Comprehensive evaluation method based on enterprise operation result and digital current situation

Abstract

The invention discloses a comprehensive evaluation method based on an enterprise operation result and a digital current situation. The method comprises five steps: S1, obtaining original operation data and original digital data of an enterprise through a data acquisition interface, and generating a standardized operation data stream and a digital data stream; s2, calculating and forming a group of quantitative digital index parameters through a maturity evaluation model; s3, inputting the quantitative operation index parameter and the quantitative digital index parameter into a dynamic weight distribution model to generate a dynamic weight coefficient; s4, generating an enterprise comprehensive competitiveness score based on the dynamic weight coefficient, the quantitative operation index parameter and the quantitative digital index parameter; and S5, outputting a comprehensive evaluation report containing optimization suggestions according to the comprehensive competitiveness score, the dynamic weight coefficient and the simulation prediction coefficient. According to the comprehensive evaluation method, the problem that enterprise operation performance evaluation and digital level evaluation are separated from each other in the prior art can be solved.

Description

Technical Field

[0001] This invention relates to the intersection of enterprise management and information technology, and specifically to a comprehensive evaluation method based on enterprise operational results and digital status. Background Technology

[0002] Currently, enterprises seeking to enhance their competitiveness through digital transformation generally face the challenge of assessing their current situation and planning their future path. Existing assessment methods mainly fall into two categories: traditional operational performance evaluation and digital transformation assessment. Traditional digital transformation focuses on financial indicators such as profit margins and revenue growth rates, as well as lagging operational indicators such as inventory turnover and customer satisfaction. While these assessments reflect past performance, they fail to explain the driving factors behind these results or make effective judgments about future potential. The other category is emerging digital transformation level assessment, which focuses on an enterprise's maturity in areas such as technology application, data management, process automation, and organizational digital culture—leading indicators. However, existing digital transformation assessment models are often disconnected from specific business performance. They typically provide a composite score or grade but struggle to answer the crucial question: "How much actual business value does achieving a certain level of digital capability actually bring to the enterprise?" This disconnect leads to several significant drawbacks: First, corporate decision-makers lack a clear understanding of the digital root causes behind operational shortcomings. For example, high customer churn rates cannot be answered by traditional assessments; is it due to insufficient customer relationship management system functionality or data incompatibility preventing precise marketing? Second, when developing digital investment plans, the lack of a quantitative correlation model between digital capabilities and business outcomes often leads to decisions relying on subjective experience or industry trends, resulting in significant uncertainty and an inability to accurately predict the business improvements that investments will bring, leading to an uncertain return on investment. Third, existing assessment results are mostly static reports, lacking dynamic simulation and forward-looking projection capabilities. Companies struggle to compare the expected effects of different development paths and cannot generate personalized, optimized upgrade roadmaps based on their own context. Therefore, the industry urgently needs a comprehensive assessment method that can deeply integrate and analyze operational results with the current digital status, providing quantifiable, simulable, and predictable data to fill the gaps in existing technologies and guide companies towards a more scientific and effective digital transformation. Summary of the Invention

[0003] In view of the shortcomings of the existing technology, the purpose of this invention is to provide a comprehensive evaluation method based on enterprise operational results and digital status, to solve the problem of the separation between enterprise operational performance evaluation and digital level evaluation in the existing technology. This invention solves this problem by constructing an automated system integrating data collection, indicator quantification, dynamic weight allocation, comprehensive evaluation, and path simulation. First, the system processes operational and digital data in parallel to generate standardized signals. Next, it uses a dynamic weight allocation model to analyze the actual impact of digital indicators on operational indicators, generating weight coefficients reflecting their inherent correlation. Then, the comprehensive evaluation and simulation engine uses these coefficients to calculate a competitiveness score and allows users to predict the specific impact of different digital investments on operational results through simulation commands. Finally, it generates a report containing root cause diagnosis and quantitative improvement suggestions, thereby accurately linking operational goals with digital construction and guiding scientific decision-making.

[0004] This invention provides a comprehensive evaluation method based on enterprise operational results and digital status, including: S1: Acquire raw operational data and raw digital data of the enterprise through the data acquisition interface, and preprocess the raw operational data and raw digital data to generate standardized operational data streams and digital data streams; S2: Based on standardized operational data flow, a set of quantitative operational indicator parameters are calculated through the indicator quantification engine, and a set of quantitative digital indicator parameters are calculated through the maturity assessment model. S3: Input the quantitative operational indicator parameters and the quantitative digital indicator parameters into the dynamic weight allocation model to generate the dynamic weight coefficients of each digital indicator on the operational indicator; S4: Based on dynamic weight coefficients, quantitative operational indicator parameters, and quantitative digital indicator parameters, the comprehensive evaluation and simulation engine are integrated to generate a comprehensive enterprise competitiveness score, triggering the improvement path simulation module to run, thereby generating simulation prediction coefficients for predicting changes in operational indicators. S5: Based on the overall competitiveness score, dynamic weighting coefficient, and simulated prediction coefficient, the report generator automatically synthesizes and outputs a comprehensive evaluation report containing optimization suggestions.

[0005] In one embodiment of the present invention, the preprocessing of the original operational data and original digitized data in step S1 specifically includes: cleaning the received original operational data and original digitized data to handle missing and outlier values, the data cleaning process being based on predefined business rules and statistical identification methods; subsequently, the cleaned data is normalized to convert it into a unified dimension and numerical range to eliminate deviations caused by differences in magnitude and unit among different indicators; during this process, an integrated data quality assessment module is simultaneously activated, which verifies the completeness, consistency, and timeliness of the input data in real time and generates data quality scoring parameters, which will be passed to the report generator as a reference for the credibility of the assessment results in subsequent steps.

[0006] In one embodiment of the present invention, in step S2, the calculation of a set of quantitative operational indicator parameters by the indicator quantification engine specifically involves: extracting basic operational values ​​of multiple dimensions from a standardized operational data stream, which at least include financial performance, market customers, internal processes, and learning and growth; then, according to predefined operational indicator calculation rules, aggregating and calculating the basic operational values ​​into a series of key performance indicator parameters; simultaneously, an industry benchmark comparison module is activated, which obtains industry benchmark data from an external database and compares the calculated key performance indicator parameters with the industry benchmark data, thereby generating a set of quantitative operational indicator parameters that include absolute levels and relative industry positions.

[0007] In one embodiment of the present invention, in step S2, the calculation of a set of quantitative digital indicator parameters by the maturity assessment model specifically involves: the maturity assessment model constructing a multi-level and multi-dimensional assessment framework, which covers at least the core areas of technical infrastructure, data management and analysis, business process digitalization, and organizational and cultural digitalization; detailed assessment questionnaires and evidence collection points are designed for each area, and an initial maturity score is assigned to each assessment point by performing pattern recognition and maturity rule matching on standardized digital data streams; subsequently, a weighted aggregation calculation layer is used to synthesize the initial maturity scores under the same dimension, and finally output a set of quantitative digital indicator parameters that can comprehensively reflect the strength of the enterprise's digital capabilities in various areas.

[0008] In one embodiment of the present invention, in step S3, the dynamic weight allocation model generates dynamic weight coefficients for each digital indicator to the operational indicator in the following manner: The model internally constructs a correlation analysis engine, which receives quantitative operational indicator parameters and quantitative digital indicator parameters, and uses a correlation rule mining algorithm to analyze the statistical correlation strength between each digital indicator and the operational indicator in the historical dataset; on this basis, a machine learning-based prediction model is further used to quantify the predictive contribution of changes in specific digital indicators to changes in key operational indicators; finally, the dynamic weight coefficient is generated by combining statistical correlation strength and predictive contribution and through a normalization process, and this signal characterizes the importance measure of each digital indicator relative to the operational results in the current enterprise context.

[0009] In one embodiment of the present invention, the dynamic weight allocation model further includes an adaptive learning mechanism that enables the model to continuously update its internal association rules and prediction model parameters based on newly input enterprise evaluation data. Specifically, after each execution of step S3, the newly generated quantitative operational indicator parameters, quantitative digital indicator parameters, and subsequent actual operational improvement data obtained from enterprise feedback will be jointly stored in a continuously growing case library. The association analysis engine and machine learning prediction model will be retrained periodically based on the updated case library, so that the generated dynamic weight coefficients can dynamically evolve over time and with changes in the enterprise environment, maintaining their accurate reflection of the current state of the enterprise.

[0010] In one embodiment of the present invention, the specific process of generating the enterprise's comprehensive competitiveness score by integrating comprehensive evaluation and simulation engine calculation in step S4 includes: First, based on the dynamic weight coefficients generated in step S3, the quantitative digital indicator parameters are weighted and summed to obtain a digital driving potential score; then, a collaborative evaluation module is invoked, which calculates the matching degree between the actual operational performance represented by the quantitative operational indicator parameters and the digital driving potential score, and generates a collaborative coefficient; finally, the digital driving potential score, the integrated value of key operational indicators, and the collaborative coefficient are integrated, and the final enterprise comprehensive competitiveness score is calculated through a nonlinear synthesis function.

[0011] In one embodiment of the present invention, in step S4, the specific method for triggering the improved path simulation module to run and generating simulated prediction coefficients for predicting changes in operational indicators is as follows: receiving a hypothetical improvement instruction from the user through an interactive interface for one or more quantitative digital indicator parameters; the simulation module constructs a digital capability improvement scenario based on the hypothetical improvement instruction; subsequently, the module calls the dynamic weight coefficients determined in step S3, especially the mapping relationship between digital indicators and operational indicators, and calculates the expected change in the affected operational indicators under this scenario through a built-in causal inference model; finally, the simulated prediction coefficients include the expected change and the predicted value of the enterprise's comprehensive competitiveness score recalculated under this change.

[0012] In one embodiment of the present invention, after generating simulation prediction coefficients, the improvement path simulation module also performs a priority ranking function for improvement paths. This function is implemented through a cost-benefit analysis submodule, which accesses a cost database storing the estimated costs of typical digital improvement measures and calculates the cost-benefit ratio of each hypothetical improvement measure by combining the expected improvement level of operational indicators indicated in the simulation prediction coefficients. Subsequently, a ranking algorithm sorts all simulated improvement measures in descending order according to the cost-benefit ratio, generating an optimal improvement sequence parameter. This optimal improvement sequence parameter is passed as a key input to the report generator in step S5 to highlight high-priority improvement recommendations in the comprehensive evaluation report.

[0013] In one embodiment of the present invention, in step S5, when the report generator automatically synthesizes the comprehensive evaluation report, in addition to integrating the comprehensive competitiveness score, dynamic weight coefficient, and simulated prediction coefficient, a root cause analysis engine is also introduced. Based on the dynamic weight coefficient, the root cause analysis engine identifies several key digital weaknesses that have the greatest impact on the current comprehensive competitiveness score of the enterprise, and associates a set of potential root cause description parameters for each key digital weakness by querying a predefined diagnostic knowledge base. These root cause description parameters and optimization suggestions together constitute the diagnostic analysis part of the report, so that the output comprehensive evaluation report not only includes the direction of improvement, but also deeply reveals the internal factors that lead to the current situation.

[0014] This invention provides a comprehensive evaluation method based on enterprise operational results and digital status. It addresses this problem by constructing an automated system integrating data collection, indicator quantification, dynamic weight allocation, comprehensive evaluation, and path simulation. First, the system processes operational and digital data in parallel, generating standardized signals. Next, it uses a dynamic weight allocation model to analyze the actual impact of digital indicators on operational indicators, generating weight coefficients reflecting their inherent correlation. Then, the comprehensive evaluation and simulation engine uses these coefficients to calculate a competitiveness score and allows users to predict the specific impact of different digital investments on operational results through simulation commands. Finally, it generates a report containing root cause diagnosis and quantitative improvement suggestions, thereby accurately linking operational goals with digital construction and guiding scientific decision-making. Attached Figure Description

[0015] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a flowchart of a comprehensive evaluation method based on enterprise operational results and digital status. Detailed Implementation

[0017] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that, unless otherwise specified, the following embodiments and features described therein can be combined with each other.

[0018] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Therefore, the drawings only show the components related to the present invention and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0019] In the following description, numerous details are explored to provide a more thorough explanation of embodiments of the invention. However, it will be apparent to those skilled in the art that embodiments of the invention may be practiced without these specific details. In other embodiments, well-known structures and devices are shown in block diagram form rather than in detail to avoid obscuring embodiments of the invention.

[0020] Please see Figure 1 The image illustrates a comprehensive evaluation method based on enterprise operational results and digital status according to the present invention. This method comprises five steps: S1: Acquiring raw operational data and raw digital data from the enterprise through a data acquisition interface, and preprocessing the raw operational data and raw digital data to generate standardized operational data streams and digital data streams; S2: Based on the standardized operational data streams, calculating a set of quantitative operational indicator parameters through an indicator quantification engine, and calculating a set of quantitative digital indicator parameters through a maturity assessment model; S3: Inputting the quantitative operational indicator parameters and quantitative digital indicator parameters into a dynamic weight allocation model to generate dynamic weight coefficients for each digital indicator on the operational indicators; S4: Based on the dynamic weight coefficients, quantitative operational indicator parameters, and quantitative digital indicator parameters, performing integrated calculations through a comprehensive evaluation and simulation engine to generate an enterprise comprehensive competitiveness score, triggering the improvement path simulation module to run, thereby generating simulation prediction coefficients for predicting changes in operational indicators; S5: Based on the comprehensive competitiveness score, dynamic weight coefficients, and simulation prediction coefficients, automatically synthesizing and outputting a comprehensive evaluation report containing optimization suggestions through a report generator.

[0021] like Figure 1As shown, its core lies in building a closed-loop, data-driven diagnostic and decision support system. This method begins with the extensive collection and precise processing of data. Specifically, it involves systematically acquiring heterogeneous raw operational and digital data from both inside and outside the enterprise through one or more data acquisition interfaces. This raw operational data typically originates from the enterprise's core business systems, such as enterprise resource planning systems, customer relationship management systems, supply chain management systems, and financial software. Its form is mostly structured database records, containing a series of micro-level facts reflecting the enterprise's actual business activities, from transaction logs and inventory records to customer work orders. The sources of raw digital data are even more extensive and complex. It includes objective technical data such as the underlying technical logs of the aforementioned business systems, application programming interface call frequency and performance indicators, and the number and growth of data tables, as well as subjective qualitative information obtained through carefully designed digital assessment questionnaires, expert interview transcripts, and system architecture document analysis. These data collectively constitute the raw materials for the assessment, but their raw state cannot be directly consumed by the model; they are usually filled with noise, inconsistencies, and dimensional differences. Therefore, preprocessing raw operational and digitized data to generate standardized operational and digitized data streams becomes a crucial foundational step. This preprocessing process is not a simple format conversion but a multi-stage, rigorous data engineering process. The first step is data cleaning, which aims to handle missing and outlier values. For missing values, the system employs different strategies based on the data type and missing pattern. For example, for continuous operational data such as daily sales, time series interpolation might be used to impute missing values ​​and maintain the continuity of business trends; for categorized digitized questionnaire data, a specific "unknown" category might be assigned to avoid introducing bias. Handling outliers relies on a combination of predefined business rules and statistical identification methods. Business rules might stipulate that a certain operational cost should not exceed a specific percentage of revenue, and values ​​exceeding this limit will be flagged. Statistical methods such as box plots or Z-score analysis are used to identify numerical points that deviate too far from the main data distribution. These identified outliers are not simply deleted but trigger an audit mechanism or are replaced with calculated values ​​within a reasonable range to ensure the data's authenticity and usability are not compromised.

[0022] Further, following data cleaning, the next step is data normalization. The purpose of this step is to transform the cleaned data from diverse sources into a unified unit of measurement and numerical range, thereby completely eliminating comparison biases caused by differences in magnitude and unit among different indicators. For example, a company's annual revenue may be in the hundreds of millions of yuan, while customer satisfaction ratings are on a percentage scale, and inventory turnover rate is measured in times. If these values ​​are directly used in calculations, the larger-scale indicator will dominate the model results, which is unreasonable. The normalization process uses mathematical transformations, such as min-max scaling, to map the data to the range of zero to one, or uses Z-score standardization to convert it into a distribution with a mean of zero and a standard deviation of one, ensuring all indicators are on a level playing field and laying the foundation for subsequent fair weighting and comprehensive calculations. During this process, an integrated data quality assessment module is simultaneously activated. This module acts like a rigorous quality inspector, performing real-time verification of the completeness, consistency, and timeliness of the input data. Completeness checks ensure that key fields are not significantly missing; consistency checks identify inconsistencies, such as a department's expenditures exceeding its budget; and timeliness assesses whether the data is updated within the required time window. This module generates a quantified data quality score parameter. While this signal doesn't directly participate in the core model's calculations, it serves as key metadata for assessing the credibility of results in subsequent steps and is passed to the final report generator. If the data quality score is too low, the report will provide a clear warning, indicating that decision-makers' conclusions based on the current data have limitations, thus demonstrating the scientific and prudent nature of the methodology. Once the data is transformed into clean, unified, and standardized signals, the methodology enters the substantive indicator quantification phase. The core task of this phase is to transform the preprocessed, standardized data signals into measurable indicator signals that represent the strength of the enterprise's operational performance and digital capabilities. This process is accomplished by two parallel, specialized computing engines: the indicator quantification engine handles the operational dimension, while the maturity assessment model handles the digital dimension.

[0023] In one embodiment of the invention, a set of quantitative operational indicator parameters is calculated using an indicator quantification engine based on standardized operational data streams. This is a process of aggregation and refinement from micro-level data to macro-level indicators. The indicator quantification engine has a pre-built structured indicator system framework, which is typically based on the classic Balanced Scorecard concept or other modern performance management theories, covering at least the core dimensions of financial performance, market and customers, internal processes, and learning and growth. The engine extracts basic operational values ​​from the standardized operational data streams, much like a miner panning for gold. For example, it extracts total revenue, total cost, and various taxes from financial transaction data; new customer numbers, churned customer numbers, and customer complaint counts from customer data; order fulfillment time, product qualification rate, and project delivery delay days from internal process data; and employee training time and core talent turnover rate from human resources data. Then, according to predefined operational indicator calculation rules in the engine's knowledge base, these basic operational values ​​are aggregated, and calculations are performed on ratios, trends, etc. For example, combining total revenue with total costs calculates the profit margin; combining total sales with average inventory value calculates inventory turnover; and combining the number of customer complaints with the total number of transactions calculates a proxy indicator for customer satisfaction. These calculations generate a series of key performance indicator (KPI) parameters, which are quantitative representations of a company's operational status. However, an isolated number often lacks context. Is a 10% profit margin good or bad? Is an inventory turnover rate of five times a year fast or slow? To give these indicator signals true evaluative meaning, an industry benchmark comparison module is activated. This module acquires industry benchmark data in real time or periodically from external industry databases, market research reports, or anonymized peer alliance data pools via a secure data connection channel. These benchmark signals may be expressed as industry averages, medians, leader percentiles, etc. The indicator quantification engine compares the calculated KPI parameters with these industry benchmark data one by one. This comparison is not just a simple comparison of numerical magnitudes, but may also calculate relative percentages, percentile rankings, or gap analyses. Through this process, previously isolated operational metrics are given a sense of position within an industry framework, ultimately generating a set of quantitative operational metric parameters that not only include absolute levels but also reveal their relative position within the industry. For example, the report will not only show "profit margin of 10 percent" but will also explicitly state that "this profit margin is higher than 70 percent of peer companies in the industry," making the assessment conclusions more insightful and action-oriented.

[0024] Meanwhile, along another parallel path, a set of quantitative digital indicator parameters is calculated using a maturity assessment model based on standardized digital data flows. This is a process of quantifying technology and governance status into capability levels, the complexity of which lies in transforming a large amount of qualitative and quantitative raw information into unified, comparable scores. The maturity assessment model itself first constructs a multi-layered, multi-dimensional assessment framework, which forms the skeleton of the model. Its design integrates internationally accepted digital capability models with best practices from enterprises. It covers at least the core areas of technological infrastructure, data management and analysis, business process digitization, and organizational and cultural digitization. The technological infrastructure area focuses on cloudification, network performance, and system architecture sophistication; the data management and analysis area focuses on data quality, data integration, and the level of data analysis and application; the business process digitization area focuses on the automation rate, online status, and end-to-end integration of core business processes; and the organizational and cultural digitization area focuses on the clarity of digital strategy, employees' digital skills, and the innovative atmosphere. For each area, the model includes detailed assessment questionnaires and evidence collection points. For example, in the "Data Integration" sub-item under the "Data Management and Analytics" domain, the questionnaire might ask whether the company has established a unified master data management platform and require evidence of the platform's management scope. In the "Business Process Digitization" domain, system logs might be analyzed to calculate the automated flow rate of order processes. The model assigns an initial maturity score to each assessment point by performing pattern recognition and maturity rule matching on standardized digitized data flows. This matching process might be based on a rule engine; for example, if evidence shows the use of containerization technology and a high rate of automated operations and maintenance, a high score would be given in the "Resilience and Operations" sub-item under "Technology Infrastructure." Natural language processing technology might also be introduced to analyze interview transcripts to determine the degree of consensus on digital strategy in "Organization and Culture." Subsequently, a weighted aggregation calculation layer begins. This layer synthesizes the initial maturity scores of various assessment points within the same dimension based on predefined weights—which may be set by domain experts or obtained through model learning of historical data. For example, in the "Data Management and Analytics" dimension, the weight of "Data Quality" might be higher than that of "Data Warehouse Capacity." Through layers of weighted aggregation, a set of structured and standardized scores is finally output, namely, quantitative digital indicator parameters. This set of signals is no longer a collection of scattered technical descriptions, but a precise metric that can comprehensively and quantitatively reflect the strength of a company's digital capabilities in various key areas, and can be compared horizontally and vertically. This provides a solid data foundation for the next step of linking digitalization with operational performance.

[0025] like Figure 1As shown, after completing the parallel quantification of enterprise operations and digital status indicators, the core innovation of this method lies in how to deeply integrate and dynamically correlate these two previously separate dimensions, thereby revealing the inherent, quantifiable causal relationship between digital capability building and business performance. This process begins with a sophisticated dynamic weight allocation model. The core task of this model is to generate a set of dynamic weight coefficients, which precisely characterize the influence of each digital indicator on key operational indicators. Its operation mechanism does not rely on fixed, pre-set weights by experts, but is achieved through a data-driven, learning-capable analysis system. The model internally constructs a correlation analysis engine as the basis for its calculations. This engine receives quantified operational indicator parameters and quantified digital indicator parameters from previous steps, treating these signals as a multi-dimensional time-series dataset reflecting the enterprise's state. The engine first uses association rule mining algorithms to scan the historical dataset, aiming to discover statistical patterns such as "when data integration indicators improve, inventory turnover indicators also tend to improve synchronously." This analysis can calculate the statistical correlation strength between various digital metrics and operational metrics, such as quantifying their linear or non-linear dependencies by calculating correlation coefficients or mutual information values. However, simple statistical correlation is insufficient to assert causal relationships. To more closely approximate causal inference, a machine learning-based predictive model is further integrated into this dynamic weight allocation model. This predictive model, such as a gradient boosting decision tree or random forest—algorithms capable of handling high-dimensional features and evaluating feature importance—is trained to predict future changes in key operational metrics based on changes in one or more digital metrics. In this process, the model learns and quantifies the predictive contribution of changes in specific digital metrics to changes in key operational metrics. For example, the model might find that an improvement in the digital metric "customer profile accuracy in the customer relationship management system" contributes far more to predicting changes in the operational metric "customer repurchase rate" than to the infrastructure metric "server virtualization rate." Ultimately, the generated dynamic weight coefficients are generated by combining the aforementioned statistical correlation strength with the predictive contribution here, and are processed through a normalization process to ensure that the sum of all weights is within a fixed range. This mechanism makes the weights no longer static, but can accurately reflect the actual importance of each digital capability relative to the operational results under the current enterprise's specific business model, industry context and historical path, thus providing a truly personalized and contextual basis for subsequent evaluation and simulation.

[0026] Specifically, to enable this model to continuously adapt to the company's development and changes in the external environment, its adaptive learning mechanism is crucial. The essence of this mechanism lies in elevating the entire evaluation system from a static evaluation tool into an intelligent agent capable of continuously evolving with accumulated experience. Specifically, after each evaluation process and the generation of the dynamic weight coefficients, the system does not consider this evaluation an endpoint. Instead, newly generated quantitative operational indicator parameters, quantitative digital indicator parameters, and subsequent operational improvement data obtained from actual operational feedback that demonstrates that digital improvement measures have indeed led to business improvements are collectively stored in a continuously growing, rigorously labeled case library. This case library becomes the fuel for system evolution. The correlation analysis engine and machine learning prediction model do not permanently remain in their initial state; they are periodically (e.g., quarterly or after accumulating a certain number of new cases) retrained based on the updated and richer case library. During this process, model parameters are updated, correlation rules are corrected, old and no longer applicable patterns are weakened, and new and stronger correlations are strengthened. This means that as time goes on, as enterprises advance their own digital transformation and the external market environment changes, the dynamic weighting coefficients generated by the system can also evolve dynamically, maintaining their ability to accurately reflect the current state of the enterprise and avoiding evaluation bias caused by outdated models. After obtaining the dynamically weighted indicator signals, the method enters the comprehensive evaluation and simulation stage. This stage aims to generate a comprehensive competitiveness score that definitively reflects the overall health of the enterprise and provides a "digital sandbox" that can anticipate the consequences of different decisions. First, the comprehensive competitiveness score of the enterprise is generated through the fusion calculation of the comprehensive evaluation and simulation engine. This process itself is a multi-level, precise calculation. The initial step is to weight and sum the set of quantitative digital indicator parameters based on the dynamic weighting coefficients generated in the previous steps. This calculation is not a simple addition of all digital scores, but a weighting based on the actual impact of each digital indicator on business results, thereby obtaining a digital driving potential score that better reflects its potential business value. This score answers the question, "With current digital capabilities, how good of a business result should theoretically be driven?"

[0027] Furthermore, a company's true value lies not only in its potential but also in the extent to which that potential is realized. Therefore, a collaborative assessment module is invoked concurrently. The core function of this module is to calculate the degree of match between the company's actual operational performance and its digital-driven potential. It receives the actual operational performance represented by quantitative operational indicator parameters and compares it with the digital-driven potential score. If the company's actual operational performance highly matches its digital potential, it indicates that digital construction has effectively translated into business results, and resource allocation is efficient. Conversely, if the digital potential is high but the operational performance is poor, it may indicate bottlenecks in other areas such as strategic execution, organizational capabilities, or the market environment. Similarly, if the operational performance is temporarily good but the digital potential is low, it may suggest that the current achievements are fragile, unsustainable, or perhaps dependent on temporary factors. This quantitative result of the matching degree is output as a synergy coefficient. Ultimately, the company's comprehensive competitiveness score is generated by fusing the digital-driven potential score, integrated key operational indicators (representing current actual results), and this crucial synergy coefficient, calculated using a non-linear synthesis function (such as geometric mean or by introducing the synergy coefficient as a multiplier). This algorithm ensures that the final score not only reflects how well a company is doing now and how well it might do in the future, but also reveals more profoundly the strategic health indicator of whether the current situation and potential are aligned, making the assessment conclusions more in-depth and forward-looking. The most valuable part of this method for decision support lies in its ability to trigger the improvement path simulation module, thereby generating simulated predictive coefficients for forecasting changes in operational indicators. This function upgrades the assessment from a static "diagnosis report" to a dynamic "strategy laboratory." Specifically, the system receives hypothetical improvement instructions from users via a graphical user interface, targeting one or more quantitative metric parameters. For example, decision-makers can slide a control to simulate improving "data governance maturity" from the current level three to the expected level four. This instruction is immediately captured by the system. Subsequently, the improvement path simulation module constructs a digital "capability improvement scenario" internally based on this hypothetical improvement instruction. Next, the core computing unit of this module is activated: it calls upon and performs calculations using a built-in causal inference model, based on the dynamic weighting coefficients determined in step three, particularly those that clearly depict the mapping relationship between "data governance maturity" and operational indicators such as "decision-making efficiency" and "compliance costs." This causal inference model goes beyond simple correlation; it attempts to simulate the extent to which the outcome variables (i.e., the affected operational indicators) will be affected when the causal variable, "data governance maturity," changes. Ultimately, the simulated predictive coefficients output by this module not only include the expected changes in these key operational indicators but also the predicted value of the enterprise's overall competitiveness score, recalculated based on these changes.This allows decision-makers to clearly and quantitatively see the potential business returns of different digital investment options before making any substantial investments, greatly improving the scientific nature and accuracy of decision-making.

[0028] This invention provides a comprehensive evaluation method based on enterprise operational results and digital status. This method addresses the problem by constructing an automated system integrating data collection, indicator quantification, dynamic weight allocation, comprehensive evaluation, and path simulation. First, the system processes operational and digital data in parallel, generating standardized signals. Next, a dynamic weight allocation model analyzes the actual impact of digital indicators on operational indicators, generating weight coefficients reflecting their inherent correlation. Then, the comprehensive evaluation and simulation engine uses these coefficients to calculate a competitiveness score and allows users to predict the specific impact of different digital investments on operational results through simulation commands. Finally, a report containing root cause diagnosis and quantitative improvement suggestions is generated, thereby accurately linking operational goals with digital construction and guiding scientific decision-making.

[0029] Therefore, the comprehensive evaluation method based on enterprise operation results and digital status of the present invention can solve the problem of the separation between enterprise operation performance evaluation and digital level evaluation in the prior art.

[0030] The above embodiments are merely illustrative of the principles and effects of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in the present invention should still be covered by the claims of the present invention.

Claims

1. A comprehensive evaluation method based on enterprise operation results and digitization status, characterized in that, The method comprises the following steps: S1: obtaining enterprise original operation data and original digital data through a data acquisition interface, and preprocessing the original operation data and the original digital data to generate standardized operation data stream and digital data stream; S2: based on the standardized operation data stream, a set of quantified operation index parameters are calculated through an index quantification engine, and a set of quantified digital index parameters are calculated through a maturity evaluation model; S3: inputting the quantified operation index parameters and the quantified digital index parameters into a dynamic weight distribution model to generate dynamic weight coefficients of each digital index on operation index; S4: based on the dynamic weight coefficients, the quantified operation index parameters and the quantified digital index parameters, a comprehensive competitiveness score of the enterprise is generated through fusion calculation of a comprehensive evaluation and simulation engine, and an improvement path simulation module is triggered to run, thereby generating a simulation prediction coefficient for predicting changes in operation index; S5: according to the comprehensive competitiveness score, the dynamic weight coefficients and the simulation prediction coefficient, a comprehensive evaluation report containing optimization suggestions is automatically synthesized and output by a report generator.

2. The method of claim 1, wherein, In step S1, the preprocessing of the original operation data and the original digital data specifically includes: data cleaning is performed on the received original operation data and the original digital data to process missing values and abnormal values therein, and the data cleaning process is based on predefined business rules and statistical identification methods; then, data normalization processing is performed on the cleaned data to convert it to a unified dimension and value range to eliminate the bias caused by the differences in magnitude and unit of different indexes; in this process, an integrated data quality evaluation module is simultaneously enabled, which checks the completeness, consistency and timeliness of the input data in real time and generates a data quality score parameter, which will be passed to the report generator as a reference basis for the credibility of the evaluation results in the subsequent steps.

3. The method of claim 1, wherein the method further comprises: In step S2, the calculation of a set of quantified operation index parameters through an index quantification engine specifically includes: extracting a plurality of dimensional basic operation values from the standardized operation data stream, which at least include financial performance, market customers, internal processes and learning growth; then, according to predefined operation index calculation rules, the basic operation values are aggregated and calculated into a series of key performance indicator parameters; at the same time, an industry benchmark comparison module is activated, which obtains industry benchmark data from an external database and compares the calculated key performance indicator parameters with the industry benchmark data, thereby generating a set of quantified operation index parameters containing absolute level and relative industry position.

4. The method of claim 1, wherein, In step S2, the formation of a set of quantitative digitalization indicator parameters is calculated by the maturity evaluation model. Specifically, the maturity evaluation model constructs a multi-level and multi-dimensional evaluation framework, which at least covers technical infrastructure, data management and analysis, business process digitalization, and organizational and cultural digitalization. Detailed evaluation questionnaires and evidence collection points are designed for each field. Through pattern recognition and maturity rule matching of the standardized digitalization data flow, an initial maturity score is assigned to each evaluation point. Then, a weighted aggregation calculation layer is used to integrate the initial maturity scores in the same dimension, and finally a set of quantitative digitalization indicator parameters is output, which can comprehensively reflect the strength of the enterprise's digitalization capability in each field.

5. The method of claim 1, wherein the method further comprises: In step S3, the dynamic weight allocation model generates dynamic weight coefficients of each digitalization indicator for the operation indicator by the following method: a correlation analysis engine is built in the model, which receives the quantitative operation indicator parameters and quantitative digitalization indicator parameters, and uses correlation rule mining algorithm to analyze the statistical correlation strength between each digitalization indicator and operation indicator in the historical data set. On this basis, a machine learning-based prediction model is further used to quantify the prediction contribution of the change of a specific digitalization indicator to the change of a key operation indicator. Finally, the dynamic weight coefficient is generated by integrating the statistical correlation strength and prediction contribution and through a normalization process. The dynamic weight coefficient represents the importance measure of each digitalization indicator relative to the operation result in the current enterprise context.

6. The method of claim 5, wherein the method further comprises: The dynamic weight allocation model also includes an adaptive learning mechanism, which enables the model to continuously update its internal correlation rules and prediction model parameters based on new input enterprise evaluation data. Specifically, after each execution of step S3, the newly generated quantitative operation indicator parameters, quantitative digitalization indicator parameters, and actual operation improvement data obtained from subsequent enterprise feedback are stored in a continuously growing case library. The correlation analysis engine and machine learning prediction model are periodically retrained based on the updated case library, so that the generated dynamic weight coefficient can dynamically evolve over time and with changes in the enterprise environment, maintaining its accurate reflection of the current situation of the enterprise.

7. The method of claim 1, wherein the method further comprises: In step S4, the fusion calculation of comprehensive evaluation and simulation engine generates the enterprise comprehensive competitiveness score. The specific process includes: first, according to the dynamic weight coefficient generated in step S3, the quantitative digitalization indicator parameters are weighted and summed to obtain a digitalization-driven potential score; then, a synergy evaluation module is called, which calculates the matching degree between the actual operation performance represented by the quantitative operation indicator parameters and the digitalization-driven potential score, and generates a synergy coefficient; finally, the digitalization-driven potential score, the integrated value of the key operation indicators, and the synergy coefficient are fused, and the final enterprise comprehensive competitiveness score is calculated through a nonlinear synthesis function.

8. The method of claim 1, wherein the method further comprises: In step S4, the trigger improvement path simulation module runs to generate simulation prediction coefficients for predicting changes in operational indicators in the following specific manner: receiving hypothetical improvement instructions for one or more quantified digitalization indicator parameters input by the user through the interactive interface; the simulation module constructs a digitalization capability improvement scenario according to the hypothetical improvement instructions; Subsequently, the module calls the dynamic weight coefficients determined in step S3, in particular the mapping relationship between the digitalization indicators and the operational indicators, and calculates the expected change in the operational indicators affected in this scenario through an embedded causal inference model; finally, the simulation prediction coefficients contain the expected change and the predicted value of the recalculated enterprise comprehensive competitiveness score under this change.

9. The method of claim 8, wherein the method further comprises: The improvement path simulation module also performs the priority sorting function of the improvement path after generating the simulation prediction coefficients; this function is realized through a cost-benefit analysis submodule that accesses a cost database storing the estimated costs of typical digitalization improvement measures and combines the expected improvement in operational indicators indicated in the simulation prediction coefficients to calculate the cost-benefit ratio of each hypothetical improvement measure; subsequently, a sorting algorithm sorts all simulated improvement measures in descending order according to the cost-benefit ratio to generate an optimal improvement sequence parameter that will be passed as a key input to the report generator in step S5 for highlighting high-priority improvement recommendations in the comprehensive evaluation report.

10. The method of claim 1, wherein the method further comprises: In step S5, when the report generator automatically synthesizes the comprehensive evaluation report, in addition to integrating the comprehensive competitiveness score, dynamic weight coefficients, and simulation prediction coefficients, it also introduces a root cause analysis engine; based on the dynamic weight coefficients, the root cause analysis engine identifies a number of key digitalization weak indicators that most constrain the current enterprise comprehensive competitiveness score and associates a set of potential root cause description parameters for each key digitalization weak indicator by querying a pre-defined diagnostic knowledge base; these root cause description parameters and the optimization recommendations together constitute the diagnostic analysis section of the report, so that the output comprehensive evaluation report not only contains improvement directions but also reveals the internal factors leading to the current situation.

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