, medium, electronic equipment and program product

Abstract

The invention relates to the technical field of big data analysis, and provides a business strategy generation method and device, a medium, electronic equipment and a program product, and the method comprises the steps: determining first weight information of business data of an object in a first dimension; determining a mutual influence degree between the business data of the first dimension and the business data of the second dimension; determining second weight information of the first dimension according to the first weight information of the business data of the first dimension and the mutual influence degree; and determining a service strategy according to the second weight information of the first dimension and the service data of the first dimension. The accuracy of the generated service strategy is improved, and waste of popularization resources is avoided.

Description

Technical Field

[0001] The present disclosure relates to the technical field of big data analysis, and in particular, to a business strategy generation method, device, medium, electronic device and program product. Background Art

[0002] With the continuous development of science and technology, more and more business data are appearing in various enterprises and industries in production and life. By analyzing these business data, enterprises can make more scientific and wise decisions and thus formulate effective business strategies.

[0003] In the related art, the weight value of business data is a fixed value set according to the impact of the business data on the business strategy. Business data of different dimensions are independent of each other, which makes the accuracy of the analysis results obtained by analyzing the business data insufficient, and thus makes the generated business strategy insufficiently accurate. Summary of the invention

[0004] The purpose of the present disclosure is to provide a business policy generation method, device, medium, electronic device and program product to solve the problems in the related art.

[0005] In a first aspect, an embodiment of the present disclosure provides a service policy generation method, which is applied to an electronic device, including: Determine first weight information of business data of the object in the first dimension; Determine the mutual influence between the business data of the first dimension and the business data of the second dimension; Determining second weight information of the first dimension according to first weight information of the business data of the first dimension and the mutual influence degree; A business strategy is determined according to the second weight information of the first dimension and the business data of the first dimension.

[0006] In a second aspect, an embodiment of the present disclosure provides a service policy generation device, which is applied to an electronic device, including: A weight information determination module, configured to determine first weight information of business data of an object in a first dimension; A mutual influence degree determination module, configured to determine the mutual influence degree between the business data of the first dimension and the business data of the second dimension; a target weight information determination module, configured to determine second weight information of the first dimension according to the first weight information of the business data of the first dimension and the mutual influence degree; The business policy determination module is configured to determine the business policy according to the second weight information of the first dimension and the business data of the first dimension.

[0007] In a third aspect, an embodiment of the present disclosure provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the business policy generation method in the first aspect.

[0008] In a fourth aspect, an embodiment of the present disclosure provides an electronic device, including: a memory having a computer program stored thereon; A processor is used to execute the computer program in the memory to implement the steps of the service policy generation method in the first aspect.

[0009] In a fifth aspect, an embodiment of the present disclosure provides a computer program product, including a computer program, which implements the steps of the business policy generation method in the first aspect when executed by a processor.

[0010] Through the above technical solution, by considering the mutual influence between the business data of objects in different dimensions and combining the first weight information of the first dimension, the second weight information of the first dimension is re-determined, and the accurate business strategy is determined based on the second weight information and the business data of the first dimension, thereby improving the accuracy of data processing and business strategy formulation.

[0011] Other features and advantages of the present disclosure will be described in detail in the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] The accompanying drawings are used to provide a further understanding of the present disclosure and constitute a part of the specification. Together with the following specific embodiments, they are used to explain the present disclosure but do not constitute a limitation of the present disclosure. In the accompanying drawings: Figure 1 The present invention is a flowchart of a method for generating a business policy according to an exemplary embodiment.

[0013] Figure 2 The figure is a block diagram of a device for generating a service policy according to an exemplary embodiment.

[0014] Figure 3 It is a block diagram of an electronic device according to an exemplary embodiment. DETAILED DESCRIPTION

[0015] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as being limited to the embodiments described herein, which are instead provided for a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not intended to limit the scope of protection of the present disclosure.

[0016] It should be understood that the various steps described in the method embodiments of the present disclosure may be performed in different orders and / or in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present disclosure is not limited in this respect.

[0017] The term "including" and its variations used herein are open inclusions, i.e., "including but not limited to". The term "based on" means "based at least in part on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". The relevant definitions of other terms will be given in the following description.

[0018] It should be noted that the concepts such as "first" and "second" mentioned in the present disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0019] It should be noted that the modifications of "one" and "plurality" mentioned in the present disclosure are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, it should be understood as "one or more".

[0020] Research has found that when business data of a certain dimension changes, it may cause business data of other dimensions to change. However, in related technologies, since business data of different dimensions are independent of each other, the set weight information cannot reflect the mutual influence between business data and business data. The fixed-size weight information set according to the influence of business data on business strategy cannot fully reflect the value of business data of each dimension, making the accuracy of the analysis results obtained by analyzing business data insufficient, thereby making the accuracy of the generated business strategy insufficient. Due to the insufficient accuracy of business strategy, it may cause a waste of promotion resources (such as human resources, network resources, etc.).

[0021] In order to solve the above technical problems, the first weight information of the business data of the object in the first dimension is determined; the mutual influence between the business data of the first dimension and the business data of the second dimension is determined; the second weight information of the first dimension is determined according to the first weight information of the first dimension and the mutual influence; the business strategy is determined according to the second weight information of the first dimension and the business data of the first dimension. By considering the mutual influence between the business data of the object in different dimensions and combining the first weight information of the first dimension, the second weight information of the first dimension is re-determined, and an accurate business strategy is determined according to the second weight information and the business data of the first dimension, so as to avoid wasting promotion resources such as human resources and network resources.

[0022] Figure 1 FIG. 1 is a flow chart of a method for generating a business policy according to an exemplary embodiment. The method for generating a business policy can be applied to electronic devices, such as Figure 1 As shown, the business strategy generation method may include steps S1 to S4.

[0023] Step S1, determining first weight information of business data of an object in a first dimension.

[0024] An object can be, but is not limited to, a company, an industry, or a product. Objects in different dimensions have different corresponding business data.

[0025] Taking an enterprise / industry as an example, the different dimensions corresponding to an enterprise / industry may be time dimension, product dimension, regional dimension, etc., and the corresponding business data may be sales volume / sales amount under the time dimension, sales volume / sales amount under the product dimension, and sales volume / sales amount under the regional dimension.

[0026] Taking a product as an example, the different dimensions corresponding to a product may be time dimension, region dimension, product model dimension, customer group dimension, etc., and the corresponding business data may be sales volume / sales amount under the time dimension, sales volume / sales amount under the region dimension, sales volume / sales amount under the product model dimension, and sales volume / sales amount under the customer group dimension.

[0027] The first dimension may refer to any one of the multiple dimensions. Unless otherwise specified, one of the dimensions will be used as an example for explanation below.

[0028] The first weight information may be the influence of the business data of the object in the first dimension on the business strategy.

[0029] The first weight information of the business data of the object in the first dimension can be determined by initial weight information set according to the impact of the business data on the business strategy, or it can be weight information determined based on the business data of the current segment and the business data of the historical time period of the object in the first dimension.

[0030] In other embodiments, the initial business data is scattered, and the scattered business data is not convenient for data analysis. Therefore, before analyzing the business data, the business data can also be preprocessed to improve the efficiency and accuracy of data analysis. The preprocessing method can be, but is not limited to, data cleaning (for example, missing value processing, outlier processing, duplicate value processing), data conversion, etc.

[0031] Step S2, determining the mutual influence between the business data of the first dimension and the business data of the second dimension.

[0032] The degree of mutual influence can be the correlation between the business data of the first dimension and the business data of the second dimension. This correlation can be a positive correlation (for example, when the business data of the first dimension increases, the dimensional data of the second dimension increases), a negative correlation (for example, when the business data of the first dimension decreases, the business data of the second dimension decreases), or no correlation.

[0033] The second dimension may be a dimension other than the first dimension among all dimensions of the object.

[0034] For example, if the object is an enterprise, all dimensions of the enterprise include time dimension, product dimension, and region dimension. When the first dimension is the time dimension, the second dimension is the product dimension and the region dimension except the time dimension.

[0035] If the first dimension refers to any one of all dimensions of the object, and the number of all dimensions is N, then the number of second dimensions is N-1, and the number of mutual influences between the business data of the first dimension and the business data of the second dimension is N-1.

[0036] Step S3, determining the second weight information of the first dimension according to the first weight information of the business data of the first dimension and the mutual influence degree.

[0037] The second weight information may be the influence of the business data of the object in the first dimension on the business strategy after considering the mutual influence basis.

[0038] Step S4, determining a business strategy based on the second weight information of the first dimension and the business data of the first dimension.

[0039] The business data of the first dimension is updated according to the second weight information of the first dimension, and then the updated business data is analyzed, and the business strategy is determined according to the analysis result.

[0040] In other embodiments, the analysis results and business strategies can also be displayed visually, which can be displayed in the form of text, or in the form of a graph, or in a combination of text and graph, and this embodiment is not limited here. For example, if the sales volume of a certain product in a certain area increases, it can be displayed in the form of an upward arrow at the corresponding position in the chart, and the upward arrow can also be marked with a bright color, or the area where the arrow is located can be highlighted and displayed separately. It is also possible to fill each type of business data into the corresponding position of the table template according to the table template selected by the user, and place the analysis results of the business data and the generated business strategy in the area specified by the user in the table, so that the user can select the required business strategy according to the needs.

[0041] By considering the mutual influence between business data of different dimensions and combining the first weight information of the first dimension, the second weight information corresponding to the first dimension is re-determined, and an accurate business strategy is determined based on the second weight information and the business data of the first dimension to avoid wasting promotion resources such as human resources and network resources.

[0042] In a possible implementation, step S1 may include step S11 and step S12.

[0043] Step S11, determining the change amount of the business data of the first dimension according to the business data of the object in the first time period of the first dimension and the business data of the object in the second time period.

[0044] The second time period is a time period before the first time period, that is, the lower limit value of the first time period is greater than the upper limit value of the second time period.

[0045] For example, if the first time period is from June 1, 2024 to June 30, 2024, the second time period may be from May 1, 2024 to May 31, 2024.

[0046] The business data of the second time period may be the business data of the previous statistical period (eg, quarter, month, week) of the business data of the first time period, or the business data of the same period of the previous year of the business data of the first time period, or the business data of other historical time periods.

[0047] For example, the business data of the first time period of the first dimension is the sales in May 2024, and the business data of the second time period of the first dimension may be the sales in April 2024 or the sales in May 2023.

[0048] The business data change amount of the first dimension may be the difference between the business data of the object in a first time period of the first dimension and the business data of the object in a second time period of the first dimension.

[0049] Step S12: determining first weight information of the business data of the object in the first dimension according to the change amount of the business data in the first dimension.

[0050] The first weight information of the business data of the object in the first dimension may be a ratio of a change in the business data in the first dimension to the business data of the object in the first dimension.

[0051] In a possible implementation, step S2 may include step S21 and step S22.

[0052] Step S21, vectorize the business data of the first dimension and the business data of the second dimension to obtain first vectorized business data and second vectorized business data. Vectorization is a process of converting business data into vector form. The data items in the business data are converted into numerical vectors. For non-numerical data in the business data (such as text, category data, etc.), they can be replaced by numerical values. For example, the commodity types are represented by unique hot encoding, electronic products: [1, 0], daily necessities: [0, 1]. For the numerical data in the business data, it can be not processed, or simplified, and can be used as a component of the vector. The first vectorized business data can be a business data vector obtained after vectorizing the business data of the first dimension.

[0053] The second vectorized business data may be a business data vector obtained by vectorizing the business data of the second dimension.

[0054] Step S22: determining the mutual influence between the business data of the first dimension and the business data of the second dimension according to the first vectorized business data and the second vectorized business data.

[0055] The geometric similarity between the first vectorized business data and the second vectorized business data is calculated, and the geometric similarity is used as the mutual influence degree between the business data of the first dimension and the business data of the second dimension.

[0056] In other embodiments, the mutual influence between the business data of the first dimension and the business data of the second dimension can be determined by calculating the geometric similarity between the first vectorized business data and the second vectorized business data and combining the historical geometric similarity between the historical first vectorized business data and the historical second vectorized business data.

[0057] In a possible implementation, the business data of the first dimension includes the business data of the first dimension within a first time period, and step S22 may include steps S221 to S223.

[0058] Step S221: determining a first similarity according to the first vectorized service data and the second vectorized service data.

[0059] The first similarity may be a geometric similarity between the first vectorized service data and the second vectorized service data within a first time period.

[0060] The calculation formula for the first similarity is:

[0061] in, represents the first similarity; Indicates the second-pole minimum difference between the business data a of the first dimension and the business data b of the second dimension, and is used to indicate the minimum distance between two curves corresponding to the business data a of the first dimension and the business data b of the second dimension at time k; Indicates the second-pole maximum difference between the business data a of the first dimension and the business data b of the second dimension, and is used to indicate the maximum distance between the two curves corresponding to the business data a of the first dimension and the business data b of the second dimension at time k; || indicates absolute value calculation; represents the business data a of the first dimension of the vectorized representation at time k, that is, the first vectorized business data; The business data b of the second dimension represented by the vectorization at time k, that is, the second vectorized business data; represents the identification coefficient, Greater than 0 and less than 1, when The larger the value is, the higher the recognition of the business data represented by the vectorization is, and the lower the efficiency of identifying the business data is; conversely, the lower the value is, the higher the efficiency of identifying the business data is. The value can be determined by user settings or system defaults.

[0062] Step S222: determine a second similarity between the business data of the first dimension and the business data of the second dimension within a second time period.

[0063] The second time period is a time period before the first time period, that is, the lower limit value of the first time period is greater than the upper limit value of the second time period.

[0064] The second similarity may be a geometric similarity between the first vectorized service data and the second vectorized service data within the second time period.

[0065] The second similarity can be obtained based on the business data of the first dimension in the second time period and the business data of the second dimension in the second time period. The business data of the first dimension in the second time period is vectorized to obtain the historical first vectorized business data, and the business data of the second dimension in the second time period is vectorized to obtain the historical second vectorized business data, and then the second similarity is determined based on the historical first vectorized business data and the historical second vectorized business data.

[0066] It should be understood that the calculation principle of the second similarity is the same as the calculation principle of the first similarity.

[0067] Step S223: determining the mutual influence between the business data of the first dimension and the business data of the second dimension according to the first similarity and the second similarity.

[0068] An average value of the first similarity and the second similarity is used as the mutual influence degree between the business data of the first dimension and the business data of the second dimension.

[0069] On the basis of the first similarity corresponding to the first time period, the second similarity corresponding to the second time period before the first time period is also considered, so that the determined mutual influence degree is more accurate and reasonable.

[0070] In a possible implementation, step S3 may include steps S31 to S33.

[0071] Step S31: determining a reference adjustment value of the business data of the first dimension according to first weight information of the business data of the first dimension.

[0072] The first weight information corresponding to the business data of all dimensions is sorted according to the numerical value (it can be in ascending order from small to large, or in descending order from large to small), and according to the first weight information of the business data of the first dimension, the first weight information corresponding to the business data of the previous dimension before the first weight information corresponding to the business data of the first dimension is sorted, and the first weight information corresponding to the business data of the next dimension after the first weight information corresponding to the business data of the first dimension is sorted, that is, the first weight information corresponding to the business data of two adjacent dimensions (one before and one after), and the reference adjustment value is determined according to the first weight information corresponding to the business data of the two adjacent dimensions and the first weight information corresponding to the business data of the first dimension. The reference adjustment value of the business data of the first dimension can be the average of the first weight information corresponding to the business data of the two adjacent dimensions and the first weight information of the business data of the first dimension.

[0073] By way of example, the first weight information of the business data of all dimensions are arranged in ascending order {Q1, Q2, Q3, Q4, ... Qn}. Taking the first weight information corresponding to the business data of the first dimension as Q3, the first weight information corresponding to the business data in the dimension after Q3 is Q4, and the first weight information corresponding to the business data in the dimension before Q3 is Q2. The reference adjustment value of the business data of the first dimension is the average of the first weight information Q2 corresponding to the business data of the previous dimension, the first weight information Q3 of the business data of the first dimension, and the first weight information Q4 corresponding to the business data of the next dimension.

[0074] It should be understood that when the first weight information of the business data of the first dimension is Q1 and there is no first weight information of the business data of the previous dimension, the first weight information of the business data of the previous dimension can be assigned as the first weight information Q1 of the business data of the first dimension to participate in the calculation.

[0075] Step S32: determining a first mutual influence degree from the mutual influence degrees.

[0076] Among them, the difference between the first mutual influence degree and the first weight information of the business data of the first dimension meets the difference condition.

[0077] The difference condition may be less than a difference threshold, or may be the N smallest differences.

[0078] A difference threshold may be set, and the mutual influence degrees whose difference with the first weight information of the business data of the first dimension is less than the difference threshold are all taken as the first mutual influence degrees.

[0079] In other embodiments, N mutual influence degrees with the smallest difference in the first weight information of the business data of the first dimension may be used as the first mutual influence degree. For example, N is 2.

[0080] Step S33, determining the second weight information based on the first mutual influence degree, the reference adjustment value, the first weight information of the business data of the first dimension, and the weight benefit coefficient.

[0081] In a possible implementation, step S33 may include steps S331 to S313.

[0082] Step S331, performing a preset operation based on the first mutual influence degree, the reference adjustment value, the first weight information of the business data of the first dimension, and the weight benefit coefficient to obtain an intermediate adjustment value.

[0083] Step S332, when the intermediate adjustment value is less than or equal to the reference adjustment value, adjust the first weight information and weight benefit coefficient of the business data of the first dimension, and re-execute the step of performing the preset operation to obtain the intermediate adjustment value until the obtained intermediate adjustment value is greater than the reference adjustment value.

[0084] Step S333: determine the intermediate adjustment value corresponding to the maximum weight benefit coefficient as the second weight information of the first dimension.

[0085] The calculation formula for the intermediate adjustment value is:

[0086] in, Indicates the intermediate adjustment value, Indicates a weighted return coefficient greater than 0; is the reference adjustment value of the business data of the first dimension; represents the first mutual influence degree; The server sets two random constants based on the user's settings, which are used to indicate the sensitivity when obtaining intermediate adjustment values. The smaller the value, the higher the sensitivity, and the higher the accuracy of the intermediate adjustment value obtained; conversely, the lower the sensitivity, the lower the accuracy of the intermediate adjustment value obtained; It means randomly selecting a random number from the random number table, and Y is less than 1 and greater than 0; Represents the first weight information of the business data of the first dimension.

[0087] Determine whether the intermediate adjustment value calculated by the above calculation formula is greater than the reference adjustment value; if the obtained intermediate adjustment value is less than or equal to the reference adjustment value, update the first weight information of the business data of the first dimension; use the updated first weight information to calculate the intermediate adjustment value and the weight benefit coefficient again; continue to determine whether the intermediate adjustment value is greater than the reference adjustment value until the adjusted intermediate adjustment value is greater than the reference adjustment value; obtain the weight benefit coefficient obtained each time to obtain a set of weight benefit coefficients; determine the weight benefit coefficient with the largest value in the set of weight benefit coefficients, and determine the intermediate adjustment value corresponding to the largest weight benefit coefficient as the second weight information of the first dimension.

[0088] The first weight information of the business data of the first dimension is updated by adding a weight benefit coefficient to the first weight information of the business data of the first dimension to obtain the updated first weight information.

[0089] In another possible implementation, step S31 may also include step S311 and step S312.

[0090] Step S311, based on the first weight information of the business data of the first dimension, the business data of the third dimension is determined, and the difference between the first weight information of the business data of the first dimension and the weight information of the business data of the third dimension is less than the preset difference. The difference between the weight information of the business data of all dimensions and the first weight information of the business data of the first dimension is calculated, and the business data of the dimension corresponding to the difference less than the preset difference is used as the business data of the third dimension.

[0091] Step S312: obtaining a reference adjustment value of the business data of the first dimension according to the first weight information of the business data of the first dimension and the weight information of the business data of the third dimension.

[0092] The average of the first weight information of the business data of the first dimension and the weight information of the business data of the third dimension is calculated to obtain a reference adjustment value of the business data of the first dimension.

[0093] In a possible implementation, step S4 may include step S41 and step S42.

[0094] Step S41, updating the business data of the first dimension according to the second weight information of the first dimension.

[0095] By multiplying the business data of the first dimension by the second weight information, the updated business data of the first dimension can be obtained.

[0096] For example, assuming that the original sales amount of Province A is as follows: Original sales amount of Province A: 1 million yuan, it is determined that the second weight information corresponding to Province A is 0.7, and the updated sales amount of Province A is 700,000 yuan.

[0097] Step S42, analyzing the updated business data and determining the business strategy based on the analysis result.

[0098] The analysis results may include, but are not limited to, profit margin, sales growth rate, repurchase rate, etc.

[0099] Business strategies can include, but are not limited to, human resource strategies, product pricing strategies, business promotion strategies, technology research and development strategies, and operational strategies.

[0100] For example, a business strategy may be to increase the quantity of a product in a certain area during a certain period of time, or to reduce the quantity of a product in a certain area during a certain period of time. Each enterprise has different development stages, and enterprises at different development stages attach importance to different business data.

[0101] In a possible implementation, the business data is business data of the enterprise to be evaluated, and generating a business strategy according to the analysis result in step S42 may include steps S421 to S424.

[0102] Step S421, determining the current development stage of the enterprise to be evaluated and the key business data of the current development stage.

[0103] Key business data can be business data that is important and has a great impact on the current development stage.

[0104] The development stages of an enterprise may include the start-up stage, development stage, stabilization stage, transformation stage, internationalization stage, etc. The key business data of different stages are different. For example, the key business data of the start-up stage may be sales volume, sales volume, etc.

[0105] The current development stage of the enterprise to be evaluated may be directly input by a user or determined based on the business data of the enterprise to be evaluated.

[0106] Step S422: determining the first content related to the key business data in the analysis result.

[0107] The first content may be a portion of the analysis result related to key business data.

[0108] For example, if you are currently in the start-up phase, key business data include sales revenue and sales volume. When the analysis results include sales growth rate, profit margin, and repurchase rate, the first content is the sales growth rate.

[0109] Step S423, determining the matching degree between the first content and the development indicator condition corresponding to the current development stage under the current business strategy.

[0110] In order to formulate business strategies more accurately, you can set development indicator conditions corresponding to the current development stage. Development indicator conditions can be quantifiable thresholds set according to different development stages.

[0111] For example, when the first content is profit margin, sales growth rate, and repurchase rate, the development indicator condition may be a profit margin threshold, a sales growth rate threshold, and a repurchase rate threshold accordingly.

[0112] The degree of matching between the first content and the development indicator conditions corresponding to the current development stage can be understood as comparing the first content with the corresponding threshold in the development indicator conditions. The smaller the difference between the first content and the corresponding threshold in its development indicator, the higher the degree of matching; the larger the difference between the first content and the corresponding threshold in its development indicator, the lower the degree of matching.

[0113] Step S424, adjusting the current business strategy according to the matching degree to obtain a new business strategy.

[0114] When the matching degree does not reach the corresponding threshold in the development index condition, the business strategy is adjusted so that the matching degree reaches the corresponding threshold in the development index condition. The greater the difference between the matching degree and the corresponding threshold in the development index condition, the greater the amplitude adjustment of the business strategy.

[0115] In a possible implementation, when the enterprise to be evaluated is in a transition period between two development stages and it is uncertain whether it is in the previous development stage or the next development stage, determining the current development stage of the enterprise to be evaluated in step S421 may include: determining key business data corresponding to the first development stage, and determining the current development stage of the enterprise to be evaluated from the first development stage and the second development stage based on the key business data corresponding to the first development stage, wherein the second development stage is the next development stage of the first development stage.

[0116] The first development stage is the previous development stage. For example, when an enterprise is transitioning from the start-up stage to the development stage, the first development stage is the start-up stage, and the second development stage is the development stage.

[0117] First determine the key business data corresponding to the first development stage, compare the key business data corresponding to the first development stage with its corresponding business data threshold, and when the key business data corresponding to the first development stage is greater than its corresponding business data threshold, determine the second development stage as the current development stage of the enterprise to be evaluated; when the key business data corresponding to the first development stage is less than or equal to its corresponding business data threshold, determine the first development stage as the current development stage of the enterprise to be evaluated.

[0118] By using the key business data corresponding to different development stages, the actual development stage of the enterprise can be determined, and accurate analysis results and business strategies can be obtained.

[0119] To implement the above method embodiment, this embodiment provides a service policy generation device, which can be applied to electronic devices. Figure 2 , Figure 2 is a block diagram of a service policy generating device according to an exemplary embodiment. The service policy generating device 600 may include: A weight information determination module 601 is configured to determine first weight information of business data of an object in a first dimension; A mutual influence degree determination module 602 is configured to determine the mutual influence degree between the business data of the first dimension and the business data of the second dimension; The target weight information determination module 603 is configured to determine the second weight information of the first dimension according to the first weight information of the business data of the first dimension and the mutual influence degree; The business policy determination module 604 is configured to determine the business policy according to the second weight information of the first dimension and the business data of the first dimension.

[0120] Optionally, the mutual influence degree determination module 602 includes: A vectorization unit is configured to perform vectorization processing on the business data of the first dimension and the business data of the second dimension to obtain first vectorized business data and second vectorized business data; The mutual influence degree determining unit is configured to determine the mutual influence degree between the service data of the first dimension and the service data of the second dimension according to the first vectorized service data and the second vectorized service data.

[0121] Optionally, the business data of the first dimension includes business data of the first dimension within a first time period; The mutual influence degree determination unit is specifically configured as follows: Determining a first similarity according to the first vectorized service data and the second vectorized service data; Determine a second similarity between the business data of the first dimension and the business data of the second dimension in a second time period; The mutual influence between the business data of the first dimension and the business data of the second dimension is determined according to the first similarity and the second similarity.

[0122] Optionally, the target weight information determination module 603 includes: A reference adjustment value determining unit, configured to determine a reference adjustment value of the business data of the first dimension according to first weight information of the business data of the first dimension; A first mutual influence degree determining unit is configured to determine a first mutual influence degree from the mutual influence degrees, wherein a difference between the first mutual influence degree and the first weight information of the business data of the first dimension satisfies a difference condition; The target weight information determination unit is configured to determine the second weight information based on the first mutual influence degree, the reference adjustment value, the first weight information of the business data of the first dimension, and the weight benefit coefficient.

[0123] Optionally, the target weight information determining unit is specifically configured as follows: Perform a preset operation based on the first mutual influence degree, the reference adjustment value, the first weight information of the business data of the first dimension, and the weight benefit coefficient to obtain an intermediate adjustment value; When the intermediate adjustment value is less than or equal to the reference adjustment value, the first weight information and the weight benefit coefficient of the business data of the first dimension are adjusted, and the step of performing the preset operation to obtain the intermediate adjustment value is re-executed until the obtained intermediate adjustment value is greater than the reference adjustment value; The intermediate adjustment value corresponding to the maximum weight benefit coefficient is determined as the second weight information of the first dimension.

[0124] Optionally, the reference adjustment value determination unit is specifically configured as follows: Determine the business data of the third dimension according to the first weight information of the business data of the first dimension, wherein the difference between the first weight information of the business data of the first dimension and the weight information of the business data of the third dimension is less than a preset difference; A reference adjustment value of the business data of the first dimension is obtained according to the first weight information of the business data of the first dimension and the weight information of the business data of the third dimension.

[0125] Optionally, the business policy determination module 604 includes: A data updating unit, configured to update the business data of the first dimension according to the second weight information of the first dimension; The business policy determination unit is configured to analyze the updated business data and determine the business policy according to the analysis result.

[0126] Optionally, the business data is business data of the enterprise to be evaluated, and the business policy determination unit is specifically configured as follows: Determine the current development stage of the enterprise to be evaluated, as well as the key business data of the current development stage; Determining first content related to key business data in the analysis result; Determine the matching degree between the first content and the development indicator conditions corresponding to the current development stage under the current business strategy; The current business strategy is adjusted according to the matching degree to obtain a new business strategy.

[0127] Optionally, the weight information determination module 601 includes: A change amount determination unit is configured to determine a change amount of business data in the first dimension according to business data of the object in a first time period in the first dimension and business data in a second time period; The weight information determination unit is configured to determine first weight information of the business data of the object in the first dimension according to the change amount of the business data in the first dimension.

[0128] Regarding the service policy generating device in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the service policy generating method, and will not be elaborated here.

[0129] Figure 3 FIG. 7 is a block diagram of an electronic device 700 according to an exemplary embodiment. Figure 3 As shown, the electronic device 700 may include: a processor 701 , a memory 702 . The electronic device 700 may also include one or more of a multimedia component 703 , an input / output (I / O) interface 704 , and a communication component 705 .

[0130] The processor 701 is used to control the overall operation of the electronic device 700 to complete all or part of the steps in the above-mentioned business policy generation method. The memory 702 is used to store various types of data to support the operation of the electronic device 700. For example, these data may include instructions for any application or method used to operate on the electronic device 700, and application-related data, such as contact data, sent and received messages, pictures, audio, video, etc. The memory 702 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read-Only Memory, referred to as EPROM), programmable read-only memory (Programmable Read-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic memory, flash memory, magnetic disk or optical disk. The multimedia component 703 may include a screen and an audio component. The screen may be, for example, a touch screen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signal may be further stored in the memory 702 or sent through the communication component 705. The audio component also includes at least one speaker for outputting audio signals. The I / O interface 704 provides an interface between the processor 701 and other interface modules, and the above-mentioned other interface modules may be a keyboard, a mouse, buttons, etc. These buttons may be virtual buttons or physical buttons. The communication component 705 is used for wired or wireless communication between the electronic device 700 and other devices. Wireless communication, such as Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G or 4G, or a combination of one or more of them, so the corresponding communication component 705 may include: Wi-Fi module, Bluetooth module, NFC module.

[0131] In an exemplary embodiment, the electronic device 700 may be implemented by one or more application specific integrated circuits (ASIC), digital signal processors (DSP), digital signal processing devices (DSPD), programmable logic devices (PLD), field programmable gate arrays (FPGA), controllers, microcontrollers, microprocessors or other electronic components to execute the above-mentioned business policy generation method.

[0132] In another exemplary embodiment, a computer-readable storage medium including program instructions is also provided, and when the program instructions are executed by a processor, the steps of the above-mentioned business policy generation method are implemented. For example, the computer-readable storage medium can be the above-mentioned memory 702 including program instructions, and the above-mentioned program instructions can be executed by the processor 701 of the electronic device 700 to complete the above-mentioned business policy generation method.

[0133] In another exemplary embodiment, the electronic device may be provided as a server.

[0134] Based on the same concept, an embodiment of the present disclosure further provides a computer program product, including a computer program, which implements the steps of the above-mentioned business policy generation method when executed by a processor.

[0135] It should be understood that the above description is only a preferred embodiment of the present disclosure and an explanation of the technical principles used. Those skilled in the art should understand that the scope of disclosure involved in the present disclosure is not limited to the technical solution formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above disclosed concept. For example, the above features are replaced with the technical features with similar functions disclosed in the present disclosure (but not limited to) to form a technical solution.

[0136] In addition, although each operation is described in a specific order, this should not be understood as requiring these operations to be performed in the specific order shown or in a sequential order. Under certain circumstances, multitasking and parallel processing may be advantageous. Similarly, although some specific implementation details are included in the above discussion, these should not be interpreted as limiting the scope of the present disclosure. Some features described in the context of a separate embodiment can also be implemented in a single embodiment in combination. On the contrary, the various features described in the context of a single embodiment can also be implemented in multiple embodiments individually or in any suitable sub-combination mode.

[0137] Although the subject matter has been described in language specific to structural features and / or method logic actions, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or actions described above. On the contrary, the specific features and actions described above are merely example forms of implementing the claims. Regarding the device in the above embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment related to the method, and will not be elaborated here.

Claims

1. A method for generating a business strategy, characterized in that: Used in electronic equipment, including: Determine first weight information of business data of the object in the first dimension; Determine the mutual influence between the business data of the first dimension and the business data of the second dimension; Determining second weight information of the first dimension according to first weight information of the business data of the first dimension and the mutual influence degree; A business strategy is determined according to the second weight information of the first dimension and the business data of the first dimension.

2. The method for generating a business strategy according to claim 1, characterized in that: The determining the mutual influence between the business data of the first dimension and the business data of the second dimension includes: Performing vectorization processing on the business data of the first dimension and the business data of the second dimension to obtain first vectorized business data and second vectorized business data; The mutual influence degree between the business data of the first dimension and the business data of the second dimension is determined according to the first vectorized business data and the second vectorized business data.

3. The method for generating a business strategy according to claim 2, characterized in that: The business data of the first dimension includes business data of the first dimension within a first time period; The determining, according to the first vectorized business data and the second vectorized business data, the mutual influence degree between the business data of the first dimension and the business data of the second dimension includes: Determining a first similarity according to the first vectorized service data and the second vectorized service data; Determine a second similarity between the business data of the first dimension and the business data of the second dimension in a second time period; The mutual influence between the business data of the first dimension and the business data of the second dimension is determined according to the first similarity and the second similarity.

4. The method for generating a business strategy according to claim 1, characterized in that: The determining, according to the first weight information of the business data of the first dimension and the mutual influence degree, the second weight information of the first dimension includes: determining a reference adjustment value of the business data of the first dimension according to first weight information of the business data of the first dimension; Determining a first mutual influence degree from the mutual influence degrees, wherein a difference between the first mutual influence degree and first weight information of the business data of the first dimension satisfies a difference condition; Based on the first mutual influence degree, the reference adjustment value, the first weight information of the business data of the first dimension, and the weight benefit coefficient, second weight information is determined.

5. The method for generating a business strategy according to claim 4, characterized in that: The determining of second weight information based on the first mutual influence degree, the reference adjustment value, the first weight information of the business data of the first dimension, and the weight benefit coefficient includes: Perform a preset operation based on the first mutual influence degree, the reference adjustment value, the first weight information of the business data of the first dimension, and the weight benefit coefficient to obtain an intermediate adjustment value; When the intermediate adjustment value is less than or equal to the reference adjustment value, the first weight information and the weight benefit coefficient of the business data of the first dimension are adjusted, and the step of performing the preset operation to obtain the intermediate adjustment value is re-executed until the intermediate adjustment value obtained is greater than the reference adjustment value; The intermediate adjustment value corresponding to the maximum weight benefit coefficient is determined as the second weight information of the first dimension.

6. The method for generating a business strategy according to claim 4, characterized in that: The determining, according to the first weight information of the business data of the first dimension, a reference adjustment value of the business data of the first dimension includes: Determining the business data of the third dimension according to the first weight information of the business data of the first dimension, wherein a difference between the first weight information of the business data of the first dimension and the weight information of the business data of the third dimension is less than a preset difference; A reference adjustment value of the business data of the first dimension is obtained according to the first weight information of the business data of the first dimension and the weight information of the business data of the third dimension.

7. The method for generating a business strategy according to claim 1, characterized in that: The determining the service strategy according to the second weight information of the first dimension and the service data of the first dimension includes: Updating the business data of the first dimension according to the second weight information of the first dimension; Analyze the updated business data and determine the business strategy based on the analysis results.

8. The method for generating a business strategy according to claim 7, characterized in that: The business data is the business data of the enterprise to be evaluated, and the business strategy is determined according to the analysis result, including: Determine the current development stage of the enterprise to be evaluated and the key business data of the current development stage; Determining first content related to the key business data in the analysis result; Determining the degree of matching between the first content and the development indicator condition corresponding to the current development stage under the current business strategy; The current business strategy is adjusted according to the matching degree to obtain a new business strategy.

9. The method for generating a business strategy according to claim 1, characterized in that: The determining of first weight information of business data of the object in the first dimension includes: Determine a change amount of the business data in the first dimension according to the business data of the object in the first time period and the business data in the second time period of the first dimension; First weight information of the business data of the object in the first dimension is determined according to the change amount of the business data of the first dimension.

10. A service strategy generating device, characterized in that: Used in electronic equipment, including: A weight information determination module, configured to determine first weight information of business data of an object in a first dimension; A mutual influence degree determination module, configured to determine the mutual influence degree between the business data of the first dimension and the business data of the second dimension; a target weight information determination module, configured to determine second weight information of the first dimension according to the first weight information of the business data of the first dimension and the mutual influence degree; The business policy determination module is configured to determine the business policy according to the second weight information of the first dimension and the business data of the first dimension.

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

12. An electronic device, characterized in that: include: a memory having a computer program stored thereon; A processor, configured to execute the computer program in the memory to implement the steps of the business policy generating method according to any one of claims 1 to 9.

13. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the business policy generating method according to any one of claims 1 to 9 are implemented.

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