Statistical analysis method and system for enterprise operation business

Abstract

The invention discloses a statistical analysis method and system for enterprise operation business. The method comprises the following steps: acquiring a data set related to the enterprise operation business; calculating a business risk index and a growth potential score of an enterprise operation business according to the business related data set; performing clustering analysis on the service related data set to obtain a plurality of types of service data subsets; and storing the business data subset of each category, the business risk index and the growth potential score for statistical analysis of enterprise operation business. According to the embodiment of the invention, the accuracy, real-time performance and practicability of enterprise operation analysis can be improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of data processing, and in particular to a statistical analysis method and system for business operations of an enterprise. Background Art

[0002] With the intensification of global market competition and the continuous changes in the economic environment, enterprises are facing increasingly complex risks and opportunities in their business operations. In this case, how to effectively evaluate and predict the risks and growth potential of business operations has become an urgent problem for corporate management and decision makers. The business activities of enterprises are not only affected by external factors such as market demand, policy changes, and competitive environment, but also by their internal operations, resource allocation, management level and other factors. In order to achieve sustainable development and competitive advantages in a complex market environment, enterprises need to use statistical analysis methods to accurately identify potential risks and growth opportunities and make corresponding strategic decisions. Summary of the invention

[0003] The purpose of the present invention is to provide a statistical analysis method and system for business operations of an enterprise, so as to solve the deficiencies in the prior art and improve the accuracy, real-timeness and practicality of business analysis of the enterprise.

[0004] An embodiment of the present application provides a statistical analysis method for business operations of an enterprise, the method comprising: Collect data sets related to the business operations of the enterprise; Calculating a business risk index and growth potential score for the business of the enterprise based on the business-related data set; Performing cluster analysis on the business-related data set to obtain business data subsets of multiple categories; The business data subset of each category, the business risk index and the growth potential score are stored for statistical analysis of the business operations of the enterprise.

[0005] Optionally, the calculation formula of the business risk index is: Among them, the is the business risk index, is the fluctuation value of the i-th business area within a certain period of time, is the risk change range of the i-th business area within a certain period of time. is the sensitivity coefficient of the i-th business area, reflecting the sensitivity of the business area to changes in external factors. is the industry risk index of the jth category, which represents the risk assessment related to the external industry environment. is a standardization factor used to adjust the dimensional differences in risk data in various business areas. is the weight value of the ith business field, m is the number of business-related industry categories, and n is the number of business fields.

[0006] Optionally, the By calculating the standard deviation of the key business indicators of the ith business area within a certain period of time, the It is obtained by calculating the difference between the maximum and minimum values ​​of the key business indicators of the ith business area within a certain period of time.

[0007] Optionally, the calculation formula of the growth potential score is: Among them, the is the growth potential score, is the current profit of the ith business area, is the cost of the ith business area, is the growth potential coefficient of the i-th business field, reflecting the potential of the business field itself. is the growth elasticity coefficient of the i-th business area, which is used to control the nonlinear relationship between profit and cost. is the demand impact coefficient of the i-th business field, is the market demand elasticity coefficient of the i-th business field, reflecting the impact of demand on the growth of this business field. is the restriction degree of the kth type of external factors on growth, and p is the number of factors related to the external environment.

[0008] Another embodiment of the present application provides a statistical analysis system for business operations of an enterprise, the system comprising: The collection module is used to collect data sets related to the business operations of the enterprise; A calculation module, used to calculate the business risk index and growth potential score of the enterprise's business operations based on the business-related data set; An analysis module, used for performing cluster analysis on the business-related data set to obtain business data subsets of multiple categories; The storage module is used to store the business data subset of each category, the business risk index and the growth potential score for statistical analysis of the enterprise's business operations.

[0009] Yet another embodiment of the present application provides a storage medium, wherein the storage medium stores a computer program, wherein the computer program is configured to execute any of the above methods when running.

[0010] Yet another embodiment of the present application provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute any of the methods described above.

[0011] Compared with the prior art, the present invention provides a statistical analysis method for an enterprise's business operations, which collects data sets related to the enterprise's business operations; calculates the business risk index and growth potential score of the enterprise's business operations based on the business-related data sets; clusters the business-related data sets to obtain business data subsets of multiple categories; stores the business data subsets of each category, the business risk index and the growth potential score for statistical analysis of the enterprise's business operations, thereby improving the accuracy, real-time nature and practicality of the enterprise's business analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 A hardware structure block diagram of a computer terminal for a statistical analysis method for business operations provided by an embodiment of the present invention; Figure 2 A flowchart of a statistical analysis method for business operations provided by an embodiment of the present invention; Figure 3 A schematic diagram of the structure of a statistical analysis system for business operations provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0013] The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, but should not be construed as limiting the present invention.

[0014] The embodiment of the present invention firstly provides a statistical analysis method for business operations of an enterprise. The method can be applied to electronic devices, such as computer terminals, specifically ordinary computers, etc.

[0015] The following describes it in detail by taking running on a computer terminal as an example. Figure 1 A hardware structure block diagram of a computer terminal for a statistical analysis method for business operations provided by an embodiment of the present invention.

[0016] like Figure 1 As shown, the computer device includes a processor, a memory, and a network interface connected via a system bus, wherein the memory may include a non-volatile storage medium and an internal memory.

[0017] The non-volatile storage medium can store an operating system and a computer program. The computer program includes program instructions, and when the program instructions are executed, the processor can execute any statistical analysis method for the business operations of the enterprise.

[0018] The processor is used to provide computing and control capabilities and support the operation of the entire computer equipment.

[0019] The internal memory provides an environment for the operation of the computer program in the non-volatile storage medium. When the computer program is executed by the processor, the processor can execute any statistical analysis method for the business operations of the enterprise.

[0020] The network interface is used for network communication, such as sending assigned tasks, etc. Those skilled in the art will understand that Figure 1 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0021] It should be understood that the processor may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among them, the general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0022] See also Figure 2 The embodiment of the present invention provides a statistical analysis method for business operations of an enterprise, which may include the following steps: S201, collect data sets related to the business operations of enterprises; This step is the first step of the statistical analysis method, which involves collecting various types of data related to business operations from the internal and external environment of the enterprise. Specifically, the collected data set should cover multiple dimensions, including but not limited to financial data (such as revenue, cost, profit, etc.), operational data (such as inventory, production efficiency, product quality, etc.), market data (such as sales volume, market demand, consumer behavior, etc.), external environment data (such as industry trends, policy changes, competition, etc.) and other factors that may affect business performance. Data collection can be achieved in a variety of ways, such as automatic export through the company's ERP system, manual research, or using the services of external data suppliers to ensure the comprehensiveness and representativeness of the data. During the collection process, the company needs to determine the key indicators that need to be collected based on specific analysis needs for subsequent risk assessment and growth potential analysis.

[0023] Business data is the basis of the entire statistical analysis process. Only by obtaining comprehensive and accurate business-related data can we provide support for the subsequent calculation of the business risk index (BRI) and the growth potential score (GPI). By systematically collecting various types of business data, enterprises can better understand their own operating conditions and market environment, provide scientific basis for decision makers, and support enterprises to carry out effective strategic planning and adjustments. At the same time, this step can also help enterprises identify potential risk factors, seize growth opportunities, and further enhance the overall stability and sustainable growth capabilities of the business. Accurate data collection is a prerequisite for ensuring the reliability and effectiveness of subsequent analysis results. Therefore, this step has far-reaching strategic significance for the long-term development of the enterprise.

[0024] In actual operation, the collection of data sets related to business operations of enterprises can be achieved through a variety of channels and methods. First, the internal financial management system and operation management system (such as ERP system, CRM system, production management system, etc.) of the enterprise are the main sources of data collection. These systems can automatically generate key data such as the company's financial statements, sales data, inventory information, production efficiency, etc., providing reliable internal operation information. Secondly, enterprises can also collect information related to the external environment through external market research, industry analysis reports, competitor data, etc. These external data are crucial for understanding industry trends, changes in market demand, and competitive situations.

[0025] For example, if a manufacturing company wants to analyze the growth potential and risks of its products in the market, it needs to collect internal production data (such as monthly output, production costs, etc.), sales data (such as sales volume and sales revenue in each region, etc.), and external market data (such as total industry demand, raw material price fluctuations, policy changes, etc.). These data will serve as the basis for risk assessment and potential analysis in subsequent analysis.

[0026] To ensure the validity and reliability of data, enterprises need to pay attention to data quality control when collecting data. First, the accuracy of the data should be ensured to avoid deviations caused by input errors or inconsistent statistical calibers. Secondly, the data should be timely to ensure that the data used reflects the current operating status and market environment. For internal system data of enterprises, an automated data collection and monitoring mechanism can be set up to ensure that the data is updated in real time while maintaining its consistency and integrity.

[0027] For example, if an enterprise finds that there is a large lag in the annual data of some financial statements when collecting financial data, it needs to communicate with relevant departments to ensure the timeliness of the data. At the same time, it is also necessary to avoid inconsistent formats due to different data sources during data integration. Cleaning and integration can be carried out through a unified standard data format for application in subsequent analysis.

[0028] The collected data needs to be stored in a structured and secure data warehouse to facilitate subsequent data analysis and mining. When storing data, enterprises should adopt appropriate storage solutions based on the type of data, data source and its sensitivity. For sensitive data such as financial data and personal information, security measures such as encryption and permission control should be taken to prevent data leakage or illegal access. The design of the data warehouse should support efficient data query, update and analysis operations to ensure that massive data can be quickly retrieved and processed in actual operations.

[0029] For example, enterprises can use cloud storage platforms to build a centralized data warehouse to ensure that all relevant data is efficiently managed in one place, and control the access rights of different users to data through multi-level permission management. In addition, regular backup and disaster recovery mechanisms ensure that data can be restored in a timely manner in the event of a system failure, without losing important historical data.

[0030] S202, calculating a business risk index and a growth potential score of the enterprise's business operations based on the business-related data set; Through statistical analysis of various business data, we can first derive quantitative indicators of risk and growth potential in each business area. The Business Risk Index (BRI) is a comprehensive indicator to measure the operating risks faced by enterprises in different fields. It takes into account the volatility, risk change range and sensitivity of external factors in each field. The Growth Potential Score (GPI) is an indicator that reflects the future development potential of each business field under the current business environment. It involves a comprehensive analysis of factors such as profit, cost, and market demand. By calculating these indices, enterprises can obtain a clearer and more systematic assessment of their operating conditions and provide data support for strategic decision-making.

[0031] The steps of calculating the business risk index (BRI) and growth potential score (GPI) of an enterprise's business operations are crucial for corporate strategic decision-making. Through these two indicators, enterprises can gain in-depth understanding of the risk status and development potential of their various business areas from different dimensions. This not only helps to timely discover potential operating risks and avoid possible operating losses, but also helps enterprises identify market opportunities and enhance growth potential. The assessment of the risk index can help the management of the enterprise take effective risk control measures, while the calculation of the growth potential score can provide a quantitative basis for resource allocation, investment decisions, etc. Overall, this calculation process will provide scientific decision-making support for the sustainable development of the enterprise, and promote the enterprise to optimize its operating strategy and enhance its market competitiveness.

[0032] Specifically, a calculation formula for a business risk index may be: The calculation formula is designed to quantify the risks of each business area of ​​the enterprise by integrating multiple factors. The purpose of constructing this formula is to capture the inherent volatility of each business area on the one hand, and to take into account the impact of the external industry environment on the business area on the other hand. The design of the formula not only takes into account the volatility and change range of each business area, but also introduces the assessment of external industry risks, thus providing a multi-dimensional risk measurement.

[0033] Among them, the is the business risk index, is the fluctuation value of the i-th business area within a certain period of time, which is used to measure the change range of the internal indicators of the area. Areas with large fluctuations usually mean higher risks. It is the risk change range of the ith business area within a certain period of time, which mainly reflects the extreme fluctuation of relevant indicators in this field within a specified period of time. is the sensitivity coefficient of the i-th business area, reflecting the sensitivity of the business area to changes in external factors. is the industry risk index of the jth category, which represents the risk assessment related to the external industry environment. It is a normalization factor used to adjust the dimensional differences of risk data in various business areas to ensure that the risks in different business areas can be reasonably compared. is the weight value of the ith business field, m is the number of business-related industry categories, and n is the number of business fields.

[0034] Comprehensively evaluate the inherent risk factors of a single field (volatility, risk changes, etc.) and the impact of the external environment on the field (such as industry risk), and through the setting of weights, ensure that the risk measurement of different fields reasonably reflects its position in the overall risk management of the enterprise. Through these parameters, enterprises can make scientific decisions when facing complex market changes.

[0035] Specifically, the By calculating the standard deviation of the key business indicators of the ith business area within a certain period of time, the It is obtained by calculating the difference between the maximum and minimum values ​​of the key business indicators of the ith business area within a certain period of time.

[0036] Sigma_i and Delta R_i are calculated by statistically analyzing the key business indicators of the i-th business area within a certain period of time. The specific methods include calculating the standard deviation and the difference in extreme values. These two indicators are used to quantify the risk and volatility of the business area. Sigma_i (volatility value) reflects the volatility of the area within a specified period of time by calculating the standard deviation of the key business indicators within the specified period of time. The greater the volatility, the higher the risk of the business area. DeltaR_i (risk change range) is obtained by calculating the difference between the maximum and minimum values ​​of the key business indicators, revealing the extreme volatility of the indicators in the area within the period of time, thereby helping to assess the potential risk changes in the area.

[0037] By calculating sigma_i and Delta R_i, companies can clearly understand the volatility and risk changes of each business area within a certain period of time. As a volatility value, sigma_i can help companies identify which business areas are unstable and need to take special risk management measures; while Delta R_i reveals whether the area has experienced extreme risk fluctuations, helping companies identify potential major risk events or abnormal changes. In actual operations, standard deviation and extreme value difference are commonly used risk measurement tools. Through these two indicators, companies can more effectively formulate corresponding risk control strategies and reduce potential operating risks.

[0038] For example, for a retail company, its key business indicators may include sales, inventory turnover, etc. By collecting monthly sales data for the past year, the fluctuation value (sigma_i) of this business area, that is, the standard deviation of sales, can be calculated. If the sales fluctuation is large, it means that the risk of this business area is high, and it may be necessary to pay attention to changes in market demand, price policy and other factors.

[0039] At the same time, by calculating the difference between the maximum and minimum sales in the field in the past year (DeltaR_i), it is possible to further assess whether there are extreme risk changes. For example, if sales drop sharply in certain months, this extreme fluctuation needs special attention, indicating that there may be sudden market changes or operational problems. Combining these two indicators, companies can conduct more accurate risk assessments in the sales field and take timely measures to deal with potential volatility risks, such as adjusting inventory management or optimizing promotion strategies.

[0040] Specifically, the calculation formula of the growth potential score is: Among them, the is the growth potential score, is the current profit of the ith business area, is the cost of the ith business area, is the growth potential coefficient of the i-th business field, reflecting the potential of the business field itself. This coefficient reflects the growth potential of the field itself, that is, the expansion capacity of the business under normal market conditions. The growth potential coefficient can be set by historical growth rate, market forecast, technological innovation or management optimization potential. For mature fields, the growth potential may be low, while for emerging fields, it may have higher potential. is the growth elasticity coefficient of the ith business area, which is used to control the nonlinear relationship between profit and cost. The growth elasticity coefficient reflects the nonlinear characteristics of the relationship between profit and cost in this area. It is used to control the relative sensitivity between profit and cost, that is, whether the cost grows exponentially while the profit increases, or vice versa. The setting of this coefficient can be determined by industry comparison analysis, historical financial data analysis, etc. is the demand impact coefficient of the ith business area, which is used to measure the impact of changes in market demand on the business area, especially the ability to cope with market fluctuations. The demand impact coefficient can be determined by analyzing the correlation between historical sales data and demand fluctuations, or by combining industry reports to predict future demand trends. is the market demand elasticity coefficient of the ith business field, reflecting the impact of demand on the growth of this business field. The market demand elasticity coefficient reflects the sensitivity of market demand to price changes of products or services in this field. The market demand elasticity coefficient can be determined by analyzing historical data on price changes and sales, or by market research and consumer behavior research. The larger the demand elasticity coefficient, the more sensitive the market is to price changes. is the restriction of the kth type of external factors on growth. This parameter measures the restriction of various factors in the external environment (such as policy changes, market competition, laws and regulations, etc.) on the growth potential of the enterprise. Enterprises can evaluate the impact of these factors on various business areas through external data collection, market research, policy analysis and other means. Usually, enterprises will determine the impact of different factors based on industry characteristics and geographical location differences. The above p is the number of factors related to the external environment.

[0041] The calculation formula of the Growth Potential Score (GPI) aims to quantify the growth potential of each business area by comprehensively evaluating the profits, costs, market demand and external environmental factors of each business area of ​​the enterprise. The significance of designing this formula is that it can fully reflect the various factors that affect the future growth of the enterprise, including the internal and external environment, the operating conditions of the business area itself, and market demand. Through a weighted combination of a series of weights, elasticity coefficients and demand influencing factors, this formula not only takes into account the economic benefits of the business itself, but also takes into account the demand elasticity of the external market and the restrictive effect of external factors, providing enterprises with a comprehensive and operational growth potential assessment model.

[0042] The example values ​​of each parameter can be set according to the actual situation. The following are example values ​​of each parameter, assuming that this is an analysis case of a manufacturing enterprise: 1. w_i (weight value of the i-th business area) - Example values: w_1 = 0.4, w_2 = 0.3, w_3 = 0.2, w_4 = 0.1.

[0043] Among them, w_1, w_2, w_3, and w_4 represent the weights of four different business areas, and the specific values ​​are allocated according to the contribution of each area to the company's total revenue, profit, etc.

[0044] 2. alpha_i (growth potential coefficient of the i-th business area) - Example values: alpha_1 = 1.2, alpha_2 = 0.8, alpha_3 = 1.5, alpha_4 = 1.0.

[0045] For example, an emerging market business area may have higher growth potential and be assigned a higher alpha value, while a mature market business area would be assigned a lower alpha value.

[0046] 3. gamma_i (growth elasticity coefficient of the i-th business area) - Example values: gamma_1 = 1.1, gamma_2 = 1.3, gamma_3 = 0.9, gamma_4 = 1.0.

[0047] For example, in some areas, the relationship between profit and cost is more sensitive, so a higher gamma value is set.

[0048] 4.eta_i (demand impact coefficient of the i-th business area) - Example values: eta_1 = 1.5, eta_2 = 1.2, eta_3 = 0.8, eta_4 = 1.0.

[0049] For example, the consumer goods sector may be more affected by fluctuations in market demand, so a higher eta value is set.

[0050] 5. Si (market demand elasticity coefficient of the i-th business area) - Example values: S_1 = 1.4, S_2 = 0.9, S_3 = 1.3, S_4 = 1.0.

[0051] For example, some business areas are more sensitive to price changes and therefore have a larger demand elasticity coefficient.

[0052] 6. D_k (the degree of restriction of growth by the kth external factor) - Example values: D_1 = 0.6, D_2 = 0.8, D_3 = 0.4.

[0053] The degree of restriction of external factors such as policies and competition is set according to the external environment of different industries and enterprises.

[0054] 7.p (number of factors related to the external environment) - Example value: p = 3.

[0055] For example, a company may be affected by three external factors: policy, market competition, and technological innovation, so p =3.

[0056] S203, performing cluster analysis on the business-related data set to obtain business data subsets of multiple categories; The business data of an enterprise is grouped and analyzed through data mining technology so that similar business areas or operating conditions can be classified into the same category. This process is usually completed through a clustering algorithm, with the aim of dividing the business operations of the enterprise into multiple subsets with similar characteristics based on the characteristics of the business data. These characteristics may include financial indicators, market performance, growth rate, etc. in the business field. By analyzing these characteristics, the similarities and differences between different businesses of the enterprise can be identified, so as to more effectively conduct risk assessment and growth potential prediction. For example, a company may operate in different market regions, and businesses in different regions may show obvious differences in sales, cost structure, customer groups, etc. Cluster analysis will help identify which market regions have similar operating characteristics, and personalized strategies or decisions can be made based on these similarities.

[0057] The significance of cluster analysis lies in that it can simplify complex and diverse business data into multiple subsets that are easy to understand and analyze, thereby helping managers identify and understand the underlying patterns of different business areas or markets. By dividing business data sets into multiple categories, companies can more clearly identify which business areas are performing well and which areas have potential risks or growth opportunities. For example, after cluster analysis, companies may find that market demand in certain regions is growing faster, while other regions may face the risk of saturation or decline in demand. This information can help managers make more targeted decisions for different categories of business, optimize resource allocation, and develop more effective market strategies.

[0058] Before performing cluster analysis, companies first need to collect relevant data about their business operations. These data may cover multiple dimensions, such as financial data (revenue, profit, cost), market data (demand, customer growth rate), operational data (capacity, production efficiency), and external environment data (such as industry growth rate, policy changes, etc.) in various business areas. For example, if a company has different businesses in multiple regions, the data set may include information such as market share, revenue, cost, and customer group characteristics in each region. After collecting the data, preprocessing is required, including missing value processing, outlier detection, and data standardization. The purpose of standardization is to eliminate the dimensional differences between different data features so that the contribution of each feature to the clustering results is more balanced. For example, different business areas of a company may have large sales differences, so before clustering, data such as sales and profits can be standardized to ensure that their influence is relatively balanced.

[0059] After the data is ready, the next step is to choose a suitable clustering algorithm for analysis. Common clustering algorithms include "k-means clustering", "hierarchical clustering" and "DBSCAN". In this case, we use the "k-means clustering" algorithm. The core idea of ​​this algorithm is to divide the data set into k categories, where k is a pre-set parameter. When choosing the k value, the company can determine a reasonable value based on previous understanding of the business field or through techniques such as the elbow rule. For example, suppose that the company operates in 10 business areas. After preliminary analysis, the company believes that these areas can be divided into 3 categories (for example: high-growth areas, stable growth areas, and low-growth areas). Therefore, the company can choose k=3, indicating that the business data set is to be divided into 3 subsets. Next, through the iterative process of the k-means algorithm, the distance between each data point and the initial cluster center is calculated, and the data points are classified into the corresponding category according to the principle of the shortest distance. The center point of each category is adjusted with each iteration until the clustering results converge.

[0060] After completing the cluster analysis, the enterprise will obtain multiple categories of business data subsets, each of which contains business areas with similar characteristics. At this point, the enterprise can conduct in-depth analysis of the business areas of each category to reveal their common characteristics and potential growth opportunities. For example, if the business area of ​​a cluster subset shows a trend of rapid growth (such as innovative products in emerging markets), the enterprise can focus on investing resources to further expand its market share; while another cluster subset may show a stable but slower growing business area (such as traditional products in mature markets). At this time, the enterprise may decide to maintain its existing share and optimize operational efficiency. Finally, the enterprise can also combine these clustering results with other classification results (such as business risk index and growth potential score) to make more accurate strategic decisions. For example, the business areas of a cluster subset may be concentrated in high-risk, high-return markets. After assessing the risks, the enterprise can choose whether to make venture capital or adopt a conservative strategy, while the businesses of other cluster subsets may operate stably in low-risk areas. The enterprise can deepen its cultivation in these areas and ensure stable cash flow.

[0061] S204, storing the business data subset of each category, the business risk index and the growth potential score for statistical analysis of the business operations of the enterprise.

[0062] In the step, "storing each category of business data subset, the business risk index and the growth potential score for statistical analysis of the business operations of the enterprise" means effectively storing the results of the cluster analysis (i.e., the business data subset), as well as the business risk index (BRI) and growth potential score (GPI) calculated by the formula. The purpose of this process is to conduct further analysis and decision support for the business data of the enterprise in the future. Specifically, the enterprise can save this data in a database or data warehouse, and each category of business data subset will be marked and stored in association with the corresponding business risk index and growth potential score. This enables enterprises to quickly access and retrieve relevant data, facilitate regular updates and long-term tracking, and thus achieve more accurate business analysis and decision-making.

[0063] The significance of this storage process is that by systematically saving the data, risk index and potential score of each business area, the enterprise can establish a centralized and structured data storage system to facilitate subsequent statistical analysis and decision-making. First, these stored data can provide a basis for subsequent trend analysis, risk assessment and market opportunity identification, and the enterprise can conduct comparative analysis in different time periods. Secondly, the stored data can provide a unified source of information for different departments or management levels, thereby promoting consistency and accuracy of decision-making. For example, the finance department can determine which areas need cost control based on the business risk index, while the marketing department can determine which markets have the potential for expansion based on the growth potential score. Through systematic storage and integration, enterprises can better manage and use these data resources.

[0064] In the first step of implementing this method, enterprises need to collect relevant business data from various data sources. These data usually include financial data (such as sales, costs, profits, etc.), market data (such as customer growth rate, market demand, product competitiveness, etc.), operational data (such as production efficiency, supply chain status, etc.) and external environment data (such as industry risks, policy changes, economic fluctuations, etc.). In order to ensure the accuracy and comparability of the data, enterprises need to pre-process the collected raw data. This includes filling or removing missing data, handling outliers, and standardizing data of different dimensions so that indicators of different business areas can be compared on the same scale. For example, sales and costs may have a large difference in numerical values, so they need to be converted into relative indicators, such as percentage change or standard deviation, so that in the subsequent cluster analysis, all indicators can fairly evaluate each business area. This pre-processing process lays the foundation for the subsequent business data clustering, risk index calculation and growth potential score calculation.

[0065] After data collection and preprocessing are completed, the enterprise will perform cluster analysis based on the business-related data sets to obtain business data subsets in multiple categories. One of the commonly used clustering algorithms is "k-means clustering", which can divide business areas into several groups based on their characteristics. For example, an enterprise may divide its business areas into "high-growth areas", "stable areas" and "low-growth areas". The core of this process is to form categories by calculating the similarity of business data. During the clustering process, the algorithm automatically divides each business area into different categories based on its characteristics (such as sales, profits, market share, etc.). After clustering is completed, the enterprise needs to calculate the business risk index (BRI) and growth potential score (GPI) for each category. The calculation of the business risk index needs to consider factors such as the volatility of each business area (such as financial fluctuations), the magnitude of risk changes, and industry risks, and combine the corresponding weights for weighted average. The calculation of the growth potential score needs to consider factors such as the current profit, cost, and market demand of each area to reflect its potential for future growth. According to these calculation formulas, the enterprise can store the risk index and potential score of each business area together with the clustering results.

[0066] After cluster analysis and index calculation, the enterprise needs to store the subset of business data, business risk index and growth potential score of each category in the database. For easy management and access, the enterprise can build a database or data warehouse to organize these data by business field, category, risk index and potential score. For example, a table containing fields such as "business field", "category", "risk index" and "growth potential score" can be created to store all business field data in the database. Each record represents a specific business field and contains relevant data of the field and corresponding risk and potential score information. In this way, the enterprise can easily access historical data, update data regularly, and conduct long-term trend analysis. For example, the management of the enterprise can determine which areas need resource optimization or increased investment by querying the risk index and growth potential score of different business categories. Based on the stored data, the enterprise can also formulate more accurate market strategies and identify potential growth opportunities or risks. Combined with data storage, the enterprise can also use visualization tools to generate reports or dashboards to further enhance the ability of data-driven decision-making.

[0067] It can be seen that data sets related to the enterprise's business operations are collected; based on the business-related data sets, the business risk index and growth potential score of the enterprise's business operations are calculated; the business-related data sets are clustered and analyzed to obtain business data subsets of multiple categories; the business data subsets of each category, the business risk index and the growth potential score are stored for statistical analysis of the enterprise's business operations, thereby improving the accuracy, real-time and practicality of the enterprise's business analysis.

[0068] Another embodiment of the present invention provides a statistical analysis system for business operations of an enterprise, see Figure 3 , the system may include: The collection module 301 is used to collect data sets related to the business operations of the enterprise; A calculation module 302, for calculating a business risk index and a growth potential score of the enterprise's business operations according to the business-related data set; An analysis module 303 is used to perform cluster analysis on the business-related data set to obtain business data subsets of multiple categories; The storage module 304 is used to store the business data subset of each category, the business risk index and the growth potential score for statistical analysis of the business operations of the enterprise.

[0069] It can be seen that data sets related to the enterprise's business operations are collected; based on the business-related data sets, the business risk index and growth potential score of the enterprise's business operations are calculated; the business-related data sets are clustered and analyzed to obtain business data subsets of multiple categories; the business data subsets of each category, the business risk index and the growth potential score are stored for statistical analysis of the enterprise's business operations, thereby improving the accuracy, real-time and practicality of the enterprise's business analysis.

[0070] An embodiment of the present invention further provides a storage medium, in which a computer program is stored, wherein the computer program is configured to execute the steps of any of the above method embodiments when running.

[0071] Specifically, in this embodiment, the above storage medium may be configured to store a computer program for performing the following steps: S201, collect data sets related to the business operations of enterprises; S202, calculating a business risk index and a growth potential score of the enterprise's business operations based on the business-related data set; S203, performing cluster analysis on the business-related data set to obtain business data subsets of multiple categories; S204, storing the business data subset of each category, the business risk index and the growth potential score for statistical analysis of the business operations of the enterprise.

[0072] It can be seen that data sets related to the enterprise's business operations are collected; based on the business-related data sets, the business risk index and growth potential score of the enterprise's business operations are calculated; the business-related data sets are clustered and analyzed to obtain business data subsets of multiple categories; the business data subsets of each category, the business risk index and the growth potential score are stored for statistical analysis of the enterprise's business operations, thereby improving the accuracy, real-time and practicality of the enterprise's business analysis.

[0073] An embodiment of the present invention further provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0074] Specifically, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0075] Specifically, in this embodiment, the processor may be configured to perform the following steps through a computer program: S201, collect data sets related to the business operations of enterprises; S202, calculating a business risk index and a growth potential score of the enterprise's business operations based on the business-related data set; S203, performing cluster analysis on the business-related data set to obtain business data subsets of multiple categories; S204, storing the business data subset of each category, the business risk index and the growth potential score for statistical analysis of the business operations of the enterprise.

[0076] It can be seen that data sets related to the enterprise's business operations are collected; based on the business-related data sets, the business risk index and growth potential score of the enterprise's business operations are calculated; the business-related data sets are clustered and analyzed to obtain business data subsets of multiple categories; the business data subsets of each category, the business risk index and the growth potential score are stored for statistical analysis of the enterprise's business operations, thereby improving the accuracy, real-time and practicality of the enterprise's business analysis.

[0077] The above describes in detail the structure, features and effects of the present invention based on the embodiments shown in the drawings. The above is only a preferred embodiment of the present invention, but the present invention is not limited to the scope of implementation shown in the drawings. Any changes made according to the concept of the present invention, or modifications to equivalent embodiments with equivalent changes, which still do not exceed the spirit covered by the description and drawings, should be within the protection scope of the present invention.

Claims

1. A statistical analysis method for business operations of an enterprise, characterized in that: The method comprises: Collect data sets related to the business operations of the enterprise; Calculating a business risk index and growth potential score for the business of the enterprise based on the business-related data set; Performing cluster analysis on the business-related data set to obtain business data subsets of multiple categories; The business data subset of each category, the business risk index and the growth potential score are stored for statistical analysis of the business operations of the enterprise.

2. The method according to claim 1, characterized in that The calculation formula of the business risk index is: Among them, the is the business risk index, is the fluctuation value of the i-th business area within a certain period of time, is the risk change range of the i-th business area within a certain period of time. is the sensitivity coefficient of the i-th business area, reflecting the sensitivity of the business area to changes in external factors. is the industry risk index of the jth category, which represents the risk assessment related to the external industry environment. is a standardization factor used to adjust the dimensional differences in risk data in various business areas. is the weight value of the ith business field, m is the number of business-related industry categories, and n is the number of business fields.

3. The method according to claim 2, characterized in that Said By calculating the standard deviation of the key business indicators of the ith business area within a certain period of time, the It is obtained by calculating the difference between the maximum and minimum values ​​of the key business indicators of the ith business area within a certain period of time.

4. The method according to claim 3, characterized in that The growth potential score is calculated as follows: Among them, the is the growth potential score, is the current profit of the ith business area, is the cost of the ith business area, is the growth potential coefficient of the i-th business field, reflecting the potential of the business field itself. is the growth elasticity coefficient of the i-th business area, which is used to control the nonlinear relationship between profit and cost. is the demand impact coefficient of the i-th business field, is the market demand elasticity coefficient of the i-th business field, reflecting the impact of demand on the growth of this business field. is the restriction degree of the kth type of external factors on growth, and p is the number of factors related to the external environment.

5. A statistical analysis system for business operations, characterized in that: The system comprises: The collection module is used to collect data sets related to the business operations of the enterprise; A calculation module, used to calculate the business risk index and growth potential score of the enterprise's business operations based on the business-related data set; An analysis module, used for performing cluster analysis on the business-related data set to obtain business data subsets of multiple categories; The storage module is used to store the business data subset of each category, the business risk index and the growth potential score for statistical analysis of the enterprise's business operations.

6. The system according to claim 5, characterized in that The calculation formula of the business risk index is: Among them, the is the business risk index, is the fluctuation value of the i-th business area within a certain period of time, is the risk change range of the i-th business area within a certain period of time. is the sensitivity coefficient of the i-th business area, reflecting the sensitivity of the business area to changes in external factors. is the industry risk index of the jth category, which represents the risk assessment related to the external industry environment. is a standardization factor used to adjust the dimensional differences in risk data in various business areas. is the weight value of the ith business field, m is the number of business-related industry categories, and n is the number of business fields.

7. The system according to claim 6, characterized in that Said By calculating the standard deviation of the key business indicators of the i-th business area within a certain period of time, the It is obtained by calculating the difference between the maximum and minimum values ​​of the key business indicators of the ith business area within a certain period of time.

8. The system according to claim 7, characterized in that The growth potential score is calculated as follows: Among them, the is the growth potential score, is the current profit of the ith business area, is the cost of the ith business area, is the growth potential coefficient of the i-th business field, reflecting the potential of the business field itself. is the growth elasticity coefficient of the i-th business area, which is used to control the nonlinear relationship between profit and cost. is the demand impact coefficient of the i-th business field, is the market demand elasticity coefficient of the i-th business field, reflecting the impact of demand on the growth of this business field. is the restriction degree of the kth type of external factors on growth, and p is the number of factors related to the external environment.

9. A storage medium, characterized in that: The storage medium stores a computer program, wherein the computer program is configured to execute the method according to any one of claims 1 to 4 when executed.

10. An electronic device comprising a memory and a processor, characterized in that: A computer program is stored in the memory, and the processor is configured to run the computer program to perform the method according to any one of claims 1 to 4.

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