Artificial intelligence-based enterprise operation efficiency analysis method and system

Abstract

The present application relates to the field of enterprise operation analysis, and particularly relates to an enterprise operation efficiency analysis method and system based on artificial intelligence. The method comprises the following steps: collecting an enterprise operation log table; classifying and marking a business unit of the enterprise operation log table, and outputting a classification label set; fitting a risk propagation distribution based on the classification label set, and constructing a risk propagation distribution graph; performing worst-case scenario pressure bearing evaluation based on the risk propagation distribution graph, and generating a pressure bearing evaluation report; simulating operation constraints on the risk propagation distribution graph, and outputting an optimal income prediction report; making differentiated improvement decisions based on the pressure bearing evaluation report and the optimal income prediction report, and outputting an operation adjustment strategy. The present application realizes accurate evaluation and optimal decision of enterprise operation efficiency under controllable risks, improves the stability of enterprise operation efficiency and the ability to continuously generate income.

Description

Technical Field

[0001] This invention relates to the field of enterprise operation analysis, and in particular to an artificial intelligence-based method and system for analyzing enterprise operation efficiency. Background Technology

[0002] In the long-term operation of an enterprise, complex and overlapping business processes, frequent departmental collaborations, unreasonable resource allocation, process bottlenecks, and risk accumulation often exist covertly. With the accelerating pace of market changes and increasing external uncertainties, single indicators or localized analytical methods are insufficient to reflect the overall operational status of the enterprise in a timely manner, easily leading to decreased efficiency, increased costs, and even systemic operational risks. Simultaneously, the correlation and risk transmission effects between different business units are becoming increasingly significant; once an anomaly occurs in one link, it can rapidly spread and have a chain reaction impact on overall operational efficiency. Existing enterprise operational efficiency analysis methods are mostly based on traditional statistical models or manual experience rules, typically relying on post-event data aggregation and manual interpretation, with limited analytical dimensions, making it difficult to dynamically model complex business scenarios. When faced with massive, multi-source, and heterogeneous operational data, these methods often suffer from slow response times, insufficient analytical accuracy, and a lack of predictive capabilities, failing to effectively identify potential efficiency bottlenecks and risks. Furthermore, existing analytical methods lack synergistic consideration between operational constraints, risk impacts, and revenue optimization, making it difficult to provide enterprises with actionable optimization decision-making solutions. Summary of the Invention

[0003] To address the aforementioned technical problems, this invention proposes an artificial intelligence-based method and system for analyzing enterprise operational efficiency, thereby resolving at least one of the aforementioned technical issues.

[0004] To achieve the above objectives, this invention provides an artificial intelligence-based method for analyzing enterprise operational efficiency, comprising the following steps: Step S1: Collect enterprise operation logs; classify and label the enterprise operation logs by business units, and output a set of classification tags; Step S2: Fit the risk propagation distribution based on the classification label set and construct a risk propagation distribution map; Step S3: Conduct a worst-case stress assessment based on the risk propagation distribution map and generate a stress assessment report; Step S4: Perform operational constraint simulation on the risk propagation distribution map and output the optimal return prediction report; Step S5: Based on the stress assessment report and the optimal profit forecast report, make differentiated improvement decisions and output operational adjustment strategies.

[0005] This specification provides an artificial intelligence-based enterprise operational efficiency analysis system for performing the artificial intelligence-based enterprise operational efficiency analysis method described above, including: The data collection unit is used to collect enterprise operation log tables; classify and label the enterprise operation log tables by business units, and output a set of classification tags; The propagation distribution unit is used to fit the risk propagation distribution based on the classification label set and construct a risk propagation distribution map. The worst-case assessment unit is used to conduct worst-case stress assessment based on the risk propagation distribution map and generate a stress assessment report. The optimal prediction unit is used to simulate the operational constraints of the risk propagation distribution map and output the optimal return prediction report. The adjustment unit is used to make differentiated improvement decisions based on the stress assessment report and the optimal profit forecast report, and output operational adjustment strategies.

[0006] The beneficial effects of this invention are specifically as follows: By centrally collecting enterprise operation logs and unifying the heterogeneous data formats from different systems and departments, a high-quality, computable data foundation is provided for subsequent analysis, avoiding the impact of information silos on operational efficiency analysis. Artificial intelligence algorithms (such as clustering, feature learning, and semantic recognition) are used to classify and label operation logs by business units, ensuring that each log entry is clearly mapped to its corresponding business link, functional module, or process node, enhancing the business interpretability of the data. By modeling the risk correlation between different business units and fitting the risk propagation distribution, the diffusion path and impact of risks from the source to each business unit are clearly depicted. A risk propagation distribution map is constructed, upgrading risk analysis from single-point, static assessment to global, dynamic propagation analysis, enabling the identification of potential "risk amplification nodes" and "key transmission channels." Through worst-case scenario assumptions, stress tests are conducted on key risk nodes to assess the enterprise's operational stability and business continuity under high-risk conditions. The stress assessment report clearly presents the vulnerability of each business unit under risk shocks, providing a clear basis for enterprises to identify "weak links." By simulating extreme risk scenarios, management can obtain actionable assessment results before risks actually occur, effectively reducing the impact of emergencies on operational efficiency. Combining risk propagation distribution maps with operational constraints such as resources, costs, and capacity, the system simulates revenue changes under different operational strategies, avoiding a sole pursuit of returns while ignoring risks. Utilizing artificial intelligence models to simulate various operational configurations, the system quickly obtains revenue forecasts for different strategy combinations, improving decision-making efficiency. The optimal revenue forecast report not only focuses on the revenue figures themselves but also comprehensively considers the degree of risk exposure, enabling the company to achieve optimal operational efficiency improvements within safe boundaries. By integrating stress assessment and revenue forecast results, differentiated improvement strategies are developed for different business units, avoiding the negative impact of a "one-size-fits-all" approach on overall efficiency. Operational adjustment strategies can guide resources towards high-return, low-risk, or high-growth potential business units, significantly improving the company's resource utilization efficiency. The system directly transforms AI analysis results into actionable operational strategies, achieving a closed-loop management model for continuous optimization of corporate operational efficiency. Attached Figure Description

[0007] Figure 1 This is a schematic diagram of the steps in the present invention, which describes an artificial intelligence-based method for analyzing enterprise operational efficiency. Figure 2 This is a detailed flowchart illustrating the implementation steps of step S1. Figure 3 This is a flowchart illustrating the detailed implementation steps of step S2. Detailed Implementation

[0008] It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of the invention.

[0009] This application provides an AI-based enterprise operational efficiency analysis method and system. The executing entities of the AI-based enterprise operational efficiency analysis method and system include, but are not limited to, the following: mechanical equipment, data processing platforms, cloud server nodes, network upload devices, etc., which can be considered general computing nodes of this application. The data processing platform includes, but is not limited to, at least one of the following: an audio / image management system, an information management system, and a cloud data management system.

[0010] Please see Figures 1 to 3 This invention provides an artificial intelligence-based method for analyzing enterprise operational efficiency, comprising the following steps: Step S1: Collect enterprise operation logs; classify and label the enterprise operation logs by business units, and output a set of classification tags; Step S2: Fit the risk propagation distribution based on the classification label set and construct a risk propagation distribution map; Step S3: Conduct a worst-case stress assessment based on the risk propagation distribution map and generate a stress assessment report; Step S4: Perform operational constraint simulation on the risk propagation distribution map and output the optimal return prediction report; Step S5: Based on the stress assessment report and the optimal profit forecast report, make differentiated improvement decisions and output operational adjustment strategies.

[0011] In the embodiments of the present invention, see Figure 1 The diagram below illustrates the steps of an artificial intelligence-based enterprise operational efficiency analysis method according to the present invention. In this example, the steps of the artificial intelligence-based enterprise operational efficiency analysis method include: Step S1: Collect enterprise operation logs; classify and label the enterprise operation logs by business units, and output a set of classification tags; In this embodiment, an enterprise operation log is generated by collecting data from financial statements, cost details, budget execution reports, staffing tables, equipment operation records, procurement and inventory ledgers, and business plan data. The collected data covers resource input, output capacity, cash flow, staffing, and asset utilization across all business units. After data collection, the operation log is standardized, including field unification, unit of measurement unification, and data type standardization, to ensure comparability and aggregability of data from different sources. Subsequently, based on business unit identification rules, various data records are categorized, core business unit attributes are extracted, and corresponding classification tags are assigned, including dimensions such as organizational structure, functional type, business scale, and risk sensitivity. During the classification and tagging process, a business unit weight parameter is introduced, assigning higher weights to core business units to highlight their impact on overall enterprise operational efficiency.

[0012] Step S2: Fit the risk propagation distribution based on the classification label set and construct a risk propagation distribution map; In this embodiment, risk propagation modeling is performed on each business unit and its relationships, and distribution fitting is applied to the risk diffusion characteristics to construct a risk propagation distribution map at the enterprise operation level. The risk propagation distribution map uses business units as nodes and the risk transmission relationships between nodes as edges. Edge weights are determined by analyzing factors such as capital dependence, output correlation, resource sharing, and operational coordination among business units. Risk propagation intensity and path decay parameters are used to describe the changes in the strength and efficiency of risk transmission during the propagation process. Distribution fitting calculates the cumulative risk value of each node under different windows using historical data and worst-case scenario assumptions, generating a risk diffusion probability distribution to characterize the spatial and temporal evolution of risk in the enterprise network. Through the quantification and visualization of risk paths, a structured risk propagation distribution map is formed, which can intuitively reflect the risk concentration areas and potential risk diffusion paths of key business units.

[0013] Step S3: Conduct a worst-case stress assessment based on the risk propagation distribution map and generate a stress assessment report; In this embodiment, the operational resilience of an enterprise under worst-case scenario conditions is assessed, generating a resilience assessment report. Worst-case scenario conditions include the maximum rate of capital depletion, the maximum profit erosion, and the maximum probability of operational failure. By mapping these conditions to risk propagation paths, the cash flow resilience, risk of financial distress, operational continuity, and management capacity of each business unit within the enterprise are analyzed point-by-point. Through simulations at multiple time points, critical operational failure points are identified, the probability of financial distress is calculated, and the potential impact on customer churn, supply chain disruption, and market share is further analyzed. The assessment results are expressed through quantitative indicators and risk level classifications, distinguishing between high, medium, and low-resilience business units, and identifying key resilience points and potential risk chains.

[0014] Step S4: Perform operational constraint simulation on the risk propagation distribution map and output the optimal return prediction report; In this embodiment, enterprise operations are optimized through simulation based on resource constraints to generate an optimal revenue forecast report. Resource constraints include the upper limit of the cost structure optimization budget, the enterprise's resource reallocation space, and the time window for business adjustments. During the simulation, resource allocation and business adjustment constraints are mapped to a risk propagation graph, correcting the risk propagation intensity and path decay, and assessing the capital utilization efficiency, output capacity, and risk exposure changes of each business unit under the constraints. Through multi-dimensional quantitative analysis of the optimized risk propagation, cost saving potential, output growth space, and risk exposure contraction magnitude are calculated, forming an indicator system that provides the optimal revenue level achievable by the enterprise under resource constraints. A structured optimal revenue forecast report is output, clearly defining the quantifiable range of cost, output, and risk improvements.

[0015] Step S5: Based on the stress assessment report and the optimal profit forecast report, make differentiated improvement decisions and output operational adjustment strategies.

[0016] In this embodiment, the pressure assessment report is compared with the optimal return forecast report to calculate the difference range of various operational indicators between the worst-case and best-case scenarios, thereby determining the risk-return quadrant of the business unit, including high-risk high-return, high-risk low-return, low-risk high-return, and low-risk low-return. Differentiated improvement strategies are formulated based on the quadrant characteristics: high-risk high-return business units immediately initiate improvement projects, clarifying improvement goals, resource investment boundaries, and high-frequency monitoring windows; high-risk low-return business units implement risk transfer or avoidance, reducing risk exposure through business scale reduction, outsourcing, or contract adjustments; low-risk high-return business units are included in the continuous optimization plan, focusing on maximizing controllable returns and optimizing resource allocation and business processes; low-risk low-return business units undergo routine monitoring, with low-frequency monitoring and early warning mechanisms set up to prevent deterioration.

[0017] In this embodiment, see Figure 2 The diagram below illustrates the detailed implementation steps of step S1. In this embodiment, the detailed implementation steps of step S1 include: Collect enterprise operation logs, including financial statements, cost details, budget execution reports, staffing lists, equipment operation records, procurement and inventory ledgers, and business plan data. Standardize the fields in the enterprise operation logs to obtain a standardized operation table; The standardized operation table is processed uniformly in terms of time dimension to construct a benchmark time axis; The standardized operation table is subjected to business entity identification to obtain multiple entity data. Determine the relationships between the multiple entity data; map the standardized operation table based on the baseline timeline and the relationships to construct the operation dataset; Based on the operational dataset, business units are classified and labeled, and a set of classification labels is output.

[0018] In this embodiment, various operational data within the enterprise are centrally collected to cover key elements of the enterprise's business activities, forming a complete operational log data foundation. The collected data includes financial statements, cost details, budget execution reports, staffing tables, equipment operation records, procurement and inventory ledgers, and business plan data. During implementation, different data collection frequencies are set according to data source categories and business attributes. For example, financial and budget data are updated monthly, personnel and inventory data are updated daily, and equipment operation data is recorded hourly or minutely. During collection, the original field names, data source identifiers, and timestamp information are retained. Data is initially screened using integrity and reasonableness verification rules, such as checking the proportion of missing fields and the reasonableness of numerical ranges. Data that clearly deviates from common business sense is marked but not directly removed, thus ensuring that the collected results accurately reflect the enterprise's operational status. A unified field specification is established based on the enterprise's operational management standards. Fields from different sources but with the same meaning are merged, and fields with similar meanings but inconsistent definitions are redefined and differentiated, such as unifying business unit identifiers, measurement units, and precision rules for monetary fields. During the field mapping process, a comprehensive judgment is made based on field name similarity, value characteristics, and business meaning. Fields with a high degree of matching are automatically mapped, while ambiguous fields are manually confirmed. Unit conversion and precision standardization are performed for numerical fields, and fixed value ranges are established for categorical fields to reduce the uncertainty brought about by free text.

[0019] Based on the enterprise's operational analysis needs, a unified time granularity is determined, such as using the day as the basic time unit, and a continuous time series covering the entire analysis period is generated. Subsequently, various operational data are mapped onto this baseline time axis according to their original time attributes: data with high recording frequency is aggregated and calculated to form statistical indicators within the corresponding time unit; data with low recording frequency is mapped to the corresponding time range through time expansion or interval allocation. Based on field semantics and business rules, information with clear identifiers is categorized into entities, such as personnel numbers corresponding to personnel entities, equipment numbers corresponding to equipment entities, and material codes corresponding to material entities. For business objects without explicit identifiers, identification is achieved through data feature similarity, behavioral patterns, and relationships; for example, project entities are identified through cost aggregation and planning data, and supplier entities are identified through transaction records. The business relationships between entities are sorted out and confirmed, and data mapping is completed in conjunction with the baseline time axis to construct a unified operational dataset. The determination of relationships includes both explicit business affiliation or configuration relationships, such as the relationship between personnel and business units, or equipment and production tasks, and potential relationships identified through data change trends and synchronization characteristics. By comparing and analyzing changes in entity data over time, collaborative or influencing relationships between different entities are identified, and association mapping rules are established accordingly. After defining these relationships, the entity data is integrated along a timeline to form an operational dataset that reflects both temporal continuity and business structure relationships. Multidimensional indicators related to business units are extracted from this dataset, including financial performance, resource allocation, operational efficiency, and execution deviations. These indicators are then filtered and combined to form a comprehensive description reflecting business characteristics. Based on this, business units are grouped according to the degree of similarity in their characteristics, and each group is named and defined in accordance with corporate management principles, resulting in classification labels with business meaning.

[0020] In this embodiment, the specific steps for classifying and labeling business units based on the operational dataset and outputting a classification label set are as follows: Calculate the total input and total output of each business unit based on the operational dataset; Based on the total input and total output, the cost deviation rate, budget execution deviation rate, human efficiency fluctuation coefficient and asset utilization abnormal value are calculated to obtain the economic indicators of each business unit. Based on the aforementioned economic indicators, analyze abnormal operational business units and mark abnormal business units. Classify and label abnormal business units, and output a set of classification labels.

[0021] In this embodiment, the resource input and operational output of each business unit within the enterprise are quantitatively summarized within a specified analysis period to form basic input-output indicators at the business unit level. The calculation scope of total input includes, but is not limited to, personnel costs, equipment depreciation costs, material consumption costs, energy costs, and external procurement expenditures. These input items are aggregated according to the affiliation of business units and accumulated according to a unified time dimension. Among them, personnel costs are allocated according to positions or staffing levels, and equipment inputs are allocated according to depreciation cycles and usage ratios. The calculation method for total output is determined based on the functional type of the business unit. For example, for production-type business units, output value or sales revenue is the main output indicator, while for support-type business units, service volume, delivery completion rate, or internal settlement value are used as equivalent output indicators. During the calculation process, input and output data are time-aligned to ensure they correspond within the same statistical period. The statistical period can be set to monthly or quarterly, and the total input and output values ​​of the business unit within that period are accumulated. This is further used to calculate multiple economic indicators reflecting operational efficiency and cost control levels, including cost deviation rate, budget execution deviation rate, labor efficiency fluctuation coefficient, and asset utilization anomalies. The cost deviation rate is measured by the proportion of deviation between actual total input and target or benchmark input. Its calculation incorporates an allowable deviation range parameter to distinguish between reasonable fluctuations and abnormal deviations. The budget execution deviation rate is calculated based on the proportion of difference between the budgeted amount and the actual amount incurred, and overspending and savings are distinguished by positive and negative directions. The labor efficiency fluctuation coefficient measures the stability of output generated by a unit of labor input. Its calculation is based on the degree of variation in unit labor efficiency within a continuous time window, the length of which can be set to 3 to 6 consecutive statistical periods. Asset utilization anomalies are identified by comparing the deviation between the actual utilization level of the business unit's assets and historical averages or reference values, and upper and lower thresholds are introduced to define the abnormal range.

[0022] The system assesses individual and combined indicators to determine whether a business unit deviates from its normal operating range. For example, when the cost deviation rate and budget execution deviation rate exceed set thresholds, the business unit is deemed to have cost control anomalies; when the human resource efficiency fluctuation coefficient is consistently higher than the upper limit of the stable range, the matching degree between human resource input and output is deemed abnormal; when abnormal asset utilization values ​​accumulate deviations over consecutive periods, asset allocation or utilization efficiency is deemed problematic. To avoid interference from short-term fluctuations, a continuous period judgment rule is introduced, requiring abnormal states to occur within at least two consecutive statistical periods before being marked. After marking abnormal operating business units, the identified abnormal business units are further classified to clarify their anomaly type and main causes, and corresponding classification label sets are output. The classification process distinguishes based on the combined characteristics of abnormal indicators. For example, business units primarily exhibiting abnormalities in cost deviation rate and budget execution deviation rate are classified as cost control anomalies, those primarily exhibiting abnormalities in human resource efficiency fluctuation coefficient are classified as human resource efficiency anomalies, and those primarily exhibiting abnormal asset utilization values ​​are classified as asset allocation anomalies. For business units exhibiting multiple anomalous characteristics, they can be classified into primary and secondary categories or labeled as composite anomalies based on the weight of the anomaly severity. The classification rules incorporate indicator weight parameters and anomaly intensity levels to distinguish between mild and severe anomalies.

[0023] In this embodiment, see Figure 3 The diagram below illustrates the detailed implementation steps of step S2. In this embodiment, the detailed implementation steps of step S2 include: Based on the classification tag set, a unit-by-unit hierarchical analysis is performed to generate abnormal characteristic results; the abnormal characteristic results include the capital scale, business scope and duration of each abnormal business unit; Based on the abnormal characteristics, calculate the impact range coefficient, financial risk coefficient, operation and maintenance interruption probability, and management failure impact degree to determine the risk association path; The risk propagation distribution is fitted to the risk association path to construct a risk propagation distribution map.

[0024] In this embodiment, based on the established set of abnormal business unit classification tags, a hierarchical analysis is conducted on each abnormal business unit to extract its abnormal characteristics within the overall enterprise operational structure. The analysis revolves around three core dimensions: the business unit's capital scale, business scope, and duration of abnormality. Capital scale is calculated by summarizing the direct and indirect costs and related capital occupation of the business unit during the abnormal period, and then proportionally converted to the enterprise's overall capital scale. Business scope is measured by identifying the number of product lines covered, the scope of service targets, or the number of related business nodes covered by the business unit, and is differentiated using a discrete level approach. The duration of abnormality is statistically analyzed based on the continuous occurrence intervals of abnormal tags on the baseline time axis, and expressed as the length of a natural cycle. During the analysis, a hierarchical weight parameter is introduced to differentiate the importance of core business units from general business units in the analysis results; for example, a higher weight coefficient is assigned to core business units. Furthermore, multi-dimensional risk indicators are calculated based on information such as capital scale, business scope, and duration to characterize the potential impact of abnormal business units on enterprise operations and determine risk correlation paths accordingly. The impact scope coefficient is calculated based on the number of upstream and downstream businesses associated with the abnormal business unit and their business scope level, used to measure the coverage of the anomaly's spread. The financial risk coefficient is assessed based on the ratio between the scale of funds involved during the anomaly period and the enterprise's risk tolerance threshold. The operational interruption probability is estimated by the ratio between the duration of the anomaly and the average recovery period of similar historical anomalies. The management failure impact is comprehensively determined by combining the management level, decision-making complexity, and frequency of anomaly recurrence of the abnormal business unit. In the calculation of risk indicators, standardized processing and weighting rules are introduced to make risk indicators of different dimensions comparable. Subsequently, based on the correlation between business units and the transmission direction of risk indicators, the correlation path of risk between different business units is determined, clarifying the possible path structure of risk spread from the source business unit to related business units.

[0025] Statistical modeling is performed to characterize the spatial and temporal distribution characteristics of risk propagation at different path nodes, focusing on the attenuation degree, propagation probability, and time delay. During the propagation distribution fitting process, business units in the risk-related paths are treated as nodes, and risk transmission relationships are considered as edges. Propagation intensity and path attenuation parameters are introduced to describe the changing trends of risk during propagation. Higher weight values ​​are assigned to risk links with high propagation probability and short path length to highlight their impact on overall operational safety.

[0026] In this embodiment, step S3 includes the following steps: Set an operational simulation cycle; Based on the operational dataset and the operational simulation period, worst-case assumptions are set; the worst-case assumptions are specifically: the maximum rate of capital consumption, the maximum profit erosion, and the maximum probability of operational failure within the period. Based on the worst-case scenario assumptions, an operational simulation of the risk propagation distribution map is performed to generate worst-case scenario risk data. Based on worst-case scenario risk data, a worst-case scenario stress assessment is conducted, and a stress assessment report is generated.

[0027] In this embodiment, the operational simulation period is determined based on the company's operating rhythm, business characteristics, and risk exposure frequency, typically using continuous natural cycles as units, such as 30 days, 90 days, or 180 days. When setting the period, it is necessary to comprehensively consider the business unit's capital turnover cycle, cost settlement cycle, and the lag characteristics of risk propagation to ensure that the simulation period can cover the entire process of risk full diffusion while avoiding excessively long periods that could distort the results. During the period setting process, minimum and maximum period length constraints are introduced to limit the scope of analysis; for example, the minimum should not be less than one complete operating cycle, and the maximum should not exceed the annual operating plan cycle. The operational simulation period is aligned with the aforementioned benchmark timeline to ensure that risk propagation paths, abnormal characteristic results, and economic indicators can all be fully mapped within this period. By clearly defining the operational simulation period, a unified time framework is provided for worst-case scenario assumptions and risk simulations.

[0028] After determining the operational simulation period, assumptions are made based on the operational dataset to determine the extreme adverse situations the company might face during that period, forming worst-case scenario assumptions. These worst-case scenario assumptions include three core aspects: the maximum rate of capital depletion within the period, the maximum profit erosion, and the maximum probability of operational failure. Specifically, the maximum rate of capital depletion is determined by amplifying historical peak cash outflows and setting an upper limit based on the capital scale characteristics of abnormal business units; the maximum profit erosion is extrapolated based on the lower edge of the profit fluctuation range, incorporating the cumulative effect of continuous profit decline; and the maximum probability of operational failure is estimated based on the frequency and duration of abnormal operational events, with higher probability weights assigned to high-risk business units. In setting these assumptions, all parameters are combined according to conservative principles to ensure that the hypothetical scenarios cover the extreme states that the company's operations might withstand.

[0029] After formulating worst-case scenario assumptions, these assumptions are applied to a pre-constructed risk propagation distribution map to simulate and analyze the evolution of risk within the operational simulation cycle. The risk intensity, propagation probability, and path weights of each node in the risk-related path are superimposed and extrapolated to simulate the process of risk spreading along different business units and business paths under the worst-case scenario assumption. During the simulation, the maximum rate of capital consumption is mapped to capital-related risk nodes, the maximum profit erosion is mapped to operating result-related nodes, and the maximum probability of operational failure is mapped to operation and maintenance and support nodes. The risk impact is then transmitted layer by layer according to the direction of risk propagation. During the propagation process, path attenuation coefficients and time delay parameters are introduced to characterize the cumulative and amplified effects of risk during propagation. After obtaining the worst-case scenario risk data, the resilience of the enterprise as a whole and each business unit under this scenario is assessed, and a corresponding resilience assessment report is generated. The resilience assessment focuses on aspects such as the degree of capital stress, operational stability, operational continuity, and management capacity. By analyzing the cumulative effect of risk data within the simulation cycle, it is determined whether the enterprise has critical stress nodes or unbearable risk ranges. During the assessment process, the risk-bearing capacity of different business units is classified and described, and the business units or business paths that will reach their risk limits first in the worst-case scenario are identified.

[0030] In this embodiment, the specific steps for conducting a worst-case scenario stress assessment based on worst-case scenario risk data and generating a stress assessment report are as follows: Based on worst-case risk data, the cash flow resilience of enterprises at multiple time points is calculated to obtain the operational critical failure point. The probability of financial distress risk is determined based on the aforementioned operational critical failure point. Based on the probability of funding disruption risk, the customer churn rate, the probability of supply chain disruption, and the expected loss of market share are calculated to obtain disruption-related data. Worst-case pressure assessment is performed based on fracture correlation data, and a pressure assessment report is generated.

[0031] In this embodiment, the cash flow resilience of an enterprise is calculated point-by-point at multiple key time points within the operational simulation cycle to identify potential operational critical failure points under extreme risk conditions. The calculation of cash flow resilience revolves around available cash balance, stable cash inflow scale, and cash outflow rate under risk scenarios. The cash outflow component focuses on factors such as increased capital consumption rate, additional expenses due to operational failures, and revenue reduction caused by profit erosion. By mapping worst-case scenario risk data to different nodes, the net cash flow and sustainable operating days at each time point are calculated, and a minimum cash safety threshold parameter is introduced to define the minimum cash level required for the enterprise to maintain basic operations. When the cash flow resilience at a certain time point declines to the point where it cannot cover continued operational needs, that time point is identified as an operational critical failure point. By analyzing the location, frequency, and overlap with key operating cycles of operational critical failure points on the timeline, a comprehensive assessment of the risk of financial distress is made. When critical failure points occur at core operating nodes or occur consecutively in multiple key time periods, the probability of financial distress increases significantly. In the process of calculating risk probability, time weight parameters and risk accumulation coefficients are introduced to reflect the characteristic that financial pressure amplifies over time. The upper and lower limits of risk probability are set in combination with the company's historical financial fluctuation range.

[0032] Further analysis of its cascading impact on key external and internal operational elements of the enterprise is conducted, calculating customer churn rate, supply chain disruption probability, and expected market share loss to form disruption-related data. Customer churn rate is estimated based on the impact of funding disruption risk on delivery capacity, service continuity, and customer trust, and a customer concentration parameter is introduced to reflect the amplifying effect of losing key customers. Supply chain disruption probability is calculated by assessing the impact of tight funding on raw material procurement, supplier settlement cycles, and cooperation stability, assigning higher risk weights to supply nodes with high dependence. Expected market share loss comprehensively considers the weakening effect of customer churn and supply chain disruption on market coverage, and expresses the potential loss scale as a proportion. A comprehensive assessment of the enterprise's overall resilience under worst-case conditions is conducted, generating a corresponding resilience assessment report. The assessment focuses on the enterprise's comprehensive stability at the funding, customer, supply chain, and market levels. By analyzing the cumulative impact of disruption-related data during the operational simulation period, key stress areas and high-risk business chains faced by the enterprise are identified. During the assessment process, different risk dimensions are described in a graded manner, distinguishing between mitigable and unacceptable risks, and clarifying the time periods and business scopes where risks are concentrated.

[0033] In this embodiment, the specific steps of step S4 are as follows: Set resource constraints; the resource constraints include the upper limit of the cost structure optimization budget, enterprise resource reallocation data, and business adjustment time window. Based on resource constraints, the risk propagation distribution map is simulated under operational constraints to generate optimal scenario simulation data. Calculate the cost-saving potential, output growth potential, and risk exposure reduction range of the optimal scenario simulation data, and output the optimal return forecast report.

[0034] In this embodiment, resource constraints mainly include three aspects: the upper limit of the cost structure optimization budget, enterprise resource reallocation data, and the business adjustment time window. The upper limit of the cost structure optimization budget limits the maximum amount of funds available for cost reduction and efficiency improvement measures. This limit is typically set based on the enterprise's acceptable capital allocation ratio and is separately identified in the overall operating budget. Enterprise resource reallocation data describes the adjustable space for resources such as personnel, funds, and equipment among different business units. By quantifying and organizing the existing resource allocation, the transferable proportions and adjustment priorities of various resources are clarified. The business adjustment time window limits the effective time range of resource adjustments and business optimization measures, set in natural cycles, and distinguishes between short-term and medium-to-long-term adjustable items. By introducing resource allocation and cost control constraints into the risk propagation path, the original risk diffusion process is modified. For example, resource reallocation reduces the risk weight of high-risk business units, and cost structure optimization alleviates the propagation intensity of nodes with financial pressure. During the simulation, the business adjustment time window limits the effective time of risk mitigation measures, making risk propagation exhibit phased changes over time. For risk paths that cannot be fully mitigated due to resource constraints, their residual risk impact is retained to accurately reflect the optimized operational status.

[0035] The potential revenue improvement that the company can achieve under this scenario is quantitatively calculated, and an optimal revenue forecast report is generated. The revenue calculation mainly focuses on three aspects: cost saving potential, output growth potential, and the extent of risk exposure reduction. Cost saving potential is assessed by comparing changes in the cost structure before and after optimization, and expressed as an achievable savings percentage. Output growth potential is calculated based on the increase in the output capacity of business units after resource reallocation, and its feasibility is judged in conjunction with the business adjustment time window. The extent of risk exposure reduction is quantitatively described by comparing the changes in the scope and intensity of risk propagation before and after the constraint simulation. During the calculation process, weight parameters are set for different revenue dimensions to reflect their relative contribution to the overall operational efficiency improvement.

[0036] In this embodiment, the specific steps of step S5 are as follows: Based on the pressure assessment report and the optimal revenue forecast report, the revenue difference is calculated to obtain the difference range of various operating indicators; Risk quadrants are defined based on the aforementioned difference range; Differentiated improvement decisions are made for the aforementioned risk quadrants, and operational adjustment strategies are output.

[0037] In this embodiment, operational indicators with clear quantitative descriptions in both types of reports are selected as comparison objects, including cost levels, cash flow resilience, risk exposure size, business continuity indicators, and output efficiency indicators. For each indicator, the assessment result value under the stress scenario and the predicted result value under the optimal scenario are extracted, and the change range of the indicator within the "worst-best" range is calculated by the difference. To enhance the stability and interpretability of the results, upper and lower limit constraints are introduced to express the difference results in intervals, for example, using the minimum improvement range, typical improvement range, and maximum potential improvement range to form the difference range. Normalization processing and weight parameters are set for different indicators to make the differences comparable under a unified scale. By selecting representative risk and return dimensions as coordinate axes, the difference range is mapped to a two-dimensional or multi-dimensional space, where the risk dimension can be represented by the change range of risk exposure or the degree of stress deterioration, and the return dimension can be represented by the cost improvement range or the output improvement space. Based on the distribution characteristics of the difference intervals across various dimensions, the space is divided into multiple risk quadrants with clearly defined meanings, such as the high-risk, low-return quadrant, the high-risk, high-return quadrant, the low-risk, low-return quadrant, and the low-risk, high-return quadrant. Quadrant boundaries are determined by setting threshold values, derived from the company's risk tolerance limits and return improvement targets. Indicator combinations falling into different quadrants are identified, enabling the company to intuitively identify which operational areas have priority for improvement and which areas require focused risk management. After defining the risk quadrants, differentiated improvement decision-making rules are established for different risk quadrants, and corresponding operational adjustment strategies are output accordingly. For operational indicators falling into the high-risk, low-return quadrant, the focus is on risk contraction and conservative adjustment strategies, such as limiting resource investment and reducing the scope of highly uncertain businesses to prevent further risk amplification. For indicators in the high-risk, high-return quadrant, a prudent optimization strategy is adopted, implementing selective resource allocation under the premise of clearly defined controllable risk boundaries. For indicators in the low-risk, low-return quadrant, the focus is on structural optimization and efficiency improvement, releasing potential value through refined adjustments. For indicators in the low-risk, high-return quadrant, they are prioritized for optimization, and operational adjustment strategies for accelerated implementation and key protection are formulated. In the strategy formulation process, strategy priority parameters and execution window constraints are introduced to distinguish between short-term actionable measures and medium- to long-term planning directions. The decision-making principles, adjustment directions, and key indicators corresponding to each quadrant are integrated to form a clear and logically consistent operational adjustment strategy.

[0038] In this embodiment, the differentiated improvement decision specifically refers to: The risk quadrants include high risk and high return, high risk and low return, low risk and high return, and low risk and low return. High-risk, high-return projects should be initiated immediately; high-risk, low-return projects should implement risk transfer and avoidance measures; low-risk, high-return projects should be included in the continuous optimization plan; and low-risk, low-return projects should be subject to routine monitoring.

[0039] In this embodiment, business units or operational indicators falling into the high-risk, high-return quadrant indicate significant potential for revenue improvement in their current state, but are accompanied by high levels of uncertainty and risk exposure. Therefore, proactive intervention and focused improvement strategies are necessary. This step first prioritizes relevant business units based on the risk quadrant mapping results, selecting those with both revenue improvement potential and risk exposure at the upper edge of the difference range as key improvement targets. Subsequently, clear improvement target ranges are set around cost structure, resource allocation, business processes, and risk control, such as limiting the acceptable upper limit of risk exposure and setting a minimum achievement percentage for revenue improvement. After the improvement project is launched, a high-frequency monitoring rhythm is set for key operational indicators, with rolling evaluations of the improvement effects conducted within a short time window to ensure that revenue potential is gradually released within a controllable range. For business units or operational indicators in the high-risk, low-return quadrant, characterized by high risk exposure and limited potential revenue improvement space, further investment or optimization is not economically justifiable. This step focuses on risk reduction, employing a combination of risk transfer and risk avoidance for these targets. The specific approach involves first identifying the types of risk sources, such as capital occupation risk, operational failure risk, or external dependency risk. Based on the risk type, corresponding transfer paths are selected, such as reducing the proportion of risk directly borne by the enterprise through business outsourcing, contract adjustments, or responsibility reassignment. For risks that cannot be effectively transferred, mitigation measures are taken by reducing business scale, decreasing resource investment intensity, or shortening business duration. During this process, minimum performance requirements are set for the effectiveness of risk transfer and mitigation, such as requiring the risk exposure to shrink to below a predetermined threshold within a certain period.

[0040] For business units or operational indicators located in the low-risk, high-return quadrant, which demonstrate strong revenue improvement under controllable risk, they are core supports for enhancing enterprise operational efficiency. This step focuses on continuous optimization, including these entities in the long-term optimization and key protection scope. Specifically, this involves analyzing the sources of their low-risk characteristics, such as stable business structures, mature management mechanisms, or good resource matching, summarizing replicable success models, and promoting them in similar business units. Resource allocation should be appropriately tilted towards these entities, for example, prioritizing their reasonable resource adjustment needs to further amplify revenue growth potential. During continuous optimization, a medium- to long-term target range is introduced to simultaneously constrain cost, output, and risk maintenance levels, ensuring that revenue growth does not come at the expense of risk accumulation. A basic monitoring frequency is set for their key indicators to monitor for trend changes or abnormal fluctuations, avoiding excessive resource investment in deep optimization to prevent input-output imbalance. During monitoring, key triggering conditions that may lead to quadrant shifts are identified, such as changes in cost structure, external environmental fluctuations, or business model adjustments. Once relevant indicators approach risk or return thresholds, a reassessment mechanism is promptly initiated. By implementing low-intervention and predictable management methods for low-risk, low-return entities, their stable operation can be maintained, leaving more flexibility for the overall allocation of corporate resources.

[0041] In this embodiment, an artificial intelligence-based enterprise operational efficiency analysis system is provided, used to execute the artificial intelligence-based enterprise operational efficiency analysis method described above, including: The data collection unit is used to collect enterprise operation log tables; classify and label the enterprise operation log tables by business units, and output a set of classification tags; The propagation distribution unit is used to fit the risk propagation distribution based on the classification label set and construct a risk propagation distribution map. The worst-case assessment unit is used to conduct worst-case stress assessment based on the risk propagation distribution map and generate a stress assessment report. The optimal prediction unit is used to simulate the operational constraints of the risk propagation distribution map and output the optimal return prediction report. The adjustment unit is used to make differentiated improvement decisions based on the stress assessment report and the optimal profit forecast report, and output operational adjustment strategies.

[0042] Therefore, the embodiments should be considered as exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of the equivalents of the application are intended to be included within the invention.

[0043] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement it. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein are implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features of the invention herein.

Claims

1. A method for analyzing enterprise operational efficiency based on artificial intelligence, characterized in that, Includes the following steps: Step S1: Collect enterprise operation logs; classify and label the enterprise operation logs by business units, and output a set of classification tags; Step S2: Fit the risk propagation distribution based on the classification label set and construct a risk propagation distribution map; Step S3: Conduct a worst-case stress assessment based on the risk propagation distribution map and generate a stress assessment report; Step S4: Perform operational constraint simulation on the risk propagation distribution map and output the optimal return prediction report; Step S5: Based on the stress assessment report and the optimal profit forecast report, make differentiated improvement decisions and output operational adjustment strategies.

2. The enterprise operational efficiency analysis method based on artificial intelligence according to claim 1, characterized in that, The specific steps of step S1 are as follows: Collect enterprise operation logs, including financial statements, cost details, budget execution reports, staffing lists, equipment operation records, procurement and inventory ledgers, and business plan data. Standardize the fields in the enterprise operation logs to obtain a standardized operation table; The standardized operation table is processed uniformly in terms of time dimension to construct a benchmark time axis; The standardized operation table is subjected to business entity identification to obtain multiple entity data; Determine the relationships between the multiple entity data; map the standardized operation table based on the baseline timeline and the relationships to construct the operation dataset; Based on the operational dataset, business units are classified and labeled, and a set of classification labels is output.

3. The enterprise operational efficiency analysis method based on artificial intelligence according to claim 2, characterized in that, The specific steps for classifying and labeling business units based on the operational dataset and outputting a classification label set are as follows: Calculate the total input and total output of each business unit based on the operational dataset; Based on the total input and total output, the cost deviation rate, budget execution deviation rate, human efficiency fluctuation coefficient and asset utilization abnormal value are calculated to obtain the economic indicators of each business unit. Based on the aforementioned economic indicators, analyze abnormal operational business units and mark abnormal business units. Classify and label abnormal business units, and output a set of classification labels.

4. The enterprise operational efficiency analysis method based on artificial intelligence according to claim 3, characterized in that, The specific steps of step S2 are as follows: Based on the classification tag set, a unit-by-unit hierarchical analysis is performed to generate abnormal characteristic results; the abnormal characteristic results include the capital scale, business scope and duration of each abnormal business unit; Based on the abnormal characteristics, calculate the impact range coefficient, financial risk coefficient, operation and maintenance interruption probability, and management failure impact degree to determine the risk association path; The risk propagation distribution is fitted to the risk association path to construct a risk propagation distribution map.

5. The enterprise operational efficiency analysis method based on artificial intelligence according to claim 4, characterized in that, The specific steps of step S3 are as follows: Set an operational simulation cycle; Based on the operational dataset and the operational simulation period, worst-case assumptions are set; the worst-case assumptions are specifically: the maximum rate of capital consumption, the maximum profit erosion, and the maximum probability of operational failure within the period. Based on the worst-case scenario assumptions, an operational simulation of the risk propagation distribution map is performed to generate worst-case scenario risk data. Based on worst-case scenario risk data, a worst-case scenario stress assessment is conducted, and a stress assessment report is generated.

6. The enterprise operational efficiency analysis method based on artificial intelligence according to claim 5, characterized in that, The specific steps for conducting a worst-case scenario stress assessment based on worst-case risk data and generating a stress assessment report are as follows: Based on worst-case risk data, the cash flow resilience of enterprises at multiple time points is calculated to obtain the operational critical failure point. The probability of financial distress risk is determined based on the aforementioned operational critical failure point. Based on the probability of funding disruption risk, the customer churn rate, the probability of supply chain disruption, and the expected loss of market share are calculated to obtain disruption-related data. Worst-case pressure assessment is performed based on fracture correlation data, and a pressure assessment report is generated.

7. The enterprise operational efficiency analysis method based on artificial intelligence according to claim 6, characterized in that, The specific steps of step S4 are as follows: Set resource constraints; the resource constraints include the upper limit of the cost structure optimization budget, enterprise resource reallocation data, and business adjustment time window. Based on resource constraints, the risk propagation distribution map is simulated under operational constraints to generate optimal scenario simulation data. Calculate the cost-saving potential, output growth potential, and risk exposure reduction range of the optimal scenario simulation data, and output the optimal return forecast report.

8. The enterprise operational efficiency analysis method based on artificial intelligence according to claim 7, characterized in that, The specific steps of step S5 are as follows: Based on the pressure assessment report and the optimal revenue forecast report, the revenue difference is calculated to obtain the difference range of various operating indicators; Risk quadrants are defined based on the aforementioned difference range; Differentiated improvement decisions are made for the aforementioned risk quadrants, and operational adjustment strategies are output.

9. The enterprise operational efficiency analysis method based on artificial intelligence according to claim 8, characterized in that, The specific details of the differentiated improvement decision are as follows: The risk quadrants include high risk and high return, high risk and low return, low risk and high return, and low risk and low return. High-risk, high-return improvement projects should be initiated immediately. High-risk, low-return strategies involve risk transfer and avoidance. Low-risk, high-return strategies will be incorporated into the continuous optimization plan. Low-risk, low-return operations are subject to routine monitoring.

10. An artificial intelligence-based enterprise operational efficiency analysis system, characterized in that, The method for performing the AI-based enterprise operational efficiency analysis as described in claim 1 includes: The data collection unit is used to collect enterprise operation log tables; classify and label the enterprise operation log tables by business units, and output a set of classification tags; The propagation distribution unit is used to fit the risk propagation distribution based on the classification label set and construct a risk propagation distribution map. Worst-case assessment unit, used to conduct worst-case stress assessment based on risk propagation distribution map and generate stress assessment report; The optimal prediction unit is used to simulate the operational constraints of the risk propagation distribution map and output the optimal return prediction report. The adjustment unit is used to make differentiated improvement decisions based on the pressure assessment report and the optimal profit forecast report, and output operational adjustment strategies.

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