A statistical analysis method and system for enterprise business operations

Abstract

The present invention discloses a statistical analysis method and system for enterprise business operations. The method includes: collecting a data set related to enterprise business operations; calculating a business risk index and a growth potential score for the enterprise business operations according to the business-related data set; performing cluster analysis on the business-related data set to obtain business data subsets of multiple categories; and storing each category of business data subset, the business risk index, and the growth potential score for use in the statistical analysis of enterprise business operations. By using the embodiments of the present invention, the accuracy, real-time performance, and practicality of enterprise business analysis can be improved.

Description

Technical Field

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

[0002] With the intensification of global market competition and the continuous change of the economic environment, enterprises face increasingly complex risks and opportunities in their business operations. In this context, how to effectively evaluate and predict the risks and growth potential of enterprise operations has become an urgent problem for enterprise 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 restricted by factors such as internal operations, resource allocation, and management level. In order to achieve sustainable development and competitive advantages in the complex market environment, enterprises need to use statistical analysis methods to accurately identify potential risks and growth opportunities and formulate corresponding strategic decisions. Summary of the Invention

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

[0004] An embodiment of the present application provides a statistical analysis method for enterprise business operations, and the method includes:

[0005] Collecting a data set related to enterprise business operations;

[0006] Calculating a business risk index and a growth potential score for enterprise business operations according to the business-related data set;

[0007] Performing cluster analysis on the business-related data set to obtain business data subsets of multiple categories;

[0008] Storing the business data subset of each category, the business risk index, and the growth potential score for statistical analysis of enterprise business operations.

[0009] Optionally, the calculation formula of the business risk index is:

[0010]

[0011] Wherein, the is the business risk index, the is the fluctuation value of the i-th business field within a certain period of time, the is the risk change range of the i-th business field within a certain period of time, the is the sensitivity coefficient of the i-th business field, reflecting the sensitivity of this business field to changes in external factors, is the industry risk index for the j-th category, representing the risk assessment related to the external industry environment. The is a standardization factor used to adjust the dimensional differences of risk data in each business area. The is the weight value of the i-th business area. Here, m is the number of business-related industry categories, and n is the number of business areas.

[0012] Optionally, the is obtained by calculating the standard deviation of the key business indicators of the i-th business area within a certain period. The is obtained by calculating the difference between the maximum and minimum values of the key business indicators of the i-th business area within a certain period.

[0013] Optionally, the calculation formula for the growth potential score is:

[0014]

[0015] wherein, the is the growth potential score, the is the current profit of the i-th business area, the is the cost of the i-th business area, the is the growth potential coefficient of the i-th business area, reflecting the potential of this business area itself. The is the growth elasticity coefficient of the i-th business area, used to control the non-linear relationship between profit and cost. The is the demand impact coefficient of the i-th business area. The is the market demand elasticity coefficient of the i-th business area, reflecting the impact of demand on the growth of this business area. The is the limitation degree of the k-th external factor on growth. Here, p is the number of factors related to the external environment.

[0016] Another embodiment of the present application provides a statistical analysis system for enterprise business operations. The system includes:

[0017] A collection module for collecting data sets related to enterprise business operations;

[0018] A calculation module for calculating the business risk index and growth potential score of enterprise business operations based on the business-related data sets;

[0019] An analysis module for performing cluster analysis on the business-related data sets to obtain business data subsets of multiple categories;

[0020] A storage module, configured to store the business data subset of each category, the business risk index, and the growth potential score for statistical analysis of enterprise business operations.

[0021] Another embodiment of the present application provides a storage medium storing a computer program, wherein the computer program is configured to execute the method described in any one of the above when running.

[0022] Another embodiment of the present application provides an electronic device including a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to run the computer program to execute the method described in any one of the above.

[0023] Compared with the prior art, a statistical analysis method for enterprise business operations provided by the present invention collects a data set related to enterprise business operations; calculates a business risk index and a growth potential score of the enterprise business operations according to the business-related data set; performs clustering analysis on the business-related data set 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 enterprise business operations, thereby being able to improve the accuracy, real-time performance, and practicality of enterprise business analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 It is a hardware structure block diagram of a computer terminal for a statistical analysis method for enterprise business operations provided by an embodiment of the present invention;

[0025] Figure 2 It is a schematic flowchart of a statistical analysis method for enterprise business operations provided by an embodiment of the present invention;

[0026] Figure 3 It is a schematic structural diagram of a statistical analysis system for enterprise business operations provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0027] The embodiments described below by referring to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0028] An embodiment of the present invention first provides a statistical analysis method for enterprise business operations, which can be applied to an electronic device, such as a computer terminal, specifically, a general computer, etc.

[0029] The following takes running on a computer terminal as an example to describe it in detail. Figure 1 It is a hardware structure block diagram of a computer terminal for a statistical analysis method for enterprise business operations provided by an embodiment of the present invention.

[0030] As shown Figure 1 in the figure, the computer device includes a processor, a memory, and a network interface connected by a system bus. Among them, the memory may include a non-volatile storage medium and an internal memory.

[0031] The non-volatile storage medium can store an operating system and a computer program. The computer program includes program instructions, which when executed, can cause the processor to execute any statistical analysis method for enterprise business operations.

[0032] The processor is used to provide computing and control capabilities to support the operation of the entire computer device.

[0033] 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, it can cause the processor to execute any statistical analysis method for enterprise business operations.

[0034] The network interface is used for network communication, such as sending assigned tasks, etc. Those skilled in the art can understand that Figure 1 the structure shown in is only a block diagram of some structures related to the solution of this application, and does not constitute a limitation on the computer device to which the solution of this application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have a different component layout.

[0035] 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 (DSPs), application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), 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.

[0036] Referring to Figure 2 , an embodiment of the present invention provides a statistical analysis method for enterprise business operations, which may include the following steps:

[0037] S201, collect a data set related to enterprise business operations;

[0038] 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 environments of the enterprise. Specifically, the collected dataset 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 situation, etc.), and other factors that may affect business performance. The data can be collected through various methods, such as automatically exporting through the enterprise's ERP system, manual research, or using the services of external data providers, to ensure the comprehensiveness and representativeness of the data. During the collection process, the enterprise needs to determine the key indicators to be collected according to specific analysis requirements for subsequent risk assessment and growth potential analysis.

[0039] Enterprise operation data is the basis of the entire statistical analysis process. Only by obtaining comprehensive and accurate business-related data can it support the calculation of subsequent Business Risk Index (BRI) and Growth Potential Index (GPI). By systematically collecting various types of business data, the enterprise can better understand its own business situation and market environment, provide a scientific basis for decision-makers, and support the enterprise in making effective strategic planning and adjustments. At the same time, this step can also help the enterprise identify potential risk factors and grasp growth opportunities, further enhancing the overall stability and sustainable growth ability of the business. Accurate data collection is the premise to ensure the reliability and effectiveness of subsequent analysis results. Therefore, this step has profound strategic significance for the long-term development of the enterprise.

[0040] In actual operation, the collection of the enterprise operation business-related dataset can be achieved through multiple channels and methods. First, the enterprise's internal financial management system and operation management systems (such as ERP system, CRM system, production management system, etc.) are the main sources of data collection. These systems can automatically generate key data such as the enterprise's financial statements, sales data, inventory information, production efficiency, etc., providing reliable internal operation information. Second, the enterprise can also collect external environment-related information through external market research, industry analysis reports, competitor data, etc. These external data are crucial for understanding industry trends, market demand changes, and competition situations.

[0041] For example, assume a manufacturing enterprise hopes to analyze the growth potential and risks of its products in the market. The enterprise needs to collect internal production data (such as monthly production volume, production cost, etc.), sales data (such as sales volume and sales revenue in each region), 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.

[0042] To ensure the validity and reliability of data, enterprises need to pay attention to data quality control when collecting data. First of all, the accuracy of 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 the data in the enterprise internal system, an automated data collection and monitoring mechanism can be set up to ensure the consistency and integrity of the data while being updated in real time.

[0043] For example, when an enterprise collects financial data and finds that there is a large lag in the annual data of some financial statements, it is necessary to communicate with relevant departments to ensure the timeliness of the data; at the same time, it is also necessary to avoid inconsistent formats caused by different data sources during data integration, and the data can be cleaned and integrated through a unified standard data format for subsequent analysis applications.

[0044] The collected data needs to be stored in a structured and secure data warehouse for subsequent data analysis and mining. When storing data, enterprises should adopt appropriate storage solutions according to the data type, data source and its sensitivity. For sensitive data such as financial data and personal information, security measures such as encryption and access 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 a large amount of data can be quickly retrieved and processed in actual operations.

[0045] For example, an enterprise can use a cloud storage platform to establish a centralized data warehouse to ensure that all relevant data is efficiently managed in one place, and at the same time, multi-level access control is used to control the access rights of different users to the data. In addition, through regular backups and disaster recovery mechanisms, it is ensured that the data can be restored in a timely manner in case of system failures and important historical data will not be lost.

[0046] S202, calculate the business risk index and growth potential score of the enterprise's business operations according to the business-related data set;

[0047] Through the statistical analysis of various business data, first of all, quantitative indicators of risks and growth potential in each business area can be obtained. The business risk index (BRI) is a comprehensive indicator to measure the business risks faced by an enterprise in different fields. It takes into account the volatility within each field, the range of risk changes, and the sensitivity to external factors. The growth potential score (GPI) is an indicator that reflects the future development potential of each business area of an enterprise in the current business environment, involving a comprehensive analysis of factors such as profit, cost, and market demand. By calculating these indexes, enterprises can obtain a clearer and more systematic assessment of their business conditions and provide data support for strategic decision-making.

[0048] The steps for calculating the Business Risk Index (BRI) and Growth Potential Index (GPI) of a company's business operations are crucial for corporate strategic decision-making. Through these two metrics, a company can gain in-depth understanding of the risk status and development potential of its various business areas from different dimensions. This not only helps to promptly identify potential business risks and avoid possible business losses, but also enables the company to identify market opportunities and enhance its growth potential. The assessment of the risk index can assist the company's management in taking effective risk control measures, while the calculation of the growth potential index 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 company, promoting the company to optimize its business strategies and enhance its market competitiveness.

[0049] Specifically, a calculation formula for the business risk index can be:

[0050]

[0051] The calculation formula is designed to quantify the risks of the company's various business areas by integrating multiple factors. The purpose of constructing this formula is: on the one hand, it can capture the inherent volatility of each business area, and on the other hand, it can take into account the impact of the external industry environment on the business area. The design of the formula not only considers the volatility and change range of each business area, but also introduces the assessment of external industry risks, thus providing a multi-dimensional risk measure.

[0052] Wherein, the is the business risk index, the is the volatility value of the i-th business area within a certain period of time, used to measure the change range of the internal indicators of this area. A business area with larger fluctuations usually means higher risks. The is the risk change range of the i-th business area within a certain period of time, mainly reflecting the extreme fluctuations of the relevant indicators within this area within the specified time. The is the sensitivity coefficient of the i-th business area, reflecting the sensitivity of this business area to changes in external factors, the is the industry risk index of the j-th category, representing the risk assessment related to the external industry environment, the is the standardization factor, used to adjust the dimensional differences of the risk data of each business area to ensure that the risks of different business areas can be reasonably compared. The is the weight value of the i-th business area, m is the number of business-related industry categories, and n is the number of business areas.

[0053] Comprehensively evaluate the internal risk factors (volatility, risk changes, etc.) of a single domain and the impact of the external environment on that domain (such as industry risks). Through the setting of weights, ensure that the risk metrics of different domains reasonably reflect their status in the overall enterprise risk management. Through these parameters, the enterprise can make scientific decisions in the face of complex market changes.

[0054] Specifically, the is obtained by calculating the standard deviation of the key business indicators of the i-th business domain within a certain period of time. The is obtained by calculating the difference between the maximum value and the minimum value of the key business indicators of the i-th business domain within a certain period of time.

[0055] The calculation methods of sigma_i and Delta R_i are by statistically analyzing the key business indicators of the i-th business domain within a certain period of time. The specific methods include calculating the standard deviation and the extreme value difference. These two indicators are used to quantify the risk and volatility of the business domain. sigma_i (volatility value) reflects the fluctuation range of this domain within this period by calculating the standard deviation of the key business indicators. The greater the fluctuation, the higher the risk of this business domain. DeltaR_i (risk change range) is obtained by calculating the difference between the maximum value and the minimum value of the key business indicators, revealing the extreme fluctuation degree of the indicators within this domain during this period, thereby helping to evaluate the potential risk changes in this domain.

[0056] By calculating sigma_i and Delta R_i, the enterprise can clearly understand the volatility and risk changes of each business domain within a certain period of time. As a volatility value, sigma_i can help the enterprise identify which business domains are unstable and need to adopt special risk management measures; while Delta R_i reveals whether this domain has experienced extreme risk fluctuations, helping the enterprise identify potential major risk events or abnormal changes. In actual operation, the standard deviation and the extreme value difference are commonly used risk measurement tools. Through these two indicators, the enterprise can more effectively formulate corresponding risk control strategies and reduce potential business risks.

[0057] For example, for a retail enterprise, its key business indicators may include sales volume, inventory turnover rate, etc. By collecting monthly sales data for the past year, the volatility value (sigma_i) of this business domain, that is, the standard deviation of the sales volume, can be calculated. If the sales volume fluctuates greatly, it indicates that the risk of this business domain is relatively high, and it may be necessary to pay attention to changes in factors such as market demand and price policies.

[0058] Meanwhile, by calculating the difference (DeltaR_i) between the maximum and minimum sales amounts in this field over the past year, it is possible to further evaluate whether there are extreme risk changes. For example, if the sales amount drops sharply in a few months, this extreme fluctuation needs to be particularly concerned about, indicating that there may be sudden market changes or operational problems. Combining these two indicators, enterprises can conduct a more accurate risk assessment of the sales field and take timely measures to respond to potential fluctuation risks, such as adjusting inventory management or optimizing promotional strategies.

[0059] Specifically, the calculation formula for the growth potential score is as follows:

[0060]

[0061] Wherein, the is the growth potential score, the is the current profit of the i-th business field, the is the cost of the i-th business field, the is the growth potential coefficient of the i-th business field, reflecting the potential of this business field itself. This coefficient reflects the growth potential of this field itself, that is, the expansion ability of this business in a normal market environment. The growth potential coefficient can be set through historical growth rates, market forecasts, technological innovations, or management optimization potentials. For mature fields, the growth potential may be relatively low, while for emerging fields, it may have higher potential. The is the growth elasticity coefficient of the i-th business field, used to control the non-linear relationship between profit and cost. The growth elasticity coefficient reflects the non-linear characteristics of the relationship between profit and cost in this field. It is used to control the relative sensitivity between profit and cost, that is, whether the cost increases exponentially while the profit increases, or vice versa. The setting of this coefficient can be determined through industry comparative analysis, historical financial data analysis, etc. is the demand impact coefficient of the i-th business field. This coefficient is used to measure the impact degree of market demand changes on this business field, especially the coping ability in the face of 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 i-th 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 determination of the market demand elasticity coefficient can be through historical data analysis of price changes and sales volumes, or through market research and consumer behavior research. The larger the demand elasticity coefficient, the more sensitive the market is to price changes. $L_k$ is the limiting degree of the $k$-th type of external factor on growth. This parameter measures the restrictive effect of various factors in the external environment (such as policy changes, market competition, laws and regulations, etc.) on the growth potential of the enterprise. The enterprise can evaluate the impact degree of these factors on each business area through means such as external data collection, market research, and policy analysis. Usually, the enterprise will determine the impact degree of different factors according to industry characteristics and geographical location differences. The said $p$ is the number of factors related to the external environment.

[0062] The calculation formula of the Growth Potential Index (GPI) aims to quantify the growth potential of each business area by comprehensively evaluating the profit, cost, market demand, and external environmental factors of the enterprise's business areas. The structural significance of designing this formula is that it can comprehensively reflect various factors affecting the future growth of the enterprise, including the internal and external environment, the operating conditions of the business areas themselves, and market demand, etc. Through the weighted synthesis of a series of weights, elasticity coefficients, and demand impact factors, this formula not only considers the economic benefits of the business itself but also the demand elasticity of the external market and the restrictive effect of external factors, providing a comprehensive and operable growth potential evaluation model for the enterprise.

[0063] The example values of each parameter can be set according to the actual situation. The following are the example values of each parameter, assuming this is an analysis case of a manufacturing enterprise:

[0064] 1. $w_i$ (weight value of the $i$-th business area)

[0065] - Example value: $w_1 = 0.4$, $w_2 = 0.3$, $w_3 = 0.2$, $w_4 = 0.1$.

[0066] Among them, $w_1$, $w_2$, $w_3$, $w_4$ respectively represent the weights of four different business areas, and the specific values are allocated according to the contributions of each area to the total revenue, profit, etc. of the enterprise.

[0067] 2. $\alpha_i$ (growth potential coefficient of the $i$-th business area)

[0068] - Example value: $\alpha_1 = 1.2$, $\alpha_2 = 0.8$, $\alpha_3 = 1.5$, $\alpha_4 = 1.0$.

[0069] For example, a business area in an emerging market may have higher growth potential and a higher $\alpha$ value is set, while a business area in a mature market is lower.

[0070] 3. $\gamma_i$ (growth elasticity coefficient of the $i$-th business area)

[0071] - Example values: gamma_1 = 1.1, gamma_2 = 1.3, gamma_3 = 0.9, gamma_4 = 1.0.

[0072] For example, in some fields, the relationship between profit and cost is more sensitive, so a higher gamma value is set.

[0073] 4. eta_i (demand impact coefficient for the i-th business area)

[0074] - Example values: eta_1 = 1.5, eta_2 = 1.2, eta_3 = 0.8, eta_4 = 1.0.

[0075] For example, the consumer goods field may be more affected by market demand fluctuations, so a higher eta value is set.

[0076] 5. S_i (market demand elasticity coefficient for the i-th business area)

[0077] - Example values: S_1 = 1.4, S_2 = 0.9, S_3 = 1.3, S_4 = 1.0.

[0078] For example, in some business areas, the sensitivity to price changes is high, so the demand elasticity coefficient is large.

[0079] 6. D_k (limitation degree of the k-th type of external factor on growth)

[0080] - Example values: D_1 = 0.6, D_2 = 0.8, D_3 = 0.4.

[0081] The limitation degrees of external factors such as policies and competition are set according to the external environments of different industries and enterprises.

[0082] 7. p (number of factors related to the external environment)

[0083] - Example values: p = 3.

[0084] For example, an enterprise may be affected by external factors in three aspects: policies, market competition, and technological innovation, so p = 3.

[0085] S203, perform clustering analysis on the business-related data set to obtain business data subsets of multiple categories;

[0086] Group analysis of enterprise operation data is carried out through data mining techniques to classify similar business areas or operation conditions into the same category. This process is usually completed through clustering algorithms, aiming to divide the business operations of an enterprise into multiple subsets with similar characteristics according to the characteristics of business data. These characteristics may include financial indicators, market performance, growth rate, etc. of the business area. By analyzing these characteristics, the similarities and differences between different businesses of the enterprise can be identified, so as to conduct risk assessment and growth potential prediction more effectively. For example, an enterprise may operate in different market regions, and the businesses in different regions may show obvious differences in terms of sales volume, cost structure, customer groups, etc. Cluster analysis will help identify which market regions have similar operation characteristics, and personalized strategies or decisions can be formulated based on these similarities.

[0087] The significance of cluster analysis lies in that it can simplify complex and diverse enterprise operation data into multiple subsets that are easy to understand and analyze, thus helping managers identify and understand the potential laws in different business areas or markets. By dividing the business data set into multiple categories, an enterprise can more clearly identify which business areas perform well and which areas have potential risks or growth opportunities. For example, after cluster analysis, an enterprise may find that the market demand in some regions is growing rapidly, while in other regions, there may be risks of demand saturation or decline. This information can help managers make more targeted decisions for different categories of businesses, optimize resource allocation, and formulate more effective market strategies.

[0088] Before conducting cluster analysis, an enterprise first needs to collect relevant data on its business operations. These data may cover multiple dimensions, such as financial data (revenue, profit, cost) of each business area, market data (demand volume, customer growth rate), operation data (production capacity, production efficiency), and external environment data (such as industry growth rate, policy changes, etc.). For example, if an enterprise has different businesses in multiple regions, the data set may include information such as market share, revenue, cost, and customer group characteristics of each region. After collecting the data, preprocessing is required, including missing value handling, outlier detection, and data standardization. The purpose of standardization is to eliminate the dimensionality differences between different data characteristics, making the contribution of each characteristic to the clustering result more balanced. For example, there may be large differences in sales volume among different business areas of an enterprise. Therefore, before clustering, data such as sales volume and profit can be standardized to ensure that their influences are relatively balanced.

[0089] After the data is prepared, the next step is to select a suitable clustering algorithm for analysis. Common clustering algorithms include "k-means clustering", "hierarchical clustering", and "DBSCAN", etc. 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 value of k, the enterprise can determine a reasonable value based on previous understanding of the business domain or through techniques such as the elbow method. For example, suppose the enterprise operates in 10 business domains. After preliminary analysis, the enterprise believes that these domains can be divided into 3 categories (for example: high-growth domains, stable-growth domains, and low-growth domains). Therefore, the enterprise 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, calculate the distance between each data point and the initial cluster center, and assign the data point to the corresponding category according to the principle of the shortest distance. The center point of each category will be continuously adjusted with each iteration until the clustering result converges.

[0090] After completing the clustering analysis, the enterprise will obtain subsets of business data for multiple categories, and each subset contains business domains with similar characteristics. At this time, the enterprise can conduct in-depth analysis of the business domains in each category to reveal their common characteristics and potential growth opportunities. For example, if the business domains in a certain clustering subset show 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 clustering subset may show stable but slowly growing business domains (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 indices and growth potential scores) to make more precise strategic decisions. For example, the business domains in a certain clustering subset may be concentrated in high-risk, high-return markets. After assessing the risks, the enterprise can choose whether to make risky investments or adopt conservative strategies, while the businesses in some other clustering subsets may operate stably in low-risk areas. The enterprise can deepen its cultivation in these areas and ensure a stable cash flow.

[0091] S204, store the business data subsets for each category, the business risk index, and the growth potential score for use in the statistical analysis of the enterprise's business operations.

[0092] In the step, "storing 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" means effectively storing the results of the clustering analysis (i.e., the business data subset), as well as the business risk index (BRI) and growth potential score (GPI) calculated through formulas. The purpose of this process is to facilitate further analysis and decision support for the enterprise's business data in the future. Specifically, the enterprise can save these data in a database or data warehouse. The business data subset of each category will be marked and stored in association with the corresponding business risk index and growth potential score. This enables the enterprise to quickly access and retrieve relevant data, facilitating regular updates and long-term tracking, thereby achieving more accurate business analysis and decision-making.

[0093] The significance of this storage process lies in that by systematically storing the data, risk index, and potential score of each business area, the enterprise can establish a centralized and structured data storage system, facilitating subsequent statistical analysis and decision-making. First, the stored data can provide a basis for subsequent trend analysis, risk assessment, and market opportunity identification. The enterprise can conduct comparative analysis over different time periods. Second, the stored data can provide a unified information source for different departments or management levels, thus promoting the 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 identify which markets have expansion potential based on the growth potential score. Through systematic storage and integration, the enterprise can better manage and utilize these data resources.

[0094] In the first step of implementing this method, the enterprise needs to collect relevant business operation data through 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.). To ensure the accuracy and comparability of the data, the enterprise needs to preprocess the collected raw data. This includes filling or removing missing data, handling outliers, and standardizing data with different dimensions so that indicators in different business areas can be compared on the same scale. For example, the sales and costs may have a large numerical difference, so they need to be converted into relative indicators, such as percentage changes or standard deviations, so that in subsequent clustering analysis, all indicators can fairly evaluate each business area. This preprocessing process lays the foundation for subsequent clustering of business data, calculation of risk index, and calculation of growth potential score.

[0095] After data collection and preprocessing are completed, the enterprise will conduct clustering analysis on the business-related data sets to obtain subsets of business data in multiple categories. One of the commonly used clustering algorithms is "k-means clustering", which can divide them into several groups according to the characteristics of the business domain. For example, an enterprise may divide its business domain into "high-growth domains", "stable domains", and "low-growth domains". The core of this process is to form categories by calculating the similarity of business data. During the clustering process, the algorithm will automatically divide them into different categories according to the characteristics of each business domain (such as sales, profit, 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 domain (such as financial volatility), the range of risk changes, and industry risks, and perform weighted averaging by combining the corresponding weights. The calculation of the growth potential score, on the other hand, needs to consider factors such as the current profit, cost, and market demand of each domain to reflect its future growth potential. The enterprise can store the risk index and potential score of each business domain together with the clustering results according to these calculation formulas.

[0096] After clustering analysis and index calculation are completed, the enterprise needs to store the subsets of business data, business risk index, and growth potential score for each category in a database. For easy management and access, the enterprise can establish a database or data warehouse and organize these data by business domain, category, risk index, and potential score. For example, a table containing fields such as "business domain", "category", "risk index", and "growth potential score" can be created to store all business domain data in the database. Each record represents a specific business domain and contains the relevant data of that domain and the corresponding risk and potential score information. In this way, the enterprise can conveniently access historical data, update data regularly, and conduct long-term trend analysis. For example, the management of the enterprise can judge which domains 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 marketing strategies, identify potential growth opportunities or risks. Combining data storage, the enterprise can also use visualization tools to generate reports or dashboards to further enhance the ability to make data-driven decisions.

[0097] It can be seen that collecting the business-related data sets of the enterprise's operations; calculating the business risk index and growth potential score of the enterprise's operations according to the business-related data sets; conducting clustering analysis on the business-related data sets to obtain subsets of business data in multiple categories; storing the subsets of business data, the business risk index, and the growth potential score for each category for statistical analysis of the enterprise's operations, so as to improve the accuracy, real-time performance, and practicality of the enterprise's operation analysis.

[0098] Another embodiment of the present invention provides a statistical analysis system for enterprise business operations. Refer to Figure 3 , the system may include:

[0099] A collection module 301, configured to collect data sets related to enterprise business operations;

[0100] A calculation module 302, configured to calculate the business risk index and growth potential score of enterprise business operations according to the business-related data sets;

[0101] An analysis module 303, configured to perform cluster analysis on the business-related data sets to obtain business data subsets of multiple categories;

[0102] A storage module 304, configured to store the business data subsets of each category, the business risk index, and the growth potential score for statistical analysis of enterprise business operations.

[0103] It can be seen that collecting data sets related to enterprise business operations; calculating the business risk index and growth potential score of enterprise business operations according to the business-related data sets; performing cluster analysis on the business-related data sets to obtain business data subsets of multiple categories; storing the business data subsets of each category, the business risk index, and the growth potential score for statistical analysis of enterprise business operations, thereby being able to improve the accuracy, real-time performance, and practicality of enterprise business analysis.

[0104] The embodiment of the present invention also provides a storage medium, in which a computer program is stored, and the computer program is configured to execute the steps in any one of the above method embodiments when running.

[0105] Specifically, in this embodiment, the above storage medium may be configured to store a computer program for executing the following steps:

[0106] S201, collecting data sets related to enterprise business operations;

[0107] S202, calculating the business risk index and growth potential score of enterprise business operations according to the business-related data sets;

[0108] S203, performing cluster analysis on the business-related data sets to obtain business data subsets of multiple categories;

[0109] S204, storing the business data subsets of each category, the business risk index, and the growth potential score for statistical analysis of enterprise business operations.

[0110] It can be seen that a data set related to the business operations of an enterprise is collected; based on the business-related data set, the business risk index and growth potential score of the enterprise's business operations are calculated; the business-related data set is subjected to cluster analysis 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 performance, and practicality of the enterprise's business analysis.

[0111] An embodiment of the present invention further provides an electronic device, including a memory and a processor, where a computer program is stored in the memory, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0112] Specifically, the above electronic device may further include a transmission device and an input / output device. Among them, the transmission device is connected to the above processor, and the input / output device is connected to the above processor.

[0113] Specifically, in this embodiment, the above processor may be configured to execute the following steps through a computer program:

[0114] S201, collect a data set related to the business operations of an enterprise;

[0115] S202, calculate the business risk index and growth potential score of the enterprise's business operations based on the business-related data set;

[0116] S203, perform cluster analysis on the business-related data set to obtain business data subsets of multiple categories;

[0117] S204, store 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.

[0118] It can be seen that a data set related to the business operations of an enterprise is collected; based on the business-related data set, the business risk index and growth potential score of the enterprise's business operations are calculated; the business-related data set is subjected to cluster analysis 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 performance, and practicality of the enterprise's business analysis.

[0119] The structure, features, and effects of the present invention have been described in detail based on the embodiments shown in the drawings. The above are only the preferred embodiments of the present invention. However, the present invention is not limited to the scope of implementation shown in the drawings. Any changes made in accordance with the concept of the present invention, or equivalent embodiments modified into equivalent changes, that still do not exceed the spirit covered by the specification and the 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; According to the business-related data set, the business risk index and growth potential score of the enterprise's business are calculated; the calculation formula of the business risk index is: Wherein, the BRI is the business risk index, the sigma_i is the fluctuation value of the ith business field within a certain period of time, the Delta R_i is the risk change amplitude of the ith business field within a certain period of time, the beta_i is the sensitivity coefficient of the ith business field, reflecting the sensitivity of the business field to changes in external factors, the R_j is the industry risk index of the jth category, indicating the risk assessment related to the external industry environment, the Z is the standardization factor used to adjust the dimensional differences of the risk data of each business field, the w_i is the weight value of the ith business field, the m is the number of business-related industry categories, and the n is the number of business fields; 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 sigma_i is obtained by calculating the standard deviation of the key business indicators of the ith business field within a certain period of time, and the Delta R_i is obtained by calculating the difference between the maximum value and the minimum value of the key business indicators of the ith business field within a certain period of time.

3. The method according to claim 2, characterized in that The growth potential score is calculated as follows: Among them, the GPI is the growth potential score, the P_i is the current profit of the ith business field, the C_i is the cost of the ith business field, the alpha_i is the growth potential coefficient of the ith business field, reflecting the potential of the business field itself, the gamma_i is the growth elasticity coefficient of the ith business field, used to control the nonlinear relationship between profit and cost, the eta_i is the demand impact coefficient of the ith business field, the S_i is the market demand elasticity coefficient of the ith business field, reflecting the impact of demand on the growth of the business field, the D_k 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.

4. 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; The calculation module is used to calculate the business risk index and growth potential score of the enterprise's business operations according to the business-related data set; the calculation formula of the business risk index is: Wherein, the BRI is the business risk index, the sigma_i is the fluctuation value of the ith business field within a certain period of time, the Delta R_i is the risk change amplitude of the ith business field within a certain period of time, the beta_i is the sensitivity coefficient of the ith business field, reflecting the sensitivity of the business field to changes in external factors, the R_j is the industry risk index of the jth category, indicating the risk assessment related to the external industry environment, the Z is the standardization factor used to adjust the dimensional differences of the risk data of each business field, the w_i is the weight value of the ith business field, the m is the number of business-related industry categories, and the n is the number of business fields; 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.

5. The system according to claim 4, characterized in that The sigma_i is obtained by calculating the standard deviation of the key business indicators of the ith business field within a certain period of time, and the Delta R_i is obtained by calculating the difference between the maximum value and the minimum value of the key business indicators of the ith business field within a certain period of time.

6. The system according to claim 5, characterized in that The growth potential score is calculated as follows: Among them, the GPI is the growth potential score, the P_i is the current profit of the ith business field, the C_i is the cost of the ith business field, the alpha_i is the growth potential coefficient of the ith business field, reflecting the potential of the business field itself, the gamma_i is the growth elasticity coefficient of the ith business field, used to control the nonlinear relationship between profit and cost, the eta_i is the demand impact coefficient of the ith business field, the S_i is the market demand elasticity coefficient of the ith business field, reflecting the impact of demand on the growth of the business field, the D_k 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.

7. 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 3 when executed.

8. 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 3.

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